diff model_validations.py @ 24:5552eda109bd draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/model_validations.py	Wed May 15 07:39:54 2019 -0400
@@ -0,0 +1,252 @@
+"""
+class
+-----
+OrderedKFold
+RepeatedOrderedKold
+
+
+function
+--------
+train_test_split
+"""
+
+import numpy as np
+import warnings
+
+from itertools import chain
+from math import ceil, floor
+from sklearn.model_selection import (GroupShuffleSplit, ShuffleSplit,
+                                     StratifiedShuffleSplit)
+from sklearn.model_selection._split import _BaseKFold, _RepeatedSplits
+from sklearn.utils import check_random_state, indexable, safe_indexing
+from sklearn.utils.validation import _num_samples, check_array
+
+
+def _validate_shuffle_split(n_samples, test_size, train_size,
+                            default_test_size=None):
+    """
+    Validation helper to check if the test/test sizes are meaningful wrt to the
+    size of the data (n_samples)
+    """
+    if test_size is None and train_size is None:
+        test_size = default_test_size
+
+    test_size_type = np.asarray(test_size).dtype.kind
+    train_size_type = np.asarray(train_size).dtype.kind
+
+    if (test_size_type == 'i' and (test_size >= n_samples or test_size <= 0)
+       or test_size_type == 'f' and (test_size <= 0 or test_size >= 1)):
+        raise ValueError('test_size={0} should be either positive and smaller'
+                         ' than the number of samples {1} or a float in the '
+                         '(0, 1) range'.format(test_size, n_samples))
+
+    if (train_size_type == 'i' and (train_size >= n_samples or train_size <= 0)
+       or train_size_type == 'f' and (train_size <= 0 or train_size >= 1)):
+        raise ValueError('train_size={0} should be either positive and smaller'
+                         ' than the number of samples {1} or a float in the '
+                         '(0, 1) range'.format(train_size, n_samples))
+
+    if train_size is not None and train_size_type not in ('i', 'f'):
+        raise ValueError("Invalid value for train_size: {}".format(train_size))
+    if test_size is not None and test_size_type not in ('i', 'f'):
+        raise ValueError("Invalid value for test_size: {}".format(test_size))
+
+    if (train_size_type == 'f' and test_size_type == 'f' and
+            train_size + test_size > 1):
+        raise ValueError(
+            'The sum of test_size and train_size = {}, should be in the (0, 1)'
+            ' range. Reduce test_size and/or train_size.'
+            .format(train_size + test_size))
+
+    if test_size_type == 'f':
+        n_test = ceil(test_size * n_samples)
+    elif test_size_type == 'i':
+        n_test = float(test_size)
+
+    if train_size_type == 'f':
+        n_train = floor(train_size * n_samples)
+    elif train_size_type == 'i':
+        n_train = float(train_size)
+
+    if train_size is None:
+        n_train = n_samples - n_test
+    elif test_size is None:
+        n_test = n_samples - n_train
+
+    if n_train + n_test > n_samples:
+        raise ValueError('The sum of train_size and test_size = %d, '
+                         'should be smaller than the number of '
+                         'samples %d. Reduce test_size and/or '
+                         'train_size.' % (n_train + n_test, n_samples))
+
+    n_train, n_test = int(n_train), int(n_test)
+
+    if n_train == 0:
+        raise ValueError(
+            'With n_samples={}, test_size={} and train_size={}, the '
+            'resulting train set will be empty. Adjust any of the '
+            'aforementioned parameters.'.format(n_samples, test_size,
+                                                train_size)
+        )
+
+    return n_train, n_test
+
+
+def train_test_split(*arrays, **options):
+    """Extend sklearn.model_selection.train_test_slit to have group split.
+
+    Parameters
+    ----------
+    *arrays : sequence of indexables with same length / shape[0]
+        Allowed inputs are lists, numpy arrays, scipy-sparse
+        matrices or pandas dataframes.
+
+    test_size : float, int or None, optional (default=None)
+        If float, should be between 0.0 and 1.0 and represent the proportion
+        of the dataset to include in the test split. If int, represents the
+        absolute number of test samples. If None, the value is set to the
+        complement of the train size. If ``train_size`` is also None, it will
+        be set to 0.25.
+
+    train_size : float, int, or None, (default=None)
+        If float, should be between 0.0 and 1.0 and represent the
+        proportion of the dataset to include in the train split. If
+        int, represents the absolute number of train samples. If None,
+        the value is automatically set to the complement of the test size.
+
+    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`.
+
+    shuffle : None or str (default='simple')
+        How to shuffle the data before splitting.
+        None, no shuffle.
+        For str, one of 'simple', 'stratified' and 'group', corresponding to
+        `ShuffleSplit`, `StratifiedShuffleSplit` and `GroupShuffleSplit`,
+        respectively.
+
+    labels : array-like or None (default=None)
+        Ignored if shuffle is None or 'simple'.
+        When shuffle='stratified', this array is used as class labels.
+        When shuffle='group', this array is used as groups.
+
+    Returns
+    -------
+    splitting : list, length=2 * len(arrays)
+        List containing train-test split of inputs.
+
+    """
+    n_arrays = len(arrays)
+    if n_arrays == 0:
+        raise ValueError("At least one array required as input")
+    test_size = options.pop('test_size', None)
+    train_size = options.pop('train_size', None)
+    random_state = options.pop('random_state', None)
+    shuffle = options.pop('shuffle', 'simple')
+    labels = options.pop('labels', None)
+
+    if options:
+        raise TypeError("Invalid parameters passed: %s" % str(options))
+
+    arrays = indexable(*arrays)
+
+    n_samples = _num_samples(arrays[0])
+    if shuffle == 'group':
+        if labels is None:
+            raise ValueError("When shuffle='group', "
+                             "labels should not be None!")
+        labels = check_array(labels, ensure_2d=False, dtype=None)
+        uniques = np.unique(labels)
+        n_samples = uniques.size
+
+    n_train, n_test = _validate_shuffle_split(n_samples, test_size, train_size,
+                                              default_test_size=0.25)
+
+    shuffle_options = dict(test_size=n_test,
+                           train_size=n_train,
+                           random_state=random_state)
+
+    if shuffle is None:
+        if labels is not None:
+            warnings.warn("The `labels` is ignored for "
+                          "shuffle being None!")
+
+        train = np.arange(n_train)
+        test = np.arange(n_train, n_train + n_test)
+
+    elif shuffle == 'simple':
+        if labels is not None:
+            warnings.warn("The `labels` is not needed and therefore "
+                          "ignored for ShuffleSplit, as shuffle='simple'!")
+
+        cv = ShuffleSplit(**shuffle_options)
+        train, test = next(cv.split(X=arrays[0], y=None))
+
+    elif shuffle == 'stratified':
+        cv = StratifiedShuffleSplit(**shuffle_options)
+        train, test = next(cv.split(X=arrays[0], y=labels))
+
+    elif shuffle == 'group':
+        cv = GroupShuffleSplit(**shuffle_options)
+        train, test = next(cv.split(X=arrays[0], y=None, groups=labels))
+
+    else:
+        raise ValueError("The argument `shuffle` only supports None, "
+                         "'simple', 'stratified' and 'group', but got `%s`!"
+                         % shuffle)
+
+    return list(chain.from_iterable((safe_indexing(a, train),
+                                    safe_indexing(a, test)) for a in arrays))
+
+
+class OrderedKFold(_BaseKFold):
+    """
+    Split into K fold based on ordered target value
+
+    Parameters
+    ----------
+    n_splits : int, default=3
+        Number of folds. Must be at least 2.
+    shuffle: bool
+    random_state: None or int
+    """
+
+    def __init__(self, n_splits=3, shuffle=False, random_state=None):
+        super(OrderedKFold, self).__init__(n_splits, shuffle, random_state)
+
+    def _iter_test_indices(self, X, y, groups=None):
+        n_samples = _num_samples(X)
+        n_splits = self.n_splits
+        y = np.asarray(y)
+        sorted_index = np.argsort(y)
+        if self.shuffle:
+            current = 0
+            rng = check_random_state(self.random_state)
+            for i in range(n_samples // int(n_splits)):
+                start, stop = current, current + n_splits
+                rng.shuffle(sorted_index[start:stop])
+                current = stop
+            rng.shuffle(sorted_index[current:])
+
+        for i in range(n_splits):
+            yield sorted_index[i:n_samples:n_splits]
+
+
+class RepeatedOrderedKFold(_RepeatedSplits):
+    """ Repeated OrderedKFold runs mutiple times with different randomization.
+
+    Parameters
+    ----------
+    n_splits : int, default=5
+        Number of folds. Must be at least 2.
+
+    n_repeats : int, default=5
+        Number of times cross-validator to be repeated.
+
+    random_state: int, RandomState instance or None. Optional
+    """
+    def __init__(self, n_splits=5, n_repeats=5, random_state=None):
+        super(RepeatedOrderedKFold, self).__init__(
+            OrderedKFold, n_repeats, random_state, n_splits=n_splits)