<commit_before>"""
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""

# Author: <NAME>, <NAME>

import numpy as np
import scipy.sparse as sp

from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings

from sklearn.metrics.pairwise import pairwise_distances
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors


def test_lsh_forest_deprecation():
    assert_warns_message(DeprecationWarning,
                         "LSHForest has poor performance and has been "
                         "deprecated in 0.19. It will be removed "
                         "in version 0.21.", LSHForest)


def test_neighbors_accuracy_with_n_candidates():
    # Checks whether accuracy increases as `n_candidates` increases.
    n_candidates_values = np.array([.1, 50, 500])
    n_samples = 100
    n_features = 10
    n_iter = 10
    n_points = 5
    rng = np.random.RandomState(42)
    accuracies = np.zeros(n_candidates_values.shape[0], dtype=float)
    X = rng.rand(n_samples, n_features)

    for i, n_candidates in enumerate(n_candidates_values):
        lshf = ignore_warnings(LSHForest, category=DeprecationWarning)(
            n_candidates=n_candidates)
        ignore_warnings(lshf.fit)(X)
        for j in range(n_iter):
            query = X[rng.randint(0, n_samples)].reshape(1, -1)

            neighbors = lshf.kneighbors(query, n_neighbors=n_points,
                                        return_distance=False)
            distances = pairwise_distances(query, X, metric='cosine')
            ranks = np.argsort(distances)[0, :n_points]

            intersection = np.intersect1d(ranks, neighbors).shape[0]
            ratio = intersection / float(n_points)
            accuracies[i] = accuracies[i] + ratio

        accuracies[i] = accuracies[i] / float(n_iter)<commit_msg>Fix random state in LSHF test (#9702)<commit_after>"""
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""

# Author: <NAME>, <NAME>

import numpy as np
import scipy.sparse as sp

from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings

from sklearn.metrics.pairwise import pairwise_distances
from sklearn.neighbors import LSHForest
from sklearn.neighbors import NearestNeighbors


def test_lsh_forest_deprecation():
    assert_warns_message(DeprecationWarning,
                         "LSHForest has poor performance and has been "
                         "deprecated in 0.19. It will be removed "
                         "in version 0.21.", LSHForest)


def test_neighbors_accuracy_with_n_candidates():
    # Checks whether accuracy increases as `n_candidates` increases.
    n_candidates_values = np.array([.1, 50, 500])
    n_samples = 100
    n_features = 10
    n_iter = 10
    n_points = 5
    rng = np.random.RandomState(42)
    accuracies = np.zeros(n_candidates_values.shape[0], dtype=float)
    X = rng.rand(n_samples, n_features)

    for i, n_candidates in enumerate(n_candidates_values):
        lshf = ignore_warnings(LSHForest, category=DeprecationWarning)(
            n_candidates=n_candidates, random_state=0)
        ignore_warnings(lshf.fit)(X)
        for j in range(n_iter):
            query = X[rng.randint(0, n_samples)].reshape(1, -1)

            neighbors = lshf.kneighbors(query, n_neighbors=n_points,
                                        return_distance=False)
            distances = pairwise_distances(query, X, metric='cosine')
            ranks = np.argsort(distances)[0, :n_points]

            intersection = np.intersect1d(ranks, neighbors).shape[0]
            ratio = intersection / float(n_points)
            accuracies[i] = accuracies[i] + ratio

        accuracies[i] = accuracies[i] / float(n_iter)
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{'dir': 'git-commits-cleaned', 'id': '6071264', 'n_tokens_mistral': 1547, 'n_tokens_neox': 1453, 'n_words': 350}