README.md in xlearn-0.1.0 vs README.md in xlearn-0.1.1
- old
+ new
@@ -8,10 +8,12 @@
- Linear models
- Factorization machines
- Field-aware factorization machines
+[](https://travis-ci.org/ankane/xlearn)
+
## Installation
First, [install xLearn](https://xlearn-doc.readthedocs.io/en/latest/install/index.html). On Mac, copy `build/lib/libxlearn_api.dylib` to `/usr/local/lib`.
Add this line to your application’s Gemfile:
@@ -20,12 +22,10 @@
gem 'xlearn'
```
## Getting Started
-This library is modeled after the [Python Scikit-learn API](https://xlearn-doc.readthedocs.io/en/latest/python_api/index.html). Some methods are missing at the moment. PRs welcome!
-
Prep your data
```ruby
x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
@@ -56,43 +56,138 @@
```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
Specify parameters
```ruby
-model = XLearn::FM.new(k: 20, epoch: 50)
+model = XLearn::Linear.new(k: 20, epoch: 50)
```
Supports the same parameters as [Python](https://xlearn-doc.readthedocs.io/en/latest/all_api/index.html)
-## Validation
+## Cross-Validation
-Pass a validation set when fitting
+Cross-validation
```ruby
-model.fit(x_train, y_train, eval_set: [x_val, y_val])
+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 Daru data frame
+
+```ruby
+Daru::DataFrame.from_csv("houses.csv")
+```
+
+Or a Numo NArray
+
+```ruby
+Numo::DFloat.new(3, 2).seq
+```
+
## Performance
-For performance, you can read data directly from files
+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")
```
-[These formats](https://xlearn-doc.readthedocs.io/en/latest/python_api/index.html#choose-machine-learning-algorithm) are supported
+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_file: "predictions.txt")
+model.predict("test.txt", out_path: "predictions.txt")
```
+
+## xLearn Installation
+
+There’s an experimental branch that includes xLearn with the gem for easiest installation.
+
+```ruby
+gem 'xlearn', github: 'ankane/xlearn', branch: 'vendor', submodules: true
+```
+
+Please file an issue if it doesn’t work for you.
+
+You can also specify the path to xLearn in an initializer:
+
+```ruby
+XLearn.ffi_lib << "/path/to/xlearn/lib/libxlearn_api.so"
+```
+
+> Use `libxlearn_api.dylib` for Mac and `xlearn_api.dll` for Windows
+
+## 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/blob/master/CHANGELOG.md)