AI Platform Training & Prediction API . projects . locations . studies

Instance Methods

trials()

Returns the trials Resource.

create(parent, body=None, studyId=None, x__xgafv=None)

Creates a study.

delete(name, x__xgafv=None)

Deletes a study.

get(name, x__xgafv=None)

Gets a study.

list(parent, x__xgafv=None)

Lists all the studies in a region for an associated project.

Method Details

create(parent, body=None, studyId=None, x__xgafv=None)
Creates a study.

Args:
  parent: string, Required. The project and location that the study belongs to.
Format: projects/{project}/locations/{location} (required)
  body: object, The request body.
    The object takes the form of:

{ # A message representing a Study.
  "inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive.
      # This should be empty if a study is ACTIVE or COMPLETED.
  "createTime": "A String", # Output only. Time at which the study was created.
  "state": "A String", # Output only. The detailed state of a study.
  "name": "A String", # Output only. The name of a study.
  "studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
    "automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
        # implementation_config is set, automated early stopping will not be run.
      "medianAutomatedStoppingConfig": { # The median automated stopping rule stops a pending trial if the trial's
          # best objective_value is strictly below the median 'performance' of all
          # completed trials reported up to the trial's last measurement.
          # Currently, 'performance' refers to the running average of the objective
          # values reported by the trial in each measurement.
        "useElapsedTime": True or False, # If true, the median automated stopping rule applies to
            # measurement.use_elapsed_time, which means the elapsed_time field of
            # the current trial's
            # latest measurement is used to compute the median objective
            # value for each completed trial.
      },
      "decayCurveStoppingConfig": {
        "useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
            # Trials Decay Curve. Otherwise, Measurement.steps will be used as the
            # x-axis.
      },
    },
    "metrics": [ # Metric specs for the study.
      { # Represents a metric to optimize.
        "goal": "A String", # Required. The optimization goal of the metric.
        "metric": "A String", # Required. The name of the metric.
      },
    ],
    "algorithm": "A String", # The search algorithm specified for the study.
    "parameters": [ # Required. The set of parameters to tune.
      { # Represents a single parameter to optimize.
        "type": "A String", # Required. The type of the parameter.
        "parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
          "values": [ # Matches values of the parent parameter with type 'DISCRETE'.
              # All values must exist in `discrete_value_spec` of parent parameter.
            3.14,
          ],
        },
        "doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
          "maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
          "minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
        },
        "integerValueSpec": { # The value spec for an 'INTEGER' parameter.
          "minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
          "maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
        },
        "categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
          "values": [ # Must be specified if type is `CATEGORICAL`.
              # The list of possible categories.
            "A String",
          ],
        },
        "parentIntValues": { # Represents the spec to match integer values from parent parameter.
          "values": [ # Matches values of the parent parameter with type 'INTEGER'.
              # All values must lie in `integer_value_spec` of parent parameter.
            "A String",
          ],
        },
        "parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
        "childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
            # matching_parent_values.
            #
            # If two items in child_parameter_specs have the same name, they must have
            # disjoint matching_parent_values.
          # Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
        ],
        "discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
          "values": [ # Must be specified if type is `DISCRETE`.
              # A list of feasible points.
              # The list should be in strictly increasing order. For instance, this
              # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
              # should not contain more than 1,000 values.
            3.14,
          ],
        },
        "parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
          "values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
              # All values must exist in `categorical_value_spec` of parent parameter.
            "A String",
          ],
        },
        "scaleType": "A String", # How the parameter should be scaled.
            # Leave unset for categorical parameters.
      },
    ],
  },
}

  studyId: string, Required. The ID to use for the study, which will become the final component of
the study's resource name.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A message representing a Study.
    "inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive.
        # This should be empty if a study is ACTIVE or COMPLETED.
    "createTime": "A String", # Output only. Time at which the study was created.
    "state": "A String", # Output only. The detailed state of a study.
    "name": "A String", # Output only. The name of a study.
    "studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
      "automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
          # implementation_config is set, automated early stopping will not be run.
        "medianAutomatedStoppingConfig": { # The median automated stopping rule stops a pending trial if the trial's
            # best objective_value is strictly below the median 'performance' of all
            # completed trials reported up to the trial's last measurement.
            # Currently, 'performance' refers to the running average of the objective
            # values reported by the trial in each measurement.
          "useElapsedTime": True or False, # If true, the median automated stopping rule applies to
              # measurement.use_elapsed_time, which means the elapsed_time field of
              # the current trial's
              # latest measurement is used to compute the median objective
              # value for each completed trial.
        },
        "decayCurveStoppingConfig": {
          "useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
              # Trials Decay Curve. Otherwise, Measurement.steps will be used as the
              # x-axis.
        },
      },
      "metrics": [ # Metric specs for the study.
        { # Represents a metric to optimize.
          "goal": "A String", # Required. The optimization goal of the metric.
          "metric": "A String", # Required. The name of the metric.
        },
      ],
      "algorithm": "A String", # The search algorithm specified for the study.
      "parameters": [ # Required. The set of parameters to tune.
        { # Represents a single parameter to optimize.
          "type": "A String", # Required. The type of the parameter.
          "parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'DISCRETE'.
                # All values must exist in `discrete_value_spec` of parent parameter.
              3.14,
            ],
          },
          "doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
            "maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
            "minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
          },
          "integerValueSpec": { # The value spec for an 'INTEGER' parameter.
            "minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
            "maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
          },
          "categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
            "values": [ # Must be specified if type is `CATEGORICAL`.
                # The list of possible categories.
              "A String",
            ],
          },
          "parentIntValues": { # Represents the spec to match integer values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'INTEGER'.
                # All values must lie in `integer_value_spec` of parent parameter.
              "A String",
            ],
          },
          "parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
          "childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
              # matching_parent_values.
              #
              # If two items in child_parameter_specs have the same name, they must have
              # disjoint matching_parent_values.
            # Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
          ],
          "discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
            "values": [ # Must be specified if type is `DISCRETE`.
                # A list of feasible points.
                # The list should be in strictly increasing order. For instance, this
                # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
                # should not contain more than 1,000 values.
              3.14,
            ],
          },
          "parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
                # All values must exist in `categorical_value_spec` of parent parameter.
              "A String",
            ],
          },
          "scaleType": "A String", # How the parameter should be scaled.
              # Leave unset for categorical parameters.
        },
      ],
    },
  }
delete(name, x__xgafv=None)
Deletes a study.

Args:
  name: string, Required. The study name. (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated
      # empty messages in your APIs. A typical example is to use it as the request
      # or the response type of an API method. For instance:
      #
      #     service Foo {
      #       rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
      #     }
      #
      # The JSON representation for `Empty` is empty JSON object `{}`.
  }
get(name, x__xgafv=None)
Gets a study.

Args:
  name: string, Required. The study name. (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A message representing a Study.
    "inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive.
        # This should be empty if a study is ACTIVE or COMPLETED.
    "createTime": "A String", # Output only. Time at which the study was created.
    "state": "A String", # Output only. The detailed state of a study.
    "name": "A String", # Output only. The name of a study.
    "studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
      "automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
          # implementation_config is set, automated early stopping will not be run.
        "medianAutomatedStoppingConfig": { # The median automated stopping rule stops a pending trial if the trial's
            # best objective_value is strictly below the median 'performance' of all
            # completed trials reported up to the trial's last measurement.
            # Currently, 'performance' refers to the running average of the objective
            # values reported by the trial in each measurement.
          "useElapsedTime": True or False, # If true, the median automated stopping rule applies to
              # measurement.use_elapsed_time, which means the elapsed_time field of
              # the current trial's
              # latest measurement is used to compute the median objective
              # value for each completed trial.
        },
        "decayCurveStoppingConfig": {
          "useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
              # Trials Decay Curve. Otherwise, Measurement.steps will be used as the
              # x-axis.
        },
      },
      "metrics": [ # Metric specs for the study.
        { # Represents a metric to optimize.
          "goal": "A String", # Required. The optimization goal of the metric.
          "metric": "A String", # Required. The name of the metric.
        },
      ],
      "algorithm": "A String", # The search algorithm specified for the study.
      "parameters": [ # Required. The set of parameters to tune.
        { # Represents a single parameter to optimize.
          "type": "A String", # Required. The type of the parameter.
          "parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'DISCRETE'.
                # All values must exist in `discrete_value_spec` of parent parameter.
              3.14,
            ],
          },
          "doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
            "maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
            "minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
          },
          "integerValueSpec": { # The value spec for an 'INTEGER' parameter.
            "minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
            "maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
          },
          "categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
            "values": [ # Must be specified if type is `CATEGORICAL`.
                # The list of possible categories.
              "A String",
            ],
          },
          "parentIntValues": { # Represents the spec to match integer values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'INTEGER'.
                # All values must lie in `integer_value_spec` of parent parameter.
              "A String",
            ],
          },
          "parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
          "childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
              # matching_parent_values.
              #
              # If two items in child_parameter_specs have the same name, they must have
              # disjoint matching_parent_values.
            # Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
          ],
          "discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
            "values": [ # Must be specified if type is `DISCRETE`.
                # A list of feasible points.
                # The list should be in strictly increasing order. For instance, this
                # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
                # should not contain more than 1,000 values.
              3.14,
            ],
          },
          "parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
            "values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
                # All values must exist in `categorical_value_spec` of parent parameter.
              "A String",
            ],
          },
          "scaleType": "A String", # How the parameter should be scaled.
              # Leave unset for categorical parameters.
        },
      ],
    },
  }
list(parent, x__xgafv=None)
Lists all the studies in a region for an associated project.

Args:
  parent: string, Required. The project and location that the study belongs to.
Format: projects/{project}/locations/{location} (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    {
    "studies": [ # The studies associated with the project.
      { # A message representing a Study.
        "inactiveReason": "A String", # Output only. A human readable reason why the Study is inactive.
            # This should be empty if a study is ACTIVE or COMPLETED.
        "createTime": "A String", # Output only. Time at which the study was created.
        "state": "A String", # Output only. The detailed state of a study.
        "name": "A String", # Output only. The name of a study.
        "studyConfig": { # Represents configuration of a study. # Required. Configuration of the study.
          "automatedStoppingConfig": { # Configuration for Automated Early Stopping of Trials. If no # Configuration for automated stopping of unpromising Trials.
              # implementation_config is set, automated early stopping will not be run.
            "medianAutomatedStoppingConfig": { # The median automated stopping rule stops a pending trial if the trial's
                # best objective_value is strictly below the median 'performance' of all
                # completed trials reported up to the trial's last measurement.
                # Currently, 'performance' refers to the running average of the objective
                # values reported by the trial in each measurement.
              "useElapsedTime": True or False, # If true, the median automated stopping rule applies to
                  # measurement.use_elapsed_time, which means the elapsed_time field of
                  # the current trial's
                  # latest measurement is used to compute the median objective
                  # value for each completed trial.
            },
            "decayCurveStoppingConfig": {
              "useElapsedTime": True or False, # If true, measurement.elapsed_time is used as the x-axis of each
                  # Trials Decay Curve. Otherwise, Measurement.steps will be used as the
                  # x-axis.
            },
          },
          "metrics": [ # Metric specs for the study.
            { # Represents a metric to optimize.
              "goal": "A String", # Required. The optimization goal of the metric.
              "metric": "A String", # Required. The name of the metric.
            },
          ],
          "algorithm": "A String", # The search algorithm specified for the study.
          "parameters": [ # Required. The set of parameters to tune.
            { # Represents a single parameter to optimize.
              "type": "A String", # Required. The type of the parameter.
              "parentDiscreteValues": { # Represents the spec to match discrete values from parent parameter.
                "values": [ # Matches values of the parent parameter with type 'DISCRETE'.
                    # All values must exist in `discrete_value_spec` of parent parameter.
                  3.14,
                ],
              },
              "doubleValueSpec": { # The value spec for a 'DOUBLE' parameter.
                "maxValue": 3.14, # Must be specified if type is `DOUBLE`. Maximum value of the parameter.
                "minValue": 3.14, # Must be specified if type is `DOUBLE`. Minimum value of the parameter.
              },
              "integerValueSpec": { # The value spec for an 'INTEGER' parameter.
                "minValue": "A String", # Must be specified if type is `INTEGER`. Minimum value of the parameter.
                "maxValue": "A String", # Must be specified if type is `INTEGER`. Maximum value of the parameter.
              },
              "categoricalValueSpec": { # The value spec for a 'CATEGORICAL' parameter.
                "values": [ # Must be specified if type is `CATEGORICAL`.
                    # The list of possible categories.
                  "A String",
                ],
              },
              "parentIntValues": { # Represents the spec to match integer values from parent parameter.
                "values": [ # Matches values of the parent parameter with type 'INTEGER'.
                    # All values must lie in `integer_value_spec` of parent parameter.
                  "A String",
                ],
              },
              "parameter": "A String", # Required. The parameter name must be unique amongst all ParameterSpecs.
              "childParameterSpecs": [ # A child node is active if the parameter's value matches the child node's
                  # matching_parent_values.
                  #
                  # If two items in child_parameter_specs have the same name, they must have
                  # disjoint matching_parent_values.
                # Object with schema name: GoogleCloudMlV1_StudyConfig_ParameterSpec
              ],
              "discreteValueSpec": { # The value spec for a 'DISCRETE' parameter.
                "values": [ # Must be specified if type is `DISCRETE`.
                    # A list of feasible points.
                    # The list should be in strictly increasing order. For instance, this
                    # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
                    # should not contain more than 1,000 values.
                  3.14,
                ],
              },
              "parentCategoricalValues": { # Represents the spec to match categorical values from parent parameter.
                "values": [ # Matches values of the parent parameter with type 'CATEGORICAL'.
                    # All values must exist in `categorical_value_spec` of parent parameter.
                  "A String",
                ],
              },
              "scaleType": "A String", # How the parameter should be scaled.
                  # Leave unset for categorical parameters.
            },
          ],
        },
      },
    ],
  }