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https://github.com/riversun/ml-fake-data-maker
Generate fake data for machine learning like regression analysis
https://github.com/riversun/ml-fake-data-maker
arff arff-generator dummy-data fake-data generator machine-learning prediction regression spark weka
Last synced: 14 days ago
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Generate fake data for machine learning like regression analysis
- Host: GitHub
- URL: https://github.com/riversun/ml-fake-data-maker
- Owner: riversun
- License: mit
- Created: 2019-07-21T10:02:20.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-13T14:44:47.000Z (over 4 years ago)
- Last Synced: 2024-12-06T02:23:13.393Z (2 months ago)
- Topics: arff, arff-generator, dummy-data, fake-data, generator, machine-learning, prediction, regression, spark, weka
- Language: Java
- Homepage:
- Size: 103 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Overview
- 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](https://opensource.org/licenses/MIT).
# How To Use
**Example Code**
Generate fake data that can be used in linear regression (or something like that).
**Maven dependency**
```xml
org.riversun
fake-data-maker
1.1.0
```
**Example code**
Generate fake data as **CSV** data
```java
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.
```shell
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
```java
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**
- [**CSV File(EN)**](https://raw.githubusercontent.com/riversun/ml-fake-data-maker/master/datasets/gem_price.csv)
- [**CSV File(JA) UTF-8 with BOM**](https://raw.githubusercontent.com/riversun/ml-fake-data-maker/master/datasets/gem_price_ja.csv)