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import unittest

import numpy as np

import kglib.kgcn.learn.metrics.report as metrics


class TestMetricsReport(unittest.TestCase):
    def test_print(self):
        y_true = np.array([0, 1, 2, 0])
        y_pred = y_true
        # expected_confusion_matrix = np.array([[2, 0, 0], [0, 1, 0], [0, 0, 1]])
        metrics.report_multiclass_metrics(y_true, y_pred)

        y_pred = [[0, 0, 1], [1, 1, 0], [0, 1, 1]]
        y_true = [[0, 1, 1], [1, 1, 0], [0, 1, 1]]
        # expected confusion matix:
        # Confusion matrix for label 0:
        # [[2 0]
        #  [0 1]]
        # Confusion matrix for label 1:
        # [[0 0]
        #  [1 2]]
        # Confusion matrix for label 2:
        # [[1 0]
        #  [0 2]]
        metrics.report_multilabel_confusion_matrix(y_true, y_pred)

        # expected confusion matrix:
        # Confusion matrix for label 0:
        # [[2 0]
        #  [0 1]]
        # Confusion matrix for label 1:
        # [[3]]
        # Confusion matrix for label 2:
        # [[1 0]
        # [0 2]]
        metrics.report_multilabel_confusion_matrix(y_true, y_true)


if __name__ == "__main__":
    unittest.main()
