easy-text-clustering 0.0.4

User-friendly, low-code text clustering

Text Clustering

This repository contains tools to easily embed and cluster texts as well as label clusters semantically and produce visualizations of those labeled clusters.

Clustering of texts in the Cosmopedia dataset.

This project is a fork of 'huggingface/text-clustering'. The following changes have been made:

1. Projection and clustering algorithms can now be selected by the user as appropriate for their use-case.
2. Each algorithm's relevant hyperparamaters can be provided by the user as a dictionary, without having to store all possible hyperparameters.
3. Visualizations can now be done interactively in 3 dimensions.
4. The pipeline can be run and re-run with new hyperparameters, or even new algorithm selections for projections and/or clustering without having to re-perform computationally expensive embedding or projections unnecessarily.
5. Texts can be batched into groups prior to clustering.
6. A simple automated test suite has been added to the repo.

Additionally, a substantial amount of documentation has been added to this repository for both the new functionality and the original functionality, improving readability and usability. This documentation is available as comments in the code and in a standalone document.

Documentation can be found here

How it works

The pipeline consists of several distinct blocks that can be customized and the whole pipeline can run in a few minutes on a consumer laptop. Each block uses existing standard methods and works quite robustly. The default pipeline is shown in the graphic below.

Text clustering pipeline.

As was true in the original repo, users can choose alternative models for Embeddings and labeling. Additionally, in this version, users can choose alternative algorithms for projection and clustering, and customize all hyperparameters for those algorithms.

Install

Install the following libraries to get started:

pip install scikit-learn umap-learn sentence_transformers faiss-cpu plotly matplotlib datasets

Clone this repository and navigate to the folder:

git clone https://github.com/billingsmoore/text-clustering.git
cd text-clustering

Basic Usage

Run pipeline and visualize results:

from src.text_clustering import ClusterClassifier
from datasets import load_dataset

SAMPLE = 100_000

texts = load_dataset("HuggingFaceTB/cosmopedia-100k", split="train").select(range(SAMPLE))["text"]

cc = ClusterClassifier()

# run the pipeline:
embs, labels, summaries = cc.fit(texts)

# show the results
cc.show()

# save 
cc.save("./cc_100k")

Load classifier and run inference:

from src.text_clustering import ClusterClassifier

cc = ClusterClassifier()

# load state
cc.load("./cc_100k")

# visualize
cc.show()

# classify new texts with k-nearest neighbour search
cluster_labels, embeddings = cc.infer(some_texts, top_k=1)

If you want to reproduce the color scheme in the plot above you can add the following code before you run cc.show():

from cycler import cycler
import matplotlib.pyplot as plt

default_cycler = (cycler(color=[
    "0F0A0A",
    "FF6600",
    "FFBE00",
    "496767",
    "87A19E",
    "FF9200",
    "0F3538",
    "F8E08E",
    "0F2021",
    "FAFAF0"])
    )
plt.rc('axes', prop_cycle=default_cycler)

If you would like to customize the plotting further the easiest way is to customize or overwrite the _show_mpl and _show_plotly methods.

Advanced Usage

from src.text_clustering import ClusterClassifier
from datasets import load_dataset

SAMPLE = 100_000

texts = load_dataset("HuggingFaceTB/cosmopedia-100k", split="train").select(range(SAMPLE))["text"]

# initialize the ClusterClassifier to use TruncatedSVD with appropriate params
# also set the clustering to use KMeans clustering with appropriate params
cc = ClusterClassifier(
    projection_algorithm='tsvd', 
    projection_args={'n_components': 5, 'n_iter': 7, 'random_state': 42},
    clustering_algorithm='kmeans',
    clustering_args={'n_clusters': 2, 'random_state': 0, 'n_init': "auto"})

# run the pipeline:
cc.fit(texts)

# show the results
cc.show()

# if results are unsatisfactory, refit with new selections
cc.fit(
    projection_algorithm='pca', 
    projection_args={'n_components': 3},
    clustering_algorithm='hdbscan',
    clustering_args={'min_cluster_size': 10})

cc.show()


# still unsatisfied? you can keep projections, but change clustering params
cc.fit(clustering_args={'min_cluster_size': 25})

cc.show()

# save when done
cc.save("./cc_100k")

Command Line Usage

You can also run the pipeline using a script with:

# run a new pipeline
python run_pipeline.py --mode run  --save_load_path './cc_100k' --n_samples 100000 --build_hf_ds
# load existing pipeline
python run_pipeline.py --mode load --save_load_path './cc_100k' --build_hf_ds
# inference mode on new texts from an input dataset
python run_pipeline.py --mode infer --save_load_path './cc_100k'  --n_samples <NB_INFERENCE_SAMPLES> --input_dataset <HF_DATA_FOR_INFERENCE>

The build_hf_ds flag builds and pushes HF datasets, for the files and clusters, that can be directly used in the FW visualization space. In infer mode, we push the clusters dataset by default.

You can also change how the clusters are labeled (multiple topics (default) vs single topic with an educational score) using the flag --topic_mode.

Examples

Check the examples folder for an example of clustering and topic labeling applied to the AutoMathText dataset, utilizing Cosmopedia's web labeling approach.