36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import pandas as pd
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from torch.nn.functional import cosine_similarity
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from app.utils.submit import string2embedding
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def _get_cosine_similarity(pred_df: pd.DataFrame, true_df: pd.DataFrame) -> float:
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predictions = pred_df["author_comment_embedding"]
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true_values = true_df["author_comment_embedding"]
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total_cos_sim = 0
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for idx in range(len(true_values)):
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pred_value = string2embedding(predictions.iloc[idx])
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gt_value = string2embedding(true_values.iloc[idx])
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if len(pred_value) != len(gt_value):
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raise ValueError(f"Embeddings have different sizes: {len(pred_value)} != {len(gt_value)}")
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cos_sim_value = cosine_similarity(pred_value.unsqueeze(0), gt_value.unsqueeze(0))
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total_cos_sim += cos_sim_value
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return float(total_cos_sim / len(true_df))
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def calculate_score(submit_path: str, gt_path: str) -> float:
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submit_df = pd.read_csv(submit_path)
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true_df = pd.read_excel(gt_path)
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submit_df = submit_df[submit_df["solution_id"].isin(true_df["id"])]
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return (_get_cosine_similarity(submit_df, true_df) - 0.6) / 0.4
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def calculate_score_and_save(submit_path: str, gt_path: str, save_path: str) -> float:
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score = calculate_score(submit_path, gt_path)
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with open(save_path, "w") as f:
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f.write(f"{score}")
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return score
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