CODEWITHSUNDEEP
pythonmachine-learningscikit-learnconfusion-matrix
tourist
tourist

Reputation: 101

Plot confusion matrix sklearn with multiple labels

I am plotting a confusion matrix for a multiple labelled data, where labels look like:

label1: 1, 0, 0, 0

label2: 0, 1, 0, 0

label3: 0, 0, 1, 0

label4: 0, 0, 0, 1

I am able to classify successfully using the below code. I only need some help to plot confusion matrix.

    for i in range(4):
        y_train= y[:,i]
        print('Train subject %d, class %s' % (subject, cols[i]))
        lr.fit(X_train[::sample,:],y_train[::sample])
        pred[:,i] = lr.predict_proba(X_test)[:,1]

I used the following code to print confusion matrix, but it always return a 2X2 matrix 

prediction = lr.predict(X_train)

print(confusion_matrix(y_train, prediction))

Upvotes: 8

Views: 32107

Answers (8)

Angelika
Angelika

Reputation: 217

You may try ConfusionMatrixDisplay from sklearn.metrics (added since v1.0):

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt


y_real = [0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 0]
y_pred = [0, 1, 0, 1, 1, 2, 2, 1, 1, 0, 2, 1, 2]
labels = ['cats', 'dogs', 'ducks']

cm = confusion_matrix(y_real, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
disp.plot()
plt.title("My confusion matrix")
plt.show()

enter image description here

Upvotes: 1

SantiagoRodriguez
SantiagoRodriguez

Reputation: 318

Another simple aproach, using seaborn`s heatmap supported with pandas dataframe

confusion_matrix = metrics.confusion_matrix(y_true=y_test, 
                                            y_pred=y_test_pred)
mc_df = pd.DataFrame(confusion_matrix,
                     index=model.classes_, 
                     columns=columns)
sns.heatmap(mc_df, annot =True, fmt="d",cmap=plt.get_cmap('Blues'))
plt.title("Confusion Matrix")

enter image description here

Upvotes: 0

Poe Dator
Poe Dator

Reputation: 4912

Just use pandas with gradient coloring:

cm = confusion_matrix(y_true, y_pred)
cm = pd.DataFrame(data=cm, columns = np.unique(y_true), index = np.unique(y_true))
cm = (cm / cm.sum(axis = 1).values.reshape(-1,1))  # to fractions of 1
cm.style.background_gradient().format(precision=2)

By now pandas has nice options for table formatting and decoration.

Upvotes: 0

Amin Ullah
Amin Ullah

Reputation: 77

I found an easy solution with sklearn and seaborn libraries.

from sklearn.metrics import confusion_matrix, classification_report
from matplotlib import pyplot as plt
import seaborn as sns

def plot_confusion_matrix(y_test,y_scores, classNames):
    y_test=np.argmax(y_test, axis=1)
    y_scores=np.argmax(y_scores, axis=1)
    classes = len(classNames)
    cm = confusion_matrix(y_test, y_scores)
    print("**** Confusion Matrix ****")
    print(cm)
    print("**** Classification Report ****")
    print(classification_report(y_test, y_scores, target_names=classNames))
    con = np.zeros((classes,classes))
    for x in range(classes):
        for y in range(classes):
            con[x,y] = cm[x,y]/np.sum(cm[x,:])

    plt.figure(figsize=(40,40))
    sns.set(font_scale=3.0) # for label size
    df = sns.heatmap(con, annot=True,fmt='.2', cmap='Blues',xticklabels= classNames , yticklabels= classNames)
    df.figure.savefig("image2.png")

classNames = ['A', 'B', 'C', 'D', 'E'] 
plot_confusion_matrix(y_test,y_scores, classNames) 
#y_test is your ground truth
#y_scores is your predicted probabilities

Upvotes: 0

AAKANKSHA DUGGAL
AAKANKSHA DUGGAL

Reputation: 43

This works the best for me :

from sklearn.metrics import multilabel_confusion_matrix
y_unique = y_test.unique()
mcm = multilabel_confusion_matrix(y_test, y_pred, labels = y_unique)
mcm

Upvotes: 1

Govardhan
Govardhan

Reputation: 11

from sklearn.metrics import multilabel_confusion_matrix

mul_c = multilabel_confusion_matrix(
    test_Y,
    pred_k,
    labels=["benign", "dos","probe","r2l","u2r"])
mul_c

Upvotes: 1

Calvin Duy Canh Tran
Calvin Duy Canh Tran

Reputation: 1098

I found a function that can plot the confusion matrix which generated from sklearn.

import numpy as np


def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True):
    """
    given a sklearn confusion matrix (cm), make a nice plot

    Arguments
    ---------
    cm:           confusion matrix from sklearn.metrics.confusion_matrix

    target_names: given classification classes such as [0, 1, 2]
                  the class names, for example: ['high', 'medium', 'low']

    title:        the text to display at the top of the matrix

    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues

    normalize:    If False, plot the raw numbers
                  If True, plot the proportions

    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph

    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """
    import matplotlib.pyplot as plt
    import numpy as np
    import itertools

    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
    plt.show()

It will look like this enter image description here

Upvotes: 16

Guiem Bosch
Guiem Bosch

Reputation: 2768

I see this is still an open issue in sklearn's repository:

https://github.com/scikit-learn/scikit-learn/issues/3452

However there have been some attempts at implementing it. From the same #3452 thread issue:

https://github.com/Magellanea/scikit-learn/commit/514287c1d5dad2f0ab4918dc4da5cf7053fe6734#diff-b04acd877dd793f28ae7be13a999ed88R187

You can check the code proposed in the function and see if that fits your needs.

Upvotes: 0

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