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.



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20195

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20152

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20158

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:



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20163

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20169

def r_squared
  @r_squared
end

#root_mean_squared_error ⇒ Float

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

Returns:

  • (Float) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20174

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20180

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20186

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) 


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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20193

def weighted_absolute_percentage_error
  @weighted_absolute_percentage_error
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1/classes.rb', line 20200

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