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| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from settings import TEST_COLUMN_NAME, BETTER_COLUMN_NAME, GROUND_TRUTH_FILE | |
| def validate_file(input_file): | |
| if len(input_file) < 1: | |
| st.write("No rows detected.") | |
| return | |
| if TEST_COLUMN_NAME not in input_file.columns.to_list(): | |
| st.write(f"No column named {TEST_COLUMN_NAME} in the file.") | |
| st.write(f"Found these columns:{input_file.columns}") | |
| return | |
| else: | |
| st.write("File loaded and confirmed.") | |
| return True | |
| def check_projections(projections): | |
| ground_truth = pd.read_csv(GROUND_TRUTH_FILE) | |
| checked_file = pd.merge(ground_truth, projections, how='left', on='bbref_id') | |
| marcel_mean_absolute_error = np.nanmean(np.abs(checked_file['ops_pred'] - checked_file[TEST_COLUMN_NAME])) | |
| better_mean_absolute_error = np.nanmean(np.abs(checked_file['ops_real'] - checked_file[BETTER_COLUMN_NAME])) | |
| return marcel_mean_absolute_error, better_mean_absolute_error | |