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.



27489
27490
27491
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27489

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) 


27398
27399
27400
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27398

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:



27403
27404
27405
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27403

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) 


27409
27410
27411
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27409

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) 


27414
27415
27416
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27414

def f1_score_at1
  @f1_score_at1
end

#f1_score_macro ⇒ Float

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

Returns:

  • (Float) 


27419
27420
27421
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27419

def f1_score_macro
  @f1_score_macro
end

#f1_score_micro ⇒ Float

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

Returns:

  • (Float) 


27424
27425
27426
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27424

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) 


27430
27431
27432
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27430

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) 


27435
27436
27437
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27435

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) 


27440
27441
27442
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27440

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) 


27446
27447
27448
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27446

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) 


27453
27454
27455
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27453

def max_predictions
  @max_predictions
end

#precision ⇒ Float

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

Returns:

  • (Float) 


27458
27459
27460
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27458

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) 


27464
27465
27466
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27464

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) 


27469
27470
27471
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27469

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) 


27476
27477
27478
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27476

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) 


27482
27483
27484
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27482

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) 


27487
27488
27489
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27487

def true_positive_count
  @true_positive_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



27494
27495
27496
27497
27498
27499
27500
27501
27502
27503
27504
27505
27506
27507
27508
27509
27510
27511
27512
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 27494

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