74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
"""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
|