77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
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import argparse
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from typing import Iterable, Optional
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import pandas as pd
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search_data = __import__('search-data')
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PREFIX: str = "./"
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def read_ground_truth(file_path: str, df: pd.DataFrame) -> Iterable[tuple[str, int]]:
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records: list[list[str]] = []
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with open(file_path) as f:
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record_tmp = []
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for line in f:
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line = line.strip()
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if line == '':
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assert len(record_tmp) == 3
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records.append(record_tmp)
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record_tmp = []
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else:
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record_tmp.append(line)
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if len(record_tmp) == 3:
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records.append(record_tmp)
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for query, name, file_name in records:
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assert file_name.startswith(PREFIX)
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file_name = file_name[len(PREFIX):]
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row = df[(df.name == name) & (df.file == file_name)]
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assert len(row) == 1
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yield query, row.index[0]
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def better_index(li: list[tuple[int, float]], e: int) -> Optional[int]:
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for i, le in enumerate(li):
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if le[0] == e:
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return i
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return None
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def main(method: str, file_path: str):
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df = search_data.load_data()
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test_set = list(read_ground_truth(file_path, df))
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precision_sum = 0
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recall_sum = 0
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for query, expected in test_set:
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indexes_values: list[tuple[int, float]] = search_data.search(query, method, df)
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idx = better_index(indexes_values, expected)
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if idx is None:
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precision = 0
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recall = 0
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else:
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precision = 1 / (idx + 1)
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recall = 1
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precision_sum += precision
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recall_sum += recall
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print("Precision: {0:.2f}%".format(precision_sum * 100 / len(test_set)))
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print("Recall: {0:.2f}%".format(recall_sum * 100 / len(test_set)))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("method", help="the method to compare similarities with", type=str)
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parser.add_argument("ground_truth_file", help="file where ground truth comes from", type=str)
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args = parser.parse_args()
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main(args.method, args.ground_truth_file)
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