As of January 1, 2020 this library no longer supports Python 2 on the latest released version.
Library versions released prior to that date will continue to be available. For more information please
visit Python 2 support on Google Cloud.
Source code for google.cloud.automl_v1.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_v1.types import annotation_payload
from google.cloud.automl_v1.types import data_items
from google.cloud.automl_v1.types import io
__protobuf__ = proto.module(
package="google.cloud.automl.v1",
manifest={
"PredictRequest",
"PredictResponse",
"BatchPredictRequest",
"BatchPredictResult",
},
)
[docs]class PredictRequest(proto.Message):
r"""Request message for
[PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
Attributes:
name (str):
Required. Name of the model requested to
serve the prediction.
payload (google.cloud.automl_v1.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_v1.types.PredictRequest.ParamsEntry]):
Additional domain-specific parameters, any string must be up
to 25000 characters long.
AutoML Vision 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.
AutoML Vision 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) The maximum number of
bounding boxes returned. The default is 100. The number of
returned bounding boxes might be limited by the server.
AutoML Tables
``feature_importance`` : (boolean) Whether
[feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
is populated in the returned list of
[TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]
objects. 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.v1.PredictionService.Predict].
Attributes:
payload (Sequence[google.cloud.automl_v1.types.AnnotationPayload]):
Prediction result.
AutoML Translation and AutoML Natural Language
Sentiment Analysis return precisely one payload.
preprocessed_input (google.cloud.automl_v1.types.ExamplePayload):
The preprocessed example that AutoML actually makes
prediction on. Empty if AutoML does not preprocess the input
example.
For AutoML Natural Language (Classification, Entity
Extraction, and Sentiment Analysis), if the input is a
document, the recognized text is returned in the
[document_text][google.cloud.automl.v1.Document.document_text]
property.
metadata (Sequence[google.cloud.automl_v1.types.PredictResponse.MetadataEntry]):
Additional domain-specific prediction response metadata.
AutoML Vision Object Detection
``max_bounding_box_count`` : (int64) The maximum number of
bounding boxes to return per image.
AutoML Natural Language Sentiment Analysis
``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 is also positive (though the least).
``sentiment_score`` is not the same as "score" and
"magnitude" from Sentiment Analysis in the Natural Language
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.v1.PredictionService.BatchPredict].
Attributes:
name (str):
Required. Name of the model requested to
serve the batch prediction.
input_config (google.cloud.automl_v1.types.BatchPredictInputConfig):
Required. The input configuration for batch
prediction.
output_config (google.cloud.automl_v1.types.BatchPredictOutputConfig):
Required. The Configuration specifying where
output predictions should be written.
params (Sequence[google.cloud.automl_v1.types.BatchPredictRequest.ParamsEntry]):
Additional domain-specific parameters for the predictions,
any string must be up to 25000 characters long.
AutoML Natural Language 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.
AutoML Vision 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.
AutoML Vision 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) The maximum number of
bounding boxes returned per image. The default is 100, the
number of bounding boxes returned might be limited by the
server. AutoML Video Intelligence 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. The default is
false.
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.
``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.
The default is false.
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.
AutoML Video Intelligence 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) The maximum number of
bounding boxes returned per image. The default is 100, the
number of bounding boxes returned might be limited by the
server.
``min_bounding_box_size`` : (float) Only bounding boxes with
shortest edge at least that long as a relative value of
video frame size are 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.v1.PredictionService.BatchPredict].
Attributes:
metadata (Sequence[google.cloud.automl_v1.types.BatchPredictResult.MetadataEntry]):
Additional domain-specific prediction response metadata.
AutoML Vision Object Detection
``max_bounding_box_count`` : (int64) The maximum number of
bounding boxes returned per image.
AutoML Video Intelligence Object Tracking
``max_bounding_box_count`` : (int64) The maximum number of
bounding boxes returned per frame.
"""
metadata = proto.MapField(proto.STRING, proto.STRING, number=1,)
__all__ = tuple(sorted(__protobuf__.manifest))