grpm_bertopic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ALfr1ZrJbM6y"
   },
   "source": [
    "# System Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Install requirements"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import subprocess\n",
    "try:\n",
    "    # Check if the module is already installed\n",
    "    importlib.import_module('torch')\n",
    "    print(\"torch is already installed.\")\n",
    "except ImportError:\n",
    "    # If the module is not installed, try installing it\n",
    "    subprocess.run(['pip3', 'install', 'torch', 'torchvision', 'torchaudio', '--index-url', 'https://download.pytorch.org/whl/cu118'])\n",
    "    print(\"torch was installed correctly.\")\n",
    "\n",
    "import torch\n",
    "print(\"Torch version:\",torch.__version__)\n",
    "print(\"Is CUDA enabled?\",torch.cuda.is_available())\n",
    "if torch.cuda.is_available():\n",
    "    print(torch.randn(1).cuda())\n",
    "else:\n",
    "    #get torch here: https://pytorch.org/get-started/locally/\n",
    "    subprocess.run(['pip3', 'uninstall', 'torch'])\n",
    "    subprocess.run(['pip3', 'install', 'torch', 'torchvision', 'torchaudio', '--index-url', 'https://download.pytorch.org/whl/cu118'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#install requirements\n",
    "!pip install nltk\n",
    "!pip install gdown\n",
    "#!pip install pandas\n",
    "!pip install bertopic\n",
    "!pip install wordcloud\n",
    "!pip install matplotlib\n",
    "#!pip install bertopic[visualization]  "
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Import packages"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 620,
     "status": "ok",
     "timestamp": 1698414716602,
     "user": {
      "displayName": "Giovanni De Filippis",
      "userId": "12250419597231658659"
     },
     "user_tz": -120
    },
    "id": "zpvxS_i2UoY_"
   },
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import os\n",
    "import io\n",
    "import sys\n",
    "import ast\n",
    "import re\n",
    "import random\n",
    "import zipfile\n",
    "import requests\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "if not 'google.colab' in sys.modules:\n",
    "    !pip install pyperclip\n",
    "    import pyperclip as pc\n",
    "\n",
    "# Plotly: Set notebook mode to work offline\n",
    "import plotly.offline as pyo\n",
    "import plotly.graph_objs as go\n",
    "pyo.init_notebook_mode()\n",
    "\n",
    "# Set data directory\n",
    "save_path = 'data/'\n",
    "\n",
    "# Set working directory\n",
    "if 'notebooks' in os.getcwd():\n",
    "    print(os.getcwd())\n",
    "    os.chdir(os.path.dirname(os.getcwd()))\n",
    "    print('cwd:', os.getcwd())\n",
    "\n",
    "\n",
    "# Define functions\n",
    "\n",
    "def simple_bool(message):\n",
    "    choose = input(message+\" (y/n): \").lower()\n",
    "    your_bool = choose in [\"y\", \"yes\",\"yea\",\"sure\"]\n",
    "    return your_bool\n",
    "\n",
    "def get_and_extract(file, dir = os.getcwd(), ext = '.zip'):\n",
    "    url='https://zenodo.org/record/8205724/files/'+file+'.zip?download=1'\n",
    "    zip_file_name = file+ext\n",
    "    extracted_folder_name = dir\n",
    "    # Download the ZIP file\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        # Extract the ZIP contents\n",
    "        with io.BytesIO(response.content) as zip_buffer:\n",
    "            with zipfile.ZipFile(zip_buffer, 'r') as zip_ref:\n",
    "                zip_ref.extractall(extracted_folder_name)\n",
    "        print(f\"ZIP file '{zip_file_name}' extracted to '{extracted_folder_name}' successfully.\")\n",
    "    else:\n",
    "        print(\"Failed to download the ZIP file.\")\n",
    "        \n",
    "def get_gitfile(url, flag='', dir = os.getcwd()):\n",
    "    url = url.replace('blob','raw')\n",
    "    response = requests.get(url)\n",
    "    file_name = flag + url.rsplit('/',1)[1]\n",
    "    file_path = os.path.join(dir, file_name)\n",
    "    if response.status_code == 200:\n",
    "        with open(file_path, 'wb') as file:\n",
    "            file.write(response.content)\n",
    "        print(f\"File downloaded successfully. Saved as {file_name}\")\n",
    "    else:\n",
    "        print(\"Unable to download the file.\")\n",
    "\n",
    "def load_preprocessed(doc_name = 'abs_preprocessed.txt'):\n",
    "    with open(save_path+doc_name, 'r',encoding='utf-8') as file:\n",
    "        docs_processed = []\n",
    "        for line in file:\n",
    "            docs_processed.append(str(line.strip()))\n",
    "\n",
    "    print(\"Imported list:\", doc_name)\n",
    "    return docs_processed\n",
    "\n",
    "#get_gitfile(\"https://raw.githubusercontent.com/johndef64/pyutilities_datascience/main/general_utilities.py\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Source Datasets\n",
    "https://zenodo.org/record/8205724  DOI: 10.5281/zenodo.8205724"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import GRPM dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"grpm_dataset/grpm_db_pcg/grpm_table_output.csv\"):\n",
    "    if simple_bool('Download source Dataset from Zenodo?'):\n",
    "        timea = datetime.now()\n",
    "        get_and_extract('grpm_dataset')\n",
    "        print('Download and extraction time ',datetime.now()-timea)\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "def pmidstr(df):\n",
    "    df['pmids'] = df['pmids'].astype(str)\n",
    "\n",
    "grpm_dataset = pd.read_csv(\"grpm_dataset/grpm_db_pcg/grpm_table_output.csv\", index_col=0).reset_index(drop=True)\n",
    "grpm_dataset_rna = pd.read_csv(\"grpm_dataset/grpm_db_rna/grpm_table_output.csv\", index_col=0).reset_index(drop=True)\n",
    "grpm_dataset_pseudo = pd.read_csv(\"grpm_dataset/grpm_db_pseudo/grpm_table_output.csv\", index_col=0).reset_index(drop=True)\n",
    "\n",
    "pmidstr(grpm_dataset)\n",
    "pmidstr(grpm_dataset_rna)\n",
    "pmidstr(grpm_dataset_pseudo)\n",
    "\n",
    "print('nbib dataset shape:',grpm_dataset.shape)\n",
    "grpm_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import NBIB dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"grpm_dataset\\grpm_db_pcg\\complete_nbibtable.csv\"):\n",
    "    if simple_bool('Download nbib-data from Zenodo?\\n (size: 5GB unpacked; average import time: 7'')'):\n",
    "        timea = datetime.now()\n",
    "        get_and_extract('nbib_data')\n",
    "        print('Download and extraction time: ',datetime.now()-timea)\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "nbib_dataset = pd.read_csv(\"grpm_dataset\\grpm_db_pcg\\complete_nbibtable.csv\", index_col=0).dropna(subset=['descriptors']).reset_index(drop=True)\n",
    "print('nbib dataset shape:',nbib_dataset.shape)\n",
    "nbib_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import MeSH Terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load reference Mesh List\n",
    "df_mesh = pd.read_csv(save_path+'ref-mesh.csv')['Preferred Label']\n",
    "list_mesh = df_mesh.to_list()\n",
    "docs_mesh = str(list_mesh)\n",
    "mesh_count = len(list_mesh)\n",
    "print('MeSH list count:',mesh_count)\n",
    "print('MeSH list sample:',list_mesh[0:10])\n",
    "#pc.copy(df_mesh.to_latex())\n",
    "#df_mesh.to_csv('S1_MeSH.csv')\n",
    "df_mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Preprocess Data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBIB filtering by MeSH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering source dataset\n",
    "timea = time.time()\n",
    "nbib_subset = pd.DataFrame(columns= nbib_dataset.columns)\n",
    "filteres_grpm = grpm_dataset[grpm_dataset.mesh.isin(list_mesh)]\n",
    "\n",
    "nbib_subset = nbib_dataset[nbib_dataset.pubmed_id.isin(filteres_grpm.pmids.drop_duplicates())]\n",
    "\n",
    "print((time.time()-timea)/60,'minutes')\n",
    "print('pubmed id count:',nbib_subset.pubmed_id.nunique())\n",
    "print('pubmed id with abstract count:',nbib_subset.abstract.nunique()-1)\n",
    "nbib_subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save/load NBIB subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if simple_bool('save nbib subset?'):\n",
    "    nbib_subset.reset_index(drop=True).to_csv(save_path+'nbib_subset_'+str(mesh_count)+'.csv')\n",
    "    \n",
    "if simple_bool('load nbib subset?'):\n",
    "    nbib_subset = pd.read_csv(save_path+'nbib_subset_251.csv', index_col=0)\n",
    "    \n",
    "print('nbib subset shape:', nbib_subset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBIB subset cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if simple_bool('load nbib_subset_clean?'):\n",
    "    nbib_subset_clean = pd.read_csv(save_path+'nbib_subset_clean_251.csv', index_col=0)\n",
    "    docs = nbib_subset_clean.combined.drop_duplicates().to_list()\n",
    "else:\n",
    "    #get only pubmed_id with abstract:\n",
    "    nbib_subset_clean = nbib_subset[['gene', 'pubmed_id', 'publication_date', 'title','abstract', 'authors', 'publication_types','place_of_publication','nlm_journal_id', 'descriptors', 'keywords',  'pii', 'doi', 'publication_status']].drop_duplicates().dropna(subset='abstract')\n",
    "    \n",
    "    # add PMID to abstract (flag):\n",
    "    nbib_subset_clean['combined'] = nbib_subset_clean['pubmed_id'].astype('str') + ' $ ' + nbib_subset_clean['abstract']\n",
    "\n",
    "def displaysub():\n",
    "    print('nbib subset shape:',nbib_subset_clean.shape)\n",
    "    print('pubmed id count:', nbib_subset_clean.pubmed_id.nunique())\n",
    "    print('abstract count:', nbib_subset_clean.abstract.nunique())\n",
    "    #nbib_subset_clean[['gene', 'pubmed_id', 'publication_date', 'authors', 'publication_types','nlm_journal_id', 'descriptors', 'keywords']].head().to_clipboard()\n",
    "    display(nbib_subset_clean.head())\n",
    "\n",
    "displaysub()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if simple_bool('save nbib_subset_clean?'):\n",
    "    nbib_subset_clean.reset_index(drop=True).to_csv(save_path+'nbib_subset_clean_'+str(mesh_count)+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Source Dataset Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteres_grpm = grpm_dataset[grpm_dataset.mesh.isin(list_mesh)]\n",
    "filteres_grpm = filteres_grpm[['gene', 'rsid', 'pmids', 'mesh']].drop_duplicates()\n",
    "filteres_grpm.describe().to_clipboard()\n",
    "\n",
    "print('Original Dataset')\n",
    "display(grpm_dataset[['gene', 'rsid', 'pmids', 'mesh']].drop_duplicates().describe())\n",
    "\n",
    "print('Filtered by MeSH')\n",
    "display(filteres_grpm.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stats\n",
    "keep_l = ['gene', 'pubmed_id', 'publication_date', 'authors', 'publication_types','nlm_journal_id', 'descriptors', 'keywords']\n",
    "keep_s = ['gene', 'pubmed_id']\n",
    "\n",
    "data = nbib_subset_clean[keep_s]#.dropna(subset=['authors'])#, 'descriptors'])\n",
    "\n",
    "# Statistics\n",
    "# Convert string representations of lists to actual lists\n",
    "data['pubmed_id'] =  data['pubmed_id'].astype(str)\n",
    "#data['authors'] = data['authors'].apply(ast.literal_eval)\n",
    "#data['publication_types'] = data['publication_types'].apply(ast.literal_eval)\n",
    "#data['descriptors'] = data['descriptors'].apply(ast.literal_eval)\n",
    "#data['keywords'] = data['keywords'].apply(ast.literal_eval)'''\n",
    "\n",
    "# Display basic statistics\n",
    "print('Data Preprocessed for Topic Modeling:')\n",
    "display(data.describe())\n",
    "\n",
    "# Number of Publications per Gene\n",
    "gene_counts = data['gene'].value_counts()\n",
    "plt.figure(figsize=(15, 5))\n",
    "gene_counts.head(60).plot(kind='bar')\n",
    "plt.title('Number of Publications per Gene')\n",
    "plt.xlabel('Gene')\n",
    "plt.ylabel('Number of Publications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load NBIB subset combined\n",
    "if 'nbib_subset_clean' not in globals():\n",
    "    if simple_bool('load nbib_subset_clean?'):\n",
    "        nbib_subset_clean = pd.read_csv(save_path+'nbib_subset_clean_251.csv', index_col=0)\n",
    "        docs = nbib_subset_clean.combined.drop_duplicates().to_list()\n",
    "else:\n",
    "    docs = nbib_subset_clean.combined.drop_duplicates().to_list()\n",
    "\n",
    "print('PMID count:',nbib_subset.pubmed_id.nunique(),  \n",
    "      '\\nabstract count:',nbib_subset_clean.abstract.nunique(), \n",
    "      '\\nPMID without abstract:',nbib_subset.pubmed_id.nunique()-nbib_subset_clean.abstract.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Corpus normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - nltk normalization (optional)\n",
    "1.  import nltk for stemming and stopword removal\n",
    "2.  remove stopwords and then stem the abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AMZYgCHPXkGb"
   },
   "outputs": [],
   "source": [
    "#download stopwords and the punkt tokenizer\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "\n",
    "#tokenize, remove stopwords and stem\n",
    "# import nltk for stemming and stopword removal\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "import time\n",
    "import random\n",
    "stop_words = set(stopwords.words('english'))\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "def stem_and_remove_stopwords(text):\n",
    "    word_tokens = word_tokenize(text)\n",
    "    lowercase_tokens = [word.lower() for word in word_tokens] #lower\n",
    "    filtered_text = [word for word in lowercase_tokens if word not in stop_words] #trimming\n",
    "    stemmed_text = [stemmer.stem(word) for word in filtered_text] #stemming\n",
    "    return \" \".join(stemmed_text)\n",
    "\n",
    "docs_to_process = docs#[100:1600]#random.sample(docs, 100)\n",
    "\n",
    "# Normalize docs\n",
    "timea = time.time()\n",
    "docs_processed = [stem_and_remove_stopwords(doc) for doc in docs_to_process]\n",
    "print(time.time()-timea)\n",
    "\n",
    "print(docs_to_process[1],'\\n')\n",
    "print(docs_processed[1])\n",
    "len(docs_processed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - no-nltk normalization \n",
    "(best for BERTopic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lower text keeping acronyms uppercase\n",
    "def lower(text):\n",
    "    # Split the text into words\n",
    "    words = text.split()\n",
    "    # Lowercase the words that don't have consecutive uppercase letters\n",
    "    processed_words = [word.lower() if not re.search('[A-Z]{2,}', word) else word for word in words]\n",
    "    # Join the processed words back into text\n",
    "    processed_text = ' '.join(processed_words)\n",
    "    return processed_text\n",
    "\n",
    "# lower text keeping genes uppercase\n",
    "''' Deprecated (too slow)\n",
    "genes = pd.concat([grpm_dataset.gene, grpm_dataset_rna.gene, grpm_dataset_pseudo.gene])\n",
    "genes= genes.drop_duplicates().reset_index(drop=True)\n",
    "genes\n",
    "\n",
    "def lower_gene(text):\n",
    "    lowered_text = text.lower()\n",
    "    for j in range(len(genes)):\n",
    "        gene = genes[j]\n",
    "        lowered_text = lowered_text.replace(' '+gene.lower()+' ', ' '+gene+' ')\n",
    "        lowered_text = lowered_text.replace('('+gene.lower()+')', '('+gene+')')\n",
    "        lowered_text = lowered_text.replace(' '+gene.lower()+',', ' '+gene+',')\n",
    "        lowered_text = lowered_text.replace(' '+gene.lower()+'.', ' '+gene+'.')\n",
    "        if j%100 == 0:\n",
    "            print(gene.ljust(10), 'remaning:',len(genes)-j)\n",
    "    return lowered_text\n",
    "'''\n",
    "\n",
    "docs_to_process = docs#[100:1600]#random.sample(docs, 100)\n",
    "\n",
    "# Normalize docs\n",
    "timea = time.time()\n",
    "sampled_docs = docs_to_process\n",
    "docs_str = str(sampled_docs)\n",
    "docs_lower = lower(docs_str)\n",
    "docs_processed = ast.literal_eval(docs_lower)\n",
    "print(time.time()-timea)\n",
    "\n",
    "print(docs_to_process[1],'\\n')\n",
    "print(docs_processed[1])\n",
    "len(docs_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save pruned and stemmed abstracts\n",
    "if simple_bool('save abs_preprocessed?'):\n",
    "    doc_name = 'abs_preprocessed_'+str(mesh_count)+'.txt'\n",
    "    with open(save_path+doc_name, 'w', encoding='utf-8') as file:\n",
    "        file.write('\\n'.join(map(str, docs_processed)))\n",
    "    print(\"List saved as \"+doc_name)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# load docs_processed.txt as a list\n",
    "if simple_bool('load abs_preprocessed?'):\n",
    "    load_preprocessed()"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zwt1lWGLztq0"
   },
   "source": [
    "# Topic modeling (TM)\n",
    "\n",
    "\n",
    "1.   Default setting - Vanilla (Extras)\n",
    "2.   Custom - choose embedding model and set clustering parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Parameters\n",
    "\n",
    "Custom settings\n",
    "https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from hdbscan import HDBSCAN\n",
    "from umap import UMAP\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from bertopic import BERTopic\n",
    "from bertopic.vectorizers import ClassTfidfTransformer\n",
    "from bertopic.representation import MaximalMarginalRelevance\n",
    "from bertopic.representation import KeyBERTInspired\n",
    "\n",
    "\n",
    "# Step 1 Extract embeddings (SBERT)\n",
    "models = [\"allenai-specter\",\n",
    "          \"all-mpnet-base-v2\",\n",
    "          \"all-MiniLM-L6-v2\"\n",
    "         ] \n",
    "    # ref: https://www.sbert.net/docs/pretrained_models.html \n",
    "\n",
    "#- allenai-specter: model trained on scientific citations and can be used to estimate the similarity of two publications. We can use it to find similar papers. \n",
    "#- all-mpnet-base-v2: general purpose model, The all-mpnet-base-v2 model provides the best quality\n",
    "#- all-MiniLM-L6-v2: 5 times faster and still offers good quality.\n",
    "\n",
    "base_embedder = models[0]  # BaseEmbedder\n",
    "sentence_transformer = SentenceTransformer(base_embedder) # SentenceTransformer\n",
    "\n",
    "\n",
    "# Step 2 - Reduce dimensionality \n",
    "# uniform manifold approximation and projection (UMAP) to reduce the dimension of embeddings\n",
    "random_state = 1337 #1000 #1337\n",
    "umap_model = UMAP(n_neighbors  = 15, #num of high dimensional neighbours\n",
    "                  n_components = 5,  # default:5 #30\n",
    "                  min_dist     = 0.0, \n",
    "                  random_state = random_state) # default:None\n",
    "            #  ref: https://stackoverflow.com/questions/71320201/how-to-fix-random-seed-for-bertopic\n",
    "\n",
    "\n",
    "# Step 3 - Cluster reduced embeddings \n",
    "# HDBSCAN (hierarchical density-based spatial clustering of applications with Noise)  to generate semantically similar document clusters. \n",
    "# Since HDBSCAN is a density-based clustering algorithm, the number of clusters is automatically chosen based on the minimum distance to be considered as a neighbor. \n",
    "min_cluster_size = 100 #5 default HDBSCAN()\n",
    "hdbscan_model = HDBSCAN(min_cluster_size = min_cluster_size,\n",
    "                        metric='euclidean', \n",
    "                        cluster_selection_method='eom', \n",
    "                        prediction_data=True)\n",
    "\n",
    "\n",
    "# Step 4 - Tokenize topics \n",
    "vectorizer_model = CountVectorizer(stop_words=\"english\", lowercase=False) # lowercase=False to keep genes uppercase\n",
    "\n",
    "\n",
    "# Step 5 - Create topic representation\n",
    "ctfidf_model = ClassTfidfTransformer(reduce_frequent_words=True) # False default\n",
    "\n",
    "\n",
    "# Step 6 - Fine-tune topic representations with a bertopic.representation model\n",
    "representation_model = MaximalMarginalRelevance(diversity = 0.7,  # 0.1 default\n",
    "                                                top_n_words = 15) # 10 default\n",
    "#representation_model = KeyBERTInspired()\n",
    "\n",
    "\n",
    "# Use the representation model in BERTopic on top of the default pipeline\n",
    "\n",
    "# All steps together\n",
    "topic_model = BERTopic(\n",
    "    min_topic_size = 10,                         # 10 default\n",
    "    top_n_words = 15,                            # 10 default\n",
    "    calculate_probabilities = True,\n",
    "    embedding_model = sentence_transformer,      # Step 1 - Extract embeddings\n",
    "    umap_model = umap_model,                     # Step 2 - Reduce dimensionality\n",
    "    hdbscan_model = hdbscan_model,               # Step 3 - Cluster reduced embeddings\n",
    "    vectorizer_model = vectorizer_model,         # Step 4 - Tokenize topics\n",
    "    ctfidf_model = ctfidf_model,                 # Step 5 - Extract topic words\n",
    "    representation_model= representation_model   # Step 6 - (Optional) Fine-tune topic represenations\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(docs_processed)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Fit Transform"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit Transform\n",
    "#topics, probs = topic_model.fit_transform(docs_processed)\n",
    "#topics_fit = topic_model.fit(docs_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## If you want to split embedding phase, use it as follows:\n",
    "\n",
    "# Step 1 Embedding documents with sentence_transformer\n",
    "embedding_file = 'nbib_subset_clean_embeddings.txt'\n",
    "\n",
    "if 'docs_processed' not in globals():\n",
    "    docs_processed = load_preprocessed()\n",
    "\n",
    "if simple_bool('Load pre-generated embeddings?'):\n",
    "    embeddings = np.loadtxt(save_path + embedding_file)\n",
    "else:\n",
    "    embeddings = sentence_transformer.encode(docs_processed, show_progress_bar=True)\n",
    "    \n",
    "# Get Topic Model using custom embeddings\n",
    "topics, probs = topic_model.fit_transform(docs_processed, embeddings=embeddings)\n",
    "#topics = topic_model.fit(docs_processed, embeddings=embeddings)\n",
    "print('random_state:',random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(embeddings.shape)\n",
    "topic_model.get_topic_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if simple_bool('save grpm_abs_embeddings?'):\n",
    "    filename = 'nbib_grpm_abs_embeddings.txt'\n",
    "    np.savetxt(save_path+filename, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if simple_bool('Save topics model?'):\n",
    "    filename= \"topic_model_seed\"+str(random_state)\n",
    "    topic_model.save(filename)\n",
    "    np.savetxt(save_path+filename+'_probs.txt', probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from bertopic import BERTopic\n",
    "if simple_bool('Load topics model?'):\n",
    "    filename = 'topic_model_251'\n",
    "    topic_model = BERTopic.load(save_path+filename)\n",
    "    probs = np.loadtxt(save_path+filename+'_probs.txt')\n",
    "\n",
    "if 'docs_processed' not in globals():\n",
    "    docs_processed = load_preprocessed()"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(topic_model.get_topic_info().head())\n",
    "pd.DataFrame(probs).head()\n",
    "#topic_model.get_document_info(docs_processed).Probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#basic custom labels \n",
    "topic_labels = topic_model.generate_topic_labels(nr_words     = 4,\n",
    "                                                 topic_prefix = True,\n",
    "                                                 word_length  = 10,\n",
    "                                                 separator    = \" \")\n",
    "common_length = 30\n",
    "padded_list = [s.ljust(common_length) + \" \"  if len(s) < common_length else s[:common_length] + \" \"  for s in topic_labels]\n",
    "\n",
    "if simple_bool('use padded list?'):\n",
    "    topic_labels = padded_list\n",
    "    \n",
    "topic_model.set_topic_labels(topic_labels)\n",
    "topic_model.get_topic_info()#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return top n words for a specific topic and their c-TF-IDF scores\n",
    "print('Top n words for a specific topic and their c-TF-IDF scores')\n",
    "topic_id = 0\n",
    "topic = topic_model.get_topic(topic_id, full=False)\n",
    "pd.DataFrame(topic)#[1].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return topics with top n words and their c-TF-IDF score\n",
    "all_topics = topic_model.get_topics()\n",
    "topic_df = pd.DataFrame(all_topics)#.T#.head()\n",
    "\n",
    "# Probablities\n",
    "probs_df = pd.DataFrame(probs)\n",
    "\n",
    "# Topic FREQ\n",
    "topic_freq = topic_model.get_topic_freq()\n",
    "outliers = topic_freq.Count[0]\n",
    "\n",
    "print('total',topic_freq.Count.sum())\n",
    "print('outliers',outliers)\n",
    "print('ingroup',topic_freq.Count.sum()-outliers)\n",
    "\n",
    "# Topic INFO\n",
    "topic_info = topic_model.get_topic_info()\n",
    "topic_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mesh_count, random_state = 251,'v2',1337\n",
    "if simple_bool('save topic info?'):\n",
    "    filename = 'topic_info_abs_seed'+str(random_state)+'.csv'\n",
    "    topic_info.to_csv(save_path+filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'docs_processed' not in locals():\n",
    "    docs_processed = load_preprocessed()\n",
    "\n",
    "doc_info = topic_model.get_document_info(docs_processed)\n",
    "doc_info['pubmed_id'] = doc_info.Document.str.split(' $ ').str[0].str.split().str[0]\n",
    "doc_info['Abstract']  = doc_info.Document.str.split('$').str[1]\n",
    "\n",
    "doc_info_s = doc_info[['Topic','pubmed_id', 'Abstract', 'Name', 'Representation', 'Top_n_words', 'Probability','Representative_document']]\n",
    "\n",
    "print(doc_info.columns)\n",
    "doc_info_s[doc_info_s.Topic == 1].reset_index().sort_values(by='index').head()#.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if simple_bool('Save document info?'):\n",
    "    filename = 'doc_info_'+str(mesh_count)+'_rndm'+str(random_state)+'.csv'\n",
    "    doc_info.to_csv(save_path+filename)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "if simple_bool('Load document info?'):\n",
    "    doc_info = pd.read_csv('data/doc_info_251_rndm1337.csv', index_col=0)\n",
    "doc_info"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get PMID and Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get pubmed_id per topic\n",
    "def get_pmids(topic_id):\n",
    "    len(doc_info[doc_info.Topic == topic_id])\n",
    "    doc_topic = doc_info[doc_info.Topic == topic_id]\n",
    "    pmid_topic = doc_topic.pubmed_id.reset_index(drop=True).astype(str)\n",
    "    return pmid_topic\n",
    "\n",
    "#get gene per topic\n",
    "def get_genes(topic_id):\n",
    "    genes_topic = grpm_dataset[grpm_dataset['pmids'].isin(get_pmids(topic_id))].gene.drop_duplicates().reset_index(drop=True)\n",
    "    return genes_topic\n",
    "\n",
    "if 'grpm_dataset' not in globals():\n",
    "    grpm_dataset = pd.read_csv(\"grpm_dataset/grpm_db_pcg/grpm_table_output.csv\", index_col=0).reset_index(drop=True)\n",
    "    grpm_dataset['pmids'] = grpm_dataset['pmids'].astype(str)\n",
    "    \n",
    "get_genes(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Representative Documents\n",
    "print(topic_info['Representative_Docs'][1])\n",
    "print('\\n',topic_info['Representation'][1])\n",
    "\n",
    "doc_info_s_rep = doc_info_s[doc_info_s.Representative_document==True]\n",
    "doc_info_s_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Print the top n words for / c-TF-IDF scores for a specific topic (here the 0)\n",
    "for n in range(2):\n",
    "    print(pd.Series(topic_model.get_topic(n))) "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic naming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate names (GPT3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isfile('pychatgpt_lite.py'):\n",
    "    get_gitfile('https://github.com/johndef64/pychatgpt/blob/main/pychatgpt_lite.py')\n",
    "\n",
    "import pychatgpt_lite as op\n",
    "op.expand_chat('''You are an helpful scientific assistant''', 'system')\n",
    "\n",
    "topic_info['CustomLabel_GPT3.5'] = ''\n",
    "free_user = False\n",
    "\n",
    "gpt_label = 'a short label of the topic. Reply only with the result.'\n",
    "gpt_description = 'a short but accurate description of the topic. Reply only with the result.'\n",
    "\n",
    "op.clearchat()\n",
    "def generate(what, label):\n",
    "    for i in range(len(topic_info)):\n",
    "        prompt1 = ''' \n",
    "        Using your knowledge of biomendical science, give a short, appropriate and specific name to the topic describing the following list of biomedical terms, max 8 words. \n",
    "        '''+str(topic_info.Representation.iloc[i])\n",
    "\n",
    "        '''\n",
    "        Example: \n",
    "        term list: \n",
    "        ['cell','mice','protein','levels','liver','lipid','role','plasma','inflammatory','activation','muscle','study','diseases','mutations','associated']\n",
    "        outout: \n",
    "        \"Role of Inflammatory Protein in Liver Diseases'\"\n",
    "        '''\n",
    "\n",
    "        if len(doc_info[doc_info.Topic == i-1]) <=1:\n",
    "            sample = 1\n",
    "        elif len(doc_info[doc_info.Topic == i-1]) <=2:\n",
    "            sample =2\n",
    "        else:\n",
    "            sample = 3\n",
    "\n",
    "        sample_docs = str(doc_info[doc_info.Topic == i-1][doc_info.Representative_document == True].sample(sample).Abstract)\n",
    "\n",
    "        prompt2 = \"\"\"\n",
    "        I have topic that contains the following documents: \"\"\" +sample_docs+ \"\"\"\n",
    "        The topic is described by the following keywords: \"\"\" +str(topic_info.Representation.iloc[i])+ \"\"\"\n",
    "        \n",
    "        Based on the above information, give me \"\"\"+what\n",
    "\n",
    "        op.send_message(prompt2, model=op.model, print_token= False, save_chat=False)\n",
    "        if label not in topic_info.columns:\n",
    "            topic_info[label] = None\n",
    "        topic_info[label][i] = str(i)+'.'+op.reply #topic_info['CustomLabel_GPT3.5'][i]\n",
    "\n",
    "        if i>=3 and i % 20 == 0:\n",
    "            print('\\nsleeping... (61 sec)')\n",
    "            time.sleep(61)\n",
    "\n",
    "        if free_user:\n",
    "            if i>=3 and i % 3 == 0:\n",
    "                print('\\nsleeping... (55 sec)')\n",
    "                time.sleep(55)\n",
    "        print('\\n')\n",
    "\n",
    "generate(gpt_label, 'CustomLabel_GPT3.5')\n",
    "generate(gpt_description, 'Description_GPT3.5')\n",
    "\n",
    "topic_labels_gpt = topic_info['CustomLabel_GPT3.5']\n",
    "topic_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if simple_bool('Save topic info GPT?'):\n",
    "    filename = 'topic_info_seed'+str(random_state)+'_GPT.csv'\n",
    "    topic_info.to_csv(save_path+filename)\n",
    "\n",
    "if simple_bool('Load topic info GPT?'):\n",
    "    topic_info = pd.read_csv(save_path+'topic_info_seed1337_GPT.csv', index_col=0) \n",
    "\n",
    "topic_info[['Topic', 'Count', 'CustomLabel_GPT3.5','Representation']]#[17:].to_clipboard(index=False)#csv('table.csv')\n",
    "#['CustomLabel_gpt-3.5'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "latex_table = topic_info[['Topic', 'Count', 'CustomLabel_GPT3.5']].to_latex(index=False)\n",
    "import pyperclip as pc\n",
    "pc.copy(latex_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RPM (requests per minute) – The maximum requests allowed per minute  \n",
    "RPD (requests per day) – The maximum requests allowed per da  \n",
    "\n",
    "TPM (tokens per minute) – The maximum tokens allowed to be sent per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'TIER': ['Free', 'Tier 1', 'Tier 2', 'Tier 3', 'Tier 4', 'Tier 5'],\n",
    "        'QUALIFICATION': ['User must be in an allowed geography', '$5 paid', '$50 paid and 7+ days since first successful payment','$100 paid and 7+ days since first successful payment', '$250 paid and 14+ days since first successful payment','$1000 paid and 30+ days since first successful payment'],\n",
    "        'MAX CREDITS': ['$100', '$100', '$250', '$500', '$1000', '$1000'],\n",
    "        'REQUEST LIMITS': ['3 RPM, 200 RPD', '500 RPM, 10K RPD', '5000 RPM', '5000 RPM', '10K RPM', '10K RPM'],\n",
    "        'TOKEN LIMITS': ['10K TPM', '20K TPM', '40K TPM', '80K TPM', '300K TPM', '300K TPM']}\n",
    "print('Openai API reference:')\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-- GOAL: PMID clusering and topic definition ('openai')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set topic labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic custom labels \n",
    "topic_model.set_topic_labels(topic_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gpt-3.5 custom labels \n",
    "topic_model.set_topic_labels(topic_labels_gpt.to_list())\n",
    "topic_model.get_topic_info().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Visualization¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Topic Visualization¶\n",
    "Evaluating topic models can be rather difficult due to the somewhat subjective nature of evaluation. Visualizing different aspects of the topic model helps in understanding the model and makes it easier to tweak the model to your liking.\n",
    "\n",
    "| Method                                      | Code                                    |\n",
    "|--------------------------------------------|-----------------------------------------|\n",
    "| Visualize Topics                            | `.visualize_topics()`                     |\n",
    "| Visualize Documents                         | `.visualize_documents()`                  |\n",
    "| Visualize Document Hierarchy                | `.visualize_hierarchical_documents()`     |\n",
    "| Visualize Topic Hierarchy                   | `.visualize_hierarchy()`                  |\n",
    "| Visualize Topic Tree                        | `.get_topic_tree(hierarchical_topics)`    |\n",
    "| Visualize Topic Terms                       | `.visualize_barchart()`                   |\n",
    "| Visualize Topic Similarity                  | `.visualize_heatmap()`                    |\n",
    "| Visualize Term Score Decline                | `.visualize_term_rank()`                  |\n",
    "| Visualize Topic Probability Distribution    | `.visualize_distribution(probs[0])`       |\n",
    "| Visualize Topics over Time                  | `.visualize_topics_over_time(topics_over_time)` |\n",
    "| Visualize Topics per Class                  | `.visualize_topics_per_class(topics_per_class)` |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize_documents (sample) ! Computationally intensive !\n",
    "\n",
    "topic_model.set_topic_labels(topic_labels)\n",
    "topic_model.visualize_documents(docs_processed, sample= 0.05, custom_labels=True)  \n",
    "#topic_model.visualize_documents(docs_processed, embeddings=embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize_documents (sample) (using embeddings, faster)\n",
    "if 'embeddings' not in locals():\n",
    "    embedding_file = 'abs_embeddings_seed1337.txt'\n",
    "    embeddings = np.loadtxt(save_path+embedding_file)\n",
    "\n",
    "topic_model.set_topic_labels(topic_labels)\n",
    "topic_model.visualize_documents(docs_processed, embeddings=embeddings,sample= 0.1, custom_labels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intertopic Distance Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis.\n",
    "\n",
    "We embed our c-TF-IDF representation of the topics in 2D using Umap and then visualize the two dimensions using plotly such that we can create an interactive view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_model.visualize_topics() #can help you visualize topics generated with their sizes and corresponding words."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Post-processing"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Evaluation\n",
    "thanks to Topic Clusterization and Intertopic Distance Map \n",
    "\n",
    "Results:\n",
    "- based on 251 Mesh, we retieverd 37.000 pepers from GRPM Dataset\n",
    "- Topic Modeling on abstracts, we got 50 Topics\n",
    "- Topic Evaluation, from 50 Topics to X0 topics\n",
    "- from 37.000 paper to 10.238 papers, XX.000 excluded and 13.YYY outliers\n",
    "- from 8.280 genes to 3.911 genes (cross papers with GRPM ds)\n",
    "- get genes network for selected topics "
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Curated Topic Evaluation  "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pertinent_topics = [ 11,28,48,18,2,5,  #cluster1:\n",
    "                     9,35, #cluster2\n",
    "                     12,#cluster3\n",
    "                     #cluster4\n",
    "                     37,32,19,40,26,33, #cluster5\n",
    "                     3,31,#cluster6\n",
    "                     #cluster7\n",
    "                     #cluster8\n",
    "                     #cluster9\n",
    "                     8,20,4,34#cluster10\n",
    "                     ]\n",
    "\n",
    "non_pertinent_topics = [ 27,47, #cluster1\n",
    "                         1,#cluster2\n",
    "                         0,30,21,49,36, #cluster3\n",
    "                         39,13,#cluster3\n",
    "                         16,39, #cluster4\n",
    "                         #cluster5\n",
    "                         29,#cluster6\n",
    "                         45,25,7,44,24,23,14,#cluster7\n",
    "                         38,50,46,10,42,17,#cluster8\n",
    "                         41,6,16,15,43,22,#cluster9\n",
    "                         #cluster10\n",
    "                         ]\n",
    "print('Selected Topics:')\n",
    "topic_info[topic_info.Topic.isin(pertinent_topics)]"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Get Representative Docs"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#Get Representative_document abstracts for pertinent topics\n",
    "doc_info_s_rep = doc_info_s[doc_info_s.Representative_document==True]\n",
    "doc_info_s_rep_pert = doc_info_s_rep[doc_info_s_rep.Topic.isin(pertinent_topics)]\n",
    "doc_info_s_rep_pert.Abstract.to_list()\n",
    "#pc.copy(str(doc_info_s_rep_pert.Abstract.to_list()))\n",
    "#pc.copy(str(doc_info_s_rep_pert.pubmed_id.to_list()))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize_documents (sample)\n",
    "if 'embeddings' not in locals():\n",
    "    embedding_file = 'abs_embeddings_seed1337.txt'\n",
    "    embeddings = np.loadtxt(save_path+embedding_file)\n",
    "    \n",
    "topic_model.set_topic_labels(topic_labels)\n",
    "topic_model.visualize_documents(docs_processed, topics= pertinent_topics, embeddings=embeddings,sample= 0.1, custom_labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "!pip install squarify"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Evaluation Results\n",
    "topic_info_pertinent = topic_info[['Topic', 'Count','CustomName', 'Representation']][topic_info.Topic.isin(pertinent_topics)]\n",
    "doc_info_s_pertinent = doc_info_s[doc_info_s.Topic.isin(pertinent_topics)]\n",
    "\n",
    "total_count = topic_info.Count.sum()\n",
    "pertinent_count = topic_info_pertinent.Count.sum()\n",
    "outliers = topic_info.Count[0]\n",
    "non_pertinent_count = total_count-outliers-pertinent_count\n",
    "\n",
    "print('total count:', total_count)\n",
    "print('outliers count:', outliers)\n",
    "print('non pertinent count:', non_pertinent_count)\n",
    "print('pertinent count:', pertinent_count)\n",
    "'''\n",
    "import plotly.express as px\n",
    "data = {\n",
    "    'Type': ['Pertinent Count', 'Non-Pertinent Count', 'Outliers'],\n",
    "    'Value': [pertinent_count, non_pertinent_count, outliers]\n",
    "}\n",
    "fig = px.treemap(data, path=['Type'], values='Value', title='Treemap')\n",
    "fig.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import squarify\n",
    "\n",
    "labels = ['Pertinent Count', 'Non-Pertinent Count', 'Outliers']\n",
    "sizes = [pertinent_count, non_pertinent_count, outliers]\n",
    "colors = ['green', 'red', 'grey']\n",
    "\n",
    "plt.figure(figsize=(4, 4))  \n",
    "squarify.plot(sizes=sizes, label=labels, color=colors, alpha=0.7)\n",
    "#plt.axis('off')\n",
    "#plt.title('Treemap')\n",
    "plt.savefig('evaluation_placeholder.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "topic_info_pertinent#.describe()"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output Dataset \n",
    "### Output Dataset Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteres_grpm = grpm_dataset[grpm_dataset.mesh.isin(list_mesh)]\n",
    "filteres_grpm = filteres_grpm[['gene', 'rsid', 'pmids', 'mesh']].drop_duplicates()\n",
    "filteres_grpm.describe().to_clipboard()\n",
    "\n",
    "pertinent_pmid = doc_info[doc_info.Topic.isin(pertinent_topics)].pubmed_id\n",
    "modeled_grpm = grpm_dataset[grpm_dataset.pmids.isin(pertinent_pmid)]\n",
    "\n",
    "print('Filtered by MeSH')\n",
    "display(filteres_grpm.describe())\n",
    "print('Selected by Topic')\n",
    "display(modeled_grpm.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Get and Enrich Output Dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "output_ds = doc_info_s_pertinent[['Topic', 'pubmed_id', 'Abstract', 'Name', 'Representation']]\n",
    "\n",
    "# filter NBIB\n",
    "filterd_nbib = nbib_subset_clean[['pubmed_id','title','abstract', 'keywords', 'descriptors']].drop_duplicates()\n",
    "filterd_nbib.pubmed_id = filterd_nbib.pubmed_id.astype(str)\n",
    "output_nbib = filterd_nbib[filterd_nbib.pubmed_id.isin(output_ds.pubmed_id)]\n",
    "output_nbib#.to_csv(save_path+'RAG_LLM_nutrigentic_dataset.csv')\n",
    "\n",
    "# MERGE NBIB & TOPICS\n",
    "output_ds_topic = pd.merge(output_ds[['Topic', 'pubmed_id', 'Name', 'Representation']], output_nbib, on='pubmed_id')\n",
    "output_ds_topic.rename(columns={'Topic':'topic_id', 'pubmed_id':'pubmed_id', 'Name':'topic_name', 'Representation':'topic_representation'}, inplace=True)\n",
    "output_ds_topic#.to_csv(save_path+'output_dataset_bertopic_nutrigentics.csv')\n",
    "\n",
    "# MERGE NBIB-TOPICS & GENE-RSID\n",
    "df = modeled_grpm[['gene', 'rsid', 'pmids']]\n",
    "transformed_df = df.groupby('pmids').aggregate({'rsid': lambda x: list(x), 'gene': lambda x: list(x)}).reset_index()\n",
    "transformed_df[['pmids', 'rsid', 'gene']]\n",
    "\n",
    "output_ds_topic_gene_rsid = pd.merge(output_ds_topic, transformed_df, left_on='pubmed_id', right_on='pmids')\n",
    "output_ds_topic_gene_rsid.rename(columns={'gene':'genes', 'rsid':'rsids'}, inplace=True)\n",
    "output_ds_topic_gene_rsid.drop(columns='pmids', inplace=True)\n",
    "output_ds_topic_gene_rsid = output_ds_topic_gene_rsid[['topic_id', 'pubmed_id', 'topic_name', 'topic_representation', 'title','abstract', 'keywords', 'descriptors', 'genes', 'rsids']]\n",
    "output_ds_topic_gene_rsid#.to_csv(save_path+'output_dataset_bertopic_nutrigentics.csv')"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Load Enriched Output Dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "output_ds_topic_gene_rsid = pd.read_csv(save_path+'output_dataset_bertopic_nutrigenetics.csv', index_col=0)\n",
    "output_ds_topic_gene_rsid"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# get enriched representative docs\n",
    "topic_id = 2\n",
    "doc_info_s_rep_pmid_bool = doc_info_s_rep[['pubmed_id', 'Representative_document']]\n",
    "output_ds_topic_gene_rsid[output_ds_topic_gene_rsid.topic_id==topic_id]\n",
    "\n",
    "# filter for representative docs\n",
    "output_ds_topic_gene_rsid_rep = output_ds_topic_gene_rsid[output_ds_topic_gene_rsid.pubmed_id.isin(doc_info_s_rep_pmid_bool.pubmed_id)]\n",
    "output_ds_topic_gene_rsid_rep[output_ds_topic_gene_rsid.topic_id==topic_id][['title','abstract','keywords']]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Extras"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# to latex\n",
    "df = topic_info[['Topic', 'Count', 'CustomLabel_GPT3.5']]\n",
    "df['CustomLabel_GPT3.5']=df['CustomLabel_GPT3.5'].str.replace(r'^.*?\\.', '', regex=True)\n",
    "df_isin = df[df.Topic.isin(pertinent_topics)]\n",
    "\n",
    "latex_table = df_isin.to_latex(index=False,caption='Pertinent Topics')\n",
    "import pyperclip as pc\n",
    "pc.copy(latex_table)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return top n words for a specific topic and their c-TF-IDF scores\n",
    "print('Top n words for a specific topic and their c-TF-IDF scores')\n",
    "for topic_id in pertinent_topics:\n",
    "    topic = topic_model.get_topic(topic_id, full=False)\n",
    "    print('\\n',topic_info.CustomName[topic_id])\n",
    "    print(pd.DataFrame(topic))#[1].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument with id -1 groups  all unattributed abstracts. We will ignore it."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Display Results"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Probablities or Distribution¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To visualize the probabilities of topic assignment\n",
    "import plotly.graph_objects as go\n",
    "fig = go.Figure()\n",
    "for n in range(3):\n",
    "    fig = topic_model.visualize_distribution(probs[n],\n",
    "                                             min_probability = 0,\n",
    "                                             custom_labels = False)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install jinja2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the topic distributions on a token-level\n",
    "topic_distr, topic_token_distr = topic_model.approximate_distribution(docs_processed, calculate_tokens=True, min_similarity=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the token-level distributions\n",
    "topic_n = 4\n",
    "df = topic_model.visualize_approximate_distribution(docs_processed[topic_n], topic_token_distr[topic_n])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_n = 2\n",
    "doc_info[doc_info.Document == docs_processed[topic_n]].reset_index().sort_values(by='index')#.head()#.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity Matrix\n",
    "Having generated topic embeddings, through both c-TF-IDF and embeddings, we can create a similarity matrix by simply applying cosine similarities through those topic embeddings. The result will be a matrix indicating how similar certain topics are to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U kaleido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 817
    },
    "executionInfo": {
     "elapsed": 489,
     "status": "ok",
     "timestamp": 1698406202416,
     "user": {
      "displayName": "Bruno Hay Mele",
      "userId": "15590879909074487118"
     },
     "user_tz": -120
    },
    "id": "B1KD6Sqp0XNB",
    "outputId": "a29cff44-bb74-4a49-cb2b-30c193ea63de"
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "def show_similarty(topics):\n",
    "    fig = go.Figure()\n",
    "    fig = topic_model.visualize_heatmap(topics=topics,\n",
    "                                        top_n_topics = 51,\n",
    "                                        #n_clusters = 20,\n",
    "                                        custom_labels=True,\n",
    "                                        width = 1100,\n",
    "                                        height = 1100)\n",
    "    #fig.write_image(\"fig1.png\", engine='kaleido')\n",
    "    fig.show()\n",
    "    \n",
    "show_similarty(None) # this is correct, in-line with the Intertopic distance map\n",
    "#show_similarty(topic_info.Topic[:len(topic_info.Topic)-10]) # !!! reorder topic doesn't work\n",
    "#show_similarty(hierarchy[:len(hierarchy)]) # !!! reorder topic doesn't work\n",
    "#show_similarty(pertinent_topics) # !!! cherry-picking topics doesn't work\n",
    "#show_similarty([7, 24,0,1,34,14,23]) \n",
    "#show_similarty([7, 14,20,14,3,4,2])\n",
    "# Conclusion: the heatmap is fixed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity Matrix (custom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pertinent_topics.sort()\n",
    "pertinent_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "topic_info[topic_info.Topic.isin(pertinent_topics)].CustomName"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "distance_matrix = cosine_similarity(np.array(topic_model.topic_embeddings_)[1:, :])\n",
    "\n",
    "# get labels\n",
    "labels = topic_info[topic_info.Topic.isin(pertinent_topics)].CustomName\n",
    "# Filter rows and columns based on indices\n",
    "pertinent_topics.sort()\n",
    "rows_to_keep = pertinent_topics\n",
    "cols_to_keep = pertinent_topics\n",
    "filtered_distance_matrix = distance_matrix[np.ix_(rows_to_keep, cols_to_keep)]\n",
    "\n",
    "fig = px.imshow(filtered_distance_matrix,\n",
    "                labels=dict(color=\"Similarity Score\"),\n",
    "                x=labels,\n",
    "                y=labels,\n",
    "                color_continuous_scale='GnBu'\n",
    "                )\n",
    "title: str = \"<b>Similarity Matrix</b>\"\n",
    "fig.update_layout(\n",
    "    title={\n",
    "        'text': f\"{title}\",\n",
    "        'y': .95,\n",
    "        'x': 0.55,\n",
    "        'xanchor': 'center',\n",
    "        'yanchor': 'top',\n",
    "        'font': dict(\n",
    "            size=22,\n",
    "            color=\"Black\")\n",
    "    },\n",
    "    width=800,\n",
    "    height=800,\n",
    "    hoverlabel=dict(\n",
    "        bgcolor=\"white\",\n",
    "        font_size=16,\n",
    "        font_family=\"Rockwell\"\n",
    "    ),\n",
    ")\n",
    "fig.update_layout(showlegend=True)\n",
    "fig.update_layout(legend_title_text='Trend')\n",
    "fig.write_image(\"heatmap_short.png\", engine=\"kaleido\", scale=3)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchy Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 712
    },
    "executionInfo": {
     "elapsed": 1647,
     "status": "ok",
     "timestamp": 1698406212282,
     "user": {
      "displayName": "Bruno Hay Mele",
      "userId": "15590879909074487118"
     },
     "user_tz": -120
    },
    "id": "X-jY41ul0XJ_",
    "outputId": "723b119f-8722-40cc-fd49-85b6a12fae86"
   },
   "outputs": [],
   "source": [
    "topic_model.visualize_hierarchy(custom_labels=True, width=700, height=600) #The topics that were created can be hierarchically reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hierarchical_topics = topic_model.hierarchical_topics(docs_processed)\n",
    "topic_model.visualize_hierarchy(custom_labels=True, width=700, height=600, hierarchical_topics=hierarchical_topics)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "hierarchical_topics = topic_model.hierarchical_topics(docs_processed)\n",
    "tree = topic_model.get_topic_tree(hierarchical_topics)\n",
    "print(tree)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terms Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wordclouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show wordcloud\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def create_wordcloud(model, topic):\n",
    "    text = {word: value for word, value in model.get_topic(topic)}\n",
    "    print(text)\n",
    "    wc = WordCloud(background_color=\"white\", max_words=1000, width=800, height=400)\n",
    "    wc.generate_from_frequencies(text)\n",
    "    plt.imshow(wc, interpolation=\"bilinear\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "def create_wordcloud_multiple(topics, output_path='wordcloud.png', dpi=300, save=True):\n",
    "    merged_dict = {}\n",
    "    for i in topics:\n",
    "        text = {word: value for word, value in topic_model.get_topic(i)}\n",
    "        merged_dict.update(text)\n",
    "    #pc.copy(str(merged_dict))\n",
    "\n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    wc = WordCloud(background_color=\"white\", max_words=1000, width=1000, height=500)\n",
    "    wc.generate_from_frequencies(merged_dict)\n",
    "\n",
    "    plt.imshow(wc, interpolation=\"bilinear\")\n",
    "    plt.axis(\"off\")\n",
    "    if save:\n",
    "        plt.savefig(output_path, dpi=dpi, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def create_wordcloud_from_corpus(corpus, output_path='wordcloud.png', dpi=300, save=True, replace=False):\n",
    "    # Combine the text corpus into a single string\n",
    "    text = ' '.join(corpus)\n",
    "    # Generate WordCloud from the text\n",
    "    wc = WordCloud(background_color=\"white\", max_words=1000, width=800, height=400)\n",
    "    wc.generate(text)\n",
    "    # Display the WordCloud\n",
    "    plt.imshow(wc, interpolation=\"bilinear\")\n",
    "    plt.axis(\"off\")\n",
    "    if save:\n",
    "        plt.savefig(output_path, dpi=dpi, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "create_wordcloud_multiple(pertinent_topics, dpi=500, save = True,output_path=save_path+'wordcloud_pert.png')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# create_wordcloud_from_corpus\n",
    "create_wordcloud_from_corpus(doc_info_s_pertinent.Abstract.to_list(), dpi=500, save = True,output_path=save_path+'wordcloud_pert.png')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Show wordcloud\n",
    "for i in pertinent_topics[:2]:\n",
    "    print('')\n",
    "    create_wordcloud(topic_model, topic=i)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topic Word score (c-TF-IDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show the selected terms for a few topics by creating bar charts out of the c-TF-IDF scores.\n",
    "topic_model.visualize_barchart(n_words = 20, \n",
    "                               topics = pertinent_topics,\n",
    "                               #top_n_topics=len(topic_info)//4,\n",
    "                              )  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Term Score Decline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Term Score Decline \n",
    "# [TF-IDF — Term Frequency-Inverse Document] #https://www.capitalone.com/tech/machine-learning/understanding-tf-idf/\n",
    "# Each topic is represented by a set of words.\n",
    "# These words, however, do not all equally represent the topic.\n",
    "# This visualization shows how many words are needed to represent a topic and at which point the beneficial effect of adding words starts to decline.\n",
    "# https://maartengr.github.io/BERTopic/getting_started/ctfidf/ctfidf.html\n",
    "topic_model.visualize_term_rank()#(log_scale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepocess dates\n",
    "import re\n",
    "if 'nbib_subset_clean' not in globals():\n",
    "    print(\"loading 'nbib_subset_clean'...\")\n",
    "    nbib_subset_clean = pd.read_csv(save_path+'nbib_subset_clean_251.csv', index_col=0)\n",
    "df_crop = nbib_subset_clean[['pubmed_id','publication_date']].drop_duplicates()\n",
    "dates = df_crop.publication_date.to_list()\n",
    "years = [date.split(' ')[0].split(\"-\")[0] for date in dates]\n",
    "len(years)\n",
    "\n",
    "for idx, date in enumerate(dates):\n",
    "  dates[idx] = \" \".join(date.split(\" \")[:2]).split(\"-\")[0].strip().split(\"/\")[0].strip()\n",
    "    \n",
    "for i in range(len(dates)):\n",
    "    if len(dates[i]) == 4: \n",
    "        dates[i] += ' Jan'\n",
    "        \n",
    "dates[:10]\n",
    "'''\n",
    "converted_dates = []\n",
    "import datetime\n",
    "for date in dates:\n",
    "    try:\n",
    "        dt = datetime.datetime.strptime(date, \"%Y %b\")\n",
    "        converted_dates.append(dt.strftime(\"%m/%Y\"))\n",
    "    except ValueError:\n",
    "        converted_dates.append('Invalid Date')\n",
    "converted_dates[:10]\n",
    "'''\n",
    "len(years), len(docs_processed)\n",
    "#pd.Series(years).drop_duplicates().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_formats = ['%Y %b',\"%Y\"]\n",
    "#topics, probs = topic_model_t.fit_transform(docs_processed,embeddings)\n",
    "topics_over_time = topic_model.topics_over_time(docs_processed, \n",
    "                                                  years, \n",
    "                                                  datetime_format= datetime_formats[1],\n",
    "                                                  global_tuning=True, \n",
    "                                                  evolution_tuning=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics_over_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#topic_model_t.visualize_topics_over_time(topics_over_time[topics_over_time[\"Timestamp\"]<2023],top_n_topics=10,width=1250,height=450).show()\n",
    "#topic_model_t.visualize_topics_over_time(topics_over_time[topics_over_time[\"Timestamp\"]<2023],top_n_topics=10,custom_labels = True, title = \"<b>Topics over Time</b>\", width=1400,height=800).show()\n",
    "\n",
    "topic_model.visualize_topics_over_time(topics_over_time,\n",
    "                                         top_n_topics=10,\n",
    "                                         custom_labels = True,\n",
    "                                         topics = pertinent_topics,\n",
    "                                         normalize_frequency = False,\n",
    "                                         title = \"<b>Topics over Time</b>\", \n",
    "                                         width=1400,\n",
    "                                         height=800).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VnIgX8Ov1DAB"
   },
   "source": [
    "# Extras"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Alternative NBIB Preprocessors"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "timea = time.time()\n",
    "\n",
    "if simple_bool('Preprocess source dataset (slow preprocessor)'):\n",
    "    nbib_subdataset = pd.DataFrame(columns= nbib_dataset.columns)\n",
    "\n",
    "    num = len(nbib_dataset)\n",
    "    for j in tqdm(range(len(nbib_dataset[:num]))):\n",
    "        descriptors = ast.literal_eval(nbib_dataset.descriptors[j])\n",
    "        descriptors_list =[]\n",
    "        for i in range(len(descriptors)):\n",
    "            descriptors_list.append(descriptors[i]['descriptor'])\n",
    "        if any(term in descriptors_list for term in list_mesh):\n",
    "            nbib_subdataset = pd.concat([nbib_subdataset, nbib_dataset.iloc[[j]]], ignore_index=True)\n",
    "    print((time.time()-timea)/60,'minutes')\n",
    "\n",
    "if simple_bool('Use alternative preprocessor (fast unaccurate)'):\n",
    "    mesh = df_mesh['Preferred Label'].drop_duplicates().dropna().str.replace('+','')\n",
    "    nbib_subdataset = nbib_dataset[nbib_dataset.descriptors.str.contains('|'.join(mesh[:len(mesh)]))]\n",
    "    print((time.time()-timea)/60,'minutes')\n",
    "\n",
    "if simple_bool('save output?'):\n",
    "    nbib_subdataset.to_csv('data'+'nbib_subset'+str(mesh_count)+'.csv')\n",
    "\n",
    "nbib_subdataset.pubmed_id.drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Train Model: Default setting"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Run model\n",
    "import time\n",
    "from hdbscan.hdbscan_ import HDBSCAN\n",
    "\n",
    "# Prepare embeddings\n",
    "\"\"\" SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. \"\"\"\n",
    "embedding_file = 'grpm_abs_embeddings_313_nonltk.txt'\n",
    "\n",
    "if simple_bool('Load pre-generated embeddings?'):\n",
    "    embeddings = np.loadtxt(save_path+embedding_file)\n",
    "else:\n",
    "    from sentence_transformers import SentenceTransformer\n",
    "\n",
    "    sentence_model = SentenceTransformer(\"all-mpnet-base-v2\")\n",
    "    embeddings = sentence_model.encode(docs_processed,\n",
    "                                       show_progress_bar=True)\n",
    "print(embeddings[0][0:5])\n",
    "print('type',type(embeddings))\n",
    "\n",
    "#Find the optimal number of clusters with HDBscan\n",
    "\"\"\" HDBSCAN: Hierarchical Density-Based Spatial Clustering of Applications with Noise \"\"\"\n",
    "timea = time.time()\n",
    "hdbscan_model = HDBSCAN(min_cluster_size=100, #15\n",
    "                        metric='euclidean',\n",
    "                        cluster_selection_method='eom',\n",
    "                        prediction_data=True)\n",
    "\n",
    "timeb = time.time()\n",
    "print((timeb - timea)/60, 'minutes')\n",
    "\n",
    "# Run BERTopic\n",
    "\"\"\" BERTopic is a topic modeling technique \"\"\"\n",
    "doc = docs #docs_processed\n",
    "\n",
    "topic_model_HBDSCAN = BERTopic(hdbscan_model=hdbscan_model).fit(docs_processed, embeddings)\n",
    "\n",
    "print((time.time() - timea)/60, 'minutes')\n",
    "'''\n",
    "LOG: GRPM EMBEDDING 17.000 ABS IN 18.797277983029684 minutes  \n",
    "LOG: GRPM EMBEDDING 44.000 ABS IN 3.5 minutes on server <***>  \n",
    "LOG: GRPM EMBEDDING 42.700 ABS IN 2.993170352776845 minutes on server <***>\n",
    "'''\n",
    "\n",
    "# Saving the array as a txt file   \n",
    "if simple_bool('save grpm_abs_embeddings?'):\n",
    "    np.savetxt(save_path+'grpm_abs_embeddings.txt', embeddings)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## accessory functions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data\n",
    "umap_data = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)\n",
    "result = pd.DataFrame(umap_data, columns=['x', 'y'])\n",
    "result['labels'] = cluster.labels_\n",
    "\n",
    "# Visualize clusters\n",
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "outliers = result.loc[result.labels == -1, :]\n",
    "clustered = result.loc[result.labels != -1, :]\n",
    "plt.scatter(outliers.x, outliers.y, color='#BDBDBD', s=0.05)\n",
    "plt.scatter(clustered.x, clustered.y, c=clustered.labels, s=0.05, cmap='hsv_r')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## optimize labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\") #base_embedder\n",
    "\n",
    "# A selected topic representation\n",
    "# 'god jesus atheists atheism belief atheist believe exist beliefs existence'\n",
    "sequence_to_classify =  \" \".join([word for word, _ in topic_model.get_topic(1)])\n",
    "\n",
    "# Our set of potential topic labels\n",
    "candidate_labels = ['brain', 'liver', 'cancer']\n",
    "\n",
    "classifier(sequence_to_classify, candidate_labels)\n",
    "\n",
    "#{'labels': ['cooking', 'dancing', 'religion'],\n",
    "# 'scores': [0.086, 0.063, 0.850],\n",
    "# 'sequence': 'god jesus atheists atheism belief atheist believe exist beliefs existence'}"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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