def train()

in code/train.py [0:0]

• 29 lines of code
• 2 McCabe index (conditional complexity)

def train(bucket, seasonality_mode, algo, daily_seasonality, changepoint_prior_scale, revision_method):
    print('**************** Training Script ***********************')
    # create train dataset
    df = pd.read_csv(filepath_or_buffer=os.environ['SM_CHANNEL_TRAIN'] + "/train.csv", header=0, index_col=0)
    hierarchy, data, region_states = prepare_data(df)
    regions = df["region"].unique().tolist()
    # create test dataset
    df_test = pd.read_csv(filepath_or_buffer=os.environ['SM_CHANNEL_TEST'] + "/test.csv", header=0, index_col=0)
    test_hierarchy, test_df, region_states = prepare_data(df_test)
    print("************** Create Root Edges *********************")
    print(hierarchy)
    print('*************** Data Type for Hierarchy *************', type(hierarchy))
    # determine estimators##################################
    if algo == "Prophet":
        print('************** Started Training Prophet Model ****************')
        estimator = HTSRegressor(model='prophet', 
                                 revision_method=revision_method, 
                                 n_jobs=4, 
                                 daily_seasonality=daily_seasonality, 
                                 changepoint_prior_scale = changepoint_prior_scale,
                                 seasonality_mode=seasonality_mode,
                                )
        # train the model
        print("************** Calling fit method ************************")
        model = estimator.fit(data, hierarchy)
        print("Prophet training is complete SUCCESS")
        
        # evaluate the model on test data
        evaluate(model, test_df, regions, region_states)
    
    ###################################################
 
    mainpref = "scikit-hts/models/"
    prefix = mainpref + "/"
    print('************************ Saving Model *************************')
    joblib.dump(estimator, os.path.join(os.environ['SM_MODEL_DIR'], "model.joblib"))
    print('************************ Model Saved Successfully *************************')

    return model