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Linlang
56ed919b2e
* update configuration docs * update configuration docs * update configuration docs
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
import json
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import pandas as pd
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from sklearn.metrics import roc_auc_score
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def prepare_for_auroc_metric(submission: pd.DataFrame, answers: pd.DataFrame, id_col: str, target_col: str) -> dict:
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# Answers checks
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assert id_col in answers.columns, f"answers dataframe should have an {id_col} column"
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assert target_col in answers.columns, f"answers dataframe should have a {target_col} column"
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# Submission checks
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if id_col not in submission.columns:
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raise InvalidSubmissionError(f"Submission should have an {id_col} column")
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if target_col not in submission.columns:
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raise InvalidSubmissionError(f"Submission should have a {target_col} column")
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if len(submission) != len(answers):
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raise InvalidSubmissionError(f"Submission and answers should have the same number of rows")
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try:
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pd.to_numeric(submission[target_col])
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except ValueError:
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raise InvalidSubmissionError(
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f"Expected {target_col} column to be numeric, got {submission[target_col].dtype} instead"
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)
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if submission[target_col].min() < 0 or submission[target_col].max() > 1:
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raise InvalidSubmissionError(
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f"Submission {target_col} column should contain probabilities,"
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" and therefore contain values between 0 and 1 inclusive"
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)
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# Sort
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submission = submission.sort_values(id_col)
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answers = answers.sort_values(id_col)
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if (submission[id_col].values != answers[id_col].values).any():
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raise InvalidSubmissionError(f"Submission and answers should have the same {id_col} values")
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roc_auc_inputs = {
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"y_true": answers[target_col].to_numpy(),
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"y_score": submission[target_col].to_numpy(),
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}
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return roc_auc_inputs
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def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
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roc_auc_inputs = prepare_for_auroc_metric(
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submission=submission, answers=answers, id_col="ID", target_col="ARF_LABEL"
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)
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return roc_auc_score(y_true=roc_auc_inputs["y_true"], y_score=roc_auc_inputs["y_score"])
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if __name__ == "__main__":
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submission_path = "submission.csv"
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gt_submission_path = "submission_test.csv"
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submission = pd.read_csv(submission_path)
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answers = pd.read_csv(gt_submission_path)
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score = grade(submission=submission, answers=answers)
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print(
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json.dumps(
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{
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"competition_id": "arf-12-hours-prediction-task",
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"score": score,
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}
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)
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)
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