BF: omitted file

nilearn/_utils/fixes/sklearn_basic_backports.py

@@ -0,0 +1,103 @@
+"""
+Back-porting some fundamental sklearn API.
+
+"""
+# Author: DOHMATOB Elvis
+
+import numbers
+import numpy as np
+import scipy.sparse as sp
+from sklearn.utils import as_float_array
+from sklearn.preprocessing import LabelBinarizer as SklLabelBinarizer
+
+
+def center_data(X, y, fit_intercept, normalize=False, copy=True,
+                sample_weight=None):
+    """
+    Centers data to have mean zero along axis 0. This is here because
+    nearly all linear models will want their data to be centered.
+
+    If sample_weight is not None, then the weighted mean of X and y
+    is zero, and not the mean itself
+    """
+    X = as_float_array(X, copy)
+    no_sample_weight = (sample_weight is None
+                        or isinstance(sample_weight, numbers.Number))
+
+    if fit_intercept:
+        if sp.issparse(X):
+            X_mean = np.zeros(X.shape[1])
+            X_std = np.ones(X.shape[1])
+        else:
+            if no_sample_weight:
+                X_mean = X.mean(axis=0)
+            else:
+                X_mean = (np.sum(X * sample_weight[:, np.newaxis], axis=0)
+                          / np.sum(sample_weight))
+            X -= X_mean
+            if normalize:
+                X_std = np.sqrt(np.sum(X ** 2, axis=0))
+                X_std[X_std == 0] = 1
+                X /= X_std
+            else:
+                X_std = np.ones(X.shape[1])
+        if no_sample_weight:
+            y_mean = y.mean(axis=0)
+        else:
+            if y.ndim <= 1:
+                y_mean = (np.sum(y * sample_weight, axis=0)
+                          / np.sum(sample_weight))
+            else:
+                # cater for multi-output problems
+                y_mean = (np.sum(y * sample_weight[:, np.newaxis], axis=0)
+                          / np.sum(sample_weight))
+        y = y - y_mean
+    else:
+        X_mean = np.zeros(X.shape[1])
+        X_std = np.ones(X.shape[1])
+        y_mean = 0. if y.ndim == 1 else np.zeros(y.shape[1], dtype=X.dtype)
+    return X, y, X_mean, y_mean, X_std
+
+
+# Cater for the fact that in 0.10 the LabelBinarizer did not have a
+# neg_label
+class _LabelBinarizer(SklLabelBinarizer):
+
+    def __init__(self, neg_label=0, pos_label=1):
+        # Always call the super class's init method
+        SklLabelBinarizer.__init__(self)
+        if neg_label >= pos_label:
+            raise ValueError("neg_label must be strictly less than pos_label.")
+
+        self.neg_label = neg_label
+        self.pos_label = pos_label
+
+    def fit_transform(self, y):
+        """Transform multi-class labels to binary labels
+
+        The output of transform is sometimes referred to by some authors as the
+        1-of-K coding scheme.
+
+        Parameters
+        ----------
+        y : numpy array of shape [n_samples] or sequence of sequences
+            Target values. In the multilabel case the nested sequences can
+            have variable lengths.
+
+        Returns
+        -------
+        Y : numpy array of shape [n_samples, n_classes]
+        """
+
+        y_ = SklLabelBinarizer.fit_transform(self, y)
+
+        if np.min(y_) == 0. and self.neg_label == -1:
+            y_ = 2. * (y_ == 1.) - 1.
+
+        return y_
+
+
+if hasattr(SklLabelBinarizer(), 'neg_label'):
+    LabelBinarizer = SklLabelBinarizer
+else:
+    LabelBinarizer = _LabelBinarizer