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| def predict_score(model_pickle_path, target_filepath, n_meta_columns, n_feature_columns): | |
| """ | |
| model_pickle_path : | |
| target_filepath : target gene expression examples with meta | |
| n_meta_columns : number of meta columns | |
| n_feature_columns : number of feature(gene)s | |
| return score DataFrame | |
| """ | |
| df0 = pd.read_csv(target_filepath, header=-1) | |
| x_header = df0.iloc[:, 0:n_meta_columns] | |
| X = df0.iloc[:, n_meta_columns:n_meta_columns + n_feature_columns] | |
| model = pickle.load(open(model_pickle_path, mode='rb')) | |
| score_vectors = model.predict_proba(X.values, output_margin=True) | |
| score_vector_df = pd.DataFrame(score_vectors) | |
| target_score_vector_df = pd.concat([x_header, score_vector_df], axis=1) | |
| return target_score_vector_df |
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