P3K range vs. xrange

larsmans · larsmans · commit 541987802f7c · 2013-02-14T02:05:35.000+01:00
diff --git a/benchmarks/bench_plot_fastkmeans.py b/benchmarks/bench_plot_fastkmeans.py
@@ -62,7 +62,7 @@ def compute_bench_2(chunks):
     means = np.array([[1, 1], [-1, -1], [1, -1], [-1, 1],
                       [0.5, 0.5], [0.75, -0.5], [-1, 0.75], [1, 0]])
     X = np.empty((0, 2))
-    for i in xrange(8):
+    for i in range(8):
         X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
     max_it = len(chunks)
     it = 0
diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py
@@ -11,6 +11,7 @@
 
 from sklearn.decomposition.nmf import NMF, _initialize_nmf
 from sklearn.datasets.samples_generator import make_low_rank_matrix
+from sklearn.externals.six.moves import xrange
 
 
 def alt_nnmf(V, r, max_iter=1000, tol=1e-3, R=None):
diff --git a/benchmarks/bench_random_projections.py b/benchmarks/bench_random_projections.py
@@ -19,6 +19,7 @@
 import scipy.sparse as sp
 
 from sklearn import clone
+from sklearn.externals.six.moves import xrange
 from sklearn.random_projection import (SparseRandomProjection,
                                        GaussianRandomProjection,
                                        johnson_lindenstrauss_min_dim)
diff --git a/benchmarks/bench_sample_without_replacement.py b/benchmarks/bench_sample_without_replacement.py
@@ -15,6 +15,7 @@
 import numpy as np
 import random
 
+from sklearn.externals.six.moves import xrange
 from sklearn.utils.random import sample_without_replacement
 
 
diff --git a/examples/applications/svm_gui.py b/examples/applications/svm_gui.py
@@ -35,6 +35,7 @@
 
 from sklearn import svm
 from sklearn.datasets import dump_svmlight_file
+from sklearn.externals.six.moves import xrange
 
 y_min, y_max = -50, 50
 x_min, x_max = -50, 50
diff --git a/examples/document_classification_20newsgroups.py b/examples/document_classification_20newsgroups.py
@@ -282,7 +282,7 @@ def predict(self, X):
 
 indices = np.arange(len(results))
 
-results = [[x[i] for x in results] for i in xrange(4)]
+results = [[x[i] for x in results] for i in range(4)]
 
 clf_names, score, training_time, test_time = results
 training_time = np.array(training_time) / np.max(training_time)
diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py
@@ -33,10 +33,11 @@
 
 import pylab as pl
 
-from sklearn.ensemble import AdaBoostClassifier
-from sklearn.tree import DecisionTreeClassifier
 from sklearn.datasets import make_gaussian_quantiles
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.externals.six.moves import xrange
 from sklearn.metrics import accuracy_score
+from sklearn.tree import DecisionTreeClassifier
 
 
 X, y = make_gaussian_quantiles(n_samples=13000, n_features=10,
diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py
@@ -62,7 +62,7 @@
 pl.axis("tight")
 
 # Plot the training points
-for i, n, c in zip(xrange(2), class_names, plot_colors):
+for i, n, c in zip(range(2), class_names, plot_colors):
     idx = np.where(y == i)
     pl.scatter(X[idx, 0], X[idx, 1],
                c=c, cmap=pl.cm.Paired,
@@ -76,7 +76,7 @@
 twoclass_output = bdt.decision_function(X)
 plot_range = (twoclass_output.min(), twoclass_output.max())
 pl.subplot(122)
-for i, n, c in zip(xrange(2), class_names, plot_colors):
+for i, n, c in zip(range(2), class_names, plot_colors):
     pl.hist(twoclass_output[y == i],
             bins=10,
             range=plot_range,
diff --git a/examples/ensemble/plot_forest_iris.py b/examples/ensemble/plot_forest_iris.py
@@ -23,6 +23,7 @@
 from sklearn.datasets import load_iris
 from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
                               AdaBoostClassifier)
+from sklearn.externals.six.moves import xrange
 from sklearn.tree import DecisionTreeClassifier
 
 # Parameters
diff --git a/examples/ensemble/plot_forest_multioutput.py b/examples/ensemble/plot_forest_multioutput.py
@@ -48,7 +48,7 @@
 pl.figure(figsize=(2. * n_faces, 2.26 * 2))
 pl.suptitle("Face completion with multi-output forests", size=16)
 
-for i in xrange(1, 1 + n_faces):
+for i in range(1, 1 + n_faces):
     face_id = np.random.randint(X_test.shape[0])
 
     true_face = np.hstack((X_test[face_id], Y_test[face_id]))
diff --git a/examples/mixture/plot_gmm_classifier.py b/examples/mixture/plot_gmm_classifier.py
@@ -32,6 +32,7 @@
 
 from sklearn import datasets
 from sklearn.cross_validation import StratifiedKFold
+from sklearn.externals.six.moves import xrange
 from sklearn.mixture import GMM
 
 
diff --git a/examples/mixture/plot_gmm_sin.py b/examples/mixture/plot_gmm_sin.py
@@ -22,6 +22,7 @@
 import matplotlib as mpl
 
 from sklearn import mixture
+from sklearn.externals.six.moves import xrange
 
 # Number of samples per component
 n_samples = 100
diff --git a/examples/tree/plot_iris.py b/examples/tree/plot_iris.py
@@ -64,7 +64,7 @@
     pl.axis("tight")
 
     # Plot the training points
-    for i, color in zip(xrange(n_classes), plot_colors):
+    for i, color in zip(range(n_classes), plot_colors):
         idx = np.where(y == i)
         pl.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                    cmap=pl.cm.Paired)
diff --git a/sklearn/covariance/shrunk_covariance_.py b/sklearn/covariance/shrunk_covariance_.py
@@ -18,6 +18,7 @@
 import numpy as np
 
 from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance
+from ..externals.six.moves import xrange
 from ..utils import array2d
 
 
diff --git a/sklearn/decomposition/factor_analysis.py b/sklearn/decomposition/factor_analysis.py
@@ -17,6 +17,7 @@
 
 
 from ..base import BaseEstimator, TransformerMixin
+from ..externals.six.moves import xrange
 from ..utils import array2d, check_arrays
 from ..utils.extmath import fast_logdet
 
diff --git a/sklearn/dummy.py b/sklearn/dummy.py
@@ -6,6 +6,7 @@
 import numpy as np
 
 from .base import BaseEstimator, ClassifierMixin, RegressorMixin
+from .externals.six.moves import xrange
 from .utils import check_random_state
 from .utils.fixes import unique
 from .utils.validation import safe_asarray
diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
@@ -44,6 +44,7 @@ class calls the ``fit`` method of each sub-estimator on random samples
 
 from ..base import ClassifierMixin, RegressorMixin
 from ..externals.joblib import Parallel, delayed, cpu_count
+from ..externals.six.moves import xrange
 from ..feature_selection.selector_mixin import SelectorMixin
 from ..metrics import r2_score
 from ..preprocessing import OneHotEncoder
diff --git a/sklearn/ensemble/partial_dependence.py b/sklearn/ensemble/partial_dependence.py
@@ -14,6 +14,7 @@
 from ..utils.extmath import cartesian
 from ..externals.joblib import Parallel, delayed
 from ..externals import six
+from ..externals.six.moves import xrange
 from ..utils import array2d
 from ..tree._tree import DTYPE
 
diff --git a/sklearn/ensemble/weight_boosting.py b/sklearn/ensemble/weight_boosting.py
@@ -26,6 +26,7 @@
 
 from .base import BaseEnsemble
 from ..base import ClassifierMixin, RegressorMixin
+from ..externals.six.moves import xrange
 from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
 from ..tree.tree import BaseDecisionTree
 from ..utils import check_arrays, check_random_state
diff --git a/sklearn/feature_extraction/tests/test_feature_hasher.py b/sklearn/feature_extraction/tests/test_feature_hasher.py
@@ -52,7 +52,7 @@ def test_feature_hasher_pairs():
 
 def test_hash_empty_input():
     n_features = 16
-    raw_X = [[], (), xrange(0)]
+    raw_X = [[], (), iter(range(0))]
 
     h = FeatureHasher(n_features=n_features, input_type="string")
     X = h.transform(raw_X)
diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -22,6 +22,7 @@
 import scipy.sparse as sp
 
 from ..base import BaseEstimator, TransformerMixin
+from ..externals.six.moves import xrange
 from ..preprocessing import normalize
 from ..utils.fixes import Counter
 from .hashing import FeatureHasher
diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py
@@ -316,7 +316,7 @@ def _transform_sparse(self, X):
         log_step_nz = self.sample_interval_ * np.log(X.data)
         step_nz = 2 * X.data * self.sample_interval_
 
-        for j in xrange(1, self.sample_steps):
+        for j in range(1, self.sample_steps):
             factor_nz = np.sqrt(step_nz /
                                 np.cosh(np.pi * j * self.sample_interval_))
 
diff --git a/sklearn/lda.py b/sklearn/lda.py
@@ -128,7 +128,7 @@ def fit(self, X, y, store_covariance=False, tol=1.0e-4):
         cov = None
         if store_covariance:
             cov = np.zeros((n_features, n_features))
-        for ind in xrange(n_classes):
+        for ind in range(n_classes):
             Xg = X[y == ind, :]
             meang = Xg.mean(0)
             means.append(meang)
diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
@@ -20,6 +20,7 @@
 from ..utils import array2d, atleast2d_or_csc, deprecated
 from ..cross_validation import check_cv
 from ..externals.joblib import Parallel, delayed
+from ..externals.six.moves import xrange
 from ..utils.extmath import safe_sparse_dot
 
 from . import cd_fast
diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
@@ -23,6 +23,7 @@
 from ..utils import array2d, arrayfuncs, as_float_array
 from ..cross_validation import check_cv
 from ..externals.joblib import Parallel, delayed
+from ..externals.six.moves import xrange
 
 
 def lars_path(X, y, Xy=None, Gram=None, max_iter=500,
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -290,7 +290,7 @@ def test_enet_multitarget():
     coef, intercept, dual_gap, eps = (estimator.coef_, estimator.intercept_,
                                       estimator.dual_gap_, estimator.eps_)
 
-    for k in xrange(n_targets):
+    for k in range(n_targets):
         estimator.fit(X, y[:, k])
         assert_array_almost_equal(coef[k, :], estimator.coef_)
         assert_array_almost_equal(intercept[k], estimator.intercept_)
diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
@@ -389,7 +389,7 @@ def test_multitarget():
         assert_array_almost_equal(Y_pred, Y_dec)
         alphas, active, coef, path = (estimator.alphas_, estimator.active_,
                                       estimator.coef_, estimator.coef_path_)
-        for k in xrange(n_targets):
+        for k in range(n_targets):
             estimator.fit(X, Y[:, k])
             y_pred = estimator.predict(X)
             assert_array_almost_equal(alphas[k], estimator.alphas_)
diff --git a/sklearn/linear_model/tests/test_passive_aggressive.py b/sklearn/linear_model/tests/test_passive_aggressive.py
@@ -82,7 +82,7 @@ def test_classifier_partial_fit():
             clf = PassiveAggressiveClassifier(C=1.0,
                                               fit_intercept=True,
                                               random_state=0)
-            for t in xrange(30):
+            for t in range(30):
                 clf.partial_fit(data, y, classes)
             score = clf.score(data, y)
             assert_greater(score, 0.79)
@@ -138,7 +138,7 @@ def test_regressor_partial_fit():
             reg = PassiveAggressiveRegressor(C=1.0,
                                              fit_intercept=True,
                                              random_state=0)
-            for t in xrange(50):
+            for t in range(50):
                 reg.partial_fit(data, y_bin)
             pred = reg.predict(data)
             assert_less(np.mean((pred - y_bin) ** 2), 1.7)
diff --git a/sklearn/linear_model/tests/test_sparse_coordinate_descent.py b/sklearn/linear_model/tests/test_sparse_coordinate_descent.py
@@ -238,7 +238,7 @@ def test_enet_multitarget():
     coef, intercept, dual_gap, eps = (estimator.coef_, estimator.intercept_,
                                       estimator.dual_gap_, estimator.eps_)
 
-    for k in xrange(n_targets):
+    for k in range(n_targets):
         estimator.fit(X, y[:, k])
         assert_array_almost_equal(coef[k, :], estimator.coef_)
         assert_array_almost_equal(intercept[k], estimator.intercept_)
diff --git a/sklearn/mixture/dpgmm.py b/sklearn/mixture/dpgmm.py
@@ -15,6 +15,7 @@
 from scipy import linalg
 from scipy.spatial.distance import cdist
 
+from ..externals.six.moves import xrange
 from ..utils import check_random_state
 from ..utils.extmath import norm, logsumexp, pinvh
 from .. import cluster
diff --git a/sklearn/neighbors/nearest_centroid.py b/sklearn/neighbors/nearest_centroid.py
@@ -12,8 +12,9 @@
 from scipy import sparse as sp
 
 from ..base import BaseEstimator, ClassifierMixin
-from ..utils.validation import check_arrays, atleast2d_or_csr
+from ..externals.six.moves import xrange
 from ..metrics.pairwise import pairwise_distances
+from ..utils.validation import check_arrays, atleast2d_or_csr
 
 
 class NearestCentroid(BaseEstimator, ClassifierMixin):
diff --git a/sklearn/qda.py b/sklearn/qda.py
@@ -11,6 +11,7 @@
 import numpy as np
 
 from .base import BaseEstimator, ClassifierMixin
+from .externals.six.moves import xrange
 from .utils.fixes import unique
 from .utils import check_arrays, array2d
 
diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
@@ -35,11 +35,12 @@
 from numpy.testing import assert_equal
 import scipy.sparse as sp
 
-from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.utils import check_random_state
-from sklearn.utils.extmath import safe_sparse_dot
-from sklearn.utils.random import sample_without_replacement
-from sklearn.utils.validation import check_arrays
+from .base import BaseEstimator, TransformerMixin
+from .externals.six.moves import xrange
+from .utils import check_random_state
+from .utils.extmath import safe_sparse_dot
+from .utils.random import sample_without_replacement
+from .utils.validation import check_arrays
 
 
 __all__ = ["SparseRandomProjection",
diff --git a/sklearn/tests/test_dummy.py b/sklearn/tests/test_dummy.py
@@ -2,6 +2,7 @@
 import numpy as np
 
 from sklearn.base import clone
+from sklearn.externals.six.moves import xrange
 from sklearn.utils.testing import (assert_array_equal,
                                    assert_equal,
                                    assert_almost_equal,
diff --git a/sklearn/tests/test_hmm.py b/sklearn/tests/test_hmm.py
@@ -520,7 +520,7 @@ def test_fit_with_init(self, params='ste', n_iter=5, verbose=False,
         learner = hmm.MultinomialHMM(self.n_components)
 
         # Create training data by sampling from the HMM.
-        train_obs = [h.sample(n=10)[0] for x in xrange(10)]
+        train_obs = [h.sample(n=10)[0] for x in range(10)]
 
         # use init_function to initialize paramerters
         learner._init(train_obs, params)
diff --git a/sklearn/tests/test_multiclass.py b/sklearn/tests/test_multiclass.py
@@ -78,7 +78,7 @@ def test_ovr_always_present():
     # Test that ovr works with classes that are always present or absent
     X = np.ones((10, 2))
     X[:5, :] = 0
-    y = [[int(i >= 5), 2, 3] for i in xrange(10)]
+    y = [[int(i >= 5), 2, 3] for i in range(10)]
     with warnings.catch_warnings(record=True):
         ovr = OneVsRestClassifier(DecisionTreeClassifier())
         ovr.fit(X, y)
diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py
@@ -19,6 +19,7 @@
 
 from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
 from ..externals import six
+from ..externals.six.moves import xrange
 from ..feature_selection.selector_mixin import SelectorMixin
 from ..utils import array2d, check_random_state
 from ..utils.validation import check_arrays
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
@@ -10,6 +10,7 @@
 
 from . import check_random_state
 from .fixes import qr_economic
+from ..externals.six.moves import xrange
 
 
 def norm(v):
diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py
@@ -218,7 +218,7 @@ def test_randomized_svd_transpose_consistency():
 def test_randomized_svd_sign_flip():
     a = np.array([[2.0, 0.0], [0.0, 1.0]])
     u1, s1, v1 = randomized_svd(a, 2, flip_sign=True, random_state=41)
-    for seed in xrange(10):
+    for seed in range(10):
         u2, s2, v2 = randomized_svd(a, 2, flip_sign=True, random_state=seed)
         assert_almost_equal(u1, u2)
         assert_almost_equal(v1, v2)
diff --git a/sklearn/utils/tests/test_random.py b/sklearn/utils/tests/test_random.py
@@ -60,7 +60,7 @@ def check_sample_int(sample_without_replacement):
     # the sample is of the correct length and contains only unique items
     n_population = 100
 
-    for n_samples in xrange(n_population + 1):
+    for n_samples in range(n_population + 1):
         s = sample_without_replacement(n_population, n_samples)
         assert_equal(len(s), n_samples)
         unique = np.unique(s)
@@ -82,14 +82,14 @@ def check_sample_int_distribution(sample_without_replacement):
     # case
     n_trials = 10000
 
-    for n_samples in xrange(n_population):
+    for n_samples in range(n_population):
         # Counting the number of combinations is not as good as counting the
         # the number of permutations. However, it works with sampling algorithm
         # that does not provide a random permutation of the subset of integer.
         n_expected = combinations(n_population, n_samples, exact=True)
 
         output = {}
-        for i in xrange(n_trials):
+        for i in range(n_trials):
             output[frozenset(sample_without_replacement(n_population,
                                                         n_samples))] = None
 
