Feat: Add google.ClassificationMetrics, google.RegressionMetrics, and google.Forecasting Metrics

google/cloud/aiplatform/metadata/schema/google/artifact_schema.py

@@ -268,3 +268,251 @@ def __init__(
             metadata=extended_metadata,
             state=state,
         )
+
+
+class ClassificationMetrics(base_artifact.BaseArtifactSchema):
+    """A Google artifact representing evaluation Classification Metrics."""
+
+    schema_title = "google.ClassificationMetrics"
+
+    def __init__(
+        self,
+        *,
+        au_prc: Optional[float] = None,
+        au_roc: Optional[float] = None,
+        log_loss: Optional[float] = None,
+        artifact_id: Optional[str] = None,
+        uri: Optional[str] = None,
+        display_name: Optional[str] = None,
+        schema_version: Optional[str] = None,
+        description: Optional[str] = None,
+        metadata: Optional[Dict] = None,
+        state: Optional[gca_artifact.Artifact.State] = gca_artifact.Artifact.State.LIVE,
+    ):
+        """Args:
+        au_prc (float):
+            Optional. The Area Under Precision-Recall Curve metric.
+            Micro-averaged for the overall evaluation.
+        au_roc (float):
+            Optional. The Area Under Receiver Operating Characteristic curve metric.
+            Micro-averaged for the overall evaluation.
+        log_loss (float): 
+            Optional. The Log Loss metric.
+        artifact_id (str):
+            Optional. The <resource_id> portion of the Artifact name with
+            the format. This is globally unique in a metadataStore:
+            projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.
+        uri (str):
+            Optional. The uniform resource identifier of the artifact file. May be empty if there is no actual
+            artifact file.
+        display_name (str):
+            Optional. The user-defined name of the Artifact.
+        schema_version (str):
+            Optional. schema_version specifies the version used by the Artifact.
+            If not set, defaults to use the latest version.
+        description (str):
+            Optional. Describes the purpose of the Artifact to be created.
+        metadata (Dict):
+            Optional. Contains the metadata information that will be stored in the Artifact.
+        state (google.cloud.gapic.types.Artifact.State):
+            Optional. The state of this Artifact. This is a
+            property of the Artifact, and does not imply or
+            capture any ongoing process. This property is
+            managed by clients (such as Vertex AI
+            Pipelines), and the system does not prescribe or
+            check the validity of state transitions.
+        """
+        extended_metadata = copy.deepcopy(metadata) if metadata else {}
+        if au_prc:
+            extended_metadata["auPrc"] = au_prc
+        if au_roc:
+            extended_metadata["auRoc"] = au_roc
+        if log_loss:
+            extended_metadata["logLoss"] = log_loss
+
+        super(ClassificationMetrics, self).__init__(
+            uri=uri,
+            artifact_id=artifact_id,
+            display_name=display_name,
+            schema_version=schema_version,
+            description=description,
+            metadata=extended_metadata,
+            state=state,
+        )
+
+
+class RegressionMetrics(base_artifact.BaseArtifactSchema):
+    """A Google artifact representing evaluation Regression Metrics."""
+
+    schema_title = "google.RegressionMetrics"
+
+    def __init__(
+        self,
+        *,
+        root_mean_squared_error: Optional[float] = None,
+        mean_absolute_error: Optional[float] = None,
+        mean_absolute_percentage_error: Optional[float] = None,
+        r_squared: Optional[float] = None,
+        root_mean_squared_log_error: Optional[float] = None,
+        artifact_id: Optional[str] = None,
+        uri: Optional[str] = None,
+        display_name: Optional[str] = None,
+        schema_version: Optional[str] = None,
+        description: Optional[str] = None,
+        metadata: Optional[Dict] = None,
+        state: Optional[gca_artifact.Artifact.State] = gca_artifact.Artifact.State.LIVE,
+    ):
+        """Args:
+        root_mean_squared_error (float):
+            Optional. Root Mean Squared Error (RMSE).
+        mean_absolute_error (float):
+            Optional. Mean Absolute Error (MAE).
+        mean_absolute_percentage_error (float):
+            Optional. Mean absolute percentage error.
+        r_squared (float):
+            Optional. Coefficient of determination as Pearson correlation coefficient.
+        root_mean_squared_log_error (float):
+            Optional. Root mean squared log error.
+        artifact_id (str):
+            Optional. The <resource_id> portion of the Artifact name with
+            the format. This is globally unique in a metadataStore:
+            projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.
+        uri (str):
+            Optional. The uniform resource identifier of the artifact file. May be empty if there is no actual
+            artifact file.
+        display_name (str):
+            Optional. The user-defined name of the Artifact.
+        schema_version (str):
+            Optional. schema_version specifies the version used by the Artifact.
+            If not set, defaults to use the latest version.
+        description (str):
+            Optional. Describes the purpose of the Artifact to be created.
+        metadata (Dict):
+            Optional. Contains the metadata information that will be stored in the Artifact.
+        state (google.cloud.gapic.types.Artifact.State):
+            Optional. The state of this Artifact. This is a
+            property of the Artifact, and does not imply or
+            capture any ongoing process. This property is
+            managed by clients (such as Vertex AI
+            Pipelines), and the system does not prescribe or
+            check the validity of state transitions.
+        """
+        extended_metadata = copy.deepcopy(metadata) if metadata else {}
+        if root_mean_squared_error:
+            extended_metadata["rootMeanSquaredError"] = root_mean_squared_error
+        if mean_absolute_error:
+            extended_metadata["meanAbsoluteError"] = mean_absolute_error
+        if mean_absolute_percentage_error:
+            extended_metadata["meanAbsolutePercentageError"] = mean_absolute_percentage_error
+        if r_squared:
+            extended_metadata["rSquared"] = r_squared
+        if root_mean_squared_log_error:
+            extended_metadata["rootMeanSquaredLogError"] = root_mean_squared_log_error
+
+        super(RegressionMetrics, self).__init__(
+            uri=uri,
+            artifact_id=artifact_id,
+            display_name=display_name,
+            schema_version=schema_version,
+            description=description,
+            metadata=extended_metadata,
+            state=state,
+        )
+
+
+class ForecastingMetrics(base_artifact.BaseArtifactSchema):
+    """A Google artifact representing evaluation Forecasting Metrics."""
+
+    schema_title = "google.ForecastingMetrics"
+
+    def __init__(
+        self,
+        *,
+        root_mean_squared_error: Optional[float] = None,
+        mean_absolute_error: Optional[float] = None,
+        mean_absolute_percentage_error: Optional[float] = None,
+        r_squared: Optional[float] = None,
+        root_mean_squared_log_error: Optional[float] = None,
+        weighted_absolute_percentage_error: Optional[float] = None,
+        root_mean_squared_percentage_error: Optional[float] = None,
+        symmetric_mean_absolute_percentage_error: Optional[float] = None,
+        artifact_id: Optional[str] = None,
+        uri: Optional[str] = None,
+        display_name: Optional[str] = None,
+        schema_version: Optional[str] = None,
+        description: Optional[str] = None,
+        metadata: Optional[Dict] = None,
+        state: Optional[gca_artifact.Artifact.State] = gca_artifact.Artifact.State.LIVE,
+    ):
+        """Args:
+        root_mean_squared_error (float):
+            Optional. Root Mean Squared Error (RMSE).
+        mean_absolute_error (float):
+            Optional. Mean Absolute Error (MAE).
+        mean_absolute_percentage_error (float):
+            Optional. Mean absolute percentage error.
+        r_squared (float):
+            Optional. Coefficient of determination as Pearson correlation coefficient.
+        root_mean_squared_log_error (float):
+            Optional. Root mean squared log error.
+        weighted_absolute_percentage_error (float):
+            Optional. Weighted Absolute Percentage Error.
+            Does not use weights, this is just what the metric is called.
+            Undefined if actual values sum to zero.
+            Will be very large if actual values sum to a very small number.
+        root_mean_squared_percentage_error (float):
+            Optional. Root Mean Square Percentage Error. Square root of MSPE.
+            Undefined/imaginary when MSPE is negative.
+        symmetric_mean_absolute_percentage_error (float):
+            Optional. Symmetric Mean Absolute Percentage Error.
+        artifact_id (str):
+            Optional. The <resource_id> portion of the Artifact name with
+            the format. This is globally unique in a metadataStore:
+            projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.
+        uri (str):
+            Optional. The uniform resource identifier of the artifact file. May be empty if there is no actual
+            artifact file.
+        display_name (str):
+            Optional. The user-defined name of the Artifact.
+        schema_version (str):
+            Optional. schema_version specifies the version used by the Artifact.
+            If not set, defaults to use the latest version.
+        description (str):
+            Optional. Describes the purpose of the Artifact to be created.
+        metadata (Dict):
+            Optional. Contains the metadata information that will be stored in the Artifact.
+        state (google.cloud.gapic.types.Artifact.State):
+            Optional. The state of this Artifact. This is a
+            property of the Artifact, and does not imply or
+            capture any ongoing process. This property is
+            managed by clients (such as Vertex AI
+            Pipelines), and the system does not prescribe or
+            check the validity of state transitions.
+        """
+        extended_metadata = copy.deepcopy(metadata) if metadata else {}
+        if root_mean_squared_error:
+            extended_metadata["rootMeanSquaredError"] = root_mean_squared_error
+        if mean_absolute_error:
+            extended_metadata["meanAbsoluteError"] = mean_absolute_error
+        if mean_absolute_percentage_error:
+            extended_metadata["meanAbsolutePercentageError"] = mean_absolute_percentage_error
+        if r_squared:
+            extended_metadata["rSquared"] = r_squared
+        if root_mean_squared_log_error:
+            extended_metadata["rootMeanSquaredLogError"] = root_mean_squared_log_error
+        if weighted_absolute_percentage_error:
+            extended_metadata["weightedAbsolutePercentageError"] = weighted_absolute_percentage_error
+        if root_mean_squared_percentage_error:
+            extended_metadata["rootMeanSquaredPercentageError"] = root_mean_squared_percentage_error
+        if symmetric_mean_absolute_percentage_error:
+            extended_metadata["symmetricMeanAbsolutePercentageError"] = symmetric_mean_absolute_percentage_error
+
+        super(ForecastingMetrics, self).__init__(
+            uri=uri,
+            artifact_id=artifact_id,
+            display_name=display_name,
+            schema_version=schema_version,
+            description=description,
+            metadata=extended_metadata,
+            state=state,
+        )

tests/unit/aiplatform/test_metadata_schema.py

@@ -461,6 +461,144 @@ def test_unmanaged_container_model_constructor_parameters_are_set_correctly(self
         assert artifact.schema_version == _TEST_SCHEMA_VERSION
 
 
+    def test_classification_metrics_title_is_set_correctly(self):
+        artifact = google_artifact_schema.ClassificationMetrics()
+        assert artifact.schema_title == "google.ClassificationMetrics"
+
+    def test_classification_metrics_constructor_parameters_are_set_correctly(self):
+        au_prc = 1.0
+        au_roc = 2.0
+        log_loss = 0.5
+
+        artifact = google_artifact_schema.ClassificationMetrics(
+            au_prc=au_prc,
+            au_roc=au_roc,
+            log_loss=log_loss,
+            artifact_id=_TEST_ARTIFACT_ID,
+            uri=_TEST_URI,
+            display_name=_TEST_DISPLAY_NAME,
+            schema_version=_TEST_SCHEMA_VERSION,
+            description=_TEST_DESCRIPTION,
+            metadata=_TEST_UPDATED_METADATA,
+        )
+        expected_metadata = {
+            "test-param1": 2.0,
+            "test-param2": "test-value-1",
+            "test-param3": False,
+            "auPrc": 1.0,
+            "auRoc": 2.0,
+            "logLoss": 0.5,
+        }
+
+        assert artifact.artifact_id == _TEST_ARTIFACT_ID
+        assert artifact.uri == _TEST_URI
+        assert artifact.display_name == _TEST_DISPLAY_NAME
+        assert artifact.description == _TEST_DESCRIPTION
+        assert json.dumps(artifact.metadata, sort_keys=True) == json.dumps(
+            expected_metadata, sort_keys=True
+        )
+        assert artifact.schema_version == _TEST_SCHEMA_VERSION
+
+
+    def test_regression_metrics_title_is_set_correctly(self):
+        artifact = google_artifact_schema.RegressionMetrics()
+        assert artifact.schema_title == "google.RegressionMetrics"
+
+    def test_regression_metrics_constructor_parameters_are_set_correctly(self):
+        root_mean_squared_error = 1.0
+        mean_absolute_error = 2.0
+        mean_absolute_percentage_error = 0.2
+        r_squared = 0.5
+        root_mean_squared_log_error = 0.9
+
+        artifact = google_artifact_schema.RegressionMetrics(
+            root_mean_squared_error=root_mean_squared_error,
+            mean_absolute_error=mean_absolute_error,
+            mean_absolute_percentage_error=mean_absolute_percentage_error,
+            r_squared=r_squared,
+            root_mean_squared_log_error=root_mean_squared_log_error,
+            artifact_id=_TEST_ARTIFACT_ID,
+            uri=_TEST_URI,
+            display_name=_TEST_DISPLAY_NAME,
+            schema_version=_TEST_SCHEMA_VERSION,
+            description=_TEST_DESCRIPTION,
+            metadata=_TEST_UPDATED_METADATA,
+        )
+        expected_metadata = {
+            "test-param1": 2.0,
+            "test-param2": "test-value-1",
+            "test-param3": False,
+            "rootMeanSquaredError": 1.0,
+            "meanAbsoluteError": 2.0,
+            "meanAbsolutePercentageError": 0.2,
+            "rSquared": 0.5,
+            "rootMeanSquaredLogError": 0.9,
+        }
+
+        assert artifact.artifact_id == _TEST_ARTIFACT_ID
+        assert artifact.uri == _TEST_URI
+        assert artifact.display_name == _TEST_DISPLAY_NAME
+        assert artifact.description == _TEST_DESCRIPTION
+        assert json.dumps(artifact.metadata, sort_keys=True) == json.dumps(
+            expected_metadata, sort_keys=True
+        )
+        assert artifact.schema_version == _TEST_SCHEMA_VERSION
+
+
+    def test_forecasting_metrics_title_is_set_correctly(self):
+        artifact = google_artifact_schema.ForecastingMetrics()
+        assert artifact.schema_title == "google.ForecastingMetrics"
+
+    def test_forecasting_metrics_constructor_parameters_are_set_correctly(self):
+        root_mean_squared_error = 1.0
+        mean_absolute_error = 2.0
+        mean_absolute_percentage_error = 0.2
+        r_squared = 0.5
+        root_mean_squared_log_error = 0.9
+        weighted_absolute_percentage_error = 4.0
+        root_mean_squared_percentage_error = 0.7
+        symmetric_mean_absolute_percentage_error = 0.8
+
+        artifact = google_artifact_schema.UnmanagedContainerModel(
+            root_mean_squared_error=root_mean_squared_error,
+            mean_absolute_error=mean_absolute_error,
+            mean_absolute_percentage_error=mean_absolute_percentage_error,
+            r_squared=r_squared,
+            root_mean_squared_log_error=root_mean_squared_log_error,
+            weighted_absolute_percentage_error=weighted_absolute_percentage_error,
+            root_mean_squared_percentage_error=root_mean_squared_percentage_error,
+            symmetric_mean_absolute_percentage_error=symmetric_mean_absolute_percentage_error,
+            artifact_id=_TEST_ARTIFACT_ID,
+            uri=_TEST_URI,
+            display_name=_TEST_DISPLAY_NAME,
+            schema_version=_TEST_SCHEMA_VERSION,
+            description=_TEST_DESCRIPTION,
+            metadata=_TEST_UPDATED_METADATA,
+        )
+        expected_metadata = {
+            "test-param1": 2.0,
+            "test-param2": "test-value-1",
+            "test-param3": False,
+            "rootMeanSquaredError": 1.0,
+            "meanAbsoluteError": 2.0,
+            "meanAbsolutePercentageError": 0.2,
+            "rSquared": 0.5,
+            "rootMeanSquaredLogError": 0.9,
+            "weightedAbsolutePercentageError": 4.0,
+            "rootMeanSquaredPercentageError": 0.7,
+            "symmetricMeanAbsolutePercentageError": 0.8,
+        }
+
+        assert artifact.artifact_id == _TEST_ARTIFACT_ID
+        assert artifact.uri == _TEST_URI
+        assert artifact.display_name == _TEST_DISPLAY_NAME
+        assert artifact.description == _TEST_DESCRIPTION
+        assert json.dumps(artifact.metadata, sort_keys=True) == json.dumps(
+            expected_metadata, sort_keys=True
+        )
+        assert artifact.schema_version == _TEST_SCHEMA_VERSION
+
+
 @pytest.mark.usefixtures("google_auth_mock")
 class TestMetadataSystemArtifactSchema:
     def setup_method(self):