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_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))