Lag#
- class Lag(lags=0, freq=None, index_out='extend', flatten_transform_index=True)[source]#
Lagging transformer. Lags time series by one or multiple lags.
Transforms a time series into a lagged version of itself. Multiple lags can be provided, as a list. Estimator-like wrapper of pandas.shift and integer index lagging.
Lags can be provided as a simple offset, lags, or pair of (lag count, frequency), with lag count an int (lags arg) and frequency a pandas frequency descriptor.
When multiple lags are provided, multiple column concatenated copies of the lagged time series will be created. Names of columns are lagname__variablename, where lagname describes the lag/freq.
If data was provided in _fit or _update, Lag transformer memorizes those indices and uses them for computing lagged values. To use only data seen in transform, use the FitInTransform compositor.
- Parameters
- lagslag offset, or list of lag offsets, optional, default=0 (identity transform)
a “lag offset” can be one of the following: int - number of periods to shift/lag time-like: DateOffset, tseries.offsets, or timedelta
time delta offset to shift/lag requires time index of transformed data to be time-like (not int)
str - time rule from pandas.tseries module, e.g., “EOM”
- freqfrequency descriptor of list of frequency descriptors, optional, default=None
if passed, must be scalar, or list of equal length to “lags” argument elements in freq correspond to elements in lags if i-th element of freq is not None, i-th element of lags must be int
this is called the “corresponding lags element” below
“frequency descriptor” can be one of the following: time-like: DateOffset, tseries.offsets, or timedelta
multiplied to corresponding “lags” element when shifting
str - offset from pd.tseries module, e.g., “D”, “M”, or time rule, e.g., “EOM”
- index_outstr, optional, one of “shift”, “original”, “extend”, default=”extend”
determines set of output indices in lagged time series “shift” - only shifted indices are retained.
Will not create NA for single lag, but can create NA for multiple lags.
“original” - only original indices are retained. Will usually create NA. “extend” - both original indices and shifted indices are retained.
Will usually create NA, possibly many, if shifted/original do not intersect.
- flatten_transform_indexbool, optional (default=True)
if True, columns of return DataFrame are flat, by “lagname__variablename” if False, columns are MultiIndex (lagname, variablename) has no effect if return mtype is one without column names
- Attributes
is_fitted
Whether fit has been called.
Examples
>>> from sktime.datasets import load_airline >>> from sktime.transformations.series.lag import Lag >>> X = load_airline()
Single lag will yield a time series with the same variables: >>> t = Lag(2) >>> Xt = t.fit_transform(X)
Multiple lags can be provided, this will result in multiple columns: >>> t = Lag([2, 4, -1]) >>> Xt = t.fit_transform(X)
The default setting of index_out will extend indices either side. To ensure that the index remains the same after transform, use index_out=”original” >>> t = Lag([2, 4, -1], index_out=”original”) >>> Xt = t.fit_transform(X)
The lag transformer may (and usually will) create NAs. (except when index_out=”shift” and there is only a single lag, or in trivial cases) This may need to be handled, e.g., if a subsequent pipeline step does not accept NA. To deal with the NAs, pipeline with the Imputer: >>> from sktime.datasets import load_airline >>> from sktime.transformations.series.impute import Imputer >>> from sktime.transformations.series.lag import Lag >>> X = load_airline() >>> >>> t = Lag([2, 4, -1]) * Imputer(“nearest”) >>> Xt = t.fit_transform(X)
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/mirror 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 tag value from estimator class (only class tags).
Get class tags from estimator class and all its parent classes.
Get fitted parameters.
Get parameter defaults for the object.
Get parameter names for the object.
get_params
([deep])Get parameters for this estimator.
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 composite.
reset
()Reset the object to a clean post-init state.
set_params
(**params)Set the parameters of this object.
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.
- 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. Equal in value to type(self)(**self.get_params(deep=False)).
- Returns
- instance of type(self), clone of self (see above)
- clone_tags(estimator, tag_names=None)[source]#
clone/mirror 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__}
- 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.
- fit(X, y=None)[source]#
Fit transformer to X, optionally to y.
- State change:
Changes state to “fitted”.
- Writes to self:
Sets is_fitted flag to True. Sets fitted model attributes ending in “_”.
- Parameters
- XSeries or Panel, any supported mtype
- Data to fit transform to, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
- subject to sktime mtype format specifications, for further details see
examples/AA_datatypes_and_datasets.ipynb
- ySeries or Panel, default=None
Additional data, e.g., labels for transformation
- 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:
Sets is_fitted flag to True. Sets fitted model attributes ending in “_”.
- Parameters
- XSeries or Panel, any supported mtype
- Data to be transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
- subject to sktime mtype format specifications, for further details see
examples/AA_datatypes_and_datasets.ipynb
- ySeries or Panel, default=None
Additional data, e.g., labels for transformation
- 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 tag value from estimator class (only class tags).
- Parameters
- tag_namestr
Name of tag value.
- tag_value_defaultany type
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 estimator class and all its parent classes.
- Returns
- collected_tagsdict
Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.
- get_fitted_params()[source]#
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Returns
- fitted_paramsdict of fitted parameters, keys are str names of parameters
parameters of components are indexed as [componentname]__[paramname]
- classmethod get_param_defaults()[source]#
Get parameter defaults for the object.
- Returns
- default_dict: dict with str keys
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 parameter names for the object.
- Returns
- param_names: list of str, alphabetically sorted list of parameter names of cls
- get_params(deep=True)[source]#
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- 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_value
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:
Fitted model attributes ending in “_”. self._is_fitted
- Parameters
- XSeries or Panel, any supported mtype
- Data to be inverse transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
- subject to sktime mtype format specifications, for further details see
examples/AA_datatypes_and_datasets.ipynb
- ySeries or Panel, default=None
Additional data, e.g., labels for transformation
- 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 composite.
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 self contains a parameter which is BaseObject
- reset()[source]#
Reset the object to a clean post-init state.
Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))
Detail behaviour: removes any object attributes, except:
hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”
runs __init__ with current values of hyper-parameters (result of get_params)
Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes
- set_params(**params)[source]#
Set the parameters of this object.
The method works on simple estimators as well as on nested objects. The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
BaseObject parameters
- Returns
- selfreference to self (after parameters have been set)
- 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 settting 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:
Fitted model attributes ending in “_”. self._is_fitted
- Parameters
- XSeries or Panel, any supported mtype
- Data to be transformed, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
- subject to sktime mtype format specifications, for further details see
examples/AA_datatypes_and_datasets.ipynb
- ySeries or Panel, default=None
Additional data, e.g., labels for transformation
- 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:
Fitted model attributes ending in “_”. self._is_fitted
- Writes to self:
May update fitted model attributes ending in “_”.
- Parameters
- XSeries or Panel, any supported mtype
- Data to fit transform to, of python type as follows:
Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,
nested pd.DataFrame, or pd.DataFrame in long/wide format
- subject to sktime mtype format specifications, for further details see
examples/AA_datatypes_and_datasets.ipynb
- ySeries or Panel, default=None
Additional data, e.g., labels for transformation
- update_paramsbool, default=True
whether the model is updated. Yes if true, if false, simply skips call. argument exists for compatibility with forecasting module.
- Returns
- selfa fitted instance of the estimator
- 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. There are currently no reserved values for transformers.
- 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) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params