reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/svm/_bounds.py

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+"""Determination of parameter bounds"""
+
+# Author: Paolo Losi
+# License: BSD 3 clause
+
+from numbers import Real
+
+import numpy as np
+
+from ..preprocessing import LabelBinarizer
+from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils.extmath import safe_sparse_dot
+from ..utils.validation import check_array, check_consistent_length
+
+
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like"],
+        "loss": [StrOptions({"squared_hinge", "log"})],
+        "fit_intercept": ["boolean"],
+        "intercept_scaling": [Interval(Real, 0, None, closed="neither")],
+    },
+    prefer_skip_nested_validation=True,
+)
+def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scaling=1.0):
+    """Return the lowest bound for C.
+
+    The lower bound for C is computed 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` is 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.
+
+    Examples
+    --------
+    >>> from sklearn.svm import l1_min_c
+    >>> from sklearn.datasets import make_classification
+    >>> X, y = make_classification(n_samples=100, n_features=20, random_state=42)
+    >>> print(f"{l1_min_c(X, y, loss='squared_hinge', fit_intercept=True):.4f}")
+    0.0044
+    """
+
+    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