sktime.series_as_features.base.estimators¶
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class
sktime.series_as_features.base.estimators.
BaseTimeSeriesForest
(base_estimator, n_estimators=100, estimator_params=(), bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, max_samples=None)[source]¶ Bases:
sklearn.ensemble._forest.BaseForest
Base class for forests of trees.
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apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.- Returns
X_leaves – For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.
- Return type
ndarray of shape (n_samples, n_estimators)
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decision_path
(X)[source]¶ Return the decision path in the forest.
New in version 0.18.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input samples. Internally, its dtype will be converted to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix
.- Returns
indicator (sparse matrix of shape (n_samples, n_nodes)) – Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. The matrix is of CSR format.
n_nodes_ptr (ndarray of shape (n_estimators + 1,)) – The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.
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fit
(X, y, sample_weight=None)[source]¶ Build a forest of trees from the training set (X, y). :param X: The training input samples. Internally, its dtype will be converted
to
dtype=np.float32
. If a sparse matrix is provided, it will be converted into a sparsecsc_matrix
.- Parameters
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – The target values (class labels in classification, real numbers in regression).
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
- Returns
self
- Return type
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