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]))