| import os | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def get_score(submission_folder = "../env"): | |
| submission_path = os.path.join(submission_folder, "submission.csv") | |
| submission = pd.read_csv(submission_path, index_col=0) | |
| preds = submission["label"].tolist() | |
| preds = [float(pred) for pred in preds] | |
| lang = "eng" | |
| test_data_path = os.path.join(submission_folder, "data", lang, f"{lang}_test.csv") | |
| df = pd.read_csv(test_data_path) | |
| scores = df["label"].tolist() | |
| scores = [float(score) for score in scores] | |
| spearman_corr = np.corrcoef(scores, preds)[0, 1] | |
| return spearman_corr | |
| if __name__ == "__main__": | |
| print(get_score()) |