diff --git a/benchmarks/bench_covertype.py b/benchmarks/bench_covertype.py
index 93ce585c79a3cdad188fc124c8cbd4bc2e394404..d71a940bc483a7353217cbc479d70af54d4a7473 100644
--- a/benchmarks/bench_covertype.py
+++ b/benchmarks/bench_covertype.py
@@ -40,9 +40,9 @@ The same task has been used in a number of papers including:
[1] http://archive.ics.uci.edu/ml/datasets/Covertype
"""
-from __future__ import division
+from __future__ import division, print_function
-print __doc__
+print(__doc__)
# Author: Peter Prettenhoer <peter.prettenhofer@gmail.com>
# License: BSD Style.
@@ -109,7 +109,7 @@ def load_data(dtype=np.float32, order='F'):
if not os.path.exists(original_archive):
# Download the data
import urllib
- print "Downloading data, Please Wait (11MB)..."
+ print("Downloading data, Please Wait (11MB)...")
opener = urllib.urlopen(
'http://archive.ics.uci.edu/ml/'
'machine-learning-databases/covtype/covtype.data.gz')
@@ -172,12 +172,14 @@ print("%s %d" % ("number of features:".ljust(25),
print("%s %d" % ("number of classes:".ljust(25),
np.unique(y_train).shape[0]))
print("%s %s" % ("data type:".ljust(25), X_train.dtype))
-print("%s %d (pos=%d, neg=%d, size=%dMB)" % ("number of train samples:".ljust(25),
- X_train.shape[0], np.sum(y_train == 1),
- np.sum(y_train == -1), int(X_train.nbytes / 1e6)))
-print("%s %d (pos=%d, neg=%d, size=%dMB)" % ("number of test samples:".ljust(25),
- X_test.shape[0], np.sum(y_test == 1),
- np.sum(y_test == -1), int(X_test.nbytes / 1e6)))
+print("%s %d (pos=%d, neg=%d, size=%dMB)"
+ % ("number of train samples:".ljust(25),
+ X_train.shape[0], np.sum(y_train == 1),
+ np.sum(y_train == -1), int(X_train.nbytes / 1e6)))
+print("%s %d (pos=%d, neg=%d, size=%dMB)"
+ % ("number of test samples:".ljust(25),
+ X_test.shape[0], np.sum(y_test == 1),
+ np.sum(y_test == -1), int(X_test.nbytes / 1e6)))
classifiers = dict()
@@ -255,10 +257,10 @@ for name in selected_classifiers:
op.error('classifier %r unknown' % name)
sys.exit(1)
-print("")
+print()
print("Training Classifiers")
print("====================")
-print("")
+print()
err, train_time, test_time = {}, {}, {}
for name in sorted(selected_classifiers):
print("Training %s ..." % name)
@@ -266,10 +268,10 @@ for name in sorted(selected_classifiers):
######################################################################
## Print classification performance
-print("")
+print()
print("Classification performance:")
print("===========================")
-print("")
+print()
def print_row(clf_type, train_time, test_time, err):
@@ -284,5 +286,5 @@ print("-" * 44)
for name in sorted(selected_classifiers, key=lambda name: err[name]):
print_row(name, train_time[name], test_time[name], err[name])
-print("")
-print("")
+print()
+print()
diff --git a/benchmarks/bench_glm.py b/benchmarks/bench_glm.py
index fed5e5bc106f867c9f5d64d05b2db264709436bc..e5cfb943262530660aa085e2bcc772df4c3a42d3 100644
--- a/benchmarks/bench_glm.py
+++ b/benchmarks/bench_glm.py
@@ -24,7 +24,7 @@ if __name__ == '__main__':
for i in range(n_iter):
- print 'Iteration %s of %s' % (i, n_iter)
+ print('Iteration %s of %s' % (i, n_iter))
n_samples, n_features = 10 * i + 3, 10 * i + 3
diff --git a/benchmarks/bench_glmnet.py b/benchmarks/bench_glmnet.py
index b9c77fbed06097fec5e1e69a9b3b89ed6a691d66..441e6ed519d0f0b13a85f80672671bade4a22728 100644
--- a/benchmarks/bench_glmnet.py
+++ b/benchmarks/bench_glmnet.py
@@ -38,9 +38,9 @@ def bench(factory, X, Y, X_test, Y_test, ref_coef):
delta = (time() - tstart)
# stop time
- print "duration: %0.3fs" % delta
- print "rmse: %f" % rmse(Y_test, clf.predict(X_test))
- print "mean coef abs diff: %f" % abs(ref_coef - clf.coef_.ravel()).mean()
+ print("duration: %0.3fs" % delta)
+ print("rmse: %f" % rmse(Y_test, clf.predict(X_test)))
+ print("mean coef abs diff: %f" % abs(ref_coef - clf.coef_.ravel()).mean())
return delta
@@ -58,9 +58,9 @@ if __name__ == '__main__':
n_informative = n_features / 10
n_test_samples = 1000
for i in range(1, n + 1):
- print '=================='
- print 'Iteration %s of %s' % (i, n)
- print '=================='
+ print('==================')
+ print('Iteration %s of %s' % (i, n))
+ print('==================')
X, Y, coef_ = make_regression(
n_samples=(i * step) + n_test_samples, n_features=n_features,
@@ -71,9 +71,9 @@ if __name__ == '__main__':
X = X[:(i * step)]
Y = Y[:(i * step)]
- print "benching scikit: "
+ print("benching scikit-learn: ")
scikit_results.append(bench(ScikitLasso, X, Y, X_test, Y_test, coef_))
- print "benching glmnet: "
+ print("benching glmnet: ")
glmnet_results.append(bench(GlmnetLasso, X, Y, X_test, Y_test, coef_))
pl.clf()
@@ -96,9 +96,9 @@ if __name__ == '__main__':
n_samples = 500
for i in range(1, n + 1):
- print '=================='
- print 'Iteration %02d of %02d' % (i, n)
- print '=================='
+ print('==================')
+ print('Iteration %02d of %02d' % (i, n))
+ print('==================')
n_features = i * step
n_informative = n_features / 10
@@ -111,9 +111,9 @@ if __name__ == '__main__':
X = X[:n_samples]
Y = Y[:n_samples]
- print "benching scikit: "
+ print("benching scikit-learn: ")
scikit_results.append(bench(ScikitLasso, X, Y, X_test, Y_test, coef_))
- print "benching glmnet: "
+ print("benching glmnet: ")
glmnet_results.append(bench(GlmnetLasso, X, Y, X_test, Y_test, coef_))
xx = np.arange(100, 100 + n * step, step)
diff --git a/benchmarks/bench_lasso.py b/benchmarks/bench_lasso.py
index c72b21eb54a0a51d3729e9a8467205b55d78db94..ccc20b684930caecef24d1b8a3398a4ccee98c07 100644
--- a/benchmarks/bench_lasso.py
+++ b/benchmarks/bench_lasso.py
@@ -19,20 +19,18 @@ from sklearn.datasets.samples_generator import make_regression
def compute_bench(alpha, n_samples, n_features, precompute):
-
lasso_results = []
lars_lasso_results = []
- n_test_samples = 0
it = 0
for ns in n_samples:
for nf in n_features:
it += 1
- print '=================='
- print 'Iteration %s of %s' % (it, max(len(n_samples),
- len(n_features)))
- print '=================='
+ print('==================')
+ print('Iteration %s of %s' % (it, max(len(n_samples),
+ len(n_features))))
+ print('==================')
n_informative = nf // 10
X, Y, coef_ = make_regression(n_samples=ns, n_features=nf,
n_informative=n_informative,
@@ -41,7 +39,7 @@ def compute_bench(alpha, n_samples, n_features, precompute):
X /= np.sqrt(np.sum(X ** 2, axis=0)) # Normalize data
gc.collect()
- print "- benching Lasso"
+ print("- benching Lasso")
clf = Lasso(alpha=alpha, fit_intercept=False,
precompute=precompute)
tstart = time()
@@ -49,7 +47,7 @@ def compute_bench(alpha, n_samples, n_features, precompute):
lasso_results.append(time() - tstart)
gc.collect()
- print "- benching LassoLars"
+ print("- benching LassoLars")
clf = LassoLars(alpha=alpha, fit_intercept=False,
normalize=False, precompute=precompute)
tstart = time()
diff --git a/benchmarks/bench_plot_fastkmeans.py b/benchmarks/bench_plot_fastkmeans.py
index 974136b75857725af166c7d1c71df7c745a00a50..c58dde0159da8cdbc6b3dd9deb164ae58ea3d60c 100644
--- a/benchmarks/bench_plot_fastkmeans.py
+++ b/benchmarks/bench_plot_fastkmeans.py
@@ -1,6 +1,7 @@
-from time import time
+from __future__ import print_function
from collections import defaultdict
+from time import time
import numpy as np
from numpy import random as nr
@@ -11,7 +12,6 @@ from sklearn.cluster.k_means_ import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
- iterations = 200
results = defaultdict(lambda: [])
chunk = 100
@@ -19,26 +19,25 @@ def compute_bench(samples_range, features_range):
for n_samples in samples_range:
for n_features in features_range:
it += 1
- print '=============================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '=============================='
- print ''
+ print('==============================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('==============================')
+ print()
data = nr.random_integers(-50, 50, (n_samples, n_features))
- print 'K-Means'
+ print('K-Means')
tstart = time()
- kmeans = KMeans(init='k-means++',
- k=10).fit(data)
+ kmeans = KMeans(init='k-means++', n_clusters=10).fit(data)
delta = time() - tstart
- print "Speed: %0.3fs" % delta
- print "Inertia: %0.5f" % kmeans.inertia_
- print ''
+ print("Speed: %0.3fs" % delta)
+ print("Inertia: %0.5f" % kmeans.inertia_)
+ print()
results['kmeans_speed'].append(delta)
results['kmeans_quality'].append(kmeans.inertia_)
- print 'Fast K-Means'
+ print('Fast K-Means')
# let's prepare the data in small chunks
mbkmeans = MiniBatchKMeans(init='k-means++',
k=10,
@@ -46,10 +45,10 @@ def compute_bench(samples_range, features_range):
tstart = time()
mbkmeans.fit(data)
delta = time() - tstart
- print "Speed: %0.3fs" % delta
- print "Inertia: %f" % mbkmeans.inertia_
- print ''
- print ''
+ print("Speed: %0.3fs" % delta)
+ print("Inertia: %f" % mbkmeans.inertia_)
+ print()
+ print()
results['minibatchkmeans_speed'].append(delta)
results['minibatchkmeans_quality'].append(mbkmeans.inertia_)
@@ -69,22 +68,22 @@ def compute_bench_2(chunks):
it = 0
for chunk in chunks:
it += 1
- print '=============================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '=============================='
- print ''
+ print('==============================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('==============================')
+ print()
- print 'Fast K-Means'
+ print('Fast K-Means')
tstart = time()
mbkmeans = MiniBatchKMeans(init='k-means++',
- k=8,
- batch_size=chunk)
+ n_clusters=8,
+ batch_size=chunk)
mbkmeans.fit(X)
delta = time() - tstart
- print "Speed: %0.3fs" % delta
- print "Inertia: %0.3fs" % mbkmeans.inertia_
- print ''
+ print("Speed: %0.3fs" % delta)
+ print("Inertia: %0.3fs" % mbkmeans.inertia_)
+ print()
results['minibatchkmeans_speed'].append(delta)
results['minibatchkmeans_quality'].append(mbkmeans.inertia_)
diff --git a/benchmarks/bench_plot_lasso_path.py b/benchmarks/bench_plot_lasso_path.py
index d79d81ca30a19407c7fd7018905f3bdce1b70fd9..e0ee81521e66b47b53fe15cfdc018d67850cfe62 100644
--- a/benchmarks/bench_plot_lasso_path.py
+++ b/benchmarks/bench_plot_lasso_path.py
@@ -2,12 +2,14 @@
The input data is mostly low rank but is a fat infinite tail.
"""
+from __future__ import print_function
+
+from collections import defaultdict
import gc
-from time import time
import sys
+from time import time
import numpy as np
-from collections import defaultdict
from sklearn.linear_model import lars_path
from sklearn.linear_model import lasso_path
@@ -24,9 +26,9 @@ def compute_bench(samples_range, features_range):
for n_samples in samples_range:
for n_features in features_range:
it += 1
- print '===================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '===================='
+ print('====================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('====================')
dataset_kwargs = {
'n_samples': n_samples,
'n_features': n_features,
@@ -35,46 +37,46 @@ def compute_bench(samples_range, features_range):
#'effective_rank': None,
'bias': 0.0,
}
- print "n_samples: %d" % n_samples
- print "n_features: %d" % n_features
+ print("n_samples: %d" % n_samples)
+ print("n_features: %d" % n_features)
X, y = make_regression(**dataset_kwargs)
gc.collect()
- print "benching lars_path (with Gram):",
+ print("benching lars_path (with Gram):", end='')
sys.stdout.flush()
tstart = time()
G = np.dot(X.T, X) # precomputed Gram matrix
Xy = np.dot(X.T, y)
lars_path(X, y, Xy=Xy, Gram=G, method='lasso')
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
results['lars_path (with Gram)'].append(delta)
gc.collect()
- print "benching lars_path (without Gram):",
+ print("benching lars_path (without Gram):", end='')
sys.stdout.flush()
tstart = time()
lars_path(X, y, method='lasso')
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
results['lars_path (without Gram)'].append(delta)
gc.collect()
- print "benching lasso_path (with Gram):",
+ print("benching lasso_path (with Gram):", end='')
sys.stdout.flush()
tstart = time()
lasso_path(X, y, precompute=True)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
results['lasso_path (with Gram)'].append(delta)
gc.collect()
- print "benching lasso_path (without Gram):",
+ print("benching lasso_path (without Gram):", end='')
sys.stdout.flush()
tstart = time()
lasso_path(X, y, precompute=False)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
results['lasso_path (without Gram)'].append(delta)
return results
diff --git a/benchmarks/bench_plot_neighbors.py b/benchmarks/bench_plot_neighbors.py
index 3cfc9883997ecabbe2551af6a9553c478a1adf79..2cc116ab5717dc5968705e4b4f57f82f3cb96d9c 100644
--- a/benchmarks/bench_plot_neighbors.py
+++ b/benchmarks/bench_plot_neighbors.py
@@ -43,8 +43,8 @@ def barplot_neighbors(Nrange=2 ** np.arange(1, 11),
N_results_query = dict([(alg, np.zeros(len(Nrange)))
for alg in algorithms])
- for i, NN in enumerate(Nrange):
- print "N = %i (%i out of %i)" % (NN, i + 1, len(Nrange))
+ for i, NN in enumerate(Nrange, 1):
+ print("N = %i (%i out of %i)" % (NN, i, len(Nrange)))
X = get_data(NN, D, dataset)
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=min(NN, k),
@@ -66,8 +66,8 @@ def barplot_neighbors(Nrange=2 ** np.arange(1, 11),
D_results_query = dict([(alg, np.zeros(len(Drange)))
for alg in algorithms])
- for i, DD in enumerate(Drange):
- print "D = %i (%i out of %i)" % (DD, i + 1, len(Drange))
+ for i, DD in enumerate(Drange, 1):
+ print("D = %i (%i out of %i)" % (DD, i, len(Drange)))
X = get_data(N, DD, dataset)
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=k,
@@ -91,8 +91,8 @@ def barplot_neighbors(Nrange=2 ** np.arange(1, 11),
X = get_data(N, DD, dataset)
- for i, kk in enumerate(krange):
- print "k = %i (%i out of %i)" % (kk, i + 1, len(krange))
+ for i, kk in enumerate(krange, 1):
+ print("k = %i (%i out of %i)" % (kk, i, len(krange)))
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=kk,
algorithm=algorithm,
diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py
index 27c903e055a8c85745b633585fb70ec0c0e3c667..abfd3208d18cecc0a0560f7874aece39c030943e 100644
--- a/benchmarks/bench_plot_nmf.py
+++ b/benchmarks/bench_plot_nmf.py
@@ -75,20 +75,20 @@ def compute_bench(samples_range, features_range, rank=50, tolerance=1e-7):
for n_samples in samples_range:
for n_features in features_range:
it += 1
- print '===================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '===================='
+ print('====================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('====================')
X = np.abs(make_low_rank_matrix(n_samples, n_features,
effective_rank=rank, tail_strength=0.2))
gc.collect()
- print "benching nndsvd-nmf: "
+ print("benching nndsvd-nmf: ")
tstart = time()
m = NMF(n_components=30, tol=tolerance, init='nndsvd').fit(X)
tend = time() - tstart
timeset['nndsvd-nmf'].append(tend)
err['nndsvd-nmf'].append(m.reconstruction_err_)
- print m.reconstruction_err_, tend
+ print(m.reconstruction_err_, tend)
gc.collect()
print "benching nndsvda-nmf: "
@@ -98,36 +98,36 @@ def compute_bench(samples_range, features_range, rank=50, tolerance=1e-7):
tend = time() - tstart
timeset['nndsvda-nmf'].append(tend)
err['nndsvda-nmf'].append(m.reconstruction_err_)
- print m.reconstruction_err_, tend
+ print(m.reconstruction_err_, tend)
gc.collect()
- print "benching nndsvdar-nmf: "
+ print("benching nndsvdar-nmf: ")
tstart = time()
m = NMF(n_components=30, init='nndsvdar',
tol=tolerance).fit(X)
tend = time() - tstart
timeset['nndsvdar-nmf'].append(tend)
err['nndsvdar-nmf'].append(m.reconstruction_err_)
- print m.reconstruction_err_, tend
+ print(m.reconstruction_err_, tend)
gc.collect()
- print "benching random-nmf"
+ print("benching random-nmf")
tstart = time()
m = NMF(n_components=30, init=None, max_iter=1000,
tol=tolerance).fit(X)
tend = time() - tstart
timeset['random-nmf'].append(tend)
err['random-nmf'].append(m.reconstruction_err_)
- print m.reconstruction_err_, tend
+ print(m.reconstruction_err_, tend)
gc.collect()
- print "benching alt-random-nmf"
+ print("benching alt-random-nmf")
tstart = time()
W, H = alt_nnmf(X, r=30, R=None, tol=tolerance)
tend = time() - tstart
timeset['alt-random-nmf'].append(tend)
err['alt-random-nmf'].append(np.linalg.norm(X - np.dot(W, H)))
- print np.linalg.norm(X - np.dot(W, H)), tend
+ print(np.linalg.norm(X - np.dot(W, H)), tend)
return timeset, err
diff --git a/benchmarks/bench_plot_omp_lars.py b/benchmarks/bench_plot_omp_lars.py
index 8b50df658b4a37db33811a9a879f5ca13acfe54e..f9d9225c1a216be80d82f8f5fde71505c4ffa3f3 100644
--- a/benchmarks/bench_plot_omp_lars.py
+++ b/benchmarks/bench_plot_omp_lars.py
@@ -3,9 +3,11 @@ regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
+from __future__ import print_function
+
import gc
-from time import time
import sys
+from time import time
import numpy as np
@@ -28,9 +30,9 @@ def compute_bench(samples_range, features_range):
for i_f, n_features in enumerate(features_range):
it += 1
n_informative = n_features / 10
- print '===================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '===================='
+ print('====================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('====================')
# dataset_kwargs = {
# 'n_train_samples': n_samples,
# 'n_test_samples': 2,
@@ -47,49 +49,49 @@ def compute_bench(samples_range, features_range):
'n_nonzero_coefs': n_informative,
'random_state': 0
}
- print "n_samples: %d" % n_samples
- print "n_features: %d" % n_features
+ print("n_samples: %d" % n_samples)
+ print("n_features: %d" % n_features)
y, X, _ = make_sparse_coded_signal(**dataset_kwargs)
X = np.asfortranarray(X)
gc.collect()
- print "benching lars_path (with Gram):",
+ print("benching lars_path (with Gram):", end='')
sys.stdout.flush()
tstart = time()
G = np.dot(X.T, X) # precomputed Gram matrix
Xy = np.dot(X.T, y)
lars_path(X, y, Xy=Xy, Gram=G, max_iter=n_informative)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
lars_gram[i_f, i_s] = delta
gc.collect()
- print "benching lars_path (without Gram):",
+ print("benching lars_path (without Gram):", end='')
sys.stdout.flush()
tstart = time()
lars_path(X, y, Gram=None, max_iter=n_informative)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
lars[i_f, i_s] = delta
gc.collect()
- print "benching orthogonal_mp (with Gram):",
+ print("benching orthogonal_mp (with Gram):", end='')
sys.stdout.flush()
tstart = time()
orthogonal_mp(X, y, precompute_gram=True,
n_nonzero_coefs=n_informative)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
omp_gram[i_f, i_s] = delta
gc.collect()
- print "benching orthogonal_mp (without Gram):",
+ print("benching orthogonal_mp (without Gram):", end='')
sys.stdout.flush()
tstart = time()
orthogonal_mp(X, y, precompute_gram=False,
n_nonzero_coefs=n_informative)
delta = time() - tstart
- print "%0.3fs" % delta
+ print("%0.3fs" % delta)
omp[i_f, i_s] = delta
results['time(LARS) / time(OMP)\n (w/ Gram)'] = (lars_gram / omp_gram)
diff --git a/benchmarks/bench_plot_svd.py b/benchmarks/bench_plot_svd.py
index b8a551c63da1a88e30b0fd69453c7e8a99011e42..ed8af0202837eb2046d87332aeaea7c189860df8 100644
--- a/benchmarks/bench_plot_svd.py
+++ b/benchmarks/bench_plot_svd.py
@@ -22,29 +22,29 @@ def compute_bench(samples_range, features_range, n_iter=3, rank=50):
for n_samples in samples_range:
for n_features in features_range:
it += 1
- print '===================='
- print 'Iteration %03d of %03d' % (it, max_it)
- print '===================='
+ print('====================')
+ print('Iteration %03d of %03d' % (it, max_it))
+ print('====================')
X = make_low_rank_matrix(n_samples, n_features,
effective_rank=rank,
tail_strength=0.2)
gc.collect()
- print "benching scipy svd: "
+ print("benching scipy svd: ")
tstart = time()
svd(X, full_matrices=False)
results['scipy svd'].append(time() - tstart)
gc.collect()
- print "benching scikit-learn randomized_svd: n_iter=0"
+ print("benching scikit-learn randomized_svd: n_iter=0")
tstart = time()
randomized_svd(X, rank, n_iter=0)
results['scikit-learn randomized_svd (n_iter=0)'].append(
time() - tstart)
gc.collect()
- print ("benching scikit-learn randomized_svd: n_iter=%d "
- % n_iter)
+ print("benching scikit-learn randomized_svd: n_iter=%d "
+ % n_iter)
tstart = time()
randomized_svd(X, rank, n_iter=n_iter)
results['scikit-learn randomized_svd (n_iter=%d)'
diff --git a/benchmarks/bench_sgd_regression.py b/benchmarks/bench_sgd_regression.py
index e07bd0add590b7fac6b6e594640542eb505629d5..60d32c8a93e0ccb7c65d4c9aee8dd0f9c80bed8b 100644
--- a/benchmarks/bench_sgd_regression.py
+++ b/benchmarks/bench_sgd_regression.py
@@ -5,7 +5,7 @@ Compares SGD regression against coordinate descent and Ridge
on synthetik data.
"""
-print __doc__
+print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD Style.
@@ -41,10 +41,10 @@ if __name__ == "__main__":
X_test = X[n_train:]
y_test = y[n_train:]
- print "======================="
- print "Round %d %d" % (i, j)
- print "n_features:", n_features
- print "n_samples:", n_train
+ print("=======================")
+ print("Round %d %d" % (i, j))
+ print("n_features:", n_features)
+ print("n_samples:", n_train)
# Shuffle data
idx = np.arange(n_train)
@@ -64,7 +64,7 @@ if __name__ == "__main__":
y_test = (y_test - mean) / std
gc.collect()
- print "- benching ElasticNet"
+ print("- benching ElasticNet")
clf = ElasticNet(alpha=alpha, rho=0.5, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
@@ -73,7 +73,7 @@ if __name__ == "__main__":
elnet_results[i, j, 1] = time() - tstart
gc.collect()
- print "- benching SGD"
+ print("- benching SGD")
n_iter = np.ceil(10 ** 4.0 / n_train)
clf = SGDRegressor(alpha=alpha, fit_intercept=False,
n_iter=n_iter, learning_rate="invscaling",
@@ -86,7 +86,7 @@ if __name__ == "__main__":
sgd_results[i, j, 1] = time() - tstart
gc.collect()
- print "- benching RidgeRegression"
+ print("- benching RidgeRegression")
clf = Ridge(alpha=alpha, fit_intercept=False)
tstart = time()
clf.fit(X_train, y_train)
diff --git a/benchmarks/bench_tree.py b/benchmarks/bench_tree.py
index 77f76d8e7049a56d2a837eb5b09936a01aeb4477..3d4c85a89159667b7c1108669bc2e1f2166696e5 100644
--- a/benchmarks/bench_tree.py
+++ b/benchmarks/bench_tree.py
@@ -63,9 +63,9 @@ def bench_scikit_tree_regressor(X, Y):
if __name__ == '__main__':
- print '============================================'
- print 'Warning: this is going to take a looong time'
- print '============================================'
+ print('============================================')
+ print('Warning: this is going to take a looong time')
+ print('============================================')
n = 10
step = 10000
@@ -73,9 +73,9 @@ if __name__ == '__main__':
dim = 10
n_classes = 10
for i in range(n):
- print '============================================'
- print 'Entering iteration %s of %s' % (i, n)
- print '============================================'
+ print('============================================')
+ print('Entering iteration %s of %s' % (i, n))
+ print('============================================')
n_samples += step
X = np.random.randn(n_samples, dim)
Y = np.random.randint(0, n_classes, (n_samples,))
@@ -102,9 +102,9 @@ if __name__ == '__main__':
dim = start_dim
for i in range(0, n):
- print '============================================'
- print 'Entering iteration %s of %s' % (i, n)
- print '============================================'
+ print('============================================')
+ print('Entering iteration %s of %s' % (i, n))
+ print('============================================')
dim += step
X = np.random.randn(100, dim)
Y = np.random.randint(0, n_classes, (100,))
diff --git a/doc/datasets/labeled_faces.rst b/doc/datasets/labeled_faces.rst
index cc7d14cffeea58d050a431ea41867b5da055a022..bcda19bd82f20b9ecd9015c6069970beee64afd5 100644
--- a/doc/datasets/labeled_faces.rst
+++ b/doc/datasets/labeled_faces.rst
@@ -43,7 +43,7 @@ classification task (hence supervised learning)::
>>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
>>> for name in lfw_people.target_names:
- ... print name
+ ... print(name)
...
Ariel Sharon
Colin Powell
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
index 71671162e60972724897ee850cc7cdc6b2edbd1a..576b99f5f8209b63fb4725a701b00ab68adf9920 100644
--- a/doc/modules/cross_validation.rst
+++ b/doc/modules/cross_validation.rst
@@ -78,7 +78,7 @@ the data and fitting a model and computing the score 5 consecutive times
The mean score and the standard deviation of the score estimate are hence given
by::
- >>> print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
+ >>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2))
Accuracy: 0.97 (+/- 0.02)
By default, the score computed at each CV iteration is the ``score``
@@ -158,11 +158,11 @@ Example of 2-fold::
>>> Y = np.array([0, 1, 0, 1])
>>> kf = KFold(len(Y), n_folds=2, indices=False)
- >>> print kf
+ >>> print(kf)
sklearn.cross_validation.KFold(n=4, n_folds=2)
>>> for train, test in kf:
- ... print train, test
+ ... print(train, test)
[False False True True] [ True True False False]
[ True True False False] [False False True True]
@@ -181,7 +181,7 @@ when creating the cross-validation procedure::
>>> kf = KFold(len(Y), n_folds=2, indices=True)
>>> for train, test in kf:
- ... print train, test
+ ... print(train, test)
[2 3] [0 1]
[0 1] [2 3]
@@ -206,11 +206,11 @@ Example of stratified 2-fold::
>>> Y = [0, 0, 0, 1, 1, 1, 0]
>>> skf = StratifiedKFold(Y, 2)
- >>> print skf
+ >>> print(skf)
sklearn.cross_validation.StratifiedKFold(labels=[0 0 0 1 1 1 0], n_folds=2)
>>> for train, test in skf:
- ... print train, test
+ ... print(train, test)
[1 4 6] [0 2 3 5]
[0 2 3 5] [1 4 6]
@@ -229,11 +229,11 @@ not waste much data as only one sample is removed from the learning set::
>>> Y = np.array([0, 1, 0, 1])
>>> loo = LeaveOneOut(len(Y))
- >>> print loo
+ >>> print(loo)
sklearn.cross_validation.LeaveOneOut(n=4)
>>> for train, test in loo:
- ... print train, test
+ ... print(train, test)
[1 2 3] [0]
[0 2 3] [1]
[0 1 3] [2]
@@ -253,11 +253,11 @@ Example of Leave-2-Out::
>>> Y = [0, 1, 0, 1]
>>> lpo = LeavePOut(len(Y), 2)
- >>> print lpo
+ >>> print(lpo)
sklearn.cross_validation.LeavePOut(n=4, p=2)
>>> for train, test in lpo:
- ... print train, test
+ ... print(train, test)
[2 3] [0 1]
[1 3] [0 2]
[1 2] [0 3]
@@ -287,11 +287,11 @@ a training set using the samples of all the experiments except one::
>>> labels = [1, 1, 2, 2]
>>> lolo = LeaveOneLabelOut(labels)
- >>> print lolo
+ >>> print(lolo)
sklearn.cross_validation.LeaveOneLabelOut(labels=[1, 1, 2, 2])
>>> for train, test in lolo:
- ... print train, test
+ ... print(train, test)
[2 3] [0 1]
[0 1] [2 3]
@@ -314,11 +314,11 @@ Example of Leave-2-Label Out::
>>> labels = [1, 1, 2, 2, 3, 3]
>>> lplo = LeavePLabelOut(labels, 2)
- >>> print lplo
+ >>> print(lplo)
sklearn.cross_validation.LeavePLabelOut(labels=[1, 1, 2, 2, 3, 3], p=2)
>>> for train, test in lplo:
- ... print train, test
+ ... print(train, test)
[4 5] [0 1 2 3]
[2 3] [0 1 4 5]
[0 1] [2 3 4 5]
@@ -344,11 +344,11 @@ Here is a usage example::
... random_state=0)
>>> len(ss)
3
- >>> print ss # doctest: +ELLIPSIS
+ >>> print(ss) # doctest: +ELLIPSIS
ShuffleSplit(5, n_iter=3, test_size=0.25, indices=True, ...)
>>> for train_index, test_index in ss:
- ... print train_index, test_index
+ ... print(train_index, test_index)
...
[1 3 4] [2 0]
[1 4 3] [0 2]
@@ -390,11 +390,11 @@ smaller than the total dataset if it is very large.
>>> bs = cross_validation.Bootstrap(9, random_state=0)
>>> len(bs)
3
- >>> print bs
+ >>> print(bs)
Bootstrap(9, n_iter=3, train_size=5, test_size=4, random_state=0)
>>> for train_index, test_index in bs:
- ... print train_index, test_index
+ ... print(train_index, test_index)
...
[1 8 7 7 8] [0 3 0 5]
[5 4 2 4 2] [6 7 1 0]
diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
index b591b733449ca0640ffb288eae7ff47e5fe3713d..ed2fdcbf687268756a64ca90d7b6e860cf97a42d 100644
--- a/doc/modules/naive_bayes.rst
+++ b/doc/modules/naive_bayes.rst
@@ -93,7 +93,7 @@ are estimated using maximum likelihood.
>>> from sklearn.naive_bayes import GaussianNB
>>> gnb = GaussianNB()
>>> y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
- >>> print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
+ >>> print("Number of mislabeled points : %d" % (iris.target != y_pred).sum())
Number of mislabeled points : 6
.. _multinomial_naive_bayes:
diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
index 367d5e9309e0ee5524d743a8ca2fd441d63de757..756c1e4fd143fcbcf7a53505e1133a3fb3339736 100644
--- a/doc/modules/neighbors.rst
+++ b/doc/modules/neighbors.rst
@@ -380,7 +380,7 @@ methods that do not make this assumption. Usage of the default
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid(metric='euclidean', shrink_threshold=None)
- >>> print clf.predict([[-0.8, -1]])
+ >>> print(clf.predict([[-0.8, -1]]))
[1]
diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst
index 7d9ec2738b09c30dd13b13cccb5539debe46764f..aa8f732d140f2d45da8c1d13f423013208981035 100644
--- a/doc/tutorial/basic/tutorial.rst
+++ b/doc/tutorial/basic/tutorial.rst
@@ -93,7 +93,7 @@ section <datasets>`.
For instance, in the case of the digits dataset, ``digits.data`` gives
access to the features that can be used to classify the digits samples::
- >>> print digits.data # doctest: +NORMALIZE_WHITESPACE
+ >>> print(digits.data) # doctest: +NORMALIZE_WHITESPACE
[[ 0. 0. 5. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 10. 0. 0.]
[ 0. 0. 0. ..., 16. 9. 0.]
diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst
index 0ec9050fb5834b1355ff69c9d3ad948990a2d33e..81e839b9595b8656bafc4b846e2f5ea3ba7615ab 100644
--- a/doc/tutorial/statistical_inference/model_selection.rst
+++ b/doc/tutorial/statistical_inference/model_selection.rst
@@ -38,7 +38,7 @@ data in *folds* that we use for training and testing::
... y_test = y_train.pop(k)
... y_train = np.concatenate(y_train)
... scores.append(svc.fit(X_train, y_train).score(X_test, y_test))
- >>> print scores
+ >>> print(scores)
[0.93489148580968284, 0.95659432387312182, 0.93989983305509184]
.. currentmodule:: sklearn.cross_validation
@@ -59,7 +59,7 @@ of indices for this purpose::
>>> from sklearn import cross_validation
>>> k_fold = cross_validation.KFold(n=6, n_folds=3, indices=True)
>>> for train_indices, test_indices in k_fold:
- ... print 'Train: %s | test: %s' % (train_indices, test_indices)
+ ... print('Train: %s | test: %s' % (train_indices, test_indices))
Train: [2 3 4 5] | test: [0 1]
Train: [0 1 4 5] | test: [2 3]
Train: [0 1 2 3] | test: [4 5]
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 3bfdef0e1199caf90e171c0803d062c08285ff04..6c4b5a3e7d059ecce382b618af96671b3f201a41 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -175,7 +175,7 @@ Linear models: :math:`y = X\beta + \epsilon`
>>> regr = linear_model.LinearRegression()
>>> regr.fit(diabetes_X_train, diabetes_y_train)
LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
- >>> print regr.coef_
+ >>> print(regr.coef_)
[ 0.30349955 -237.63931533 510.53060544 327.73698041 -814.13170937
492.81458798 102.84845219 184.60648906 743.51961675 76.09517222]
@@ -328,7 +328,7 @@ application of Occam's razor: `prefer simpler models`.
Lasso(alpha=0.025118864315095794, copy_X=True, fit_intercept=True,
max_iter=1000, normalize=False, positive=False, precompute='auto',
tol=0.0001, warm_start=False)
- >>> print regr.coef_
+ >>> print(regr.coef_)
[ 0. -212.43764548 517.19478111 313.77959962 -160.8303982 -0.
-187.19554705 69.38229038 508.66011217 71.84239008]
diff --git a/doc/tutorial/statistical_inference/unsupervised_learning.rst b/doc/tutorial/statistical_inference/unsupervised_learning.rst
index 6990ab07d7d4ec263c8017676d7d02eef9116b16..13d0b4e67c7ad5c4b9af6a38a8470ce4cb181e7b 100644
--- a/doc/tutorial/statistical_inference/unsupervised_learning.rst
+++ b/doc/tutorial/statistical_inference/unsupervised_learning.rst
@@ -40,9 +40,9 @@ algorithms. The simplest clustering algorithm is
>>> k_means = cluster.KMeans(n_clusters=3)
>>> k_means.fit(X_iris) # doctest: +ELLIPSIS
KMeans(copy_x=True, init='k-means++', ...
- >>> print k_means.labels_[::10]
+ >>> print(k_means.labels_[::10])
[1 1 1 1 1 0 0 0 0 0 2 2 2 2 2]
- >>> print y_iris[::10]
+ >>> print(y_iris[::10])
[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]
.. |k_means_iris_bad_init| image:: ../../auto_examples/cluster/images/plot_cluster_iris_3.png
@@ -276,7 +276,7 @@ data by projecting on a principal subspace.
>>> pca = decomposition.PCA()
>>> pca.fit(X)
PCA(copy=True, n_components=None, whiten=False)
- >>> print pca.explained_variance_ # doctest: +SKIP
+ >>> print(pca.explained_variance_) # doctest: +SKIP
[ 2.18565811e+00 1.19346747e+00 8.43026679e-32]
>>> # As we can see, only the 2 first components are useful
diff --git a/examples/ensemble/plot_adaboost_hastie_10_2.py b/examples/ensemble/plot_adaboost_hastie_10_2.py
index db9da0dca04dd980d3bcbe1f6173035568d33152..4c8f83aeae5040f94376314459373b7d4e2b58a4 100644
--- a/examples/ensemble/plot_adaboost_hastie_10_2.py
+++ b/examples/ensemble/plot_adaboost_hastie_10_2.py
@@ -18,7 +18,7 @@ whereas real SAMME.R uses the predicted class probabilities.
.. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
"""
-print __doc__
+print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>,
# Noel Dawe <noel.dawe@gmail.com>
diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py
index e70f923b3983850a19e13e035f55f1f6437c0d08..3e9ec6e53b4cf280c7e6cc2a17409e4e2444f6af 100644
--- a/examples/ensemble/plot_adaboost_multiclass.py
+++ b/examples/ensemble/plot_adaboost_multiclass.py
@@ -23,7 +23,7 @@ therefore are not shown.
.. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
"""
-print __doc__
+print(__doc__)
# Author: Noel Dawe <noel.dawe@gmail.com>
#
diff --git a/examples/ensemble/plot_adaboost_regression.py b/examples/ensemble/plot_adaboost_regression.py
index 0daf557d6dafa88be0411611d4ed763188dc4dba..9a419c2ba4dbd7ade9d45d71b976baff1cc64158 100644
--- a/examples/ensemble/plot_adaboost_regression.py
+++ b/examples/ensemble/plot_adaboost_regression.py
@@ -12,7 +12,7 @@ detail.
.. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
"""
-print __doc__
+print(__doc__)
import numpy as np
diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py
index 18afe629f978e698264ae712ab65e53dd25a0be7..290e841fb64a5588cd270209a639eb4273441b00 100644
--- a/examples/ensemble/plot_adaboost_twoclass.py
+++ b/examples/ensemble/plot_adaboost_twoclass.py
@@ -16,7 +16,7 @@ containing a desired purity of class B, for example, by only selecting samples
with a decision score above some value.
"""
-print __doc__
+print(__doc__)
import pylab as pl
import numpy as np
diff --git a/examples/exercises/plot_cv_digits.py b/examples/exercises/plot_cv_digits.py
index 6529df4d9ecfeee52ec66ea21313f5cc2fcda5ac..dde717902d6b1a302d6aba018f1fae98657a6022 100644
--- a/examples/exercises/plot_cv_digits.py
+++ b/examples/exercises/plot_cv_digits.py
@@ -6,7 +6,7 @@ Cross-validation on Digits Dataset Exercise
This exercise is used in the :ref:`cv_generators_tut` part of the
:ref:`model_selection_tut` section of the :ref:`stat_learn_tut_index`.
"""
-print __doc__
+print(__doc__)
import numpy as np
diff --git a/examples/mixture/plot_gmm_classifier.py b/examples/mixture/plot_gmm_classifier.py
index af6ffeb766f61367595ebd5427d8717cfb8d436f..03b043cd3273a919a4a46e51e17493b333ffc925 100644
--- a/examples/mixture/plot_gmm_classifier.py
+++ b/examples/mixture/plot_gmm_classifier.py
@@ -19,7 +19,7 @@ crosses. The iris dataset is four-dimensional. Only the first two
dimensions are shown here, and thus some points are separated in other
dimensions.
"""
-print __doc__
+print(__doc__)
# Author: Ron Weiss <ronweiss@gmail.com>, Gael Varoquaux
# License: BSD Style.
diff --git a/examples/mixture/plot_gmm_selection.py b/examples/mixture/plot_gmm_selection.py
index 4f9b46a95fad04029b167221bf06ce0753866798..4fa015b9523559426ce5871286d4f0403532c7bb 100644
--- a/examples/mixture/plot_gmm_selection.py
+++ b/examples/mixture/plot_gmm_selection.py
@@ -14,7 +14,7 @@ Unlike Bayesian procedures, such inferences are prior-free.
In that case, the model with 2 components and full covariance
(which corresponds to the true generative model) is selected.
"""
-print __doc__
+print(__doc__)
import itertools
diff --git a/examples/plot_confusion_matrix.py b/examples/plot_confusion_matrix.py
index d4de1c4d93fd58382df6b11912f61f2ed901b0f0..4585303dc3c110103d2a0b5fe99fa097ec8105e6 100644
--- a/examples/plot_confusion_matrix.py
+++ b/examples/plot_confusion_matrix.py
@@ -31,7 +31,7 @@ y_ = classifier.fit(X[:half], y[:half]).predict(X[half:])
# Compute confusion matrix
cm = confusion_matrix(y[half:], y_)
-print cm
+print(cm)
# Show confusion matrix
pl.matshow(cm)
diff --git a/sklearn/cluster/spectral.py b/sklearn/cluster/spectral.py
index 67aa0ddb896ec39b7f632fdedd5b6c7cbb0473a3..876435a27dda5d28d067be55129d9057b2194e0a 100644
--- a/sklearn/cluster/spectral.py
+++ b/sklearn/cluster/spectral.py
@@ -137,7 +137,7 @@ def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,
U, S, Vh = np.linalg.svd(t_svd)
svd_restarts += 1
except LinAlgError:
- print "SVD did not converge, randomizing and trying again"
+ print("SVD did not converge, randomizing and trying again")
break
ncut_value = 2.0 * (n_samples - S.sum())
diff --git a/sklearn/covariance/tests/test_covariance.py b/sklearn/covariance/tests/test_covariance.py
index 177b4fb8b998e010d487de9e63118a44a27bc557..d5606117d929d73f8a28e8be49c5be7e901bb68b 100644
--- a/sklearn/covariance/tests/test_covariance.py
+++ b/sklearn/covariance/tests/test_covariance.py
@@ -44,7 +44,7 @@ def test_covariance():
cov.error_norm, emp_cov, norm='foo')
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
- print np.amin(mahal_dist), np.amax(mahal_dist)
+ print(np.amin(mahal_dist), np.amax(mahal_dist))
assert(np.amin(mahal_dist) > 0)
# test with n_features = 1
diff --git a/sklearn/covariance/tests/test_robust_covariance.py b/sklearn/covariance/tests/test_robust_covariance.py
index 7f953ee5dc554b05649930e8068fcbf3b79a0805..51e0b0e3878c58b751accf736b232de0a0bb61d7 100644
--- a/sklearn/covariance/tests/test_robust_covariance.py
+++ b/sklearn/covariance/tests/test_robust_covariance.py
@@ -82,7 +82,7 @@ def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
- print clf.threshold
+ print(clf.threshold)
assert_raises(Exception, clf.predict, X)
assert_raises(Exception, clf.decision_function, X)
clf.fit(X)
diff --git a/sklearn/datasets/base.py b/sklearn/datasets/base.py
index da957030df5253dc1768ec6272dbd4529b3f4bce..97a2268c4815e7d42b29afa0bce263dddbc1cd93 100644
--- a/sklearn/datasets/base.py
+++ b/sklearn/datasets/base.py
@@ -283,7 +283,7 @@ def load_digits(n_class=10):
>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
- >>> print digits.data.shape
+ >>> print(digits.data.shape)
(1797, 64)
>>> import pylab as pl #doctest: +SKIP
>>> pl.gray() #doctest: +SKIP
@@ -391,7 +391,7 @@ def load_boston():
--------
>>> from sklearn.datasets import load_boston
>>> boston = load_boston()
- >>> print boston.data.shape
+ >>> print(boston.data.shape)
(506, 13)
"""
module_path = dirname(__file__)
diff --git a/sklearn/datasets/california_housing.py b/sklearn/datasets/california_housing.py
index c421a9227698e4ef5f1d6fb4aebecaeb172ea8ef..7c866df8ba3ffb2cf31566f7afad6f69b8e64a0b 100644
--- a/sklearn/datasets/california_housing.py
+++ b/sklearn/datasets/california_housing.py
@@ -60,8 +60,7 @@ def fetch_california_housing(data_home=None, download_if_missing=True):
if not exists(data_home):
makedirs(data_home)
if not exists(join(data_home, TARGET_FILENAME)):
- print 'downloading Cal. housing from %s to %s' % (DATA_URL,
- data_home)
+ print('downloading Cal. housing from %s to %s' % (DATA_URL, data_home))
fhandle = urllib2.urlopen(DATA_URL)
buf = BytesIO(fhandle.read())
zip_file = ZipFile(buf)
diff --git a/sklearn/datasets/samples_generator.py b/sklearn/datasets/samples_generator.py
index 31ac8f0233c6125d30d4a6bad85bf173f3f41516..0aa9eaee4e8ea3609d578172e393c3f0f4ad7dff 100644
--- a/sklearn/datasets/samples_generator.py
+++ b/sklearn/datasets/samples_generator.py
@@ -638,7 +638,7 @@ def make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0,
>>> from sklearn.datasets.samples_generator import make_blobs
>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
... random_state=0)
- >>> print X.shape
+ >>> print(X.shape)
(10, 2)
>>> y
array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])
diff --git a/sklearn/decomposition/factor_analysis.py b/sklearn/decomposition/factor_analysis.py
index 71efb637115a5a6f93267ac6481300a3cde30093..f63ddaf4af214f35d08da1666067d875b1b105e5 100644
--- a/sklearn/decomposition/factor_analysis.py
+++ b/sklearn/decomposition/factor_analysis.py
@@ -162,7 +162,7 @@ class FactorAnalysis(BaseEstimator, TransformerMixin):
psi = np.maximum(var - np.sum(W ** 2, axis=0), SMALL)
else:
if self.verbose:
- print "Did not converge"
+ print("Did not converge")
self.components_ = W
self.noise_variance_ = psi
diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py
index 0d8221c899a8e99677e28898f1b008be3031ff12..6ea91845b2612d55fa6a0d33080311101b941c44 100644
--- a/sklearn/feature_extraction/image.py
+++ b/sklearn/feature_extraction/image.py
@@ -280,7 +280,7 @@ def extract_patches_2d(image, patch_size, max_patches=None, random_state=None):
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> patches = image.extract_patches_2d(one_image, (2, 2))
- >>> print patches.shape
+ >>> print(patches.shape)
(9, 2, 2)
>>> patches[0]
array([[0, 1],
diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
index 46931c48b284cfe80f76b2ae2b5a6d86614f5862..229e6fad5ac739b5b4683ad8f59e3c6f23442b77 100644
--- a/sklearn/linear_model/tests/test_least_angle.py
+++ b/sklearn/linear_model/tests/test_least_angle.py
@@ -168,7 +168,7 @@ def test_no_path_all_precomputed():
alphas_, active_, coef_path_ = linear_model.lars_path(
X, y, method="lasso", Gram=G, Xy=Xy, alpha_min=0.9)
- print "---"
+ print("---")
alpha_, active, coef = linear_model.lars_path(
X, y, method="lasso", Gram=G, Xy=Xy, alpha_min=0.9, return_path=False)
diff --git a/sklearn/manifold/mds.py b/sklearn/manifold/mds.py
index 380d98fa052270492540556559b2e609ff5e268c..5e96f98025de4199b303d5e6eec74fdec0d5094b 100644
--- a/sklearn/manifold/mds.py
+++ b/sklearn/manifold/mds.py
@@ -119,12 +119,12 @@ def _smacof_single(similarities, metric=True, n_components=2, init=None,
dis = np.sqrt((X ** 2).sum(axis=1)).sum()
if verbose == 2:
- print 'it: %d, stress %s' % (it, stress)
+ print('it: %d, stress %s' % (it, stress))
if old_stress is not None:
if(old_stress - stress / dis) < eps:
if verbose:
- print 'breaking at iteration %d with stress %s' % (it,
- stress)
+ print('breaking at iteration %d with stress %s' % (it,
+ stress))
break
old_stress = stress / dis
diff --git a/sklearn/metrics/cluster/supervised.py b/sklearn/metrics/cluster/supervised.py
index a4b999013e3733eafe394fbee35c75ad3153e754..9e687c3f598678e2777fba40bac77a429e1fa796 100644
--- a/sklearn/metrics/cluster/supervised.py
+++ b/sklearn/metrics/cluster/supervised.py
@@ -312,20 +312,20 @@ def homogeneity_score(labels_true, labels_pred):
Non-pefect labelings that futher split classes into more clusters can be
perfectly homogeneous::
- >>> print ("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
+ >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 1, 2]))
... # doctest: +ELLIPSIS
1.0...
- >>> print ("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
+ >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 2, 3]))
... # doctest: +ELLIPSIS
1.0...
Clusters that include samples from different classes do not make for an
homogeneous labeling::
- >>> print ("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
+ >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 1, 0, 1]))
... # doctest: +ELLIPSIS
0.0...
- >>> print ("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
+ >>> print("%.6f" % homogeneity_score([0, 0, 1, 1], [0, 0, 0, 0]))
... # doctest: +ELLIPSIS
0.0...
@@ -384,17 +384,17 @@ def completeness_score(labels_true, labels_pred):
Non-pefect labelings that assign all classes members to the same clusters
are still complete::
- >>> print completeness_score([0, 0, 1, 1], [0, 0, 0, 0])
+ >>> print(completeness_score([0, 0, 1, 1], [0, 0, 0, 0]))
1.0
- >>> print completeness_score([0, 1, 2, 3], [0, 0, 1, 1])
+ >>> print(completeness_score([0, 1, 2, 3], [0, 0, 1, 1]))
1.0
If classes members are splitted across different clusters, the
assignment cannot be complete::
- >>> print completeness_score([0, 0, 1, 1], [0, 1, 0, 1])
+ >>> print(completeness_score([0, 0, 1, 1], [0, 1, 0, 1]))
0.0
- >>> print completeness_score([0, 0, 0, 0], [0, 1, 2, 3])
+ >>> print(completeness_score([0, 0, 0, 0], [0, 1, 2, 3]))
0.0
"""
diff --git a/sklearn/neighbors/nearest_centroid.py b/sklearn/neighbors/nearest_centroid.py
index 5a9d0c1a6ca896e4e159b1570639ca7e4ac0236a..b313e5121dc69e818b16e12cade5b80d26761178 100644
--- a/sklearn/neighbors/nearest_centroid.py
+++ b/sklearn/neighbors/nearest_centroid.py
@@ -46,7 +46,7 @@ class NearestCentroid(BaseEstimator, ClassifierMixin):
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid(metric='euclidean', shrink_threshold=None)
- >>> print clf.predict([[-0.8, -1]])
+ >>> print(clf.predict([[-0.8, -1]]))
[1]
See also
diff --git a/sklearn/svm/base.py b/sklearn/svm/base.py
index 23949eead75ec45a3d166483aeff836a9e24880a..3ae7ec49d60f2a97b5b8dd470a2d5710e3feead5 100644
--- a/sklearn/svm/base.py
+++ b/sklearn/svm/base.py
@@ -1,3 +1,5 @@
+from __future__ import print_function
+
import numpy as np
import scipy.sparse as sp
import warnings
@@ -180,7 +182,7 @@ class BaseLibSVM(BaseEstimator):
fit = self._sparse_fit if self._sparse else self._dense_fit
if self.verbose: # pragma: no cover
- print '[LibSVM]',
+ print('[LibSVM]', end='')
fit(X, y, sample_weight, solver_type, kernel)
self.shape_fit_ = X.shape
@@ -681,7 +683,7 @@ class BaseLibLinear(BaseEstimator):
rnd = check_random_state(self.random_state)
if self.verbose:
- print '[LibLinear]',
+ print('[LibLinear]', end='')
self.raw_coef_ = train(X, y, self._get_solver_type(), self.tol,
self._get_bias(), self.C,
self.class_weight_,
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 7314be71b2ea7803e3b73e051ca590fa82507398..65c1592c4619754cbc9c7f802ed4e29d76497666 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -5,6 +5,8 @@ General tests for all estimators in sklearn.
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD Style.
+from __future__ import print_function
+
import os
import warnings
import sys
@@ -139,13 +141,13 @@ def test_estimators_sparse_data():
clf.fit(X, y)
except TypeError, e:
if not 'sparse' in repr(e):
- print ("Estimator %s doesn't seem to fail gracefully on "
- "sparse data" % name)
+ print("Estimator %s doesn't seem to fail gracefully on "
+ "sparse data" % name)
traceback.print_exc(file=sys.stdout)
raise e
except Exception, exc:
- print ("Estimator %s doesn't seem to fail gracefully on "
- "sparse data" % name)
+ print("Estimator %s doesn't seem to fail gracefully on "
+ "sparse data" % name)
traceback.print_exc(file=sys.stdout)
raise exc
@@ -207,9 +209,9 @@ def test_transformers():
else:
assert_equal(X_pred.shape[0], n_samples)
except Exception as e:
- print trans
- print e
- print
+ print(trans)
+ print(e)
+ print()
succeeded = False
continue
@@ -263,13 +265,13 @@ def test_transformers_sparse_data():
trans.fit(X, y)
except TypeError, e:
if not 'sparse' in repr(e):
- print ("Estimator %s doesn't seem to fail gracefully on "
- "sparse data" % name)
+ print("Estimator %s doesn't seem to fail gracefully on "
+ "sparse data" % name)
traceback.print_exc(file=sys.stdout)
raise e
except Exception, exc:
- print ("Estimator %s doesn't seem to fail gracefully on "
- "sparse data" % name)
+ print("Estimator %s doesn't seem to fail gracefully on "
+ "sparse data" % name)
traceback.print_exc(file=sys.stdout)
raise exc
@@ -623,8 +625,8 @@ def test_regressors_train():
assert_greater(reg.score(X, y_), 0.5)
except Exception as e:
print(reg)
- print e
- print
+ print(e)
+ print()
succeeded = False
assert_true(succeeded)
diff --git a/sklearn/utils/arpack.py b/sklearn/utils/arpack.py
index 17dbcaafc1e2396f12ad2940d18c0ec216c26d63..8aa753d081da68dfdd1f16cff58c5a332b230140 100644
--- a/sklearn/utils/arpack.py
+++ b/sklearn/utils/arpack.py
@@ -1408,7 +1408,7 @@ def _eigsh(A, k=6, M=None, sigma=None, which='LM', v0=None, ncv=None,
>>> vals, vecs = eigsh(id, k=6)
>>> vals # doctest: +SKIP
array([ 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])
- >>> print vecs.shape
+ >>> print(vecs.shape)
(13, 6)
References