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Harmony Python library

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You can also join our Discord server! If you found Harmony helpful, you can leave us a review!

What does Harmony do?

Quick start with the code

Read our guide to contributing to Harmony here or read CONTRIBUTING.md.

You can run the walkthrough Python notebook in Google Colab with a single click: <a href="https://colab.research.google.com/github/harmonydata/harmony/blob/main/Harmony_example_walkthrough.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

You can also download an R markdown notebook to run in R Studio: <a href="https://harmonydata.ac.uk/harmony_r_example.nb.html" target="_parent"><img src="https://img.shields.io/badge/RStudio-4285F4" alt="Open In R Studio"/></a>

You can run the walkthrough R notebook in Google Colab with a single click: <a href="https://colab.research.google.com/github/harmonydata/experiments/blob/main/Harmony_R_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> View the PDF documentation of the R package on CRAN

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The Harmony Project

Harmony is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try Harmony at https://harmonydata.ac.uk/app and you can read our blog at https://harmonydata.ac.uk/blog/.

Who to contact?

You can contact Harmony team at https://harmonydata.ac.uk/, or Thomas Wood at https://fastdatascience.com/.

🖥 Installation instructions (video)

Installing Harmony

🖱 Looking to try Harmony in the browser?

Visit: https://harmonydata.ac.uk/app/

You can also visit our blog at https://harmonydata.ac.uk/

✅ You need Tika if you want to extract instruments from PDFs

Download and install Java if you don't have it already. Download and install Apache Tika and run it on your computer https://tika.apache.org/download.html

java -jar tika-server-standard-2.3.0.jar

Requirements

You need a Windows, Linux or Mac system with

🖥 Installing Harmony Python package

You can install from PyPI.

pip install harmonydata

Loading all models

Harmony uses spaCy to help with text extraction from PDFs. spaCy models can be downloaded with the following command in Python:

import harmony
harmony.download_models()

Matching example instruments

instruments = harmony.example_instruments["CES_D English"], harmony.example_instruments["GAD-7 Portuguese"]
questions, similarity, query_similarity, new_vectors_dict = harmony.match_instruments(instruments)

How to load a PDF, Excel or Word into an instrument

harmony.load_instruments_from_local_file("gad-7.pdf")

Optional environment variables

As an alternative to downloading models, you can set environment variables so that Harmony calls spaCy on a remote server. This is only necessary if you are making a server deployment of Harmony.

Creating instruments from a list of strings

You can also create instruments quickly from a list of strings

from harmony import create_instrument_from_list, match_instruments
instrument1 = create_instrument_from_list(["I feel anxious", "I feel nervous"])
instrument2 = create_instrument_from_list(["I feel afraid", "I feel worried"])
all_questions, similarity, query_similarity, new_vectors_dict = match_instruments([instrument1, instrument2])

Loading instruments from PDFs

If you have a local file, you can load it into a list of Instrument instances:

from harmony import load_instruments_from_local_file
instruments = load_instruments_from_local_file("gad-7.pdf")

Matching instruments

Once you have some instruments, you can match them with each other with a call to match_instruments.

from harmony import match_instruments
all_questions, similarity, query_similarity, new_vectors_dict = match_instruments(instruments)

⇗⇗ Using a different vectorisation function

Harmony defaults to sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (HuggingFace link). However you can use other sentence transformers from HuggingFace by setting the environment HARMONY_SENTENCE_TRANSFORMER_PATH before importing Harmony:

export HARMONY_SENTENCE_TRANSFORMER_PATH=sentence-transformers/distiluse-base-multilingual-cased-v2

Using OpenAI or other LLMs for vectorisation

Any word vector representation can be used by Harmony. The below example works for OpenAI's text-embedding-ada-002 model as of July 2023, provided you have create a paid OpenAI account. However, since LLMs are progressing rapidly, we have chosen not to integrate Harmony directly into the OpenAI client libraries, but instead allow you to pass Harmony any vectorisation function of your choice.

import numpy as np
from harmony import match_instruments_with_function, example_instruments
from openai import OpenAI

client = OpenAI()
model_name = "text-embedding-ada-002"
def convert_texts_to_vector(texts):
    vectors = client.embeddings.create(input = texts, model=model_name).data
    return np.asarray([vectors[i].embedding for i in range(len(vectors))])
instruments = example_instruments["CES_D English"], example_instruments["GAD-7 Portuguese"]
all_questions, similarity, query_similarity, new_vectors_dict = match_instruments_with_function(instruments, None, convert_texts_to_vector)

💻 Do you want to run Harmony in your browser locally?

Download and install Docker:

Open a Terminal and run

docker run -p 8000:8000 -p 3000:3000 harmonydata/harmonylocal

Then go to http://localhost:3000 in your browser.

Looking for the Harmony API?

Visit: https://github.com/harmonydata/harmonyapi

Docker images

If you are a Docker user, you can run Harmony from a pre-built Docker image.

Contributing to Harmony

If you'd like to contribute to this project, you can contact us at https://harmonydata.ac.uk/ or make a pull request on our Github repository. You can also raise an issue.

Developing Harmony

🧪 Automated tests

Test code is in tests/ folder using unittest.

The testing tool tox is used in the automation with GitHub Actions CI/CD. Since the PDF extraction also needs Java and Tika installed, you cannot run the unit tests without first installing Java and Tika. See above for instructions.

🧪 Use tox locally

Install tox and run it:

pip install tox
tox

In our configuration, tox runs a check of source distribution using check-manifest (which requires your repo to be git-initialized (git init) and added (git add .) at least), setuptools's check, and unit tests using pytest. You don't need to install check-manifest and pytest though, tox will install them in a separate environment.

The automated tests are run against several Python versions, but on your machine, you might be using only one version of Python, if that is Python 3.9, then run:

tox -e py39

Thanks to GitHub Actions' automated process, you don't need to generate distribution files locally.

⚙️Continuous integration/deployment to PyPI

This package is based on the template https://pypi.org/project/example-pypi-package/

This package

⚙️Re-releasing the package manually

The code to re-release Harmony on PyPI is as follows:

source activate py311
pip install twine
rm -rf dist
python setup.py sdist
twine upload dist/*

‎😃💁 Who worked on Harmony?

Harmony is a collaboration project between Ulster University, University College London, the Universidade Federal de Santa Maria, and Fast Data Science. Harmony is funded by Wellcome as part of the Wellcome Data Prize in Mental Health.

The core team at Harmony is made up of:

📜 License

MIT License. Copyright (c) 2023 Ulster University (https://www.ulster.ac.uk)

📜 How do I cite Harmony?

You can cite our validation paper:

McElroy, Wood, Bond, Mulvenna, Shevlin, Ploubidis, Scopel Hoffmann, Moltrecht, Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data. BMC Psychiatry 24, 530 (2024), https://doi.org/10.1186/s12888-024-05954-2

A BibTeX entry for LaTeX users is

@article{mcelroy2024using,
  title={Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data},
  author={McElroy, Eoin and Wood, Thomas and Bond, Raymond and Mulvenna, Maurice and Shevlin, Mark and Ploubidis, George B and Hoffmann, Mauricio Scopel and Moltrecht, Bettina},
  journal={BMC psychiatry},
  volume={24},
  number={1},
  pages={530},
  year={2024},
  publisher={Springer}
}