org.riversun.ml.fakedatamaker._ExampleEn Maven / Gradle / Ivy

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Generate fake data for regression analysis
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package org.riversun.ml.fakedatamaker;

import java.io.File;

/**
 * 
 * Generate fake data that can be used in linear regression analysis
 *
 */
class _ExampleEn {

    public static void main(String[] args) {

        // Set base price
        double basePrice = 10;

        // Create attributes
        Attribute material = new Attribute(
                // category name
                "material",
                // new AttributeNominal(categorical value,Weight given to objective variable)
                new AttributeNominal("Diamond", 20),
                new AttributeNominal("Platinum", 15),
                new AttributeNominal("Gold", 10),
                new AttributeNominal("Silver", 3));

        Attribute brand = new Attribute(
                "brand",
                new AttributeNominal("WorldTopBrand", 8.0),
                new AttributeNominal("FamouseBrand", 4.5),
                new AttributeNominal("NationalBrand", 2.0),
                new AttributeNominal("NoBrand", 1.0));

        Attribute shop = new Attribute(
                "shop",
                new AttributeNominal("BrandStore", 1.7),
                new AttributeNominal("DepartmentStore", 1.5),
                new AttributeNominal("MassRetailer", 1.2),
                new AttributeNominal("DiscountStore", 1.1));

        Attribute shape = new Attribute(
                "shape",
                new AttributeNominal("Ring", 1.10),
                new AttributeNominal("Neckless", 1.07),
                new AttributeNominal("Earrings", 1.05),
                new AttributeNominal("Brooch", 1.05),
                new AttributeNominal("Brace", 1.15));

        Attribute weightg = new Attribute("weight",
                new AttributeNumeric(10, 60, ComputeMethod.LOG10, 1));

        FakeDataSet ds = new FakeDataSet.Builder()
                .type(DataType.REGRESSION)
                .outputFormat(OutputFormat.CSV)// CSV or ARFF
                .nameOfData("gemsales")
                .addAttr(material)
                .addAttr(shape)
                .addAttr(weightg)
                .addAttr(brand)
                .addAttr(shop)
                .compliantListener(new DataRuleCompliantListener() {
                    @Override
                    public boolean isCompliant(AttributeCheck check) {

                        if (check.nominalEquals("brand", "NoBrand") && check.nominalEquals("shop", "BrandStore")) {
                            // No-brand has no "BrandStore"
                            return false;
                        }
                        if (check.nominalEquals("brand", "WorldTopBrand") &&
                                (check.nominalEquals("shop", "DiscountStore")) || check.nominalEquals("shop", "MassRetailer")) {
                            // WorldTopBrands are not handled at "DiscountStores" or "mass retailers"
                            return false;
                        }
                        if (check.nominalEquals("brand", "FamouseBrand") &&
                                (check.nominalEquals("shop", "DiscountStore"))) {
                            // FamouseBrands are not handled at "DiscountStore"
                            return false;
                        }

                        return true;
                    }
                })
                .numOfLines(500)// num of data
                .targetLabel("price")// target label
                .targetInitialValue(basePrice)
                .valueVolatility(0.0)
                //.seed(1)// random seed >0
                .withHeader(true)
                .withId(true)
                .build();

        // ds.save(new File("c:/temp/gem_price.csv"), "UTF-8");//save generated data
        System.out.println(ds.get());// print generated data

    }
}