locsec/AMMICO
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AMMICO/build/doctrees/nbsphinx/notebooks/Example text.ipynb

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "dcaa3da1",
"metadata": {},
"source": [
"# Notebook for text extraction on image\n",
"\n",
"The text extraction and analysis is carried out using a variety of tools: \n",
"\n",
"1. Text extraction from the image using [google-cloud-vision](https://cloud.google.com/vision) \n",
"1. Language detection of the extracted text using [Googletrans](https://py-googletrans.readthedocs.io/en/latest/) \n",
"1. Translation into English or other languages using [Googletrans](https://py-googletrans.readthedocs.io/en/latest/) \n",
"1. Cleaning of the text using [spacy](https://spacy.io/) \n",
"1. Spell-check using [TextBlob](https://textblob.readthedocs.io/en/dev/index.html) \n",
"1. Subjectivity analysis using [TextBlob](https://textblob.readthedocs.io/en/dev/index.html) \n",
"1. Text summarization using [transformers](https://huggingface.co/docs/transformers/index) pipelines\n",
"1. Sentiment analysis using [transformers](https://huggingface.co/docs/transformers/index) pipelines \n",
"1. Named entity recognition using [transformers](https://huggingface.co/docs/transformers/index) pipelines \n",
"1. Topic analysis using [BERTopic](https://github.com/MaartenGr/BERTopic) \n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f43f327c",
"metadata": {
"execution": {
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"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cf362e60",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:23:00.797570Z",
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}
},
"outputs": [],
"source": [
"import os\n",
"import ammico\n",
"from ammico import utils as mutils\n",
"from ammico import display as mdisplay"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "fddba721",
"metadata": {},
"source": [
"We select a subset of image files to try the text extraction on, see the `limit` keyword. The `find_files` function finds image files within a given directory: "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "27675810",
"metadata": {
"execution": {
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}
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"outputs": [],
"source": [
"# Here you need to provide the path to your google drive folder\n",
"# or local folder containing the images\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3a7dfe11",
"metadata": {},
"source": [
"We need to initialize the main dictionary that contains all information for the images and is updated through each subsequent analysis:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8b32409f",
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"mydict = mutils.initialize_dict(images)"
]
},
{
"cell_type": "markdown",
"id": "7b8b929f",
"metadata": {},
"source": [
"## Google cloud vision API\n",
"\n",
"For this you need an API key and have the app activated in your google console. The first 1000 images per month are free (July 2022)."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cbf74c0b-52fe-4fb8-b617-f18611e8f986",
"metadata": {},
"source": [
"```\n",
"os.environ[\n",
" \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
"] = \"your-credentials.json\"\n",
"```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0891b795-c7fe-454c-a45d-45fadf788142",
"metadata": {},
"source": [
"## Inspect the elements per image\n",
"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
"Here, we display the text extraction and translation results provided by the above libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c6ecc88",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:23:14.449813Z",
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"shell.execute_reply": "2023-06-23T12:23:15.481213Z"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "__init__() got an unexpected keyword argument 'identify'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m analysis_explorer \u001b[38;5;241m=\u001b[39m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAnalysisExplorer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midentify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-on-image\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m analysis_explorer\u001b[38;5;241m.\u001b[39mrun_server(port\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8054\u001b[39m)\n",
"\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'identify'"
]
}
],
"source": [
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"text-on-image\")\n",
"analysis_explorer.run_server(port=8054)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9c3e72b5-0e57-4019-b45e-3e36a74e7f52",
"metadata": {},
"source": [
"## Or directly analyze for further processing\n",
"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded. Set the keyword `analyse_text` to `True` if you want the text to be analyzed (spell check, subjectivity, text summary, sentiment, NER)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
"metadata": {
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"iopub.status.idle": "2023-06-23T12:24:30.756386Z",
"shell.execute_reply": "2023-06-23T12:24:30.748510Z"
}
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"outputs": [
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"Downloading (…)ve/f2482bf/vocab.txt: 0.00B [00:00, ?B/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Downloading (…)ve/f2482bf/vocab.txt: 213kB [00:00, 12.7MB/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"for key in mydict:\n",
" mydict[key] = ammico.text.TextDetector(\n",
" mydict[key], analyse_text=True\n",
" ).analyse_image()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3c063eda",
"metadata": {},
"source": [
"## Convert to dataframe and write csv\n",
"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5709c2cd",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:30.784996Z",
"iopub.status.busy": "2023-06-23T12:24:30.784319Z",
"iopub.status.idle": "2023-06-23T12:24:30.818628Z",
"shell.execute_reply": "2023-06-23T12:24:30.818032Z"
}
},
"outputs": [],
"source": [
"outdict = mutils.append_data_to_dict(mydict)\n",
"df = mutils.dump_df(outdict)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ae182eb7",
"metadata": {},
"source": [
"Check the dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c4f05637",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:30.821898Z",
"iopub.status.busy": "2023-06-23T12:24:30.821537Z",
"iopub.status.idle": "2023-06-23T12:24:30.888205Z",
"shell.execute_reply": "2023-06-23T12:24:30.887540Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>text</th>\n",
" <th>text_language</th>\n",
" <th>text_english</th>\n",
" <th>text_summary</th>\n",
" <th>sentiment</th>\n",
" <th>sentiment_score</th>\n",
" <th>entity</th>\n",
" <th>entity_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>data/106349S_por.png</td>\n",
" <td>NEWS URGENTE SAMSUNG AO VIVO Rio de Janeiro NO...</td>\n",
" <td>pt</td>\n",
" <td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>\n",
" <td>NEW COUNTING METHOD RJ City HALL EXCLUDES 1,1...</td>\n",
" <td>NEGATIVE</td>\n",
" <td>0.99</td>\n",
" <td>[Rio de Janeiro, C, ##IT, P, ##NA, ##LTO]</td>\n",
" <td>[LOC, ORG, LOC, LOC, ORG, LOC]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>data/102141_2_eng.png</td>\n",
" <td>CORONAVIRUS QUARANTINE CORONAVIRUS OUTBREAK BE...</td>\n",
" <td>en</td>\n",
" <td>CORONAVIRUS QUARANTINE CORONAVIRUS OUTBREAK BE...</td>\n",
" <td>Coronavirus QUARANTINE CORONAVIRUS OUTBREAK</td>\n",
" <td>NEGATIVE</td>\n",
" <td>0.98</td>\n",
" <td>[CORONAVIRUS, ##AR, ##TI, ##RONAVIR, ##C, Co]</td>\n",
" <td>[ORG, MISC, MISC, ORG, MISC, MISC]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>data/102730_eng.png</td>\n",
" <td>400 DEATHS GET E-BOOK X AN Corporation ncy Ser...</td>\n",
" <td>en</td>\n",
" <td>400 DEATHS GET E-BOOK X AN Corporation ncy Ser...</td>\n",
" <td>A municipal worker sprays disinfectant on his...</td>\n",
" <td>NEGATIVE</td>\n",
" <td>0.99</td>\n",
" <td>[AN Corporation ncy Services, Ahmedabad, RE, #...</td>\n",
" <td>[ORG, LOC, PER, ORG]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename text \\\n",
"0 data/106349S_por.png NEWS URGENTE SAMSUNG AO VIVO Rio de Janeiro NO... \n",
"1 data/102141_2_eng.png CORONAVIRUS QUARANTINE CORONAVIRUS OUTBREAK BE... \n",
"2 data/102730_eng.png 400 DEATHS GET E-BOOK X AN Corporation ncy Ser... \n",
"\n",
" text_language text_english \\\n",
"0 pt NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO... \n",
"1 en CORONAVIRUS QUARANTINE CORONAVIRUS OUTBREAK BE... \n",
"2 en 400 DEATHS GET E-BOOK X AN Corporation ncy Ser... \n",
"\n",
" text_summary sentiment \\\n",
"0 NEW COUNTING METHOD RJ City HALL EXCLUDES 1,1... NEGATIVE \n",
"1 Coronavirus QUARANTINE CORONAVIRUS OUTBREAK NEGATIVE \n",
"2 A municipal worker sprays disinfectant on his... NEGATIVE \n",
"\n",
" sentiment_score entity \\\n",
"0 0.99 [Rio de Janeiro, C, ##IT, P, ##NA, ##LTO] \n",
"1 0.98 [CORONAVIRUS, ##AR, ##TI, ##RONAVIR, ##C, Co] \n",
"2 0.99 [AN Corporation ncy Services, Ahmedabad, RE, #... \n",
"\n",
" entity_type \n",
"0 [LOC, ORG, LOC, LOC, ORG, LOC] \n",
"1 [ORG, MISC, MISC, ORG, MISC, MISC] \n",
"2 [ORG, LOC, PER, ORG] "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "eedf1e47",
"metadata": {},
"source": [
"Write the csv file - here you should provide a file path and file name for the csv file to be written."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bf6c9ddb",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:30.891561Z",
"iopub.status.busy": "2023-06-23T12:24:30.891007Z",
"iopub.status.idle": "2023-06-23T12:24:30.905301Z",
"shell.execute_reply": "2023-06-23T12:24:30.904714Z"
}
},
"outputs": [],
"source": [
"# Write the csv\n",
"df.to_csv(\"./data_out.csv\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4bc8ac0a",
"metadata": {},
"source": [
"## Topic analysis\n",
"The topic analysis is carried out using [BERTopic](https://maartengr.github.io/BERTopic/index.html) using an embedded model through a [spaCy](https://spacy.io/) pipeline."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4931941b",
"metadata": {},
"source": [
"BERTopic takes a list of strings as input. The more items in the list, the better for the topic modeling. If the below returns an error for `analyse_topic()`, the reason can be that your dataset is too small.\n",
"\n",
"You can pass which dataframe entry you would like to have analyzed. The default is `text_english`, but you could for example also select `text_summary` or `text_english_correct` setting the keyword `analyze_text` as so:\n",
"\n",
"`ammico.text.PostprocessText(mydict=mydict, analyze_text=\"text_summary\").analyse_topic()`\n",
"\n",
"### Option 1: Use the dictionary as obtained from the above analysis."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a3450a61",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:30.908227Z",
"iopub.status.busy": "2023-06-23T12:24:30.907705Z",
"iopub.status.idle": "2023-06-23T12:24:45.015459Z",
"shell.execute_reply": "2023-06-23T12:24:45.013545Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading data from dict.\n",
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting en-core-web-md==3.5.0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.5.0/en_core_web_md-3.5.0-py3-none-any.whl (42.8 MB)\n",
"\u001b[?25l 0.0/42.8 MB ? eta -:--:--\r",
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]
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"\r",
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"\r",
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"\u001b[2K ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 42.8/42.8 MB 50.0 MB/s eta 0:00:00\n",
"\u001b[?25h"
]
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]
},
{
"name": "stdout",
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"text": [
"Requirement already satisfied: click<9.0.0,>=7.1.1 in /opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (8.1.3)\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installing collected packages: en-core-web-md\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully installed en-core-web-md-3.5.0\n",
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the package via spacy.load('en_core_web_md')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"[notice] A new release of pip is available: 23.0.1 -> 23.1.2\n",
"[notice] To update, run: pip install --upgrade pip\n"
]
},
{
"ename": "TypeError",
"evalue": "Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:2868\u001b[0m, in \u001b[0;36mBERTopic._reduce_dimensionality\u001b[0;34m(self, embeddings, y, partial_fit)\u001b[0m\n\u001b[1;32m 2867\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2868\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mumap_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2684\u001b[0m, in \u001b[0;36mUMAP.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 2683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_, aux_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_embed_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2685\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2686\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2687\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2688\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# JH why raw data?\u001b[39;49;00m\n\u001b[1;32m 2689\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2690\u001b[0m \u001b[38;5;66;03m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[39;00m\n\u001b[1;32m 2691\u001b[0m \u001b[38;5;66;03m# coordinates of np.nan. These will be filtered by our plotting functions automatically.\u001b[39;00m\n\u001b[1;32m 2692\u001b[0m \u001b[38;5;66;03m# They also prevent users from being deceived a distance query to one of these points.\u001b[39;00m\n\u001b[1;32m 2693\u001b[0m \u001b[38;5;66;03m# Might be worth moving this into simplicial_set_embedding or _fit_embed_data\u001b[39;00m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2717\u001b[0m, in \u001b[0;36mUMAP._fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m 2714\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A method wrapper for simplicial_set_embedding that can be\u001b[39;00m\n\u001b[1;32m 2715\u001b[0m \u001b[38;5;124;03mreplaced by subclasses.\u001b[39;00m\n\u001b[1;32m 2716\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msimplicial_set_embedding\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2718\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2719\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2720\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2721\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initial_alpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2722\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2723\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_b\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2724\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepulsion_strength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2725\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnegative_sample_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2726\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2727\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2728\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2729\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2730\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2731\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdensmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2732\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_densmap_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2733\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_dens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2734\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2735\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2736\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_metric\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meuclidean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ml2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2737\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 2738\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2739\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtqdm_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2740\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:1078\u001b[0m, in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m 1076\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspectral\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1077\u001b[0m \u001b[38;5;66;03m# We add a little noise to avoid local minima for optimization to come\u001b[39;00m\n\u001b[0;32m-> 1078\u001b[0m initialisation \u001b[38;5;241m=\u001b[39m \u001b[43mspectral_layout\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1081\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1083\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1086\u001b[0m expansion \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10.0\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(initialisation)\u001b[38;5;241m.\u001b[39mmax()\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/spectral.py:332\u001b[0m, in \u001b[0;36mspectral_layout\u001b[0;34m(data, graph, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2000000\u001b[39m:\n\u001b[0;32m--> 332\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meigsh\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 334\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSM\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mncv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_lanczos_vectors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1605\u001b[0m, in \u001b[0;36meigsh\u001b[0;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(A):\n\u001b[0;32m-> 1605\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for sparse A with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N. Use scipy.linalg.eigh(A.toarray()) or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1607\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m reduce k.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(A, LinearOperator):\n",
"\u001b[0;31mTypeError\u001b[0m: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# make a list of all the text_english entries per analysed image from the mydict variable as above\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m topic_model, topic_df, most_frequent_topics \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPostprocessText\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mmydict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmydict\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse_topic\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/text.py:221\u001b[0m, in \u001b[0;36mPostprocessText.analyse_topic\u001b[0;34m(self, return_topics)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBERTopic excited with an error - maybe your dataset is too small?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 221\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopics, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_text_english\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# return the topic list\u001b[39;00m\n\u001b[1;32m 223\u001b[0m topic_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopic_model\u001b[38;5;241m.\u001b[39mget_topic_info()\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:356\u001b[0m, in \u001b[0;36mBERTopic.fit_transform\u001b[0;34m(self, documents, embeddings, y)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseed_topic_list \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 355\u001b[0m y, embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_guided_topic_modeling(embeddings)\n\u001b[0;32m--> 356\u001b[0m umap_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce_dimensionality\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;66;03m# Cluster reduced embeddings\u001b[39;00m\n\u001b[1;32m 359\u001b[0m documents, probabilities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cluster_embeddings(umap_embeddings, documents, y\u001b[38;5;241m=\u001b[39my)\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:2872\u001b[0m, in \u001b[0;36mBERTopic._reduce_dimensionality\u001b[0;34m(self, embeddings, y, partial_fit)\u001b[0m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 2870\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe dimensionality reduction algorithm did not contain the `y` parameter and\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2871\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m therefore the `y` parameter was not used\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2872\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mumap_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2874\u001b[0m umap_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mumap_model\u001b[38;5;241m.\u001b[39mtransform(embeddings)\n\u001b[1;32m 2875\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReduced dimensionality\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2684\u001b[0m, in \u001b[0;36mUMAP.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 2681\u001b[0m \u001b[38;5;28mprint\u001b[39m(ts(), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConstruct embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_, aux_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_embed_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2685\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2686\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2687\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2688\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# JH why raw data?\u001b[39;49;00m\n\u001b[1;32m 2689\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2690\u001b[0m \u001b[38;5;66;03m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[39;00m\n\u001b[1;32m 2691\u001b[0m \u001b[38;5;66;03m# coordinates of np.nan. These will be filtered by our plotting functions automatically.\u001b[39;00m\n\u001b[1;32m 2692\u001b[0m \u001b[38;5;66;03m# They also prevent users from being deceived a distance query to one of these points.\u001b[39;00m\n\u001b[1;32m 2693\u001b[0m \u001b[38;5;66;03m# Might be worth moving this into simplicial_set_embedding or _fit_embed_data\u001b[39;00m\n\u001b[1;32m 2694\u001b[0m disconnected_vertices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgraph_\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mflatten() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2717\u001b[0m, in \u001b[0;36mUMAP._fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m 2713\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fit_embed_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, n_epochs, init, random_state):\n\u001b[1;32m 2714\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"A method wrapper for simplicial_set_embedding that can be\u001b[39;00m\n\u001b[1;32m 2715\u001b[0m \u001b[38;5;124;03m replaced by subclasses.\u001b[39;00m\n\u001b[1;32m 2716\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msimplicial_set_embedding\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2718\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2719\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2720\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2721\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initial_alpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2722\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2723\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_b\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2724\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepulsion_strength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2725\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnegative_sample_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2726\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2727\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2728\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2729\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2730\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2731\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdensmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2732\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_densmap_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2733\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_dens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2734\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2735\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2736\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_metric\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meuclidean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ml2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2737\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 2738\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2739\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtqdm_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2740\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:1078\u001b[0m, in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m 1073\u001b[0m embedding \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39muniform(\n\u001b[1;32m 1074\u001b[0m low\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m10.0\u001b[39m, high\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m, size\u001b[38;5;241m=\u001b[39m(graph\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], n_components)\n\u001b[1;32m 1075\u001b[0m )\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 1076\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspectral\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1077\u001b[0m \u001b[38;5;66;03m# We add a little noise to avoid local minima for optimization to come\u001b[39;00m\n\u001b[0;32m-> 1078\u001b[0m initialisation \u001b[38;5;241m=\u001b[39m \u001b[43mspectral_layout\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1081\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1083\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1086\u001b[0m expansion \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10.0\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(initialisation)\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m 1087\u001b[0m embedding \u001b[38;5;241m=\u001b[39m (initialisation \u001b[38;5;241m*\u001b[39m expansion)\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m 1088\u001b[0m np\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m 1089\u001b[0m ) \u001b[38;5;241m+\u001b[39m random_state\u001b[38;5;241m.\u001b[39mnormal(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1092\u001b[0m np\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m 1093\u001b[0m )\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/spectral.py:332\u001b[0m, in \u001b[0;36mspectral_layout\u001b[0;34m(data, graph, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2000000\u001b[39m:\n\u001b[0;32m--> 332\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meigsh\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 334\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSM\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mncv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_lanczos_vectors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 342\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m scipy\u001b[38;5;241m.\u001b[39msparse\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mlobpcg(\n\u001b[1;32m 343\u001b[0m L, random_state\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39m(L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], k)), largest\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-8\u001b[39m\n\u001b[1;32m 344\u001b[0m )\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1605\u001b[0m, in \u001b[0;36meigsh\u001b[0;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)\u001b[0m\n\u001b[1;32m 1600\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N for N * N square matrix. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1601\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempting to use scipy.linalg.eigh instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1602\u001b[0m \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(A):\n\u001b[0;32m-> 1605\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for sparse A with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N. Use scipy.linalg.eigh(A.toarray()) or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1607\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m reduce k.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(A, LinearOperator):\n\u001b[1;32m 1609\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for LinearOperator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA with k >= N.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mTypeError\u001b[0m: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k."
]
}
],
"source": [
"# make a list of all the text_english entries per analysed image from the mydict variable as above\n",
"topic_model, topic_df, most_frequent_topics = ammico.text.PostprocessText(\n",
" mydict=mydict\n",
").analyse_topic()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "95667342",
"metadata": {},
"source": [
"### Option 2: Read in a csv\n",
"Not to analyse too many images on google Cloud Vision, use the csv output to obtain the text (when rerunning already analysed images)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5530e436",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:45.019909Z",
"iopub.status.busy": "2023-06-23T12:24:45.019678Z",
"iopub.status.idle": "2023-06-23T12:24:46.513284Z",
"shell.execute_reply": "2023-06-23T12:24:46.512283Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading data from df.\n"
]
},
{
"ename": "TypeError",
"evalue": "Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:2868\u001b[0m, in \u001b[0;36mBERTopic._reduce_dimensionality\u001b[0;34m(self, embeddings, y, partial_fit)\u001b[0m\n\u001b[1;32m 2867\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2868\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mumap_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2684\u001b[0m, in \u001b[0;36mUMAP.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 2683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_, aux_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_embed_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2685\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2686\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2687\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2688\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# JH why raw data?\u001b[39;49;00m\n\u001b[1;32m 2689\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2690\u001b[0m \u001b[38;5;66;03m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[39;00m\n\u001b[1;32m 2691\u001b[0m \u001b[38;5;66;03m# coordinates of np.nan. These will be filtered by our plotting functions automatically.\u001b[39;00m\n\u001b[1;32m 2692\u001b[0m \u001b[38;5;66;03m# They also prevent users from being deceived a distance query to one of these points.\u001b[39;00m\n\u001b[1;32m 2693\u001b[0m \u001b[38;5;66;03m# Might be worth moving this into simplicial_set_embedding or _fit_embed_data\u001b[39;00m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2717\u001b[0m, in \u001b[0;36mUMAP._fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m 2714\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"A method wrapper for simplicial_set_embedding that can be\u001b[39;00m\n\u001b[1;32m 2715\u001b[0m \u001b[38;5;124;03mreplaced by subclasses.\u001b[39;00m\n\u001b[1;32m 2716\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msimplicial_set_embedding\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2718\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2719\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2720\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2721\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initial_alpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2722\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2723\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_b\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2724\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepulsion_strength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2725\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnegative_sample_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2726\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2727\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2728\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2729\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2730\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2731\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdensmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2732\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_densmap_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2733\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_dens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2734\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2735\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2736\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_metric\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meuclidean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ml2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2737\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 2738\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2739\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtqdm_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2740\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:1078\u001b[0m, in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m 1076\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspectral\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1077\u001b[0m \u001b[38;5;66;03m# We add a little noise to avoid local minima for optimization to come\u001b[39;00m\n\u001b[0;32m-> 1078\u001b[0m initialisation \u001b[38;5;241m=\u001b[39m \u001b[43mspectral_layout\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1081\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1083\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1086\u001b[0m expansion \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10.0\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(initialisation)\u001b[38;5;241m.\u001b[39mmax()\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/spectral.py:332\u001b[0m, in \u001b[0;36mspectral_layout\u001b[0;34m(data, graph, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2000000\u001b[39m:\n\u001b[0;32m--> 332\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meigsh\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 334\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSM\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mncv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_lanczos_vectors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1605\u001b[0m, in \u001b[0;36meigsh\u001b[0;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(A):\n\u001b[0;32m-> 1605\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for sparse A with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N. Use scipy.linalg.eigh(A.toarray()) or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1607\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m reduce k.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(A, LinearOperator):\n",
"\u001b[0;31mTypeError\u001b[0m: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m input_file_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_out.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m topic_model, topic_df, most_frequent_topics \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPostprocessText\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_csv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcsv_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_file_path\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyse_topic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreturn_topics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/text.py:221\u001b[0m, in \u001b[0;36mPostprocessText.analyse_topic\u001b[0;34m(self, return_topics)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBERTopic excited with an error - maybe your dataset is too small?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 221\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopics, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_text_english\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# return the topic list\u001b[39;00m\n\u001b[1;32m 223\u001b[0m topic_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopic_model\u001b[38;5;241m.\u001b[39mget_topic_info()\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:356\u001b[0m, in \u001b[0;36mBERTopic.fit_transform\u001b[0;34m(self, documents, embeddings, y)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseed_topic_list \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 355\u001b[0m y, embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_guided_topic_modeling(embeddings)\n\u001b[0;32m--> 356\u001b[0m umap_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce_dimensionality\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;66;03m# Cluster reduced embeddings\u001b[39;00m\n\u001b[1;32m 359\u001b[0m documents, probabilities \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cluster_embeddings(umap_embeddings, documents, y\u001b[38;5;241m=\u001b[39my)\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/bertopic/_bertopic.py:2872\u001b[0m, in \u001b[0;36mBERTopic._reduce_dimensionality\u001b[0;34m(self, embeddings, y, partial_fit)\u001b[0m\n\u001b[1;32m 2869\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 2870\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe dimensionality reduction algorithm did not contain the `y` parameter and\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2871\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m therefore the `y` parameter was not used\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2872\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mumap_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2874\u001b[0m umap_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mumap_model\u001b[38;5;241m.\u001b[39mtransform(embeddings)\n\u001b[1;32m 2875\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReduced dimensionality\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2684\u001b[0m, in \u001b[0;36mUMAP.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 2681\u001b[0m \u001b[38;5;28mprint\u001b[39m(ts(), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConstruct embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2684\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_, aux_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_embed_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2685\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2686\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2687\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2688\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# JH why raw data?\u001b[39;49;00m\n\u001b[1;32m 2689\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2690\u001b[0m \u001b[38;5;66;03m# Assign any points that are fully disconnected from our manifold(s) to have embedding\u001b[39;00m\n\u001b[1;32m 2691\u001b[0m \u001b[38;5;66;03m# coordinates of np.nan. These will be filtered by our plotting functions automatically.\u001b[39;00m\n\u001b[1;32m 2692\u001b[0m \u001b[38;5;66;03m# They also prevent users from being deceived a distance query to one of these points.\u001b[39;00m\n\u001b[1;32m 2693\u001b[0m \u001b[38;5;66;03m# Might be worth moving this into simplicial_set_embedding or _fit_embed_data\u001b[39;00m\n\u001b[1;32m 2694\u001b[0m disconnected_vertices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgraph_\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mflatten() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:2717\u001b[0m, in \u001b[0;36mUMAP._fit_embed_data\u001b[0;34m(self, X, n_epochs, init, random_state)\u001b[0m\n\u001b[1;32m 2713\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fit_embed_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, n_epochs, init, random_state):\n\u001b[1;32m 2714\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"A method wrapper for simplicial_set_embedding that can be\u001b[39;00m\n\u001b[1;32m 2715\u001b[0m \u001b[38;5;124;03m replaced by subclasses.\u001b[39;00m\n\u001b[1;32m 2716\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msimplicial_set_embedding\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2718\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2719\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2720\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2721\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_initial_alpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2722\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_a\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2723\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_b\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2724\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepulsion_strength\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2725\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnegative_sample_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2726\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2727\u001b[0m \u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2728\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2729\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2730\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2731\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdensmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2732\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_densmap_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2733\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_dens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2734\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_distance_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2735\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_output_metric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2736\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput_metric\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meuclidean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ml2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2737\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 2738\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2739\u001b[0m \u001b[43m \u001b[49m\u001b[43mtqdm_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtqdm_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2740\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/umap_.py:1078\u001b[0m, in \u001b[0;36msimplicial_set_embedding\u001b[0;34m(data, graph, n_components, initial_alpha, a, b, gamma, negative_sample_rate, n_epochs, init, random_state, metric, metric_kwds, densmap, densmap_kwds, output_dens, output_metric, output_metric_kwds, euclidean_output, parallel, verbose, tqdm_kwds)\u001b[0m\n\u001b[1;32m 1073\u001b[0m embedding \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39muniform(\n\u001b[1;32m 1074\u001b[0m low\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m10.0\u001b[39m, high\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m, size\u001b[38;5;241m=\u001b[39m(graph\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], n_components)\n\u001b[1;32m 1075\u001b[0m )\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 1076\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(init, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m init \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspectral\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1077\u001b[0m \u001b[38;5;66;03m# We add a little noise to avoid local minima for optimization to come\u001b[39;00m\n\u001b[0;32m-> 1078\u001b[0m initialisation \u001b[38;5;241m=\u001b[39m \u001b[43mspectral_layout\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1080\u001b[0m \u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1081\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1083\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetric_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1086\u001b[0m expansion \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10.0\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(initialisation)\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m 1087\u001b[0m embedding \u001b[38;5;241m=\u001b[39m (initialisation \u001b[38;5;241m*\u001b[39m expansion)\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m 1088\u001b[0m np\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m 1089\u001b[0m ) \u001b[38;5;241m+\u001b[39m random_state\u001b[38;5;241m.\u001b[39mnormal(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1092\u001b[0m np\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m 1093\u001b[0m )\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/umap/spectral.py:332\u001b[0m, in \u001b[0;36mspectral_layout\u001b[0;34m(data, graph, dim, random_state, metric, metric_kwds)\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2000000\u001b[39m:\n\u001b[0;32m--> 332\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meigsh\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 334\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSM\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mncv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_lanczos_vectors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43mtol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1e-4\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m \u001b[49m\u001b[43mv0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 342\u001b[0m eigenvalues, eigenvectors \u001b[38;5;241m=\u001b[39m scipy\u001b[38;5;241m.\u001b[39msparse\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39mlobpcg(\n\u001b[1;32m 343\u001b[0m L, random_state\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39m(L\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], k)), largest\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-8\u001b[39m\n\u001b[1;32m 344\u001b[0m )\n",
"File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1605\u001b[0m, in \u001b[0;36meigsh\u001b[0;34m(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)\u001b[0m\n\u001b[1;32m 1600\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N for N * N square matrix. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1601\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempting to use scipy.linalg.eigh instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1602\u001b[0m \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(A):\n\u001b[0;32m-> 1605\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for sparse A with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk >= N. Use scipy.linalg.eigh(A.toarray()) or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1607\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m reduce k.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(A, LinearOperator):\n\u001b[1;32m 1609\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use scipy.linalg.eigh for LinearOperator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA with k >= N.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mTypeError\u001b[0m: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k."
]
}
],
"source": [
"input_file_path = \"data_out.csv\"\n",
"topic_model, topic_df, most_frequent_topics = ammico.text.PostprocessText(\n",
" use_csv=True, csv_path=input_file_path\n",
").analyse_topic(return_topics=10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0b6ef6d7",
"metadata": {},
"source": [
"### Access frequent topics\n",
"A topic of `-1` stands for an outlier and should be ignored. Topic count is the number of occurence of that topic. The output is structured from most frequent to least frequent topic."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "43288cda-61bb-4ff1-a209-dcfcc4916b1f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:46.516495Z",
"iopub.status.busy": "2023-06-23T12:24:46.516261Z",
"iopub.status.idle": "2023-06-23T12:24:46.550184Z",
"shell.execute_reply": "2023-06-23T12:24:46.549551Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'topic_df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtopic_df\u001b[49m)\n",
"\u001b[0;31mNameError\u001b[0m: name 'topic_df' is not defined"
]
}
],
"source": [
"print(topic_df)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b3316770",
"metadata": {},
"source": [
"### Get information for specific topic\n",
"The most frequent topics can be accessed through `most_frequent_topics` with the most occuring topics first in the list."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "db14fe03",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:46.554159Z",
"iopub.status.busy": "2023-06-23T12:24:46.552888Z",
"iopub.status.idle": "2023-06-23T12:24:46.584494Z",
"shell.execute_reply": "2023-06-23T12:24:46.583894Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'most_frequent_topics' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m topic \u001b[38;5;129;01min\u001b[39;00m \u001b[43mmost_frequent_topics\u001b[49m:\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTopic:\u001b[39m\u001b[38;5;124m\"\u001b[39m, topic)\n",
"\u001b[0;31mNameError\u001b[0m: name 'most_frequent_topics' is not defined"
]
}
],
"source": [
"for topic in most_frequent_topics:\n",
" print(\"Topic:\", topic)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d10f701e",
"metadata": {},
"source": [
"### Topic visualization\n",
"The topics can also be visualized. Careful: This only works if there is sufficient data (quantity and quality)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2331afe6",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:46.587622Z",
"iopub.status.busy": "2023-06-23T12:24:46.587263Z",
"iopub.status.idle": "2023-06-23T12:24:46.616640Z",
"shell.execute_reply": "2023-06-23T12:24:46.616059Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'topic_model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241m.\u001b[39mvisualize_topics()\n",
"\u001b[0;31mNameError\u001b[0m: name 'topic_model' is not defined"
]
}
],
"source": [
"topic_model.visualize_topics()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f4eaf353",
"metadata": {},
"source": [
"### Save the model\n",
"The model can be saved for future use."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e5e8377c",
"metadata": {
"execution": {
"iopub.execute_input": "2023-06-23T12:24:46.619565Z",
"iopub.status.busy": "2023-06-23T12:24:46.619271Z",
"iopub.status.idle": "2023-06-23T12:24:46.648645Z",
"shell.execute_reply": "2023-06-23T12:24:46.648043Z"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'topic_model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmisinfo_posts\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mNameError\u001b[0m: name 'topic_model' is not defined"
]
}
],
"source": [
"topic_model.save(\"misinfo_posts\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c94edb9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.17"
},
"vscode": {
"interpreter": {
"hash": "da98320027a74839c7141b42ef24e2d47d628ba1f51115c13da5d8b45a372ec2"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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