It looks like one function not tested by pytest

[Robert]

On Tuesday, January 12, 2016 at 3:36:13 PM UTC-5, Robert wrote:
> Hi,
> 
> I modify a test suite, and simplified to the below content.
> I am interested in function:
> test_bad_covariance_type()
> 
> I think that it tests for wrong type input, i.e. 
> 'badcovariance_type'
> will generate error, which is expected. Thus, it passes test.
> 
> When a correct type, such as 'diag', is given, there will be no error 
> generated. pytest will declare a failure.
> 
> 
> Unfortunately, it will always pass the pytest for both cases.
> 
> What is wrong with my code or my understanding?
> 
> 
> Thanks,
> 
> 
> 
> 
> //////////////
> from __future__ import absolute_import
> 
> from unittest import TestCase
> 
> import numpy as np
> import pytest
> 
> from hmmlearn import hmm
> 
> from . import log_likelihood_increasing, make_covar_matrix, normalized
> 
> 
> class GaussianHMMTestMixin(object):
>     covariance_type = None  # set by subclasses
> 
>     def setUp(self):
>         self.prng = prng = np.random.RandomState(10)
>         self.n_components = n_components = 3
>         self.n_features = n_features = 3
>         self.startprob = prng.rand(n_components)
>         self.startprob = self.startprob / self.startprob.sum()
>         self.transmat = prng.rand(n_components, n_components)
>         self.transmat /= np.tile(self.transmat.sum(axis=1)[:, np.newaxis],
>                                  (1, n_components))
>         self.means = prng.randint(-20, 20, (n_components, n_features))
>         self.covars = dict(
>             (cv_type, make_covar_matrix(cv_type, n_components, n_features))
>             for cv_type in ["spherical", "tied", "diag", "full"])
>         self.expanded_covars = {
>             'spherical': [np.eye(n_features) * cov
>                           for cov in self.covars['spherical']],
>             'diag': [np.diag(cov) for cov in self.covars['diag']],
>             'tied': [self.covars['tied']] * n_components,
>             'full': self.covars['full'],
>         }
> 
>     def test_bad_covariance_type(self):
>         with pytest.raises(ValueError):
>             #h = hmm.GaussianHMM(20, covariance_type='badcovariance_type')
>             h = hmm.GaussianHMM(covariance_type='diag')
>             h.means_ = self.means
>             h.covars_ = []
>             h.startprob_ = self.startprob
>             h.transmat_ = self.transmat
>             h._check()
> 
>     def test_fit(self, params='stmc', n_iter=5, **kwargs):
>         h = hmm.GaussianHMM(self.n_components, self.covariance_type)
>         h.startprob_ = self.startprob
>         h.transmat_ = normalized(
>             self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
>         h.means_ = 20 * self.means
>         h.covars_ = self.covars[self.covariance_type]
> 
>         lengths = [10] * 10
>         X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
> 
>         # Mess up the parameters and see if we can re-learn them.
>         # TODO: change the params and uncomment the check
>         h.fit(X, lengths=lengths)
> 
> 
>         
> 
> class TestGaussianHMMWithSphericalCovars(GaussianHMMTestMixin, TestCase):
>     covariance_type = 'spherical'
> 
>     def test_fit_startprob_and_transmat(self):
>         self.test_fit('st')
>                         
>     def test_bad_covariance_type0(self):
>         self.test_bad_covariance_type()

It turns out that it always raises error, because there are other errors
besides the covariance_type object. That is, pytest works as expected.
Thanks,