ColumnEnsembleClassifier#
- class ColumnEnsembleClassifier(estimators, remainder='drop', verbose=False)[source]#
Applies estimators to columns of an array or pandas DataFrame.
This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be ensembled to form a single output.
- Parameters
- estimatorslist of tuples
List of (name, estimator, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.
- namestring
Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using
set_params
and searched in grid search.- estimatoror {‘drop’}
Estimator must support fit and predict_proba. Special-cased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns.
column(s) : array-like of string or int, slice, boolean mask array or callable.
- remainder{‘drop’, ‘passthrough’} or estimator, default ‘drop’
By default, only the specified columns in transformations are transformed and combined in the output, and the non-specified columns are dropped. (default of
'drop'
). By specifyingremainder='passthrough'
, all remaining columns that were not specified in transformations will be automatically passed through. This subset of columns is concatenated with the output of the transformations. By settingremainder
to be an estimator, the remaining non-specified columns will use theremainder
estimator. The estimator must support fit and transform.
- Attributes
is_fitted
Whether fit has been called.
Examples
>>> from sktime.classification.dictionary_based import ContractableBOSS >>> from sktime.classification.interval_based import CanonicalIntervalForest >>> from sktime.datasets import load_basic_motions >>> X_train, y_train = load_basic_motions(split="train") >>> X_test, y_test = load_basic_motions(split="test") >>> cboss = ContractableBOSS( ... n_parameter_samples=4, max_ensemble_size=2, random_state=0 ... ) >>> cif = CanonicalIntervalForest( ... n_estimators=2, n_intervals=4, att_subsample_size=4, random_state=0 ... ) >>> estimators = [("cBOSS", cboss, 5), ("CIF", cif, [3, 4])] >>> col_ens = ColumnEnsembleClassifier(estimators=estimators) >>> col_ens.fit(X_train, y_train) ColumnEnsembleClassifier(...) >>> y_pred = col_ens.predict(X_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 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 parameters of 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.
Check if the object is composite.
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])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
(**kwargs)Set the parameters of estimator.
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) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.
- 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__}
- 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)[source]#
Fit time series classifier to training data.
- Parameters
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- or of any other supported Panel mtype
for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- y1D np.array of int, of shape [n_instances] - class labels for fitting
indices correspond to instance indices in X
- Returns
- selfReference to self.
Notes
Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.
- fit_predict(X, y, cv=None, change_state=True) numpy.ndarray [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
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- or of any other supported Panel mtype
for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- y1D np.array of int, of shape [n_instances] - class labels for fitting
indices correspond to instance indices in X
- 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
- y1D np.array of int, of shape [n_instances] - predicted class labels
indices correspond to instance indices in X if cv is passed, -1 indicates entries not seen in union of test sets
- fit_predict_proba(X, y, cv=None, change_state=True) numpy.ndarray [source]#
Fit and predict labels probabilities for sequences in X.
Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.
- Parameters
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- or of any other supported Panel mtype
for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- y1D np.array of int, of shape [n_instances] - class labels for fitting
indices correspond to instance indices in X
- 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
- y2D array of shape [n_instances, n_classes] - predicted class probabilities
1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j
- 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 parameters of estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained sub-objects that are estimators.
- Returns
- paramsmapping of string to any
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_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 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
- 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, of cls.save(path)
- classmethod load_from_serial(serial)[source]#
Load object from serialized memory container.
- Parameters
- serial1st element of output of cls.save(None)
- Returns
- deserialized self resulting in output serial, of cls.save(None)
- predict(X) numpy.ndarray [source]#
Predicts labels for sequences in X.
- Parameters
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- 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
- y1D np.array of int, of shape [n_instances] - predicted class labels
indices correspond to instance indices in X
- predict_proba(X) numpy.ndarray [source]#
Predicts labels probabilities for sequences in X.
- Parameters
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- 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
- y2D array of shape [n_instances, n_classes] - predicted class probabilities
1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j
- 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)[source]#
Save serialized self to bytes-like object or to (.zip) file.
Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file
saved 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 file estimator.zip will be stored in /home/stored/.
- Returns
- if path is None - in-memory serialized self
- if path is file location - ZipFile with reference to the file
- score(X, y) float [source]#
Scores predicted labels against ground truth labels on X.
- Parameters
- X3D np.array (any number of dimensions, equal length series)
of shape [n_instances, n_dimensions, series_length]
- or 2D np.array (univariate, equal length series)
of shape [n_instances, series_length]
- or pd.DataFrame with each column a dimension, each cell a pd.Series
(any number of dimensions, equal or unequal length series)
- or of any other supported Panel mtype
for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- y1D np.ndarray of int, of shape [n_instances] - class labels (ground truth)
indices correspond to instance indices in X
- 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.
- Returns
- selfreference to self.
Notes
Changes object state, copies configs in config_dict to self._config_dynamic.