8000 Merge remote-tracking branch 'upstream/master' into labelencoder-sets · scikit-learn/scikit-learn@ea189ce · GitHub
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jorisvandenbosschejorisvandenbossche
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Merge remote-tracking branch 'upstream/master' into labelencoder-sets
2 parents 662da67 + a320c08 commit ea189ce

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‎.gitignore

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@@ -54,6 +54,7 @@ benchmarks/bench_covertype_data/
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*.prefs
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.pydevproject
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.idea
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.vscode
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*.c
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*.cpp

‎.travis.yml

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@@ -56,8 +56,8 @@ matrix:
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# installed from their CI wheels in a virtualenv with the Python
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# interpreter provided by travis.
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- python: 3.6
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env: DISTRIB="scipy-dev-wheels"
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if: type = cron
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env: DISTRIB="scipy-dev"
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if: type = cron OR commit_message ~ /\[scipy-dev\]/
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install: source build_tools/travis/install.sh
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script: bash build_tools/travis/test_script.sh

‎benchmarks/bench_plot_incremental_pca.py

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@@ -13,7 +13,7 @@
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from collections import defaultdict
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import matplotlib.pyplot as plt
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from sklearn.datasets import fetch_lfw_people
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from sklearn.decomposition import IncrementalPCA, RandomizedPCA, PCA
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from sklearn.decomposition import IncrementalPCA, PCA
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def plot_results(X, y, label):
@@ -37,7 +37,6 @@ def plot_feature_times(all_times, batch_size, all_components, data):
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plot_results(all_components, all_times['pca'], label="PCA")
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plot_results(all_components, all_times['ipca'],
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label="IncrementalPCA, bsize=%i" % batch_size)
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plot_results(all_components, all_times['rpca'], label="RandomizedPCA")
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plt.legend(loc="upper left")
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plt.suptitle("Algorithm runtime vs. n_components\n \
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LFW, size %i x %i" % data.shape)
@@ -50,7 +49,6 @@ def plot_feature_errors(all_errors, batch_size, all_components, data):
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plot_results(all_components, all_errors['pca'], label="PCA")
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plot_results(all_components, all_errors['ipca'],
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label="IncrementalPCA, bsize=%i" % batch_size)
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plot_results(all_components, all_errors['rpca'], label="RandomizedPCA")
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plt.legend(loc="lower left")
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plt.suptitle("Algorithm error vs. n_components\n"
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"LFW, size %i x %i" % data.shape)
@@ -61,7 +59,6 @@ def plot_feature_errors(all_errors, batch_size, all_components, data):
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def plot_batch_times(all_times, n_features, all_batch_sizes, data):
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plt.figure()
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plot_results(all_batch_sizes, all_times['pca'], label="PCA")
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plot_results(all_batch_sizes, all_times['rpca'], label="RandomizedPCA")
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plot_results(all_batch_sizes, all_times['ipca'], label="IncrementalPCA")
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plt.legend(loc="lower left")
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plt.suptitle("Algorithm runtime vs. batch_size for n_components %i\n \
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all_errors = defaultdict(list)
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for n_components in all_features:
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pca = PCA(n_components=n_components)
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rpca = RandomizedPCA(n_components=n_components, random_state=1999)
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ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
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results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
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('ipca', ipca),
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('rpca', rpca)]}
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('ipca', ipca)]}
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for k in sorted(results_dict.keys()):
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all_times[k].append(results_dict[k]['time'])
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all_times = defaultdict(list)
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all_errors = defaultdict(list)
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pca = PCA(n_components=n_components)
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rpca = RandomizedPCA(n_components=n_components, random_state=1999)
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rpca = PCA(n_components=n_components, svd_solver='randomized',
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random_state=1999)
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results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
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('rpca', rpca)]}
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all_errors['ipca'].append(results_dict['ipca']['error'])
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plot_batch_times(all_times, n_components, batch_sizes, data)
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# RandomizedPCA error is always worse (approx 100x) than other PCA
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# tests
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plot_batch_errors(all_errors, n_components, batch_sizes, data)
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faces = fetch_lfw_people(resize=.2, min_faces_per_person=5)

‎build_tools/travis/install.sh

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@@ -78,7 +78,7 @@ elif [[ "$DISTRIB" == "ubuntu" ]]; then
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source testvenv/bin/activate
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pip install pytest pytest-cov cython==$CYTHON_VERSION
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elif [[ "$DISTRIB" == "scipy-dev-wheels" ]]; then
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elif [[ "$DISTRIB" == "scipy-dev" ]]; then
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# Set up our own virtualenv environment to avoid travis' numpy.
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# This venv points to the python interpreter of the travis build
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# matrix.

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