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Generate matrix plot for confusion matrix with pretty annotations.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix
def cm_analysis(y_true, y_pred, filename, labels, ymap=None, figsize=(10,10)):
"""
Generate matrix plot of confusion matrix with pretty annotations.
The plot image is saved to disk.
args:
y_true: true label of the data, with shape (nsamples,)
y_pred: prediction of the data, with shape (nsamples,)
filename: filename of figure file to save
labels: string array, name the order of class labels in the confusion matrix.
use `clf.classes_` if using scikit-learn models.
with shape (nclass,).
ymap: dict: any -> string, length == nclass.
if not None, map the labels & ys to more understandable strings.
Caution: original y_true, y_pred and labels must align.
figsize: the size of the figure plotted.
"""
if ymap is not None:
y_pred = [ymap[yi] for yi in y_pred]
y_true = [ymap[yi] for yi in y_true]
labels = [ymap[yi] for yi in labels]
cm = confusion_matrix(y_true, y_pred, labels=labels)
cm_sum = np.sum(cm, axis=1, keepdims=True)
cm_perc = cm / cm_sum.astype(float) * 100
annot = np.empty_like(cm).astype(str)
nrows, ncols = cm.shape
for i in range(nrows):
for j in range(ncols):
c = cm[i, j]
p = cm_perc[i, j]
if i == j:
s = cm_sum[i]
annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s)
elif c == 0:
annot[i, j] = ''
else:
annot[i, j] = '%.1f%%\n%d' % (p, c)
cm = pd.DataFrame(cm, index=labels, columns=labels)
cm.index.name = 'Actual'
cm.columns.name = 'Predicted'
fig, ax = plt.subplots(figsize=figsize)
sns.heatmap(cm, annot=annot, fmt='', ax=ax)
plt.savefig(filename)
@krishnakanthnakka
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krishnakanthnakka commented Feb 5, 2020

PFA the data at https://drive.google.com/open?id=12Gx_O0Sjn0xocGQ-2jvW7Z03-61my29i

Thanks for prompt response.

@koursera
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koursera commented Oct 5, 2020

Hi Hitvoice - this is awesome! Thanks so much posting this. I'm trying it out and see an odd thing with my binary classification CM. I've posted your version and what I get from using confusion_matrix. You can see that the classes are transposed and I'm wondering if I'm doing something wrong. I'd like to keep this aligned to what confusion_matrix displays so I thought I'd ask.

Screen Shot 2020-10-05 at 5 58 04 PM

Screen Shot 2020-10-05 at 5 57 00 PM

@hitvoice
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hitvoice commented Oct 28, 2020

Hi Hitvoice - this is awesome! Thanks so much posting this. I'm trying it out and see an odd thing with my binary classification CM. I've posted your version and what I get from using confusion_matrix. You can see that the classes are transposed and I'm wondering if I'm doing something wrong. I'd like to keep this aligned to what confusion_matrix displays so I thought I'd ask.

Screen Shot 2020-10-05 at 5 58 04 PM Screen Shot 2020-10-05 at 5 57 00 PM

What do you mean by "the classes are transposed"? Do you mean the order of 0 and 1?

@koursera
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koursera commented Nov 3, 2020

Do you mean the order of 0 and 1?

Yes, I was looking to align this with sklearn's confusion matrix for consistency. Perhaps a parameter could be passed to indicate the order since various references have a different convention for the axes.

@hitvoice
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hitvoice commented Nov 4, 2020

You can see from the code that the matrix is indeed computed from sklearn's "confusion_matrix" function. How did you get the first figure? If you prefer that kind of style, you can reorder the dataframe columns by cm = cm[cm.columns[::-1]] before creating the plot.

@kalkite
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kalkite commented Oct 11, 2021

@hitvoice, it's really appreciated work. how to changet the clf.classes_ to other, for instance my classess are from 0 to 8, but i want to change those labels to string.
confusion_matrix

@hitvoice
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hitvoice commented Oct 17, 2021

@rajeshkalakoti pass the value of ymap (dict[Any,str]) to cm_analysis. You can configure the class names in ymap.

@kalkite
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kalkite commented Oct 24, 2021

for instance, labels are like "class-1, class-2,class-3, class-4,class-5, class-6,class-7, class-8,classs-9", how to configure it?

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