HIVECOTEV2#
- class HIVECOTEV2(stc_params=None, drcif_params=None, arsenal_params=None, tde_params=None, time_limit_in_minutes=0, save_component_probas=False, verbose=0, n_jobs=1, random_state=None)[source]#
Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) V2.
An ensemble of the STC, DrCIF, Arsenal and TDE classifiers from different feature representations using the CAWPE structure as described in [1].
- Parameters
- stc_paramsdict or None, default=None
Parameters for the ShapeletTransformClassifier module. If None, uses the default parameters with a 2 hour transform contract.
- drcif_paramsdict or None, default=None
Parameters for the DrCIF module. If None, uses the default parameters with n_estimators set to 500.
- arsenal_paramsdict or None, default=None
Parameters for the Arsenal module. If None, uses the default parameters.
- tde_paramsdict or None, default=None
Parameters for the TemporalDictionaryEnsemble module. If None, uses the default parameters.
- time_limit_in_minutesint, default=0
Time contract to limit build time in minutes, overriding n_estimators/n_parameter_samples for each component. Default of 0 means n_estimators/n_parameter_samples for each component is used.
- save_component_probasbool, default=False
When predict/predict_proba is called, save each HIVE-COTEV2 component probability predictions in component_probas.
- verboseint, default=0
Level of output printed to the console (for information only).
- n_jobsint, default=1
The number of jobs to run in parallel for both fit and predict.
-1
means using all processors.- random_stateint or None, default=None
Seed for random number generation.
- Attributes
- n_classes_int
The number of classes.
- classes_list
The unique class labels.
- stc_weight_float
The weight for STC probabilities.
- drcif_weight_float
The weight for DrCIF probabilities.
- arsenal_weight_float
The weight for Arsenal probabilities.
- tde_weight_float
The weight for TDE probabilities.
- component_probasdict
Only used if save_component_probas is true. Saved probability predictions for each HIVE-COTEV2 component.
See also
HIVECOTEV1
,ShapeletTransformClassifier
,DrCIF
,Arsenal
,TemporalDictionaryEnsemble
Notes
For the Java version, see `https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/ tsml/classifiers/hybrids/HIVE_COTE.java`_.
References
- 1
Middlehurst, Matthew, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall. “HIVE-COTE 2.0: a new meta ensemble for time series classification.” Machine Learning (2021).
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 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 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.
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_params
(**params)Set the parameters of this object.
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. 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)[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 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.
- 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.
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
- 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_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)