Function that plots the Lift Curve of a Machine Learning Model

Plot_Lift_Curve.py

# Function that plots a Lift Curve using the real label values of a dataset and the probability predictions of a Machine Learning Algorithm/model
# @Params:
# y_val: real labels of the data
# y_pred: probability predictions for such data
# step: how big we want the steps in the percentiles to be

def plot_Lift_curve(y_val, y_pred, step=0.01):
    
    #Define an auxiliar dataframe to plot the curve
    aux_lift = pd.DataFrame()
    #Create a real and predicted column for our new DataFrame and assign values
    aux_lift['real'] = y_val
    aux_lift['predicted'] = y_pred
    #Order the values for the predicted probability column:
    aux_lift.sort_values('predicted',ascending=False,inplace=True)
    
    #Create the values that will go into the X axis of our plot
    x_val = np.arange(step,1+step,step)
    #Calculate the ratio of ones in our data
    ratio_ones = aux_lift['real'].sum() / len(aux_lift)
    #Create an empty vector with the values that will go on the Y axis our our plot
    y_v = []
    
    #Calculate for each x value its correspondent y value
    for x in x_val:
        num_data = int(np.ceil(x*len(aux_lift))) #The ceil function returns the closest integer bigger than our number 
        data_here = aux_lift.iloc[:num_data,:]   # ie. np.ceil(1.4) = 2
        ratio_ones_here = data_here['real'].sum()/len(data_here)
        y_v.append(ratio_ones_here / ratio_ones)
           
   #Plot the figure
    fig, axis = plt.subplots()
    fig.figsize = (40,40)
    axis.plot(x_val, y_v, 'g-', linewidth = 3, markersize = 5)
    axis.plot(x_val, np.ones(len(x_val)), 'k-')
    axis.set_xlabel('Proportion of sample')
    axis.set_ylabel('Lift')
    plt.title('Lift Curve')
    plt.show()