WindowSummarizer#
- class WindowSummarizer(lag_feature=None, n_jobs=-1, target_cols=None, truncate=None)[source]#
Transformer for extracting time series features.
The WindowSummarizer transforms input series to features based on a provided dictionary of window summarizer, window shifts and window lengths.
- Parameters:
- n_jobsint, optional (default=-1)
The number of jobs to run in parallel for applying the window functions.
-1
means using all processors.- target_cols: list of str, optional (default = None)
Specifies which columns in X to target for applying the window functions.
None
will target the first column- lag_feature: dict of str and list, optional (default = dict containing first lag)
Dictionary specifying as key the type of function to be used and as value the argument
window
. For the functionlag
, the argumentwindow
is an integer or a list of integers giving thelag
values to be used. For all other functions, the argumentwindow
is a list with the argumentslag
andwindow length
.lag
defines how far back in the past the window starts,window length
gives the length of the window across which to apply the function. For multiple different windows, provide a list of lists.Please see below a graphical representation of the logic using the following symbols:
z
= time stamp that the window is summarized to.Part of the window if
lag
is between 0 and1-window_length
, otherwise not part of the window.x
= (other) time stamps in the window which is summarized*
= observations, past or future, not part of the windowThe summarization function is applied to the window consisting of x and potentially z.
For
window = [1, 3]
, we have alag
of 1 andwindow_length
of 3 to target the three last days (exclusive z) that were observed. Summarization is done across windows like this:|---------------------------| | * * * * * * * * x x x z * | |---------------------------|
For
window = [0, 3]
, we have alag
of 0 andwindow_length
of 3 to target the three last days (inclusive z) that were observed. Summarization is done across windows like this:|---------------------------| | * * * * * * * * x x z * * | |---------------------------|
Special case
lag
: Since lags are frequently used and window length is redundant, you only need to provide a list oflag
values. Sowindow = [1]
will result in the first lag:|---------------------------| | * * * * * * * * * * x z * | |---------------------------|
And
window = [1, 4]
will result in the first and fourth lag:|---------------------------| | * * * * * * * x * * x z * | |---------------------------|
- key: either custom function call (to be provided by user) or
- str corresponding to native pandas window function:
“sum”,
“mean”,
“median”,
“std”,
“var”,
“kurt”,
“min”,
“max”,
“corr”,
“cov”,
“skew”,
“sem”
See also: https://pandas.pydata.org/docs/reference/window.html.
The column generated will be named after the key provided, followed by the lag parameter and the window_length (if not a lag).
- second value (window): list of integers
List containing lag and window_length parameters.
- truncate: str, optional (default = None)
Defines how to deal with NAs that were created as a result of applying the functions in the lag_feature dict across windows that are longer than the remaining history of data. For example a lag config of [14, 7] cannot be fully applied for the first 20 observations of the targeted column. A lag_feature of [[8, 14], [1, 28]] cannot be correctly applied for the first 21 resp. 28 observations of the targeted column. Possible values to deal with those NAs:
None
“bfill”
None will keep the NAs generated, and would leave it for the user to choose an estimator that can correctly deal with observations with missing values, “bfill” will fill the NAs by carrying the first observation backwards.
- Returns:
- X: pd.DataFrame
Contains all transformed columns as well as non-transformed columns. The raw inputs to transformed columns will be dropped.
- self: reference to self
- Attributes:
- truncate_startint
See section Parameters - truncate for a more detailed explanation of truncation as a result of applying windows of certain lengths across past observations. Truncate_start will give the maximum of observations that are filled with NAs across all arguments of the lag_feature when truncate is set to None.
Examples
>>> import pandas as pd >>> from sktime.transformations.series.summarize import WindowSummarizer >>> from sktime.datasets import load_airline, load_longley >>> from sktime.forecasting.naive import NaiveForecaster >>> from sktime.forecasting.base import ForecastingHorizon >>> from sktime.forecasting.compose import ForecastingPipeline >>> from sktime.split import temporal_train_test_split >>> y = load_airline() >>> kwargs = { ... "lag_feature": { ... "lag": [1], ... "mean": [[1, 3], [3, 6]], ... "std": [[1, 4]], ... } ... } >>> transformer = WindowSummarizer(**kwargs) >>> y_transformed = transformer.fit_transform(y)
Example with transforming multiple columns of exogeneous features
>>> y, X = load_longley() >>> y_train, y_test, X_train, X_test = temporal_train_test_split(y, X) >>> fh = ForecastingHorizon(X_test.index, is_relative=False) >>> # Example transforming only X >>> pipe = ForecastingPipeline( ... steps=[ ... ("a", WindowSummarizer(n_jobs=1, target_cols=["POP", "GNPDEFL"])), ... ("b", WindowSummarizer(n_jobs=1, target_cols=["GNP"], **kwargs)), ... ("forecaster", NaiveForecaster(strategy="drift")), ... ] ... ) >>> pipe_return = pipe.fit(y_train, X_train) >>> y_pred1 = pipe_return.predict(fh=fh, X=X_test)
Example with transforming multiple columns of exogeneous features as well as the y column
>>> Z_train = pd.concat([X_train, y_train], axis=1) >>> Z_test = pd.concat([X_test, y_test], axis=1) >>> pipe = ForecastingPipeline( ... steps=[ ... ("a", WindowSummarizer(n_jobs=1, target_cols=["POP", "TOTEMP"])), ... ("b", WindowSummarizer(**kwargs, n_jobs=1, target_cols=["GNP"])), ... ("forecaster", NaiveForecaster(strategy="drift")), ... ] ... ) >>> pipe_return = pipe.fit(y_train, Z_train) >>> y_pred2 = pipe_return.predict(fh=fh, X=Z_test)
Methods
Check if the estimator has been fitted.
clone
()Obtain a clone of the object with same hyper-parameters.
clone_tags
(estimator[, tag_names])Clone tags from another estimator as dynamic override.
create_test_instance
([parameter_set])Construct Estimator instance if possible.
create_test_instances_and_names
([parameter_set])Create list of all test instances and a list of names for them.
fit
(X[, y])Fit transformer to X, optionally to y.
fit_transform
(X[, y])Fit to data, then transform it.
get_class_tag
(tag_name[, tag_value_default])Get a class tag's value.
Get class tags from the class and all its parent classes.
Get config flags for self.
get_fitted_params
([deep])Get fitted parameters.
Get object's parameter defaults.
Get object's parameter names.
get_params
([deep])Get a dict of parameters values for this object.
get_tag
(tag_name[, tag_value_default, ...])Get tag value from estimator class and dynamic tag overrides.
get_tags
()Get tags from estimator class and dynamic tag overrides.
get_test_params
([parameter_set])Return testing parameter settings for the estimator.
inverse_transform
(X[, y])Inverse transform X and return an inverse transformed version.
Check if the object is composed of other BaseObjects.
load_from_path
(serial)Load object from file location.
load_from_serial
(serial)Load object from serialized memory container.
reset
()Reset the object to a clean post-init state.
save
([path, serialization_format])Save serialized self to bytes-like object or to (.zip) file.
set_config
(**config_dict)Set config flags to given values.
set_params
(**params)Set the parameters of this object.
set_random_state
([random_state, deep, ...])Set random_state pseudo-random seed parameters for self.
set_tags
(**tag_dict)Set dynamic tags to given values.
transform
(X[, y])Transform X and return a transformed version.
update
(X[, y, update_params])Update transformer with X, optionally y.
- classmethod get_test_params(parameter_set='default')[source]#
Return testing parameter settings for the estimator.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return
"default"
set.
- Returns:
- paramsdict or list of dict, default = {}
Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e.,
MyClass(**params)
orMyClass(**params[i])
creates a valid test instance.create_test_instance
uses the first (or only) dictionary inparams
- check_is_fitted()[source]#
Check if the estimator has been fitted.
- Raises:
- NotFittedError
If the estimator has not been fitted yet.
- clone()[source]#
Obtain a clone of the object with same hyper-parameters.
A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.
- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
Notes
If successful, equal in value to
type(self)(**self.get_params(deep=False))
.
- clone_tags(estimator, tag_names=None)[source]#
Clone tags from another estimator as dynamic override.
- Parameters:
- estimatorestimator inheriting from :class:BaseEstimator
- tag_namesstr or list of str, default = None
Names of tags to clone. If None then all tags in estimator are used as tag_names.
- Returns:
- Self
Reference to self.
Notes
Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.
- classmethod create_test_instance(parameter_set='default')[source]#
Construct Estimator instance if possible.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.
- Returns:
- instanceinstance of the class with default parameters
Notes
get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.
- classmethod create_test_instances_and_names(parameter_set='default')[source]#
Create list of all test instances and a list of names for them.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.
- Returns:
- objslist of instances of cls
i-th instance is cls(**cls.get_test_params()[i])
- nameslist of str, same length as objs
i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}
- fit(X, y=None)[source]#
Fit transformer to X, optionally to y.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True
possibly coerced to inner type or update_data compatible type by reference, when possible
model attributes (ending in “_”) : dependent on estimator
- Parameters:
- Xtime series in sktime compatible data container format
Data to fit transform to, of sktime type as follows: Series: interpreted as single time series
pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)
- Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)
- Hierarchical: pd.DataFrame with 3- or more-level MultiIndex
highest (rightmost) level of MultiIndex is time
for more details on sktime mtype format specifications, and additional valid type specifications, refer to
examples/AA_datatypes_and_datasets.ipynb
- yoptional, time series in sktime compatible data format, default=None
Additional data, e.g., labels for transformation some transformers require this, see class docstring for details
- Returns:
- selfa fitted instance of the estimator
- fit_transform(X, y=None)[source]#
Fit to data, then transform it.
Fits the transformer to X and y and returns a transformed version of X.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True
possibly coerced to inner type or update_data compatible type by reference, when possible
model attributes (ending in “_”) : dependent on estimator
- Parameters:
- Xtime series in sktime compatible data container format
Data to transform, of sktime type as follows: Series: interpreted as single time series
pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)
- Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)
- Hierarchical: pd.DataFrame with 3- or more-level MultiIndex
highest (rightmost) level of MultiIndex is time
for more details on sktime mtype format specifications, and additional valid type specifications, refer to
examples/AA_datatypes_and_datasets.ipynb
- yoptional, time series in sktime compatible data format, default=None
Additional data, e.g., labels for transformation some transformers require this, see class docstring for details
- Returns:
- transformed version of X
- type depends on type of X and scitype:transform-output tag:
- X | tf-output | type of return |
|----------|————–|------------------------| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |
- instances in return correspond to instances in X
- combinations not in the table are currently not supported
- Explicitly, with examples:
- if X is Series (e.g., pd.DataFrame) and transform-output is Series
then the return is a single Series of the same mtype Example: detrending a single series
- if X is Panel (e.g., pd-multiindex) and transform-output is Series
- then the return is Panel with same number of instances as X
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
- if X is Series or Panel and transform-output is Primitives
then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series
- if X is Series and transform-output is Panel
then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X
- classmethod get_class_tag(tag_name, tag_value_default=None)[source]#
Get a class tag’s value.
Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.
- Parameters:
- tag_namestr
Name of tag value.
- tag_value_defaultany
Default/fallback value if tag is not found.
- Returns:
- tag_value
Value of the tag_name tag in self. If not found, returns tag_value_default.
- classmethod get_class_tags()[source]#
Get class tags from the class and all its parent classes.
Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.
- Returns:
- collected_tagsdict
Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.
- get_config()[source]#
Get config flags for self.
- Returns:
- config_dictdict
Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.
- get_fitted_params(deep=True)[source]#
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
Whether to return fitted parameters of components.
If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.
- Returns:
- fitted_paramsdict with str-valued keys
Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:
always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc
- classmethod get_param_defaults()[source]#
Get object’s parameter defaults.
- Returns:
- default_dict: dict[str, Any]
Keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.
- classmethod get_param_names()[source]#
Get object’s parameter names.
- Returns:
- param_names: list[str]
Alphabetically sorted list of parameter names of cls.
- get_params(deep=True)[source]#
Get a dict of parameters values for this object.
- Parameters:
- deepbool, default=True
Whether to return parameters of components.
If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.
- Returns:
- paramsdict with str-valued keys
Dictionary of parameters, paramname : paramvalue keys-value pairs include:
always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc
- get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#
Get tag value from estimator class and dynamic tag overrides.
- Parameters:
- tag_namestr
Name of tag to be retrieved
- tag_value_defaultany type, optional; default=None
Default/fallback value if tag is not found
- raise_errorbool
whether a ValueError is raised when the tag is not found
- Returns:
- tag_valueAny
Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.
- Raises:
- ValueError if raise_error is True i.e. if tag_name is not in
- self.get_tags().keys()
- get_tags()[source]#
Get tags from estimator class and dynamic tag overrides.
- Returns:
- collected_tagsdict
Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.
- inverse_transform(X, y=None)[source]#
Inverse transform X and return an inverse transformed version.
- Currently it is assumed that only transformers with tags
“scitype:transform-input”=”Series”, “scitype:transform-output”=”Series”,
have an inverse_transform.
- State required:
Requires state to be “fitted”.
Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform
- Parameters:
- Xtime series in sktime compatible data container format
Data to inverse transform, of sktime type as follows: Series: interpreted as single time series
pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)
- Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)
- Hierarchical: pd.DataFrame with 3- or more-level MultiIndex
highest (rightmost) level of MultiIndex is time
for more details on sktime mtype format specifications, and additional valid type specifications, refer to
examples/AA_datatypes_and_datasets.ipynb
- yoptional, time series in sktime compatible data format, default=None
Additional data, e.g., labels for transformation some transformers require this, see class docstring for details
- Returns:
- inverse transformed version of X
of the same type as X, and conforming to mtype format specifications
- is_composite()[source]#
Check if the object is composed of other BaseObjects.
A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.
- Returns:
- composite: bool
Whether an object has any parameters whose values are BaseObjects.
- classmethod load_from_path(serial)[source]#
Load object from file location.
- Parameters:
- serialresult of ZipFile(path).open(“object)
- Returns:
- deserialized self resulting in output at
path
, ofcls.save(path)
- deserialized self resulting in output at
- classmethod load_from_serial(serial)[source]#
Load object from serialized memory container.
- Parameters:
- serial1st element of output of
cls.save(None)
- serial1st element of output of
- Returns:
- deserialized self resulting in output
serial
, ofcls.save(None)
- deserialized self resulting in output
- reset()[source]#
Reset the object to a clean post-init state.
Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:
hyper-parameters = arguments of __init__
object attributes containing double-underscores, i.e., the string “__”
Class and object methods, and class attributes are also unaffected.
- Returns:
- self
Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.
Notes
Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))
- save(path=None, serialization_format='pickle')[source]#
Save serialized self to bytes-like object or to (.zip) file.
Behaviour: if
path
is None, returns an in-memory serialized self ifpath
is a file location, stores self at that location as a zip filesaved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).
- Parameters:
- pathNone or file location (str or Path)
if None, self is saved to an in-memory object if file location, self is saved to that file location. If:
path=”estimator” then a zip file
estimator.zip
will be made at cwd. path=”/home/stored/estimator” then a zip fileestimator.zip
will be stored in/home/stored/
.- serialization_format: str, default = “pickle”
Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.
- Returns:
- if
path
is None - in-memory serialized self - if
path
is file location - ZipFile with reference to the file
- if
- set_config(**config_dict)[source]#
Set config flags to given values.
- Parameters:
- config_dictdict
Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:
- displaystr, “diagram” (default), or “text”
how jupyter kernels display instances of self
“diagram” = html box diagram representation
“text” = string printout
- print_changed_onlybool, default=True
whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.
- warningsstr, “on” (default), or “off”
whether to raise warnings, affects warnings from sktime only
“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime
- backend:parallelstr, optional, default=”None”
backend to use for parallelization when broadcasting/vectorizing, one of
“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses
joblib.Parallel
“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
“dask”: uses
dask
, requiresdask
package in environment
- backend:parallel:paramsdict, optional, default={} (no parameters passed)
additional parameters passed to the parallelization backend as config. Valid keys depend on the value of
backend:parallel
:“None”: no additional parameters,
backend_params
is ignored“loky”, “multiprocessing” and “threading”: default
joblib
backends any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
, with the exception ofbackend
which is directly controlled bybackend
. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
. Any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
,backend
must be passed as a key ofbackend_params
in this case. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“dask”: any valid keys for
dask.compute
can be passed, e.g.,scheduler
- input_conversionstr, one of “on” (default), “off”, or valid mtype string
controls input checks and conversions, for
_fit
,_transform
,_inverse_transform
,_update
"on"
- input check and conversion is carried out"off"
- input check and conversion are not carried out before passing data to inner methodsvalid mtype string - input is assumed to specified mtype, conversion is carried out but no check
- output_conversionstr, one of “on”, “off”, valid mtype string
controls output conversion for
_transform
,_inverse_transform
"on"
- if input_conversion is “on”, output conversion is carried out"off"
- output of_transform
,_inverse_transform
is directly returnedvalid mtype string - output is converted to specified mtype
- Returns:
- selfreference to self.
Notes
Changes object state, copies configs in config_dict to self._config_dynamic.
- set_params(**params)[source]#
Set the parameters of this object.
The method works on simple estimators as well as on composite objects. Parameter key strings
<component>__<parameter>
can be used for composites, i.e., objects that contain other objects, to access<parameter>
in the component<component>
. The string<parameter>
, without<component>__
, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name<parameter>
.- Parameters:
- **paramsdict
BaseObject parameters, keys must be
<component>__<parameter>
strings. __ suffixes can alias full strings, if unique among get_params keys.
- Returns:
- selfreference to self (after parameters have been set)
- set_random_state(random_state=None, deep=True, self_policy='copy')[source]#
Set random_state pseudo-random seed parameters for self.
Finds
random_state
named parameters viaestimator.get_params
, and sets them to integers derived fromrandom_state
viaset_params
. These integers are sampled from chain hashing viasample_dependent_seed
, and guarantee pseudo-random independence of seeded random generators.Applies to
random_state
parameters inestimator
depending onself_policy
, and remaining component estimators if and only ifdeep=True
.Note: calls
set_params
even ifself
does not have arandom_state
, or none of the components have arandom_state
parameter. Therefore,set_random_state
will reset anyscikit-base
estimator, even those without arandom_state
parameter.- Parameters:
- random_stateint, RandomState instance or None, default=None
Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.
- deepbool, default=True
Whether to set the random state in sub-estimators. If False, will set only
self
’srandom_state
parameter, if exists. If True, will setrandom_state
parameters in sub-estimators as well.- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
estimator.random_state
is set to inputrandom_state
“keep” :
estimator.random_state
is kept as is“new” :
estimator.random_state
is set to a new random state,
derived from input
random_state
, and in general different from it
- Returns:
- selfreference to self
- set_tags(**tag_dict)[source]#
Set dynamic tags to given values.
- Parameters:
- **tag_dictdict
Dictionary of tag name: tag value pairs.
- Returns:
- Self
Reference to self.
Notes
Changes object state by setting tag values in tag_dict as dynamic tags in self.
- transform(X, y=None)[source]#
Transform X and return a transformed version.
- State required:
Requires state to be “fitted”.
Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _transform
- Parameters:
- Xtime series in sktime compatible data container format
Data to fit transform to, of sktime type as follows: Series: interpreted as single time series
pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)
- Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)
- Hierarchical: pd.DataFrame with 3- or more-level MultiIndex
highest (rightmost) level of MultiIndex is time
for more details on sktime mtype format specifications, and additional valid type specifications, refer to
examples/AA_datatypes_and_datasets.ipynb
- yoptional, time series in sktime compatible data format, default=None
Additional data, e.g., labels for transformation some transformers require this, see class docstring for details
- Returns:
- transformed version of X
- type depends on type of X and scitype:transform-output tag:
transform
X
-output
type of return
Series
Primitives
pd.DataFrame (1-row)
Panel
Primitives
pd.DataFrame
Series
Series
Series
Panel
Series
Panel
Series
Panel
Panel
- instances in return correspond to instances in X
- combinations not in the table are currently not supported
- Explicitly, with examples:
- if X is Series (e.g., pd.DataFrame) and transform-output is Series
then the return is a single Series of the same mtype Example: detrending a single series
- if X is Panel (e.g., pd-multiindex) and transform-output is Series
- then the return is Panel with same number of instances as X
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
- if X is Series or Panel and transform-output is Primitives
then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series
- if X is Series and transform-output is Panel
then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X
- update(X, y=None, update_params=True)[source]#
Update transformer with X, optionally y.
- State required:
Requires state to be “fitted”.
Accesses in self: _is_fitted : must be True _X : accessed by _update and by update_data, if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _update
Writes to self: _X : updated by values in X, via update_data, if remember_data tag is True fitted model attributes (ending in “_”) : only if update_params=True
type and nature of update are dependent on estimator
- Parameters:
- Xtime series in sktime compatible data container format
Data to update transform with, of sktime type as follows: Series: interpreted as single time series
pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)
- Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)
- Hierarchical: pd.DataFrame with 3- or more-level MultiIndex
highest (rightmost) level of MultiIndex is time
for more details on sktime mtype format specifications, and additional valid type specifications, refer to
examples/AA_datatypes_and_datasets.ipynb
- yoptional, time series in sktime compatible data format, default=None
Additional data, e.g., labels for transformation some transformers require this, see class docstring for details
- Returns:
- selfa fitted instance of the estimator