sklearn confusion_matrix displaying with wrong dimensions / tick marks at wrong spots

Question

I'm trying to display a confusion matrix and can't for the life of my figure out why it refuses to display in an appropriate manner. Here's my code:

import numpy as np
import itertools
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.winter):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize=30)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, fontsize=20)
    plt.yticks(tick_marks, classes, fontsize=20)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", 
                 color="white" if cm[i, j] < thresh else "black", fontsize=40)

    plt.tight_layout()
    plt.ylabel('True label', fontsize=30)
    plt.xlabel('Predicted label', fontsize=30)

    return plt

cm = confusion_matrix(y_test, y_predicted_counts)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Unsure','No','Yes'], normalize=False, title='Confusion matrix')
plt.show()
print(cm)

And this is what is displayed:

bad confusion matrix

Any help would be appreciated. Thanks in advance.

Does this answer your question? [matplotlib/seaborn: first and last row cut in half of heatmap plot](https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot) — user2653663, Dec 03 '19 at 12:52

score 1 · Accepted Answer · answered Nov 29 '19 at 21:20

For the call to imshow you need to specify origin='lower' (the default is 'upper'; they probably changed this at some time and the scikit-learn docs didn't update their example). So the following should do the trick:

plt.imshow(cm, interpolation='nearest', cmap=cmap, origin='lower')
#                                                    ^
#                                                    |
# added origin='lower'  ------------------------------

This fixed the problem. Thank you so much! – polkadotdoor Dec 07 '19 at 19:40 — polkadotdoor, Dec 07 '19 at 19:40

score 0 · Answer 2 · edited Jun 20 '20 at 09:12

Using Matplotlib

If you want to keep your matplotlib implementation, just add plt.ylim(-0.5,2.5) at the end of your plot_confusion_matrix function:

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.winter):
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize=30)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, fontsize=20)
    plt.yticks(tick_marks, classes, fontsize=20)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.

    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", 
                 color="white" if cm[i, j] < thresh else "black", fontsize=40)

    plt.tight_layout()
    plt.ylabel('True label', fontsize=30)
    plt.xlabel('Predicted label', fontsize=30)
    plt.ylim(-0.5, 2.5)  # <-- SOLUTION 

    return plt

Using Seaborn

you can try the seaborn package for plotting heatmaps:

from sklearn.metrics import confusion_matrix
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt   

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.winter):
  cm_df = pd.DataFrame(cm, columns=classes, index = classes)
  cm_df.index.name = 'Actual'
  cm_df.columns.name = 'Predicted'
  plt.figure(figsize = (10,7))
  sn.set(font_scale=1.4)#for label size
  ax =sn.heatmap(cm_df, cmap=cmap, annot=True,annot_kws={"size": 16},fmt="d")# font size
  plt.title(title)
  bottom, top = ax.get_ylim()
  ax.set_ylim(bottom + 0.5, top - 0.5)
  plt.show()

plot_confusion_matrix(cm, classes=['Unsure','No','Yes'], normalize=False, title='Confusion matrix')

Confusion Matrix Result

Hope this works for you!

score 0 · Answer 3 · answered Dec 03 '19 at 12:51

0

It's likely that you're using matplotlib 3.1.1 which broke the tick default behaviour. Upgrade to 3.1.2 or downgrade to 3.1.0 to fix the issue.

answered Dec 03 '19 at 12:51

user2653663

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sklearn confusion_matrix displaying with wrong dimensions / tick marks at wrong spots

3 Answers3

Using Matplotlib

Using Seaborn