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Add precision and recall in return eval_model #172

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Nov 11, 2021
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Add precision and recall in return eval_model #172

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6 changes: 3 additions & 3 deletions medcat/utils/meta_cat/ml_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import numpy as np

from scipy.special import softmax
from sklearn.metrics import classification_report, f1_score
from sklearn.metrics import classification_report, f1_score, precision_recall_fscore_support
from torch import nn
import torch.optim as optim

Expand Down Expand Up @@ -258,7 +258,7 @@ def eval_model(model, data, config, tokenizer):

score_average = config.train['score_average']
predictions = np.argmax(np.concatenate(all_logits, axis=0), axis=1)
f1 = f1_score(y_eval, predictions, average=score_average)
precision, recall, f1, support = precision_recall_fscore_support(y_eval, predictions, average=score_average)

examples = {'FP': {}, 'FN': {}, 'TP': {}}
id2category_value = {v: k for k, v in config.general['category_value2id'].items()}
Expand All @@ -277,4 +277,4 @@ def eval_model(model, data, config, tokenizer):
else:
examples['TP'][y] = examples['TP'].get(y, []) + [(info, text)]

return {'f1': f1, 'examples': examples}
return {'precision': precision, 'recall': recall, 'f1': f1, 'examples': examples}