# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
import proto  # type: ignore

from google.cloud.aiplatform_v1beta1.types import explanation
from google.protobuf import struct_pb2  # type: ignore
from google.protobuf import timestamp_pb2  # type: ignore


__protobuf__ = proto.module(
    package="google.cloud.aiplatform.v1beta1", manifest={"ModelEvaluation",},
)


class ModelEvaluation(proto.Message):
    r"""A collection of metrics calculated by comparing Model's
    predictions on all of the test data against annotations from the
    test data.

    Attributes:
        name (str):
            Output only. The resource name of the
            ModelEvaluation.
        metrics_schema_uri (str):
            Output only. Points to a YAML file stored on Google Cloud
            Storage describing the
            [metrics][google.cloud.aiplatform.v1beta1.ModelEvaluation.metrics]
            of this ModelEvaluation. The schema is defined as an OpenAPI
            3.0.2 `Schema
            Object <https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject>`__.
        metrics (google.protobuf.struct_pb2.Value):
            Output only. Evaluation metrics of the Model. The schema of
            the metrics is stored in
            [metrics_schema_uri][google.cloud.aiplatform.v1beta1.ModelEvaluation.metrics_schema_uri]
        create_time (google.protobuf.timestamp_pb2.Timestamp):
            Output only. Timestamp when this
            ModelEvaluation was created.
        slice_dimensions (Sequence[str]):
            Output only. All possible
            [dimensions][ModelEvaluationSlice.slice.dimension] of
            ModelEvaluationSlices. The dimensions can be used as the
            filter of the
            [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices]
            request, in the form of ``slice.dimension = <dimension>``.
        model_explanation (google.cloud.aiplatform_v1beta1.types.ModelExplanation):
            Output only. Aggregated explanation metrics
            for the Model's prediction output over the data
            this ModelEvaluation uses. This field is
            populated only if the Model is evaluated with
            explanations, and only for AutoML tabular
            Models.
        explanation_specs (Sequence[google.cloud.aiplatform_v1beta1.types.ModelEvaluation.ModelEvaluationExplanationSpec]):
            Output only. Describes the values of
            [ExplanationSpec][google.cloud.aiplatform.v1beta1.ExplanationSpec]
            that are used for explaining the predicted values on the
            evaluated data.
    """

    class ModelEvaluationExplanationSpec(proto.Message):
        r"""

        Attributes:
            explanation_type (str):
                Explanation type.

                For AutoML Image Classification models, possible values are:

                -  ``image-integrated-gradients``
                -  ``image-xrai``
            explanation_spec (google.cloud.aiplatform_v1beta1.types.ExplanationSpec):
                Explanation spec details.
        """

        explanation_type = proto.Field(proto.STRING, number=1,)
        explanation_spec = proto.Field(
            proto.MESSAGE, number=2, message=explanation.ExplanationSpec,
        )

    name = proto.Field(proto.STRING, number=1,)
    metrics_schema_uri = proto.Field(proto.STRING, number=2,)
    metrics = proto.Field(proto.MESSAGE, number=3, message=struct_pb2.Value,)
    create_time = proto.Field(proto.MESSAGE, number=4, message=timestamp_pb2.Timestamp,)
    slice_dimensions = proto.RepeatedField(proto.STRING, number=5,)
    model_explanation = proto.Field(
        proto.MESSAGE, number=8, message=explanation.ModelExplanation,
    )
    explanation_specs = proto.RepeatedField(
        proto.MESSAGE, number=9, message=ModelEvaluationExplanationSpec,
    )


__all__ = tuple(sorted(__protobuf__.manifest))
