def setUp(self):
"""Initialize data used in the tests."""
prediction_field = "MEAL_TYPE"
start_level = 1
include_only = None
negate = False
n_processes = 1
self.problem_data = build_problem_data(group_map_lines, mapping_lines, prediction_field, start_level, include_only, negate, n_processes, parse_object_string=parse_object_string)
initial_feature_ids = self.problem_data.get_group_ids(start_level)
self.initial_feature_vector = FeatureVector([self.problem_data.get_feature_record(start_level, id) for id in initial_feature_ids])
self.split_proportion = 0.5
self.split_abun_coef = 0
self.split_score_coef = -1
self.merge_proportion = 0.5
self.merge_abun_coef = -1
self.merge_score_coef = 0
self.delete_proportion = 0.0
self.delete_abun_coef = -1
self.delete_score_coef = -1
self.group_actions = [
SplitAction(self.problem_data, self.split_proportion, self.split_abun_coef, self.split_score_coef),
MergeAction(self.problem_data, self.merge_proportion, self.merge_abun_coef, self.merge_score_coef),
DeleteAction(self.problem_data, self.delete_proportion, self.delete_abun_coef, self.delete_score_coef)
]
self.n_generate = 2
self.action_vector_generator = ActionVectorGenerator(self.group_actions, self.n_generate, self.problem_data)
class ActionVectorGeneratorTests(TestCase):
"""Tests for functions in the action_vector_generator module."""
def setUp(self):
"""Initialize data used in the tests."""
prediction_field = "MEAL_TYPE"
start_level = 1
include_only = None
negate = False
n_processes = 1
self.problem_data = build_problem_data(group_map_lines, mapping_lines, prediction_field, start_level, include_only, negate, n_processes, parse_object_string=parse_object_string)
initial_feature_ids = self.problem_data.get_group_ids(start_level)
self.initial_feature_vector = FeatureVector([self.problem_data.get_feature_record(start_level, id) for id in initial_feature_ids])
self.split_proportion = 0.5
self.split_abun_coef = 0
self.split_score_coef = -1
self.merge_proportion = 0.5
self.merge_abun_coef = -1
self.merge_score_coef = 0
self.delete_proportion = 0.0
self.delete_abun_coef = -1
self.delete_score_coef = -1
self.group_actions = [
SplitAction(self.problem_data, self.split_proportion, self.split_abun_coef, self.split_score_coef),
MergeAction(self.problem_data, self.merge_proportion, self.merge_abun_coef, self.merge_score_coef),
DeleteAction(self.problem_data, self.delete_proportion, self.delete_abun_coef, self.delete_score_coef)
]
self.n_generate = 2
self.action_vector_generator = ActionVectorGenerator(self.group_actions, self.n_generate, self.problem_data)
def test_generate_action_scores(self):
"""
Test the score generation process.
"""
n_iterations = 10
feature_vector = self.initial_feature_vector
max_length = 10000
for i in range(n_iterations):
#Generate action scores for each group in a feature vector
if len(feature_vector.rec_list) > max_length:
feature_vector.rec_list = feature_vector.rec_list[-max_length:]
outcome = ModelOutcome(feature_vector, [r for r in range(len(feature_vector.get_record_list()))], prediction_quality=0, predictions=[])
action_scores = self.action_vector_generator.generate_action_scores(outcome)
#Test required invariants over the returned score values, like None for inapplicable actions
self.assertEquals(len(action_scores), len(self.group_actions))
for action_index in range(len(action_scores)):
scores = action_scores[action_index]
self.assertEquals(len(scores), len(feature_vector.get_record_list()))
action = self.group_actions[action_index]
if isinstance(action, MergeAction):
for feature_index in range(len(scores)):
if scores[feature_index] == None:
self.assertEquals(feature_vector.get_record_list()[feature_index].get_scope(), 0)
else:
self.assertNotEquals(feature_vector.get_record_list()[feature_index].get_scope(), 0)
elif isinstance(action, SplitAction):
for feature_index in range(len(scores)):
if scores[feature_index] == None:
self.assertEquals(feature_vector.get_record_list()[feature_index].get_scope(), self.problem_data.get_max_scope())
else:
self.assertNotEquals(feature_vector.get_record_list()[feature_index].get_scope(), self.problem_data.get_max_scope())
#Have the vector generator create a new vector to test
feature_vector = self.action_vector_generator.generate_action_vector(outcome)[0]