IndividualOrdinalTDE#
- class IndividualOrdinalTDE(window_size=10, word_length=8, norm=False, levels=1, igb=False, alphabet_size=4, bigrams=True, dim_threshold=0.85, max_dims=20, typed_dict=True, n_jobs=1, random_state=None)[source]#
Single O-TDE classifier, an ordinal version of the IndividualTDE from [R246383869600-2].
Base classifier for the O-TDE classifier. Implementation of single O-TDE base model from [R246383869600-1].
Overview: input “n” series of length “m” and IndividualOrdinalTDE performs a SFA transform to form a sparse dictionary of discretised words. The binning thresholds are obtained from a DecisionTreeRegressor which considers as splitting criterion the friedman mse metric. Then, histograms are formed from the discretised words for each time series.
fit involves finding “n” histograms.
predict uses 1 nearest neighbor with the histogram intersection distance function.
- Parameters:
- window_sizeint, default=10
Size of the window to use in the SFA transform.
- word_lengthint, default=8
Length of word to use to use in the SFA transform.
- normbool, default=False
Whether to normalize SFA words by dropping the first Fourier coefficient.
- levelsint, default=1
The number of spatial pyramid levels for the SFA transform.
- igbbool, default=False
Whether to use Information Gain Binning (IGB) or Multiple Coefficient Binning (MCB) for the SFA transform.
- alphabet_sizedefault=4
Number of possible letters (values) for each word.
- bigramsbool, default=False
Whether to record word bigrams in the SFA transform.
- dim_thresholdfloat, default=0.85
Accuracy threshold as a propotion of the highest accuracy dimension for words extracted from each dimensions. Only applicable for multivariate data.
- max_dimsint, default=20
Maximum number of dimensions words are extracted from. Only applicable for multivariate data.
- typed_dictbool, default=True
Use a numba TypedDict to store word counts. May increase memory usage, but will be faster for larger datasets.
- 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
The number of classes.
- classes_list
The classes labels.
- n_instances_int
The number of train cases.
- n_dims_int
The number of dimensions per case.
- series_length_int
The length of each series.
See also
TemporalDictinaryEnsemble
,SFA
Notes
For the Java version, see TSML.
References
Examples
>>> from aeon.classification.ordinal_classification import IndividualOrdinalTDE >>> from aeon.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 = IndividualOrdinalTDE() >>> clf.fit(X_train, y_train) IndividualOrdinalTDE(...) >>> 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_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.
get_tags
()Get tags from estimator class.
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 time series 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_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:
- estimatorobject
Estimator 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 channels, equal length series)
of shape (n_instances, n_channels, n_timepoints)
- or 2D np.array (univariate, equal length series)
of shape (n_instances, n_timepoints)
- or list of numpy arrays (any number of channels, unequal length series)
of shape [n_instances], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i
other types are allowed and converted into one of the above.
- y1D np.array, 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.
See also
get_tag
Get a single tag from an object.
get_tags
Get all tags from an object.
get_class_tag
Get a single tag from a class.
Examples
>>> from aeon.classification import DummyClassifier >>> DummyClassifier.get_class_tag("capability:multivariate") True
- 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.
Uses dynamic tag overrides.
- Parameters:
- tag_namestr
Name of tag to be retrieved.
- tag_value_defaultany type, 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()
See also
get_tags
Get all tags from an object.
get_clas_tags
Get all tags from a class.
get_class_tag
Get a single tag from a class.
Examples
>>> from aeon.classification import DummyClassifier >>> d = DummyClassifier() >>> d.get_tag("capability:multivariate") True
- get_tags()[source]#
Get tags from estimator class.
Includes the dynamic tag overrides.
- Returns:
- dict
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.
See also
get_tag
Get a single tag from an object.
get_clas_tags
Get all tags from a class.
get_class_tag
Get a single tag from a class.
Examples
>>> from aeon.classification import DummyClassifier >>> d = DummyClassifier() >>> tags = d.get_tags()
- 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.
- 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:
- serialobject
Result 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:
- serialobject
First element of output of cls.save(None).
- Returns:
- deserialized self resulting in output serial, of cls.save(None).
- predict(X) ndarray [source]#
Predicts labels for time series in X.
- Parameters:
- X3D np.array (any number of channels, equal length series)
of shape (n_instances, n_channels, n_timepoints)
- or 2D np.array (univariate, equal length series)
of shape (n_instances, n_timepoints)
- or list of numpy arrays (any number of channels, unequal length series)
of shape [n_instances], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i
other types are allowed and converted into one of the above.
- Returns:
- y1D np.array, of shape [n_instances] - predicted class labels
indices correspond to instance indices in X
- predict_proba(X) ndarray [source]#
Predicts labels probabilities for sequences in X.
- Parameters:
- X3D np.array (any number of channels, equal length series)
of shape (n_instances, n_channels, n_timepoints)
- or 2D np.array (univariate, equal length series)
of shape (n_instances, n_timepoints)
- or list of numpy arrays (any number of channels, unequal length series)
of shape [n_instances], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i
other types are allowed and converted into one of the above.
- Returns:
- y2D array of shape (n_cases, n_classes) - predicted class probabilities
First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated 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 channels, equal length series)
of shape (n_instances, n_channels, n_timepoints)
- or 2D np.array (univariate, equal length series)
of shape (n_instances, n_timepoints)
- or list of numpy arrays (any number of channels, unequal length series)
of shape [n_instances], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i
other types are allowed and converted into one of the above.
- y1D np.ndarray 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)