Plotting multiple confusion matrix side by side

Question

I am new here. This is my first question that I hope to get an answer from experts. I have 5 classifier models that I am trying to plot their confusion matrix.

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import collections

classifiers = {
    "Naive Bayes": GaussianNB(),
    "LogisiticRegression": LogisticRegression(),
    "KNearest": KNeighborsClassifier(),
    "Support Vector Classifier": SVC(),
    "DecisionTreeClassifier": DecisionTreeClassifier(),
}

and then

from sklearn.metrics import confusion_matrix
for key, classifier in classifiers.items(): 
    y_pred = classifier.fit(X_train, y_train).predict(X_test)
    cf_matrix=confusion_matrix(y_test, y_pred)
    print(cf_matrix)

which gives me This

now I am trying to plot them with below code but no data is shown on the plots.

fig, axn = plt.subplots(1,5, sharex=True, sharey=True)
cbar_ax = fig.add_axes([.91, .3, .03, .4])

for i, ax in enumerate(axn.flat):
    sns.heatmap(cf_matrix, ax=ax,
                cbar=i == 0,
                vmin=0, vmax=1,
                cbar_ax=None if i else cbar_ax)

fig.tight_layout(rect=[0, 0, .9, 1])

Here is how it looks like

Can someone please help me get this done?

user_6347629 · Answer

sklearn provides plotting capability on confusion_matrix. There are two ways to do it,

I used the second way here, because removing colorbar was quite verbose in first way (having multiple colorbars looks very cluttered).

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

classifiers = {
    "Naive Bayes": GaussianNB(),
    "LogisiticRegression": LogisticRegression(),
    "KNearest": KNeighborsClassifier(),
    "Support Vector Classifier": SVC(),
    "DecisionTreeClassifier": DecisionTreeClassifier(),
}


iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y)


f, axes = plt.subplots(1, 5, figsize=(20, 5), sharey='row')

for i, (key, classifier) in enumerate(classifiers.items()):
    y_pred = classifier.fit(X_train, y_train).predict(X_test)
    cf_matrix = confusion_matrix(y_test, y_pred)
    disp = ConfusionMatrixDisplay(cf_matrix,
                                  display_labels=iris.target_names)
    disp.plot(ax=axes[i], xticks_rotation=45)
    disp.ax_.set_title(key)
    disp.im_.colorbar.remove()
    disp.ax_.set_xlabel('')
    if i!=0:
        disp.ax_.set_ylabel('')

f.text(0.4, 0.1, 'Predicted label', ha='left')
plt.subplots_adjust(wspace=0.40, hspace=0.1)


f.colorbar(disp.im_, ax=axes)
plt.show()

user_12258459 · Answer

You need to store the confusion matrix somewhere, so for if I use an example dataset:

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns

data = load_breast_cancer()
scaler = StandardScaler()

X_df = pd.DataFrame(data.data, columns=data.feature_names)
X_df = scaler.fit_transform(X_df)
y_df = pd.DataFrame(data.target, columns=['target'])

X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2, random_state=11)

And store it in a similar dictionary:

from sklearn.metrics import confusion_matrix
cf_matrix = dict.fromkeys(classifiers.keys())
for key, classifier in classifiers.items(): 
    y_pred = classifier.fit(X_train, y_train.values.ravel()).predict(X_test)
    cf_matrix[key]=confusion_matrix(y_test, y_pred)

Then you can plot it:

fig, axn = plt.subplots(1,5, sharex=True, sharey=True,figsize=(12,2))

for i, ax in enumerate(axn.flat):
    k = list(cf_matrix)[i]
    sns.heatmap(cf_matrix[k], ax=ax,cbar=i==4)
    ax.set_title(k,fontsize=8)

Plotting multiple confusion matrix side by side

2 Answers