chat-intents

ChatIntents provides a method for automatically clustering and applying descriptive group labels to short text documents containing dialogue intents. It uses UMAP for performing dimensionality reduction on user-supplied document embeddings and HDSBCAN for performing the clustering. Hyperparameters are automatically tuned by performing a Bayesian search (using hyperopt) on a constrained optimization of an objective function using user-supplied bounds.

See the associated Medium post for additional description and motivation.

Installation

Installation can be done using PyPI:

pip install chatintents

Note: Depending on your system setup and environment, you may encounter an error associated with the pip install of HDSBCAN (failure to build the hdbscan wheel). This is a known issue with HDSCAN and has several possible solutions. If you are already using a conda virtual environment, an easy solution is to conda install HDBSCAN before installing the chatintents package using:

conda install -c conda-forge hdbscan

Sentence embeddings

The chatintents package doesn't include or specify how to create the sentence embeddings of the documents. Two popular pre-trained embedding models, as shown in the tutorial notebook, are the Unversal Sentence Encoder (USE) and Sentence Transformers.

Sentence Transformers can be installed by:

pip install -U sentence-transformers

Universal Sentence Encoder requires installing

pip install tensorflow
pip install --upgrade tensorflow-hub

Quick Start

The below example uses a Sentence Transformer model to embed the messages and create a model instance:

import chatintents
from chatintents import ChatIntents

from sentence_transformers import SentenceTransformer

all_intents = list(docs['text'])
model = SentenceTransformer('all-mpnet-base-v2')
embeddings = model.encode(all_intents)

model = ChatIntents(embeddings, 'st1')

Creating a ChatIntents instance requires inputs of an embedding representation of all documents and a short-text string description of the model (no spaces).

Generating clusters

Methods are provided for generating clusters using user-supplied hyperparameters, from random search, and from a Bayesian search.

User-supplied hyperparameters and manually scoring

clusters = model.generate_clusters(n_neighbors = 15, 
                                   n_components = 5, 
                                   min_cluster_size = 5, 
                                   min_samples = None,
                                   random_state=42)

labels, cost = model.score_clusters(clusters)

Random search

To run 100 evaluations of randomly-selected hyperparameter values within user-supplied ranges:

space = {
        "n_neighbors": range(12,16),
        "n_components": range(3,7),
        "min_cluster_size": range(2,15),
        "min_samples": range(2,15)
    }

df_random = model.random_search(space, 100)

Bayesian search

Perform a Bayesian search of the hyperparameter space using hyperopt and user-supplied upper and lower bounds for the number of expected clusters:

hspace = {
    "n_neighbors": hp.choice('n_neighbors', range(3,16)),
    "n_components": hp.choice('n_components', range(3,16)),
    "min_cluster_size": hp.choice('min_cluster_size', range(2,16)),
    "min_samples": None,
    "random_state": 42
}

label_lower = 30
label_upper = 100
max_evals = 100

model.bayesian_search(space=hspace,
                      label_lower=label_lower, 
                      label_upper=label_upper, 
                      max_evals=max_evals)

Running the bayesian_search method on a model instance saves the best parameters and best clusters to that instance as variables. For example:

>>> model.best_params

{'min_cluster_size': 5,
 'min_samples': None,
 'n_components': 11,
 'n_neighbors': 3,
 'random_state': 42}

Applying labels to best clusters from Bayesian search

After running the bayesian_search method to identify the best clusters for a given embedding model, descriptive labels can then be applied with:

df_summary, labeled_docs = model.apply_and_summarize_labels(docs[['text']])

This yields two results. The df_summary dataframe summarizing the count and descriptive label of each group:

and the labeled_docs dataframe with each document in the dataset and it's associated cluster number and descriptiive label:

Evaluating performance if ground truth is known

Two methods are also supplied for evaluating and comparing the performance of different models if the ground truth labels happen to be known:

models = [model_use, model_st1, model_st2, model_st3]

df_comparison, labeled_docs_all_models = chatintents.evaluate_models(docs[['text', 
                                                                           'category']],
                                                                           models)

An example df_comparison dataframe comparing model performance is shown below:

Tutorial

See this tutorial notebook for an example of using the chatintents package for comparing four different models on a dataset.