// Copyright 2019 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.
// Note: this file is purely for documentation. Any contents are not expected
// to be loaded as the JS file.
/**
* Contains annotation details specific to text sentiment.
*
* @property {number} sentiment
* Output only. The sentiment with the semantic, as given to the
* AutoMl.ImportData when populating the dataset from which the model used
* for the prediction had been trained.
* The sentiment values are between 0 and
* Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
* with higher value meaning more positive sentiment. They are completely
* relative, i.e. 0 means least positive sentiment and sentiment_max means
* the most positive from the sentiments present in the train data. Therefore
* e.g. if train data had only negative sentiment, then sentiment_max, would
* be still negative (although least negative).
* The sentiment shouldn't be confused with "score" or "magnitude"
* from the previous Natural Language Sentiment Analysis API.
*
* @typedef TextSentimentAnnotation
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TextSentimentAnnotation definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/text_sentiment.proto}
*/
const TextSentimentAnnotation = {
// This is for documentation. Actual contents will be loaded by gRPC.
};
/**
* Model evaluation metrics for text sentiment problems.
*
* @property {number} precision
* Output only. Precision.
*
* @property {number} recall
* Output only. Recall.
*
* @property {number} f1Score
* Output only. The harmonic mean of recall and precision.
*
* @property {number} meanAbsoluteError
* Output only. Mean absolute error. Only set for the overall model
* evaluation, not for evaluation of a single annotation spec.
*
* @property {number} meanSquaredError
* Output only. Mean squared error. Only set for the overall model
* evaluation, not for evaluation of a single annotation spec.
*
* @property {number} linearKappa
* Output only. Linear weighted kappa. Only set for the overall model
* evaluation, not for evaluation of a single annotation spec.
*
* @property {number} quadraticKappa
* Output only. Quadratic weighted kappa. Only set for the overall model
* evaluation, not for evaluation of a single annotation spec.
*
* @property {Object} confusionMatrix
* Output only. Confusion matrix of the evaluation.
* Only set for the overall model evaluation, not for evaluation of a single
* annotation spec.
*
* This object should have the same structure as [ConfusionMatrix]{@link google.cloud.automl.v1beta1.ConfusionMatrix}
*
* @property {string[]} annotationSpecId
* Output only. The annotation spec ids used for this evaluation.
* Deprecated .
*
* @typedef TextSentimentEvaluationMetrics
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TextSentimentEvaluationMetrics definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/text_sentiment.proto}
*/
const TextSentimentEvaluationMetrics = {
// This is for documentation. Actual contents will be loaded by gRPC.
};