Deprecate training_data on subpredictors.

src/citrine/__version__.py

@@ -1 +1 @@
-__version__ = "3.4.8"
+__version__ = "3.5.0"

src/citrine/informatics/predictors/auto_ml_predictor.py

@@ -1,5 +1,6 @@
 from typing import List, Optional, Set
 
+from deprecation import deprecated
 from gemd.enumeration.base_enumeration import BaseEnumeration
 
 from citrine._rest.resource import Resource
@@ -52,7 +53,7 @@ class AutoMLPredictor(Resource["AutoMLPredictor"], PredictorNode):
     estimators: Optional[Set[AutoMLEstimator]]
         Set of estimators to consider during during AutoML model selection.
         If None is provided, defaults to AutoMLEstimator.RANDOM_FOREST.
-    training_data: Optional[List[DataSource]]
+    training_data: Optional[List[DataSource]] (deprecated)
         Sources of training data. Each can be either a CSV or an GEM Table. Candidates from
         multiple data sources will be combined into a flattened list and de-duplicated by uid and
         identifiers. De-duplication is performed if a uid or identifier is shared between two or
@@ -69,7 +70,7 @@ class AutoMLPredictor(Resource["AutoMLPredictor"], PredictorNode):
         'estimators',
         default={AutoMLEstimator.RANDOM_FOREST}
     )
-    training_data = _properties.List(
+    _training_data = _properties.List(
         _properties.Object(DataSource),
         'training_data',
         default=[]
@@ -90,7 +91,22 @@ def __init__(self,
         self.inputs: List[Descriptor] = inputs
         self.estimators: Set[AutoMLEstimator] = estimators or {AutoMLEstimator.RANDOM_FOREST}
         self.outputs = outputs
-        self.training_data: List[DataSource] = training_data or []
+        # self.training_data: List[DataSource] = training_data or []
+        if training_data:
+            self.training_data: List[DataSource] = training_data
+
+    @property
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data must be accessed through the top-level GraphPredictor.'")
+    def training_data(self):
+        """[DEPRECATED] Retrieve training data associated with this node."""
+        return self._training_data
+
+    @training_data.setter
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data should only be added to the top-level GraphPredictor.'")
+    def training_data(self, value):
+        self._training_data = value
 
     def __str__(self):
         return '<AutoMLPredictor {!r}>'.format(self.name)

src/citrine/informatics/predictors/mean_property_predictor.py

@@ -1,10 +1,12 @@
-from typing import List, Optional, Mapping, Union
+from typing import List, Mapping, Optional, Union
+
+from deprecation import deprecated
 
 from citrine._rest.resource import Resource
 from citrine._serialization import properties as _properties
 from citrine.informatics.data_sources import DataSource
 from citrine.informatics.descriptors import (
-    FormulationDescriptor, RealDescriptor, CategoricalDescriptor
+    CategoricalDescriptor, FormulationDescriptor, RealDescriptor
 )
 from citrine.informatics.predictors import PredictorNode
 
@@ -79,7 +81,7 @@ class MeanPropertyPredictor(Resource["MeanPropertyPredictor"], PredictorNode):
         ),
         'default_properties'
     )
-    training_data = _properties.List(
+    _training_data = _properties.List(
         _properties.Object(DataSource), 'training_data', default=[]
     )
 
@@ -104,7 +106,22 @@ def __init__(self,
         self.impute_properties: bool = impute_properties
         self.label: Optional[str] = label
         self.default_properties: Optional[Mapping[str, Union[str, float]]] = default_properties
-        self.training_data: List[DataSource] = training_data or []
+        # self.training_data: List[DataSource] = training_data or []
+        if training_data:
+            self.training_data: List[DataSource] = training_data
 
     def __str__(self):
         return '<MeanPropertyPredictor {!r}>'.format(self.name)
+
+    @property
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data must be accessed through the top-level GraphPredictor.'")
+    def training_data(self):
+        """[DEPRECATED] Retrieve training data associated with this node."""
+        return self._training_data
+
+    @training_data.setter
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data should only be added to the top-level GraphPredictor.'")
+    def training_data(self, value):
+        self._training_data = value

src/citrine/informatics/predictors/simple_mixture_predictor.py

@@ -1,5 +1,7 @@
 from typing import List, Optional
 
+from deprecation import deprecated
+
 from citrine._rest.resource import Resource
 from citrine._serialization import properties
 from citrine.informatics.data_sources import DataSource
@@ -28,7 +30,7 @@ class SimpleMixturePredictor(Resource["SimpleMixturePredictor"], PredictorNode):
 
     """
 
-    training_data = properties.List(properties.Object(DataSource), 'training_data', default=[])
+    _training_data = properties.List(properties.Object(DataSource), 'training_data', default=[])
 
     typ = properties.String('type', default='SimpleMixture', deserializable=False)
 
@@ -39,7 +41,8 @@ def __init__(self,
                  training_data: Optional[List[DataSource]] = None):
         self.name: str = name
         self.description: str = description
-        self.training_data: List[DataSource] = training_data or []
+        if training_data:
+            self.training_data: List[DataSource] = training_data
 
     def __str__(self):
         return '<SimpleMixturePredictor {!r}>'.format(self.name)
@@ -53,3 +56,16 @@ def input_descriptor(self) -> FormulationDescriptor:
     def output_descriptor(self) -> FormulationDescriptor:
         """The output formulation descriptor with key 'Flat Formulation'."""
         return FormulationDescriptor.flat()
+
+    @property
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data must be accessed through the top-level GraphPredictor.'")
+    def training_data(self):
+        """[DEPRECATED] Retrieve training data associated with this node."""
+        return self._training_data
+
+    @training_data.setter
+    @deprecated(deprecated_in="3.5.0", removed_in="4.0.0",
+                details="Training data should only be added to the top-level GraphPredictor.'")
+    def training_data(self, value):
+        self._training_data = value

tests/informatics/test_predictors.py

@@ -327,6 +327,24 @@ def test_auto_ml_multiple_outputs(auto_ml_multiple_outputs):
     assert built.dump()['outputs'] == [z.dump(), y.dump()]
 
 
+def test_auto_ml_deprecated_training_data(auto_ml):
+    with pytest.deprecated_call():
+        pred = AutoMLPredictor(
+            name='AutoML Predictor',
+            description='Predicts z from inputs w and x',
+            inputs=auto_ml.inputs,
+            outputs=auto_ml.outputs,
+            training_data=[GemTableDataSource(table_id=uuid.uuid4(), table_version=1)]
+        )
+
+    new_training_data = [GemTableDataSource(table_id=uuid.uuid4(), table_version=2)]
+    with pytest.deprecated_call():
+        pred.training_data = new_training_data
+
+    with pytest.deprecated_call():
+        assert pred.training_data == new_training_data
+
+
 def test_ing_to_formulation_initialization(ing_to_formulation_predictor):
     """Make sure the correct fields go to the correct places for an ingredients to formulation predictor."""
     assert ing_to_formulation_predictor.name == 'Ingredients to formulation predictor'
@@ -361,6 +379,28 @@ def test_mean_property_round_robin(mean_property_predictor):
     assert len(cat_props) == 1
 
 
+def test_mean_property_training_data_deprecated(mean_property_predictor):
+    with pytest.deprecated_call():
+        pred = MeanPropertyPredictor(
+            name='Mean property predictor',
+            description='Computes mean ingredient properties',
+            input_descriptor=mean_property_predictor.input_descriptor,
+            properties=mean_property_predictor.properties,
+            p=2.5,
+            impute_properties=True,
+            default_properties=mean_property_predictor.default_properties,
+            label=mean_property_predictor.label,
+            training_data=[GemTableDataSource(table_id=uuid.uuid4(), table_version=1)]
+        )
+
+    new_training_data = [GemTableDataSource(table_id=uuid.uuid4(), table_version=2)]
+    with pytest.deprecated_call():
+        pred.training_data = new_training_data
+
+    with pytest.deprecated_call():
+        assert pred.training_data == new_training_data
+
+
 def test_label_fractions_property_initialization(label_fractions_predictor):
     """Make sure the correct fields go to the correct places for a label fraction predictor."""
     assert label_fractions_predictor.name == 'Label fractions predictor'
@@ -379,6 +419,22 @@ def test_simple_mixture_predictor_initialization(simple_mixture_predictor):
     assert str(simple_mixture_predictor) == expected_str
 
 
+def test_simplex_mixture_training_data_deprecated():
+    with pytest.deprecated_call():
+        pred = SimpleMixturePredictor(
+            name='Simple mixture predictor',
+            description='Computes mean ingredient properties',
+            training_data=[GemTableDataSource(table_id=uuid.uuid4(), table_version=1)]
+        )
+
+    new_training_data = [GemTableDataSource(table_id=uuid.uuid4(), table_version=2)]
+    with pytest.deprecated_call():
+        pred.training_data = new_training_data
+
+    with pytest.deprecated_call():
+        assert pred.training_data == new_training_data
+
+
 def test_ingredient_fractions_property_initialization(ingredient_fractions_predictor):
     """Make sure the correct fields go to the correct places for an ingredient fractions predictor."""
     assert ingredient_fractions_predictor.name == 'Ingredient fractions predictor'

tests/serialization/test_predictors.py

@@ -19,7 +19,8 @@ def test_auto_ml_deserialization(valid_auto_ml_predictor_data):
     assert predictor.inputs[0] == RealDescriptor("x", lower_bound=0, upper_bound=100, units="")
     assert len(predictor.outputs) == 1
     assert predictor.outputs[0] == RealDescriptor("z", lower_bound=0, upper_bound=100, units="")
-    assert len(predictor.training_data) == 0
+    with pytest.deprecated_call():
+        assert len(predictor.training_data) == 0
 
 
 def test_polymorphic_auto_ml_deserialization(valid_auto_ml_predictor_data):
@@ -31,7 +32,8 @@ def test_polymorphic_auto_ml_deserialization(valid_auto_ml_predictor_data):
     assert predictor.inputs[0] == RealDescriptor("x", lower_bound=0, upper_bound=100, units="")
     assert len(predictor.outputs) == 1
     assert predictor.outputs[0] == RealDescriptor("z", lower_bound=0, upper_bound=100, units="")
-    assert len(predictor.training_data) == 0
+    with pytest.deprecated_call():
+        assert len(predictor.training_data) == 0
 
 
 def test_legacy_serialization(valid_auto_ml_predictor_data):