Uploaded Test files

venv/Lib/site-packages/sklearn/svm/_bounds.py

@@ -0,0 +1,74 @@
+"""Determination of parameter bounds"""
+# Author: Paolo Losi
+# License: BSD 3 clause
+
+import numpy as np
+
+from ..preprocessing import LabelBinarizer
+from ..utils.validation import check_consistent_length, check_array
+from ..utils.validation import _deprecate_positional_args
+from ..utils.extmath import safe_sparse_dot
+
+
+@_deprecate_positional_args
+def l1_min_c(X, y, *, loss='squared_hinge', fit_intercept=True,
+             intercept_scaling=1.0):
+    """
+    Return the lowest bound for C such that for C in (l1_min_C, infinity)
+    the model is guaranteed not to be empty. This applies to l1 penalized
+    classifiers, such as LinearSVC with penalty='l1' and
+    linear_model.LogisticRegression with penalty='l1'.
+
+    This value is valid if class_weight parameter in fit() is not set.
+
+    Parameters
+    ----------
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Training vector, where n_samples in the number of samples and
+        n_features is the number of features.
+
+    y : array-like of shape (n_samples,)
+        Target vector relative to X.
+
+    loss : {'squared_hinge', 'log'}, default='squared_hinge'
+        Specifies the loss function.
+        With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
+        With 'log' it is the loss of logistic regression models.
+
+    fit_intercept : bool, default=True
+        Specifies if the intercept should be fitted by the model.
+        It must match the fit() method parameter.
+
+    intercept_scaling : float, default=1.0
+        when fit_intercept is True, instance vector x becomes
+        [x, intercept_scaling],
+        i.e. a "synthetic" feature with constant value equals to
+        intercept_scaling is appended to the instance vector.
+        It must match the fit() method parameter.
+
+    Returns
+    -------
+    l1_min_c : float
+        minimum value for C
+    """
+    if loss not in ('squared_hinge', 'log'):
+        raise ValueError('loss type not in ("squared_hinge", "log")')
+
+    X = check_array(X, accept_sparse='csc')
+    check_consistent_length(X, y)
+
+    Y = LabelBinarizer(neg_label=-1).fit_transform(y).T
+    # maximum absolute value over classes and features
+    den = np.max(np.abs(safe_sparse_dot(Y, X)))
+    if fit_intercept:
+        bias = np.full((np.size(y), 1), intercept_scaling,
+                       dtype=np.array(intercept_scaling).dtype)
+        den = max(den, abs(np.dot(Y, bias)).max())
+
+    if den == 0.0:
+        raise ValueError('Ill-posed l1_min_c calculation: l1 will always '
+                         'select zero coefficients for this data')
+    if loss == 'squared_hinge':
+        return 0.5 / den
+    else:  # loss == 'log':
+        return 2.0 / den