Class: OCI::AiLanguage::Models::TextClassificationModelMetrics

Inherits:
Object
  • Object
show all
Defined in:
lib/oci/ai_language/models/text_classification_model_metrics.rb

Overview

Model level text classification metrics

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(attributes = {}) ⇒ TextClassificationModelMetrics

Initializes the object

Parameters:

  • attributes (Hash) (defaults to: {}) —

    Model attributes in the form of hash

Options Hash (attributes):

  • :accuracy (Float) —

    The value to assign to the #accuracy property

  • :micro_f1 (Float) —

    The value to assign to the #micro_f1 property

  • :micro_precision (Float) —

    The value to assign to the #micro_precision property

  • :micro_recall (Float) —

    The value to assign to the #micro_recall property

  • :macro_f1 (Float) —

    The value to assign to the #macro_f1 property

  • :macro_precision (Float) —

    The value to assign to the #macro_precision property

  • :macro_recall (Float) —

    The value to assign to the #macro_recall property

  • :weighted_f1 (Float) —

    The value to assign to the #weighted_f1 property

  • :weighted_precision (Float) —

    The value to assign to the #weighted_precision property

  • :weighted_recall (Float) —

    The value to assign to the #weighted_recall property



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 103

def initialize(attributes = {})
  return unless attributes.is_a?(Hash)

  # convert string to symbol for hash key
  attributes = attributes.each_with_object({}) { |(k, v), h| h[k.to_sym] = v }

  self.accuracy = attributes[:'accuracy'] if attributes[:'accuracy']

  self.micro_f1 = attributes[:'microF1'] if attributes[:'microF1']

  raise 'You cannot provide both :microF1 and :micro_f1' if attributes.key?(:'microF1') && attributes.key?(:'micro_f1')

  self.micro_f1 = attributes[:'micro_f1'] if attributes[:'micro_f1']

  self.micro_precision = attributes[:'microPrecision'] if attributes[:'microPrecision']

  raise 'You cannot provide both :microPrecision and :micro_precision' if attributes.key?(:'microPrecision') && attributes.key?(:'micro_precision')

  self.micro_precision = attributes[:'micro_precision'] if attributes[:'micro_precision']

  self.micro_recall = attributes[:'microRecall'] if attributes[:'microRecall']

  raise 'You cannot provide both :microRecall and :micro_recall' if attributes.key?(:'microRecall') && attributes.key?(:'micro_recall')

  self.micro_recall = attributes[:'micro_recall'] if attributes[:'micro_recall']

  self.macro_f1 = attributes[:'macroF1'] if attributes[:'macroF1']

  raise 'You cannot provide both :macroF1 and :macro_f1' if attributes.key?(:'macroF1') && attributes.key?(:'macro_f1')

  self.macro_f1 = attributes[:'macro_f1'] if attributes[:'macro_f1']

  self.macro_precision = attributes[:'macroPrecision'] if attributes[:'macroPrecision']

  raise 'You cannot provide both :macroPrecision and :macro_precision' if attributes.key?(:'macroPrecision') && attributes.key?(:'macro_precision')

  self.macro_precision = attributes[:'macro_precision'] if attributes[:'macro_precision']

  self.macro_recall = attributes[:'macroRecall'] if attributes[:'macroRecall']

  raise 'You cannot provide both :macroRecall and :macro_recall' if attributes.key?(:'macroRecall') && attributes.key?(:'macro_recall')

  self.macro_recall = attributes[:'macro_recall'] if attributes[:'macro_recall']

  self.weighted_f1 = attributes[:'weightedF1'] if attributes[:'weightedF1']

  raise 'You cannot provide both :weightedF1 and :weighted_f1' if attributes.key?(:'weightedF1') && attributes.key?(:'weighted_f1')

  self.weighted_f1 = attributes[:'weighted_f1'] if attributes[:'weighted_f1']

  self.weighted_precision = attributes[:'weightedPrecision'] if attributes[:'weightedPrecision']

  raise 'You cannot provide both :weightedPrecision and :weighted_precision' if attributes.key?(:'weightedPrecision') && attributes.key?(:'weighted_precision')

  self.weighted_precision = attributes[:'weighted_precision'] if attributes[:'weighted_precision']

  self.weighted_recall = attributes[:'weightedRecall'] if attributes[:'weightedRecall']

  raise 'You cannot provide both :weightedRecall and :weighted_recall' if attributes.key?(:'weightedRecall') && attributes.key?(:'weighted_recall')

  self.weighted_recall = attributes[:'weighted_recall'] if attributes[:'weighted_recall']
end

Instance Attribute Details

#accuracy ⇒ Float

[Required] The fraction of the labels that were correctly recognised .

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 13

def accuracy
  @accuracy
end

#macro_f1 ⇒ Float

[Required] F1-score, is a measure of a modelu2019s accuracy on a dataset

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 29

def macro_f1
  @macro_f1
end

#macro_precision ⇒ Float

[Required] Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 33

def macro_precision
  @macro_precision
end

#macro_recall ⇒ Float

[Required] Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 37

def macro_recall
  @macro_recall
end

#micro_f1 ⇒ Float

[Required] F1-score, is a measure of a modelu2019s accuracy on a dataset

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 17

def micro_f1
  @micro_f1
end

#micro_precision ⇒ Float

[Required] Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 21

def micro_precision
  @micro_precision
end

#micro_recall ⇒ Float

[Required] Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 25

def micro_recall
  @micro_recall
end

#weighted_f1 ⇒ Float

F1-score, is a measure of a modelu2019s accuracy on a dataset

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 41

def weighted_f1
  @weighted_f1
end

#weighted_precision ⇒ Float

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 45

def weighted_precision
  @weighted_precision
end

#weighted_recall ⇒ Float

Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

  • (Float) 


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 49

def weighted_recall
  @weighted_recall
end

Class Method Details

.attribute_map ⇒ Object

Attribute mapping from ruby-style variable name to JSON key.



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 52

def self.attribute_map
  {
    # rubocop:disable Style/SymbolLiteral
    'accuracy': :'accuracy',
    'micro_f1': :'microF1',
    'micro_precision': :'microPrecision',
    'micro_recall': :'microRecall',
    'macro_f1': :'macroF1',
    'macro_precision': :'macroPrecision',
    'macro_recall': :'macroRecall',
    'weighted_f1': :'weightedF1',
    'weighted_precision': :'weightedPrecision',
    'weighted_recall': :'weightedRecall'
    # rubocop:enable Style/SymbolLiteral
  }
end

.swagger_types ⇒ Object

Attribute type mapping.



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 70

def self.swagger_types
  {
    # rubocop:disable Style/SymbolLiteral
    'accuracy': :'Float',
    'micro_f1': :'Float',
    'micro_precision': :'Float',
    'micro_recall': :'Float',
    'macro_f1': :'Float',
    'macro_precision': :'Float',
    'macro_recall': :'Float',
    'weighted_f1': :'Float',
    'weighted_precision': :'Float',
    'weighted_recall': :'Float'
    # rubocop:enable Style/SymbolLiteral
  }
end

Instance Method Details

#==(other) ⇒ Object

Checks equality by comparing each attribute.

Parameters:

  • other (Object) —

    the other object to be compared



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 173

def ==(other)
  return true if equal?(other)

  self.class == other.class &&
    accuracy == other.accuracy &&
    micro_f1 == other.micro_f1 &&
    micro_precision == other.micro_precision &&
    micro_recall == other.micro_recall &&
    macro_f1 == other.macro_f1 &&
    macro_precision == other.macro_precision &&
    macro_recall == other.macro_recall &&
    weighted_f1 == other.weighted_f1 &&
    weighted_precision == other.weighted_precision &&
    weighted_recall == other.weighted_recall
end

#build_from_hash(attributes) ⇒ Object

Builds the object from hash

Parameters:

  • attributes (Hash) —

    Model attributes in the form of hash

Returns:

  • (Object) —

    Returns the model itself



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 212

def build_from_hash(attributes)
  return nil unless attributes.is_a?(Hash)

  self.class.swagger_types.each_pair do |key, type|
    if type =~ /^Array<(.*)>/i
      # check to ensure the input is an array given that the the attribute
      # is documented as an array but the input is not
      if attributes[self.class.attribute_map[key]].is_a?(Array)
        public_method("#{key}=").call(
          attributes[self.class.attribute_map[key]]
            .map { |v| OCI::Internal::Util.convert_to_type(Regexp.last_match(1), v) }
        )
      end
    elsif !attributes[self.class.attribute_map[key]].nil?
      public_method("#{key}=").call(
        OCI::Internal::Util.convert_to_type(type, attributes[self.class.attribute_map[key]])
      )
    end
    # or else data not found in attributes(hash), not an issue as the data can be optional
  end

  self
end

#eql?(other) ⇒ Boolean

Parameters:

  • other (Object) —

    the other object to be compared

Returns:

  • (Boolean)

See Also:

  • `==` method


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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 192

def eql?(other)
  self == other
end

#hash ⇒ Fixnum

Calculates hash code according to all attributes.

Returns:

  • (Fixnum) —

    Hash code



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 201

def hash
  [accuracy, micro_f1, micro_precision, micro_recall, macro_f1, macro_precision, macro_recall, weighted_f1, weighted_precision, weighted_recall].hash
end

#to_hash ⇒ Hash

Returns the object in the form of hash

Returns:

  • (Hash) —

    Returns the object in the form of hash



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 245

def to_hash
  hash = {}
  self.class.attribute_map.each_pair do |attr, param|
    value = public_method(attr).call
    next if value.nil? && !instance_variable_defined?("@#{attr}")

    hash[param] = _to_hash(value)
  end
  hash
end

#to_s ⇒ String

Returns the string representation of the object

Returns:

  • (String) —

    String presentation of the object



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# File 'lib/oci/ai_language/models/text_classification_model_metrics.rb', line 239

def to_s
  to_hash.to_s
end