https://github.com/dirkschumacher/ftrljs
FTRL-Proximal online learning algorithm for logistic regression in javascript
https://github.com/dirkschumacher/ftrljs
ftrl javascript logistic-regression machine-learning-algorithms online-learning-algorithms
Last synced: 3 months ago
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FTRL-Proximal online learning algorithm for logistic regression in javascript
- Host: GitHub
- URL: https://github.com/dirkschumacher/ftrljs
- Owner: dirkschumacher
- Created: 2019-07-06T10:14:28.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-01T12:40:24.000Z (about 2 years ago)
- Last Synced: 2025-03-10T15:11:29.759Z (3 months ago)
- Topics: ftrl, javascript, logistic-regression, machine-learning-algorithms, online-learning-algorithms
- Language: JavaScript
- Size: 17.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
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README
# Dense FTRL-Proximal online learning algorithm for logistic regression
An _experimental_ implementation of the FTRL online learning algorithm for logistic regression in plain javascript.
Implemented as in this
[paper](https://www.eecs.tufts.edu/%7Edsculley/papers/ad-click-prediction.pdf).
```js
const optimizer = ftrl(
3, // the number of features in your training examples
10, // lambda1 - use this parameter for l1 regularitzation
2, // lambda2 - use this parameter for l2 regularitzation
0.1, // alpha - parameters that control the learning rate (see Eq. 2)
1 // beta - parameters that control the learning rate (see Eq. 2). 1 seems to be a good default value here
)
// pass one training example (an array) and the result (a number) at a time
optimizer.fit([1, 2, 3], 1)
optimizer.fit([1, 4, 3], 1)
optimizer.fit([1, 7, 3], 0)
// you can then predict the result for unseen examples
optimizer.predict([2, 3, 4])
// or just get the weights
optimizer.weights()
// ore store everything for later
const checkpoint = optimizer.save()
// ...
const newOptimizer = ftrl()
newOptimizer.load(checkpoint)
```
## API
* `ftrl()` creates a new optimizer/model
* `fit()` fits a single training example (array + result)
* `predict()` takes an array and returns the prediction (number between 0 and 1)
* `weights()` computes and returns the weights
* `save()` export the current state of the optimizer to an object
* `load()` restore a optimizer from a previously saved state
## Memory
The algorithm stores three dense arrays in the size of the features. If your number of features is too large, a sparse implementation of the algorithm can be used.
## References
McMahan, H. Brendan, et al. “Ad click prediction: a view from the
trenches.” Proceedings of the 19th ACM SIGKDD international conference
on Knowledge discovery and data mining. ACM, 2013.