52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
import os
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import numpy as np
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import pandas as pd
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import sklearn.metrics
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import argparse
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--output_path', type=str, help='')
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parser.add_argument('--answer_path', type=str, help='')
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parser.add_argument('--task', type=str, default="binary", help='default:binary, possible other options:{chemprot}')
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args = parser.parse_args()
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testdf = pd.read_csv(args.answer_path, sep="\t", header=None)
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preddf = pd.read_csv(args.output_path, sep="\t", header=None)
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# binary
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if args.task == "binary":
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pred = [preddf.iloc[i].tolist() for i in preddf.index]
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pred_class = [np.argmax(v) for v in pred]
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pred_prob_one = [v[1] for v in pred]
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p,r,f,s = sklearn.metrics.precision_recall_fscore_support(y_pred=pred_class, y_true=testdf["label"])
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results = dict()
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results["f1 score"] = f[1]
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results["recall"] = r[1]
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results["precision"] = p[1]
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results["specificity"] = r[0]
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# chemprot
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# micro-average of 5 target classes
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# see "Potent pairing: ensemble of long short-term memory networks and support vector machine for chemical-protein relation extraction (Mehryary, 2018)" for details
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if args.task == "chemprot":
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pred = [preddf.iloc[i].tolist() for i in preddf.index]
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pred_class = [np.argmax(v) for v in pred]
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str_to_int_mapper = dict()
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testdf.iloc[:,3] = testdf.iloc[:, 3].fillna("False")
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for i,v in enumerate(sorted(testdf.iloc[:,3].unique())):
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str_to_int_mapper[v] = i
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test_answer = [str_to_int_mapper[v] for v in testdf.iloc[:,3]]
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p,r,f,s = sklearn.metrics.precision_recall_fscore_support(y_pred=pred_class, y_true=test_answer, labels=[0,1,2,3,4], average="micro")
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results = dict()
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results["f1 score"] = f
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results["recall"] = r
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results["precision"] = p
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for k,v in results.items():
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print("{:11s} : {:.2%}".format(k,v))
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