Python 如何绘制scikit学习分类报告？
是否可以使用matplotlib scikit学习分类报告进行绘图？。假设我这样打印分类报告：Python 如何绘制scikit学习分类报告？,python,numpy,matplotlib,scikit-learn,Python,Numpy,Matplotlib,Scikit Learn,是否可以使用matplotlib scikit学习分类报告进行绘图？。假设我这样打印分类报告： print '\n*Classification Report:\n', classification_report(y_test, predictions) confusion_matrix_graph = confusion_matrix(y_test, predictions) plot_classification_report(classificationReport, with_
print '\n*Classification Report:\n', classification_report(y_test, predictions)
confusion_matrix_graph = confusion_matrix(y_test, predictions)
plot_classification_report(classificationReport, with_avg_total=True)
Clasification Report:
precision recall f1-score support
1 0.62 1.00 0.76 66
2 0.93 0.93 0.93 40
3 0.59 0.97 0.73 67
4 0.47 0.92 0.62 272
5 1.00 0.16 0.28 413
avg / total 0.77 0.57 0.49 858
import matplotlib.pyplot as plt
cm = [[0.50, 1.00, 0.67],
[0.00, 0.00, 0.00],
[1.00, 0.67, 0.80]]
labels = ['class 0', 'class 1', 'class 2']
fig, ax = plt.subplots()
h = ax.matshow(cm)
fig.colorbar(h)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
ax.set_xlabel('Predicted')
ax.set_ylabel('Ground truth')
为此，我刚刚编写了一个函数
plot\u classification\u report（）def plot_classification_report(cr, title='Classification report ', with_avg_total=False, cmap=plt.cm.Blues):
lines = cr.split('\n')
classes = []
plotMat = []
for line in lines[2 : (len(lines) - 3)]:
#print(line)
t = line.split()
# print(t)
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
print(v)
plotMat.append(v)
if with_avg_total:
aveTotal = lines[len(lines) - 1].split()
classes.append('avg/total')
vAveTotal = [float(x) for x in t[1:len(aveTotal) - 1]]
plotMat.append(vAveTotal)
plt.imshow(plotMat, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
x_tick_marks = np.arange(3)
y_tick_marks = np.arange(len(classes))
plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45)
plt.yticks(y_tick_marks, classes)
plt.tight_layout()
plt.ylabel('Classes')
plt.xlabel('Measures')
sampleClassificationReport = """ precision recall f1-score support
1 0.62 1.00 0.76 66
2 0.93 0.93 0.93 40
3 0.59 0.97 0.73 67
4 0.47 0.92 0.62 272
5 1.00 0.16 0.28 413
avg / total 0.77 0.57 0.49 858"""
plot_classification_report(sampleClassificationReport)
from sklearn.metrics import classification_report
classificationReport = classification_report(y_true, y_pred, target_names=target_names)
plot_classification_report(classificationReport)
和_avg_totalprint '\n*Classification Report:\n', classification_report(y_test, predictions)
confusion_matrix_graph = confusion_matrix(y_test, predictions)
plot_classification_report(classificationReport, with_avg_total=True)
import matplotlib.pyplot as plt
import numpy as np
def show_values(pc, fmt="%.2f", **kw):
'''
Heatmap with text in each cell with matplotlib's pyplot
Source: https://stackoverflow.com/a/25074150/395857
By HYRY
'''
from itertools import izip
pc.update_scalarmappable()
ax = pc.get_axes()
#ax = pc.axes# FOR LATEST MATPLOTLIB
#Use zip BELOW IN PYTHON 3
for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
def cm2inch(*tupl):
'''
Specify figure size in centimeter in matplotlib
Source: https://stackoverflow.com/a/22787457/395857
By gns-ank
'''
inch = 2.54
if type(tupl[0]) == tuple:
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):
'''
Inspired by:
- https://stackoverflow.com/a/16124677/395857
- https://stackoverflow.com/a/25074150/395857
'''
# Plot it out
fig, ax = plt.subplots()
#c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)
c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)
# set tick labels
#ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)
ax.set_xticklabels(xticklabels, minor=False)
ax.set_yticklabels(yticklabels, minor=False)
# set title and x/y labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Remove last blank column
plt.xlim( (0, AUC.shape[1]) )
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
# Add color bar
plt.colorbar(c)
# Add text in each cell
show_values(c)
# Proper orientation (origin at the top left instead of bottom left)
if correct_orientation:
ax.invert_yaxis()
ax.xaxis.tick_top()
# resize
fig = plt.gcf()
#fig.set_size_inches(cm2inch(40, 20))
#fig.set_size_inches(cm2inch(40*4, 20*4))
fig.set_size_inches(cm2inch(figure_width, figure_height))
def plot_classification_report(classification_report, title='Classification report ', cmap='RdBu'):
'''
Plot scikit-learn classification report.
Extension based on https://stackoverflow.com/a/31689645/395857
'''
lines = classification_report.split('\n')
classes = []
plotMat = []
support = []
class_names = []
for line in lines[2 : (len(lines) - 2)]:
t = line.strip().split()
if len(t) < 2: continue
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
support.append(int(t[-1]))
class_names.append(t[0])
print(v)
plotMat.append(v)
print('plotMat: {0}'.format(plotMat))
print('support: {0}'.format(support))
xlabel = 'Metrics'
ylabel = 'Classes'
xticklabels = ['Precision', 'Recall', 'F1-score']
yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup in enumerate(support)]
figure_width = 25
figure_height = len(class_names) + 7
correct_orientation = False
heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)
def main():
sampleClassificationReport = """ precision recall f1-score support
Acacia 0.62 1.00 0.76 66
Blossom 0.93 0.93 0.93 40
Camellia 0.59 0.97 0.73 67
Daisy 0.47 0.92 0.62 272
Echium 1.00 0.16 0.28 413
avg / total 0.77 0.57 0.49 858"""
plot_classification_report(sampleClassificationReport)
plt.savefig('test_plot_classif_report.png', dpi=200, format='png', bbox_inches='tight')
plt.close()
if __name__ == "__main__":
main()
#cProfile.run('main()') # if you want to do some profiling
导入matplotlib.pyplot作为plt
将numpy作为np导入
def显示_值（pc，fmt=“%.2f”，**kw）：
'''
使用matplotlib的pyplot在每个单元格中显示文本的热图
资料来源：https://stackoverflow.com/a/25074150/395857
海瑞
'''
从itertools导入izip
pc.更新_scalarmappable（）
ax=pc.获取轴（）
#ax=pc.axes#用于最新的MATPLOTLIB
#在Python3中使用下面的zip
对于p，color，izip中的值（pc.get_path（），pc.get_facecolors（），pc.get_array（））：
x、 y=p.顶点[：-2，：]平均值（0）
如果np.all（颜色[：3]>0.5）：
颜色=（0.0,0.0,0.0）
其他：
颜色=（1.0,1.0,1.0）
最大文本（x，y，fmt%值，ha=“center”，va=“center”，color=color，**千瓦）
def cm2inch（*tupl）：
'''
在matplotlib中指定地物尺寸（以厘米为单位）
资料来源：https://stackoverflow.com/a/22787457/395857
由gns ank提供
'''
英寸=2.54
如果类型（tupl[0]）==tuple:
返回元组（元组[0]中i的i/英寸）
其他：
返回元组（元组中i的i/英寸）
def热图（AUC、标题、xlabel、ylabel、xticklabel、yticklabels、图宽=40、图高=20、正确方向=False、cmap='RdBu'）：
'''
灵感来自：
- https://stackoverflow.com/a/16124677/395857
- https://stackoverflow.com/a/25074150/395857
'''
#把它画出来
图，ax=plt.子批次（）
#c=ax.pcolor（AUC，edgecolors='k'，linestyle='虚线'，线宽=0.2，cmap='RdBu'，vmin=0.0，vmax=1.0）
c=ax.pcolor（AUC，edgecolors='k'，linestyle='虚线'，线宽=0.2，cmap=cmap）
#将主刻度放在每个单元格的中间
最大设定值（np.arange（AUC.shape[0]）+0.5，小调=假）
ax.set_xticks（np.arange（AUC.shape[1]）+0.5，minor=False）
#设置勾号标签
#ax.set_xticklabels（np.arange（1，AUC.shape[1]+1），minor=False）
ax.setxticklabels（xticklabels，minor=False）
ax.set_yticklabels（yticklabels，minor=False）
#设置标题和x/y标签
标题（标题）
plt.xlabel（xlabel）
plt.ylabel（ylabel）
#删除最后一个空白列
plt.xlim（（0，AUC.shape[1]））
#关掉所有的滴答声
ax=plt.gca（）
对于ax.xaxis.get_major_ticks（）中的t：
t、 tick1On=False
t、 tick2On=False
对于ax.yaxis.get_major_ticks（）中的t：
t、 tick1On=False
t、 tick2On=False
#添加颜色栏
打印颜色条（c）
#在每个单元格中添加文本
显示_值（c）
#方向正确（原点位于左上角，而不是左下角）
如果方向正确：
ax.invert_yaxis（）
ax.xaxis.tick_top（）
#调整大小
图=plt.gcf（）
#图设置尺寸英寸（厘米2英寸（40,20））
#图设置尺寸英寸（厘米2英寸（40*4，20*4））
图设置尺寸英寸（cm2英寸（图宽、图高））
def绘图分类报告（分类报告，标题class='classification report'，cmap='RdBu'）：
'''
绘制scikit学习分类报告。
基于https://stackoverflow.com/a/31689645/395857
'''
行=分类报告。拆分（'\n'）
类别=[]
plotMat=[]
支持=[]
类名称=[]
对于行中的行[2:（len（line）-2）]：
t=line.strip（）.split（）
如果len（t）<2：继续
class.append（t[0]）
v=[t中x的浮点（x）[1:len（t）-1]]
support.append（int（t[-1]））
类名称。追加（t[0]）
印刷品（五）
plotMat.append（v）
打印（'plotMat:{0}'。格式（plotMat））
打印（'支持：{0}'。格式（支持））
xlabel='Metrics'
ylabel='Classes'
xticklabels=[“精度”、“回忆”、“F1分数”]
yticklabels=['{0}（{1}'）。idx的格式（类名称[idx]，sup），枚举中的sup（支持）]
图_宽度=25
图\u高度=长度（类别名称）+7
方向正确=错误
热图（np.数组（plotMat）、标题、xlabel、ylabel、xticklabel、yticklabels、地物宽度、地物高度、正确方位、cmap=cmap）
def main（）：
sampleClassificationReport=“”精确召回f1分数支持
相思树0.62 1.00 0.76 66
开花0.930.930.9340
山茶花0.59 0.97 0.73 67
黛西0.47 0.92 0.62 272
Echium 1.00 0.16 0.28 413
平均/总计0.77 0.57 0.49 858“
绘图分类报告（样本分类报告）
plt.savefig（'test\u plot\u classif\u report.png'，dpi=200，format='png'，bbox\u inches='tight'）
plt.close（）
如果名称=“\uuuuu main\uuuuuuuu”：
main（）
#如果要进行一些分析，请运行（'main（）'）#
这是我的简单解决方案，使用seaborn热图
import seaborn as sns
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
import matplotlib.pyplot as plt
y = np.random.randint(low=0, high=10, size=100)
y_p = np.random.randint(low=0, high=10, size=100)
def plot_classification_report(y_tru, y_prd, figsize=(10, 10), ax=None):
plt.figure(figsize=figsize)
xticks = ['precision', 'recall', 'f1-score', 'support']
yticks = list(np.unique(y_tru))
yticks += ['avg']
rep = np.array(precision_recall_fscore_support(y_tru, y_prd)).T
avg = np.mean(rep, axis=0)
avg[-1] = np.sum(rep[:, -1])
rep = np.insert(rep, rep.shape[0], avg, axis=0)
sns.heatmap(rep,
annot=True,
cbar=False,
xticklabels=xticks,
yticklabels=yticks,
ax=ax)
plot_classification_report(y, y_p)
在这里，您可以得到与的相同的绘图，但代码要短得多（可以放入单个函数）
导入
import numpy as np
import seaborn as sns
from sklearn.metrics import classification_report
import pandas as pd
true = np.random.randint(0, 10, size=100)
pred = np.random.randint(0, 10, size=100)
labels = np.arange(10)
target_names = list("ABCDEFGHI")
clf_report = classification_report(true,
pred,
labels=labels,
target_names=target_names,
output_dict=True)
# .iloc[:-1, :] to exclude support
sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True)
import matplotlib.pyplot as plt
import numpy as np
def show_values(pc, fmt="%.2f", **kw):
'''
Heatmap with text in each cell with matplotlib's pyplot
Source: https://stackoverflow.com/a/25074150/395857
By HYRY
'''
pc.update_scalarmappable()
ax = pc.axes
#ax = pc.axes# FOR LATEST MATPLOTLIB
#Use zip BELOW IN PYTHON 3
for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
def cm2inch(*tupl):
'''
Specify figure size in centimeter in matplotlib
Source: https://stackoverflow.com/a/22787457/395857
By gns-ank
'''
inch = 2.54
if type(tupl[0]) == tuple:
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):
'''
Inspired by:
- https://stackoverflow.com/a/16124677/395857
- https://stackoverflow.com/a/25074150/395857
'''
# Plot it out
fig, ax = plt.subplots()
#c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)
c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap, vmin=0.0, vmax=1.0)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)
# set tick labels
#ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)
ax.set_xticklabels(xticklabels, minor=False)
ax.set_yticklabels(yticklabels, minor=False)
# set title and x/y labels
plt.title(title, y=1.25)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Remove last blank column
plt.xlim( (0, AUC.shape[1]) )
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1line.set_visible(False)
t.tick2line.set_visible(False)
for t in ax.yaxis.get_major_ticks():
t.tick1line.set_visible(False)
t.tick2line.set_visible(False)
# Add color bar
plt.colorbar(c)
# Add text in each cell
show_values(c)
# Proper orientation (origin at the top left instead of bottom left)
if correct_orientation:
ax.invert_yaxis()
ax.xaxis.tick_top()
# resize
fig = plt.gcf()
#fig.set_size_inches(cm2inch(40, 20))
#fig.set_size_inches(cm2inch(40*4, 20*4))
fig.set_size_inches(cm2inch(figure_width, figure_height))
def plot_classification_report(classification_report, number_of_classes=2, title='Classification report ', cmap='RdYlGn'):
'''
Plot scikit-learn classification report.
Extension based on https://stackoverflow.com/a/31689645/395857
'''
lines = classification_report.split('\n')
#drop initial lines
lines = lines[2:]
classes = []
plotMat = []
support = []
class_names = []
for line in lines[: number_of_classes]:
t = list(filter(None, line.strip().split(' ')))
if len(t) < 4: continue
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
support.append(int(t[-1]))
class_names.append(t[0])
plotMat.append(v)
xlabel = 'Metrics'
ylabel = 'Classes'
xticklabels = ['Precision', 'Recall', 'F1-score']
yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup in enumerate(support)]
figure_width = 10
figure_height = len(class_names) + 3
correct_orientation = True
heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)
plt.show()
def plot_classification_report(cr, title='Classification report ', with_avg_total=False, cmap=plt.cm.Blues):
lines = cr.split('\n')
classes = []
plotMat = []
for line in lines[2 : (len(lines) - 6)]: rt
t = line.split()
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
plotMat.append(v)
if with_avg_total:
aveTotal = lines[len(lines) - 1].split()
classes.append('avg/total')
vAveTotal = [float(x) for x in t[1:len(aveTotal) - 1]]
plotMat.append(vAveTotal)
plt.figure(figsize=(12,48))
#plt.imshow(plotMat, interpolation='nearest', cmap=cmap) THIS also works but the scale is not good neither the colors for many classes(200)
#plt.colorbar()
plt.title(title)
x_tick_marks = np.arange(3)
y_tick_marks = np.arange(len(classes))
plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45)
plt.yticks(y_tick_marks, classes)
plt.tight_layout()
plt.ylabel('Classes')
plt.xlabel('Measures')
import seaborn as sns
sns.heatmap(plotMat, annot=True)
reportstr = classification_report(true_classes, y_pred,target_names=class_labels_no_spaces)
plot_classification_report(reportstr)
import matplotlib.pyplot as plt
import numpy as np
def show_values(pc, fmt="%.2f", **kw):
'''
Heatmap with text in each cell with matplotlib's pyplot
Source: https://stackoverflow.com/a/25074150/395857
By HYRY
'''
pc.update_scalarmappable()
ax = pc.axes
#ax = pc.axes# FOR LATEST MATPLOTLIB
#Use zip BELOW IN PYTHON 3
for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
def cm2inch(*tupl):
'''
Specify figure size in centimeter in matplotlib
Source: https://stackoverflow.com/a/22787457/395857
By gns-ank
'''
inch = 2.54
if type(tupl[0]) == tuple:
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):
'''
Inspired by:
- https://stackoverflow.com/a/16124677/395857
- https://stackoverflow.com/a/25074150/395857
'''
# Plot it out
fig, ax = plt.subplots()
#c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)
c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)
# set tick labels
#ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)
ax.set_xticklabels(xticklabels, minor=False)
ax.set_yticklabels(yticklabels, minor=False)
# set title and x/y labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Remove last blank column
plt.xlim( (0, AUC.shape[1]) )
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
# Add color bar
plt.colorbar(c)
# Add text in each cell
show_values(c)
# Proper orientation (origin at the top left instead of bottom left)
if correct_orientation:
ax.invert_yaxis()
ax.xaxis.tick_top()
# resize
fig = plt.gcf()
#fig.set_size_inches(cm2inch(40, 20))
#fig.set_size_inches(cm2inch(40*4, 20*4))
fig.set_size_inches(cm2inch(figure_width, figure_height))
def plot_classification_report(classification_report, title='Classification report ', cmap='RdBu'):
'''
Plot scikit-learn classification report.
Extension based on https://stackoverflow.com/a/31689645/395857
'''
lines = classification_report.split('\n')
classes = []
plotMat = []
support = []
class_names = []
for line in lines[2 : (len(lines) - 4)]:
t = line.strip().split()
if len(t) < 2: continue
classes.append(t[0])
v = [float(x) for x in t[1: len(t) - 1]]
support.append(int(t[-1]))
class_names.append(t[0])
print(v)
plotMat.append(v)
print('plotMat: {0}'.format(plotMat))
print('support: {0}'.format(support))
xlabel = 'Metrics'
ylabel = 'Classes'
xticklabels = ['Precision', 'Recall', 'F1-score']
yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup in enumerate(support)]
figure_width = 25
figure_height = len(class_names) + 7
correct_orientation = False
heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)
def main():
# OLD
# sampleClassificationReport = """ precision recall f1-score support
#
# Acacia 0.62 1.00 0.76 66
# Blossom 0.93 0.93 0.93 40
# Camellia 0.59 0.97 0.73 67
# Daisy 0.47 0.92 0.62 272
# Echium 1.00 0.16 0.28 413
#
# avg / total 0.77 0.57 0.49 858"""
# NEW
sampleClassificationReport = """ precision recall f1-score support
1 1.00 0.33 0.50 9
2 0.50 1.00 0.67 9
3 0.86 0.67 0.75 9
4 0.90 1.00 0.95 9
5 0.67 0.89 0.76 9
6 1.00 1.00 1.00 9
7 1.00 1.00 1.00 9
8 0.90 1.00 0.95 9
9 0.86 0.67 0.75 9
10 1.00 0.78 0.88 9
11 1.00 0.89 0.94 9
12 0.90 1.00 0.95 9
13 1.00 0.56 0.71 9
14 1.00 1.00 1.00 9
15 0.60 0.67 0.63 9
16 1.00 0.56 0.71 9
17 0.75 0.67 0.71 9
18 0.80 0.89 0.84 9
19 1.00 1.00 1.00 9
20 1.00 0.78 0.88 9
21 1.00 1.00 1.00 9
22 1.00 1.00 1.00 9
23 0.27 0.44 0.33 9
24 0.60 1.00 0.75 9
25 0.56 1.00 0.72 9
26 0.18 0.22 0.20 9
27 0.82 1.00 0.90 9
28 0.00 0.00 0.00 9
29 0.82 1.00 0.90 9
30 0.62 0.89 0.73 9
31 1.00 0.44 0.62 9
32 1.00 0.78 0.88 9
33 0.86 0.67 0.75 9
34 0.64 1.00 0.78 9
35 1.00 0.33 0.50 9
36 1.00 0.89 0.94 9
37 0.50 0.44 0.47 9
38 0.69 1.00 0.82 9
39 1.00 0.78 0.88 9
40 0.67 0.44 0.53 9
accuracy 0.77 360
macro avg 0.80 0.77 0.76 360
weighted avg 0.80 0.77 0.76 360
"""
plot_classification_report(sampleClassificationReport)
plt.savefig('test_plot_classif_report.png', dpi=200, format='png', bbox_inches='tight')
plt.close()
if __name__ == "__main__":
main()
#cProfile.run('main()') # if you want to do some profiling