Class: Google::Apis::BigqueryV2::TrainingOptions
- Inherits:
-
Object
- Object
- Google::Apis::BigqueryV2::TrainingOptions
- Includes:
- Core::Hashable, Core::JsonObjectSupport
- Defined in:
- generated/google/apis/bigquery_v2/classes.rb,
generated/google/apis/bigquery_v2/representations.rb,
generated/google/apis/bigquery_v2/representations.rb
Instance Attribute Summary collapse
-
#data_split_column ⇒ String
The column to split data with.
-
#data_split_eval_fraction ⇒ Float
The fraction of evaluation data over the whole input data.
-
#data_split_method ⇒ String
The data split type for training and evaluation, e.g.
-
#distance_type ⇒ String
Distance type for clustering models.
-
#early_stop ⇒ Boolean
(also: #early_stop?)
Whether to stop early when the loss doesn't improve significantly any more (compared to min_relative_progress).
-
#initial_learn_rate ⇒ Float
Specifies the initial learning rate for the line search learn rate strategy.
-
#input_label_columns ⇒ Array<String>
Name of input label columns in training data.
-
#kmeans_initialization_column ⇒ String
The column used to provide the initial centroids for kmeans algorithm when kmeans_initialization_method is CUSTOM.
-
#kmeans_initialization_method ⇒ String
The method used to initialize the centroids for kmeans algorithm.
-
#l1_regularization ⇒ Float
L1 regularization coefficient.
-
#l2_regularization ⇒ Float
L2 regularization coefficient.
-
#label_class_weights ⇒ Hash<String,Float>
Weights associated with each label class, for rebalancing the training data.
-
#learn_rate ⇒ Float
Learning rate in training.
-
#learn_rate_strategy ⇒ String
The strategy to determine learn rate for the current iteration.
-
#loss_type ⇒ String
Type of loss function used during training run.
-
#max_iterations ⇒ Fixnum
The maximum number of iterations in training.
-
#min_relative_progress ⇒ Float
When early_stop is true, stops training when accuracy improvement is less than 'min_relative_progress'.
-
#model_uri ⇒ String
[Beta] Google Cloud Storage URI from which the model was imported.
-
#num_clusters ⇒ Fixnum
Number of clusters for clustering models.
-
#optimization_strategy ⇒ String
Optimization strategy for training linear regression models.
-
#warm_start ⇒ Boolean
(also: #warm_start?)
Whether to train a model from the last checkpoint.
Instance Method Summary collapse
-
#initialize(**args) ⇒ TrainingOptions
constructor
A new instance of TrainingOptions.
-
#update!(**args) ⇒ Object
Update properties of this object.
Methods included from Core::JsonObjectSupport
Methods included from Core::Hashable
Constructor Details
#initialize(**args) ⇒ TrainingOptions
Returns a new instance of TrainingOptions.
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5784 def initialize(**args) update!(**args) end |
Instance Attribute Details
#data_split_column ⇒ String
The column to split data with. This column won't be used as a feature.
- When data_split_method is CUSTOM, the corresponding column should be boolean. The rows with true value tag are eval data, and the false are training data.
- When data_split_method is SEQ, the first DATA_SPLIT_EVAL_FRACTION
rows (from smallest to largest) in the corresponding column are used
as training data, and the rest are eval data. It respects the order
in Orderable data types:
https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data-
type-properties
Corresponds to the JSON property
dataSplitColumn
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5668 def data_split_column @data_split_column end |
#data_split_eval_fraction ⇒ Float
The fraction of evaluation data over the whole input data. The rest
of data will be used as training data. The format should be double.
Accurate to two decimal places.
Default value is 0.2.
Corresponds to the JSON property dataSplitEvalFraction
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5676 def data_split_eval_fraction @data_split_eval_fraction end |
#data_split_method ⇒ String
The data split type for training and evaluation, e.g. RANDOM.
Corresponds to the JSON property dataSplitMethod
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5681 def data_split_method @data_split_method end |
#distance_type ⇒ String
Distance type for clustering models.
Corresponds to the JSON property distanceType
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5686 def distance_type @distance_type end |
#early_stop ⇒ Boolean Also known as: early_stop?
Whether to stop early when the loss doesn't improve significantly
any more (compared to min_relative_progress). Used only for iterative
training algorithms.
Corresponds to the JSON property earlyStop
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5693 def early_stop @early_stop end |
#initial_learn_rate ⇒ Float
Specifies the initial learning rate for the line search learn rate
strategy.
Corresponds to the JSON property initialLearnRate
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5700 def initial_learn_rate @initial_learn_rate end |
#input_label_columns ⇒ Array<String>
Name of input label columns in training data.
Corresponds to the JSON property inputLabelColumns
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5705 def input_label_columns @input_label_columns end |
#kmeans_initialization_column ⇒ String
The column used to provide the initial centroids for kmeans algorithm
when kmeans_initialization_method is CUSTOM.
Corresponds to the JSON property kmeansInitializationColumn
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5711 def kmeans_initialization_column @kmeans_initialization_column end |
#kmeans_initialization_method ⇒ String
The method used to initialize the centroids for kmeans algorithm.
Corresponds to the JSON property kmeansInitializationMethod
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5716 def kmeans_initialization_method @kmeans_initialization_method end |
#l1_regularization ⇒ Float
L1 regularization coefficient.
Corresponds to the JSON property l1Regularization
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5721 def l1_regularization @l1_regularization end |
#l2_regularization ⇒ Float
L2 regularization coefficient.
Corresponds to the JSON property l2Regularization
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5726 def l2_regularization @l2_regularization end |
#label_class_weights ⇒ Hash<String,Float>
Weights associated with each label class, for rebalancing the
training data. Only applicable for classification models.
Corresponds to the JSON property labelClassWeights
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5732 def label_class_weights @label_class_weights end |
#learn_rate ⇒ Float
Learning rate in training. Used only for iterative training algorithms.
Corresponds to the JSON property learnRate
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5737 def learn_rate @learn_rate end |
#learn_rate_strategy ⇒ String
The strategy to determine learn rate for the current iteration.
Corresponds to the JSON property learnRateStrategy
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5742 def learn_rate_strategy @learn_rate_strategy end |
#loss_type ⇒ String
Type of loss function used during training run.
Corresponds to the JSON property lossType
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5747 def loss_type @loss_type end |
#max_iterations ⇒ Fixnum
The maximum number of iterations in training. Used only for iterative
training algorithms.
Corresponds to the JSON property maxIterations
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5753 def max_iterations @max_iterations end |
#min_relative_progress ⇒ Float
When early_stop is true, stops training when accuracy improvement is
less than 'min_relative_progress'. Used only for iterative training
algorithms.
Corresponds to the JSON property minRelativeProgress
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5760 def min_relative_progress @min_relative_progress end |
#model_uri ⇒ String
[Beta] Google Cloud Storage URI from which the model was imported. Only
applicable for imported models.
Corresponds to the JSON property modelUri
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5766 def model_uri @model_uri end |
#num_clusters ⇒ Fixnum
Number of clusters for clustering models.
Corresponds to the JSON property numClusters
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5771 def num_clusters @num_clusters end |
#optimization_strategy ⇒ String
Optimization strategy for training linear regression models.
Corresponds to the JSON property optimizationStrategy
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5776 def optimization_strategy @optimization_strategy end |
#warm_start ⇒ Boolean Also known as: warm_start?
Whether to train a model from the last checkpoint.
Corresponds to the JSON property warmStart
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5781 def warm_start @warm_start end |
Instance Method Details
#update!(**args) ⇒ Object
Update properties of this object
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# File 'generated/google/apis/bigquery_v2/classes.rb', line 5789 def update!(**args) @data_split_column = args[:data_split_column] if args.key?(:data_split_column) @data_split_eval_fraction = args[:data_split_eval_fraction] if args.key?(:data_split_eval_fraction) @data_split_method = args[:data_split_method] if args.key?(:data_split_method) @distance_type = args[:distance_type] if args.key?(:distance_type) @early_stop = args[:early_stop] if args.key?(:early_stop) @initial_learn_rate = args[:initial_learn_rate] if args.key?(:initial_learn_rate) @input_label_columns = args[:input_label_columns] if args.key?(:input_label_columns) @kmeans_initialization_column = args[:kmeans_initialization_column] if args.key?(:kmeans_initialization_column) @kmeans_initialization_method = args[:kmeans_initialization_method] if args.key?(:kmeans_initialization_method) @l1_regularization = args[:l1_regularization] if args.key?(:l1_regularization) @l2_regularization = args[:l2_regularization] if args.key?(:l2_regularization) @label_class_weights = args[:label_class_weights] if args.key?(:label_class_weights) @learn_rate = args[:learn_rate] if args.key?(:learn_rate) @learn_rate_strategy = args[:learn_rate_strategy] if args.key?(:learn_rate_strategy) @loss_type = args[:loss_type] if args.key?(:loss_type) @max_iterations = args[:max_iterations] if args.key?(:max_iterations) @min_relative_progress = args[:min_relative_progress] if args.key?(:min_relative_progress) @model_uri = args[:model_uri] if args.key?(:model_uri) @num_clusters = args[:num_clusters] if args.key?(:num_clusters) @optimization_strategy = args[:optimization_strategy] if args.key?(:optimization_strategy) @warm_start = args[:warm_start] if args.key?(:warm_start) end |