Class: Eps::BaseEstimator
- Inherits:
-
Object
- Object
- Eps::BaseEstimator
show all
- Defined in:
- lib/eps/base_estimator.rb
Class Method Summary
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Instance Method Summary
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Constructor Details
#initialize(data = nil, y = nil, **options) ⇒ BaseEstimator
Returns a new instance of BaseEstimator.
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# File 'lib/eps/base_estimator.rb', line 3
def initialize(data = nil, y = nil, **options)
train(data, y, **options) if data
end
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Class Method Details
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# File 'lib/eps/base_estimator.rb', line 74
def self.(data, features)
vocabulary = {}
function_mapping = {}
derived_fields = {}
data.css("LocalTransformations DerivedField, TransformationDictionary DerivedField").each do |n|
name = n.attribute("name")&.value
field = n.css("FieldRef").attribute("field").value
value = n.css("Constant").text
field = field[10..-2] if field =~ /\Alowercase\(.+\)\z/
next if value.empty?
(vocabulary[field] ||= []) << value
function_mapping[field] = n.css("Apply").attribute("function").value
derived_fields[name] = [field, value]
end
functions = {}
data.css("TransformationDictionary DefineFunction").each do |n|
name = n.attribute("name").value
text_index = n.css("TextIndex")
functions[name] = {
tokenizer: Regexp.new(text_index.attribute("wordSeparatorCharacterRE").value),
case_sensitive: text_index.attribute("isCaseSensitive")&.value == "true"
}
end
text_features = {}
function_mapping.each do |field, function|
text_features[field] = functions[function].merge(vocabulary: vocabulary[field])
features[field] = "text"
end
[text_features, derived_fields]
end
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.load_pmml(data) ⇒ Object
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# File 'lib/eps/base_estimator.rb', line 40
def self.load_pmml(data)
if data.is_a?(String)
data = Nokogiri::XML(data) { |config| config.strict }
end
model = new
model.instance_variable_set("@pmml", data)
model.instance_variable_set("@evaluator", yield(data))
model
end
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Instance Method Details
#evaluate(data, y = nil, target: nil) ⇒ Object
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# File 'lib/eps/base_estimator.rb', line 31
def evaluate(data, y = nil, target: nil)
data, target = prep_data(data, y, target || @target)
Eps.metrics(data.label, predict(data))
end
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#predict(data) ⇒ Object
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# File 'lib/eps/base_estimator.rb', line 7
def predict(data)
singular = data.is_a?(Hash)
data = [data] if singular
data = Eps::DataFrame.new(data)
@evaluator.features.each do |k, type|
values = data.columns[k]
raise ArgumentError, "Missing column: #{k}" if !values
column_type = Utils.column_type(values.compact, k) if values
if !column_type.nil?
if (type == "numeric" && column_type != "numeric") || (type != "numeric" && column_type != "categorical")
raise ArgumentError, "Bad type for column #{k}: Expected #{type} but got #{column_type}"
end
end
end
predictions = @evaluator.predict(data)
singular ? predictions.first : predictions
end
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#summary(extended: false) ⇒ Object
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# File 'lib/eps/base_estimator.rb', line 50
def summary(extended: false)
str = String.new("")
if @validation_set
y_true = @validation_set.label
y_pred = predict(@validation_set)
case @target_type
when "numeric"
metric_name = "RMSE"
v = Metrics.rmse(y_true, y_pred)
metric_value = v.round >= 1000 ? v.round.to_s : "%.3g" % v
else
metric_name = "accuracy"
metric_value = "%.1f%%" % (100 * Metrics.accuracy(y_true, y_pred)).round(1)
end
str << "Validation %s: %s\n\n" % [metric_name, metric_value]
end
str << _summary(extended: extended)
str
end
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#to_pmml ⇒ Object
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# File 'lib/eps/base_estimator.rb', line 36
def to_pmml
(@pmml ||= generate_pmml).to_xml
end
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