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pylab_examples_Examples 27_boxplot_demo2.	H.Kamifuji .

boxplot_demo2_01.py

　ジョシュ・ヘマンさん、例を頂きありがとうございます。

　異なる特性を持つ5つの異なる確率分布からいくつかのデータを生成する。私たちは、データの IID ブートストラップリサンプルが元のサンプルの分布特性をどのように保持しているかを試したいと思っています。ボックスプロットはこの評価を行うための視覚的ツールの 1 つです

"""
Thanks Josh Hemann for the example
"""

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon


# Generate some data from five different probability distributions,
# each with different characteristics. We want to play with how an IID
# bootstrap resample of the data preserves the distributional
# properties of the original sample, and a boxplot is one visual tool
# to make this assessment
numDists = 5
randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
               'Triangular(2,9,11)']
N = 500
np.random.seed(0)
norm = np.random.normal(1, 1, N)
logn = np.random.lognormal(1, 1, N)
expo = np.random.exponential(1, N)
gumb = np.random.gumbel(6, 4, N)
tria = np.random.triangular(2, 9, 11, N)

# Generate some random indices that we'll use to resample the original data
# arrays. For code brevity, just use the same random indices for each array
# bootstrapIndices = np.random.random_integers(0, N - 1, N)
bootstrapIndices = np.random.randint(0, N - 1, N)
normBoot = norm[bootstrapIndices]
expoBoot = expo[bootstrapIndices]
gumbBoot = gumb[bootstrapIndices]
lognBoot = logn[bootstrapIndices]
triaBoot = tria[bootstrapIndices]

data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
        tria, triaBoot]

fig, ax1 = plt.subplots(figsize=(10, 6))
fig.canvas.set_window_title('A Boxplot Example')
plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)

bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='red', marker='+')

# Add a horizontal grid to the plot, but make it very light in color
# so we can use it for reading data values but not be distracting
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
               alpha=0.5)

# Hide these grid behind plot objects
ax1.set_axisbelow(True)
ax1.set_title('Comparison of IID Bootstrap Resampling Across Five Distributions')
ax1.set_xlabel('Distribution')
ax1.set_ylabel('Value')

# Now fill the boxes with desired colors
boxColors = ['darkkhaki', 'royalblue']
numBoxes = numDists*2
medians = list(range(numBoxes))
for i in range(numBoxes):
    box = bp['boxes'][i]
    boxX = []
    boxY = []
    for j in range(5):
        boxX.append(box.get_xdata()[j])
        boxY.append(box.get_ydata()[j])
    boxCoords = list(zip(boxX, boxY))
    # Alternate between Dark Khaki and Royal Blue
    k = i % 2
    boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
    ax1.add_patch(boxPolygon)
    # Now draw the median lines back over what we just filled in
    med = bp['medians'][i]
    medianX = []
    medianY = []
    for j in range(2):
        medianX.append(med.get_xdata()[j])
        medianY.append(med.get_ydata()[j])
        plt.plot(medianX, medianY, 'k')
        medians[i] = medianY[0]
    # Finally, overplot the sample averages, with horizontal alignment
    # in the center of each box
    plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
             color='w', marker='*', markeredgecolor='k')

# Set the axes ranges and axes labels
ax1.set_xlim(0.5, numBoxes + 0.5)
top = 40
bottom = -5
ax1.set_ylim(bottom, top)
xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2))
plt.setp(xtickNames, rotation=45, fontsize=8)

# Due to the Y-axis scale being different across samples, it can be
# hard to compare differences in medians across the samples. Add upper
# X-axis tick labels with the sample medians to aid in comparison
# (just use two decimal places of precision)
pos = np.arange(numBoxes) + 1
upperLabels = [str(np.round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
    k = tick % 2
    ax1.text(pos[tick], top - (top*0.05), upperLabels[tick],
             horizontalalignment='center', size='x-small', weight=weights[k],
             color=boxColors[k])

# Finally, add a basic legend
plt.figtext(0.80, 0.08, str(N) + ' Random Numbers',
            backgroundcolor=boxColors[0], color='black', weight='roman',
            size='x-small')
plt.figtext(0.80, 0.045, 'IID Bootstrap Resample',
            backgroundcolor=boxColors[1],
            color='white', weight='roman', size='x-small')
plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver',
            weight='roman', size='medium')
plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman',
            size='x-small')

plt.show()

実行結果( boxplot_demo2_01.png )

　

　Python 3.11.2 見直しました。上記のコードでは、下記のエラーが発生します。

_:\boxplot_demo2.py:28: DeprecationWarning: This function is deprecated. Please call randint(0, 499 + 1) instead
　　　bootstrapIndices = np.random.random_integers(0, N - 1, N)
Traceback (most recent call last):
　File "_:\boxplot_demo2.py", line 39, in <module>
　　　fig.canvas.set_window_title('A Boxplot Example')
　　　^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'FigureCanvasTkAgg' object has no attribute 'set_window_title'

　matplotlib 内部のエラーのようです。matplotlib の改修（先祖帰りバグの改修）を待つしかない。

　Python 3.11.6 (matplotlib 3.7.1) では、下記のようなエラーがあり、実行できない。

Traceback (most recent call last):
  File "M:\______\boxplot_demo2_01.py", line 40, in 
    fig.canvas.set_window_title('A Boxplot Example')
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'FigureCanvasTkAgg' object has no attribute 'set_window_title'

　Python 3.12.0 (matplotlib 3.8.1) では、下記のようなエラーがあり、実行できない。

Traceback (most recent call last):
  File "E:\______\boxplot_demo2_01.py", line 40, in 
    fig.canvas.set_window_title('A Boxplot Example')
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'FigureCanvasTkAgg' object has no attribute 'set_window_title'

　代替サンプルが見つからないので、自力で改修しました。
　fig.canvas.set_window_title('A Boxplot Example') が使えないようなので、fig, ax1 = plt.subplots(figsize=(10, 6), num="A Boxplot Example") のように、num キーワードで指定しました。

39,40c39,40
< fig, ax1 = plt.subplots(figsize=(10, 6))
< fig.canvas.set_window_title('A Boxplot Example')
---
> fig, ax1 = plt.subplots(figsize=(10, 6), num="A Boxplot Example")
> # fig.canvas.set_window_title('A Boxplot Example')

"""
Thanks Josh Hemann for the example
"""

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon


# Generate some data from five different probability distributions,
# each with different characteristics. We want to play with how an IID
# bootstrap resample of the data preserves the distributional
# properties of the original sample, and a boxplot is one visual tool
# to make this assessment
numDists = 5
randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
               'Triangular(2,9,11)']
N = 500
np.random.seed(0)
norm = np.random.normal(1, 1, N)
logn = np.random.lognormal(1, 1, N)
expo = np.random.exponential(1, N)
gumb = np.random.gumbel(6, 4, N)
tria = np.random.triangular(2, 9, 11, N)

# Generate some random indices that we'll use to resample the original data
# arrays. For code brevity, just use the same random indices for each array
# bootstrapIndices = np.random.random_integers(0, N - 1, N)
bootstrapIndices = np.random.randint(0, N - 1, N)
normBoot = norm[bootstrapIndices]
expoBoot = expo[bootstrapIndices]
gumbBoot = gumb[bootstrapIndices]
lognBoot = logn[bootstrapIndices]
triaBoot = tria[bootstrapIndices]

data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
        tria, triaBoot]

fig, ax1 = plt.subplots(figsize=(10, 6), num="A Boxplot Example")
# fig.canvas.set_window_title('A Boxplot Example')
plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)

bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='red', marker='+')

# Add a horizontal grid to the plot, but make it very light in color
# so we can use it for reading data values but not be distracting
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
               alpha=0.5)

# Hide these grid behind plot objects
ax1.set_axisbelow(True)
ax1.set_title('Comparison of IID Bootstrap Resampling Across Five Distributions')
ax1.set_xlabel('Distribution')
ax1.set_ylabel('Value')

# Now fill the boxes with desired colors
boxColors = ['darkkhaki', 'royalblue']
numBoxes = numDists*2
medians = list(range(numBoxes))
for i in range(numBoxes):
    box = bp['boxes'][i]
    boxX = []
    boxY = []
    for j in range(5):
        boxX.append(box.get_xdata()[j])
        boxY.append(box.get_ydata()[j])
    boxCoords = list(zip(boxX, boxY))
    # Alternate between Dark Khaki and Royal Blue
    k = i % 2
    boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
    ax1.add_patch(boxPolygon)
    # Now draw the median lines back over what we just filled in
    med = bp['medians'][i]
    medianX = []
    medianY = []
    for j in range(2):
        medianX.append(med.get_xdata()[j])
        medianY.append(med.get_ydata()[j])
        plt.plot(medianX, medianY, 'k')
        medians[i] = medianY[0]
    # Finally, overplot the sample averages, with horizontal alignment
    # in the center of each box
    plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
             color='w', marker='*', markeredgecolor='k')

# Set the axes ranges and axes labels
ax1.set_xlim(0.5, numBoxes + 0.5)
top = 40
bottom = -5
ax1.set_ylim(bottom, top)
xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2))
plt.setp(xtickNames, rotation=45, fontsize=8)

# Due to the Y-axis scale being different across samples, it can be
# hard to compare differences in medians across the samples. Add upper
# X-axis tick labels with the sample medians to aid in comparison
# (just use two decimal places of precision)
pos = np.arange(numBoxes) + 1
upperLabels = [str(np.round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
    k = tick % 2
    ax1.text(pos[tick], top - (top*0.05), upperLabels[tick],
             horizontalalignment='center', size='x-small', weight=weights[k],
             color=boxColors[k])

# Finally, add a basic legend
plt.figtext(0.80, 0.08, str(N) + ' Random Numbers',
            backgroundcolor=boxColors[0], color='black', weight='roman',
            size='x-small')
plt.figtext(0.80, 0.045, 'IID Bootstrap Resample',
            backgroundcolor=boxColors[1],
            color='white', weight='roman', size='x-small')
plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver',
            weight='roman', size='medium')
plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman',
            size='x-small')

plt.show()

　Python 3.11.6 (matplotlib 3.7.1) 及び Python 3.12.0 (matplotlib 3.8.1) 共に、正常実行です。

　

参照ページ

pylab_examples_Examples code: boxplot_demo2.py

リリースノート

2023/11/22 Ver=1.04 Python 3.12.0 (matplotlib 3.8.1)で確認
2023/11/22 Ver=1.04 Python 3.11.6 (matplotlib 3.7.1)で確認
2023/04/01 Ver=1.03 Python 3.11.2 で確認
2020/10/31 Ver=1.01 Python 3.7.8 で確認
2018/11/28 Ver=1.01 初版リリース

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