TimeSeriesLloyds#
- class TimeSeriesLloyds(n_clusters: int = 8, init_algorithm: Union[str, Callable] = 'random', metric: Union[str, Callable] = 'euclidean', n_init: int = 10, max_iter: int = 300, tol: float = 1e-06, verbose: bool = False, random_state: Optional[Union[int, RandomState]] = None, distance_params: Optional[dict] = None)[source]#
Abstact class that implements time series Lloyds algorithm.
- Parameters:
- n_clusters: int, defaults = 8
The number of clusters to form as well as the number of centroids to generate.
- init_algorithm: str, defaults = ‘forgy’
Method for initializing cluster centers. Any of the following are valid: [‘kmeans++’, ‘random’, ‘forgy’]
- metric: str or Callable, defaults = ‘dtw’
Distance metric to compute similarity between time series. Any of the following are valid: [‘dtw’, ‘euclidean’, ‘erp’, ‘edr’, ‘lcss’, ‘squared’, ‘ddtw’, ‘wdtw’, ‘wddtw’]
- n_init: int, defaults = 10
Number of times the k-means algorithm will be run with different centroid seeds. The final result will be the best output of n_init consecutive runs in terms of inertia.
- max_iter: int, defaults = 30
Maximum number of iterations of the k-means algorithm for a single run.
- tol: float, defaults = 1e-6
Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.
- verbose: bool, defaults = False
Verbosity mode.
- random_state: int or np.random.RandomState instance or None, defaults = None
Determines random number generation for centroid initialization.
- distance_params: dict, defaults = None
Dictonary containing kwargs for the distance metric being used.
- Attributes:
- cluster_centers_: np.ndarray (3d array of shape (n_clusters, n_dimensions,
series_length)) Time series that represent each of the cluster centers. If the algorithm stops before fully converging these will not be consistent with labels_.
- labels_: np.ndarray (1d array of shape (n_instance,))
Labels that is the index each time series belongs to.
- inertia_: float
Sum of squared distances of samples to their closest cluster center, weighted by the sample weights if provided.
- n_iter_: int
Number of iterations run.
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_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[, 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.
- 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: Union[DataFrame, ndarray], y=None) 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_channels,
- series_length)).
- y: ignored, exists for API consistency reasons.
- Returns:
- self:
Fitted estimator.
- fit_predict(X: Union[DataFrame, ndarray], y=None) 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_channels, series_length)). 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.
- 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(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.
- 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)
- predict(X: Union[DataFrame, ndarray], y=None) 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_channels, series_length)). 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.
- 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_channels, series_length]
for list of other mtypes, see datatypes.SCITYPE_REGISTER
- 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=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_channels, series_length)). 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.
- 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)