JarCastingOverview
- 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.
How To Use
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,400
Example 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
}
Download example data for learning and regression
Data file is also MIT licensed.
Gem Prices
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