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Source code for google.cloud.automl_v1beta1.types.prediction_service

# -*- 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.automl_v1beta1.types import annotation_payload
from google.cloud.automl_v1beta1.types import data_items
from google.cloud.automl_v1beta1.types import io


__protobuf__ = proto.module(
    package="google.cloud.automl.v1beta1",
    manifest={
        "PredictRequest",
        "PredictResponse",
        "BatchPredictRequest",
        "BatchPredictResult",
    },
)


[docs]class PredictRequest(proto.Message): r"""Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. Attributes: name (str): Required. Name of the model requested to serve the prediction. payload (google.cloud.automl_v1beta1.types.ExamplePayload): Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve. params (Sequence[google.cloud.automl_v1beta1.types.PredictRequest.ParamsEntry]): Additional domain-specific parameters, any string must be up to 25000 characters long. - For Image Classification: ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. - For Image Object Detection: ``score_threshold`` - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. ``max_bounding_box_count`` - (int64) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server. - For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false. """ name = proto.Field(proto.STRING, number=1,) payload = proto.Field(proto.MESSAGE, number=2, message=data_items.ExamplePayload,) params = proto.MapField(proto.STRING, proto.STRING, number=3,)
[docs]class PredictResponse(proto.Message): r"""Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. Attributes: payload (Sequence[google.cloud.automl_v1beta1.types.AnnotationPayload]): Prediction result. Translation and Text Sentiment will return precisely one payload. preprocessed_input (google.cloud.automl_v1beta1.types.ExamplePayload): The preprocessed example that AutoML actually makes prediction on. Empty if AutoML does not preprocess the input example. - For Text Extraction: If the input is a .pdf file, the OCR'ed text will be provided in [document_text][google.cloud.automl.v1beta1.Document.document_text]. metadata (Sequence[google.cloud.automl_v1beta1.types.PredictResponse.MetadataEntry]): Additional domain-specific prediction response metadata. - For Image Object Detection: ``max_bounding_box_count`` - (int64) At most that many bounding boxes per image could have been returned. - For Text Sentiment: ``sentiment_score`` - (float, deprecated) A value between -1 and 1, -1 maps to least positive sentiment, while 1 maps to the most positive one and the higher the score, the more positive the sentiment in the document is. Yet these values are relative to the training data, so e.g. if all data was positive then -1 will be also positive (though the least). The sentiment_score shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. """ payload = proto.RepeatedField( proto.MESSAGE, number=1, message=annotation_payload.AnnotationPayload, ) preprocessed_input = proto.Field( proto.MESSAGE, number=3, message=data_items.ExamplePayload, ) metadata = proto.MapField(proto.STRING, proto.STRING, number=2,)
[docs]class BatchPredictRequest(proto.Message): r"""Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict]. Attributes: name (str): Required. Name of the model requested to serve the batch prediction. input_config (google.cloud.automl_v1beta1.types.BatchPredictInputConfig): Required. The input configuration for batch prediction. output_config (google.cloud.automl_v1beta1.types.BatchPredictOutputConfig): Required. The Configuration specifying where output predictions should be written. params (Sequence[google.cloud.automl_v1beta1.types.BatchPredictRequest.ParamsEntry]): Required. Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. - For Text Classification: ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. - For Image Classification: ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. - For Image Object Detection: ``score_threshold`` - (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. ``max_bounding_box_count`` - (int64) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. - For Video Classification : ``score_threshold`` - (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5. ``segment_classification`` - (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is "true". ``shot_classification`` - (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". ``1s_interval_classification`` - (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. The default is "false". - For Tables: feature_importance - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. The default is false. - For Video Object Tracking: ``score_threshold`` - (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. ``max_bounding_box_count`` - (int64) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. ``min_bounding_box_size`` - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be returned. Value in 0 to 1 range. Default is 0. """ name = proto.Field(proto.STRING, number=1,) input_config = proto.Field( proto.MESSAGE, number=3, message=io.BatchPredictInputConfig, ) output_config = proto.Field( proto.MESSAGE, number=4, message=io.BatchPredictOutputConfig, ) params = proto.MapField(proto.STRING, proto.STRING, number=5,)
[docs]class BatchPredictResult(proto.Message): r"""Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict]. Attributes: metadata (Sequence[google.cloud.automl_v1beta1.types.BatchPredictResult.MetadataEntry]): Additional domain-specific prediction response metadata. - For Image Object Detection: ``max_bounding_box_count`` - (int64) At most that many bounding boxes per image could have been returned. - For Video Object Tracking: ``max_bounding_box_count`` - (int64) At most that many bounding boxes per frame could have been returned. """ metadata = proto.MapField(proto.STRING, proto.STRING, number=1,)
__all__ = tuple(sorted(__protobuf__.manifest))