# 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 # # https://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. module Google module Cloud module AutoML module V1beta1 # Request message for # {Google::Cloud::AutoML::V1beta1::PredictionService::Predict PredictionService::Predict}. # @!attribute [rw] name # @return [String] # Name of the model requested to serve the prediction. # @!attribute [rw] payload # @return [Google::Cloud::AutoML::V1beta1::ExamplePayload] # Required. Payload to perform a prediction on. The payload must match the # problem type that the model was trained to solve. # @!attribute [rw] params # @return [Hash{String => String}] # 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][[google.cloud.automl.v1beta1.TablesModelColumnInfo.feature_importance] # should be populated in the returned # # [TablesAnnotation(-s)][[google.cloud.automl.v1beta1.TablesAnnotation]. # The default is false. class PredictRequest; end # Response message for # {Google::Cloud::AutoML::V1beta1::PredictionService::Predict PredictionService::Predict}. # @!attribute [rw] payload # @return [Array] # Prediction result. # Translation and Text Sentiment will return precisely one payload. # @!attribute [rw] preprocessed_input # @return [Google::Cloud::AutoML::V1beta1::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 # {Google::Cloud::AutoML::V1beta1::Document#document_text document_text}. # @!attribute [rw] metadata # @return [Hash{String => String}] # 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. class PredictResponse; end # Request message for # {Google::Cloud::AutoML::V1beta1::PredictionService::BatchPredict PredictionService::BatchPredict}. # @!attribute [rw] name # @return [String] # Name of the model requested to serve the batch prediction. # @!attribute [rw] input_config # @return [Google::Cloud::AutoML::V1beta1::BatchPredictInputConfig] # Required. The input configuration for batch prediction. # @!attribute [rw] output_config # @return [Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig] # Required. The Configuration specifying where output predictions should # be written. # @!attribute [rw] params # @return [Hash{String => String}] # 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 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. class BatchPredictRequest; end # Result of the Batch Predict. This message is returned in # {Google::Longrunning::Operation#response response} of the operation returned # by the # {Google::Cloud::AutoML::V1beta1::PredictionService::BatchPredict PredictionService::BatchPredict}. # @!attribute [rw] metadata # @return [Hash{String => String}] # 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. class BatchPredictResult; end end end end end