accuracy_script.py

def calculate_model_accuracy_metrics(actual, predicted):
    """
    Output model accuracy metrics, comparing predicted values
    to actual values.
    Arguments:
        actual: list. Time series of actual values.
        predicted: list. Time series of predicted values
    Outputs:
        Forecast bias metrics, mean absolute error, mean squared error,
        and root mean squared error in the console
    """
    #Calculate forecast bias
    forecast_errors = [actual[i]-predicted[i] for i in range(len(actual))]
    bias = sum(forecast_errors) * 1.0/len(actual)
    print('Bias: %f' % bias)
    #Calculate mean absolute error
    mae = mean_absolute_error(actual, predicted)
    print('MAE: %f' % mae)
    #Calculate mean squared error and root mean squared error
    mse = mean_squared_error(actual, predicted)
    print('MSE: %f' % mse)
    rmse = sqrt(mse)
    print('RMSE: %f' % rmse)

#Execute in the main block

#Un-difference the data
for i in range(1,len(master_df.index)-1):
    master_df.at[i,'Electricity_Price_Transformed']= master_df.at[i-1,'Electricity_Price_Transformed']+master_df.at[i,'Electricity_Price_Transformed_Differenced_PostProcess']
    
#Back-transform the data
master_df.loc[:,'Predicted_Electricity_Price']=np.exp(master_df['Electricity_Price_Transformed'])
    
#Compare the forecasted data to the real data
print(master_df[master_df['Predicted']==1][['Date','Electricity_Price', 'Predicted_Electricity_Price']])

#Evaluate the accuracy of the results
calculate_model_accuracy_metrics(list(master_df[master_df['Predicted']==1]['Electricity_Price']), 
                                    list(master_df[master_df['Predicted']==1 ['Predicted_Electricity_Price']))