RandomIntervalClassifier

class RandomIntervalClassifier(n_intervals=100, interval_transformers=None, estimator=None, n_jobs=1, random_state=None)

Random Interval Classifier.

This classifier simply transforms the input data using the RandomIntervals transformer and builds a provided estimator using the transformed data.

Parameters

n_intervalsint, default=100,

The number of intervals of random length, position and dimension to be extracted.

interval_transformerstransformer or list of transformers, default=None,

Transformer(s) used to extract features from each interval. If None, defaults to the Catch22 transformer.

estimatorsklearn classifier, default=None

An sklearn estimator to be built using the transformed data. Defaults to a Rotation Forest with 200 trees.

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, integer.

Attributes

n_classes_int

Number of classes. Extracted from the data.

classes_ndarray of shape (n_classes)

Holds the label for each class.

See also

RandomIntervals

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.

classmethod get_test_params(parameter_set='default')

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()

Check if the estimator has been fitted.

Raises

NotFittedError

If the estimator has not been fitted yet.

clone()

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.

Notes

Also equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)

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')

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')

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)

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

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

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)

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()

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()

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)

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()

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()

Get object’s parameter names.

Returns

param_names: list[str]

Alphabetically sorted list of parameter names of cls.

get_params(deep=True)

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)

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()

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()

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

Whether fit has been called.

classmethod load_from_path(serial)

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)

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

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

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()

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)

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

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)

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)

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)

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.