TSCGridSearchCV#
- class TSCGridSearchCV(estimator, param_grid, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False, tune_by_variable=False)[source]#
Exhaustive search over specified parameter values for an estimator.
Adapts sklearn GridSearchCV for sktime time series classifiers
Optimizes hyper-parameters of
estimators
by exhaustive grid search.- Parameters:
- estimatorestimator object
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a
score
function, orscoring
must be passed.- param_griddict or list of dictionaries
Dictionary with parameters names (
str
) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.- scoringstr, callable, list, tuple or dict, default=None
Strategy to evaluate the performance of the cross-validated model on the test set.
If scoring represents a single score, one can use:
a single string (see The scoring parameter: defining model evaluation rules);
a callable (see Defining your scoring strategy from metric functions) that returns a single value.
If scoring represents multiple scores, one can use:
a list or tuple of unique strings;
a callable returning a dictionary where the keys are the metric names and the values are the metric scores;
a dictionary with metric names as keys and callables a values.
- n_jobsint, default=None
Number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- refitbool, str, or callable, default=True
Refit an estimator using the best found parameters on the whole dataset. If
False
, thepredict
andpredict_proba
will not work.For multiple metric evaluation, this needs to be a
str
denoting the scorer that would be used to find the best parameters for refitting the estimator at the end.Where there are considerations other than maximum score in choosing a best estimator,
refit
can be set to a function which returns the selectedbest_index_
givencv_results_
. In that case, thebest_estimator_
andbest_params_
will be set according to the returnedbest_index_
while thebest_score_
attribute will not be available.The refitted estimator is made available at the
best_estimator_
attribute and permits usingpredict
directly on thisGridSearchCV
instance.Also for multiple metric evaluation, the attributes
best_index_
,best_score_
andbest_params_
will only be available ifrefit
is set and all of them will be determined w.r.t this specific scorer.See
scoring
parameter to know more about multiple metric evaluation.- cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross validation,
integer, to specify the number of folds in a
(Stratified)KFold
,An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and
y
is either binary or multiclass,StratifiedKFold
is used. In all other cases,KFold
is used. These splitters are instantiated withshuffle=False
so the splits will be the same across calls.Refer User Guide for the various cross-validation strategies that can be used here.
- verboseint
Controls the verbosity: the higher, the more messages.
>1 : the computation time for each fold and parameter candidate is displayed;
>2 : the score is also displayed;
>3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation.
- pre_dispatchint, or str, default=’2*n_jobs’
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
An int, giving the exact number of total jobs that are spawned
A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
- error_score‘raise’ or numeric, default=np.nan
Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.
- return_train_scorebool, default=False
If
False
, thecv_results_
attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.- tune_by_variablebool, optional (default=False)
Whether to tune parameter by each time series variable separately, in case of multivariate data passed to the tuning estimator. Only applies if time series passed are strictly multivariate. If True, clones of the estimator will be fit to each variable separately, and are available in fields of the classifiers_ attribute. Has the same effect as applying ColumnEnsembleClassifier wrapper to self. If False, the same best parameter is selected for all variables.
- Attributes:
- cv_results_dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be imported into a pandas
DataFrame
.For multi-metric evaluation, the scores for all the scorers are available in the
cv_results_
dict at the keys ending with that scorer’s name ('_<scorer_name>'
) instead of'_score'
shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.)- best_estimator_estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if
refit=False
.See
refit
parameter for more information on allowed values.- best_score_float
Mean cross-validated score of the best_estimator
For multi-metric evaluation, this is present only if
refit
is specified.This attribute is not available if
refit
is a function.- best_params_dict
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is present only if
refit
is specified.- best_index_int
The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting.The dict at
search.cv_results_['params'][search.best_index_]
gives the parameter setting for the best model, that gives the highest mean score (search.best_score_
).For multi-metric evaluation, this is present only if
refit
is specified.- scorer_function or a dict
Scorer function used on the held out data to choose the best parameters for the model.
For multi-metric evaluation, this attribute holds the validated
scoring
dict which maps the scorer key to the scorer callable.- n_splits_int
The number of cross-validation splits (folds/iterations).
- refit_time_float
Seconds used for refitting the best model on the whole dataset.
This is present only if
refit
is not False.- multimetric_bool
Whether or not the scorers compute several metrics.
- classes_ndarray of shape (n_classes,)
The classes labels. This is present only if
refit
is specified and the underlying estimator is a classifier.- n_features_in_int
Number of features seen during fit. Only defined if
best_estimator_
is defined (see the documentation for therefit
parameter for more details) and thatbest_estimator_
exposesn_features_in_
when fit.- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Only defined if
best_estimator_
is defined (see the documentation for therefit
parameter for more details) and thatbest_estimator_
exposesfeature_names_in_
when fit.
See also
ParameterGrid
Generates all the combinations of a hyperparameter grid.
train_test_split
Utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.
sklearn.metrics.make_scorer
Make a scorer from a performance metric or loss function.
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 time series classifier to training data.
fit_predict
(X, y[, cv, change_state])Fit and predict labels for sequences in X.
fit_predict_proba
(X, y[, cv, change_state])Fit and predict labels probabilities for sequences in X.
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.
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.
predict
(X)Predicts labels for sequences in X.
Predicts labels probabilities for sequences in X.
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.
score
(X, y)Scores predicted labels against ground truth labels on X.
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.
- 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. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.
- 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)[source]#
Fit time series classifier to training data.
- State change:
Changes state to “fitted”.
- Writes to self:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
- Parameters:
- Xsktime compatible time series panel data container, Panel scitype, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- Returns:
- selfReference to self.
- fit_predict(X, y, cv=None, change_state=True)[source]#
Fit and predict labels for sequences in X.
Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.
- Writes to self, if change_state=True:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
Does not update state if change_state=False.
- Parameters:
- Xsktime compatible time series panel data container, Panel scitype, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- cvNone, int, or sklearn cross-validation object, optional, default=None
None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)
where multiple X_train, y_train, X_test are obtained from cv folds returned y is union over all test fold predictions cv test folds must be non-intersecting
- intequivalent to cv=KFold(cv, shuffle=True, random_state=x),
i.e., k-fold cross-validation predictions out-of-sample random_state x is taken from self if exists, otherwise x=None
- change_statebool, optional (default=True)
- if False, will not change the state of the classifier,
i.e., fit/predict sequence is run with a copy, self does not change
- if True, will fit self to the full X and y,
end state will be equivalent to running fit(X, y)
- Returns:
- y_predsktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] predicted class labels 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X 1D np.npdarray, if y univariate (one dimension) otherwise, same type as y passed in fit
- fit_predict_proba(X, y, cv=None, change_state=True)[source]#
Fit and predict labels probabilities for sequences in X.
Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.
- Writes to self, if change_state=True:
Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.
Does not update state if change_state=False.
- Parameters:
- Xsktime compatible time series panel data container, Panel scitype, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- cvNone, int, or sklearn cross-validation object, optional, default=None
None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)
where multiple X_train, y_train, X_test are obtained from cv folds returned y is union over all test fold predictions cv test folds must be non-intersecting
int : equivalent to cv=Kfold(int), i.e., k-fold cross-validation predictions
- change_statebool, optional (default=True)
- if False, will not change the state of the classifier,
i.e., fit/predict sequence is run with a copy, self does not change
- if True, will fit self to the full X and y,
end state will be equivalent to running fit(X, y)
- Returns:
- y_pred2D np.array of int, of shape [n_instances, n_classes]
predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1
- 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 objectif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
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.
- 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
- predict(X)[source]#
Predicts labels for sequences in X.
- Parameters:
- Xsktime compatible time series panel data container, Panel scitype, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- Returns:
- y_predsktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] predicted class labels 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X 1D np.npdarray, if y univariate (one dimension) otherwise, same type as y passed in fit
- predict_proba(X)[source]#
Predicts labels probabilities for sequences in X.
- Parameters:
- Xsktime compatible time series panel data container, Panel scitype, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- Returns:
- y_pred2D np.array of int, of shape [n_instances, n_classes]
predicted class label probabilities 0-th indices correspond to instance indices in X 1-st indices correspond to class index, in same order as in self.classes_ entries are predictive class probabilities, summing to 1
- 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
- score(X, y) float [source]#
Scores predicted labels against ground truth labels on X.
- Parameters:
- Xsktime compatible time series panel data container, e.g.,
pd-multiindex: pd.DataFrame with columns = variables, index = pd.MultiIndex with first level = instance indices, second level = time indices numpy3D: 3D np.array (any number of dimensions, equal length series) of shape [n_instances, n_dimensions, series_length] or of any other supported Panel mtype for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- ysktime compatible tabular data container, Table scitype
1D iterable, of shape [n_instances] or 2D iterable, of shape [n_instances, n_dimensions] class labels for fitting 0-th indices correspond to instance indices in X 1-st indices (if applicable) correspond to multioutput vector indices in X supported sktime types: np.ndarray (1D, 2D), pd.Series, pd.DataFrame
- Returns:
- float, accuracy score of predict(X) vs y
- 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
- 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