SummaryClassifier¶
- class SummaryClassifier(summary_stats='default', estimator=None, n_jobs=1, random_state=None)[source]¶
Summary statistic classifier.
This classifier simply transforms the input data using the SevenNumberSummary transformer and builds a provided estimator using the transformed data.
- Parameters:
- summary_stats[“default”, “percentiles”, “bowley”, “tukey”], default=”default”
The summary statistics to compute. The options are as follows, with float denoting the percentile value extracted from the series:
“default”: mean, std, min, max, 0.25, 0.5, 0.75
“percentiles”: 0.215, 0.887, 0.25, 0.5, 0.75, 0.9113, 0.9785
“bowley”: min, max, 0.1, 0.25, 0.5, 0.75, 0.9
“tukey”: min, max, 0.125, 0.25, 0.5, 0.75, 0.875
- estimatorsklearn classifier, default=None
An sklearn estimator to be built using the transformed data. Defaults to a Random Forest with 200 trees.
- n_jobsint, default=1
The number of jobs to run in parallel for both fit and predict.
-1
means using all processors.- random_stateint, RandomState instance or None, default=None
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
- Attributes:
- n_classes_int
Number of classes. Extracted from the data.
- classes_ndarray of shape (n_classes)
Holds the label for each class.
- estimator_sklearn classifier
The fitted estimator.
- transformer_SevenNumberSummary
The fitted transformer.
See also
SummaryTransformer
SummaryRegressor
Examples
>>> from aeon.classification.feature_based import SummaryClassifier >>> from sklearn.ensemble import RandomForestClassifier >>> from aeon.datasets import load_unit_test >>> X_train, y_train = load_unit_test(split="train") >>> X_test, y_test = load_unit_test(split="test") >>> clf = SummaryClassifier(estimator=RandomForestClassifier(n_estimators=5)) >>> clf.fit(X_train, y_train) SummaryClassifier(...) >>> y_pred = clf.predict(X_test)
Methods
clone
([random_state])Obtain a clone of the object with the same hyperparameters.
fit
(X, y)Fit time series classifier to training data.
fit_predict
(X, y, **kwargs)Fits the classifier and predicts class labels for X.
fit_predict_proba
(X, y, **kwargs)Fits the classifier and predicts class label probabilities for X.
get_class_tag
(tag_name[, raise_error, ...])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.
Sklearn metadata routing.
get_params
([deep])Get parameters for this estimator.
get_tag
(tag_name[, raise_error, ...])Get tag value from estimator class.
get_tags
()Get tags from estimator.
predict
(X)Predicts class labels for time series in X.
Predicts class label probabilities for time series in X.
reset
([keep])Reset the object to a clean post-init state.
score
(X, y[, metric, use_proba, metric_params])Scores predicted labels against ground truth labels on X.
set_params
(**params)Set the parameters of this estimator.
set_tags
(**tag_dict)Set dynamic tags to given values.
- clone(random_state=None)[source]¶
Obtain a clone of the object with the same hyperparameters.
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 totype(self)(**self.get_params(deep=False))
.- Parameters:
- random_stateint, RandomState instance, or None, default=None
Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.
- Returns:
- estimatorobject
Instance of
type(self)
, clone of self (see above)
- fit(X, y) BaseCollectionEstimator [source]¶
Fit time series classifier to training data.
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
. Other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.- ynp.ndarray
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X.
- Returns:
- selfBaseClassifier
Reference 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, **kwargs) ndarray [source]¶
Fits the classifier and predicts class labels for X.
fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.
Classifiers which override _fit_predict will have the
capability:train_estimate
tag set to True.Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
. other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.- ynp.ndarray
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X.- kwargsdict
key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function
_fit_predict
is not overridden. If_fit_predict
is overridden, kwargs may not function as expected. If_fit_predict
is not overridden, valid input iscv_size
integer, which is the number of cross validation folds to use to estimate train data. Ifcv_size
is not passed, the default is 10. Ifcv_size
is greater than the minimum number of samples in any class, it is set to this minimum.
- Returns:
- predictionsnp.ndarray
shape
[n_cases]
- predicted class labels indices correspond to instance indices in
- fit_predict_proba(X, y, **kwargs) ndarray [source]¶
Fits the classifier and predicts class label probabilities for X.
fit_predict_proba produces probability estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.
Classifiers which override _fit_predict_proba will have the
capability:train_estimate
tag set to True.Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict_proba(X)
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
. other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.- ynp.ndarray
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X.- kwargsdict
key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function
_fit_predict
is not overridden. If_fit_predict
is overridden, kwargs may not function as expected. If_fit_predict
is not overridden, valid input iscv_size
integer, which is the number of cross validation folds to use to estimate train data. Ifcv_size
is not passed, the default is 10. Ifcv_size
is greater than the minimum number of samples in any class, it is set to this minimum.
- Returns:
- probabilitiesnp.ndarray
2D 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
- classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class (only class tags).
- Parameters:
- tag_namestr
Name of tag value.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in cls. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if
raise_error
is True andtag_name
is not inself.get_tags().keys()
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 and tag value pairs. Collected from
_tags
class attribute via nested inheritance. These are not overridden by dynamic tags set byset_tags
or class__init__
calls.
- get_fitted_params(deep=True)[source]¶
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.
- Returns:
- fitted_paramsdict
Fitted parameter names mapped to their values.
- 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, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class.
Includes dynamic and overridden tags.
- Parameters:
- tag_namestr
Name of tag to be retrieved.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in self. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if raise_error is
True
andtag_name
is not inself.get_tags().keys()
Examples
>>> from aeon.classification import DummyClassifier >>> d = DummyClassifier() >>> d.get_tag("capability:multivariate") True
- get_tags()[source]¶
Get tags from estimator.
Includes dynamic and overridden tags.
- Returns:
- collected_tagsdict
Dictionary of tag name and tag value pairs. Collected from
_tags
class attribute via nested inheritance and then any overridden and new tags from__init__
orset_tags
.
- predict(X) ndarray [source]¶
Predicts class labels for time series in X.
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.
- Returns:
- predictionsnp.ndarray
1D np.array of float, of shape (n_cases) - predicted class labels indices correspond to instance indices in X
- predict_proba(X) ndarray [source]¶
Predicts class label probabilities for time series in X.
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
. other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.
- Returns:
- probabilitiesnp.ndarray
2D 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(keep=None)[source]¶
Reset the object to a clean post-init state.
After a
self.reset()
call, self is equal or similar in value totype(self)(**self.get_params(deep=False))
, assuming no other attributes were kept usingkeep
.- Detailed 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 hyperparameters (result ofget_params
)- Not affected by the reset are:
object attributes containing double-underscores class and object methods, class attributes any attributes specified in the
keep
argument
- Parameters:
- keepNone, str, or list of str, default=None
If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.
- Returns:
- selfobject
Reference to self.
- score(X, y, metric='accuracy', use_proba=False, metric_params=None) float [source]¶
Scores predicted labels against ground truth labels on X.
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, n_timepoints)
or 2D np.array (univariate, equal length series) of shape(n_cases, n_timepoints)
or list of numpy arrays (any number of channels, unequal length series) of shape[n_cases]
, 2D np.array(n_channels, n_timepoints_i)
, wheren_timepoints_i
is length of seriesi
. other types are allowed and converted into one of the above.Different estimators have different capabilities to handle different types of input. If
self.get_tag("capability:multivariate")
is False, they cannot handle multivariate series, so eithern_channels == 1
is true or X is 2D of shape(n_cases, n_timepoints)
. Ifself.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability for is passed.- ynp.ndarray
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X.- metricUnion[str, callable], default=”accuracy”,
Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.
- use_probabool, default=False,
Argument to check if scorer works on probability estimates or not.
- metric_paramsdict, default=None,
Contains parameters to be passed to the scoring function. If None, no parameters are passed.
- Returns:
- scorefloat
Accuracy score of predict(X) vs y.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.