Cleaning up dead code and malformed docs.

docs/source/workflows/code_examples.rst

@@ -66,7 +66,7 @@ This pattern is also extremely useful for performing optimization over complex o
         }
     )
 
-    physical_properties_predictor = AutoMLModel(
+    physical_properties_predictor = AutoMLPredictor(
         name = 'physical properties model',
         inputs = [
             wheat_flour_quantity,

docs/source/workflows/predictors.rst

@@ -251,16 +251,16 @@ The following example demonstrates how to use a :class:`~citrine.informatics.pre
     ml_predictor = AutoMLPredictor(
         name='ML Model for Density',
         description='Predict the density, given molecular features of the solvent',
-        inputs = features,
-        output = [output_desc]
+        inputs=features,
+        outputs=[output_desc]
     )
 
     # use a graph predictor to wrap together the featurizer and the machine learning model
     graph_predictor = GraphPredictor(
         name='Density from solvent molecular structure',
         description='Predict the density from the solvent molecular structure using molecular structure features.',
-        predictors = [featurizer, ml_predictor],
-        training_data = [GemTableDataSource(table_id=training_data_table_uid, table_version=training_data_table_version)] # training data shared by all sub-predictors
+        predictors=[featurizer, ml_predictor],
+        training_data=[GemTableDataSource(table_id=training_data_table_uid, table_version=training_data_table_version)] # training data shared by all sub-predictors
     )
 
     # register or update predictor by name

src/citrine/informatics/predictor_evaluator.py

@@ -55,7 +55,7 @@ def name(self) -> str:
 
         A name is required by all evaluators because it is used as the top-level key
         in the results returned by a
-        :class:`citrine.informatics.workflows.PredictorEvaluationWorkflow`.
+        :class:`citrine.informatics.executions.predictor_evaluation.PredictorEvaluation`.
         As such, the names of all evaluators within a single workflow must be unique.
         """
         raise NotImplementedError  # pragma: no cover

tests/conftest.py

@@ -819,18 +819,6 @@ def generic_entity():
     }
 
 
-@pytest.fixture
-def predictor_evaluation_execution_dict(generic_entity):
-    ret = deepcopy(generic_entity)
-    ret.update({
-        "workflow_id": str(uuid.uuid4()),
-        "predictor_id": str(uuid.uuid4()),
-        "predictor_version": random.randint(1, 10),
-        "evaluator_names": ["Example evaluator"]
-    })
-    return ret
-
-
 @pytest.fixture
 def design_execution_dict(generic_entity):
     ret = generic_entity.copy()
@@ -872,15 +860,3 @@ def example_generation_results():
             }
         }]
     }
-
-
-
-@pytest.fixture
-def predictor_evaluation_workflow_dict(generic_entity, example_cv_evaluator_dict, example_holdout_evaluator_dict):
-    ret = deepcopy(generic_entity)
-    ret.update({
-        "name": "Example PEW",
-        "description": "Example PEW for testing",
-        "evaluators": [example_cv_evaluator_dict, example_holdout_evaluator_dict]
-    })
-    return ret

tests/utils/factories.py

@@ -517,19 +517,6 @@ class CrossValidationEvaluatorFactory(factory.DictFactory):
     type = "CrossValidationEvaluator"
 
 
-class PredictorEvaluationWorkflowFactory(factory.DictFactory):
-    id = factory.Faker('uuid4')
-    name = factory.Faker("company")
-    description = factory.Faker("catch_phrase")
-    archived = False
-    evaluators = factory.List([factory.SubFactory(CrossValidationEvaluatorFactory)])
-    type = "PredictorEvaluationWorkflow"
-    # TODO  Create Trait and status_detail content
-    status = "SUCCEEDED"
-    status_description = "READY"
-    status_detail = []
-
-
 class PredictorEvaluationDataFactory(factory.DictFactory):
     evaluators = factory.List([factory.SubFactory(CrossValidationEvaluatorFactory)])