5_deep_learning_doc2vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv('data_videos/dataframes/data.csv' )\n",
    "docs = df['titulo_video'].to_list() \n",
    "docs[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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>titulo_video</th>\n",
       "      <th>nombre_canal</th>\n",
       "      <th>keywords</th>\n",
       "      <th>lngs</th>\n",
       "      <th>bigrams</th>\n",
       "      <th>trigrams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMT2 - Extracting Emails from your Gmail Inbox...</td>\n",
       "      <td>DigitalSreeni</td>\n",
       "      <td>['amt2', 'extracting', 'emails', 'gmail', 'inb...</td>\n",
       "      <td>en</td>\n",
       "      <td>[('amt2', 'extracting'), ('extracting', 'email...</td>\n",
       "      <td>[('amt2', 'extracting', 'emails'), ('extractin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Read emails with Python + Gmail API (part 1)</td>\n",
       "      <td>ian samir</td>\n",
       "      <td>['read', 'emails', 'python', 'gmail', 'api', '...</td>\n",
       "      <td>en</td>\n",
       "      <td>[('read', 'emails'), ('emails', 'python'), ('p...</td>\n",
       "      <td>[('read', 'emails', 'python'), ('emails', 'pyt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logra Más Con Menos Esfuerzo - Esencialismo de...</td>\n",
       "      <td>Mis Propias Finanzas</td>\n",
       "      <td>['logra', 'menos', 'esfuerzo', 'esencialismo',...</td>\n",
       "      <td>es</td>\n",
       "      <td>[('logra', 'menos'), ('menos', 'esfuerzo'), ('...</td>\n",
       "      <td>[('logra', 'menos', 'esfuerzo'), ('menos', 'es...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Estratégia de trading con ChatGPT 4 Code Inter...</td>\n",
       "      <td>Rodrigo Prieto</td>\n",
       "      <td>['estratégia', 'trading', 'chatgpt', '4', 'cod...</td>\n",
       "      <td>es</td>\n",
       "      <td>[('estratégia', 'trading'), ('trading', 'chatg...</td>\n",
       "      <td>[('estratégia', 'trading', 'chatgpt'), ('tradi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AI &amp; Machine Learning in Finance: AI Applicati...</td>\n",
       "      <td>Swedish House of Finance</td>\n",
       "      <td>['ai', 'machine', 'learning', 'finance', 'ai',...</td>\n",
       "      <td>en</td>\n",
       "      <td>[('ai', 'machine'), ('machine', 'learning'), (...</td>\n",
       "      <td>[('ai', 'machine', 'learning'), ('machine', 'l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        titulo_video  \\\n",
       "0  AMT2 - Extracting Emails from your Gmail Inbox...   \n",
       "1       Read emails with Python + Gmail API (part 1)   \n",
       "2  Logra Más Con Menos Esfuerzo - Esencialismo de...   \n",
       "3  Estratégia de trading con ChatGPT 4 Code Inter...   \n",
       "4  AI & Machine Learning in Finance: AI Applicati...   \n",
       "\n",
       "               nombre_canal  \\\n",
       "0             DigitalSreeni   \n",
       "1                 ian samir   \n",
       "2      Mis Propias Finanzas   \n",
       "3            Rodrigo Prieto   \n",
       "4  Swedish House of Finance   \n",
       "\n",
       "                                            keywords lngs  \\\n",
       "0  ['amt2', 'extracting', 'emails', 'gmail', 'inb...   en   \n",
       "1  ['read', 'emails', 'python', 'gmail', 'api', '...   en   \n",
       "2  ['logra', 'menos', 'esfuerzo', 'esencialismo',...   es   \n",
       "3  ['estratégia', 'trading', 'chatgpt', '4', 'cod...   es   \n",
       "4  ['ai', 'machine', 'learning', 'finance', 'ai',...   en   \n",
       "\n",
       "                                             bigrams  \\\n",
       "0  [('amt2', 'extracting'), ('extracting', 'email...   \n",
       "1  [('read', 'emails'), ('emails', 'python'), ('p...   \n",
       "2  [('logra', 'menos'), ('menos', 'esfuerzo'), ('...   \n",
       "3  [('estratégia', 'trading'), ('trading', 'chatg...   \n",
       "4  [('ai', 'machine'), ('machine', 'learning'), (...   \n",
       "\n",
       "                                            trigrams  \n",
       "0  [('amt2', 'extracting', 'emails'), ('extractin...  \n",
       "1  [('read', 'emails', 'python'), ('emails', 'pyt...  \n",
       "2  [('logra', 'menos', 'esfuerzo'), ('menos', 'es...  \n",
       "3  [('estratégia', 'trading', 'chatgpt'), ('tradi...  \n",
       "4  [('ai', 'machine', 'learning'), ('machine', 'l...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['amt2', 'extracting', 'emails', 'gmail', 'inbox', 'using', 'python'],\n",
       " ['read', 'emails', 'python', 'gmail', 'api', 'part', '1'],\n",
       " ['logra', 'menos', 'esfuerzo', 'esencialismo', 'greg', 'mckeown']]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_keywords = df['keywords'].to_list()\n",
    "df_all_keywords = [eval(item) for item in df_all_keywords]\n",
    "df_all_keywords[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embeddings\n",
    "\n",
    "- Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec\n",
    "### Word embeddings\n",
    " Word2Vec is a shallow neural network that learns to predict the probability of a word given its context (CBOW) or the context given a word (skip-gram).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.00511473  0.00782683  0.01041726  0.01057132 -0.00815265 -0.02039944\n",
      "  0.00836665  0.02793838 -0.00979028 -0.00846289  0.00278681 -0.01590751\n",
      " -0.00597193  0.00892309 -0.00048311 -0.00715872  0.00601779 -0.01282972\n",
      " -0.00756324 -0.02491398  0.0134604   0.00778747  0.01528325 -0.00563193\n",
      "  0.00278978 -0.00245629 -0.00630619  0.00066459 -0.01581367 -0.00651979\n",
      "  0.00114517  0.00045304  0.0107509  -0.01517092 -0.00395195  0.00538746\n",
      "  0.00980562 -0.0114839  -0.004585   -0.01789439 -0.01141596 -0.00123141\n",
      " -0.01202405 -0.00482905  0.00636645 -0.00385481 -0.01421676  0.01042087\n",
      "  0.00634265  0.0192273  -0.00518767  0.00013857 -0.00906032 -0.00156027\n",
      "  0.00233014  0.00101459  0.01014011 -0.00811525 -0.01495156  0.01408792\n",
      "  0.00019018  0.00445199 -0.00571124 -0.00567787 -0.00986879  0.00929032\n",
      "  0.00116037  0.01144193 -0.01008357  0.01236423  0.0037243   0.01618353\n",
      "  0.00900159 -0.01366363  0.01255707  0.00138204  0.0088453  -0.00336442\n",
      " -0.01208779 -0.00874171 -0.00478684  0.00232736  0.00144729  0.01914699\n",
      " -0.00680048 -0.00052855  0.00415084  0.00623241  0.00530874  0.01195677\n",
      "  0.01085199  0.00329412  0.00601795  0.00087848  0.02473818  0.01301591\n",
      " -0.0042909  -0.01432658  0.00106467  0.00731332]\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "sentences = df_all_keywords\n",
    "model = Word2Vec(sentences, min_count=1, vector_size=100, workers=4, sg=0)\n",
    "print(model.wv['python'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doc2Vec\n",
    "### Paragraph Vector\n",
    "- Doc2Vec learns vector representations of documents by combining the word vectors with a document-level vector.\n",
    "- The Doc2Vec architecture is similar to Word2Vec in that it consists of two architectures: Distributed Memory (DM) and Distributed Bag of Words (DBOW)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.4139877e-03 -6.1110700e-03 -9.9155419e-03  8.5891020e-03\n",
      "  3.6325967e-03  7.2583160e-04 -1.0333649e-02 -5.0694784e-03\n",
      " -1.0059312e-02  1.7683017e-03  2.7178917e-03  4.5237099e-03\n",
      " -4.3862527e-03 -3.2503910e-03 -3.3440832e-03 -8.8531049e-03\n",
      "  2.2432373e-03  9.4026932e-03 -9.7185150e-03 -3.7962885e-03\n",
      " -3.6952880e-03  2.6198702e-03 -5.3488431e-03  2.4366097e-03\n",
      "  5.8872434e-03 -8.2072653e-03 -8.3953403e-03 -1.0134435e-02\n",
      "  4.9977317e-03 -9.0100253e-03  5.9141763e-03  6.7420155e-03\n",
      " -6.8102251e-03 -4.4218870e-03 -1.3849449e-03  1.5864470e-03\n",
      " -1.6065371e-03 -8.6576724e-03 -3.7907127e-03  1.7870113e-03\n",
      " -2.4654448e-03 -7.1741305e-03  4.3514082e-03 -9.0532433e-03\n",
      "  2.5728685e-03 -4.8580561e-03  7.0596969e-04 -2.1948903e-03\n",
      "  5.4416400e-03 -8.0194585e-03 -2.1066519e-03 -9.6736898e-05\n",
      " -6.1471276e-03 -6.6350130e-03 -1.7675686e-03  8.6590350e-03\n",
      " -1.6409742e-03  3.4970753e-03 -5.8272369e-03  8.7508243e-03\n",
      "  2.7194584e-03  9.2350235e-03  4.0652170e-03 -4.7569438e-03\n",
      "  2.1126568e-03 -4.6872259e-03  5.9493245e-03  1.7672065e-03\n",
      " -2.3995561e-03 -5.7527032e-03 -7.9941098e-03 -6.9401367e-04\n",
      " -8.8311825e-03 -9.0657873e-03 -8.0368361e-03  2.2585844e-03\n",
      " -6.8354472e-03 -7.8171156e-03  2.2170469e-03  2.1390971e-03\n",
      "  7.9490719e-03  4.6224417e-03 -9.3037290e-03 -9.6136770e-05\n",
      "  7.6654959e-03  2.9902647e-03  2.5722904e-03 -5.0084381e-03\n",
      "  6.5932698e-03  1.9428869e-03 -7.6393965e-03  7.4150288e-03\n",
      " -1.0096794e-02 -8.1699295e-03 -4.7927010e-03  1.0026427e-02\n",
      "  3.1518314e-03 -2.1184362e-03  9.1221705e-03  2.7930881e-03]\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "\n",
    "documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(sentences)]\n",
    "model = Doc2Vec(documents, min_count=1, vector_size=100, workers=4, dm=0)\n",
    "print(model.dv[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Toc2Vec\n",
    "### Paragraph Vector\n",
    "- Top2Vec is an unsupervised machine-learning model designed for topic modelling and document clustering. The model is based on a combination of document embeddings and clustering techniques, which enables it to identify topics and clusters in large text datasets without the need for explicit topic labelling.\n",
    "1. Document Embedding\n",
    "2. Low Dimensional Document Embedding\n",
    "3. Identifying Dense Areas of Documents\n",
    "4. Finding the Closest Word Vectors to the Topic Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Usuario\\Documents\\Jhonier\\proyectos_personales\\ScrapyTube\\venv\\lib\\site-packages\\umap\\distances.py:1071: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "c:\\Users\\Usuario\\Documents\\Jhonier\\proyectos_personales\\ScrapyTube\\venv\\lib\\site-packages\\umap\\distances.py:1086: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "c:\\Users\\Usuario\\Documents\\Jhonier\\proyectos_personales\\ScrapyTube\\venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2023-08-29 17:33:08,198 - top2vec - INFO - Pre-processing documents for training\n",
      "2023-08-29 17:33:15,543 - top2vec - INFO - Creating joint document/word embedding\n",
      "2023-08-29 17:36:04,139 - top2vec - INFO - Creating lower dimension embedding of documents\n",
      "2023-08-29 17:36:29,197 - top2vec - INFO - Finding dense areas of documents\n",
      "2023-08-29 17:36:31,878 - top2vec - INFO - Finding topics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from top2vec import Top2Vec\n",
    "# from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "# newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))\n",
    "model = Top2Vec(documents=docs, speed=\"learn\", workers=8)\n",
    "\n",
    "model.get_num_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BERTopic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bertopic import BERTopic\n",
    "from sentence_transformers import SentenceTransformer, util\n",
    "from umap import UMAP\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_model = CountVectorizer(stop_words=\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 7.81 s\n",
      "Wall time: 14.3 s\n"
     ]
    }
   ],
   "source": [
    "model_embedding = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "corpus_embeddings = model_embedding.encode(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BERTopic(\n",
    "    n_gram_range=(1, 2, 3),\n",
    "    vectorizer_model=vectorizer_model,\n",
    "    nr_topics='auto',\n",
    "    min_topic_size=10,\n",
    "    calculate_probabilities=True).fit(docs, corpus_embeddings )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics, probabilities = model.transform(docs, corpus_embeddings )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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       "    Topic  Count\n",
       "0      -1   1117\n",
       "1       0    814\n",
       "7       1    196\n",
       "12      2    114\n",
       "10      3     95\n",
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       "29      7     38\n",
       "5       8     36\n",
       "22      9     33\n",
       "17     10     32\n",
       "11     11     31\n",
       "23     12     30\n",
       "9      13     30\n",
       "14     14     29\n",
       "18     15     25\n",
       "33     16     24\n",
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       "15     20     21\n",
       "24     21     19\n",
       "4      22     18\n",
       "32     23     17\n",
       "6      24     17\n",
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       "13     29     13\n",
       "30     30     13\n",
       "35     31     13\n",
       "31     32     12\n",
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   "source": [
    "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(corpus_embeddings)\n",
    "model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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