diff --git a/Harmony_example_walkthrough.ipynb b/Harmony_example_walkthrough.ipynb index 254f197..7a3a9dc 100644 --- a/Harmony_example_walkthrough.ipynb +++ b/Harmony_example_walkthrough.ipynb @@ -1,10417 +1,9829 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "dEAhNlpexeK_" + }, + "source": [ + "![The Harmony Project logo](https://raw.githubusercontent.com/harmonydata/brand/main/Logo/PNG/%D0%BB%D0%BE%D0%B3%D0%BE%20%D1%84%D1%83%D0%BB-05.png)\n", + "\n", + "🌐 harmonydata.ac.uk\n", + "\"Harmony\n", + "\"Harmony\n", + "\"Harmony\n", + "\"Harmony\n", + "\"Harmony\n", + "\"Harmony\n", + "\n", + "# Harmony walkthrough - Python library\n", + "\n", + "You can run this notebook in Google Colab: \"Open\n", + "\n", + "This notebook shows how you can use Harmony to find the similarity matrix between two questionnaires from Harmony's database, and a third questionnaire which you input here (Norwegian GAD-7).\n", + "\n", + "Harmony is a data harmonisation tool that uses natural language\n", + "processing to recognise where questions in questionnaires are semantically similar. Harmony is a collaboration project between [Ulster University](https://ulster.ac.uk/), [University College London](https://ucl.ac.uk/), the [Universidade Federal de Santa Maria](https://www.ufsm.br/), and [Fast Data Science](http://fastdatascience.com/). Harmony is funded by [Wellcome](https://wellcome.org/) as part of the [Wellcome Data Prize in Mental Health](https://wellcome.org/grant-funding/schemes/wellcome-mental-health-data-prize).\n", + "\n", + "This walkthrough lets you compare items where questions have already been extracted from the PDFs. If you want to process PDFs, you also need to install\n", + "Java and [Apache Tika](https://tika.apache.org/) - see the Harmony README.\n", + "\n", + "![my badge](https://badgen.net/badge/Status/In%20Development/orange)\n", + "\n", + "[![PyPI package](https://img.shields.io/badge/pip%20install-harmonydata-brightgreen)](https://pypi.org/project/harmonydata/)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lcG2aGGGxqZD" + }, + "source": [ + "## Install the Harmony Python library from Pypi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "37805a8efe0c42f1b204654aa011a4f9": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": 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" \"Ikke klart å slutte å bekymre deg eller kontrolleren bekymringene dine\"],\n", + " instrument_name=\"GAD-7 Norwegian\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mujvuUjrxV1G" + }, + "outputs": [], + "source": [ + "instruments = [harmony.example_instruments[\"CES_D English\"],\n", + " harmony.example_instruments[\"GAD-7 Portuguese\"],\n", + " gad_7_norwegian]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FOHRcHrl6K7I" + }, + "outputs": [], + "source": [ + "questions, similarity, query_similarity, new_vectors_dict = harmony.match_instruments(instruments)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g3nhELT60CNy" + }, + "source": [ + "See the questions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YinOWR8oxXRu", + "outputId": "1fcd23eb-939a-466e-a834-bc835d58be2b" + }, + "outputs": [], + "source": [ + "for q in questions:\n", + " print (q.question_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "coFWB74m0K_i" + }, + "source": [ + "See the similarity matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lAdAESnBzF4s", + "outputId": "509a176d-fde6-4062-beb4-9db834dae1ee" + }, + "outputs": [], + "source": [ + "similarity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the similarity matrix" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "![The Harmony Project logo](https://raw.githubusercontent.com/harmonydata/brand/main/Logo/PNG/%D0%BB%D0%BE%D0%B3%D0%BE%20%D1%84%D1%83%D0%BB-05.png)\n", - "\n", - "🌐 harmonydata.ac.uk\n", - "\"Harmony\n", - "\"Harmony\n", - "\"Harmony\n", - "\"Harmony\n", - "\"Harmony\n", - "\"Harmony\n", - "\n", - "# Harmony walkthrough - Python library\n", - "\n", - "You can run this notebook in Google Colab: \"Open\n", - "\n", - "This notebook shows how you can use Harmony to find the similarity matrix between two questionnaires from Harmony's database, and a third questionnaire which you input here (Norwegian GAD-7).\n", - "\n", - "Harmony is a data harmonisation tool that uses natural language\n", - "processing to recognise where questions in questionnaires are semantically similar. Harmony is a collaboration project between [Ulster University](https://ulster.ac.uk/), [University College London](https://ucl.ac.uk/), the [Universidade Federal de Santa Maria](https://www.ufsm.br/), and [Fast Data Science](http://fastdatascience.com/). Harmony is funded by [Wellcome](https://wellcome.org/) as part of the [Wellcome Data Prize in Mental Health](https://wellcome.org/grant-funding/schemes/wellcome-mental-health-data-prize).\n", - "\n", - "This walkthrough lets you compare items where questions have already been extracted from the PDFs. If you want to process PDFs, you also need to install\n", - "Java and [Apache Tika](https://tika.apache.org/) - see the Harmony README.\n", - "\n", - "![my badge](https://badgen.net/badge/Status/In%20Development/orange)\n", - "\n", - "[![PyPI package](https://img.shields.io/badge/pip%20install-harmonydata-brightgreen)](https://pypi.org/project/harmonydata/)\n" + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(similarity, cmap='hot', interpolation='nearest')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate a crosswalk table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from harmony.matching.generate_crosswalk_table import generate_crosswalk_table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = 0.6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YMDJuv-q7wq9" + }, + "outputs": [], + "source": [ + "df_crosswalk_table = generate_crosswalk_table(questions, similarity, threshold)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_crosswalk_table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "ukds", + "language": "python", + "name": "ukds" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + }, + 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- { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:90: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'cached_download' (from 'huggingface_hub.file_download') is deprecated and will be removed from version '0.26'. Use `hf_hub_download` instead.\n", - " warnings.warn(warning_message, FutureWarning)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - ".gitattributes: 0%| | 0.00/1.02k [00:00