8000 TST Extend tests for `scipy.sparse.*array` in `sklearn/metrics/tests/test_dist_metrics.py` by Tialo · Pull Request #27229 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

TST Extend tests for scipy.sparse.*array in sklearn/metrics/tests/test_dist_metrics.py #27229

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Sep 13, 2023
Merged

TST Extend tests for scipy.sparse.*array in sklearn/metrics/tests/test_dist_metrics.py #27229

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
41 changes: 24 additions & 17 deletions sklearn/metrics/tests/test_dist_metrics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@

import numpy as np
import pytest
import scipy.sparse as sp
from scipy.spatial.distance import cdist

from sklearn.metrics import DistanceMetric
Expand All @@ -15,7 +14,7 @@
)
from sklearn.utils import check_random_state
from sklearn.utils._testing import assert_allclose, create_memmap_backed_data
from sklearn.utils.fixes import parse_version, sp_version
from sklearn.utils.fixes import CSR_CONTAINERS, parse_version, sp_version


def dist_func(x1, x2, p):
Expand Down Expand Up @@ -61,10 +60,11 @@ def dist_func(x1, x2, p):
"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
)
@pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
def test_cdist(metric_param_grid, X, Y):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_cdist(metric_param_grid, X, Y, csr_container):
metric, param_grid = metric_param_grid
keys = param_grid.keys()
X_csr, Y_csr = sp.csr_matrix(X), sp.csr_matrix(Y)
X_csr, Y_csr = csr_container(X), csr_container(Y)
for vals in itertools.product(*param_grid.values()):
kwargs = dict(zip(keys, vals))
rtol_dict = {}
Expand Down Expand Up @@ -110,7 +110,8 @@ def test_cdist(metric_param_grid, X, Y):
@pytest.mark.parametrize(
"X_bool, Y_bool", [(X_bool, Y_bool), (X_bool_mmap, Y_bool_mmap)]
)
def test_cdist_bool_metric(metric, X_bool, Y_bool):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_cdist_bool_metric(metric, X_bool, Y_bool, csr_container):
D_scipy_cdist = cdist(X_bool, Y_bool, metric)

dm = DistanceMetric.get_metric(metric)
Expand All @@ -119,7 +120,7 @@ def test_cdist_bool_metric(metric, X_bool, Y_bool):

# DistanceMetric.pairwise must be consistent
# on all combinations of format in {sparse, dense}².
X_bool_csr, Y_bool_csr = sp.csr_matrix(X_bool), sp.csr_matrix(Y_bool)
X_bool_csr, Y_bool_csr = csr_container(X_bool), csr_container(Y_bool)

D_sklearn = dm.pairwise(X_bool, Y_bool)
assert D_sklearn.flags.c_contiguous
Expand All @@ -142,10 +143,11 @@ def test_cdist_bool_metric(metric, X_bool, Y_bool):
"metric_param_grid", METRICS_DEFAULT_PARAMS, ids=lambda params: params[0]
)
@pytest.mark.parametrize("X", [X64, X32, X_mmap])
def test_pdist(metric_param_grid, X):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_pdist(metric_param_grid, X, csr_container):
metric, param_grid = metric_param_grid
keys = param_grid.keys()
X_csr = sp.csr_matrix(X)
X_csr = csr_container(X)
for vals in itertools.product(*param_grid.values()):
kwargs = dict(zip(keys, vals))
rtol_dict = {}
Expand Down Expand Up @@ -215,13 +217,14 @@ def test_distance_metrics_dtype_consistency(metric_param_grid):

@pytest.mark.parametrize("metric", BOOL_METRICS)
@pytest.mark.parametrize("X_bool", [X_bool, X_bool_mmap])
def test_pdist_bool_metrics(metric, X_bool):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_pdist_bool_metrics(metric, X_bool, csr_container):
D_scipy_pdist = cdist(X_bool, X_bool, metric)
dm = DistanceMetric.get_metric(metric)
D_sklearn = dm.pairwise(X_bool)
assert_allclose(D_sklearn, D_scipy_pdist)

X_bool_csr = sp.csr_matrix(X_bool)
X_bool_csr = csr_container(X_bool)
D_sklearn = dm.pairwise(X_bool_csr)
assert_allclose(D_sklearn, D_scipy_pdist)

Expand Down Expand Up @@ -259,12 +262,13 @@ def test_pickle_bool_metrics(metric, X_bool):


@pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
def test_haversine_metric(X, Y):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_haversine_metric(X, Y, csr_container):
# The Haversine DistanceMetric only works on 2 features.
X = np.asarray(X[:, :2])
Y = np.asarray(Y[:, :2])

X_csr, Y_csr = sp.csr_matrix(X), sp.csr_matrix(Y)
X_csr, Y_csr = csr_container(X), csr_container(Y)

# Haversine is not supported by scipy.special.distance.{cdist,pdist}
# So we reimplement it to have a reference.
Expand Down Expand Up @@ -360,11 +364,14 @@ def test_readonly_kwargs():
[
(np.array([1, 1.5, -13]), ValueError, "w cannot contain negative weights"),
(np.array([1, 1.5, np.nan]), ValueError, "w contains NaN"),
(
sp.csr_matrix([1, 1.5, 1]),
TypeError,
"A sparse matrix was passed, but dense data is required",
),
*[
(
csr_container([1, 1.5, 1]),
TypeError,
"A sparse matrix was passed, but dense data is required",
)
for csr_container in CSR_CONTAINERS
],
(np.array(["a", "b", "c"]), ValueError, "could not convert string to float"),
(np.array([]), ValueError, "a minimum of 1 is required"),
],
Expand Down
0