feat: added support for conditional parameters in hyperparameter tuni…

…ng (#1544) * feat: added support for conditional parameters in hyperparameter tuning * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * fixing unit tests * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * fixed all failing tests * addressed PR comments * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md * 🦉 Updates from OwlBot post-processor See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
googleapis · Aug 2, 2022 · 744cc38 · 744cc38
1 parent 3526b3e
commit 744cc38
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diff --git a/google/cloud/aiplatform/hyperparameter_tuning.py b/google/cloud/aiplatform/hyperparameter_tuning.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-# Copyright 2021 Google LLC
+# Copyright 2022 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -29,6 +29,10 @@
     "unspecified": gca_study_compat.StudySpec.ParameterSpec.ScaleType.SCALE_TYPE_UNSPECIFIED,
 }
 
+_INT_VALUE_SPEC = "integer_value_spec"
+_DISCRETE_VALUE_SPEC = "discrete_value_spec"
+_CATEGORICAL_VALUE_SPEC = "categorical_value_spec"
+
 
 class _ParameterSpec(metaclass=abc.ABCMeta):
     """Base class represents a single parameter to optimize."""
@@ -77,10 +81,30 @@ def _to_parameter_spec(
         self, parameter_id: str
     ) -> gca_study_compat.StudySpec.ParameterSpec:
         """Converts this parameter to ParameterSpec."""
-        # TODO: Conditional parameters
+        conditions = []
+        if self.conditional_parameter_spec is not None:
+            for (conditional_param_id, spec) in self.conditional_parameter_spec.items():
+                condition = (
+                    gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec()
+                )
+                if self._parameter_spec_value_key == _INT_VALUE_SPEC:
+                    condition.parent_int_values = gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition(
+                        values=spec.parent_values
+                    )
+                elif self._parameter_spec_value_key == _CATEGORICAL_VALUE_SPEC:
+                    condition.parent_categorical_values = gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition(
+                        values=spec.parent_values
+                    )
+                elif self._parameter_spec_value_key == _DISCRETE_VALUE_SPEC:
+                    condition.parent_discrete_values = gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition(
+                        values=spec.parent_values
+                    )
+                condition.parameter_spec = spec._to_parameter_spec(conditional_param_id)
+                conditions.append(condition)
         parameter_spec = gca_study_compat.StudySpec.ParameterSpec(
             parameter_id=parameter_id,
             scale_type=_SCALE_TYPE_MAP.get(getattr(self, "scale", "unspecified")),
+            conditional_parameter_specs=conditions,
         )
 
         setattr(
@@ -105,6 +129,8 @@ def __init__(
         min: float,
         max: float,
         scale: str,
+        conditional_parameter_spec: Optional[Dict[str, "_ParameterSpec"]] = None,
+        parent_values: Optional[Sequence[Union[int, float, str]]] = None,
     ):
         """
         Value specification for a parameter in ``DOUBLE`` type.
@@ -120,9 +146,16 @@ def __init__(
                 Required. The type of scaling that should be applied to this parameter.
 
                 Accepts: 'linear', 'log', 'reverse_log'
+            conditional_parameter_spec (Dict[str, _ParameterSpec]):
+                Optional. The conditional parameters associated with the object. The dictionary key
+                is the ID of the conditional parameter and the dictionary value is one of
+                `IntegerParameterSpec`, `CategoricalParameterSpec`, or `DiscreteParameterSpec`
+            parent_values (Sequence[Union[int, float, str]]):
+                Optional. This argument is only needed when the object is a conditional parameter
+                and specifies the parent parameter's values for which the condition applies.
         """
 
-        super().__init__()
+        super().__init__(conditional_parameter_spec, parent_values)
 
         self.min = min
         self.max = max
@@ -142,6 +175,8 @@ def __init__(
         min: int,
         max: int,
         scale: str,
+        conditional_parameter_spec: Optional[Dict[str, "_ParameterSpec"]] = None,
+        parent_values: Optional[Sequence[Union[int, float, str]]] = None,
     ):
         """
         Value specification for a parameter in ``INTEGER`` type.
@@ -157,9 +192,18 @@ def __init__(
                 Required. The type of scaling that should be applied to this parameter.
 
                 Accepts: 'linear', 'log', 'reverse_log'
+            conditional_parameter_spec (Dict[str, _ParameterSpec]):
+                Optional. The conditional parameters associated with the object. The dictionary key
+                is the ID of the conditional parameter and the dictionary value is one of
+                `IntegerParameterSpec`, `CategoricalParameterSpec`, or `DiscreteParameterSpec`
+            parent_values (Sequence[int]):
+                Optional. This argument is only needed when the object is a conditional parameter
+                and specifies the parent parameter's values for which the condition applies.
         """
-
-        super().__init__()
+        super().__init__(
+            conditional_parameter_spec=conditional_parameter_spec,
+            parent_values=parent_values,
+        )
 
         self.min = min
         self.max = max
@@ -177,15 +221,26 @@ class CategoricalParameterSpec(_ParameterSpec):
     def __init__(
         self,
         values: Sequence[str],
+        conditional_parameter_spec: Optional[Dict[str, "_ParameterSpec"]] = None,
+        parent_values: Optional[Sequence[Union[int, float, str]]] = None,
     ):
         """Value specification for a parameter in ``CATEGORICAL`` type.
 
         Args:
             values (Sequence[str]):
                 Required. The list of possible categories.
+            conditional_parameter_spec (Dict[str, _ParameterSpec]):
+                Optional. The conditional parameters associated with the object. The dictionary key
+                is the ID of the conditional parameter and the dictionary value is one of
+                `IntegerParameterSpec`, `CategoricalParameterSpec`, or `DiscreteParameterSpec`
+            parent_values (Sequence[str]):
+                Optional. This argument is only needed when the object is a conditional parameter
+                and specifies the parent parameter's values for which the condition applies.
         """
-
-        super().__init__()
+        super().__init__(
+            conditional_parameter_spec=conditional_parameter_spec,
+            parent_values=parent_values,
+        )
 
         self.values = values
 
@@ -202,6 +257,8 @@ def __init__(
         self,
         values: Sequence[float],
         scale: str,
+        conditional_parameter_spec: Optional[Dict[str, "_ParameterSpec"]] = None,
+        parent_values: Optional[Sequence[Union[int, float, str]]] = None,
     ):
         """Value specification for a parameter in ``DISCRETE`` type.
 
@@ -216,9 +273,18 @@ def __init__(
             Required. The type of scaling that should be applied to this parameter.
 
             Accepts: 'linear', 'log', 'reverse_log'
+        conditional_parameter_spec (Dict[str, _ParameterSpec]):
+            Optional. The conditional parameters associated with the object. The dictionary key
+            is the ID of the conditional parameter and the dictionary value is one of
+            `IntegerParameterSpec`, `CategoricalParameterSpec`, or `DiscreteParameterSpec`
+        parent_values (Sequence[float]):
+            Optional. This argument is only needed when the object is a conditional parameter
+            and specifies the parent parameter's values for which the condition applies.
         """
-
-        super().__init__()
+        super().__init__(
+            conditional_parameter_spec=conditional_parameter_spec,
+            parent_values=parent_values,
+        )
 
         self.values = values
         self.scale = scale
diff --git a/tests/unit/aiplatform/test_hyperparameter_tuning_job.py b/tests/unit/aiplatform/test_hyperparameter_tuning_job.py
@@ -76,6 +76,13 @@
 
 _TEST_LABELS = {"my_hp_key": "my_hp_value"}
 
+_TEST_CONDITIONAL_PARAMETER_DECAY = hpt.DoubleParameterSpec(
+    min=1e-07, max=1, scale="linear", parent_values=[32, 64]
+)
+_TEST_CONDITIONAL_PARAMETER_LR = hpt.DoubleParameterSpec(
+    min=1e-07, max=1, scale="linear", parent_values=[4, 8, 16]
+)
+
 _TEST_BASE_HYPERPARAMETER_TUNING_JOB_PROTO = gca_hyperparameter_tuning_job_compat.HyperparameterTuningJob(
     display_name=_TEST_DISPLAY_NAME,
     study_spec=gca_study_compat.StudySpec(
@@ -109,8 +116,34 @@
                 parameter_id="batch_size",
                 scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
                 discrete_value_spec=gca_study_compat.StudySpec.ParameterSpec.DiscreteValueSpec(
-                    values=[16, 32]
+                    values=[4, 8, 16, 32, 64]
                 ),
+                conditional_parameter_specs=[
+                    gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec(
+                        parent_discrete_values=gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition(
+                            values=[32, 64]
+                        ),
+                        parameter_spec=gca_study_compat.StudySpec.ParameterSpec(
+                            double_value_spec=gca_study_compat.StudySpec.ParameterSpec.DoubleValueSpec(
+                                min_value=1e-07, max_value=1
+                            ),
+                            scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
+                            parameter_id="decay",
+                        ),
+                    ),
+                    gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec(
+                        parent_discrete_values=gca_study_compat.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition(
+                            values=[4, 8, 16]
+                        ),
+                        parameter_spec=gca_study_compat.StudySpec.ParameterSpec(
+                            double_value_spec=gca_study_compat.StudySpec.ParameterSpec.DoubleValueSpec(
+                                min_value=1e-07, max_value=1
+                            ),
+                            scale_type=gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
+                            parameter_id="learning_rate",
+                        ),
+                    ),
+                ],
             ),
         ],
         algorithm=gca_study_compat.StudySpec.Algorithm.RANDOM_SEARCH,
@@ -388,7 +421,12 @@ def test_create_hyperparameter_tuning_job(
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -454,7 +492,12 @@ def test_create_hyperparameter_tuning_job_with_timeout(
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -515,7 +558,12 @@ def test_run_hyperparameter_tuning_job_with_fail_raises(
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -574,7 +622,12 @@ def test_run_hyperparameter_tuning_job_with_fail_at_creation(self):
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -639,7 +692,12 @@ def test_hyperparameter_tuning_job_get_state_raises_without_run(self):
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32, 64], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -697,7 +755,12 @@ def test_create_hyperparameter_tuning_job_with_tensorboard(
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,
@@ -769,7 +832,12 @@ def test_create_hyperparameter_tuning_job_with_enable_web_access(
                     values=["relu", "sigmoid", "elu", "selu", "tanh"]
                 ),
                 "batch_size": hpt.DiscreteParameterSpec(
-                    values=[16, 32], scale="linear"
+                    values=[4, 8, 16, 32, 64],
+                    scale="linear",
+                    conditional_parameter_spec={
+                        "decay": _TEST_CONDITIONAL_PARAMETER_DECAY,
+                        "learning_rate": _TEST_CONDITIONAL_PARAMETER_LR,
+                    },
                 ),
             },
             parallel_trial_count=_TEST_PARALLEL_TRIAL_COUNT,