- Library to generate fake data for machine learningu (like prediction with linearregression,randomforest,gbt etc.)
- Support CSV and ARFF format
It is licensed under MIT.
Example Code
Generate fake data that can be used in linear regression (or something like that).
Maven dependency
<dependency>
<groupId>org.riversun</groupId>
<artifactId>fake-data-maker</artifactId>
<version>1.1.0</version>
</dependency>Example code
Generate fake data as CSV data
import java.io.File;
/**
*
* Generate fake data that can be used in linear regression analysis
*
*/
class Example {
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(20)//num of data
.targetLabel("price")//target label
.targetInitialValue(basePrice)
.valueVolatility(0.0)
.withHeader(true)
.withId(true)
.build();
//ds.save(new File("c:/temp/data.arff"), "UTF-8");//save generated data
System.out.println(ds.get());//print generated data
}
}Result You can get like this CSV file.
In the case of this data, it is possible to predict the price of the gem (objective variable ) from the attribute (explanatory variable) using linear regression etc.
id,material,shape,weight,brand,shop,price
0,Gold,Brace,42,NationalBrand,DepartmentStore,561
1,Gold,Earrings,51,NoBrand,DepartmentStore,269
2,Silver,Ring,49,WorldTopBrand,DepartmentStore,672
3,Gold,Earrings,43,WorldTopBrand,BrandStore,2337
4,Platinum,Ring,10,FamouseBrand,BrandStore,1279
5,Diamond,Neckless,42,NoBrand,DiscountStore,383
6,Gold,Earrings,13,WorldTopBrand,BrandStore,1603
7,Gold,Brace,59,FamouseBrand,BrandStore,1558
8,Platinum,Earrings,47,WorldTopBrand,DepartmentStore,3173
9,Silver,Ring,38,NationalBrand,DepartmentStore,156
10,Diamond,Neckless,25,FamouseBrand,BrandStore,2299
11,Platinum,Ring,34,FamouseBrand,BrandStore,1940
12,Gold,Brooch,21,NoBrand,DiscountStore,154
13,Gold,Earrings,18,WorldTopBrand,DepartmentStore,1607
14,Gold,Earrings,35,NoBrand,DiscountStore,178
15,Platinum,Ring,37,NationalBrand,BrandStore,881
16,Silver,Brooch,39,NoBrand,DiscountStore,55
17,Gold,Earrings,43,NationalBrand,DepartmentStore,514
18,Silver,Brace,35,FamouseBrand,BrandStore,409
19,Platinum,Brooch,13,NationalBrand,DiscountStore,393
20,Gold,Earrings,53,NationalBrand,DiscountStore,400Example code
Generate fake data as ARFF data
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.ARFF)//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(20)//num of data
.targetLabel("price")//target label
.targetInitialValue(basePrice)
.valueVolatility(0.0)
.withHeader(true)
.withId(true)
.build();
//ds.save(new File("c:/temp/data.arff"), "UTF-8");//save generated data
System.out.println(ds.get());//print generated data
}Data file is also MIT licensed.
Purpose Predict sales price from gem attributes
Format
id,material,shape,weight,brand,shop,price
0,Gold,Brace,42,NationalBrand,DepartmentStore,561
1,Gold,Earrings,51,NoBrand,DepartmentStore,269
...
Data File