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reformat #66

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Aug 29, 2023
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reformat #66

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79 changes: 44 additions & 35 deletions src/locpix/scripts/img_seg/membrane_performance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,18 +21,21 @@
import os
import numpy as np
from locpix.preprocessing import datastruc

# from locpix.visualise.performance import plot_pr_curve , generate_binary_conf_matrix
import locpix.evaluate.metrics as metrics
from sklearn.metrics import precision_recall_curve, auc
import polars as pl
from datetime import datetime

# import matplotlib.pyplot as plt
# from locpix.visualise import vis_img
import argparse
from locpix.scripts.img_seg import membrane_performance_config
import json
import time


def main():

parser = argparse.ArgumentParser(
Expand Down Expand Up @@ -186,40 +189,41 @@ def main():
# output_seg_imgs_test = os.path.join(
# project_folder,
# f"membrane_performance/{method}/membrane/seg_images/test/{fold}",
#)
# )
# output_seg_imgs_val = os.path.join(
# project_folder,
# f"membrane_performance/{method}/membrane/seg_images/val/{fold}",
#)
#output_train_pr = os.path.join(
# )
# output_train_pr = os.path.join(
# project_folder,
# f"membrane_performance/{method}/membrane/train_pr/{fold}",
#)
#output_test_pr = os.path.join(
# project_folder, f"membrane_performance/{method}/membrane/test_pr/{fold}"
#)
#output_val_pr = os.path.join(
# )
# output_test_pr = os.path.join(
# project_folder, f"membrane_performance/{method}/membrane/test_pr/
# {fold}"
# )
# output_val_pr = os.path.join(
# project_folder, f"membrane_performance/{method}/membrane/val_pr/{fold}"
#)
# )
output_metrics = os.path.join(
project_folder, f"membrane_performance/{method}/membrane/metrics/{fold}"
)
#output_conf_matrix = os.path.join(
# output_conf_matrix = os.path.join(
# project_folder,
# f"membrane_performance/{method}/membrane/conf_matrix/{fold}",
#)
# )

# create output folders
folders = [
output_df_folder_test,
output_df_folder_val,
# output_seg_imgs_val,
# output_seg_imgs_test,
#output_train_pr,
#output_val_pr,
#output_test_pr,
# output_train_pr,
# output_val_pr,
# output_test_pr,
output_metrics,
#output_conf_matrix,
# output_conf_matrix,
]

for folder in folders:
Expand Down Expand Up @@ -270,9 +274,9 @@ def main():
pr, rec, pr_threshold = precision_recall_curve(
gt_list, prob_list, pos_label=1
)
baseline = len(gt[gt == 1]) / len(gt)
baseline = len(gt_list[gt_list == 1]) / len(gt_list)

# pr, recall saved for train
# pr, recall saved for train
save_loc = os.path.join(output_metrics, f"train_{date}.txt")
lines = ["Overall results", "-----------"]
lines.append(f"prcurve_pr: {list(pr)}")
Expand All @@ -282,7 +286,7 @@ def main():
f.writelines("\n".join(lines))

# plot pr curve
#save_loc = os.path.join(output_train_pr, "_curve.pkl")
# save_loc = os.path.join(output_train_pr, "_curve.pkl")
# plot_pr_curve(
# ax_train,
# method.capitalize(),
Expand Down Expand Up @@ -417,7 +421,7 @@ def main():
baseline = len(gt[gt == 1]) / len(gt)

# plot pr curve
#save_loc = os.path.join(output_test_pr, "_curve.pkl")
# save_loc = os.path.join(output_test_pr, "_curve.pkl")
# plot_pr_curve(
# ax_test,
# method.capitalize(),
Expand All @@ -430,14 +434,16 @@ def main():
# # pickle=True,
# )
pr_auc = auc(rec, pr)
add_metrics = {"pr_auc": pr_auc,
"prcurve_pr": list(pr),
"prcurve_rec": list(rec),
"prcurve_baseline": baseline}
add_metrics = {
"pr_auc": pr_auc,
"prcurve_pr": list(pr),
"prcurve_rec": list(rec),
"prcurve_baseline": baseline,
}

# metric calculations based on final prediction
save_loc = os.path.join(output_metrics, f"test_{date}.txt")
agg_results = metrics.aggregated_metrics(
_ = metrics.aggregated_metrics(
output_df_folder_test,
save_loc,
gt_label_map,
Expand Down Expand Up @@ -549,10 +555,8 @@ def main():
)
baseline = len(gt[gt == 1]) / len(gt)



# plot pr curve
#save_loc = os.path.join(output_val_pr, "_curve.pkl")
# save_loc = os.path.join(output_val_pr, "_curve.pkl")
# plot_pr_curve(
# ax_val,
# method.capitalize(),
Expand All @@ -565,14 +569,16 @@ def main():
# # pickle=True,
# )
pr_auc = auc(rec, pr)
add_metrics = {"pr_auc": pr_auc,
"prcurve_pr": list(pr),
"prcurve_rec": list(rec),
"prcurve_baseline": baseline}
add_metrics = {
"pr_auc": pr_auc,
"prcurve_pr": list(pr),
"prcurve_rec": list(rec),
"prcurve_baseline": baseline,
}

# metric calculations based on final prediction
save_loc = os.path.join(output_metrics, f"val_{date}.txt")
agg_results =metrics.aggregated_metrics(
_ = metrics.aggregated_metrics(
output_df_folder_val,
save_loc,
gt_label_map,
Expand Down Expand Up @@ -602,9 +608,12 @@ def main():
# ax_test.legend([handles_test[idx] for idx in order],
# [methods[idx] for idx in order])

# fig_train.savefig(os.path.join(output_overlay_pr_curves, "_train.png"), dpi=600)
# fig_test.savefig(os.path.join(output_overlay_pr_curves, "_test.png"), dpi=600)
# fig_val.savefig(os.path.join(output_overlay_pr_curves, "_val.png"), dpi=600)
# fig_train.savefig(os.path.join(output_overlay_pr_curves,
# "_train.png"), dpi=600)
# fig_test.savefig(os.path.join(output_overlay_pr_curves,
# "_test.png"), dpi=600)
# fig_val.savefig(os.path.join(output_overlay_pr_curves,
# "_val.png"), dpi=600)

# save yaml file
yaml_save_loc = os.path.join(project_folder, "membrane_performance.yaml")
Expand Down
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