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https://github.com/ankane/xlearn
High performance factorization machines for Ruby
https://github.com/ankane/xlearn
Last synced: 3 months ago
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High performance factorization machines for Ruby
- Host: GitHub
- URL: https://github.com/ankane/xlearn
- Owner: ankane
- License: apache-2.0
- Archived: true
- Created: 2019-10-12T02:02:09.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-02-03T01:42:18.000Z (almost 2 years ago)
- Last Synced: 2024-03-31T08:08:26.607Z (7 months ago)
- Language: Ruby
- Homepage:
- Size: 84 KB
- Stars: 53
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# xLearn Ruby
[xLearn](https://github.com/aksnzhy/xlearn) - the high performance machine learning library - for Ruby
Supports:
- Linear models
- Factorization machines
- Field-aware factorization machines
[](https://github.com/ankane/xlearn-ruby/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "xlearn"
```
## Getting Started
Prep your data
```ruby
x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
```
Train a model
```ruby
model = XLearn::Linear.new(task: "reg")
model.fit(x, y)
```
Use `XLearn::FM` for factorization machines and `XLearn::FFM` for field-aware factorization machines
Make predictions
```ruby
model.predict(x)
```
Save the model to a file
```ruby
model.save_model("model.bin")
```
Load the model from a file
```ruby
model.load_model("model.bin")
```
Save a text version of the model
```ruby
model.save_txt("model.txt")
```
Pass a validation set
```ruby
model.fit(x_train, y_train, eval_set: [x_val, y_val])
```
Train online
```ruby
model.partial_fit(x_train, y_train)
```
Get the bias term, linear term, and latent factors
```ruby
model.bias_term
model.linear_term
model.latent_factors # fm and ffm only
```
## Parameters
Pass parameters - default values below
```ruby
XLearn::FM.new(
task: "binary", # binary (classification), reg (regression)
metric: nil, # acc, prec, recall, f1, auc, mae, mape, rmse, rmsd
lr: 0.2, # learning rate
lambda: 0.00002, # lambda for l2 regularization
k: 4, # latent factors for fm and ffm
alpha: 0.3, # hyper parameter for ftrl
beta: 1.0, # hyper parameter for ftrl
lambda_1: 0.00001, # hyper parameter for ftrl
lambda_2: 0.00002, # hyper parameter for ftrl
epoch: 10, # number of epochs
fold: 3, # number of folds
opt: "adagrad", # sgd, adagrad, ftrl
block_size: 500, # block size for on-disk training in MB
early_stop: true, # use early stopping
stop_window: 2, # size of stop window for early stopping
sign: false, # convert predition output to 0 and 1
sigmoid: false, # convert predition output using sigmoid
seed: 1 # random seed to shuffle data set
)
```
## Cross-Validation
Cross-validation
```ruby
model.cv(x, y)
```
Specify the number of folds
```ruby
model.cv(x, y, folds: 5)
```
## Data
Data can be an array of arrays
```ruby
[[1, 2, 3], [4, 5, 6]]
```
Or a Numo array
```ruby
Numo::NArray.cast([[1, 2, 3], [4, 5, 6]])
```
Or a Rover data frame
```ruby
Rover.read_csv("houses.csv")
```
Or a Daru data frame
```ruby
Daru::DataFrame.from_csv("houses.csv")
```
## Performance
For large datasets, read data directly from files
```ruby
model.fit("train.txt", eval_set: "validate.txt")
model.predict("test.txt")
model.cv("train.txt")
```
For linear models and factorization machines, use CSV:
```txt
label,value_1,value_2,...,value_n
```
Or the `libsvm` format (better for sparse data):
```txt
label index_1:value_1 index_2:value_2 ... index_n:value_n
```
> You can also use commas instead of spaces for separators
For field-aware factorization machines, use the `libffm` format:
```txt
label field_1:index_1:value_1 field_2:index_2:value_2 ...
```
> You can also use commas instead of spaces for separators
You can also write predictions directly to a file
```ruby
model.predict("test.txt", out_path: "predictions.txt")
```
## Credits
This library is modeled after xLearn’s [Scikit-learn API](https://xlearn-doc.readthedocs.io/en/latest/python_api/index.html).
## History
View the [changelog](https://github.com/ankane/xlearn-ruby/blob/master/CHANGELOG.md)
## Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- [Report bugs](https://github.com/ankane/xlearn-ruby/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/xlearn-ruby/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/xlearn-ruby.git
cd xlearn-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test
```