Graphical Pipelines
Graphical Pipelines
Content of this Notebook:
Understanding what are graphical pipelines
Understanding the API of graphical pipelines
Examples of simple pipelines and how they can be implemented with graphical pipelines.
More complex graphical pipeline (Forecasting + GridSearch)
Grid search with a graphical pipeline
What are Graphical Pipelines?
Recap sequential pipelines:
Many tasks are non-sequential. To solve this two possibilities exist:
Nesting Sequential Pipelines.
Using Graphical Pipelines.
Thus, there is the generalised graphial pipeline.
Graphical means that different steps may share the same predecessor or provide their outputs to the same successor (the dataflows can branch and merge).
Generalised means that the pipeline can be used for multiple tasks (e.g. forecasting, classification, …).
Note
The graphical pipeline is a new feature, Thus, if you are considering any issues, we would be happy to get feedback on the graphical pipeline.
Potential Use-Cases
There exist various potential use-case for the graphical pipeline. In the following, we focus on a forecasting and a classification pipeline. #### Forecasting Use-Case for Graphical Pipelines
The input of forecasters depends on the output of other forecasters, which same the same input.
Forecaster could use the same preprocessing (branching of data flow)
Forecaster could use outputs of multiple predeccessors (merging of data flow)
Note: The current experimental state of the graphical pipeline does not fully support this use-case. However, we are working on this. If you are interested in this use-case and want to contribute, please contact us.
Credits
The graphical pipeline was first developed by pyWATTS [1] and was then adapted for sktime. The original implementation can be found pyWATTS. pyWATTS is a open source library developed at the Institute of Applied Informatics and Automation at the KIT and funded by HelmholtzAI.
[1] Heidrich, Benedikt, et al. “pyWATTS: Python workflow automation tool for time series.” arXiv preprint arXiv:2106.10157 (2021).
Note: The current experimental state of the graphical pipeline does not fully support this use-case. However, we are working on this. If you are interested in this use-case and want to contribute, please contact us.
How to build a Graphical Pipeline
Let us first visualise a simple forecasting pipeline, we want to construct:
Then we are having to ways on how to construct this pipeline with the graphical pipeline
Pass all steps to the pipeline during initialisation as for the sequential pipelines.
[1]:
from sktime.forecasting.sarimax import SARIMAX
from sktime.pipeline.pipeline import Pipeline
from sktime.transformations.difference import Differencer
differencer = Differencer()
general_pipeline = Pipeline(
[
{"skobject": differencer, "name": "differencer", "edges": {"X": "y"}},
{
"skobject": SARIMAX(),
"name": "sarimax",
"edges": {"X": "X", "y": "differencer"},
},
{
"skobject": differencer,
"name": "differencer_inv",
"edges": {"X": "sarimax"},
"method": "inverse_transform",
},
]
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
Create a pipeline object and add the steps one by one.
[2]:
general_pipeline = Pipeline()
differencer = Differencer()
general_pipeline = general_pipeline.add_step(
differencer, "differencer", edges={"X": "y"}
)
general_pipeline = general_pipeline.add_step(
SARIMAX(), "sarimax", edges={"X": "X", "y": "differencer"}
)
general_pipeline = general_pipeline.add_step(
differencer, "differencer_inv", edges={"X": "sarimax"}, method="inverse_transform"
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
Explanation of the parameters
The add_step’s parameter or key of the dicts in the step list during initialisation are:
skobject: The sktime object added to the pipeline
name: The name of the step
edges: The keys of the dictionary indicate the input of the skobject (X or y), and the values are the names of the steps that should be connected to the input argument. Note subsetting using
__and feature union via lists are supported.method: The skobject’s method that should be called. If not provided, the default method would be inferred based on the added skobject. This parameter is used for the inverse_transform method. Optional.
kwargs: Additional keyword arguments passed to the sktime object. Optional.
Now let us fit the pipeline and make a prediction
[3]:
from sktime.datasets import load_longley
from sktime.forecasting.model_selection import temporal_train_test_split
y, X = load_longley()
y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
general_pipeline.fit(y=y_train, X=X_train, fh=[1, 2, 3, 4])
general_pipeline.predict(X=X_test)
[3]:
1959 67213.735362
1960 68328.076310
1961 68737.861398
1962 71322.894026
Freq: A-DEC, Name: TOTEMP, dtype: float64
Further Examples
Classification Pipeline
A simple classification pipeline implemented using the graphical pipeline.
[4]:
from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
from sktime.transformations.exponent import ExponentTransformer
general_pipeline = Pipeline()
general_pipeline = general_pipeline.add_step(
ExponentTransformer(), "exponent", edges={"X": "X"}
)
general_pipeline = general_pipeline.add_step(
KNeighborsTimeSeriesClassifier(), "classifier", edges={"X": "exponent", "y": "y"}
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
Or alternatively defined using the constructor API.
[5]:
general_pipeline = Pipeline(
[
{"skobject": ExponentTransformer(), "name": "exponent", "edges": {"X": "X"}},
{
"skobject": KNeighborsTimeSeriesClassifier(),
"name": "classifier",
"edges": {"X": "exponent", "y": "y"},
},
]
)
This pipeline can be visualised as follows:
[6]:
from sktime.datasets import load_arrow_head
X, y = load_arrow_head(split="train", return_X_y=True)
general_pipeline.fit(X=X, y=y)
general_pipeline.predict(X=X)
[6]:
array(['0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0',
'1', '2', '0', '1', '2', '0', '1', '2', '0', '1', '2', '0', '1',
'2', '0', '1', '2', '0', '1', '2', '0', '1', '2'], dtype='<U1')
A More Complex Example
The considered use-case is to forecast the inflation using forecasts of the real gross domestic product, real disposable personal income, and the unemployment rate. Furthermore the unemployment rate is forecasted using the same features except the unemployment rate itself.
The data is taken from the macrodata dataset from the statsmodels package.
Note We stick with the add_step in the following.
Create Graphical Pipeline Instance
[7]:
pipe = Pipeline()
pipe.set_config(warnings="off")
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
[7]:
Pipeline()Please rerun this cell to show the HTML repr or trust the notebook.
Pipeline()
Add Preprocessing
[8]:
from sklearn.preprocessing import StandardScaler
from sktime.transformations.adapt import TabularToSeriesAdaptor
from sktime.transformations.detrend import Deseasonalizer
pipe = pipe.add_step(
TabularToSeriesAdaptor(StandardScaler()),
name="scaler",
edges={"X": "X__realgdp_realdpi_unemp"},
)
pipe = pipe.add_step(
Deseasonalizer(sp=4), name="deseasonalizer", edges={"X": "X__realgdp_realdpi"}
)
Add forecastesr for GDP and DPI
[9]:
from sklearn.linear_model import Lasso, Ridge
from sktime.forecasting.compose import make_reduction
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_gdp",
edges={"y": "deseasonalizer__realgdp"},
)
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_dpi",
edges={"y": "deseasonalizer__realdpi"},
)
Add Forecaster for unemployment rate that depends on forecasts of GDP and DPI
[10]:
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_unemp",
edges={
"y": "scaler__unemp",
"X": [
"forecaster_gdp",
"forecaster_dpi",
],
},
)
Add forecaster for the inflation that depends on forecasted DPI and unemployment rate
[11]:
pipe = pipe.add_step(
make_reduction(Ridge(), windows_identical=False, window_length=5),
name="forecaster_inflation",
edges={"X": ["forecaster_dpi", "forecaster_unemp"], "y": "y"},
)
Load data and split them into train and test
[12]:
from sktime.datasets import load_macroeconomic
from sktime.forecasting.base import ForecastingHorizon
data = load_macroeconomic()
X = data[["realgdp", "realdpi", "unemp"]]
y = data[["infl"]]
fh = ForecastingHorizon([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
y_train, y_test, X_train, X_test = temporal_train_test_split(y, X=X, fh=fh)
X_train
[12]:
| realgdp | realdpi | unemp | |
|---|---|---|---|
| Period | |||
| 1959Q1 | 2710.349 | 1886.9 | 5.8 |
| 1959Q2 | 2778.801 | 1919.7 | 5.1 |
| 1959Q3 | 2775.488 | 1916.4 | 5.3 |
| 1959Q4 | 2785.204 | 1931.3 | 5.6 |
| 1960Q1 | 2847.699 | 1955.5 | 5.2 |
| ... | ... | ... | ... |
| 2005Q3 | 12683.153 | 9308.0 | 5.0 |
| 2005Q4 | 12748.699 | 9358.7 | 4.9 |
| 2006Q1 | 12915.938 | 9533.8 | 4.7 |
| 2006Q2 | 12962.462 | 9617.3 | 4.7 |
| 2006Q3 | 12965.916 | 9662.5 | 4.7 |
191 rows × 3 columns
[13]:
pipe.fit(y=y_train, X=X_train, fh=fh)
result = pipe.predict(X=None, fh=y_test.index)
result
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
[13]:
| infl | |
|---|---|
| Period | |
| 2006Q4 | 3.090428 |
| 2007Q1 | 1.676421 |
| 2007Q2 | 0.219586 |
| 2007Q3 | 1.570087 |
| 2007Q4 | 0.350137 |
| 2008Q1 | 0.438966 |
| 2008Q2 | 0.615457 |
| 2008Q3 | 0.119022 |
| 2008Q4 | 0.257887 |
| 2009Q1 | 0.129785 |
| 2009Q2 | -0.056094 |
| 2009Q3 | -0.066123 |
[14]:
((result - y_test) ** 2).mean()
[14]:
infl 20.103326
dtype: float64
Grid Search with graphical pipeline
This pipeline has multiple parameters that might be tested to find the configurations. These parameters include:
which forecaster should be used for which variable ->
MultiplexForecasterwhat should be the hyperparameters of the forecaster
which features should be used for the different forecasters -> Tune the edges of the graphical pipeline!
Since we do forecasting, we use the ForecastingGridSearchCV.
Create blue print of the pipeline
[15]:
from sktime.forecasting.compose import MultiplexForecaster
pipe = Pipeline()
sklearn_scaler = StandardScaler()
sktime_scaler = TabularToSeriesAdaptor(sklearn_scaler)
deseasonalizer = Deseasonalizer(sp=4)
pipe = pipe.add_step(
sktime_scaler, name="scaler", edges={"X": "X__realgdp_realdpi_unemp"}
)
pipe = pipe.add_step(
deseasonalizer, name="deseasonalizer", edges={"X": "X__realgdp_realdpi"}
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_gdp",
edges={"y": "deseasonalizer__realgdp"},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_dpi",
edges={"y": "deseasonalizer__realdpi"},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_unemp",
edges={
"y": "scaler__unemp",
"X": [
"forecaster_gdp",
"forecaster_dpi",
],
},
)
pipe = pipe.add_step(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
name="forecaster_inflation",
edges={"X": ["forecaster_dpi", "forecaster_unemp"], "y": "y"},
)
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
Specify the parameter grid:
The keys of the dictionary are the parameters’ in the pipeline, and the values specify which options should be tested. Keys have the following structure: parameter of a step <step_name>__skobject__<parameter-name> and input edges of a step <step-name>__edges_<Xory>.
[16]:
param_grid = {
"forecaster_inflation__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_unemp__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_dpi__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_gdp__skobject__selected_forecaster": ["ridge", "lasso"],
"forecaster_inflation__edges__X": [
["forecaster_unemp"],
["forecaster_unemp", "forecaster_dpi"],
],
"forecaster_unemp__edges__X": [
[],
["forecaster_dpi"],
["forecaster_gdp", "forecaster_dpi"],
],
"deseasonalizer__edges__X": ["X__realgdp_realdpi", "scaler__realgdp_realdpi"],
}
Initialise the gridsearch using pipeline, cross-validation strategy, scoring, and param_grid.
[17]:
from sktime.forecasting.model_selection import (
ForecastingGridSearchCV,
SlidingWindowSplitter,
)
from sktime.performance_metrics.forecasting import mean_absolute_error
gridcv = ForecastingGridSearchCV(
pipe,
cv=SlidingWindowSplitter(
window_length=len(X_train) - 20,
step_length=4,
fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
),
scoring=mean_absolute_error,
param_grid=param_grid,
)
Call fit on the gridsearch object.
[18]:
gridcv.fit(y=y_train, X=X_train)
/Users/benediktheidrich/code/sktime/sktime/forecasting/model_selection/_tune.py:201: UserWarning: in ForecastingGridSearchCV, n_jobs and pre_dispatch parameters are deprecated and will be removed in 0.27.0. Please use n_jobs and pre_dispatch directly in the backend_params argument instead.
warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.644e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+05, tolerance: 9.351e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+05, tolerance: 5.168e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e+01, tolerance: 1.702e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.703e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.733e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+05, tolerance: 9.857e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.170e+05, tolerance: 5.465e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.735e+01, tolerance: 1.701e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.193e+02, tolerance: 1.679e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.690e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+05, tolerance: 1.035e+05
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.280e+05, tolerance: 5.757e+04
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.956e+01, tolerance: 1.697e-02
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+02, tolerance: 1.665e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/step.py:217: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences
input_data[step_name] = pd.concat(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01
model = cd_fast.enet_coordinate_descent(
/Users/benediktheidrich/code/sktime/sktime/pipeline/pipeline.py:160: UserWarning: This generalised graphical pipeline is experimental, with all the usual risks of edge features. For mature alternatives, use single-purpose pipelines and compositors, such as TransformedTargetForecaster, ForecastingPipeline, ClassificationPipeline, etc., see for instance notebooks 01_forecasting.ipynb and 02_classification.ipynb athttps://github.com/sktime/sktime/blob/main/examples/.
warnings.warn(
/Users/benediktheidrich/.pyenv/versions/3.11.9/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.843e-01
model = cd_fast.enet_coordinate_descent(
[18]:
ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12],
step_length=4,
window_length=171),
forecaster=Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'},
'kwargs': {},
'method': None,
'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'},
'kwargs': {},
'method': Non...
'forecaster_inflation__edges__X': [['forecaster_unemp'],
['forecaster_unemp',
'forecaster_dpi']],
'forecaster_inflation__skobject__selected_forecaster': ['ridge',
'lasso'],
'forecaster_unemp__edges__X': [[],
['forecaster_dpi'],
['forecaster_gdp',
'forecaster_dpi']],
'forecaster_unemp__skobject__selected_forecaster': ['ridge',
'lasso']},
scoring=<function mean_absolute_error at 0x172c7e980>)Please rerun this cell to show the HTML repr or trust the notebook.ForecastingGridSearchCV(cv=SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12],
step_length=4,
window_length=171),
forecaster=Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'},
'kwargs': {},
'method': None,
'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'},
'kwargs': {},
'method': Non...
'forecaster_inflation__edges__X': [['forecaster_unemp'],
['forecaster_unemp',
'forecaster_dpi']],
'forecaster_inflation__skobject__selected_forecaster': ['ridge',
'lasso'],
'forecaster_unemp__edges__X': [[],
['forecaster_dpi'],
['forecaster_gdp',
'forecaster_dpi']],
'forecaster_unemp__skobject__selected_forecaster': ['ridge',
'lasso']},
scoring=<function mean_absolute_error at 0x172c7e980>)SlidingWindowSplitter(fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], step_length=4,
window_length=171)Pipeline(steps=[{'edges': {'X': 'X__realgdp_realdpi_unemp'}, 'kwargs': {},
'method': None, 'name': 'scaler',
'skobject': TabularToSeriesAdaptor(transformer=StandardScaler())},
{'edges': {'X': 'X__realgdp_realdpi'}, 'kwargs': {},
'method': None, 'name': 'deseasonalizer',
'skobject': Deseasonalizer(sp=4)},
{'edges': {'y': 'deseasonalizer__realgdp'}, 'kwargs': {},
'method': None...
window_length=5))])},
{'edges': {'X': ['forecaster_dpi', 'forecaster_unemp'],
'y': 'y'},
'kwargs': {}, 'method': None, 'name': 'forecaster_inflation',
'skobject': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])}])Examine the results of the gridsearch
[19]:
gridcv.cv_results_
[19]:
| mean_test__DynamicForecastingErrorMetric | mean_fit_time | mean_pred_time | params | rank_test__DynamicForecastingErrorMetric | |
|---|---|---|---|---|---|
| 0 | 1.539329 | 0.075673 | 0.023929 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 107.5 |
| 1 | 1.720565 | 0.076208 | 0.025338 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 119.5 |
| 2 | 1.394329 | 0.141800 | 0.046452 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 97.5 |
| 3 | 1.942051 | 0.151181 | 0.045115 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 129.5 |
| 4 | 2.033714 | 0.160442 | 0.059711 | {'deseasonalizer__edges__X': 'X__realgdp_reald... | 136.0 |
| ... | ... | ... | ... | ... | ... |
| 187 | 1.329079 | 0.096761 | 0.037935 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 188 | 1.329079 | 0.109997 | 0.040909 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 189 | 1.329079 | 0.100659 | 0.047549 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 190 | 1.329079 | 0.105045 | 0.055426 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
| 191 | 1.329079 | 0.106906 | 0.039190 | {'deseasonalizer__edges__X': 'scaler__realgdp_... | 48.5 |
192 rows × 5 columns
Using the fitted grid search to make a prediction with the best hyperparameters
[20]:
result = gridcv.predict(X=None, fh=y_test.index)
result
[20]:
| infl | |
|---|---|
| Period | |
| 2006Q4 | 2.188182 |
| 2007Q1 | 2.124281 |
| 2007Q2 | 1.045280 |
| 2007Q3 | 1.857716 |
| 2007Q4 | 1.790664 |
| 2008Q1 | 1.649457 |
| 2008Q2 | 1.874361 |
| 2008Q3 | 1.855627 |
| 2008Q4 | 1.858207 |
| 2009Q1 | 1.909693 |
| 2009Q2 | 1.905106 |
| 2009Q3 | 1.910452 |
How to implement a bit simpler version of the pipeline above by nesting sequential pipelines
Simplifcation: The forecasting of the unemployment rate is not dependent on the GDP and DPI.
Create sequential pipelines for forecasting the GDP, DPI and unemployment rate.
[21]:
from sktime.forecasting.compose import ColumnEnsembleForecaster, ForecastX
from sktime.transformations.subset import ColumnSelect
forecasting_pipeline_gdp = (
ColumnSelect(["realgdp"]) # To train the forecaster only on the realgdp column
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
forecasting_pipeline_dpi = (
ColumnSelect(["realdpi"])
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
forecasting_pipeline_unemp = (
ColumnSelect(["unemp"])
* Deseasonalizer()
* MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
)
)
Use ColumnEnsembleForecaster to combine the forecasts of the DPI, GDP, UNEMP. (Union of forecasts)
[22]:
input_inflation_forecast = ColumnEnsembleForecaster(
[
("realdpi", forecasting_pipeline_dpi, "realdpi"),
("realgdp", forecasting_pipeline_gdp, "realgdp"),
("unemp", forecasting_pipeline_unemp, "unemp"),
]
)
Create the inflation forecaster.
[23]:
inflation_forecast = ForecastX(
MultiplexForecaster(
[
(
"ridge",
make_reduction(Ridge(), windows_identical=False, window_length=5),
),
(
"lasso",
make_reduction(Lasso(), windows_identical=False, window_length=5),
),
]
),
input_inflation_forecast,
)
[24]:
inflation_forecast.fit(y=y_train, X=X_train, fh=fh)
[24]:
ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length...
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
forecaster_y=MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]))Please rerun this cell to show the HTML repr or trust the notebook.ForecastX(forecaster_X=ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length...
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
forecaster_y=MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]))ColumnSelect(columns=['realdpi'])
Deseasonalizer()
Ridge()
Lasso()
ColumnSelect(columns=['realgdp'])
Deseasonalizer()
Ridge()
Lasso()
ColumnSelect(columns=['unemp'])
Deseasonalizer()
Ridge()
Lasso()
Ridge()
Lasso()
[25]:
inflation_forecast.predict()
[25]:
| infl | |
|---|---|
| 2006Q4 | 3.979318 |
| 2007Q1 | 2.347512 |
| 2007Q2 | 1.443598 |
| 2007Q3 | 3.914533 |
| 2007Q4 | 2.533117 |
| 2008Q1 | 3.278010 |
| 2008Q2 | 3.861517 |
| 2008Q3 | 3.487510 |
| 2008Q4 | 4.195074 |
| 2009Q1 | 4.294984 |
| 2009Q2 | 4.433578 |
| 2009Q3 | 4.858610 |
[26]:
inflation_forecast.get_params(True)
[26]:
{'behaviour': 'update',
'columns': None,
'fh_X': None,
'fit_behaviour': 'use_actual',
'forecaster_X': ColumnEnsembleForecaster(forecasters=[('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realdpi'),
('r...
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realgdp'),
('unemp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')]),
'forecaster_y': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__forecasters': [('realdpi',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realdpi'),
('realgdp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'realgdp'),
('unemp',
TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'unemp')],
'forecaster_X__realdpi': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__realgdp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__unemp': TransformedTargetForecaster(steps=[ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])]),
'forecaster_X__realdpi__steps': [ColumnSelect(columns=['realdpi']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__realdpi__ColumnSelect': ColumnSelect(columns=['realdpi']),
'forecaster_X__realdpi__Deseasonalizer': Deseasonalizer(),
'forecaster_X__realdpi__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__realdpi__ColumnSelect__columns': ['realdpi'],
'forecaster_X__realdpi__ColumnSelect__index_treatment': 'remove',
'forecaster_X__realdpi__ColumnSelect__integer_treatment': 'col',
'forecaster_X__realdpi__Deseasonalizer__model': 'additive',
'forecaster_X__realdpi__Deseasonalizer__sp': 1,
'forecaster_X__realdpi__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_X__realdpi__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__realdpi__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__realdpi__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__realdpi__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__copy_X': True,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__fit_intercept': True,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__realdpi__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__realdpi__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__realdpi__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__realdpi__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__copy_X': True,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__fit_intercept': True,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__realdpi__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_X__realgdp__steps': [ColumnSelect(columns=['realgdp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__realgdp__ColumnSelect': ColumnSelect(columns=['realgdp']),
'forecaster_X__realgdp__Deseasonalizer': Deseasonalizer(),
'forecaster_X__realgdp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__realgdp__ColumnSelect__columns': ['realgdp'],
'forecaster_X__realgdp__ColumnSelect__index_treatment': 'remove',
'forecaster_X__realgdp__ColumnSelect__integer_treatment': 'col',
'forecaster_X__realgdp__Deseasonalizer__model': 'additive',
'forecaster_X__realgdp__Deseasonalizer__sp': 1,
'forecaster_X__realgdp__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_X__realgdp__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__realgdp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__realgdp__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__realgdp__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__copy_X': True,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__fit_intercept': True,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__realgdp__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__realgdp__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__realgdp__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__realgdp__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__copy_X': True,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__fit_intercept': True,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__realgdp__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_X__unemp__steps': [ColumnSelect(columns=['unemp']),
Deseasonalizer(),
MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))])],
'forecaster_X__unemp__ColumnSelect': ColumnSelect(columns=['unemp']),
'forecaster_X__unemp__Deseasonalizer': Deseasonalizer(),
'forecaster_X__unemp__MultiplexForecaster': MultiplexForecaster(forecasters=[('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(),
window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(),
window_length=5))]),
'forecaster_X__unemp__ColumnSelect__columns': ['unemp'],
'forecaster_X__unemp__ColumnSelect__index_treatment': 'remove',
'forecaster_X__unemp__ColumnSelect__integer_treatment': 'col',
'forecaster_X__unemp__Deseasonalizer__model': 'additive',
'forecaster_X__unemp__Deseasonalizer__sp': 1,
'forecaster_X__unemp__MultiplexForecaster__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_X__unemp__MultiplexForecaster__selected_forecaster': None,
'forecaster_X__unemp__MultiplexForecaster__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_X__unemp__MultiplexForecaster__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator': Ridge(),
'forecaster_X__unemp__MultiplexForecaster__ridge__pooling': 'local',
'forecaster_X__unemp__MultiplexForecaster__ridge__transformers': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__window_length': 5,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__alpha': 1.0,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__copy_X': True,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__fit_intercept': True,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__max_iter': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__positive': False,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__random_state': None,
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__solver': 'auto',
'forecaster_X__unemp__MultiplexForecaster__ridge__estimator__tol': 0.0001,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator': Lasso(),
'forecaster_X__unemp__MultiplexForecaster__lasso__pooling': 'local',
'forecaster_X__unemp__MultiplexForecaster__lasso__transformers': None,
'forecaster_X__unemp__MultiplexForecaster__lasso__window_length': 5,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__alpha': 1.0,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__copy_X': True,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__fit_intercept': True,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__max_iter': 1000,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__positive': False,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__precompute': False,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__random_state': None,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__selection': 'cyclic',
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__tol': 0.0001,
'forecaster_X__unemp__MultiplexForecaster__lasso__estimator__warm_start': False,
'forecaster_y__forecasters': [('ridge',
RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5)),
('lasso',
RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5))],
'forecaster_y__selected_forecaster': None,
'forecaster_y__ridge': RecursiveTabularRegressionForecaster(estimator=Ridge(), window_length=5),
'forecaster_y__lasso': RecursiveTabularRegressionForecaster(estimator=Lasso(), window_length=5),
'forecaster_y__ridge__estimator': Ridge(),
'forecaster_y__ridge__pooling': 'local',
'forecaster_y__ridge__transformers': None,
'forecaster_y__ridge__window_length': 5,
'forecaster_y__ridge__estimator__alpha': 1.0,
'forecaster_y__ridge__estimator__copy_X': True,
'forecaster_y__ridge__estimator__fit_intercept': True,
'forecaster_y__ridge__estimator__max_iter': None,
'forecaster_y__ridge__estimator__positive': False,
'forecaster_y__ridge__estimator__random_state': None,
'forecaster_y__ridge__estimator__solver': 'auto',
'forecaster_y__ridge__estimator__tol': 0.0001,
'forecaster_y__lasso__estimator': Lasso(),
'forecaster_y__lasso__pooling': 'local',
'forecaster_y__lasso__transformers': None,
'forecaster_y__lasso__window_length': 5,
'forecaster_y__lasso__estimator__alpha': 1.0,
'forecaster_y__lasso__estimator__copy_X': True,
'forecaster_y__lasso__estimator__fit_intercept': True,
'forecaster_y__lasso__estimator__max_iter': 1000,
'forecaster_y__lasso__estimator__positive': False,
'forecaster_y__lasso__estimator__precompute': False,
'forecaster_y__lasso__estimator__random_state': None,
'forecaster_y__lasso__estimator__selection': 'cyclic',
'forecaster_y__lasso__estimator__tol': 0.0001,
'forecaster_y__lasso__estimator__warm_start': False}
Comparison graphical pipeline with nesting of sequential pipelines
Advantages of graphical pipelines
Enable an easy implementation of complex pipelines
By nesting sequential pipelines, even a simplified version of the graphical pipeline is very complicat to implement.
By nesting sequential pipelines, some graphical pipelines are not possible to implement (e.g., the example with coupled ForecastX).
Preprocessing steps can not be shared between the different forecasters.
The parameter structure can be very complex for the sequential pipelines.
In a complex scenario, how would you fine-tune the edges?
Advantages of sequential pipelines
Constructing simple pipelines is very easy.
Inverse operations are automatically applied.
This is a mature feature compared to the experimental graphical pipeline.
When to use what?
If your pipeline does not need much of nested pipelines and is mainly sequential, you should probably stick with the standard pipeline implementation.
If your pipeline should represent a complex scenario with multiple forecasters, that are influencing other ones, you might want to use the graphical pipeline since it makes it easier to write the codel
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