It looks like one function not tested by pytest

[Robert]

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()