# Copyright 2021 The Kubeflow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Artifact types corresponding to Google Cloud Resources produced and consumed by GCPC components.
These artifact types can be used in your custom KFP SDK components similarly to
other [KFP SDK
artifacts](https://www.kubeflow.org/docs/components/pipelines/v2/data-types/artifacts/).
If you wish to produce Google artifacts from your own components, it is
recommended that you use [Containerized Python
Components](https://www.kubeflow.org/docs/components/pipelines/v2/components/containerized-python-components/).
You should assign metadata to the Google artifacts according to the artifact's
schema (provided by each artifact's `.schema` attribute).
"""
__all__ = [
'VertexModel',
'VertexEndpoint',
'VertexBatchPredictionJob',
'VertexDataset',
'BQMLModel',
'BQTable',
'UnmanagedContainerModel',
'ClassificationMetrics',
'RegressionMetrics',
'ForecastingMetrics',
]
import textwrap
from typing import Any, Dict, Optional
from kfp import dsl
_RESOURCE_NAME_KEY = 'resourceName'
[docs]class VertexModel(dsl.Artifact):
"""An artifact representing a Vertex AI [Model resource](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.models)."""
schema_title = 'google.VertexModel'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.VertexModel
type: object
properties:
resourceName:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
uri: str,
model_resource_name: str,
) -> 'VertexModel':
# fmt: off
"""Create a VertexModel artifact instance.
Args:
name: The artifact name.
uri: the Vertex Model resource uri, in a form of https://{service-endpoint}/v1/projects/{project}/locations/{location}/models/{model}, where {service-endpoint} is one of the supported service endpoints at https://cloud.google.com/vertex-ai/docs/reference/rest#rest_endpoints
model_resource_name: The name of the Model resource, in a form of projects/{project}/locations/{location}/models/{model}. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.models/get
Returns:
VertexModel instance.
"""
# fmt: on
return cls(
name=name,
uri=uri,
metadata={_RESOURCE_NAME_KEY: model_resource_name},
)
[docs]class VertexEndpoint(dsl.Artifact):
"""An artifact representing a Vertex AI [Endpoint resource](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints)."""
schema_title = 'google.VertexEndpoint'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.VertexEndpoint
type: object
properties:
resourceName:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
uri: str,
endpoint_resource_name: str,
) -> 'VertexEndpoint':
# fmt: off
"""Create a VertexEndpoint artifact instance.
Args:
name: The artifact name.
uri: the Vertex Endpoint resource uri, in a form of https://{service-endpoint}/v1/projects/{project}/locations/{location}/endpoints/{endpoint}, where {service-endpoint} is one of the supported service endpoints at https://cloud.google.com/vertex-ai/docs/reference/rest#rest_endpoints
endpoint_resource_name: The name of the Endpoint resource, in a form of projects/{project}/locations/{location}/endpoints/{endpoint}. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.endpoints/get
Returns:
VertexEndpoint instance.
"""
# fmt: on
return cls(
name=name,
uri=uri,
metadata={_RESOURCE_NAME_KEY: endpoint_resource_name},
)
[docs]class VertexBatchPredictionJob(dsl.Artifact):
"""An artifact representing a Vertex AI [BatchPredictionJob resource](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#resource:-batchpredictionjob)."""
schema_title = 'google.VertexBatchPredictionJob'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.VertexBatchPredictionJob
type: object
properties:
resourceName:
type: string
bigqueryOutputTable:
type: string
gcsOutputDirectory:
type: string
bigqueryOutputDataset:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
uri: str,
job_resource_name: str,
bigquery_output_table: Optional[str] = None,
bigquery_output_dataset: Optional[str] = None,
gcs_output_directory: Optional[str] = None,
) -> 'VertexBatchPredictionJob':
# fmt: off
"""Create a VertexBatchPredictionJob artifact instance.
Args:
name: The artifact name.
uri: the Vertex Batch Prediction resource uri, in a form of https://{service-endpoint}/v1/projects/{project}/locations/{location}/batchPredictionJobs/{batchPredictionJob}, where {service-endpoint} is one of the supported service endpoints at https://cloud.google.com/vertex-ai/docs/reference/rest#rest_endpoints
job_resource_name: The name of the batch prediction job resource, in a form of projects/{project}/locations/{location}/batchPredictionJobs/{batchPredictionJob}. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs/get
bigquery_output_table: The name of the BigQuery table created, in predictions_<timestamp> format, into which the prediction output is written. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#outputinfo
bigquery_output_dataset: The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#outputinfo
gcs_output_directory: The full path of the Cloud Storage directory created, into which the prediction output is written. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#outputinfo
Returns:
VertexBatchPredictionJob instance.
"""
# fmt: on
return cls(
name=name,
uri=uri,
metadata={
_RESOURCE_NAME_KEY: job_resource_name,
'bigqueryOutputTable': bigquery_output_table,
'bigqueryOutputDataset': bigquery_output_dataset,
'gcsOutputDirectory': gcs_output_directory,
},
)
[docs]class VertexDataset(dsl.Artifact):
"""An artifact representing a Vertex AI [Dataset resource](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.datasets)."""
schema_title = 'google.VertexDataset'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.VertexDataset
type: object
properties:
resourceName:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
uri: str,
dataset_resource_name: str,
) -> 'VertexDataset':
# fmt: off
"""Create a VertexDataset artifact instance.
Args:
name: The artifact name.
uri: the Vertex Dataset resource uri, in a form of https://{service-endpoint}/v1/projects/{project}/locations/{location}/datasets/{datasets_name}, where {service-endpoint} is one of the supported service endpoints at https://cloud.google.com/vertex-ai/docs/reference/rest#rest_endpoints
dataset_resource_name: The name of the Dataset resource, in a form of projects/{project}/locations/{location}/datasets/{datasets_name}. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.datasets/get
Returns:
VertexDataset instance.
"""
return cls(
uri=uri,
name=name,
metadata={_RESOURCE_NAME_KEY: dataset_resource_name},
)
[docs]class BQMLModel(dsl.Artifact):
"""An artifact representing a Google Cloud [BQML Model resource](https://cloud.google.com/bigquery/docs/reference/rest/v2/models)."""
schema_title = 'google.BQMLModel'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.BQMLModel
type: object
properties:
projectId:
type: string
datasetId:
type: string
modelId:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
project_id: str,
dataset_id: str,
model_id: str,
) -> 'BQMLModel':
# fmt: off
"""Create a BQMLModel artifact instance.
Args:
name: The artifact name.
project_id: The ID of the project containing this model.
dataset_id: The ID of the dataset containing this model.
model_id: The ID of the model. For more details, see https://cloud.google.com/bigquery/docs/reference/rest/v2/models#ModelReference
Returns:
BQMLModel instance.
"""
# fmt: on
return cls(
name=name,
uri=f'https://www.googleapis.com/bigquery/v2/projects/{project_id}/datasets/{dataset_id}/models/{model_id}',
metadata={
'projectId': project_id,
'datasetId': dataset_id,
'modelId': model_id,
},
)
[docs]class BQTable(dsl.Artifact):
"""An artifact representing a Google Cloud [BQ Table resource](https://cloud.google.com/bigquery/docs/reference/rest/v2/tables)."""
schema_title = 'google.BQTable'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.BQTable
type: object
properties:
projectId:
type: string
datasetId:
type: string
tableId:
type: string
expirationTime:
type: string""")
[docs] @classmethod
def create(
cls,
name: str,
project_id: str,
dataset_id: str,
table_id: str,
) -> 'BQTable':
# fmt: off
"""Create a BQTable artifact instance.
Args:
name: The artifact name.
project_id: The ID of the project containing this table.
dataset_id: The ID of the dataset containing this table.
table_id: The ID of the table. For more details, see https://cloud.google.com/bigquery/docs/reference/rest/v2/TableReference
Returns:
BQTable instance.
"""
# fmt: on
return cls(
name=name,
uri=f'https://www.googleapis.com/bigquery/v2/projects/{project_id}/datasets/{dataset_id}/tables/{table_id}',
metadata={
'projectId': project_id,
'datasetId': dataset_id,
'tableId': table_id,
},
)
[docs]class UnmanagedContainerModel(dsl.Artifact):
"""An artifact representing a Vertex AI [unmanaged container model](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ModelContainerSpec)."""
schema_title = 'google.UnmanagedContainerModel'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.UnmanagedContainerModel
type: object
properties:
predictSchemata:
type: object
properties:
instanceSchemaUri:
type: string
parametersSchemaUri:
type: string
predictionSchemaUri:
type: string
containerSpec:
type: object
properties:
imageUri:
type: string
command:
type: array
items:
type: string
args:
type: array
items:
type: string
env:
type: array
items:
type: object
properties:
name:
type: string
value:
type: string
ports:
type: array
items:
type: object
properties:
containerPort:
type: integer
predictRoute:
type: string
healthRoute:
type: string""")
[docs] @classmethod
def create(
cls,
predict_schemata: Dict[str, str],
container_spec: Dict[str, Any],
) -> 'UnmanagedContainerModel':
# fmt: off
"""Create a UnmanagedContainerModel artifact instance.
Args:
predict_schemata: Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/PredictSchemata
container_spec: Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification. For more details, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/ModelContainerSpec
Returns:
UnmanagedContainerModel instance.
"""
# fmt: on
return cls(
metadata={
'predictSchemata': predict_schemata,
'containerSpec': container_spec,
}
)
[docs]class ClassificationMetrics(dsl.Artifact):
"""An artifact representing evaluation [classification metrics](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/evaluate-model#classification_1)."""
schema_title = 'google.ClassificationMetrics'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.ClassificationMetrics
type: object
properties:
aggregationType:
type: string
enum: - AGGREGATION_TYPE_UNSPECIFIED - MACRO_AVERAGE - MICRO_AVERAGE
aggregationThreshold:
type: number
format: float
recall:
type: number
format: float
precision:
type: number
format: float
f1_score:
type: number
format: float
accuracy:
type: number
format: float
auPrc:
type: number
format: float
auRoc:
type: number
format: float
logLoss:
type: number
format: float
confusionMatrix:
type: object
properties:
rows:
type: array
items:
type: array
items:
type: integer
format: int64
annotationSpecs:
type: array
items:
type: object
properties:
id:
type: string
displayName:
type: string
confidenceMetrics:
type: array
items:
type: object
properties:
confidenceThreshold:
type: number
format: float
recall:
type: number
format: float
precision:
type: number
format: float
f1Score:
type: number
format: float
maxPredictions:
type: integer
format: int32
falsePositiveRate:
type: number
format: float
accuracy:
type: number
format: float
truePositiveCount:
type: integer
format: int64
falsePositiveCount:
type: integer
format: int64
falseNegativeCount:
type: integer
format: int64
trueNegativeCount:
type: integer
format: int64
recallAt1:
type: number
format: float
precisionAt1:
type: number
format: float
falsePositiveRateAt1:
type: number
format: float
f1ScoreAt1:
type: number
format: float
confusionMatrix:
type: object
properties:
rows:
type: array
items:
type: array
items:
type: integer
format: int64
annotationSpecs:
type: array
items:
type: object
properties:
id:
type: string
displayName:
type: string""")
[docs] @classmethod
def create(
cls,
name: str = 'evaluation_metrics',
recall: Optional[float] = None,
precision: Optional[float] = None,
f1_score: Optional[float] = None,
accuracy: Optional[float] = None,
au_prc: Optional[float] = None,
au_roc: Optional[float] = None,
log_loss: Optional[float] = None,
) -> 'ClassificationMetrics':
# fmt: off
"""Create a ClassificationMetrics artifact instance.
Args:
name: The artifact name.
recall: Recall (True Positive Rate) for the given confidence threshold.
precision: Precision for the given confidence threshold.
f1_score: The harmonic mean of recall and precision.
accuracy: Accuracy is the fraction of predictions given the correct label.
au_prc: The Area Under Precision-Recall Curve metric.
au_roc: The Area Under Receiver Operating Characteristic curve metric.
log_loss: The Log Loss metric.
Returns:
ClassificationMetrics instance.
"""
# fmt: on
metadata = {}
if recall is not None:
metadata['recall'] = recall
if precision is not None:
metadata['precision'] = precision
if f1_score is not None:
metadata['f1Score'] = f1_score
if accuracy is not None:
metadata['accuracy'] = accuracy
if au_prc is not None:
metadata['auPrc'] = au_prc
if au_roc is not None:
metadata['auRoc'] = au_roc
if log_loss is not None:
metadata['logLoss'] = log_loss
return cls(
name=name,
metadata=metadata,
)
[docs]class RegressionMetrics(dsl.Artifact):
"""An artifact representing evaluation [regression metrics](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/evaluate-model#regression_1)."""
schema_title = 'google.RegressionMetrics'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.RegressionMetrics
type: object
properties:
rootMeanSquaredError:
type: number
format: float
meanAbsoluteError:
type: number
format: float
meanAbsolutePercentageError:
type: number
format: float
rSquared:
type: number
format: float
rootMeanSquaredLogError:
type: number
format: float""")
[docs] @classmethod
def create(
cls,
name: str = 'evaluation_metrics',
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,
) -> 'RegressionMetrics':
# fmt: off
"""Create a RegressionMetrics artifact instance.
Args:
name: The artifact name.
root_mean_squared_error: Root Mean Squared Error (RMSE).
mean_absolute_error: Mean Absolute Error (MAE).
mean_absolute_percentage_error: Mean absolute percentage error.
r_squared: Coefficient of determination as Pearson correlation coefficient.
root_mean_squared_log_error: Root mean squared log error.
Returns:
RegressionMetrics instance.
"""
# fmt: on
metadata = {}
if root_mean_squared_error is not None:
metadata['rootMeanSquaredError'] = root_mean_squared_error
if mean_absolute_error is not None:
metadata['meanAbsoluteError'] = mean_absolute_error
if mean_absolute_percentage_error is not None:
metadata['meanAbsolutePercentageError'] = mean_absolute_percentage_error
if r_squared is not None:
metadata['rSquared'] = r_squared
if root_mean_squared_log_error is not None:
metadata['rootMeanSquaredLogError'] = root_mean_squared_log_error
return cls(
name=name,
metadata=metadata,
)
[docs]class ForecastingMetrics(dsl.Artifact):
"""An artifact representing evaluation [forecasting metrics](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/evaluate-model#metrics)."""
schema_title = 'google.ForecastingMetrics'
schema_version = '0.0.1'
schema = textwrap.dedent("""\
title: google.ForecastingMetrics
type: object
properties:
rootMeanSquaredError:
type: number
format: float
meanAbsoluteError:
type: number
format: float
meanAbsolutePercentageError:
type: number
format: float
rSquared:
type: number
format: float
rootMeanSquaredLogError:
type: number
format: float
weightedAbsolutePercentageError:
type: number
format: float
rootMeanSquaredPercentageError:
type: number
format: float
symmetricMeanAbsolutePercentageError:
type: number
format: float
quantileMetrics:
type: array
items:
type: object
properties:
quantile:
type: number
format: double
scaledPinballLoss:
type: number
format: float
observedQuantile:
type: number
format: double""")
[docs] @classmethod
def create(
cls,
name: str = 'evaluation_metrics',
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,
) -> 'ForecastingMetrics':
# fmt: off
"""Create a ForecastingMetrics artifact instance.
Args:
name: The artifact name.
root_mean_squared_error: Root Mean Squared Error (RMSE).
mean_absolute_error: Mean Absolute Error (MAE).
mean_absolute_percentage_error: Mean absolute percentage error.
r_squared: Coefficient of determination as Pearson correlation coefficient.
root_mean_squared_log_error: Root mean squared log error.
weighted_absolute_percentage_error: 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: Root Mean Square Percentage Error. Square root of MSPE. Undefined/imaginary when MSPE is negative.
symmetric_mean_absolute_percentage_error: Symmetric Mean Absolute Percentage Error.
Returns:
ForecastingMetrics instance.
"""
# fmt: on
metadata = {}
if root_mean_squared_error is not None:
metadata['rootMeanSquaredError'] = root_mean_squared_error
if mean_absolute_error is not None:
metadata['meanAbsoluteError'] = mean_absolute_error
if mean_absolute_percentage_error is not None:
metadata['meanAbsolutePercentageError'] = mean_absolute_percentage_error
if r_squared is not None:
metadata['rSquared'] = r_squared
if root_mean_squared_log_error is not None:
metadata['rootMeanSquaredLogError'] = root_mean_squared_log_error
if weighted_absolute_percentage_error is not None:
metadata['weightedAbsolutePercentageError'] = (
weighted_absolute_percentage_error
)
if root_mean_squared_percentage_error is not None:
metadata['rootMeanSquaredPercentageError'] = (
root_mean_squared_percentage_error
)
if symmetric_mean_absolute_percentage_error is not None:
metadata['symmetricMeanAbsolutePercentageError'] = (
symmetric_mean_absolute_percentage_error
)
return cls(
name=name,
metadata=metadata,
)