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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, ROW_NUMBER | |
| def validate_file(input_file): | |
| if len(input_file) != ROW_NUMBER: # row number for test data | |
| st.write("Incorrect number of rows--please make sure you have the same number as in the input file.") | |
| 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(checked_file): | |
| ground_truth = pd.read_csv(GROUND_TRUTH_FILE) | |
| ground_truth = ground_truth.rename({'RF_STUFF': 'pred_run_value'}, axis=1) | |
| ground_truth[TEST_COLUMN_NAME] = checked_file[TEST_COLUMN_NAME] | |
| rfstuff_mean_absolute_error = np.nanmedian(np.abs(ground_truth['pred_run_value'] - checked_file[TEST_COLUMN_NAME])) | |
| better_mean_absolute_error = np.nanmedian(np.abs(ground_truth['run_value'] - checked_file[BETTER_COLUMN_NAME])) | |
| return rfstuff_mean_absolute_error, better_mean_absolute_error | |