Class: OCI::AiAnomalyDetection::Models::ModelTrainingResults

Inherits:
Object
  • Object
show all
Defined in:
lib/oci/ai_anomaly_detection/models/model_training_results.rb

Overview

Specifies the details for an Anomaly Detection model trained with MSET.

Constant Summary collapse

ALGORITHM_ENUM = 
[
  ALGORITHM_MULTIVARIATE_MSET = 'MULTIVARIATE_MSET'.freeze,
  ALGORITHM_UNIVARIATE_OCSVM = 'UNIVARIATE_OCSVM'.freeze,
  ALGORITHM_UNKNOWN_ENUM_VALUE = 'UNKNOWN_ENUM_VALUE'.freeze
].freeze

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(attributes = {}) ⇒ ModelTrainingResults

Initializes the object

Parameters:

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

    Model attributes in the form of hash

Options Hash (attributes):



95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 95

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.fap = attributes[:'fap'] if attributes[:'fap']

  self.multivariate_fap = attributes[:'multivariateFap'] if attributes[:'multivariateFap']

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

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

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

  self.window_size = attributes[:'windowSize'] if attributes[:'windowSize']

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

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

  self.is_training_goal_achieved = attributes[:'isTrainingGoalAchieved'] unless attributes[:'isTrainingGoalAchieved'].nil?

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

  self.is_training_goal_achieved = attributes[:'is_training_goal_achieved'] unless attributes[:'is_training_goal_achieved'].nil?

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

  self.signal_details = attributes[:'signalDetails'] if attributes[:'signalDetails']

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

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

  self.row_reduction_details = attributes[:'rowReductionDetails'] if attributes[:'rowReductionDetails']

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

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

Instance Attribute Details

#algorithm ⇒ String

Actual algorithm used to train the model

Returns:

  • (String) 


28
29
30
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 28

def algorithm
  @algorithm
end

#fap ⇒ Float

[Required] The final-achieved model accuracy metric on individual value level

Returns:

  • (Float) 


20
21
22
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 20

def fap
  @fap
end

#is_training_goal_achieved ⇒ BOOLEAN

A boolean value to indicate if train goal/targetFap is achieved for trained model

Returns:

  • (BOOLEAN) 


36
37
38
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 36

def is_training_goal_achieved
  @is_training_goal_achieved
end

#multivariate_fap ⇒ Float

The model accuracy metric on timestamp level.

Returns:

  • (Float) 


24
25
26
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 24

def multivariate_fap
  @multivariate_fap
end

#row_reduction_details ⇒ OCI::AiAnomalyDetection::Models::RowReductionDetails

Returns:



47
48
49
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 47

def row_reduction_details
  @row_reduction_details
end

#signal_details ⇒ Array<OCI::AiAnomalyDetection::Models::PerSignalDetails>

The list of signal details.

Returns:



44
45
46
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 44

def signal_details
  @signal_details
end

#warning ⇒ String

A warning message to explain the reason when targetFap cannot be achieved for trained model

Returns:

  • (String) 


40
41
42
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 40

def warning
  @warning
end

#window_size ⇒ Integer

Window size defined during training or deduced by the algorithm.

Returns:

  • (Integer) 


32
33
34
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 32

def window_size
  @window_size
end

Class Method Details

.attribute_map ⇒ Object

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



50
51
52
53
54
55
56
57
58
59
60
61
62
63
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 50

def self.attribute_map
  {
    # rubocop:disable Style/SymbolLiteral
    'fap': :'fap',
    'multivariate_fap': :'multivariateFap',
    'algorithm': :'algorithm',
    'window_size': :'windowSize',
    'is_training_goal_achieved': :'isTrainingGoalAchieved',
    'warning': :'warning',
    'signal_details': :'signalDetails',
    'row_reduction_details': :'rowReductionDetails'
    # rubocop:enable Style/SymbolLiteral
  }
end

.swagger_types ⇒ Object

Attribute type mapping.



66
67
68
69
70
71
72
73
74
75
76
77
78
79
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 66

def self.swagger_types
  {
    # rubocop:disable Style/SymbolLiteral
    'fap': :'Float',
    'multivariate_fap': :'Float',
    'algorithm': :'String',
    'window_size': :'Integer',
    'is_training_goal_achieved': :'BOOLEAN',
    'warning': :'String',
    'signal_details': :'Array<OCI::AiAnomalyDetection::Models::PerSignalDetails>',
    'row_reduction_details': :'OCI::AiAnomalyDetection::Models::RowReductionDetails'
    # 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



158
159
160
161
162
163
164
165
166
167
168
169
170
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 158

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

  self.class == other.class &&
    fap == other.fap &&
    multivariate_fap == other.multivariate_fap &&
    algorithm == other.algorithm &&
    window_size == other.window_size &&
    is_training_goal_achieved == other.is_training_goal_achieved &&
    warning == other.warning &&
    signal_details == other.signal_details &&
    row_reduction_details == other.row_reduction_details
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



195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 195

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


175
176
177
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 175

def eql?(other)
  self == other
end

#hash ⇒ Fixnum

Calculates hash code according to all attributes.

Returns:

  • (Fixnum) —

    Hash code



184
185
186
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 184

def hash
  [fap, multivariate_fap, algorithm, window_size, is_training_goal_achieved, warning, signal_details, row_reduction_details].hash
end

#to_hash ⇒ Hash

Returns the object in the form of hash

Returns:

  • (Hash) —

    Returns the object in the form of hash



228
229
230
231
232
233
234
235
236
237
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 228

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



222
223
224
 
# File 'lib/oci/ai_anomaly_detection/models/model_training_results.rb', line 222

def to_s
  to_hash.to_s
end