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regression_pathology_features.py
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regression_pathology_features.py
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"""
Run regression using a pathology characteristic as the independent variable and
evaluation metric as the dependent variable.
"""
from argparse import ArgumentParser
import pandas as pd
from eval_constants import LOCALIZATION_TASKS
from utils import format_ci, run_linear_regression
def normalize(column):
if column.min() == column.max():
return column
else:
return (column-column.min())/(column.max()-column.min())
def run_features_regression(args):
evaluate_hb = eval(args.evaluate_hb)
# read localization performance
pred_iou_results = pd.read_csv(args.pred_iou_results)
pred_hitmiss_results = pd.read_csv(args.pred_hitmiss_results)
if evaluate_hb:
hb_iou_results = pd.read_csv(args.hb_iou_results)
hb_hitmiss_results = pd.read_csv(args.hb_hitmiss_results)
# read geometric features
instance_df = pd.read_csv(f'{args.features_dir}/num_instances.csv').\
drop(['img_id'], axis=1)
area_df = pd.read_csv(f'{args.features_dir}/area_ratio.csv').\
drop(['img_id'], axis=1)
elongation_df = pd.read_csv(f'{args.features_dir}/elongation.csv').\
drop(['img_id'], axis=1)
rec_area_ratio_df = pd.read_csv(f'{args.features_dir}/rec_area_ratio.csv').\
drop(['img_id'], axis=1)
irrectangularity_df = 1-rec_area_ratio_df # irrectangularity = 1-(area_ratio)
# get cxrs with at least one ground-truth segmentation for any pathology
neg_idx = instance_df[instance_df.eq(0).all(1)].index.values
pos_idx = [i for i in instance_df.index.values if i not in neg_idx]
instance_df_pos = instance_df.iloc[pos_idx]
area_df_pos = area_df.iloc[pos_idx]
elongation_df_pos = elongation_df.iloc[pos_idx]
irrectangularity_df_pos = irrectangularity_df.iloc[pos_idx]
# create regression dataframe
if evaluate_hb:
regression_cols = ['pred_iou', 'iou_diff', 'pred_hitmiss', 'hitmiss_diff']
else:
regression_cols = ['pred_iou', 'pred_hitmiss']
for y in regression_cols:
overall_regression = pd.DataFrame()
coef_summary = pd.DataFrame(columns = ['lower', 'upper', 'mean',
'coef_pval', 'corr', 'corr_pval', 'corr_lower', 'corr_upper', 'feature'])
for task in sorted(LOCALIZATION_TASKS):
df = pd.DataFrame()
data = {'pred_iou': pred_iou_results[task].values,
'pred_hitmiss': pred_hitmiss_results[task].values,
'n_instance': instance_df_pos[task].values,
'area_ratio':area_df_pos[task].values,
'elongation': elongation_df_pos[task].values,
'irrectangularity': irrectangularity_df_pos[task].values,
'img_id': pred_iou_results['img_id']}
if evaluate_hb:
data['iou_diff'] = hb_iou_results[task].values \
- pred_iou_results[task].values
data['hitmiss_diff'] = hb_hitmiss_results[task].values \
- pred_hitmiss_results[task].values
df = pd.DataFrame(data)
# get cxrs with ground-truth segmentation for this task
df = df[df.n_instance>0]
overall_regression = pd.concat([overall_regression, df])
features = ['n_instance', 'area_ratio', 'elongation', 'irrectangularity']
for feature in features:
overall_regression[feature] = normalize(overall_regression[feature])
results = run_linear_regression(overall_regression, 'Overall', y, feature)
coef_summary = pd.concat([coef_summary, results])
coef_summary = coef_summary.apply(format_ci,
bonferroni_correction=4,
axis=1)\
[['task', 'feature', 'Linear regression coefficients']]
coef_summary.to_csv(f'{args.save_dir}/regression_features_{y}.csv',
index=False)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument('--features_dir', type=str,
help='directory with four csv files: area_ratio.csv, \
elongation.csv, num_instances.csv`, and \
rec_area_ratio.csv.')
parser.add_argument('--pred_iou_results', type=str,
help='path to csv file with saliency method IoU results \
for each CXR and each pathology.')
parser.add_argument('--pred_hitmiss_results', type=str,
help='path to csv file with saliency method hit/miss \
results for each CXR and each pathology.')
parser.add_argument('--evaluate_hb', type=str, default='False',
help='if true, evaluate human benchmark in addition to \
saliency method.')
parser.add_argument('--hb_iou_results', type=str,
help='path to csv file with human benchmark IoU results \
for each CXR and each pathology.')
parser.add_argument('--hb_hitmiss_results', type=str,
help='path to csv file with human benchmark hit/miss \
results for each CXR and each pathology.')
parser.add_argument('--save_dir', type=str, default='.',
help='where to save regression results')
args = parser.parse_args()
assert args.evaluate_hb in ['True', 'False'], \
"`evaluate_hb` flag must be either `True` or `False`"
run_features_regression(args)