evaluate_model.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import pandas as pd
import seaborn as sns


def evaluate_model(clf, X_test, y_test, y_pred, valid_y, classes, classesName, pathSave):

    #classifier, label_list, test_x, valid_y, title = "Confusion matrix"):
    precision = []
    recall = []
    f1 = []
    support = []
    weighted_avg = None
    accuracy = None

    df = pd.DataFrame(columns= ['className', 'precision', 'recall', 'f1-score', 'support', 'FP', 'FN', 'TP', 'TN'])
    report = classification_report( y_pred, valid_y, output_dict = True)
    for c in classes:
        precision.append(report[c]['precision'])
        recall.append(report[c]['recall'])
        f1.append(report[c]['f1-score'])
        support.append(report[c]['support'])

    accuracy = report['accuracy']
    weighted_avg = report['weighted avg']
    cnf_matrix = confusion_matrix(valid_y, y_pred)
    FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)
    FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)
    TP = np.diag(cnf_matrix)
    TN = cnf_matrix.sum() - (FP + FN + TP)

    df['className'] = classesName
    df['precision'] = precision
    df['recall'] = recall
    df['f1-score'] = f1
    df['support'] = support
    df['FP'] = FP
    df['FN'] = FN
    df['TP'] = TP
    df['TN'] = TN
    #disp = plot_confusion_matrix(classifier, test_x, valid_y,
    #                                 display_labels= label_list,
    #                                 cmap=plt.cm.Blues,
    #                                 normalize=None)
    #disp.ax_.set_title(title)

    #print(title)
    #print(disp.confusion_matrix)

    #plt.show()
    plt.rcParams["font.size"] = 3
    plot_confusion_matrix(clf, X_test, y_test)
    plt.savefig(pathSave)
    return df, accuracy, weighted_avg