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How to use test_assertion method in pytest-play

Best Python code snippet using pytest-play_python
test_HMMGeneral.py
Source:test_HMMGeneral.py
...22    """Letters as the emissions of the model.23    """24    letters = ['A', 'B']25# -- helper functions26def test_assertion(name, result, expected):27    """Helper function to test an assertion and print out a reasonable error.28    """29    assert result == expected, "Expected %s, got %s for %s" \30           % (expected, result, name)31class MarkovModelBuilderTest(unittest.TestCase):32    def setUp(self):33        self.mm_builder = MarkovModel.MarkovModelBuilder(NumberAlphabet(),34                                                         LetterAlphabet())35    def test_test_initialize(self):36        """Making sure MarkovModelBuilder is initialized correctly.37        """38        expected_transition_prob = {}39        expected_transition_pseudo = {}40        expected_emission_prob = {('2', 'A'): 0, ('1', 'A'): 0,41                                  ('1', 'B'): 0, ('2', 'B'): 0}42        expected_emission_pseudo = {('2', 'A'): 1, ('1', 'A'): 1,43                                    ('1', 'B'): 1, ('2', 'B'): 1}44        assertions = []45        test_assertion("Transition prob", self.mm_builder.transition_prob,46                          expected_transition_prob)47        test_assertion("Transition pseudo",48                          self.mm_builder.transition_pseudo,49                          expected_transition_pseudo)50        test_assertion("Emission prob", self.mm_builder.emission_prob,51                           expected_emission_prob)52        test_assertion("Emission pseudo", self.mm_builder.emission_pseudo,53                           expected_emission_pseudo)54    def test_allow_all_transitions(self):55        """Testing allow_all_transitions.56        """57        self.mm_builder.allow_all_transitions()58        expected_prob = {('2', '1'): 0, ('1', '1'): 0,59                         ('1', '2'): 0, ('2', '2'): 0}60        expected_pseudo = {('2', '1'): 1, ('1', '1'): 1,61                           ('1', '2'): 1, ('2', '2'): 1}62        test_assertion("Probabilities", self.mm_builder.transition_prob,63                       expected_prob)64        test_assertion("Pseudo counts", self.mm_builder.transition_pseudo,65                       expected_pseudo)66    def test_set_initial_probabilities(self):67        self.mm_builder.set_initial_probabilities({})68        test_assertion("Equal initial probabilities by default",69                       self.mm_builder.initial_prob, {'1': 0.5, '2': 0.5})70        # initial probability sum > 1, should raise an exception71        self.assertRaises(72            Exception,73            self.mm_builder.set_initial_probabilities,74            {'1': 0.6, '2': 0.5})75        # referencing invalid states should raise an exception76        self.assertRaises(77            Exception,78            self.mm_builder.set_initial_probabilities,79            {'666': 0.1})80        self.mm_builder.set_initial_probabilities({'1': 0.2})81        test_assertion("One default initial probability",82                       self.mm_builder.initial_prob, {'1': 0.2, '2': 0.8})83        self.mm_builder.set_initial_probabilities({'1': 0.9, '2': 0.1})84        test_assertion("Set initial probabilities",85                       self.mm_builder.initial_prob, {'1': 0.9, '2': 0.1})86    def test_set_equal_probabilities(self):87        self.mm_builder.allow_transition('1', '2', 0.05)88        self.mm_builder.allow_transition('2', '1', 0.95)89        self.mm_builder.set_equal_probabilities()90        test_assertion("Equal initial probabilities",91                       self.mm_builder.initial_prob,92                       {'1': 0.5, '2': 0.5})93        test_assertion("Equal transition probabilities",94                       self.mm_builder.transition_prob,95                       {('1', '2'): 0.5, ('2', '1'): 0.5})96        test_assertion("Equal emission probabilities",97                       self.mm_builder.emission_prob,98                       {('2', 'A'): 0.25, ('1', 'B'): 0.25,99                        ('1', 'A'): 0.25, ('2', 'B'): 0.25})100    def test_set_random_probabilities(self):101        self.mm_builder.allow_transition('1', '2', 0.05)102        self.mm_builder.allow_transition('2', '1', 0.95)103        self.mm_builder.set_random_probabilities()104        test_assertion("Number of initial probabilities",105                       len(self.mm_builder.initial_prob),106                       len(self.mm_builder._state_alphabet.letters))107        # To test this more thoroughly, perhaps mock random.random() and108        # verify that it's being called as expected?109class HiddenMarkovModelTest(unittest.TestCase):110    def setUp(self):111        self.mm_builder = MarkovModel.MarkovModelBuilder(NumberAlphabet(),112                                                    LetterAlphabet())113    def test_transitions_from(self):114        """Testing the calculation of transitions_from115        """116        self.mm_builder.allow_transition('1', '2', 1.0)117        self.mm_builder.allow_transition('2', '1', 0.5)118        self.mm_builder.allow_transition('2', '2', 0.5)119        self.mm_builder.set_initial_probabilities({})120        self.mm = self.mm_builder.get_markov_model()121        state_1 = self.mm.transitions_from("1")122        expected_state_1 = ["2"]123        state_1.sort()124        expected_state_1.sort()125        test_assertion("States reached by transitions from state 1",126                       state_1, expected_state_1)127        state_2 = self.mm.transitions_from("2")128        expected_state_2 = ["1", "2"]129        state_2.sort()130        expected_state_2.sort()131        test_assertion("States reached by transitions from state 2",132                       state_2, expected_state_2)133        fake_state = self.mm.transitions_from("Fake")134        expected_fake_state = []135        test_assertion("States reached by transitions from a fake transition",136                       fake_state, expected_fake_state)137    def test_transitions_to(self):138        """Testing the calculation of transitions_to139        """140        self.mm_builder.allow_transition('1', '1', 0.5)141        self.mm_builder.allow_transition('1', '2', 0.5)142        self.mm_builder.allow_transition('2', '1', 1.0)143        self.mm_builder.set_initial_probabilities({})144        self.mm = self.mm_builder.get_markov_model()145        state_1 = self.mm.transitions_to("1")146        expected_state_1 = ["1", "2"]147        state_1.sort()148        expected_state_1.sort()149        test_assertion("States with transitions to state 1",150                       state_1, expected_state_1)151        state_2 = self.mm.transitions_to("2")152        expected_state_2 = ["1"]153        state_2.sort()154        expected_state_2.sort()155        test_assertion("States with transitions to state 2",156                       state_2, expected_state_2)157        fake_state = self.mm.transitions_to("Fake")158        expected_fake_state = []159        test_assertion("States with transitions to a fake transition",160                       fake_state, expected_fake_state)161    def test_allow_transition(self):162        """Testing allow_transition163        """164        self.mm_builder.allow_transition('1', '2', 1.0)165        self.mm_builder.set_initial_probabilities({})166        self.mm = self.mm_builder.get_markov_model()167        state_1 = self.mm.transitions_from("1")168        expected_state_1 = ["2"]169        state_1.sort()170        expected_state_1.sort()171        test_assertion("States reached by transitions from state 1",172                       state_1, expected_state_1)173        state_2 = self.mm.transitions_from("2")174        expected_state_2 = []175        state_2.sort()176        expected_state_2.sort()177        test_assertion("States reached by transitions from state 2",178                       state_2, expected_state_2)179        state_1 = self.mm.transitions_to("1")180        expected_state_1 = []181        state_1.sort()182        expected_state_1.sort()183        test_assertion("States with transitions to state 1",184                       state_1, expected_state_1)185        state_2 = self.mm.transitions_to("2")186        expected_state_2 = ["1"]187        state_2.sort()188        expected_state_2.sort()189        test_assertion("States with transitions to state 2",190                       state_2, expected_state_2)191    def test_simple_hmm(self):192        """Test a simple model with 2 states and 2 symbols.193        """194        # set initial probabilities195        prob_initial = [0.4, 0.6]196        self.mm_builder.set_initial_probabilities(197                {'1': prob_initial[0], '2': prob_initial[1]})198        # set transition probabilities199        prob_transition = [[0.35, 0.65], [0.45, 0.55]]200        self.mm_builder.allow_transition('1', '1', prob_transition[0][0])201        self.mm_builder.allow_transition('1', '2', prob_transition[0][1])202        self.mm_builder.allow_transition('2', '1', prob_transition[1][0])203        self.mm_builder.allow_transition('2', '2', prob_transition[1][1])204        # set emission probabilities205        prob_emission = [[0.45, 0.55], [0.75, 0.25]]206        self.mm_builder.set_emission_score('1', 'A', prob_emission[0][0])207        self.mm_builder.set_emission_score('1', 'B', prob_emission[0][1])208        self.mm_builder.set_emission_score('2', 'A', prob_emission[1][0])209        self.mm_builder.set_emission_score('2', 'B', prob_emission[1][1])210        # Check all two letter sequences using a brute force calculation211        model = self.mm_builder.get_markov_model()212        for first_letter in LetterAlphabet.letters:213            for second_letter in LetterAlphabet.letters:214                observed_emissions = [first_letter, second_letter]215                viterbi = model.viterbi(observed_emissions, NumberAlphabet)216                self._checkSimpleHmm(prob_initial, prob_transition,217                                 prob_emission, viterbi, observed_emissions)218    def _checkSimpleHmm(self, prob_initial, prob_transition, prob_emission,219                         viterbi, observed_emissions):220        max_prob = 0221        # expected first and second states in the sequence, calculated below222        seq_first_state = None223        seq_second_state = None224        # convert the observed letters 'A' or 'B' into 0 or 1225        letter1 = ord(observed_emissions[0]) - ord('A')226        letter2 = ord(observed_emissions[1]) - ord('A')227        for first_state in NumberAlphabet.letters:228            for second_state in NumberAlphabet.letters:229                # compute the probability of the state sequence first_state,230                # second_state emitting the observed_emissions231                state1 = ord(first_state) - ord('1')232                state2 = ord(second_state) - ord('1')233                prob = prob_initial[state1] * prob_emission[state1][letter1] *\234                    prob_transition[state1][state2] *\235                    prob_emission[state2][letter2]236                if prob > max_prob:237                    seq_first_state = first_state238                    seq_second_state = second_state239                    max_prob = prob240        max_prob = math.log(max_prob)241        seq = viterbi[0]242        prob = viterbi[1]243        test_assertion("state sequence",244                       str(seq),245                       seq_first_state + seq_second_state)246        test_assertion("log probability", round(prob, 11), round(max_prob, 11))247    def test_non_ergodic(self):248        """Test a non-ergodic model (meaning that some transitions are not249        allowed).250        """251        # make state '1' the initial state252        prob_1_initial = 1.0253        self.mm_builder.set_initial_probabilities(254                {'1': prob_1_initial})255        # probabilities of transitioning from state 1 to 1, and 1 to 2256        prob_1_to_1 = 0.5257        prob_1_to_2 = 0.5258        # set up allowed transitions259        self.mm_builder.allow_transition('1', '1', prob_1_to_1)260        self.mm_builder.allow_transition('1', '2', prob_1_to_2)261        # Emission probabilities262        # In state 1 the most likely emission is A, in state 2 the most263        # likely emission is B. (Would be simpler just to use 1.0 and 0.0264        # emission probabilities here, but the algorithm blows up on zero265        # probabilities because of the conversion to log space.)266        prob_1_A = 0.95267        prob_1_B = 0.05268        prob_2_A = 0.05269        prob_2_B = 0.95270        # set emission probabilities271        self.mm_builder.set_emission_score('1', 'A', prob_1_A)272        self.mm_builder.set_emission_score('1', 'B', prob_1_B)273        self.mm_builder.set_emission_score('2', 'A', prob_2_A)274        self.mm_builder.set_emission_score('2', 'B', prob_2_B)275        # run the Viterbi algorithm to find the most probable state path276        model = self.mm_builder.get_markov_model()277        observed_emissions = ['A', 'B']278        viterbi = model.viterbi(observed_emissions, NumberAlphabet)279        seq = viterbi[0]280        prob = viterbi[1]281        # the most probable path must be from state 1 to state 2282        test_assertion("most probable path", str(seq), '12')283        # The probability of that path is the probability of starting in284        # state 1, then emitting an A, then transitioning 1 -> 2, then285        # emitting a B.286        # Note that probabilities are converted into log space.287        expected_prob = math.log(prob_1_initial)\288        + math.log(prob_1_A)\289        + math.log(prob_1_to_2)\290        + math.log(prob_2_B)291        test_assertion("log probability of most probable path",292                       prob, expected_prob)293class ScaledDPAlgorithmsTest(unittest.TestCase):294    def setUp(self):295        # set up our Markov Model296        mm_builder = MarkovModel.MarkovModelBuilder(NumberAlphabet(),297                                                    LetterAlphabet())298        mm_builder.allow_all_transitions()299        mm_builder.set_equal_probabilities()300        mm = mm_builder.get_markov_model()301        # now set up a test sequence302        emission_seq = Seq("ABB", LetterAlphabet())303        state_seq = Seq("", NumberAlphabet())304        training_seq = Trainer.TrainingSequence(emission_seq, state_seq)305        # finally set up the DP...
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