properties_1.py

from sklearn import metrics
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
def plot_confusion_metrix(y_test,model_test):
    cm = metrics.confusion_matrix(y_test, model_test)
    plt.figure(1)
    plt.clf()
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)
    classNames = ['Nondemented','Demented']
    plt.title('Confusion Matrix')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    tick_marks = np.arange(len(classNames))
    plt.xticks(tick_marks, classNames)
    plt.yticks(tick_marks, classNames)
    s = [['TN','FP'], ['FN', 'TP']]
    for i in range(2):
        for j in range(2):
            plt.text(j,i, str(s[i][j])+" = "+str(cm[i][j]))
    plt.show()


def report_performance(model,model_test,y_test):

    #model_test = model.predict(X_test)

    print("\n\nConfusion Matrix:")
    print("{0}".format(metrics.confusion_matrix(y_test, model_test)))
    print("\n\nClassification Report: ")
    print(metrics.classification_report(y_test, model_test))
    #cm = metrics.confusion_matrix(y_test, model_test)
    plot_confusion_metrix(y_test, model_test)

def roc_curves(model,X_test,y_test):
    predictions_test = model.predict(X_test)
    fpr, tpr, thresholds = roc_curve(predictions_test,y_test)
    roc_auc = auc(fpr, tpr)

    plt.figure()
    plt.plot(fpr, tpr, color='darkorange', lw=1, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
    
def accuracy(model,X_test):
    pred = model.predict(X_test)
    accu = metrics.accuracy_score(y_test,pred)
    print("\nAcuuracy Of the Model: ",accu,"\n\n")