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chabud-ecml-pkdd2023 / create_sample_submission.py

lc-col

Uploaded create_sample_submission.py

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	import numpy as np
	import pandas as pd
	import h5py

	from trimesh.voxel.runlength import dense_to_brle
	from pathlib import Path
	from collections import defaultdict

	from typing import Any, Union, Dict, Literal
	from numpy.typing import NDArray

	# class RandomModel:
	# def __init__(self, shape):
	# self.shape = shape
	# return

	# def __call__(self, input):
	# # input is ignored, just generate some random predictions
	# return np.random.randint(0, 2, size=self.shape, dtype=bool)

	class FixedModel:
	def __init__(self, shape) -> None:
	self.shape = shape
	return

	def __call__(self, input) -> Any:
	# input is ignored, just generate a mask filled with zeros, with fixed pixels set to 1
	mask = np.zeros(self.shape, dtype=bool)
	mask[100:250, 100:250] = True
	return mask

	def retrieve_validation_fold(path: Union[str, Path]) -> Dict[str, NDArray]:
	result = defaultdict(dict)
	with h5py.File(path, 'r') as fp:
	for uuid, values in fp.items():
	if values.attrs['fold'] != 0:
	continue

	result[uuid]['post'] = values['post_fire'][...]
	# result[uuid]['pre'] = values['pre_fire'][...]

	return dict(result)

	def compute_submission_mask(id: str, mask: NDArray):
	brle = dense_to_brle(mask.astype(bool).flatten())
	return {"id": id, "rle_mask": brle, "index": np.arange(len(brle))}

	if __name__ == '__main__':
	validation_fold = retrieve_validation_fold('train_eval.hdf5')

	# use a list to accumulate results
	result = []
	# instantiate the model
	model = FixedModel(shape=(512, 512))
	for uuid in validation_fold:
	input_images = validation_fold[uuid]

	# perform the prediction
	predicted = model(input_images)
	# convert the prediction in RLE format
	encoded_prediction = compute_submission_mask(uuid, predicted)
	result.append(pd.DataFrame(encoded_prediction))

	# concatenate all dataframes
	submission_df = pd.concat(result)
	submission_df.to_csv('predictions.csv', index=False)