confusion_matrix.py

from sklearn.metrics import confusion_matrix

def conf_matrix(model, X_test):
    y_pred = model.predict(X_test)            # calculate predictions
    cm = confusion_matrix(y_test, y_pred)     # defining the confusion matrix
    tn, fp, fn, tp = cm.ravel()               # assigning the elements of the confusion matrix to variables
    print(f"True Negatives: {tn}")            # print those variables
    print(f"False Positives: {fp}")
    print(f"False Negatives: {fn}")
    print(f"True Positives: {tp}")            # return the confusion matrix as dataframe
    return pd.DataFrame(cm, 
                        columns = ['Pred Deep Philosophy','Pred Shower Thoughts'], 
                        index = ['Act Deep Philosphy', 'Act Shower Thoughts'])