Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

Returns a new instance of GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics.



51712
51713
51714
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51712

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#confidence_threshold ⇒ Float

Metrics are computed with an assumption that the Model never returns predictions with score lower than this value. Corresponds to the JSON property confidenceThreshold

Returns:

  • (Float) 


51621
51622
51623
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51621

def confidence_threshold
  @confidence_threshold
end

#confusion_matrix ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SchemaModelevaluationMetricsConfusionMatrix

Confusion matrix of the evaluation for this confidence_threshold. Corresponds to the JSON property confusionMatrix

Returns:



51626
51627
51628
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51626

def confusion_matrix
  @confusion_matrix
end

#f1_score ⇒ Float

The harmonic mean of recall and precision. For summary metrics, it computes the micro-averaged F1 score. Corresponds to the JSON property f1Score

Returns:

  • (Float) 


51632
51633
51634
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51632

def f1_score
  @f1_score
end

#f1_score_at1 ⇒ Float

The harmonic mean of recallAt1 and precisionAt1. Corresponds to the JSON property f1ScoreAt1

Returns:

  • (Float) 


51637
51638
51639
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51637

def f1_score_at1
  @f1_score_at1
end

#f1_score_macro ⇒ Float

Macro-averaged F1 Score. Corresponds to the JSON property f1ScoreMacro

Returns:

  • (Float) 


51642
51643
51644
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51642

def f1_score_macro
  @f1_score_macro
end

#f1_score_micro ⇒ Float

Micro-averaged F1 Score. Corresponds to the JSON property f1ScoreMicro

Returns:

  • (Float) 


51647
51648
51649
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51647

def f1_score_micro
  @f1_score_micro
end

#false_negative_count ⇒ Fixnum

The number of ground truth labels that are not matched by a Model created label. Corresponds to the JSON property falseNegativeCount

Returns:

  • (Fixnum) 


51653
51654
51655
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51653

def false_negative_count
  @false_negative_count
end

#false_positive_count ⇒ Fixnum

The number of Model created labels that do not match a ground truth label. Corresponds to the JSON property falsePositiveCount

Returns:

  • (Fixnum) 


51658
51659
51660
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51658

def false_positive_count
  @false_positive_count
end

#false_positive_rate ⇒ Float

False Positive Rate for the given confidence threshold. Corresponds to the JSON property falsePositiveRate

Returns:

  • (Float) 


51663
51664
51665
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51663

def false_positive_rate
  @false_positive_rate
end

#false_positive_rate_at1 ⇒ Float

The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. Corresponds to the JSON property falsePositiveRateAt1

Returns:

  • (Float) 


51669
51670
51671
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51669

def false_positive_rate_at1
  @false_positive_rate_at1
end

#max_predictions ⇒ Fixnum

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 confidenceThreshold. Corresponds to the JSON property maxPredictions

Returns:

  • (Fixnum) 


51676
51677
51678
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51676

def max_predictions
  @max_predictions
end

#precision ⇒ Float

Precision for the given confidence threshold. Corresponds to the JSON property precision

Returns:

  • (Float) 


51681
51682
51683
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51681

def precision
  @precision
end

#precision_at1 ⇒ Float

The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. Corresponds to the JSON property precisionAt1

Returns:

  • (Float) 


51687
51688
51689
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51687

def precision_at1
  @precision_at1
end

#recall ⇒ Float

Recall (True Positive Rate) for the given confidence threshold. Corresponds to the JSON property recall

Returns:

  • (Float) 


51692
51693
51694
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51692

def recall
  @recall
end

#recall_at1 ⇒ Float

The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each DataItem. Corresponds to the JSON property recallAt1

Returns:

  • (Float) 


51699
51700
51701
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51699

def recall_at1
  @recall_at1
end

#true_negative_count ⇒ Fixnum

The number of labels that were not created by the Model, but if they would, they would not match a ground truth label. Corresponds to the JSON property trueNegativeCount

Returns:

  • (Fixnum) 


51705
51706
51707
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51705

def true_negative_count
  @true_negative_count
end

#true_positive_count ⇒ Fixnum

The number of Model created labels that match a ground truth label. Corresponds to the JSON property truePositiveCount

Returns:

  • (Fixnum) 


51710
51711
51712
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51710

def true_positive_count
  @true_positive_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



51717
51718
51719
51720
51721
51722
51723
51724
51725
51726
51727
51728
51729
51730
51731
51732
51733
51734
51735
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 51717

def update!(**args)
  @confidence_threshold = args[:confidence_threshold] if args.key?(:confidence_threshold)
  @confusion_matrix = args[:confusion_matrix] if args.key?(:confusion_matrix)
  @f1_score = args[:f1_score] if args.key?(:f1_score)
  @f1_score_at1 = args[:f1_score_at1] if args.key?(:f1_score_at1)
  @f1_score_macro = args[:f1_score_macro] if args.key?(:f1_score_macro)
  @f1_score_micro = args[:f1_score_micro] if args.key?(:f1_score_micro)
  @false_negative_count = args[:false_negative_count] if args.key?(:false_negative_count)
  @false_positive_count = args[:false_positive_count] if args.key?(:false_positive_count)
  @false_positive_rate = args[:false_positive_rate] if args.key?(:false_positive_rate)
  @false_positive_rate_at1 = args[:false_positive_rate_at1] if args.key?(:false_positive_rate_at1)
  @max_predictions = args[:max_predictions] if args.key?(:max_predictions)
  @precision = args[:precision] if args.key?(:precision)
  @precision_at1 = args[:precision_at1] if args.key?(:precision_at1)
  @recall = args[:recall] if args.key?(:recall)
  @recall_at1 = args[:recall_at1] if args.key?(:recall_at1)
  @true_negative_count = args[:true_negative_count] if args.key?(:true_negative_count)
  @true_positive_count = args[:true_positive_count] if args.key?(:true_positive_count)
end