ProximityForest#
- class ProximityForest(random_state=None, n_estimators=100, distance_measure=None, get_distance_measure=None, get_exemplars=<function get_one_exemplar_per_class_proximity>, get_gain=<function gini_gain>, verbosity=0, max_depth=inf, is_leaf=<function pure>, n_jobs=1, n_stump_evaluations=5, find_stump=None, setup_distance_measure_getter=<function setup_all_distance_measure_getter>)[source]#
Proximity Forest class.
Models a decision tree forest which uses distance measures to partition data [1].
- Parameters
- random_staterandom, default = None
seed for reproducibility
- n_estimatorsint, default=100
The number of trees in the forest.
- distance_measure: default = None
- get_exemplars: default=get_one_exemplar_per_class_proximity
- get_gain: default=gini_gain
function to score the quality of a split
- verbosity: default=0
- max_depth: default=np.math.inf
maximum depth of the tree
- is_leaf: default=pure
function to decide when to mark a node as a leaf node
- n_jobs: default=int, 1
number of jobs to run in parallel *across threads”
- n_stump_evaluations: int, default=5
- find_stump: default=None, function to find the best split of data
- setup_distance_measure_getter=setup_all_distance_measure_getter
- Attributes
is_fitted
Whether fit has been called.
Notes
- 1
Ben Lucas et al., “Proximity Forest: an effective and scalable distance-based classifier for time series”,Data Mining and Knowledge Discovery, 33(3): 607-635, 2019 https://arxiv.org/abs/1808.10594
Java wrapper of authors original https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/distance_based/ProximityForestWrapper.java Java version https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/distance_based/proximity/ProximityForest.java
Examples
>>> from sktime.classification.distance_based import ProximityForest >>> 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 = ProximityForest( ... n_estimators=2, max_depth=2, n_stump_evaluations=1 ... ) >>> clf.fit(X_train, y_train) ProximityForest(...) >>> y_pred = clf.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/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 config flags for self.
get_fitted_params
([deep])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_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')[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 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_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 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_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
- selfreference to self (after parameters have been set)