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.



35825
35826
35827
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35825

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) 


35734
35735
35736
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35734

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:



35739
35740
35741
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35739

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) 


35745
35746
35747
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35745

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) 


35750
35751
35752
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35750

def f1_score_at1
  @f1_score_at1
end

#f1_score_macro ⇒ Float

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

Returns:

  • (Float) 


35755
35756
35757
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35755

def f1_score_macro
  @f1_score_macro
end

#f1_score_micro ⇒ Float

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

Returns:

  • (Float) 


35760
35761
35762
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35760

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) 


35766
35767
35768
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35766

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) 


35771
35772
35773
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35771

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) 


35776
35777
35778
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35776

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) 


35782
35783
35784
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35782

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) 


35789
35790
35791
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35789

def max_predictions
  @max_predictions
end

#precision ⇒ Float

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

Returns:

  • (Float) 


35794
35795
35796
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35794

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) 


35800
35801
35802
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35800

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) 


35805
35806
35807
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35805

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) 


35812
35813
35814
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35812

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) 


35818
35819
35820
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35818

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) 


35823
35824
35825
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35823

def true_positive_count
  @true_positive_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



35830
35831
35832
35833
35834
35835
35836
35837
35838
35839
35840
35841
35842
35843
35844
35845
35846
35847
35848
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 35830

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