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fasttext.js

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FastText for Node.js

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## What is FastText and FastText.js
[FastText](https://github.com/facebookresearch/fastText) is a library for efficient learning of word representations and sentence classification. FastText is provided by Facebook Inc. 
`FastText.js` is a `JavaScript` library  that wraps `FastText` to run smoothly within `node`.

## What's New
Added support for M1 Apple processor.

# Table of Contents
- [FastText.js APIs](https://github.com/loretoparisi/fasttext.js#fasttextjs-apis)
- [How to Install](https://github.com/loretoparisi/fasttext.js#how-to-install)
	* [Install via NPM](https://github.com/loretoparisi/fasttext.js#install-via-npm)
	* [Install via Docker](https://github.com/loretoparisi/fasttext.js#install-via-docker)
	* [WASM](https://github.com/loretoparisi/fasttext.js#wasm) :new:
- [How to Use](https://github.com/loretoparisi/fasttext.js#how-to-use)
	* [Train](https://github.com/loretoparisi/fasttext.js#train)
	* [Train Supervised](https://github.com/loretoparisi/fasttext.js#train-supervised)
	* [Train Unsupervised](https://github.com/loretoparisi/fasttext.js#train-unsupervised)
	* [Track Progress](https://github.com/loretoparisi/fasttext.js#track-progress)
	* [Test](https://github.com/loretoparisi/fasttext.js#test)
	* [Predict](https://github.com/loretoparisi/fasttext.js#predict)
	* [Nearest Neighbor](https://github.com/loretoparisi/fasttext.js#nearest-neighbor)
– [Tools](https://github.com/loretoparisi/fasttext.js#tools)
	* [Confusion Matrix](https://github.com/loretoparisi/fasttext.js#confusion-matrix) :new:
- [Examples](https://github.com/loretoparisi/fasttext.js#examples)
	* [Train](https://github.com/loretoparisi/fasttext.js#train-1)
	* [Test](https://github.com/loretoparisi/fasttext.js#test-1)
    * [Test Labels](https://github.com/loretoparisi/fasttext.js#test-labels) :new:
	* [Predict](https://github.com/loretoparisi/fasttext.js#predict-1)
    * [Multi-label](https://github.com/loretoparisi/fasttext.js#multi-label) :new:
	* [Run a Prediction Server](https://github.com/loretoparisi/fasttext.js#run-a-prediction-server)
    * [Run a Prediction CLI](https://github.com/loretoparisi/fasttext.js#run-a-prediction-cli) :new:
	* [Language Identification Server](https://github.com/loretoparisi/fasttext.js#language-identificaton-server)
    * [Train Language Identification Model](https://github.com/loretoparisi/fasttext.js#train-language-identification-model) :new:
- [Training set and Test set format](https://github.com/loretoparisi/fasttext.js#training-set-and-test-set-format)
- [Datasets](https://github.com/loretoparisi/fasttext.js#datasets) :new:
- [Models](https://github.com/loretoparisi/fasttext.js#models) :new:
- [Other Versions](https://github.com/loretoparisi/fasttext.js#other-versions)
– [Supported Platforms](https://github.com/loretoparisi/fasttext.js#supported-platforms)
	* [External Binary](https://github.com/loretoparisi/fasttext.js#external-binary)
– [How It Works](https://github.com/loretoparisi/fasttext.js#how-it-works)
– [Disclaimer](https://github.com/loretoparisi/fasttext.js#disclaimer)
– [Acknowledgments](https://github.com/loretoparisi/fasttext.js#acknowledgments)

## FastText.js APIs
This version of `FastText.js` comes with the following `JavaScript` APIs

```javascript
FastText.new(options)
FastText.load()
FastText.loadnn() [NEW API]
FastText.word2vec() [NEW API]
FastText.train()
FastText.test()
FastText.predict(string)
FastText.nn() [NEW API]
```

## How to Install

```bash
git clone https://github.com/loretoparisi/fasttext.js.git
cd fasttext.js
npm install
```

### Install via NPM
`FastText.js` is available as a npm module [here](https://www.npmjs.com/package/fasttext.js). To add the package to your project

```bash
npm install --save fasttext.js
```

### Install via Docker
Build the docker image 
```bash
docker build -t fasttext.js .
```

This will update the latest `FastText` linux binaries from source to `lib/bin/linux`.

Now running the image on the docker host binding the port 3000 it is possibile to run the server example:
```bash
docker build -t fasttext.js .
docker run --rm -it -p 3000:3000 fasttext.js node fasttext.js/examples/server.js 
```

To serve a different custom model a volume can be used and passing the `MODEL` environment variable

```bash
docker run -v /models/:/models --rm -it -p 3000:3000 -e MODEL=/models/my_model.bin fasttext.js node fasttext.js/examples/server.js

```

### WASM
We provide a WASM compiled binary of `FastText` to be used natively NodeJS. Please check `/wasm` folder for library and examples. :new:

## How to Use

### Train
You can specify all the train parameters supported by fastText as a json object

```javascript
train: {
    // number of concurrent threads
    thread: 8,
    // verbosity level [2]
    verbose: 4,
    // number of negatives sampled [5]
    neg: 5,
    // loss function {ns, hs, softmax} [ns]
    loss: process.env.TRAIN_LOSS || 'ns',
    // learning rate [0.05]
    lr: process.env.TRAIN_LR || 0.05,
    // change the rate of updates for the learning rate [100]
    lrUpdateRate: 100,
    // max length of word ngram [1]
    wordNgrams: process.env.TRAIN_NGRAM || 1,
    // minimal number of word occurences
    minCount: 1,
    // minimal number of word occurences
    minCountLabel: 1,
    // size of word vectors [100]
    dim: process.env.TRAIN_DIM || 100,
    // size of the context window [5]
    ws: process.env.TRAIN_WS || 5,
    //  number of epochs [5]
    epoch: process.env.TRAIN_EPOCH || 5,
    // number of buckets [2000000]
    bucket: 2000000,
    // min length of char ngram [3]
    minn: process.env.TRAIN_MINN || 3,
    // max length of char ngram [6]
    maxn: process.env.TRAIN_MAXN || 6,
    // sampling threshold [0.0001]
    t: 0.0001,
    // load pre trained word vectors from unsupervised model
    pretrainedVectors: process.env.WORD2VEC || ''
    }
```

#### Train Supervised
To train the model you must specificy the training set as `trainFile` and the file where the model must be serialized as `serializeTo`. All the `FastText` supervised options are supported. See [here](https://github.com/facebookresearch/fastText#full-documentation) for more details about training options. Note that `serializeTo` does not need to have the file extension in. A `bin` extension for the quantized model will be automatically added. You can use the `pretrainedVectors` option to load an unsupervised pre-trained model. Please use the `word2vec` api to train this model.

```javascript
var fastText = new FastText({
    serializeTo: './band_model',
    trainFile: './band_train.txt'
});
fastText.train()
.then(done=> {
    console.log("train done.");
})
.catch(error => {
    console.error(error);
})
```

#### Train Unsupervised
To train an unsupervised model use the `word2vec` api. You can specify the words representation to train using `word2vec.model` parameter set to `skipgram` or `cbow` and use the `train` parameters as usual:

```javascript
fastText.word2vec()
    .then(done => {
    })
    .catch(error => {
        console.error("Train error", error);
    })
```

## Track Progress
It is possible to track the training progress using the callback parameters `trainProgress` in the options:

```javascript
{
    trainCallback: function(res) {
        console.log( "\t"+JSON.stringify(res) );
    }
}
```

This will print out the training progress in the following format:

```json
{
    "progress":17.2,
    "words":174796,
    "lr":0.041382,
    "loss":1.232538,
    "eta":"0h0m",
    "eta_msec":0
}
```

that is the parsed JSON representation of `fasttext` output

```
Progress:  17.2% words/sec/thread:  174796 lr:  0.041382 loss:  1.232538 ETA:   0h 0m
```

where `eta_msec` represents the ETA (estimated time of arrival) in milliseconds.

So a typical progress will fire the `trainCallback` several times like

```json
{"progress":0.6,"loss":4.103271,"lr":0.0498,"words":21895,"eta":"0h9m","eta_msec":540000}
{"progress":0.6,"loss":3.927083,"lr":0.049695,"words":21895,"eta":"0h6m","eta_msec":360000}
{"progress":0.6,"loss":3.927083,"lr":0.049695,"words":21895,"eta":"0h6m","eta_msec":360000}
{"progress":0.6,"loss":3.676603,"lr":0.049611,"words":26813,"eta":"0h5m","eta_msec":300000}
{"progress":0.6,"loss":3.676603,"lr":0.049611,"words":26813,"eta":"0h5m","eta_msec":300000}
{"progress":0.6,"loss":3.345654,"lr":0.04949,"words":33691,"eta":"0h4m","eta_msec":240000}
{"progress":1.2,"loss":3.345654,"lr":0.04949,"words":39604,"eta":"0h4m","eta_msec":240000}
```

Until the progress will reach 100%:

```json
{"progress":99,"loss":0.532964,"lr":0.000072,"words":159556,"eta":"0h0m","eta_msec":0}
{"progress":99,"loss":0.532964,"lr":0.000072,"words":159556,"eta":"0h0m","eta_msec":0}
{"progress":99,"loss":0.532392,"lr":-0.000002,"words":159482,"eta":"0h0m","eta_msec":0}
{"progress":100,"loss":0.532392,"lr":0,"words":159406,"eta":"0h0m","eta_msec":0}
{"progress":100,"loss":0.532392,"lr":0,"words":159406,"eta":"0h0m","eta_msec":0}
```

__NOTE__. Please note that some approximation errors may occur in the output values.

### Test
To test your model you must specificy the test set file as `testFile` and the model file to be loaded as `loadModel`. Optionally you can specificy the precision and recall at `k` (P@k and R@k) passing the object `test: { precisionRecall: k }`.

```javascript
var fastText = new FastText({
    loadModel: './band_model.bin',
    testFile:  './band_test.txt'
});
fastText.test()
.then(evaluation=> {
    console.log("test done.",evaluation);
})
.catch(error => {
    console.error(error);
})
```

The `evaluation` will contain the precision `P`, recall `R` and number of samples `N` as a json object `{ P: '0.278', R: '0.278' }`.
If a train is called just before test the `evaluation` will contain the number of words `W` and the number of labels `L` as well as a json object: `{ W: 524, L: 2, N: '18', P: '0.278', R: '0.278' }`:

```javascript
fastText.train()
.then(done=> {
    return fastText.test();
})
.then(evaluation=> {
    console.log("train & test done.",evaluation);
})
.catch(error => {
    console.error("train error",error);
})
```

### Test-Labels
:new: The api `testLabels` evaluate labels `F1-Score`, `Recall` and `Precision` for each label in the model.

```javascript
var fastText = new FastText({
    loadModel: './band_model.bin',
    testFile:  './band_test.txt'
});
fastText.testLabels()
.then(evaluation=> {
    console.log("test-labels:",evaluation);
})
.catch(error => {
    console.error(error);
})
```

This will print out the values `F1`, `P` and `R` for each label.

```json
[
  {
    "ham": {
      "F1": "0.172414",
      "P": "0.094340",
      "R": "1.000000"
    }
  },
  {
    "spam": {
      "F1": "0.950495",
      "P": "0.905660",
      "R": "1.000000"
    }
  }
]
```

### Predict
To inference your model with new data and predict the label you must specify the model file to be loaded as `loadModel`. You can then call the `load` method once, and `predict(string)` to classify a string. Optionally you can specify the `k` most likely labels to print for each line as `predict: { precisionRecall: k }`

```javascript
var sample="Our Twitter run by the band and crew to give you an inside look into our lives on the road. Get #FutureHearts now: http://smarturl.it/futurehearts";
fastText.load()
.then(done => {
    return fastText.predict(sample);
})
.then(labels=> {
    console.log("TEXT:", sample, "\nPREDICT:",labels );
    sample="LBi Software provides precisely engineered, customer-focused #HRTECH solutions. Our flagship solution, LBi HR HelpDesk, is a SaaS #HR Case Management product.";
    return fastText.predict(sample);
})
.then(labels=> {
    console.log("TEXT:", sample, "\nPREDICT:",labels );
    fastText.unload();
})
.catch(error => {
    console.error(error);
});
```

### Multi-Label
A multi-label example will have a list of columns of labels marked by `__label__` prefix

```csv
__label__sauce __label__cheese How much does potato starch affect a cheese sauce recipe?
```
FastText.js will automatically handle multiple labels in the dataset for training and testing, please run the `multilabel.js` example to test it out:

```sh
cd examples/
node multilabel.js
```

The `train()` and `test()` will print out:
```sh
N	3000
P@1	0.000333
R@1	0.000144
Number of examples: 3000
exec:fasttext end.
exec:fasttext exit.
{ N: '3000', P: '0.000333', R: '0.000144' }
```

while the `testLabels` api will print out the labels array:

```
[ '__label__equipment',
  '__label__cast',
  '__label__oven',
  '__label__sauce',
  '__label__indian',
  '__label__breakfast',
  '__label__chili',
  '__label__spicy',
  '__label__bread',
  '__label__eggs',
  '__label__baking',
   ...
```

and by labels scores:

```sh
F1-Score : 0.175182  Precision : 0.096000  Recall : 1.000000   __label__baking
F1-Score : 0.150432  Precision : 0.081333  Recall : 1.000000   __label__food-safety
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__substitutions
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__equipment
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__bread
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__chicken
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__storage-method
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__eggs
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__meat
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__sauce
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__cake
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__flavor
F1-Score : --------  Precision : --------  Recall : 0.000000   __label__freezing
...
```

### Nearest Neighbor
To get the nearest neighbor words for a given term use the `nn` api:

```javascript
fastText.loadnn()
.then(labels=> {
    return fastText.nn(text)
})
.then(labels=> {
    console.log("Nearest Neighbor\n", JSON.stringify(labels, null, 2));
})
.catch(error => {
    console.error("predict error",error);
});
```

## Tools
We provide some support tools and script.
### Confusion Matrix
To evaluate the [Confusion Matrix](https://en.wikipedia.org/wiki/Confusion_matrix) of a model, please use the `confusion.sh` script provided in the `tools/` folder. The script requires `sklearn` and `matplotlib` installed on the system:

```bash
$ cd tools/
$ ./confusion.sh 
Usage: ./confusion.sh DATASET_FILE MODEL_FILE [LABEL_COLUMS, def:1] [LABEL_NORMALIZED, default|normalize, def:default] [PLOT, 0|1, def:0]
```

You must specify the dataset file path that has been used to train the model and the model file path. If the label golumn is different than the first column, plese specify the `LABEL_COLUMN` column index. If the dataset must be normalized, having a different label prefix or no one, please use the value `normalize`:

```bash
cd examples/
node train
cd ..
cd tools/
./confusion.sh ../examples/dataset/sms.tsv ../examples/models/sms_model.bin 1 normalize 1
```

If the dataset has the fasttext label prefix i.e. `__label__`, please set the  parameter `NORMALIZE_LABEL` to `default`:

```bash
./confusion.sh ../examples/dataset/sms.tsv ../examples/models/sms_model.bin 1 default
```

The script will calculate the predictions against the dataset and build confusion matrix using `sklearn`

```bash
./confusion.sh ../examples/dataset/sms.tsv ../examples/models/sms_model.bin 1
Platform is Darwin
Normalizing dataset ../examples/dataset/sms.tsv...
Splitting 1 label colums...
Calculating predictions...
Calculating confusion matrix...
Test labels:1324 (sample)
['spam']
labels:{'ham', 'spam'}
Predicted labels:1324 (sample)
['ham']
Accuracy: 0.756797583082
[[1002    0]
 [ 322    0]]
Confusion matrix
[[ 1.  0.]
 [ 1.  0.]]
```

and and visualize it using `matplotlib`:

## Examples
A folder `examples` contains several usage examples of `FastText.js`.
### Train

```bash
$ cd examples/
$ node train.js 
train [ 'supervised',
  '-input',
  '/var/folders/_b/szqwdfn979n4fdg7f2j875_r0000gn/T/trainfile.csv',
  '-output',
  './data/band_model',
  '-dim',
  10,
  '-lr',
  0.1,
  '-wordNgrams',
  2,
  '-minCount',
  1,
  '-bucket',
  10000000,
  '-epoch',
  5,
  '-thread',
  4 ]
Read 0M words
Number of words:  517
Number of labels: 2
Progress: 100.0%  words/sec/thread: 1853435  lr: 0.000000  loss: 0.681683  eta: 0h0m -14m 
exec:fasttext end.
exec:fasttext exit.
train done.
task:fasttext pid:41311 terminated due to receipt of signal:null
```

### Test

```bash
$ cd examples/
$ node test.js 
test [ 'test',
  './data/band_model.bin',
  '/var/folders/_b/szqwdfn979n4fdg7f2j875_r0000gn/T/trainfile.csv',
  1 ]
Number of examples: 18
exec:fasttext end.
exec:fasttext exit.
test done.
task:fasttext pid:41321 terminated due to receipt of signal:null
```

### Predict

```bash
$ cd examples/
$ node predict.js 
TEXT: our twitter run by the band and crew to give you an inside look into our lives on the road .  get #futurehearts now  http //smarturl . it/futurehearts PREDICT: BAND
TEXT: lbi software provides precisely engineered ,  customer-focused #hrtech solutions .  our flagship solution ,  lbi hr helpdesk ,  is a saas #hr case management product .  PREDICT: ORGANIZATION
```

### Run a Prediction Server
We run a model and serve predictions via a simple node `http` api. We first download the example hosted models:

```bash
cd examples/models
./models.sh
```

We now run the `server.js` example that will create a `http` server. We can export the `MODEL` env that will point to the local pretrained model, and a optional `PORT` parameter were the server is going to listen (default `PORT` is 3000):

```bash
$ cd examples/
$ export MODEL=models/lid.176.ftz
$ node server.js 
model loaded
server is listening on 3000
```

Try this simple server api passing a `text` parameter like:

http://localhost:3000/?text=LBi%20Software%20provides%20precisely%20engineered,%20customer-focused%20#HRTECH

http://localhost:3000/?text=Our%20Twitter%20run%20by%20the%20band%20and%20crew%20to%20give%20you%20an%20inside%20look%20into%20our%20lives%20on%20the%20road.%20Get%20#FutureHearts

The server api will response in json format

```json
{
	"response_time": 0.001,
	"predict": [{
			"label": "BAND",
			"score": "0.5"
		},
		{
			"label": "ORGANIZATION",
			"score": "0.498047"
		}
	]
}
```

### Run a Prediction CLI
To run a prediction Command Line Interface, please specify the env `MODEL` of the model file to run and use the example script `cli`:

```bash
cd examples/
export MODEL=models/langid_model.bin
node cli
Loading model...
model loaded.
Welcome to FastText.js CLI
Type exit or CTRL-Z to exit
> hello how are you?
> [ { label: 'EN', score: '0.988627' },
  { label: 'BN', score: '0.000935369' } ]
> das is seher gut!
> [ { label: 'DE', score: '0.513201' },
  { label: 'EN', score: '0.411016' } ]
rien ne va plus
> [ { label: 'FR', score: '0.951547' },
  { label: 'EO', score: '0.00760891' } ]
exit
> model unloaded.
  ```

### Language Identificaton Server
In this example we use the fastText compressed languages model (176 languages) we host.

```bash
cd examples/
export MODEL=./data/lid.176.ftz 
export PORT=9001
node server
```

and then

http://localhost:9001/?text=%EB%9E%84%EB%9E%84%EB%9D%BC%20%EC%B0%A8%EC%B0%A8%EC%B0%A8%20%EB%9E%84%EB%9E%84%EB%9D%BC\n%EB%9E%84%EB%9E%84%EB%9D%BC%20%EC%B0%A8%EC%B0%A8%EC%B0%A8%20%EC%9E%A5%EC%9C%A4%EC%A0%95%20%ED%8A%B8%EC%9C%84%EC%8A%A4%ED%8A%B8%20%EC%B6%A4%EC%9D%84%20%EC%B6%A5%EC%8B%9C%EB%8B%A4

that will be correctly detected as KO:

```json
{
	"response_time": 0.001,
	"predict": [{
			"label": "KO",
			"score": "1"
		},
		{
			"label": "TR",
			"score": "1.95313E-08"
		}
	]
}
```

### Train Language Identification Model
We train a langauge identification model from scratch. Please see in the [examples/models](https://github.com/loretoparisi/fasttext.js/tree/master/examples/models) folder.

## Training set and Test set format
The `trainFile` and `testFile` are a TSV or CSV file where the fist column is the label, the second column is the text sample. `FastText.js` will try to normalize the dataset to the `FastText` format using `FastText.prepareDataset` method. You do not have to call this method explicitly by the way, `FastText.js` will do for you. For more info see [here](https://github.com/facebookresearch/fastText#text-classification).

## Datasets
We host some example datasets in order to train, test and predict FastText models on the fly. For more info how to download and work with datasets, please see in the [examples/datasets](https://github.com/loretoparisi/fasttext.js/tree/master/examples/dataset) folder.

## Models
We host some example pretrained models. For more info how to download and work with pretrained models, please see in the [examples/models](https://github.com/loretoparisi/fasttext.js/tree/master/examples/models) folder.

## Other Versions
- [FastText Python Package](https://pypi.python.org/pypi/fasttext)
- [FastText for Windows](http://cs.mcgill.ca/~mxia3/FastText-for-Windows/)
- [Nodejs binding for fasttext representation and classification.](https://github.com/vunb/node-fasttext)

## Supported Platforms
In this release `FastText.js` comes with precompiled binaries for `linux`, `macOS` and `Windows` in the `lib/bin/` folder. The Windows version is a 64-bit compiled version. It requires the [Visual C++ Redistributable for Visual Studio 2015](http://www.microsoft.com/en-us/download/details.aspx?id=48145) components. See [here](http://cs.mcgill.ca/~mxia3/FastText-for-Windows/) for more info about the Windows version.
### External Binary
To use an external binary version use the `bin` option to specify the executable absolute path:

```javascript
var fastText = new FastText({
    bin: '/usr/local/bin/fasttext'
    loadModel: DATA_ROOT + '/sms_model.bin' // must specifiy filename and ext
});
```

A `executable not found in path` error will be thrown if the executable has not been found in the specified path.

## How It Works
Precompiled binaries runs `FastText` natively. A node `child_process` spawn will fork a new `FastText` native process tu run at OS speed, manage the state, the errors and the output of the process to the JavaScript API.

## Disclaimer
For more info about `FastText` and `FastText` license see [here](https://github.com/facebookresearch/fastText).

## Acknowledgments
I thank you the following devs that helped me to improve [FastText.js](https://github.com/loretoparisi/fasttext.js)
- [shoegazerstella](https://github.com/shoegazerstella)

fasttext.js

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