Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetrics

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

Overview

Metrics for forecasting evaluation results.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetrics

Returns a new instance of GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetrics.



15515
15516
15517
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15515

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

Instance Attribute Details

#mean_absolute_error ⇒ Float

Mean Absolute Error (MAE). Corresponds to the JSON property meanAbsoluteError

Returns:

  • (Float) 


15472
15473
15474
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15472

def mean_absolute_error
  @mean_absolute_error
end

#mean_absolute_percentage_error ⇒ Float

Mean absolute percentage error. Infinity when there are zeros in the ground truth. Corresponds to the JSON property meanAbsolutePercentageError

Returns:

  • (Float) 


15478
15479
15480
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15478

def mean_absolute_percentage_error
  @mean_absolute_percentage_error
end

#quantile_metrics ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaModelevaluationMetricsForecastingEvaluationMetricsQuantileMetricsEntry>

The quantile metrics entries for each quantile. Corresponds to the JSON property quantileMetrics

Returns:



15483
15484
15485
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15483

def quantile_metrics
  @quantile_metrics
end

#r_squared ⇒ Float

Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant. Corresponds to the JSON property rSquared

Returns:

  • (Float) 


15489
15490
15491
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15489

def r_squared
  @r_squared
end

#root_mean_squared_error ⇒ Float

Root Mean Squared Error (RMSE). Corresponds to the JSON property rootMeanSquaredError

Returns:

  • (Float) 


15494
15495
15496
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15494

def root_mean_squared_error
  @root_mean_squared_error
end

#root_mean_squared_log_error ⇒ Float

Root mean squared log error. Undefined when there are negative ground truth values or predictions. Corresponds to the JSON property rootMeanSquaredLogError

Returns:

  • (Float) 


15500
15501
15502
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15500

def root_mean_squared_log_error
  @root_mean_squared_log_error
end

#root_mean_squared_percentage_error ⇒ Float

Root Mean Square Percentage Error. Square root of MSPE. Undefined/imaginary when MSPE is negative. Corresponds to the JSON property rootMeanSquaredPercentageError

Returns:

  • (Float) 


15506
15507
15508
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15506

def root_mean_squared_percentage_error
  @root_mean_squared_percentage_error
end

#weighted_absolute_percentage_error ⇒ Float

Weighted Absolute Percentage Error. Does not use weights, this is just what the metric is called. Undefined if actual values sum to zero. Will be very large if actual values sum to a very small number. Corresponds to the JSON property weightedAbsolutePercentageError

Returns:

  • (Float) 


15513
15514
15515
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15513

def weighted_absolute_percentage_error
  @weighted_absolute_percentage_error
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



15520
15521
15522
15523
15524
15525
15526
15527
15528
15529
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 15520

def update!(**args)
  @mean_absolute_error = args[:mean_absolute_error] if args.key?(:mean_absolute_error)
  @mean_absolute_percentage_error = args[:mean_absolute_percentage_error] if args.key?(:mean_absolute_percentage_error)
  @quantile_metrics = args[:quantile_metrics] if args.key?(:quantile_metrics)
  @r_squared = args[:r_squared] if args.key?(:r_squared)
  @root_mean_squared_error = args[:root_mean_squared_error] if args.key?(:root_mean_squared_error)
  @root_mean_squared_log_error = args[:root_mean_squared_log_error] if args.key?(:root_mean_squared_log_error)
  @root_mean_squared_percentage_error = args[:root_mean_squared_percentage_error] if args.key?(:root_mean_squared_percentage_error)
  @weighted_absolute_percentage_error = args[:weighted_absolute_percentage_error] if args.key?(:weighted_absolute_percentage_error)
end