ML之FE：特征工程中常用的五大数据集划分方法(特殊类型数据分割，如时间序列数据分割法)讲解及其代码
ML之FE：特征工程中常用的五大数据集划分方法(特殊类型数据分割，如时间序列数据分割法)讲解及其代码
特殊类型数据分割
5.1、时间序列数据分割TimeSeriesSplit
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class TimeSeriesSplit Found at: sklearn.model_selection._split class TimeSeriesSplit(_BaseKFold): """Time Series cross-validator .. versionadded:: 0.18 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 :class:`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 :ref:`User Guide <cross_validation>`. Parameters ---------- n_splits : int, default=5. Number of splits. Must be at least 2. .. versionchanged:: 0.22 . ``n_splits`` default value changed from 3 to 5. max_train_size : int, default=None. Maximum size for a single training set. |
提供训练/测试索引，以分割时间序列数据样本，在训练/测试集中，在固定的时间间隔观察。在每次分割中，测试索引必须比以前更高，因此在交叉验证器中变换是不合适的。这个交叉验证对象是KFold 的变体。在第k次分割中，它返回第k次折叠作为序列集，返回第(k+1)次折叠作为测试集。 注意，与标准的交叉验证方法不同，连续训练集是之前那些训练集的超集。 更多信息请参见:ref: ' User Guide <cross_validation> '。</cross_validation> 参数 ---------- n_splits :int，默认=5。数量的分裂。必须至少是2. ..versionchanged:: 0.22。' ' n_split ' ' '默认值从3更改为5。 max_train_size : int，默认None。单个训练集的最大容量。 |
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Examples -------- >>> import numpy as np >>> from sklearn.model_selection import TimeSeriesSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> tscv = TimeSeriesSplit() >>> print(tscv) TimeSeriesSplit(max_train_size=None, n_splits=5) >>> 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] TRAIN: [0 1 2 3] TEST: [4] TRAIN: [0 1 2 3 4] TEST: [5] Notes ----- The training set has size ``i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1)`` in the ``i``th split, with a test set of size ``n_samples//(n_splits + 1)``, where ``n_samples`` is the number of samples. |
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""" @_deprecate_positional_args def __init__(self, n_splits=5, *, max_train_size=None): super().__init__(n_splits, shuffle=False, random_state=None) self.max_train_size = max_train_size def split(self, X, y=None, groups=None): """Generate indices to split data into training and test set. Parameters ---------- X : array-like of 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 of shape (n_samples,). Always ignored, exists for compatibility. groups : array-like of shape (n_samples,). Always ignored, exists for compatibility. Yields ------ train : ndarray. The training set indices for that split. test : ndarray. The testing set indices for that split. """ X, y, groups = indexable(X, y, groups) n_samples = _num_samples(X) n_splits = self.n_splits n_folds = n_splits + 1 if n_folds > n_samples: raise ValueError( ("Cannot have number of folds ={0} greater than the number of samples: {1}."). format(n_folds, n_samples)) indices = np.arange(n_samples) test_size = n_samples // n_folds test_starts = range(test_size + n_samples % n_folds, n_samples, test_size) for test_start in test_starts: if self.max_train_size and self.max_train_size < test_start: yield indices[test_start - self.max_train_size:test_start], indices [test_start:test_start + test_size] else: yield indices[:test_start], indices[test_start:test_start + test_size] |
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Examples -------- >>> import numpy as np >>> from sklearn.model_selection import TimeSeriesSplit >>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]]) >>> y = np.array([1, 2, 3, 4, 5, 6]) >>> tscv = TimeSeriesSplit() >>> print(tscv) TimeSeriesSplit(max_train_size=None, n_splits=5) >>> 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] TRAIN: [0 1 2 3] TEST: [4] TRAIN: [0 1 2 3 4] TEST: [5] Notes ----- The training set has size ``i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1)`` in the ``i``th split, with a test set of size ``n_samples//(n_splits + 1)``, where ``n_samples`` is the number of samples. |
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""" @_deprecate_positional_args def __init__(self, n_splits=5, *, max_train_size=None): super().__init__(n_splits, shuffle=False, random_state=None) self.max_train_size = max_train_size def split(self, X, y=None, groups=None): """Generate indices to split data into training and test set. Parameters ---------- X : array-like of 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 of shape (n_samples,). Always ignored, exists for compatibility. groups : array-like of shape (n_samples,). Always ignored, exists for compatibility. Yields ------ train : ndarray. The training set indices for that split. test : ndarray. The testing set indices for that split. """ X, y, groups = indexable(X, y, groups) n_samples = _num_samples(X) n_splits = self.n_splits n_folds = n_splits + 1 if n_folds > n_samples: raise ValueError( ("Cannot have number of folds ={0} greater than the number of samples: {1}."). format(n_folds, n_samples)) indices = np.arange(n_samples) test_size = n_samples // n_folds test_starts = range(test_size + n_samples % n_folds, n_samples, test_size) for test_start in test_starts: if self.max_train_size and self.max_train_size < test_start: yield indices[test_start - self.max_train_size:test_start], indices [test_start:test_start + test_size] else: yield indices[:test_start], indices[test_start:test_start + test_size] |