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import argparse
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils.paths import LOGS_PATH, DATA_VIS_PATH, DATA_PATH
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"extension_name",
type=str,
help="Experiment extension name"
)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
extension_name = args.extension_name
# thresholds = np.array([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95])
thresholds = np.array([0.89])
f1_mean, f1_std = [], []
precision_mean, precision_std = [], []
recall_mean, recall_std = [], []
csv_pattern = os.path.join(LOGS_PATH, f"dl_evaluation_{extension_name}", f"dl_evaluation_{extension_name}_{{}}.csv")
for threshold in thresholds:
performance_csv_filename = csv_pattern.format(threshold)
perf_df = pd.read_csv(performance_csv_filename)
mean_row = perf_df.iloc[-2]
std_row = perf_df.iloc[-1]
# Precision, Recall, F1Score
f1_mean.append(mean_row['F1Score'])
f1_std.append(std_row['F1Score'])
precision_mean.append(mean_row['Precision'])
precision_std.append(std_row['Precision'])
recall_mean.append(mean_row['Recall'])
recall_std.append(std_row['Recall'])
f1_mean, f1_std = np.array(f1_mean), np.array(f1_std)
precision_mean, precision_std = np.array(precision_mean), np.array(precision_std)
recall_mean, recall_std = np.array(recall_mean), np.array(recall_std)
print(f1_mean, precision_mean, recall_mean)
# df_to_save = pd.DataFrame({'threshold': thresholds,
# 'f1score_mean': f1_mean, 'f1score_std': f1_std,
# 'precision_mean': precision_mean, 'precision_std': precision_std,
# 'recall_mean': recall_mean, 'recall_std': recall_std})
# csv_filename = os.path.join(DATA_PATH, f"performance_threshold_{extension_name}.csv")
# df_to_save.to_csv(csv_filename, index=False)
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