BaseClusterer#
- class BaseClusterer(n_clusters: Optional[int] = None)[source]#
Abstract base class for time series clusterer.
- Parameters
- n_clusters: int, defaults = None
Number of clusters for model.
- Attributes
is_fitted
Whether fit has been called.
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 clusterer to training data.
fit_predict
(X[, y])Compute cluster centers and predict cluster index for each time series.
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[, y])Predict the closest cluster each sample in X belongs to.
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])Score the quality of the clusterer.
set_params
(**params)Set the parameters of this object.
set_tags
(**tag_dict)Set dynamic tags to given values.
- fit(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) sktime.base._base.BaseEstimator [source]#
Fit time series clusterer to training data.
- Parameters
- XTraining time series instances to cluster. np.ndarray (2d or 3d array of
- shape (n_instances, series_length) or shape (n_instances, n_dimensions,
- series_length)) or pd.DataFrame (where each column is a dimension, each cell
- is a pd.Series (any number of dimensions, equal or unequal length series)).
- Converted to type _tags[“X_inner_mtype”]
- y: ignored, exists for API consistency reasons.
- Returns
- self:
Fitted estimator.
- predict(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) numpy.ndarray [source]#
Predict the closest cluster each sample in X belongs to.
- Parameters
- Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape
(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to predict their cluster indexes.
- y: ignored, exists for API consistency reasons.
- Returns
- np.ndarray (1d array of shape (n_instances,))
Index of the cluster each time series in X belongs to.
- fit_predict(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) numpy.ndarray [source]#
Compute cluster centers and predict cluster index for each time series.
Convenience method; equivalent of calling fit(X) followed by predict(X)
- Parameters
- Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape
(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to train clusterer and then have indexes each belong to return.
- y: ignored, exists for API consistency reasons.
- Returns
- np.ndarray (1d array of shape (n_instances,))
Index of the cluster each time series in X belongs to.
- predict_proba(X)[source]#
Predicts labels probabilities for sequences in X.
Default behaviour is to call _predict and set the predicted class probability to 1, other class probabilities to 0. Override if better estimates are obtainable.
- Parameters
- Xguaranteed to be of a type in self.get_tag(“X_inner_mtype”)
- if self.get_tag(“X_inner_mtype”) = “numpy3D”:
3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
- if self.get_tag(“X_inner_mtype”) = “nested_univ”:
pd.DataFrame with each column a dimension, each cell a pd.Series
for list of other 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
- score(X, y=None) float [source]#
Score the quality of the clusterer.
- Parameters
- Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape
(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to train clusterer and then have indexes each belong to return.
- y: ignored, exists for API consistency reasons.
- Returns
- scorefloat
Score of the clusterer.
- 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.
- 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.
- 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.
- 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
- 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)
- 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
- 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)