src/utils.py

import pandas as pd
def norm_sNavie(df):
    df_normalized = df.copy()
    seasonal_naive_row = df[df['model'] == 'seasonal_naive'].iloc[0]
    print('df: ',df)
    for column in df.columns:
        if column != 'model':  # We skip normalizing the 'model' column
            df_normalized[column] = df[column] / seasonal_naive_row[column]
    return df_normalized

def pivot_df(file_name, tab_name):
    df = pd.read_csv(file_name)
    if tab_name == 'univariate':
        df['univariate'] = df['univariate'].replace({True: 'univariate', False: 'multivariate'})
        df.rename(columns={'univariate': 'variate_type'}, inplace=True)
        tab_name = 'variate_type'
    df_melted = pd.melt(df, id_vars=[tab_name, 'model'], var_name='metric', value_name='value')
    df_melted['metric'] = df_melted['metric'].replace({
        'eval_metrics/MAPE[0.5]': 'MAPE',
        'eval_metrics/mean_weighted_sum_quantile_loss': 'CRPS'
    })
    df_pivot = df_melted.pivot_table(index='model', columns=[tab_name, 'metric'], values='value')
    df_pivot.columns = [f'{tab_name} ({metric})' for tab_name, metric in df_pivot.columns]
    # df_pivot.to_csv('pivoted_df.csv')
    # print(df_pivot)
    df_pivot = df_pivot.reset_index()
    return df_pivot