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.



17970
17971
17972
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17970

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) 


17927
17928
17929
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17927

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) 


17933
17934
17935
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17933

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:



17938
17939
17940
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17938

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) 


17944
17945
17946
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17944

def r_squared
  @r_squared
end

#root_mean_squared_error ⇒ Float

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

Returns:

  • (Float) 


17949
17950
17951
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17949

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) 


17955
17956
17957
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17955

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) 


17961
17962
17963
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17961

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) 


17968
17969
17970
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17968

def weighted_absolute_percentage_error
  @weighted_absolute_percentage_error
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



17975
17976
17977
17978
17979
17980
17981
17982
17983
17984
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17975

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