Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics

Returns a new instance of GoogleCloudAiplatformV1SchemaModelevaluationMetricsClassificationEvaluationMetricsConfidenceMetrics.



27636
27637
27638
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27636

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) 


27545
27546
27547
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27545

def confidence_threshold
  @confidence_threshold
end

#confusion_matrix ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaModelevaluationMetricsConfusionMatrix

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

Returns:



27550
27551
27552
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27550

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) 


27556
27557
27558
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27556

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) 


27561
27562
27563
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27561

def f1_score_at1
  @f1_score_at1
end

#f1_score_macro ⇒ Float

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

Returns:

  • (Float) 


27566
27567
27568
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27566

def f1_score_macro
  @f1_score_macro
end

#f1_score_micro ⇒ Float

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

Returns:

  • (Float) 


27571
27572
27573
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27571

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) 


27577
27578
27579
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27577

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) 


27582
27583
27584
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27582

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) 


27587
27588
27589
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27587

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) 


27593
27594
27595
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27593

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) 


27600
27601
27602
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27600

def max_predictions
  @max_predictions
end

#precision ⇒ Float

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

Returns:

  • (Float) 


27605
27606
27607
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27605

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) 


27611
27612
27613
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27611

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) 


27616
27617
27618
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27616

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) 


27623
27624
27625
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27623

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) 


27629
27630
27631
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27629

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) 


27634
27635
27636
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27634

def true_positive_count
  @true_positive_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



27641
27642
27643
27644
27645
27646
27647
27648
27649
27650
27651
27652
27653
27654
27655
27656
27657
27658
27659
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27641

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