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camelyon17 / data /evaluation /evaluate.py

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	"""
	Evaluation script for CAMELYON17.
	"""

	import sklearn.metrics
	import pandas as ps

	import argparse

	#----------------------------------------------------------------------------------------------------

	def calculate_kappa(reference, submission):
	"""
	Calculate inter-annotator agreement with quadratic weighted Kappa.

	Args:
	reference (pandas.DataFrame): List of labels assigned by the organizers.
	submission (pandas.DataFrame): List of labels assigned by participant.

	Returns:
	float: Kappa score.

	Raises:
	ValueError: Unknown stage in reference.
	ValueError: Patient missing from submission.
	ValueError: Unknown stage in submission.
	"""

	# The accepted stages are pN0, pN0(i+), pN1mi, pN1, pN2 as described on the website. During parsing all strings converted to lowercase.
	#
	stage_list = ['pn0', 'pn0(i+)', 'pn1mi', 'pn1', 'pn2']

	# Extract the patient pN stages from the tables for evaluation.
	#
	reference_map = {df_row['patient']: df_row['stage'].lower() for _, df_row in reference.iterrows() if df_row['patient'].lower().endswith('.zip')}
	submission_map = {df_row['patient']: df_row['stage'].lower() for _, df_row in submission.iterrows() if df_row['patient'].lower().endswith('.zip')}

	# Reorganize data into lists with the same patient order and check consistency.
	#
	reference_stage_list = []
	submission_stage_list = []
	for patient_id, reference_stage in reference_map.items():
	# Check consistency: all stages must be from the official stage list and there must be a submission for each patient in the ground truth.
	#
	submission_stage = submission_map[patient_id].lower()

	if reference_stage not in stage_list:
	raise ValueError('Unknown stage in reference: \'{stage}\''.format(stage=reference_stage))
	if patient_id not in submission_map:
	raise ValueError('Patient missing from submission: \'{patient}\''.format(patient=patient_id))
	if submission_stage not in stage_list:
	raise ValueError('Unknown stage in submission: \'{stage}\''.format(stage=submission_map[patient_id]))

	# Add the pair to the lists.
	#
	reference_stage_list.append(reference_stage)
	submission_stage_list.append(submission_stage)

	# Return the Kappa score.
	#
	return sklearn.metrics.cohen_kappa_score(y1=reference_stage_list, y2=submission_stage_list, labels=stage_list, weights='quadratic')

	#----------------------------------------------------------------------------------------------------

	def collect_arguments():
	"""
	Collect command line arguments.

	Returns:
	(str, str): The parsed reference and submission CSV file paths.
	"""

	# Configure argument parser.
	#
	argument_parser = argparse.ArgumentParser(description='Calculate inter-annotator agreement.')

	argument_parser.add_argument('-r', '--reference', required=True, type=str, help='reference CSV path')
	argument_parser.add_argument('-s', '--submission', required=True, type=str, help='submission CSV path')

	# Parse arguments.
	#
	arguments = vars(argument_parser.parse_args())

	# Collect arguments.
	#
	parsed_reference_path = arguments['reference']
	parsed_submission_path = arguments['submission']

	# Print parsed parameters.
	#
	print(argument_parser.description)
	print('Reference: {path}'.format(path=parsed_reference_path))
	print('Submission: {path}'.format(path=parsed_submission_path))

	return parsed_reference_path, parsed_submission_path

	#----------------------------------------------------------------------------------------------------


	if __name__ == '__main__':

	# Parse parameters.
	#
	reference_path, submission_path = collect_arguments()

	# Load tables to Pandas data frames.
	#
	reference_df = ps.read_csv(reference_path)
	submission_df = ps.read_csv(submission_path)

	# Calculate kappa score.
	#
	try:
	kappa_score = calculate_kappa(reference=reference_df, submission=submission_df)
	except Exception as exception:
	print(exception)
	else:
	print('Score: {score}'.format(score=kappa_score))