{ "cells": [ { "cell_type": "markdown", "id": "13525c0a", "metadata": {}, "source": [ "# Visualization Experiments" ] }, { "cell_type": "markdown", "id": "99b6a77c", "metadata": {}, "source": [ "Lets load the data artefacts to local memory. These files are to be downloaded from S3 as the pipeline automatically uploads them to the pre-configured S3 bucket." ] }, { "cell_type": "code", "execution_count": 24, "id": "455ba9af", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m2023-06-14 18:01:52.751\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfile_util\u001b[0m:\u001b[36mdownload_files\u001b[0m:\u001b[36m36\u001b[0m - \u001b[1mDownloading file df.pkl\u001b[0m\n", "\u001b[32m2023-06-14 18:01:56.546\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mfile_util\u001b[0m:\u001b[36mdownload_files\u001b[0m:\u001b[36m36\u001b[0m - \u001b[1mDownloading file mappings.pkl\u001b[0m\n" ] } ], "source": [ "from file_util import download_files\n", "import pickle\n", "\n", "# Download files from S3 bucket. You can download multiple files at a time by passing a list of names\n", "files_to_download = [\"df.pkl\", \"mappings.pkl\"]\n", "download_files(files_to_download)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "fcef3f4d", "metadata": {}, "outputs": [], "source": [ "# Download spacy model for the first time\n", "!spacy download en_core_web_md\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "01aa4dc3", "metadata": {}, "outputs": [], "source": [ "import spacy\n", "\n", "spaCy_model = \"en_core_web_md\"\n", "nlp = spacy.load(spaCy_model)\n", "stopwords = nlp.Defaults.stop_words\n" ] }, { "cell_type": "markdown", "id": "2e344353", "metadata": {}, "source": [ "## Example template 1" ] }, { "cell_type": "markdown", "id": "ec7c2c55", "metadata": {}, "source": [ "## Scatter plot of transcription with Topic modelling" ] }, { "cell_type": "markdown", "id": "71921ceb", "metadata": {}, "source": [ "Change the values of \"category\", \"category_name\" to one agenda topic and change the value of \"not_category_name\" and see different plots." ] }, { "cell_type": "code", "execution_count": 17, "id": "43e01074", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import scattertext as st\n", "\n", "\n", "def plot_topic_modelling_and_word_to_sentence_search(df, cat_1, cat_1_name, cat_2_name):\n", " df = df.assign(parse=lambda df: df.text.apply(st.whitespace_nlp_with_sentences))\n", "\n", " corpus = st.CorpusFromParsedDocuments(\n", " df, category_col='ts_to_topic_mapping_top_1', parsed_col='parse'\n", " ).build().get_unigram_corpus().remove_terms(stopwords, ignore_absences=True).compact(st.AssociationCompactor(2000))\n", " \n", " html = st.produce_scattertext_explorer(\n", " corpus,\n", " category=cat_1, category_name=cat_1_name, not_category_name=cat_2_name,\n", " minimum_term_frequency=0, pmi_threshold_coefficient=0,\n", " width_in_pixels=1000,\n", " transform=st.Scalers.dense_rank\n", " )\n", " open('./demo_compact.html', 'w').write(html)\n", "\n", "plot_topic_modelling_and_word_to_sentence_search(df,\n", " cat_1=\"TAM\",\n", " cat_1_name=\"TAM\",\n", " cat_2_name=\"Churn\")\n", "\n", "# once you are done, check the generated HTML file\n" ] }, { "cell_type": "markdown", "id": "3c8ef97d", "metadata": {}, "source": [ "## Timeline visualizer" ] }, { "cell_type": "code", "execution_count": 25, "id": "fa95284d", "metadata": {}, "outputs": [], "source": [ "mappings = pickle.load(open(\"mappings.pkl\", \"rb\"))" ] }, { "cell_type": "code", "execution_count": 26, "id": "7d588df9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{(0.0, 46.16): 'TAM',\n", " (46.16, 52.32): 'Founders',\n", " (52.32, 58.0): 'Founders',\n", " (59.92, 69.92): 'Founders',\n", " (69.92, 75.6): 'Founders',\n", " (75.6, 80.48): 'AGENDA',\n", " (80.48, 86.24): 'Founders',\n", " (86.24, 91.6): 'Founders',\n", " (92.32, 98.64): 'Founders',\n", " (99.36, 103.12): 'Founders',\n", " (103.12, 110.0): 'Founders',\n", " (110.0, 114.0): 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2484.8),\n", " 'Product market fit': (2472.44, 2474.96),\n", " 'Unit economics': (1290.96, 1296.56),\n", " 'Churn': (1036.32, 1043.84),\n", " 'Business': (2211.64, 2214.76)},\n", " {'Founders': (2370.0, 2377.0),\n", " 'TAM': (2484.8, 2526.44),\n", " 'AGENDA': (2521.59, 2527.91),\n", " 'Product market fit': (2481.16, 2484.8),\n", " 'Churn': (2377.0, 2387.44),\n", " 'Business': (2104.88, 2110.4)}]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mappings" ] }, { "cell_type": "code", "execution_count": null, "id": "edb167a7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }