class RandomIntervalClassifier(BaseClassifier):
"""Random Interval Classifier.
This classifier simply transforms the input data using the RandomIntervals
transformer and builds a provided estimator using the transformed data.
Parameters
----------
n_intervals : int, default=100,
The number of intervals of random length, position and dimension to be
extracted.
interval_transformers : transformer or list of transformers, default=None,
Transformer(s) used to extract features from each interval. If None, defaults to
the Catch22 transformer.
estimator : sklearn classifier, default=None
An sklearn estimator to be built using the transformed data. Defaults to a
Rotation Forest with 200 trees.
n_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, default=None
Seed for random, integer.
Attributes
----------
n_classes_ : int
Number of classes. Extracted from the data.
classes_ : ndarray of shape (n_classes)
Holds the label for each class.
See Also
--------
RandomIntervals
Examples
--------
>>> from sktime.classification.feature_based import RandomIntervalClassifier
>>> from sktime.contrib.vector_classifiers._rotation_forest import RotationForest
>>> from sktime.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 = RandomIntervalClassifier(
... n_intervals=5,
... estimator=RotationForest(n_estimators=10),
... )
>>> clf.fit(X_train, y_train)
RandomIntervalClassifier(...)
>>> y_pred = clf.predict(X_test)
"""
_tags = {
"capability:multivariate": True,
"capability:multithreading": True,
}
def __init__(
self,
n_intervals=100,
interval_transformers=None,
estimator=None,
n_jobs=1,
random_state=None,
):
self.n_intervals = n_intervals
self.interval_transformers = interval_transformers
self.estimator = estimator
self.n_jobs = n_jobs
self.random_state = random_state
self._transformer = None
self._estimator = None
super(RandomIntervalClassifier, self).__init__()
def _fit(self, X, y):
"""Fit a pipeline on cases (X,y), where y is the target variable.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
interval_transformers = (Catch22(outlier_norm=True, replace_nans=True)
if self.interval_transformers is None else
self.interval_transformers)
self._transformer = RandomIntervals(
n_intervals=self.n_intervals,
transformers=interval_transformers,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._estimator = _clone_estimator(
RotationForest() if self.estimator is None else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self._threads_to_use
X_t = self._transformer.fit_transform(X, y)
self._estimator.fit(X_t, y)
return self
def _predict(self, X):
"""Predict class values of n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predictions for.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
return self._estimator.predict(self._transformer.transform(X))
def _predict_proba(self, X):
"""Predict class probabilities for n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
m = getattr(self._estimator, "predict_proba", None)
if callable(m):
return self._estimator.predict_proba(
self._transformer.transform(X))
else:
dists = np.zeros((X.shape[0], self.n_classes_))
preds = self._estimator.predict(self._transformer.transform(X))
for i in range(0, X.shape[0]):
dists[i, self._class_dictionary[preds[i]]] = 1
return dists
class RandomIntervalClassifier(BaseClassifier):
"""Random Interval Classifier.
This classifier simply transforms the input data using the RandomIntervals
transformer and builds a provided estimator using the transformed data.
Parameters
----------
n_intervals : int, default=100,
The number of intervals of random length, position and dimension to be
extracted.
interval_transformers : transformer or list of transformers, default=None,
Transformer(s) used to extract features from each interval. If None, defaults to
the Catch22 transformer.
estimator : sklearn classifier, default=None
An sklearn estimator to be built using the transformed data. Defaults to a
Rotation Forest with 200 trees.
n_jobs : int, default=1
The number of jobs to run in parallel for both `fit` and `predict`.
``-1`` means using all processors.
random_state : int or None, default=None
Seed for random, integer.
Attributes
----------
n_classes_ : int
Number of classes. Extracted from the data.
classes_ : ndarray of shape (n_classes)
Holds the label for each class.
See Also
--------
RandomIntervals
Examples
--------
>>> from sktime.classification.feature_based import RandomIntervalClassifier
>>> from sktime._contrib.vector_classifiers._rotation_forest import RotationForest
>>> from sktime.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 = RandomIntervalClassifier(
... n_intervals=3,
... estimator=RotationForest(n_estimators=5),
... )
>>> clf.fit(X_train, y_train)
RandomIntervalClassifier(...)
>>> y_pred = clf.predict(X_test)
"""
_tags = {
"capability:multivariate": True,
"capability:multithreading": True,
"classifier_type": "interval",
}
def __init__(
self,
n_intervals=100,
interval_transformers=None,
estimator=None,
n_jobs=1,
random_state=None,
):
self.n_intervals = n_intervals
self.interval_transformers = interval_transformers
self.estimator = estimator
self.n_jobs = n_jobs
self.random_state = random_state
self._transformer = None
self._estimator = None
super(RandomIntervalClassifier, self).__init__()
def _fit(self, X, y):
"""Fit a pipeline on cases (X,y), where y is the target variable.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
interval_transformers = (Catch22(outlier_norm=True, replace_nans=True)
if self.interval_transformers is None else
self.interval_transformers)
self._transformer = RandomIntervals(
n_intervals=self.n_intervals,
transformers=interval_transformers,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._estimator = _clone_estimator(
RotationForest() if self.estimator is None else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self._threads_to_use
X_t = self._transformer.fit_transform(X, y)
self._estimator.fit(X_t, y)
return self
def _predict(self, X) -> np.ndarray:
"""Predict class values of n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predictions for.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
return self._estimator.predict(self._transformer.transform(X))
def _predict_proba(self, X) -> np.ndarray:
"""Predict class probabilities for n instances in X.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The data to make predict probabilities for.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
m = getattr(self._estimator, "predict_proba", None)
if callable(m):
return self._estimator.predict_proba(
self._transformer.transform(X))
else:
dists = np.zeros((X.shape[0], self.n_classes_))
preds = self._estimator.predict(self._transformer.transform(X))
for i in range(0, X.shape[0]):
dists[i, self._class_dictionary[preds[i]]] = 1
return dists
@classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, 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
-------
params : dict 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`.
"""
from sklearn.ensemble import RandomForestClassifier
from sktime.transformations.series.summarize import SummaryTransformer
if parameter_set == "results_comparison":
return {
"n_intervals":
3,
"estimator":
RandomForestClassifier(n_estimators=10),
"interval_transformers":
SummaryTransformer(
summary_function=("mean", "std", "min", "max"),
quantiles=(0.25, 0.5, 0.75),
),
}
else:
return {
"n_intervals":
2,
"estimator":
RandomForestClassifier(n_estimators=2),
"interval_transformers":
SummaryTransformer(summary_function=("mean", "min", "max"), ),
}