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Source code for google.cloud.automl_v1.types.text_sentiment
# -*- 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 classification
__protobuf__ = proto.module(
package="google.cloud.automl.v1",
manifest={"TextSentimentAnnotation", "TextSentimentEvaluationMetrics",},
)
[docs]class TextSentimentAnnotation(proto.Message):
r"""Contains annotation details specific to text sentiment.
Attributes:
sentiment (int):
Output only. The sentiment with the semantic, as given to
the
[AutoMl.ImportData][google.cloud.automl.v1.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.
"""
sentiment = proto.Field(proto.INT32, number=1,)
[docs]class TextSentimentEvaluationMetrics(proto.Message):
r"""Model evaluation metrics for text sentiment problems.
Attributes:
precision (float):
Output only. Precision.
recall (float):
Output only. Recall.
f1_score (float):
Output only. The harmonic mean of recall and
precision.
mean_absolute_error (float):
Output only. Mean absolute error. Only set
for the overall model evaluation, not for
evaluation of a single annotation spec.
mean_squared_error (float):
Output only. Mean squared error. Only set for
the overall model evaluation, not for evaluation
of a single annotation spec.
linear_kappa (float):
Output only. Linear weighted kappa. Only set
for the overall model evaluation, not for
evaluation of a single annotation spec.
quadratic_kappa (float):
Output only. Quadratic weighted kappa. Only
set for the overall model evaluation, not for
evaluation of a single annotation spec.
confusion_matrix (google.cloud.automl_v1.types.ClassificationEvaluationMetrics.ConfusionMatrix):
Output only. Confusion matrix of the
evaluation. Only set for the overall model
evaluation, not for evaluation of a single
annotation spec.
"""
precision = proto.Field(proto.FLOAT, number=1,)
recall = proto.Field(proto.FLOAT, number=2,)
f1_score = proto.Field(proto.FLOAT, number=3,)
mean_absolute_error = proto.Field(proto.FLOAT, number=4,)
mean_squared_error = proto.Field(proto.FLOAT, number=5,)
linear_kappa = proto.Field(proto.FLOAT, number=6,)
quadratic_kappa = proto.Field(proto.FLOAT, number=7,)
confusion_matrix = proto.Field(
proto.MESSAGE,
number=8,
message=classification.ClassificationEvaluationMetrics.ConfusionMatrix,
)
__all__ = tuple(sorted(__protobuf__.manifest))