Use numpy assert allclose for array testing (#56)

pkeilbach · Dec 20, 2023 · f269d6b · f269d6b
1 parent 6e7c247
commit f269d6b
Showing 1 changed file with 12 additions and 11 deletions.
diff --git a/tests/htwgnlp/test_naive_bayes.py b/tests/htwgnlp/test_naive_bayes.py
@@ -4,7 +4,7 @@
 import pandas as pd
 import pytest
 
-from htwgnlp.naive_bayes import NaiveBayes
+from htwgnlp.naive_bayes_private import NaiveBayes
 
 model = NaiveBayes()
 
@@ -107,7 +107,8 @@ def trained_log_ratios(train_samples, train_samples_labels):
 )
 def test_set_logprior(labels, expected):
  model.logprior = labels
- assert model.logprior == expected
+ np.testing.assert_allclose(model.logprior, expected)
+ # assert model.logprior == expected
 
 
 @pytest.mark.parametrize(
@@ -171,15 +172,15 @@ def test_get_log_ratios(trained_log_ratios):
  assert isinstance(trained_log_ratios, pd.Series)
  assert trained_log_ratios.index.size == 9
 
- assert trained_log_ratios.loc["happy"] == np.log(0.15 / 0.05)
- assert trained_log_ratios.loc["sad"] == np.log(0.05 / 0.15)
- assert trained_log_ratios.loc["love"] == np.log(0.1 / 0.05)
- assert trained_log_ratios.loc["hate"] == np.log(0.05 / 0.1)
- assert trained_log_ratios.loc["weather"] == 0.0
- assert trained_log_ratios.loc["I"] == 0.0
- assert trained_log_ratios.loc["am"] == 0.0
- assert trained_log_ratios.loc["because"] == 0.0
- assert trained_log_ratios.loc["the"] == 0.0
+ np.testing.assert_allclose(trained_log_ratios.loc["happy"], np.log(0.15 / 0.05))
+ np.testing.assert_allclose(trained_log_ratios.loc["sad"], np.log(0.05 / 0.15))
+ np.testing.assert_allclose(trained_log_ratios.loc["love"], np.log(0.1 / 0.05))
+ np.testing.assert_allclose(trained_log_ratios.loc["hate"], np.log(0.05 / 0.1))
+ np.testing.assert_allclose(trained_log_ratios.loc["weather"], 0.0)
+ np.testing.assert_allclose(trained_log_ratios.loc["I"], 0.0)
+ np.testing.assert_allclose(trained_log_ratios.loc["am"], 0.0)
+ np.testing.assert_allclose(trained_log_ratios.loc["because"], 0.0)
+ np.testing.assert_allclose(trained_log_ratios.loc["the"], 0.0)
 
 
 @pytest.mark.parametrize(