Notice: This repository is now retired, and has been migrated to GitLab due to GitHub Copilot.
A text clustering example written for CSC715M Natural Language Processing at De La Salle University (Term 3, A.Y. 2019-2020)
An autoencoder is a neural network that aims to reconstruct a given input. It learns to do so by learning the most salient features of a data. These salient features are encoded in the latent space, i.e. feature representation with lower dimensions than the original feature space. We can use the latent code of an autoencoder for downstream tasks such as classification, regression, and clustering. In this simple example, we use the latent code representation of a text for clustering using the k-Means algorithm. This work was not intended to reach state-of-the-art performance, but rather to show that the latent code from an autoencoder can be used for downstream tasks, as how we use features from principal components analysis, linear discriminant analysis, and locally linear embedding.
It is recommended to create a virtual environment for this repository to isolate the dependencies by the modules.
$ virtualenv venv --python=python3 # we use python 3
$ source venv/bin/activate
$ pip install -r requirements.txt
Next, train the autoencoder model, and cluster the AG News
dataset by running the train.py module.
$ python -m modules.train
The train.py exports the trained autoencoder model, the fitted clustering
model, and the TF-IDF vectorizer used to vectorize the text data. These assets
can be used for an inference at a later time.
We can use the exported assets for inference by running the run.py module. It
will accept a quoted string as the text to cluster. Note that the quotation
marks are needed since the module is using sys.argv for getting the command
arguments.
$ python -m modules.run "brown in line after livingston sack preston , livingston have sacked allan preston as manager . the former hearts and st johnstone defender and his assistant , alan kernaghan , were dismissed after a run of seven defeats left the club"
[INFO] Loading the trained autoencoder model...
[SUCCESS] Trained autoencoder ready for use.
Input text: brown in line after livingston sack preston , livingston have sacked allan preston as manager . the former hearts and st johnstone defender and his assistant , alan kernaghan , were dismissed after a run of seven defeats left the club
Predicted cluster: 0
$ python -m modules.run "lockheed profit jumps on it , jet demand , < p> \< /p> < p> new york ( reuters ) - no . 1 u . s . defense contractor lockheed\martin corp . < lmt . n> reported a 41 percent rise in quarterly\profit on tuesday , beating wall street forecasts , as demand\soared for its combat aircraft and information technology\services . < /p> "
[INFO] Loading the trained autoencoder model...
[SUCCESS] Trained autoencoder ready for use.
Input text: lockheed profit jumps on it , jet demand , < p> \< /p> < p> new york ( reuters ) - no . 1 u . s . defense contractor lockheed\martin corp . < lmt . n> reported a 41 percent rise in quarterly\profit on tuesday , beating wall street forecasts , as demand\soared for its combat aircraft and information technology\services . < /p>
Predicted cluster: 3
$ python -m modules.run "japan narrowly escapes recession , new figures show japan ' s economy is barely staying out of recession with annual growth of just 0 . 2 in the third quarter . "
[INFO] Loading the trained autoencoder model...
[SUCCESS] Trained autoencoder ready for use.
Input text: japan narrowly escapes recession , new figures show japan ' s economy is barely staying out of recession with annual growth of just 0 . 2 in the third quarter .
Predicted cluster: 3
$ python -m modules.run "google lowers its ipo price range , san jose , calif . - in a sign that google inc . ' s initial public offering isn ' t as popular as expected , the company lowered its estimated price range to between \$85 and \$95 per share , down from the earlier prediction of \$108 and \$135 per share . . . "
[INFO] Loading the trained autoencoder model...
[SUCCESS] Trained autoencoder ready for use.
Input text: google lowers its ipo price range , san jose , calif . - in a sign that google inc . ' s initial public offering isn ' t as popular as expected , the company lowered its estimated price range to between $85 and $95 per share , down from the earlier prediction of $108 and $135 per share . . .
Predicted cluster: 1
We can also use our simple API for clustering.
First, we run the server with,
$ uvicorn modules.api:app --reload
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: Started reloader process [4076975] using statreload
INFO: Started server process [4077021]
INFO: Waiting for application startup.
INFO: Application startup complete.
After running the server, we can now use our API,
>>> import requests
>>> text = "brown in line after livingston sack preston , livingston have
sacked allan preston as manager . the former hearts and st johnstone
defender and his assistant , alan kernaghan , were dismissed after a run of
seven defeats left the club"
>>> r = requests.post("http://127.0.0.1:8000/api/v1/cluster", json={"text": text})
>>> r.json()
{'text': 'brown in line after livingston sack preston , livingston have sacked
allan preston as manager . the former hearts and st johnstone defender and his
assistant , alan kernaghan , were dismissed after a run of seven defeats left
the club',
'cluster index': 0}AG News Clustering with Autoencoder
Copyright (C) 2020 Abien Fred Agarap
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.