8000 EXA change normed to density in matplotlib calls in examples (#12718) · scikit-learn/scikit-learn@f70b1ff · GitHub
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EXA change normed to density in matplotlib calls in examples (#12718)
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-7
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‎examples/compose/plot_transformed_target.py

Lines changed: 13 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -20,7 +20,9 @@
2020
from __future__ import print_function, division
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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from distutils.version import LooseVersion
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print(__doc__)
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@@ -34,6 +36,13 @@
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from sklearn.compose import TransformedTargetRegressor
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from sklearn.metrics import median_absolute_error, r2_score
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39+
40+
# `normed` is being deprecated in favor of `density` in histograms
41+
if LooseVersion(matplotlib.__version__) >= '2.1':
42+
density_param = {'density': True}
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else:
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density_param = {'normed': True}
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###############################################################################
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# A synthetic random regression problem is generated. The targets ``y`` are
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# modified by: (i) translating all targets such that all entries are
@@ -54,13 +63,13 @@
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f, (ax0, ax1) = plt.subplots(1, 2)
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57-
ax0.hist(y, bins=100, normed=True)
66+
ax0.hist(y, bins=100, **density_param)
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ax0.set_xlim([0, 2000])
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ax0.set_ylabel('Probability')
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ax0.set_xlabel('Target')
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ax0.set_title('Target distribution')
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63-
ax1.hist(y_trans, bins=100, normed=True)
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ax1.hist(y_trans, bins=100, **density_param)
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ax1.set_ylabel('Probability')
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ax1.set_xlabel('Target')
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ax1.set_title('Transformed target distribution')
@@ -139,12 +148,12 @@
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f, (ax0, ax1) = plt.subplots(1, 2)
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142-
ax0.hist(y, bins=100, normed=True)
151+
ax0.hist(y, bins=100, **density_param)
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ax0.set_ylabel('Probability')
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ax0.set_xlabel('Target')
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ax0.set_title('Target distribution')
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147-
ax1.hist(y_trans, bins=100, normed=True)
156+
ax1.hist(y_trans, bins=100, **density_param)
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ax1.set_ylabel('Probability')
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ax1.set_xlabel('Target')
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ax1.set_title('Transformed target distribution')

‎examples/neighbors/plot_kde_1d.py

Lines changed: 9 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -29,10 +29,17 @@
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# Author: Jake Vanderplas <jakevdp@cs.washington.edu>
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#
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
34+
from distutils.version import LooseVersion
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from scipy.stats import norm
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from sklearn.neighbors import KernelDensity
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38+
# `normed` is being deprecated in favor of `density` in histograms
39+
if LooseVersion(matplotlib.__version__) >= '2.1':
40+
density_param = {'density': True}
41+
else:
42+
density_param = {'normed': True}
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#----------------------------------------------------------------------
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# Plot the progression of histograms to kernels
@@ -47,11 +54,11 @@
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fig.subplots_adjust(hspace=0.05, wspace=0.05)
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# histogram 1
50-
ax[0, 0].hist(X[:, 0], bins=bins, fc='#AAAAFF', normed=True)
57+
ax[0, 0].hist(X[:, 0], bins=bins, fc='#AAAAFF', **density_param)
5158
ax[0, 0].text(-3.5, 0.31, "Histogram")
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# histogram 2
54-
ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc='#AAAAFF', normed=True)
61+
ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc='#AAAAFF', **density_param)
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ax[0, 1].text(-3.5, 0.31, "Histogram, bins shifted")
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# tophat KDE

‎examples/plot_johnson_lindenstrauss_bound.py

Lines changed: 9 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -94,13 +94,21 @@
9494
import sys
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from time import time
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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from distutils.version import LooseVersion
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from sklearn.random_projection import johnson_lindenstrauss_min_dim
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from sklearn.random_projection import SparseRandomProjection
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from sklearn.datasets import fetch_20newsgroups_vectorized
101103
from sklearn.datasets import load_digits
102104
from sklearn.metrics.pairwise import euclidean_distances
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106+
# `normed` is being deprecated in favor of `density` in histograms
107+
if LooseVersion(matplotlib.__version__) >= '2.1':
108+
density_param = {'density': True}
109+
else:
110+
density_param = {'normed': True}
111+
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# Part 1: plot the theoretical dependency between n_components_min and
105113
# n_samples
106114

@@ -187,7 +195,7 @@
187195
% (np.mean(rates), np.std(rates)))
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189197
plt.figure()
190-
plt.hist(rates, bins=50, normed=True, range=(0., 2.), edgecolor='k')
198+
plt.hist(rates, bins=50, range=(0., 2.), edgecolor='k', **density_param)
191199
plt.xlabel("Squared distances rate: projected / original")
192200
plt.ylabel("Distribution of samples pairs")
193201
plt.title("Histogram of pairwise distance rates for n_components=%d" %

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