sktime.classification.compose

class sktime.classification.compose.ColumnEnsembleClassifier(estimators, remainder='drop', verbose=False)[source]

Bases: sktime.classification.compose._column_ensemble.BaseColumnEnsembleClassifier

Applies estimators to columns of an array or pandas DataFrame.

This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be ensembled to form a single output.

Parameters

estimators (list of tuples) –

List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.

namestring

Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using set_params and searched in grid search.

Estimatorestimator or {‘drop’}

Estimator must support fit and predict_proba. Special-cased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns

column(s) : string or int, array-like of string or int, slice, boolean mask array or callable

remainder{‘drop’, ‘passthrough’} or estimator, default ‘drop’

By default, only the specified columns in transformations are transformed and combined in the output, and the non-specified columns are dropped. (default of 'drop'). By specifying remainder='passthrough', all remaining columns that were not specified in transformations will be automatically passed through. This subset of columns is concatenated with the output of the transformations. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

set_params(**kwargs)[source]

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns

Return type

self

class sktime.classification.compose.ComposableTimeSeriesForestClassifier(estimator=None, n_estimators=100, criterion='entropy', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, max_samples=None)[source]

Bases: sktime.series_as_features.base.estimators._ensemble.BaseTimeSeriesForest, sktime.classification.base.BaseClassifier

Time-Series Forest Classifier.

@article{DENG2013142,

title = {A time series forest for classification and feature extraction}, journal = {Information Sciences}, volume = {239}, pages = {142 - 153}, year = {2013}, issn = {0020-0255}, doi = {https://doi.org/10.1016/j.ins.2013.02.030}, url = {http://www.sciencedirect.com/science/article/pii/S0020025513001473}, author = {Houtao Deng and George Runger and Eugene Tuv and Martyanov Vladimir}, keywords = {Decision tree, Ensemble, Entrance gain, Interpretability,

Large margin, Time series classification}

}

A time series forest is a meta estimator and an adaptation of the random forest for time-series/panel data that fits a number of decision tree classifiers on various sub-samples of a transformed dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).

Parameters
  • estimator (Pipeline) – A pipeline consisting of series-to-tabular transformations and a decision tree classifier as final estimator.

  • n_estimators (integer, optional (default=200)) – The number of trees in the forest.

  • criterion (string, optional (default="entropy")) – The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is tree-specific. Default is “entropy”

  • max_depth (integer or None, optional (default=None)) – The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

  • min_samples_split (int, float, optional (default=2)) –

    The minimum number of samples required to split an internal node: - If int, then consider min_samples_split as the minimum number. - If float, then min_samples_split is a fraction and

    ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  • min_samples_leaf (int, float, optional (default=1)) –

    The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider min_samples_leaf as the minimum number. - If float, then min_samples_leaf is a fraction and

    ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  • min_weight_fraction_leaf (float, optional (default=0.)) – The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

  • max_features (int, float, string or None, optional (default=None)) –

    The number of features to consider when looking for the best split: - If int, then consider max_features features at each split. - If float, then max_features is a fraction and

    int(max_features * n_features) features are considered at each split.

    • If “auto”, then max_features=sqrt(n_features).

    • If “sqrt”, then max_features=sqrt(n_features) (same as “auto”).

    • If “log2”, then max_features=log2(n_features).

    • If None, then max_features=n_features.

    Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

  • max_leaf_nodes (int or None, optional (default=None)) – Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

  • min_impurity_decrease (float, optional (default=0.)) –

    A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:

    N_t / N * (impurity - N_t_R / N_t * right_impurity
                        - N_t_L / N_t * left_impurity)
    

    where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child. N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

  • min_impurity_split (float or None, (default=None)) – Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

  • bootstrap (boolean, optional (default=False)) – Whether bootstrap samples are used when building trees.

  • oob_score (bool (default=False)) – Whether to use out-of-bag samples to estimate the generalization accuracy.

  • n_jobs (int or None, optional (default=None)) – The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

  • random_state (int, RandomState instance or None, optional (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.

  • verbose (int, optional (default=0)) – Controls the verbosity when fitting and predicting.

  • warm_start (bool, optional (default=False)) – When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.

  • class_weight (dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None)) – Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

  • max_samples (int or float, default=None) –

    If bootstrap is True, the number of samples to draw from X to train each base estimator. - If None (default), then draw X.shape[0] samples. - If int, then draw max_samples samples. - If float, then draw max_samples * X.shape[0] samples. Thus,

    max_samples should be in the interval (0, 1).

estimators_[source]

The collection of fitted sub-estimators.

Type

list of DecisionTreeClassifier

classes_[source]

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

Type

array of shape = [n_classes] or a list of such arrays

n_classes_[source]

The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).

Type

int or list

n_columns[source]

The number of features when fit is performed.

Type

int

n_outputs_[source]

The number of outputs when fit is performed.

Type

int

feature_importances_[source]

The normalised feature values at each time index of the time series forest

Type

data frame of shape = [n_timepoints, n_features]

oob_score_[source]

Score of the training dataset obtained using an out-of-bag estimate.

Type

float

oob_decision_function_[source]

Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN.

Type

array of shape = [n_samples, n_classes]

predict(X)[source]

Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees. :param X: 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 sparse csr_matrix.

Returns

y – The predicted classes.

Return type

array-like of shape (n_samples,) or (n_samples, n_outputs)

predict_log_proba(X)[source]

Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest. :param X: 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 sparse csr_matrix.

Returns

p – such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

Return type

array of shape (n_samples, n_classes), or a list of n_outputs

predict_proba(X)[source]

Predict class probabilities for X. The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. :param X: 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 sparse csr_matrix.

Returns

p – such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

Return type

array of shape = [n_samples, n_classes], or a list of n_outputs