Class: Rust::Models::Regression::LinearRegressionModel

Inherits:
RegressionModel show all
Defined in:
lib/rust/models/regression.rb

Overview

Represents a linear regression model in R.

Instance Attribute Summary

Attributes inherited from RegressionModel

#data, #dependent_variable, #options

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from RegressionModel

#actuals, #backward_selection, #coefficients, #fitted, #initialize, #load_in_r_as, #method_missing, #model, #mse, #r_2, #r_2_adjusted, #r_hash, #residuals, #significant_variables, #summary, #variables

Methods inherited from RustDatatype

#load_in_r_as, #r_hash, #r_mirror, #r_mirror_to

Constructor Details

This class inherits a constructor from Rust::Models::Regression::RegressionModel

Dynamic Method Handling

This class handles dynamic methods through the method_missing method in the class Rust::Models::Regression::RegressionModel

Class Method Details

.can_pull?(type, klass) ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/rust/models/regression.rb', line 222

def self.can_pull?(type, klass)
    return type == "list" && klass == self.r_model_name
end

.generate(dependent_variable, independent_variables, data, **options) ⇒ Object

Generates a linear regression model, given its dependent_variable and independent_variables and its data. options can be specified and directly passed to the model.



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# File 'lib/rust/models/regression.rb', line 244

def self.generate(dependent_variable, independent_variables, data, **options)
    RegressionModel.generate(
        LinearRegressionModel,
        self.r_model_name, 
        dependent_variable, 
        independent_variables, 
        data, 
        **options
    )
end

.pull_priority ⇒ Object 



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# File 'lib/rust/models/regression.rb', line 226

def self.pull_priority
    1
end

.pull_variable(variable, type, klass) ⇒ Object 



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# File 'lib/rust/models/regression.rb', line 230

def self.pull_variable(variable, type, klass)
    model = Rust::RustDatatype.pull_variable(variable, Rust::List)
    
    return LinearRegressionModel.new(model)
end

.r_model_name ⇒ Object 



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# File 'lib/rust/models/regression.rb', line 236

def self.r_model_name
    "lm"
end

Instance Method Details

#predict(line) ⇒ Object 



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# File 'lib/rust/models/regression.rb', line 302

def predict(line)
    self.to_proc.call(line)
end

#to_proc ⇒ Object

Returns the model as a proc that can be used to predict values



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# File 'lib/rust/models/regression.rb', line 258

def to_proc
    proc do |unnormalized_data|
        data = Rust::DataFrame.new(["__TOREM__"])
        unnormalized_data.rows.times do
            data << [0]
        end
        unnormalized_data.colnames.each do |col|
            if (unnormalized_data|col)[0].is_a?(Numeric)
                newcol = Rust::DataFrame.new([col])
                (unnormalized_data|col).each do |v|
                    newcol << [v]
                end
                data.cbind!(newcol)
            else
                (unnormalized_data|col).uniq.each do |val|
                    newcol = Rust::DataFrame.new([col + val])
                    (unnormalized_data|col).each do |v|
                        if v == val
                            newcol << [1]
                        else
                            newcol << [0]
                        end
                    end
                    data.cbind!(newcol)
                end
            end
        end
        data.delete_column("__TOREM__")
        value = 0
        @variables.each do |var|
            p var
            if var.name == "(Intercept)"
                value += var.coefficient
            else
                if data.colnames.include?(var.name)
                    value += (data|var.name)[0] * var.coefficient
                end
            end
        end

        value
    end
end