def evaluate()

in code/train.py [0:0]

• 17 lines of code
• 3 McCabe index (conditional complexity)

def evaluate(model, test_df, regions, region_states):
    print("******* Making Predictions for 90days in future *********")
    predictions = model.predict(steps_ahead=90)
    test_preds = predictions.query('index > "2009-04-29"').copy()
    print('******************************', test_preds.shape)
    print('******************************', test_preds.head())
    ## root level metrics
    print('************ Root Level Metrics ************')
    total_mse = mean_squared_error(test_df['total'], test_preds['total'])
    print('Total: MSE: {}\n'.format(total_mse))
    
    print('************ Region Level Metrics ************')
    for region in regions:
            mse = mean_squared_error(test_df[region], test_preds[region])
            print('{}: MSE: {}\n'.format(region,mse))
        
    print('************ State Level Metrics ************')
    for state in region_states:
            mse = mean_squared_error(test_df[state], test_preds[state])
            print('{}:MSE: {}\n'.format(state,mse))