# frozen_string_literal: true
# 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.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module AutoML
module V1
# Contains annotation details specific to classification.
# @!attribute [rw] score
# @return [::Float]
# Output only. A confidence estimate between 0.0 and 1.0. A higher value
# means greater confidence that the annotation is positive. If a user
# approves an annotation as negative or positive, the score value remains
# unchanged. If a user creates an annotation, the score is 0 for negative or
# 1 for positive.
class ClassificationAnnotation
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Model evaluation metrics for classification problems.
# Note: For Video Classification this metrics only describe quality of the
# Video Classification predictions of "segment_classification" type.
# @!attribute [rw] au_prc
# @return [::Float]
# Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
# for the overall evaluation.
# @!attribute [rw] au_roc
# @return [::Float]
# Output only. The Area Under Receiver Operating Characteristic curve metric.
# Micro-averaged for the overall evaluation.
# @!attribute [rw] log_loss
# @return [::Float]
# Output only. The Log Loss metric.
# @!attribute [rw] confidence_metrics_entry
# @return [::Array<::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry>]
# Output only. Metrics for each confidence_threshold in
# 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
# position_threshold = INT32_MAX_VALUE.
# ROC and precision-recall curves, and other aggregated metrics are derived
# from them. The confidence metrics entries may also be supplied for
# additional values of position_threshold, but from these no aggregated
# metrics are computed.
# @!attribute [rw] confusion_matrix
# @return [::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfusionMatrix]
# Output only. Confusion matrix of the evaluation.
# Only set for MULTICLASS classification problems where number
# of labels is no more than 10.
# Only set for model level evaluation, not for evaluation per label.
# @!attribute [rw] annotation_spec_id
# @return [::Array<::String>]
# Output only. The annotation spec ids used for this evaluation.
class ClassificationEvaluationMetrics
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Metrics for a single confidence threshold.
# @!attribute [rw] confidence_threshold
# @return [::Float]
# Output only. Metrics are computed with an assumption that the model
# never returns predictions with score lower than this value.
# @!attribute [rw] position_threshold
# @return [::Integer]
# Output only. Metrics are computed with an assumption that the model
# always returns at most this many predictions (ordered by their score,
# descendingly), but they all still need to meet the confidence_threshold.
# @!attribute [rw] recall
# @return [::Float]
# Output only. Recall (True Positive Rate) for the given confidence
# threshold.
# @!attribute [rw] precision
# @return [::Float]
# Output only. Precision for the given confidence threshold.
# @!attribute [rw] false_positive_rate
# @return [::Float]
# Output only. False Positive Rate for the given confidence threshold.
# @!attribute [rw] f1_score
# @return [::Float]
# Output only. The harmonic mean of recall and precision.
# @!attribute [rw] recall_at1
# @return [::Float]
# Output only. The Recall (True Positive Rate) when only considering the
# label that has the highest prediction score and not below the confidence
# threshold for each example.
# @!attribute [rw] precision_at1
# @return [::Float]
# Output only. The precision when only considering the label that has the
# highest prediction score and not below the confidence threshold for each
# example.
# @!attribute [rw] false_positive_rate_at1
# @return [::Float]
# Output only. The False Positive Rate when only considering the label that
# has the highest prediction score and not below the confidence threshold
# for each example.
# @!attribute [rw] f1_score_at1
# @return [::Float]
# Output only. The harmonic mean of {::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry#recall_at1 recall_at1} and {::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfidenceMetricsEntry#precision_at1 precision_at1}.
# @!attribute [rw] true_positive_count
# @return [::Integer]
# Output only. The number of model created labels that match a ground truth
# label.
# @!attribute [rw] false_positive_count
# @return [::Integer]
# Output only. The number of model created labels that do not match a
# ground truth label.
# @!attribute [rw] false_negative_count
# @return [::Integer]
# Output only. The number of ground truth labels that are not matched
# by a model created label.
# @!attribute [rw] true_negative_count
# @return [::Integer]
# Output only. The number of labels that were not created by the model,
# but if they would, they would not match a ground truth label.
class ConfidenceMetricsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Confusion matrix of the model running the classification.
# @!attribute [rw] annotation_spec_id
# @return [::Array<::String>]
# Output only. IDs of the annotation specs used in the confusion matrix.
# For Tables CLASSIFICATION
# [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
# only list of [annotation_spec_display_name-s][] is populated.
# @!attribute [rw] display_name
# @return [::Array<::String>]
# Output only. Display name of the annotation specs used in the confusion
# matrix, as they were at the moment of the evaluation. For Tables
# CLASSIFICATION
# [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
# distinct values of the target column at the moment of the model
# evaluation are populated here.
# @!attribute [rw] row
# @return [::Array<::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfusionMatrix::Row>]
# Output only. Rows in the confusion matrix. The number of rows is equal to
# the size of `annotation_spec_id`.
# `row[i].example_count[j]` is the number of examples that have ground
# truth of the `annotation_spec_id[i]` and are predicted as
# `annotation_spec_id[j]` by the model being evaluated.
class ConfusionMatrix
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Output only. A row in the confusion matrix.
# @!attribute [rw] example_count
# @return [::Array<::Integer>]
# Output only. Value of the specific cell in the confusion matrix.
# The number of values each row has (i.e. the length of the row) is equal
# to the length of the `annotation_spec_id` field or, if that one is not
# populated, length of the {::Google::Cloud::AutoML::V1::ClassificationEvaluationMetrics::ConfusionMatrix#display_name display_name} field.
class Row
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
end
# Type of the classification problem.
module ClassificationType
# An un-set value of this enum.
CLASSIFICATION_TYPE_UNSPECIFIED = 0
# At most one label is allowed per example.
MULTICLASS = 1
# Multiple labels are allowed for one example.
MULTILABEL = 2
end
end
end
end
end