!713 add UT for calc_hyper_param_importance function

Merge pull request !713 from shenghong96/calc_hyper_importance

tests/ut/optimizer/test_utils.py

@@ -14,8 +14,11 @@
 # ============================================================================
 """Test the model module."""
 import numpy as np
+import pandas as pd
 import pytest
 
+from mindinsight.optimizer.common.exceptions import SamplesNotEnoughError, CorrelationNanError
+from mindinsight.optimizer.utils.importances import calc_hyper_param_importance
 from mindinsight.optimizer.utils.utils import is_simple_numpy_number, calc_histogram
 
 
@@ -33,3 +36,26 @@ def test_calc_histogram():
     assert output[0][1] == pytest.approx(0.6, 1e-6)
     assert output[1][1] == pytest.approx(0.6, 1e-6)
     assert output[0][2] == pytest.approx(2.0, 1e-6)
+
+
+def test_calc_hyper_param_importance_exception_1():
+    """Test calc_hyper_param_importance function when number of samples is less or equal than 2"""
+    flattened_lineage = {'epoch': [10, 10], 'accuracy': [32, 32]}
+    with pytest.raises(SamplesNotEnoughError) as info:
+        calc_hyper_param_importance(pd.DataFrame(flattened_lineage), 'epoch', 'accuracy')
+    assert "Number of samples is less or equal than 2." in str(info.value)
+
+
+def test_calc_hyper_param_importance_exception_2():
+    """Test calc_hyper_param_importance function when correlation equals to NaN"""
+    flattened_lineage = {'epoch': [10, 10, 10], 'accuracy': [0.6432, 0.6281, 0.6692]}
+    with pytest.raises(CorrelationNanError) as info:
+        calc_hyper_param_importance(pd.DataFrame(flattened_lineage), 'epoch', 'accuracy')
+    assert "Correlation is nan!" in str(info.value)
+
+
+def test_calc_hyper_param_importance():
+    """Test calc_hyper_param_importance function"""
+    flattened_lineage = {'epoch': [10, 20, 30], 'accuracy': [30, 40, 50]}
+    result = calc_hyper_param_importance(pd.DataFrame(flattened_lineage), 'epoch', 'accuracy')
+    assert result == pytest.approx(1.0, 1e-6)