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MAINT Parameters validation for svm.l1_min_c #24865

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Merged
merged 8 commits into from
Nov 18, 2022
Merged

MAINT Parameters validation for svm.l1_min_c #24865

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6df7131
added parameter validation for l1_min_c function
glevv Nov 8, 2022
bb3256a
added to validation list
glevv Nov 8, 2022
7f76300
fix brackets
glevv Nov 8, 2022
8bd4d81
change val params for intercept scaling
glevv Nov 8, 2022
16f5f8b
fixed imports
glevv Nov 8, 2022
a35e9f3
updated tests
glevv Nov 9, 2022
1eb260d
removed unnecessary tests
glevv Nov 9, 2022
d04b4f2
Merge branch 'main' into l1-min-c-param-val
glevv Nov 13, 2022
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14 changes: 12 additions & 2 deletions sklearn/svm/_bounds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,13 +2,25 @@
# Author: Paolo Losi
# License: BSD 3 clause

from numbers import Real

import numpy as np

from ..preprocessing import LabelBinarizer
from ..utils.validation import check_consistent_length, check_array
from ..utils.extmath import safe_sparse_dot
from ..utils._param_validation import StrOptions, Interval, validate_params


@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")],
}
)
def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scaling=1.0):
"""Return the lowest bound for C.

Expand Down Expand Up @@ -49,8 +61,6 @@ def l1_min_c(X, y, *, loss="squared_hinge", fit_intercept=True, intercept_scalin
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)
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12 changes: 0 additions & 12 deletions sklearn/svm/tests/test_bounds.py
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Expand Up @@ -35,13 +35,6 @@ def test_l1_min_c(loss, X_label, Y_label, intercept_label):
check_l1_min_c(X, Y, loss, **intercept_params)


def test_l1_min_c_l2_loss():
# loss='l2' should raise ValueError
msg = "loss type not in"
with pytest.raises(ValueError, match=msg):
l1_min_c(dense_X, Y1, loss="l2")


def check_l1_min_c(X, y, loss, fit_intercept=True, intercept_scaling=1.0):
min_c = l1_min_c(
X,
Expand Down Expand Up @@ -76,11 +69,6 @@ def test_ill_posed_min_c():
l1_min_c(X, y)


def test_unsupported_loss():
with pytest.raises(ValueError):
l1_min_c(dense_X, Y1, loss="l1")


_MAX_UNSIGNED_INT = 4294967295


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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -10,6 +10,7 @@

PARAM_VALIDATION_FUNCTION_LIST = [
"sklearn.cluster.kmeans_plusplus",
"sklearn.svm.l1_min_c",
]


Expand Down