CanonicalIntervalForestRegressor¶
- class CanonicalIntervalForestRegressor(base_estimator=None, n_estimators=200, n_intervals='sqrt', min_interval_length=3, max_interval_length=inf, att_subsample_size=8, time_limit_in_minutes=None, contract_max_n_estimators=500, use_pycatch22=False, random_state=None, n_jobs=1, parallel_backend=None)[source]¶
Canonical Interval Forest (CIF) Regressor.
Implementation of the interval-based forest making use of the catch22 feature set on randomly selected intervals described in Middlehurst et al. (2020). [1]
Overview: Input “n” series with “d” dimensions of length “m”. For each tree
Sample n_intervals intervals of random position and length
Subsample att_subsample_size catch22 or summary statistic attributes randomly
Randomly select dimension for each interval
Calculate attributes for each interval, concatenate to form new data set
Build a decision tree on new data set
ensemble the trees with averaged label estimates
- Parameters:
- base_estimatorBaseEstimator or None, default=None
scikit-learn BaseEstimator used to build the interval ensemble. If None, use a simple decision tree.
- n_estimatorsint, default=200
Number of estimators to build for the ensemble.
- n_intervalsint, str, list or tuple, default=”sqrt”
Number of intervals to extract per tree for each series_transformers series.
An int input will extract that number of intervals from the series, while a str input will return a function of the series length (may differ per series_transformers output) to extract that number of intervals. Valid str inputs are:
“sqrt”: square root of the series length.
- “sqrt-div”: sqrt of series length divided by the number
of series_transformers.
A list or tuple of ints and/or strs will extract the number of intervals using the above rules and sum the results for the final n_intervals. i.e. [4, “sqrt”] will extract sqrt(n_timepoints) + 4 intervals.
Different number of intervals for each series_transformers series can be specified using a nested list or tuple. Any list or tuple input containing another list or tuple must be the same length as the number of series_transformers.
- min_interval_lengthint, float, list, or tuple, default=3
Minimum length of intervals to extract from series. float inputs take a proportion of the series length to use as the minimum interval length.
Different minimum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.
- max_interval_lengthint, float, list, or tuple, default=np.inf
Maximum length of intervals to extract from series. float inputs take a proportion of the series length to use as the maximum interval length.
Different maximum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.
- att_subsample_sizeint, float, list, tuple or None, default=None
The number of attributes to subsample for each estimator. If None, use all
If int, use that number of attributes for all estimators. If float, use that proportion of attributes for all estimators.
Different subsample sizes for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.
- time_limit_in_minutesint, default=0
Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators are used.
- contract_max_n_estimatorsint, default=500
Max number of estimators when time_limit_in_minutes is set.
- use_pycatch22bool, optional, default=False
Wraps the C based pycatch22 implementation for aeon. (https://github.com/DynamicsAndNeuralSystems/pycatch22). This requires the
pycatch22
package to be installed if True.- 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.
- n_jobsint, default=1
The number of jobs to run in parallel for both fit and predict.
-1
means using all processors.- parallel_backendstr, ParallelBackendBase instance or None, default=None
Specify the parallelisation backend implementation in joblib, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.
- Attributes:
- n_cases_int
The number of train cases in the training set.
- n_channels_int
The number of dimensions per case in the training set.
- n_timepoints_int
The length of each series in the training set.
- total_intervals_int
Total number of intervals per tree from all representations.
- estimators_list of shape (n_estimators) of BaseEstimator
The collections of estimators trained in fit.
- intervals_list of shape (n_estimators) of TransformerMixin
Stores the interval extraction transformer for all estimators.
See also
CanonicalIntervalForestClassifier
DrCIFRegressor
References
[1]Matthew Middlehurst and James Large and Anthony Bagnall. “The Canonical Interval Forest (CIF) Classifier for Time Series Classification.” IEEE International Conference on Big Data 2020
Examples
>>> from aeon.regression.interval_based import CanonicalIntervalForestRegressor >>> from aeon.testing.data_generation import make_example_3d_numpy >>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12, ... return_y=True, regression_target=True, ... random_state=0) >>> reg = CanonicalIntervalForestRegressor(n_estimators=10, random_state=0) >>> reg.fit(X, y) CanonicalIntervalForestRegressor(n_estimators=10, random_state=0) >>> reg.predict(X) array([0.7252543 , 1.45657786, 0.95608366, 1.64399016, 0.42385504, 0.65113978, 1.01919317, 1.30157483, 1.66017354, 0.2900776 ])
Methods
clone
([random_state])Obtain a clone of the object with the same hyperparameters.
fit
(X, y)Fit time series regressor to training data.
fit_predict
(X, y)Fits the regressor and predicts class labels 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 target variable for time series in X.
reset
([keep])Reset the object to a clean post-init state.
score
(X, y[, metric, 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.
temporal_importance_curves
([return_dict, ...])Calculate the temporal importance curves for each feature.
- 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 regressor 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 either
n_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, of shape
(n_cases)
- regression targets (ground truth) for fitting indices corresponding to instance indices in X.
- Returns:
- selfBaseRegressor
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) ndarray [source]¶
Fits the regressor 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.
Regressors 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 either
n_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, of shape
(n_cases)
- regression targets (ground truth) for fitting indices corresponding to instance indices in X.
- Returns:
- predictionsnp.ndarray
1D np.array of float, of shape (n_cases) - predicted regression labels indices correspond to instance indices in X
- 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 target variable 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 either
n_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 regression labels indices correspond to instance indices in X
- 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='r2', 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 either
n_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, of shape
(n_cases)
- regression targets (ground truth) for fitting indices corresponding to instance indices in X.- metricUnion[str, callable], default=”r2”,
Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.
- metric_paramsdict, default=None,
Contains parameters to be passed to the scoring function. If None, no parameters are passed.
- Returns:
- scorefloat
MSE 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.
- set_tags(**tag_dict)[source]¶
Set dynamic tags to given values.
- Parameters:
- **tag_dictdict
Dictionary of tag name and tag value pairs.
- Returns:
- selfobject
Reference to self.
- temporal_importance_curves(return_dict=False, normalise_time_points=False)[source]¶
Calculate the temporal importance curves for each feature.
Can be finicky with transformers currently.
- Parameters:
- return_dictbool, default=False
If True, return a dictionary of curves. If False, return a list of names and a list of curves.
- normalise_time_pointsbool, default=False
If True, normalise the time points for each feature to the number of splits that used that feature. If False, return the sum of the information gain for each split.
- Returns:
- nameslist of str
The names of the features.
- curveslist of np.ndarray
The temporal importance curves for each feature.