pyts.classification
.TimeSeriesForest¶

class
pyts.classification.
TimeSeriesForest
(n_estimators=500, n_windows=1.0, min_window_size=1, criterion='entropy', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, class_weight=None, ccp_alpha=0.0, max_samples=None)[source]¶ A random forest classifier for time series.
A random forest is a meta estimator that fits a number of decision tree classifiers on various subsamples of the dataset and uses averaging to improve the predictive accuracy and control overfitting.
This transformer extracts 3 features from each window: the mean, the standard deviation and the slope. The total number of features is thus equal to
3 * n_windows
. Then a random forest is built using these features as input data.Parameters:  n_estimators : int (default = 500)
The number of trees in the forest.
 n_windows : int or float (default = 1.)
The number of windows from which features are extracted.
 min_window_size : int or float (default = 1)
The minimum length of the windows. If float, it represents a percentage of the size of each time series.
 criterion : str (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 treespecific.
 max_depth : integer or None (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 or float (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 andceil(min_samples_split * n_samples)
are the minimum number of samples for each split.
 If int, then consider
 min_samples_leaf : int or float (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. If int, then consider
min_samples_leaf
as the minimum number.  If float, then
min_samples_leaf
is a fraction andceil(min_samples_leaf * n_samples)
are the minimum number of samples for each node.
 If int, then consider
 min_weight_fraction_leaf : float (default = 0.)
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.
 max_features : int, float, str or None (default = “sqrt”)
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 andint(max_features * n_features)
features are considered at each split.  If “sqrt”, then
max_features=sqrt(n_features)
.  If “log2”, then
max_features=log2(n_features)
.  If None, then
max_features=n_features
.
 If int, then consider
 max_leaf_nodes : int or None (default = None)
Grow trees with
max_leaf_nodes
in bestfirst fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float (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, andN_t_R
is the number of samples in the right child. bootstrap : bool (default = True)
Whether bootstrap samples are used when building trees. If False, the whole datset is used to build each tree.
 oob_score : bool (default = False)
Whether to use outofbag samples to estimate the generalization accuracy.
 n_jobs : int or None, optional (default = None)
The number of jobs to run in parallel.
fit()
,predict()
,decision_path()
andapply()
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. random_state : int, RandomState instance or None (default = None)
Controls both the randomness of the bootstrapping of the samples used when building trees (if
bootstrap=True
) and the sampling of the features to consider when looking for the best split at each node (ifmax_features < n_features
). verbose : int (default = 0)
Controls the verbosity when fitting and predicting.
 class_weight : dict, “balanced”, “balanced_subsample” or None (default = None)
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one.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. ccp_alpha : float (default = 0.)
Complexity parameter used for Minimal CostComplexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. It must be nonnegative. max_samples : int, float or None (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).
 If None (default), then draw
Notes
The default values for the parameters controlling the size of the trees (e.g.
max_depth
,min_samples_leaf
, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data,
max_features=n_features
andbootstrap=False
, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting,random_state
has to be fixed.References
[1] H. Deng, G. Runger, E. Tuv and M. Vladimir, “A Time Series Forest for Classification and Feature Extraction”. Information Sciences, 239, 142153 (2013). [2] Leo Breiman, “Random Forests”, Machine Learning, 45(1), 532, 2001. Examples
>>> from pyts.datasets import load_gunpoint >>> from pyts.classification import TimeSeriesForest >>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True) >>> clf = TimeSeriesForest(random_state=43) >>> clf.fit(X_train, y_train) TimeSeriesForest(...) >>> clf.score(X_test, y_test) 0.97333...
Attributes:  estimator_ : DecisionTreeClassifier
The child estimator template used to create the collection of fitted subestimators.
 classes_ : array, shape = (n_classes,)
The classes labels.
 estimators_ : list of DecisionTreeClassifier
The collection of fitted subestimators.
 feature_importances_ : array, shape = (n_features,)
The feature importances (the higher, the more important the feature).
 indices_ : array, shape = (n_windows, 2)
The indices for the windows. The first column consists of the starting indices (included) of the windows. The second column consists of the ending indices (excluded) of the windows.
 n_features_in_ : int
The number of features when
fit
is performed. oob_decision_function_ : None or array, shape = (n_samples, n_classes)
Decision function computed with outofbag 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. This attribute is not None only when
oob_score
is True. oob_score_ : None or float
Score of the training dataset obtained using an outofbag estimate. This attribute is not None only when
oob_score
is True.
Methods
__init__
([n_estimators, n_windows, …])Initialize self. apply
(X)Apply trees in the forest to X, return leaf indices. decision_path
(X)Return the decision path in the forest. fit
(X, y)Fit the model according to the given training data. get_params
([deep])Get parameters for this estimator. predict
(X)Predict class for X. predict_proba
(X)Predict class probabilities for X. score
(X, y)Return the mean accuracy on the given test data and labels. set_params
(**params)Set the parameters of this estimator. 
__init__
(n_estimators=500, n_windows=1.0, min_window_size=1, criterion='entropy', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, class_weight=None, ccp_alpha=0.0, max_samples=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

apply
(X)[source]¶ Apply trees in the forest to X, return leaf indices.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Univariate time series.
Returns:  X_leaves : array_like, shape = (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

decision_path
(X)[source]¶ Return the decision path in the forest.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Univariate time series.
Returns:  indicator : sparse csr array, shape = (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes.
 n_nodes_ptr : array, shape = (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the ith estimator.

fit
(X, y)[source]¶ Fit the model according to the given training data.
It build a forest of trees from the training set.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Univariate time series.
 y : arraylike, shape = (n_samples,)
Class labels for each sample.
Returns:  self : object

get_params
(deep=True)¶ Get parameters for this estimator.
Parameters:  deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:  params : dict
Parameter names mapped to their values.

predict
(X)[source]¶ Predict class for X.
The predicted class of an input time series 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.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Univariate time series.
Returns:  y : array, shape = (n_samples,)
The predicted classes.

predict_proba
(X)[source]¶ Predict class probabilities for X.
The predicted class probabilities of an input time series 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.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Univariate time series.
Returns:  p : array, shape = (n_samples, n_classes)
The class probabilities of the input time series. The order of the classes corresponds to that in the attribute classes_.

score
(X, y)[source]¶ Return the mean accuracy on the given test data and labels.
Parameters:  X : arraylike, shape = (n_samples, n_timestamps)
Test samples.
 y : arraylike, shape = (n_samples,)
True labels for X.
Returns:  score : float
Mean accuracy of self.predict(X) wrt. y.

set_params
(**params)¶ 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:  **params : dict
Estimator parameters.
Returns:  self : estimator instance
Estimator instance.