From eeb6dcc4cc898af1a1c395a8e76eeee00d58c25f Mon Sep 17 00:00:00 2001
From: Andreas Mueller <amueller@ais.uni-bonn.de>
Date: Sun, 6 May 2012 16:22:39 +0200
Subject: [PATCH] ENH use random states everywhere, never call np.random.
---
sklearn/cluster/tests/test_hierarchical.py | 23 ++++++++--------
sklearn/cluster/tests/test_k_means.py | 3 ++-
.../tests/test_robust_covariance.py | 4 +--
sklearn/decomposition/tests/test_fastica.py | 4 +--
sklearn/ensemble/tests/test_forest.py | 6 ++---
.../ensemble/tests/test_gradient_boosting.py | 11 ++++----
.../tests/test_feature_select.py | 9 ++++---
sklearn/hmm.py | 6 ++---
sklearn/linear_model/tests/test_logistic.py | 8 +++---
sklearn/linear_model/tests/test_ridge.py | 27 +++++++++----------
sklearn/linear_model/tests/test_sgd.py | 20 +++++++-------
sklearn/manifold/tests/test_locally_linear.py | 6 ++---
sklearn/metrics/tests/test_metrics.py | 4 +--
sklearn/metrics/tests/test_pairwise.py | 2 --
sklearn/neighbors/tests/test_ball_tree.py | 14 +++++-----
sklearn/neighbors/tests/test_neighbors.py | 13 +++++----
sklearn/svm/tests/test_sparse.py | 3 ++-
sklearn/tests/test_kernel_approximation.py | 5 ++--
sklearn/tests/test_multiclass.py | 3 ++-
sklearn/tests/test_preprocessing.py | 10 +++----
sklearn/utils/tests/test_shortest_path.py | 6 ++---
sklearn/utils/tests/test_weighted_mode.py | 8 +++---
22 files changed, 100 insertions(+), 95 deletions(-)
diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
index 5e0da5f92b..53962a631f 100644
--- a/sklearn/cluster/tests/test_hierarchical.py
+++ b/sklearn/cluster/tests/test_hierarchical.py
@@ -17,9 +17,9 @@ def test_structured_ward_tree():
"""
Check that we obtain the correct solution for structured ward tree.
"""
- np.random.seed(0)
+ rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
- X = np.random.randn(50, 100)
+ X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
children, n_components, n_leaves = ward_tree(X.T, connectivity)
n_nodes = 2 * X.shape[1] - 1
@@ -30,8 +30,8 @@ def test_unstructured_ward_tree():
"""
Check that we obtain the correct solution for unstructured ward tree.
"""
- np.random.seed(0)
- X = np.random.randn(50, 100)
+ rnd = np.random.RandomState(0)
+ X = rnd.randn(50, 100)
children, n_nodes, n_leaves = ward_tree(X.T)
n_nodes = 2 * X.shape[1] - 1
assert_true(len(children) + n_leaves == n_nodes)
@@ -41,9 +41,9 @@ def test_height_ward_tree():
"""
Check that the height of ward tree is sorted.
"""
- np.random.seed(0)
+ rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
- X = np.random.randn(50, 100)
+ X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
children, n_nodes, n_leaves = ward_tree(X.T, connectivity)
n_nodes = 2 * X.shape[1] - 1
@@ -54,9 +54,9 @@ def test_ward_clustering():
"""
Check that we obtain the correct number of clusters with Ward clustering.
"""
- np.random.seed(0)
+ rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
- X = np.random.randn(100, 50)
+ X = rnd.randn(100, 50)
connectivity = grid_to_graph(*mask.shape)
clustering = Ward(n_clusters=10, connectivity=connectivity)
clustering.fit(X)
@@ -67,9 +67,9 @@ def test_ward_agglomeration():
"""
Check that we obtain the correct solution in a simplistic case
"""
- np.random.seed(0)
+ rnd = np.random.RandomState(0)
mask = np.ones([10, 10], dtype=np.bool)
- X = np.random.randn(50, 100)
+ X = rnd.randn(50, 100)
connectivity = grid_to_graph(*mask.shape)
ward = WardAgglomeration(n_clusters=5, connectivity=connectivity)
ward.fit(X)
@@ -98,10 +98,11 @@ def test_scikit_vs_scipy():
"""
from scipy.sparse import lil_matrix
n, p, k = 10, 5, 3
+ rnd = np.random.RandomState(0)
connectivity = lil_matrix(np.ones((n, n)))
for i in range(5):
- X = .1 * np.random.normal(size=(n, p))
+ X = .1 * rnd.normal(size=(n, p))
X -= 4 * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
diff --git a/sklearn/cluster/tests/test_k_means.py b/sklearn/cluster/tests/test_k_means.py
index fe9226c1cc..b735ab3454 100644
--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -43,7 +43,8 @@ def test_square_norms():
def test_kmeans_dtype():
- X = np.random.normal(size=(40, 2))
+ rnd = np.random.RandomState(0)
+ X = rnd.normal(size=(40, 2))
X = (X * 10).astype(np.uint8)
km = KMeans(n_init=1).fit(X)
with warnings.catch_warnings(record=True) as w:
diff --git a/sklearn/covariance/tests/test_robust_covariance.py b/sklearn/covariance/tests/test_robust_covariance.py
index b34374bb8a..74cb39047b 100644
--- a/sklearn/covariance/tests/test_robust_covariance.py
+++ b/sklearn/covariance/tests/test_robust_covariance.py
@@ -72,8 +72,8 @@ def test_outlier_detection():
"""
"""
- np.random.RandomState(0)
- X = np.random.randn(100, 10)
+ rnd = np.random.RandomState(0)
+ X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
clf.fit(X)
y_pred = clf.predict(X)
diff --git a/sklearn/decomposition/tests/test_fastica.py b/sklearn/decomposition/tests/test_fastica.py
index 5f8e6dc3c2..d958462ad1 100644
--- a/sklearn/decomposition/tests/test_fastica.py
+++ b/sklearn/decomposition/tests/test_fastica.py
@@ -51,7 +51,7 @@ def test_fastica(add_noise=False):
""" Test the FastICA algorithm on very simple data.
"""
# scipy.stats uses the global RNG:
- np.random.seed(0)
+ rng = np.random.RandomState(0)
n_samples = 1000
# Generate two sources:
s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
@@ -67,7 +67,7 @@ def test_fastica(add_noise=False):
m = np.dot(mixing, s)
if add_noise:
- m += 0.1 * np.random.randn(2, 1000)
+ m += 0.1 * rng.randn(2, 1000)
center_and_norm(m)
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index 56f6f20986..0eabf44b48 100644
--- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py
@@ -30,15 +30,15 @@ true_result = [-1, 1, 1]
# also load the iris dataset
# and randomly permute it
iris = datasets.load_iris()
-np.random.seed([1])
-perm = np.random.permutation(iris.target.size)
+rng = np.random.RandomState(0)
+perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# also load the boston dataset
# and randomly permute it
boston = datasets.load_boston()
-perm = np.random.permutation(boston.target.size)
+perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.target[perm]
diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py
index 15a641fdab..276a737f1a 100644
--- a/sklearn/ensemble/tests/test_gradient_boosting.py
+++ b/sklearn/ensemble/tests/test_gradient_boosting.py
@@ -20,17 +20,18 @@ y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]
+rng = np.random.RandomState(0)
# also load the boston dataset
# and randomly permute it
boston = datasets.load_boston()
-perm = np.random.permutation(boston.target.size)
+perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.target[perm]
# also load the iris dataset
# and randomly permute it
iris = datasets.load_iris()
-perm = np.random.permutation(iris.target.size)
+perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
@@ -248,7 +249,7 @@ def test_check_inputs_predict():
assert_raises(ValueError, clf.predict, x)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
- clf.fit(X, np.random.rand(len(X)))
+ clf.fit(X, rng.rand(len(X)))
x = np.array([1.0, 2.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
@@ -312,6 +313,6 @@ def test_degenerate_targets():
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
clf.fit(X, np.ones(len(X)))
- clf.predict(np.random.rand(2))
+ clf.predict(rng.rand(2))
assert_array_equal(np.ones((1,), dtype=np.float64),
- clf.predict(np.random.rand(2)))
+ clf.predict(rng.rand(2)))
diff --git a/sklearn/feature_selection/tests/test_feature_select.py b/sklearn/feature_selection/tests/test_feature_select.py
index 0975ed10d0..c579a1b13c 100644
--- a/sklearn/feature_selection/tests/test_feature_select.py
+++ b/sklearn/feature_selection/tests/test_feature_select.py
@@ -19,8 +19,9 @@ from sklearn.datasets.samples_generator import make_classification, \
def test_f_oneway_vs_scipy_stats():
"""Test that our f_oneway gives the same result as scipy.stats"""
- X1 = np.random.randn(10, 3)
- X2 = 1 + np.random.randn(10, 3)
+ rng = np.random.RandomState(0)
+ X1 = rng.randn(10, 3)
+ X2 = 1 + rng.randn(10, 3)
f, pv = stats.f_oneway(X1, X2)
f2, pv2 = f_oneway(X1, X2)
assert_true(np.allclose(f, f2))
@@ -67,8 +68,8 @@ def test_f_regression_input_dtype():
Test whether f_regression returns the same value
for any numeric data_type
"""
-
- X = np.random.rand(10, 20)
+ rng = np.random.RandomState(0)
+ X = rng.rand(10, 20)
y = np.arange(10).astype(np.int)
F1, pv1 = f_regression(X, y)
diff --git a/sklearn/hmm.py b/sklearn/hmm.py
index 605c6e7ddf..e8c2542fb1 100644
--- a/sklearn/hmm.py
+++ b/sklearn/hmm.py
@@ -135,7 +135,7 @@ class _BaseHMM(BaseEstimator):
self._algorithm = algorithm
else:
self._algorithm = "viterbi"
- self.random_state = random_state
+ self.random_state = check_random_state(random_state)
def eval(self, obs):
"""Compute the log probability under the model and compute posteriors
@@ -900,8 +900,8 @@ class MultinomialHMM(_BaseHMM):
super(MultinomialHMM, self)._init(obs, params=params)
if 'e' in params:
- emissionprob = normalize(np.random.rand(self.n_components,
- self.n_symbols), 1)
+ emissionprob = normalize(self.random_state.rand(self.n_components,
+ self.n_symbols), 1)
self.emissionprob_ = emissionprob
def _initialize_sufficient_statistics(self):
diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
index 6f517580be..7946141059 100644
--- a/sklearn/linear_model/tests/test_logistic.py
+++ b/sklearn/linear_model/tests/test_logistic.py
@@ -77,7 +77,8 @@ def test_predict_iris():
def test_inconsistent_input():
"""Test that an exception is raised on inconsistent input"""
- X_ = np.random.random((5, 10))
+ rng = np.random.RandomState(0)
+ X_ = rng.random_sample((5, 10))
y_ = np.ones(X_.shape[0])
clf = logistic.LogisticRegression()
@@ -87,9 +88,8 @@ def test_inconsistent_input():
assert_raises(ValueError, clf.fit, X, y_wrong)
# Wrong dimensions for test data
- assert_raises(ValueError,
- clf.fit(X_, y_).predict,
- np.random.random((3, 12)))
+ assert_raises(ValueError, clf.fit(X_, y_).predict,
+ rng.random_sample((3, 12)))
@raises(ValueError)
diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
index e7ba5b33cd..6b5cec7293 100644
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -17,11 +17,11 @@ from sklearn.linear_model.ridge import RidgeClassifierCV
from sklearn.cross_validation import KFold
+rng = np.random.RandomState(0)
diabetes = datasets.load_diabetes()
-
X_diabetes, y_diabetes = diabetes.data, diabetes.target
ind = np.arange(X_diabetes.shape[0])
-np.random.shuffle(ind)
+rng.shuffle(ind)
ind = ind[:200]
X_diabetes, y_diabetes = X_diabetes[ind], y_diabetes[ind]
@@ -30,8 +30,6 @@ iris = datasets.load_iris()
X_iris = sp.csr_matrix(iris.data)
y_iris = iris.target
-np.random.seed(0)
-
DENSE_FILTER = lambda X: X
SPARSE_FILTER = lambda X: sp.csr_matrix(X)
@@ -46,8 +44,8 @@ def test_ridge():
# With more samples than features
n_samples, n_features = 6, 5
- y = np.random.randn(n_samples)
- X = np.random.randn(n_samples, n_features)
+ y = rng.randn(n_samples)
+ X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
@@ -59,8 +57,8 @@ def test_ridge():
# With more features than samples
n_samples, n_features = 5, 10
- y = np.random.randn(n_samples)
- X = np.random.randn(n_samples, n_features)
+ y = rng.randn(n_samples)
+ X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=alpha)
ridge.fit(X, y)
assert_greater(ridge.score(X, y), .9)
@@ -73,8 +71,8 @@ def test_ridge_shapes():
"""Test shape of coef_ and intercept_
"""
n_samples, n_features = 5, 10
- X = np.random.randn(n_samples, n_features)
- y = np.random.randn(n_samples)
+ X = rng.randn(n_samples, n_features)
+ y = rng.randn(n_samples)
Y1 = y[:, np.newaxis]
Y = np.c_[y, 1 + y]
@@ -97,8 +95,8 @@ def test_ridge_intercept():
"""Test intercept with multiple targets GH issue #708
"""
n_samples, n_features = 5, 10
- X = np.random.randn(n_samples, n_features)
- y = np.random.randn(n_samples)
+ X = rng.randn(n_samples, n_features)
+ y = rng.randn(n_samples)
Y = np.c_[y, 1. + y]
ridge = Ridge()
@@ -140,9 +138,8 @@ def test_ridge_vs_lstsq():
# we need more samples than features
n_samples, n_features = 5, 4
- np.random.seed(0)
- y = np.random.randn(n_samples)
- X = np.random.randn(n_samples, n_features)
+ y = rng.randn(n_samples)
+ X = rng.randn(n_samples, n_features)
ridge = Ridge(alpha=0., fit_intercept=False)
ols = LinearRegression(fit_intercept=False)
diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
index fbd26e4cdf..53ae5f3b3c 100644
--- a/sklearn/linear_model/tests/test_sgd.py
+++ b/sklearn/linear_model/tests/test_sgd.py
@@ -279,9 +279,9 @@ class DenseSGDClassifierTestCase(unittest.TestCase, CommonTest):
def test_sgd_l1(self):
"""Test L1 regularization"""
n = len(X4)
- np.random.seed(13)
+ rng = np.random.RandomState(13)
idx = np.arange(n)
- np.random.shuffle(idx)
+ rng.shuffle(idx)
X = X4[idx, :]
Y = Y4[idx, :]
@@ -350,8 +350,8 @@ class DenseSGDClassifierTestCase(unittest.TestCase, CommonTest):
X, y = iris.data, iris.target
X = preprocessing.scale(X)
idx = np.arange(X.shape[0])
- np.random.seed(13)
- np.random.shuffle(idx)
+ rng = np.random.RandomState(0)
+ rng.shuffle(idx)
X = X[idx]
y = y[idx]
clf = self.factory(alpha=0.0001, n_iter=1000,
@@ -528,6 +528,7 @@ class DenseSGDRegressorTestCase(unittest.TestCase):
def test_sgd_least_squares_fit(self):
xmin, xmax = -5, 5
n_samples = 100
+ rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
@@ -540,8 +541,7 @@ class DenseSGDRegressorTestCase(unittest.TestCase):
assert_greater(score, 0.99)
# simple linear function with noise
- y = 0.5 * X.ravel() \
- + np.random.randn(n_samples, 1).ravel()
+ y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = self.factory(loss='squared_loss', alpha=0.1, n_iter=20,
fit_intercept=False)
@@ -552,6 +552,7 @@ class DenseSGDRegressorTestCase(unittest.TestCase):
def test_sgd_huber_fit(self):
xmin, xmax = -5, 5
n_samples = 100
+ rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
@@ -564,8 +565,7 @@ class DenseSGDRegressorTestCase(unittest.TestCase):
assert_greater(score, 0.99)
# simple linear function with noise
- y = 0.5 * X.ravel() \
- + np.random.randn(n_samples, 1).ravel()
+ y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = self.factory(loss="huber", p=0.1, alpha=0.1, n_iter=20,
fit_intercept=False)
@@ -577,11 +577,11 @@ class DenseSGDRegressorTestCase(unittest.TestCase):
"""Check that the SGD ouput is consistent with coordinate descent"""
n_samples, n_features = 1000, 5
- np.random.seed(0)
+ rng = np.random.RandomState(0)
X = np.random.randn(n_samples, n_features)
# ground_truth linear model that generate y from X and to which the
# models should converge if the regularizer would be set to 0.0
- ground_truth_coef = np.random.randn(n_features)
+ ground_truth_coef = rng.randn(n_features)
y = np.dot(X, ground_truth_coef)
# XXX: alpha = 0.1 seems to cause convergence problems
diff --git a/sklearn/manifold/tests/test_locally_linear.py b/sklearn/manifold/tests/test_locally_linear.py
index a45c8addb8..3e246640a4 100644
--- a/sklearn/manifold/tests/test_locally_linear.py
+++ b/sklearn/manifold/tests/test_locally_linear.py
@@ -65,14 +65,14 @@ def test_lle_simple_grid():
def test_lle_manifold():
+ rng = np.random.RandomState(0)
# similar test on a slightly more complex manifold
X = np.array(list(product(range(20), repeat=2)))
X = np.c_[X, X[:, 0] ** 2 / 20]
- X = X + 1e-10 * np.random.uniform(size=X.shape)
+ X = X + 1e-10 * rng.uniform(size=X.shape)
n_components = 2
clf = manifold.LocallyLinearEmbedding(n_neighbors=5,
- n_components=n_components,
- random_state=0)
+ n_components=n_components, random_state=0)
tol = 1.5
N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
diff --git a/sklearn/metrics/tests/test_metrics.py b/sklearn/metrics/tests/test_metrics.py
index e37213bdf4..d409866d12 100644
--- a/sklearn/metrics/tests/test_metrics.py
+++ b/sklearn/metrics/tests/test_metrics.py
@@ -53,8 +53,8 @@ def make_prediction(dataset=None, binary=False):
half = int(n_samples / 2)
# add noisy features to make the problem harder and avoid perfect results
- np.random.seed(0)
- X = np.c_[X, np.random.randn(n_samples, 200 * n_features)]
+ rng = np.random.RandomState(0)
+ X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
# run classifier, get class probabilities and label predictions
clf = svm.SVC(kernel='linear', probability=True)
diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
index 29109c1ba7..43c0ee25e3 100644
--- a/sklearn/metrics/tests/test_pairwise.py
+++ b/sklearn/metrics/tests/test_pairwise.py
@@ -17,8 +17,6 @@ from ..pairwise import pairwise_kernel_functions
from ..pairwise import check_pairwise_arrays
from ..pairwise import _parallel_pairwise
-np.random.seed(0)
-
def test_pairwise_distances():
""" Test the pairwise_distance helper function. """
diff --git a/sklearn/neighbors/tests/test_ball_tree.py b/sklearn/neighbors/tests/test_ball_tree.py
index 4480475e0d..891f80960d 100644
--- a/sklearn/neighbors/tests/test_ball_tree.py
+++ b/sklearn/neighbors/tests/test_ball_tree.py
@@ -8,11 +8,13 @@ from sklearn import neighbors
# Note: simple tests of BallTree.query() and BallTree.query_radius()
# are contained within the tests of test_neighbors.py
+rng = np.random.RandomState(0)
+
def test_warning_flag(n_samples=100, n_features=3, k=3):
"""test that discarding identical distances triggers warning flag"""
- X = np.random.random(size=(n_samples, n_features))
- q = np.random.random(size=n_features)
+ X = rng.random_sample(size=(n_samples, n_features))
+ q = rng.random_sample(size=n_features)
bt = neighbors.BallTree(X[:-1], leaf_size=5)
dist, ind = bt.query(q, k=k)
@@ -38,7 +40,7 @@ def test_warning_flag(n_samples=100, n_features=3, k=3):
def test_ball_tree_query_radius(n_samples=100, n_features=10):
- X = 2 * np.random.random(size=(n_samples, n_features)) - 1
+ X = 2 * rng.random_sample(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
@@ -56,7 +58,7 @@ def test_ball_tree_query_radius(n_samples=100, n_features=10):
def test_ball_tree_query_radius_distance(n_samples=100, n_features=10):
- X = 2 * np.random.random(size=(n_samples, n_features)) - 1
+ X = 2 * rng.random_sample(size=(n_samples, n_features)) - 1
query_pt = np.zeros(n_features, dtype=float)
eps = 1E-15 # roundoff error can cause test to fail
@@ -76,7 +78,7 @@ def test_ball_tree_query_radius_distance(n_samples=100, n_features=10):
def test_ball_tree_pickle():
import pickle
- X = np.random.random(size=(10, 3))
+ X = rng.random_sample(size=(10, 3))
bt1 = neighbors.BallTree(X, leaf_size=1)
ind1, dist1 = bt1.query(X)
for protocol in (0, 1, 2):
@@ -88,7 +90,7 @@ def test_ball_tree_pickle():
def test_ball_tree_p_distance():
- X = np.random.random(size=(100, 5))
+ X = rng.random_sample(size=(100, 5))
for p in (1, 2, 3, 4, np.inf):
bt = neighbors.BallTree(X, leaf_size=10, p=p)
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index 19a56da28b..6481645e79 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -11,9 +11,10 @@ from scipy.spatial import cKDTree
from sklearn import neighbors, datasets
+rng = np.random.RandomState(0)
# load and shuffle iris dataset
iris = datasets.load_iris()
-perm = np.random.permutation(iris.target.size)
+perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
@@ -39,8 +40,8 @@ def _weight_func(dist):
def test_warn_on_equidistant(n_samples=100, n_features=3, k=3):
"""test the production of a warning if equidistant points are discarded"""
- X = np.random.random(size=(n_samples, n_features))
- q = np.random.random(size=n_features)
+ X = rng.random_sample(size=(n_samples, n_features))
+ q = rng.random_sample(size=n_features)
neigh = neighbors.NearestNeighbors(n_neighbors=k)
neigh.fit(X[:-1])
@@ -75,8 +76,6 @@ def test_unsupervised_kneighbors(n_samples=20, n_features=5,
n_query_pts=2, n_neighbors=5,
random_state=0):
"""Test unsupervised neighbors methods"""
- rng = np.random.RandomState(random_state)
-
X = rng.rand(n_samples, n_features)
test = rng.rand(n_query_pts, n_features)
@@ -103,7 +102,7 @@ def test_unsupervised_kneighbors(n_samples=20, n_features=5,
def test_unsupervised_inputs():
"""test the types of valid input into NearestNeighbors"""
- X = np.random.random((10, 3))
+ X = rng.random_sample((10, 3))
nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1)
nbrs_fid.fit(X)
@@ -462,7 +461,7 @@ def test_neighbors_badargs():
neighbors.NearestNeighbors,
algorithm='blah')
- X = np.random.random((10, 2))
+ X = rng.random_sample((10, 2))
for cls in (neighbors.KNeighborsClassifier,
neighbors.RadiusNeighborsClassifier,
diff --git a/sklearn/svm/tests/test_sparse.py b/sklearn/svm/tests/test_sparse.py
index 38bd4d8d16..c6fd3cb913 100644
--- a/sklearn/svm/tests/test_sparse.py
+++ b/sklearn/svm/tests/test_sparse.py
@@ -26,7 +26,8 @@ true_result2 = [1, 2, 3]
iris = datasets.load_iris()
# permute
-perm = np.random.permutation(iris.target.size)
+rng = np.random.RandomState(0)
+perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
# sparsify
diff --git a/sklearn/tests/test_kernel_approximation.py b/sklearn/tests/test_kernel_approximation.py
index 0f177fae2a..6868fe0835 100644
--- a/sklearn/tests/test_kernel_approximation.py
+++ b/sklearn/tests/test_kernel_approximation.py
@@ -7,8 +7,9 @@ from sklearn.kernel_approximation import SkewedChi2Sampler
from sklearn.metrics.pairwise import rbf_kernel
# generate data
-X = np.random.uniform(size=(300, 50))
-Y = np.random.uniform(size=(300, 50))
+rng = np.random.RandomState(0)
+X = rng.random_sample(size=(300, 50))
+Y = rng.random_sample(size=(300, 50))
X /= X.sum(axis=1)[:, np.newaxis]
Y /= Y.sum(axis=1)[:, np.newaxis]
diff --git a/sklearn/tests/test_multiclass.py b/sklearn/tests/test_multiclass.py
index 62f05ad5bd..6256f8eb81 100644
--- a/sklearn/tests/test_multiclass.py
+++ b/sklearn/tests/test_multiclass.py
@@ -20,7 +20,8 @@ from sklearn import svm
from sklearn import datasets
iris = datasets.load_iris()
-perm = np.random.permutation(iris.target.size)
+rng = np.random.RandomState(0)
+perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
n_classes = 3
diff --git a/sklearn/tests/test_preprocessing.py b/sklearn/tests/test_preprocessing.py
index cf156bd385..5a16a6f9ca 100644
--- a/sklearn/tests/test_preprocessing.py
+++ b/sklearn/tests/test_preprocessing.py
@@ -21,8 +21,6 @@ from sklearn.preprocessing import scale
from sklearn import datasets
from sklearn.linear_model.stochastic_gradient import SGDClassifier
-np.random.seed(0)
-
iris = datasets.load_iris()
@@ -440,7 +438,8 @@ def test_label_binarizer_multilabel_unlabeled():
def test_center_kernel():
"""Test that KernelCenterer is equivalent to Scaler in feature space"""
- X_fit = np.random.random((5, 4))
+ rng = np.random.RandomState(0)
+ X_fit = rng.random_sample((5, 4))
scaler = Scaler(with_std=False)
scaler.fit(X_fit)
X_fit_centered = scaler.transform(X_fit)
@@ -453,7 +452,7 @@ def test_center_kernel():
assert_array_almost_equal(K_fit_centered, K_fit_centered2)
# center predict time matrix
- X_pred = np.random.random((2, 4))
+ X_pred = rng.random_sample((2, 4))
K_pred = np.dot(X_pred, X_fit.T)
X_pred_centered = scaler.transform(X_pred)
K_pred_centered = np.dot(X_pred_centered, X_fit_centered.T)
@@ -462,7 +461,8 @@ def test_center_kernel():
def test_fit_transform():
- X = np.random.random((5, 4))
+ rng = np.random.RandomState(0)
+ X = rng.random_sample((5, 4))
for obj in ((Scaler(), Normalizer(), Binarizer())):
X_transformed = obj.fit(X).transform(X)
X_transformed2 = obj.fit_transform(X)
diff --git a/sklearn/utils/tests/test_shortest_path.py b/sklearn/utils/tests/test_shortest_path.py
index a6fa27eff0..7d876a000d 100644
--- a/sklearn/utils/tests/test_shortest_path.py
+++ b/sklearn/utils/tests/test_shortest_path.py
@@ -30,14 +30,14 @@ def floyd_warshall_slow(graph, directed=False):
def generate_graph(N=20):
#sparse grid of distances
- dist_matrix = np.random.random((N, N))
+ rng = np.random.RandomState(0)
+ dist_matrix = rng.random_sample((N, N))
#make symmetric: distances are not direction-dependent
dist_matrix += dist_matrix.T
#make graph sparse
- i = (np.random.randint(N, size=N * N / 2),
- np.random.randint(N, size=N * N / 2))
+ i = (rng.randint(N, size=N * N / 2), rng.randint(N, size=N * N / 2))
dist_matrix[i] = 0
#set diagonal to zero
diff --git a/sklearn/utils/tests/test_weighted_mode.py b/sklearn/utils/tests/test_weighted_mode.py
index e58e31c524..85ef08fae2 100644
--- a/sklearn/utils/tests/test_weighted_mode.py
+++ b/sklearn/utils/tests/test_weighted_mode.py
@@ -7,7 +7,8 @@ from scipy import stats
def test_uniform_weights():
# with uniform weights, results should be identical to stats.mode
- x = np.random.randint(10, size=(10, 5))
+ rng = np.random.RandomState(0)
+ x = rng.randint(10, size=(10, 5))
weights = np.ones(x.shape)
for axis in (None, 0, 1):
@@ -23,8 +24,9 @@ def test_random_weights():
# with a score that is easily reproduced
mode_result = 6
- x = np.random.randint(mode_result, size=(100, 10))
- w = np.random.random(x.shape)
+ rng = np.random.RandomState(0)
+ x = rng.randint(mode_result, size=(100, 10))
+ w = rng.random_sample(x.shape)
x[:, :5] = mode_result
w[:, :5] += 1
--
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