hipom_data_mapping/train/mapping_pattern/mapping_prediction/predict.py

import pandas as pd
import os
import glob
from inference import Inference

checkpoint_directory =  '../'

def infer_and_select(fold):
    print(f"Inference for fold {fold}")
    # import test data
    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/test_all.csv"
    df = pd.read_csv(data_path, skipinitialspace=True)

    # get target data
    data_path = f"../../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
    train_df = pd.read_csv(data_path, skipinitialspace=True)
    # processing to help with selection later
    train_df['thing_property'] = train_df['thing'] + " " + train_df['property']


    ##########################################
    # run inference
    # checkpoint
    # Use glob to find matching paths
    directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
    # Use glob to find matching paths
    # path is usually checkpoint_fold_1/checkpoint-<step number>
    # we are guaranteed to save only 1 checkpoint from training
    pattern = 'checkpoint-*'
    checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]


    infer = Inference(checkpoint_path)
    infer.prepare_dataloader(df, batch_size=256, max_length=128)
    thing_prediction_list, property_prediction_list = infer.generate()

    # add labels too
    # thing_actual_list, property_actual_list = decode_preds(pred_labels)
    # Convert the list to a Pandas DataFrame
    df_out = pd.DataFrame({
        'p_thing': thing_prediction_list, 
        'p_property': property_prediction_list
    })
    # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing']
    # df_out['p_property_correct'] = df_out['p_property'] == df_out['property']
    df = pd.concat([df, df_out], axis=1)

    # we can save the t5 generation output here
    df.to_csv(f"exports/result_group_{fold}.csv", index=False)

    # here we want to evaluate mapping accuracy within the valid in mdm data only
    in_mdm = df['MDM']
    condition_correct_thing = df['p_thing'] == df['thing_pattern']
    condition_correct_property = df['p_property'] == df['property_pattern']
    prediction_mdm_correct = sum(condition_correct_thing & condition_correct_property & in_mdm)
    pred_correct_proportion = prediction_mdm_correct/sum(in_mdm)

    # write output to file output.txt
    with open("output.txt", "a") as f:
        print(f'Accuracy for fold {fold}: {pred_correct_proportion}', file=f)

###########################################  
# Execute for all folds

# reset file before writing to it
with open("output.txt", "w") as f:
    print('', file=f)

for fold in [1,2,3,4,5]:
    infer_and_select(fold)