DrCIF#

class DrCIF(n_estimators=200, n_intervals=None, att_subsample_size=10, min_interval=4, max_interval=None, base_estimator='CIT', time_limit_in_minutes=0.0, contract_max_n_estimators=500, save_transformed_data=False, n_jobs=1, random_state=None)[source]#

Diverse Representation Canonical Interval Forest Classifier (DrCIF).

Extension of the CIF algorithm using multple representations. Implementation of the interval based forest making use of the catch22 feature set on randomly selected intervals on the base series, periodogram representation and differences representation described in the HIVE-COTE 2.0 paper Middlehurst et al (2021). [1]

Overview: Input “n” series with “d” dimensions of length “m”. For each tree

Ensemble the trees with averaged probability estimates

Parameters

n_estimatorsint, default=200: Number of estimators to build for the ensemble.
n_intervalsint, length 3 list of int or None, default=None: Number of intervals to extract per representation per tree as an int for all representations or list for individual settings, if None extracts (4 + (sqrt(representation_length) * sqrt(n_dims)) / 3) intervals.
att_subsample_sizeint, default=10: Number of catch22 or summary statistic attributes to subsample per tree.
min_intervalint or length 3 list of int, default=4: Minimum length of an interval per representation as an int for all representations or list for individual settings.
max_intervalint, length 3 list of int or None, default=None: Maximum length of an interval per representation as an int for all representations or list for individual settings, if None set to (representation_length / 2).
base_estimatorBaseEstimator or str, default=”CIT”: Base estimator for the ensemble, can be supplied a sklearn BaseEstimator or a string for suggested options. “DTC” uses the sklearn DecisionTreeClassifier using entropy as a splitting measure (sklearn.tree.DecisionTreeClassifier). “CIT” uses the sktime ContinuousIntervalTree, an implementation of the original tree used with embedded attribute processing for faster predictions (sktime.classification.interval_based.ContinuousIntervalTree). In order to pass parameters to estimators, pass a BaseEstimator instance.
time_limit_in_minutesint, default=0: Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators is used.
contract_max_n_estimatorsint, default=500: Max number of estimators when time_limit_in_minutes is set.
save_transformed_databool, default=False: Save the data transformed in fit for use in _get_train_probs.
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.
n_instances_int: The number of train cases.
n_dims_int: The number of dimensions per case.
series_length_int: The length of each series.
classes_list: The classes labels.
total_intervals_int: Total number of intervals per tree from all representations.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.
intervals_list of shape (n_estimators) of ndarray with shape (total_intervals,2): Stores indexes of each intervals start and end points for all classifiers.
atts_list of shape (n_estimators) of array with shape (att_subsample_size): Attribute indexes of the subsampled catch22 or summary statistic for all classifiers.
dims_list of shape (n_estimators) of array with shape (total_intervals): The dimension to extract attributes from each interval for all classifiers.
transformed_data_list of shape (n_estimators) of ndarray with shape
(n_instances,total_intervals * att_subsample_size): The transformed dataset for all classifiers. Only saved when save_transformed_data is true.

See also

CanonicalIntervalForest

Notes

For the Java version, see TSML.

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.” arXiv preprint arXiv:2104.07551 (2021).

Examples

>>> from sktime.classification.interval_based import DrCIF
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True) 
>>> clf = DrCIF(
...     n_estimators=3, n_intervals=2, att_subsample_size=2
... ) 
>>> clf.fit(X_train, y_train) 
DrCIF(...)
>>> y_pred = clf.predict(X_test) 

Methods

`check_is_fitted`()	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`()	Get class tags from the class and all its parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object’s parameter defaults.
`get_param_names`()	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.
`is_composite`()	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.
`predict_proba`(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`(**params)	Set the parameters of this object.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

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__.

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)

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)

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)

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)

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)

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 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.

property is_fitted[source]#: Whether fit has been called.

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)

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)

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:

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)

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.

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

self: Reference 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.

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.