report_intent.py

from sklearn.metrics import roc_auc_score, precision_score, recall_score, precision_recall_fscore_support
import numpy as np



def report(train_true, train_preds, test_true, test_preds, intents):
    print('Train AUC-ROC:', roc_auc_score(train_true, train_preds))
    print('Test AUC-ROC:', roc_auc_score(test_true, test_preds))

    train_preds = np.round(train_preds)
    test_preds = np.round(test_preds)

    print('TRAIN: (with repeats)')
    print("     type     precision     recall     f1-score     support")

    for ind, intent in enumerate(intents):
        scores = np.asarray(precision_recall_fscore_support(train_true[:, ind], train_preds[:, ind]))[:, 1]
        print("%s \t %f \t %f \t %f \t %f" % (intent, scores[0], scores[1], scores[2], scores[3]))

    print('TEST:')
    print("     type     precision     recall     f1-score     support")

    f1_scores = []

    for ind, intent in enumerate(intents):
        scores = np.asarray(precision_recall_fscore_support(test_true[:, ind], test_preds[:, ind]))[:, 1]
        print("%s \t %f \t %f \t %f \t %f" % (intent, scores[0], scores[1], scores[2], scores[3]))
        f1_scores.append(scores[2])

    return(f1_scores)