def setUp(self):
super(TestMakePipeline, self).setUp()
self.env_vars = {key: value for key, value in \
self.config['before_run']['environment_variables'].items()}
self.runner = PipelineGenerator(self.config)
self.scripts1 = ['./script_a --option 0.1', './script_b 0.2']
self.scripts2 = ['./script_a --option 0.3', './script_b 0.4']
def test_run_optimum_within_bounds(self):
generator = PipelineGenerator(self.config_with_optimum(15, 3))
designer = generator.new_designer_from_config(
model_selection='manual', manual_formula=self.full_formula)
design = designer.new_design()
pipeline = generator.new_pipeline_collection(design)
cmd = '{script}'
executor = self.__class__.executor(workdir=self.work_dir,
base_command=cmd)
results = executor.run_pipeline_collection(pipeline)
optimum = designer.update_factors_from_response(results)
expected_optimum = {'FactorA': 15, 'FactorB': 3}
self.assertTrue(optimum.converged)
for factor in design.columns:
self.assertTrue(
np.isclose(optimum.predicted_optimum[factor],
expected_optimum[factor]))
def test_screening_finds_optimum_at_origin(self):
generator = PipelineGenerator(self.config_with_optimum(0, 0))
designer = generator.new_designer_from_config(
skip_screening=False,
model_selection='manual',
manual_formula=self.full_formula)
design = designer.new_design()
pipeline = generator.new_pipeline_collection(design)
cmd = '{script}'
executor = self.__class__.executor(workdir=self.work_dir,
base_command=cmd)
results = executor.run_pipeline_collection(pipeline)
optimum = designer.update_factors_from_response(results)
self.assertEqual(designer._phase, 'optimization')
self.assertFalse(optimum.converged)
expected_optimum = {'FactorA': 0, 'FactorB': 0}
for factor in design.columns:
self.assertTrue(
np.isclose(optimum.predicted_optimum[factor],
expected_optimum[factor]))
def test_run_optimum_outside_bounds(self):
generator = PipelineGenerator(self.config_with_optimum(5, 5))
designer = generator.new_designer_from_config(
model_selection='manual', manual_formula=self.full_formula)
design = designer.new_design()
pipeline = generator.new_pipeline_collection(design)
cmd = '{script}'
executor = self.__class__.executor(workdir=self.work_dir,
base_command=cmd)
results = executor.run_pipeline_collection(pipeline)
optimum = designer.update_factors_from_response(results)
expected_optimum = {'FactorA': 5, 'FactorB': 5}
self.assertFalse(optimum.converged)
for factor in design.columns:
# Optimum is outside of bounds and optimization should be
# constrained to be inside design space. So the expected
# optimum should not have been predicted after a single
# iteration.
self.assertFalse(
np.isclose(optimum.predicted_optimum[factor],
expected_optimum[factor]))
def setUp(self):
script_one = {
'script': './script_a',
'factors': {
'FactorA': {
'factor_name': 'Factor A',
'script_option': '--option'
}
}
}
script_two = {
'script': './script_b {% FactorB %}',
'factors': {
'FactorB': {
'factor_name': 'Factor B',
'substitute': True
}
}
}
design_spec = {
'type': 'CCC',
'factors': {
'FactorA': {
'min': 0,
'max': 1,
'low_init': 0,
'high_init': .2
},
'FactorB': {
'min': 0,
'max': 1,
'low_init': .1,
'high_init': .3
}
},
'responses': {
'ResponseA': {'criterion': 'maximize'}
}
}
self.work_dir = os.path.join(os.getcwd(), 'work_dir')
os.makedirs(self.work_dir, exist_ok=True)
self.env_vars = {'MYPATH': '~/a/path'}
before = {'environment_variables': self.env_vars}
self.outfile = 'my_results.txt'
self.config = {
'working_directory': self.work_dir,
'design': design_spec,
'results_file': self.outfile,
'before_run': before,
'pipeline': ['ScriptOne', 'ScriptTwo'],
'ScriptOne': script_one,
'ScriptTwo': script_two
}
self.design = pd.DataFrame([
['One', .1, .2],
['Two', .3, .4]
], columns=['Exp Id', 'FactorA', 'FactorB'])
self.generator = PipelineGenerator(copy.deepcopy(self.config))
self.pipeline = self.generator.new_pipeline_collection(self.design,'Exp Id')
def test_bad_config_raises_valueerror(self):
# Create bad pipeline by "skipping" one step in pipeline
bad_config = copy.deepcopy(self.config)
bad_config['pipeline'].pop()
self.assertRaises(ValueError, lambda: PipelineGenerator(bad_config))
def test_creating_doesnt_crash(self):
generator = PipelineGenerator(self.config)