ai-content-maker/.venv/Lib/site-packages/sklearn/neighbors/tests/test_graph.py

102 lines
3.5 KiB
Python

import numpy as np
import pytest
from sklearn.metrics import euclidean_distances
from sklearn.neighbors import KNeighborsTransformer, RadiusNeighborsTransformer
from sklearn.neighbors._base import _is_sorted_by_data
from sklearn.utils._testing import assert_array_equal
def test_transformer_result():
# Test the number of neighbors returned
n_neighbors = 5
n_samples_fit = 20
n_queries = 18
n_features = 10
rng = np.random.RandomState(42)
X = rng.randn(n_samples_fit, n_features)
X2 = rng.randn(n_queries, n_features)
radius = np.percentile(euclidean_distances(X), 10)
# with n_neighbors
for mode in ["distance", "connectivity"]:
add_one = mode == "distance"
nnt = KNeighborsTransformer(n_neighbors=n_neighbors, mode=mode)
Xt = nnt.fit_transform(X)
assert Xt.shape == (n_samples_fit, n_samples_fit)
assert Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
assert Xt.format == "csr"
assert _is_sorted_by_data(Xt)
X2t = nnt.transform(X2)
assert X2t.shape == (n_queries, n_samples_fit)
assert X2t.data.shape == (n_queries * (n_neighbors + add_one),)
assert X2t.format == "csr"
assert _is_sorted_by_data(X2t)
# with radius
for mode in ["distance", "connectivity"]:
add_one = mode == "distance"
nnt = RadiusNeighborsTransformer(radius=radius, mode=mode)
Xt = nnt.fit_transform(X)
assert Xt.shape == (n_samples_fit, n_samples_fit)
assert not Xt.data.shape == (n_samples_fit * (n_neighbors + add_one),)
assert Xt.format == "csr"
assert _is_sorted_by_data(Xt)
X2t = nnt.transform(X2)
assert X2t.shape == (n_queries, n_samples_fit)
assert not X2t.data.shape == (n_queries * (n_neighbors + add_one),)
assert X2t.format == "csr"
assert _is_sorted_by_data(X2t)
def _has_explicit_diagonal(X):
"""Return True if the diagonal is explicitly stored"""
X = X.tocoo()
explicit = X.row[X.row == X.col]
return len(explicit) == X.shape[0]
def test_explicit_diagonal():
# Test that the diagonal is explicitly stored in the sparse graph
n_neighbors = 5
n_samples_fit, n_samples_transform, n_features = 20, 18, 10
rng = np.random.RandomState(42)
X = rng.randn(n_samples_fit, n_features)
X2 = rng.randn(n_samples_transform, n_features)
nnt = KNeighborsTransformer(n_neighbors=n_neighbors)
Xt = nnt.fit_transform(X)
assert _has_explicit_diagonal(Xt)
assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
Xt = nnt.transform(X)
assert _has_explicit_diagonal(Xt)
assert np.all(Xt.data.reshape(n_samples_fit, n_neighbors + 1)[:, 0] == 0)
# Using transform on new data should not always have zero diagonal
X2t = nnt.transform(X2)
assert not _has_explicit_diagonal(X2t)
@pytest.mark.parametrize("Klass", [KNeighborsTransformer, RadiusNeighborsTransformer])
def test_graph_feature_names_out(Klass):
"""Check `get_feature_names_out` for transformers defined in `_graph.py`."""
n_samples_fit = 20
n_features = 10
rng = np.random.RandomState(42)
X = rng.randn(n_samples_fit, n_features)
est = Klass().fit(X)
names_out = est.get_feature_names_out()
class_name_lower = Klass.__name__.lower()
expected_names_out = np.array(
[f"{class_name_lower}{i}" for i in range(est.n_samples_fit_)],
dtype=object,
)
assert_array_equal(names_out, expected_names_out)