Source code for nlp_architect.utils.metrics

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# Copyright 2017-2018 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
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from __future__ import absolute_import, division, print_function, unicode_literals

from scipy.stats import pearsonr, spearmanr
from seqeval.metrics import classification_report, precision_score, recall_score, f1_score
from sklearn.metrics import f1_score as classification_f1_score

[docs]def get_conll_scores(predictions, y, y_lex, unk="O"): """Get Conll style scores (precision, recall, f1) """ if isinstance(predictions, list): predictions = predictions[-1] test_p = predictions if len(test_p.shape) > 2: test_p = test_p.argmax(2) test_y = y if len(test_y.shape) > 2: test_y = test_y.argmax(2) prediction_data = [] for n in range(test_y.shape[0]): test_yval = [] for i in list(test_y[n]): try: test_yval.append(y_lex[i]) except KeyError: pass test_pval = [unk] * len(test_yval) for e, i in enumerate(list(test_p[n])[: len(test_pval)]): try: test_pval[e] = y_lex[i] except KeyError: pass prediction_data.append((test_yval, test_pval)) y_true, y_pred = list(zip(*prediction_data)) return classification_report(y_true, y_pred, digits=3)
[docs]def simple_accuracy(preds, labels): """return simple accuracy """ return (preds == labels).mean()
[docs]def accuracy(preds, labels): """return simple accuracy in expected dict format """ acc = simple_accuracy(preds, labels) return {"acc": acc}
[docs]def acc_and_f1(preds, labels): """return accuracy and f1 score """ acc = simple_accuracy(preds, labels) f1 = classification_f1_score(y_true=labels, y_pred=preds) return { "acc": acc, "f1": f1, "acc_and_f1": (acc + f1) / 2, }
[docs]def pearson_and_spearman(preds, labels): """get pearson and spearman correlation """ pearson_corr = pearsonr(preds, labels)[0] spearman_corr = spearmanr(preds, labels)[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, }
[docs]def tagging(preds, labels): p = precision_score(labels, preds) r = recall_score(labels, preds) f1 = f1_score(labels, preds) return p, r, f1