BOSSEnsemble#
- class BOSSEnsemble(threshold=0.92, max_ensemble_size=500, max_win_len_prop=1, min_window=10, typed_dict=True, save_train_predictions=False, n_jobs=1, random_state=None)[source]#
Ensemble of bag of Symbolic Fourier Approximation Symbols (BOSS).
Implementation of BOSS Ensemble from Schäfer (2015). [1]
Overview: Input “n” series of length “m” and BOSS performs a grid search over a set of parameter values, evaluating each with a LOOCV. It then retains all ensemble members within 92% of the best by default for use in the ensemble. There are three primary parameters:
alpha: alphabet size
w: window length
l: word length.
For any combination, a single BOSS slides a window length “w” along the series. The w length window is shortened to an “l” length word through taking a Fourier transform and keeping the first l/2 complex coefficients. These “l” coefficients are then discretized into alpha possible values, to form a word length “l”. A histogram of words for each series is formed and stored.
Fit involves finding “n” histograms.
Predict uses 1 nearest neighbor with a bespoke BOSS distance function.
- Parameters
- thresholdfloat, default=0.92
Threshold used to determine which classifiers to retain. All classifiers within percentage threshold of the best one are retained.
- max_ensemble_sizeint or None, default=500
Maximum number of classifiers to retain. Will limit number of retained classifiers even if more than max_ensemble_size are within threshold.
- max_win_len_propint or float, default=1
Maximum window length as a proportion of the series length.
- min_windowint, default=10
Minimum window size.
- typed_dictbool, default=True
Use a numba TypedDict to store word counts. May increase memory usage, but will be faster for larger datasets. As the Dict cannot be pickled currently, there will be some overhead converting it to a python dict with multiple threads and pickling.
- save_train_predictionsbool, default=False
Save the ensemble member train predictions in fit for use in _get_train_probs leave-one-out cross-validation.
- 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_list
The classes labels.
- n_instances_int
Number of instances. Extracted from the data.
- n_estimators_int
The final number of classifiers used. Will be <= max_ensemble_size if max_ensemble_size has been specified.
- series_length_int
Length of all series (assumed equal).
- estimators_list
List of DecisionTree classifiers.
See also
Notes
For the Java version, see TSML.
References
- 1
Patrick Schäfer, “The BOSS is concerned with time series classification in the presence of noise”, Data Mining and Knowledge Discovery, 29(6): 2015 https://link.springer.com/article/10.1007/s10618-014-0377-7
Examples
>>> from sktime.classification.dictionary_based import BOSSEnsemble >>> 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 = BOSSEnsemble(max_ensemble_size=3) >>> clf.fit(X_train, y_train) BOSSEnsemble(...) >>> 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.
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 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.
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.
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.
- 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.
- 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
- 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
- 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)