8000 Rename `TimeSeriesCV` to `TimeSeriesSplit` (#7245) · TomDLT/scikit-learn@9f457f5 · GitHub
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Rename TimeSeriesCV to TimeSeriesSplit (scikit-learn#7245)
* rename TimeSeriesCV to TimeSeriesSplit * Add TimeSeriesSplit * Add whats new
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‎doc/modules/classes.rst

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@@ -174,6 +174,7 @@ Splitter Classes
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model_selection.LabelShuffleSplit
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model_selection.StratifiedShuffleSplit
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model_selection.PredefinedSplit
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model_selection.TimeSeriesSplit
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Splitter Functions
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------------------

‎doc/modules/cross_validation.rst

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@@ -533,13 +533,13 @@ between training and testing instances (yielding poor estimates of
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generalisation error) on time series data. Therefore, it is very important
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to evaluate our model for time series data on the "future" observations
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least like those that are used to train the model. To achieve this, one
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solution is provided by :class:`TimeSeriesCV`.
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solution is provided by :class:`TimeSeriesSplit`.
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TimeSeriesCV
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TimeSeriesSplit
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-----------------------
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:class:`TimeSeriesCV` is a variation of *k-fold* which
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:class:`TimeSeriesSplit` is a variation of *k-fold* which
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returns first :math:`k` folds as train set and the :math:`(k+1)` th
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fold as test set. Note that unlike standard cross-validation methods,
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successive training sets are supersets of those that come before them.
@@ -551,13 +551,13 @@ that are observed at fixed time intervals.
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Example of 3-split time series cross-validation on a dataset with 6 samples::
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>>> from sklearn.model_selection import TimeSeriesCV
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>>> from sklearn.model_selection import TimeSeriesSplit
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>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
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>>> y = np.array([1, 2, 3, 4, 5, 6])
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>>> tscv = TimeSeriesCV(n_splits=3)
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>>> tscv = TimeSeriesSplit(n_splits=3)
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>>> print(tscv) # doctest: +NORMALIZE_WHITESPACE
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TimeSeriesCV(n_splits=3)
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TimeSeriesSplit(n_splits=3)
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>>> for train, test in tscv.split(X):
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... print("%s %s" % (train, test))
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[0 1 2] [3]

‎doc/whats_new.rst

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@@ -141,6 +141,12 @@ New features
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<https://github.com/scikit-learn/scikit-learn/pull/6954>`_) by `Nelson
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Liu`_
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- Added new cross-validation splitter
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:class:`model_selection.TimeSeriesSplit` to handle time series data.
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(`#6586
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<https://github.com/scikit-learn/scikit-learn/pull/6586>`_) by `YenChen
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Lin`_
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Enhancements
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............
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‎sklearn/model_selection/__init__.py

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@@ -2,7 +2,7 @@
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from ._split import KFold
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from ._split import LabelKFold
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from ._split import StratifiedKFold
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from ._split import TimeSeriesCV
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from ._split import TimeSeriesSplit
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from ._split import LeaveOneLabelOut
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from ._split import LeaveOneOut
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from ._split import LeavePLabelOut
@@ -28,7 +28,7 @@
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__all__ = ('BaseCrossValidator',
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'GridSearchCV',
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'TimeSeriesCV',
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'TimeSeriesSplit',
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'KFold',
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'LabelKFold',
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'LabelShuffleSplit',

‎sklearn/model_selection/_split.py

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@@ -635,7 +635,7 @@ def split(self, X, y, labels=None):
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return super(StratifiedKFold, self).split(X, y, labels)
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class TimeSeriesCV(_BaseKFold):
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class TimeSeriesSplit(_BaseKFold):
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"""Time Series cross-validator
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Provides train/test indices to split time series data samples
@@ -659,12 +659,12 @@ class TimeSeriesCV(_BaseKFold):
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Examples
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--------
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>>> from sklearn.model_selection import TimeSeriesCV
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>>> from sklearn.model_selection import TimeSeriesSplit
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>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
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>>> y = np.array([1, 2, 3, 4])
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>>> tscv = TimeSeriesCV(n_splits=3)
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>>> tscv = TimeSeriesSplit(n_splits=3)
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>>> print(tscv) # doctest: +NORMALIZE_WHITESPACE
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TimeSeriesCV(n_splits=3)
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TimeSeriesSplit(n_splits=3)
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>>> for train_index, test_index in tscv.split(X):
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... print("TRAIN:", train_index, "TEST:", test_index)
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... X_train, X_test = X[train_index], X[test_index]
@@ -681,9 +681,9 @@ class TimeSeriesCV(_BaseKFold):
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where ``n_samples`` is the number of samples.
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"""
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def __init__(self, n_splits=3):
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super(TimeSeriesCV, self).__init__(n_splits,
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shuffle=False,
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random_state=None)
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super(TimeSeriesSplit, self).__init__(n_splits,
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shuffle=False,
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random_state=None)
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def split(self, X, y=None, labels=None):
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"""Generate indices to split data into training and test set.

‎sklearn/model_selection/tests/test_split.py

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@@ -30,7 +30,7 @@
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from sklearn.model_selection import KFold
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from sklearn.model_selection import StratifiedKFold
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from sklearn.model_selection import LabelKFold
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from sklearn.model_selection import TimeSeriesCV
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from sklearn.model_selection import TimeSeriesSplit
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from sklearn.model_selection import LeaveOneOut
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from sklearn.model_selection import LeaveOneLabelOut
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from sklearn.model_selection import LeavePOut
@@ -1004,9 +1004,9 @@ def test_time_series_cv():
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# Should fail if there are more folds than samples
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assert_raises_regexp(ValueError, "Cannot have number of folds.*greater",
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next,
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TimeSeriesCV(n_splits=7).split(X))
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TimeSeriesSplit(n_splits=7).split(X))
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tscv = TimeSeriesCV(2)
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tscv = TimeSeriesSplit(2)
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# Manually check that Time Series CV preserves the data
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# ordering on toy datasets
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assert_array_equal(train, [0, 1, 2, 3])
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assert_array_equal(test, [4, 5])
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splits = TimeSeriesCV(2).split(X)
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splits = TimeSeriesSplit(2).split(X)
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train, test = next(splits)
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assert_array_equal(train, [0, 1, 2])
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assert_array_equal(test, [5, 6])
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# Check get_n_splits returns the correct number of splits
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splits = TimeSeriesCV(2).split(X)
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splits = TimeSeriesSplit(2).split(X)
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n_splits_actual = len(list(splits))
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assert_equal(n_splits_actual, tscv.get_n_splits())
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assert_equal(n_splits_actual, 2)

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