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class sklearn.model_selection.TimeSeriesSplit(n_splits=3)
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Time Series cross-validator
Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.
This cross-validation object is a variation of
KFold
. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set.Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them.
Read more in the User Guide.
Parameters: n_splits : int, default=3
Number of splits. Must be at least 1.
Notes
The training set has size
i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1)
in thei``th split, with a test set of size ``n_samples//(n_splits + 1)
, wheren_samples
is the number of samples.Examples
>>> from sklearn.model_selection import TimeSeriesSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4]) >>> tscv = TimeSeriesSplit(n_splits=3) >>> print(tscv) TimeSeriesSplit(n_splits=3) >>> for train_index, test_index in tscv.split(X): ... print("TRAIN:", train_index, "TEST:", test_index) ... X_train, X_test = X[train_index], X[test_index] ... y_train, y_test = y[train_index], y[test_index] TRAIN: [0] TEST: [1] TRAIN: [0 1] TEST: [2] TRAIN: [0 1 2] TEST: [3]
Methods
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the cross-validator split
(X[, y, groups])Generate indices to split data into training and test set. -
__init__(n_splits=3)
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get_n_splits(X=None, y=None, groups=None)
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Returns the number of splitting iterations in the cross-validator
Parameters: X : object
Always ignored, exists for compatibility.
y : object
Always ignored, exists for compatibility.
groups : object
Always ignored, exists for compatibility.
Returns: n_splits : int
Returns the number of splitting iterations in the cross-validator.
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split(X, y=None, groups=None)
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Generate indices to split data into training and test set.
Parameters: X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape (n_samples,)
Always ignored, exists for compatibility.
groups : array-like, with shape (n_samples,), optional
Always ignored, exists for compatibility.
Returns: train : ndarray
The training set indices for that split.
test : ndarray
The testing set indices for that split.
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model_selection.TimeSeriesSplit()
2017-01-15 04:24:21
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