8000 TST Extend tests for `scipy.sparse.*array` in `sklearn/linear_model/tests/test_sag.py` by Tialo · Pull Request #27206 · scikit-learn/scikit-learn · GitHub
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TST Extend tests for scipy.sparse.*array in sklearn/linear_model/tests/test_sag.py #27206

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Merged
merged 1 commit into from
Sep 13, 2023
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TST Extend tests for scipy.sparse.*array in sklearn/linear_model/tests/test_sag.py #27206

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49 changes: 29 additions & 20 deletions sklearn/linear_model/tests/test_sag.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@

import numpy as np
import pytest
import scipy.sparse as sp
from scipy.special import logsumexp

from sklearn._loss.loss import HalfMultinomialLoss
Expand All @@ -27,6 +26,7 @@
assert_array_almost_equal,
)
from sklearn.utils.extmath import row_norms
from sklearn.utils.fixes import CSR_CONTAINERS

iris = load_iris()

Expand Down Expand Up @@ -356,7 +356,8 @@ def test_regressor_matching():


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_sag_pobj_matches_logistic_regression():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_pobj_matches_logistic_regression(csr_container):
"""tests if the sag pobj matches log reg"""
n_samples = 100
alpha = 1.0
Expand All @@ -383,7 +384,7 @@ def test_sag_pobj_matches_logistic_regression():
)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)
clf3.fit(X, y)

pobj1 = get_pobj(clf1.coef_, alpha, X, y, log_loss)
Expand All @@ -396,7 +397,8 @@ def test_sag_pobj_matches_logistic_regression():


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_sag_pobj_matches_ridge_regression():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_pobj_matches_ridge_regression(csr_container):
"""tests if the sag pobj matches ridge reg"""
n_samples = 100
n_features = 10
Expand Down Expand Up @@ -427,7 +429,7 @@ def test_sag_pobj_matches_ridge_regression():
)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)
clf3.fit(X, y)

pobj1 = get_pobj(clf1.coef_, alpha, X, y, squared_loss)
Expand All @@ -440,7 +442,8 @@ def test_sag_pobj_matches_ridge_regression():


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_sag_regressor_computed_correctly():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_regressor_computed_correctly(csr_container):
"""tests if the sag regressor is computed correctly"""
alpha = 0.1
n_features = 10
Expand All @@ -465,7 +468,7 @@ def test_sag_regressor_computed_correctly():
clf2 = clone(clf1)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)

spweights1, spintercept1 = sag_sparse(
X,
Expand Down Expand Up @@ -551,7 +554,8 @@ def test_get_auto_step_size():


@pytest.mark.parametrize("seed", range(3)) # locally tested with 1000 seeds
def test_sag_regressor(seed):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_regressor(seed, csr_container):
"""tests if the sag regressor performs well"""
xmin, xmax = -5, 5
n_samples = 300
Expand All @@ -573,7 +577,7 @@ def test_sag_regressor(seed):
)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)
score1 = clf1.score(X, y)
score2 = clf2.score(X, y)
assert score1 > 0.98
Expand All @@ -585,15 +589,16 @@ def test_sag_regressor(seed):
clf1 = Ridge(tol=tol, solver="sag", max_iter=max_iter, alpha=alpha * n_samples)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)
score1 = clf1.score(X, y)
score2 = clf2.score(X, y)
assert score1 > 0.45
assert score2 > 0.45


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_sag_classifier_computed_correctly():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_classifier_computed_correctly(csr_container):
"""tests if the binary classifier is computed correctly"""
alpha = 0.1
n_samples = 50
Expand All @@ -619,7 +624,7 @@ def test_sag_classifier_computed_correctly():
clf2 = clone(clf1)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)

spweights, spintercept = sag_sparse(
X,
Expand Down Expand Up @@ -649,7 +654,8 @@ def test_sag_classifier_computed_correctly():


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_sag_multiclass_computed_correctly():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sag_multiclass_computed_correctly(csr_container):
"""tests if the multiclass classifier is computed correctly"""
alpha = 0.1
n_samples = 20
Expand All @@ -672,7 +678,7 @@ def test_sag_multiclass_computed_correctly():
clf2 = clone(clf1)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)

coef1 = []
intercept1 = []
Expand Down Expand Up @@ -720,7 +726,8 @@ def test_sag_multiclass_computed_correctly():
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)


def test_classifier_results():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_classifier_results(csr_container):
"""tests if classifier results match target"""
alpha = 0.1
n_features = 20
Expand All @@ -742,15 +749,16 @@ def test_classifier_results():
clf2 = clone(clf1)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)
pred1 = clf1.predict(X)
pred2 = clf2.predict(X)
assert_almost_equal(pred1, y, decimal=12)
assert_almost_equal(pred2, y, decimal=12)


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_binary_classifier_class_weight():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_binary_classifier_class_weight(csr_container):
"""tests binary classifier with classweights for each class"""
alpha = 0.1
n_samples = 50
Expand Down Expand Up @@ -778,7 +786,7 @@ def test_binary_classifier_class_weight():
clf2 = clone(clf1)

clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)

le = LabelEncoder()
class_weight_ = compute_class_weight(class_weight, classes=np.unique(y), y=y)
Expand Down Expand Up @@ -813,7 +821,8 @@ def test_binary_classifier_class_weight():


@pytest.mark.filterwarnings("ignore:The max_iter was reached")
def test_multiclass_classifier_class_weight():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_multiclass_classifier_class_weight(csr_container):
"""tests multiclass with classweights for each class"""
alpha = 0.1
n_samples = 20
Expand All @@ -837,7 +846,7 @@ def test_multiclass_classifier_class_weight():
)
clf2 = clone(clf1)
clf1.fit(X, y)
clf2.fit(sp.csr_matrix(X), y)
clf2.fit(csr_container(X), y)

le = LabelEncoder()
class_weight_ = compute_class_weight(class_weight, classes=np.unique(y), y=y)
Expand Down
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