PyCaret 2 NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 NLP Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 31-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>country</th>\n",
       "      <th>en</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>nonpayment</th>\n",
       "      <th>sector</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>\"Banco Esperanza\" is a group of 10 women looki...</td>\n",
       "      <td>F</td>\n",
       "      <td>1225</td>\n",
       "      <td>partner</td>\n",
       "      <td>Retail</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>\"Caminemos Hacia Adelante\" or \"Walking Forward...</td>\n",
       "      <td>F</td>\n",
       "      <td>1975</td>\n",
       "      <td>lender</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>\"Creciendo Por La Union\" is a group of 10 peop...</td>\n",
       "      <td>F</td>\n",
       "      <td>2175</td>\n",
       "      <td>partner</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>\"Cristo Vive\" (\"Christ lives\" is a group of 10...</td>\n",
       "      <td>F</td>\n",
       "      <td>1425</td>\n",
       "      <td>partner</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>\"Cristo Vive\" is a large group of 35 people, 2...</td>\n",
       "      <td>F</td>\n",
       "      <td>4025</td>\n",
       "      <td>partner</td>\n",
       "      <td>Food</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country                                                 en  \\\n",
       "0  Dominican Republic  \"Banco Esperanza\" is a group of 10 women looki...   \n",
       "1  Dominican Republic  \"Caminemos Hacia Adelante\" or \"Walking Forward...   \n",
       "2  Dominican Republic  \"Creciendo Por La Union\" is a group of 10 peop...   \n",
       "3  Dominican Republic  \"Cristo Vive\" (\"Christ lives\" is a group of 10...   \n",
       "4  Dominican Republic  \"Cristo Vive\" is a large group of 35 people, 2...   \n",
       "\n",
       "  gender  loan_amount nonpayment    sector  status  \n",
       "0      F         1225    partner    Retail       0  \n",
       "1      F         1975     lender  Clothing       0  \n",
       "2      F         2175    partner  Clothing       0  \n",
       "3      F         1425    partner  Clothing       0  \n",
       "4      F         4025    partner      Food       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('kiva')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row1_col0\" class=\"data row1 col0\" >Documents</td>\n",
       "                        <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row1_col1\" class=\"data row1 col1\" >6818</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row2_col0\" class=\"data row2 col0\" >Vocab Size</td>\n",
       "                        <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row2_col1\" class=\"data row2 col1\" >10671</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row3_col0\" class=\"data row3 col0\" >Custom Stopwords</td>\n",
       "                        <td id=\"T_7444a5f4_d32c_11ea_b9c8_5076af549583row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19abbcba888>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.nlp import *\n",
    "nlp1 = setup(data, target = 'en', session_id=123, log_experiment=True, log_plots = True, experiment_name='kiva1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Name</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lda</th>\n",
       "      <td>Latent Dirichlet Allocation</td>\n",
       "      <td>gensim/models/ldamodel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lsi</th>\n",
       "      <td>Latent Semantic Indexing</td>\n",
       "      <td>gensim/models/lsimodel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdp</th>\n",
       "      <td>Hierarchical Dirichlet Process</td>\n",
       "      <td>gensim/models/hdpmodel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rp</th>\n",
       "      <td>Random Projections</td>\n",
       "      <td>gensim/models/rpmodel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmf</th>\n",
       "      <td>Non-Negative Matrix Factorization</td>\n",
       "      <td>sklearn.decomposition.NMF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Name                  Reference\n",
       "ID                                                               \n",
       "lda        Latent Dirichlet Allocation     gensim/models/ldamodel\n",
       "lsi           Latent Semantic Indexing     gensim/models/lsimodel\n",
       "hdp     Hierarchical Dirichlet Process     gensim/models/hdpmodel\n",
       "rp                  Random Projections      gensim/models/rpmodel\n",
       "nmf  Non-Negative Matrix Factorization  sklearn.decomposition.NMF"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = create_model('lda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf = create_model('nmf', num_topics = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Assign Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>en</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>nonpayment</th>\n",
       "      <th>sector</th>\n",
       "      <th>status</th>\n",
       "      <th>Topic_0</th>\n",
       "      <th>Topic_1</th>\n",
       "      <th>Topic_2</th>\n",
       "      <th>Topic_3</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Perc_Dominant_Topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>group woman look receive small loan take small...</td>\n",
       "      <td>F</td>\n",
       "      <td>1225</td>\n",
       "      <td>partner</td>\n",
       "      <td>Retail</td>\n",
       "      <td>0</td>\n",
       "      <td>0.443424</td>\n",
       "      <td>0.170630</td>\n",
       "      <td>0.001865</td>\n",
       "      <td>0.384082</td>\n",
       "      <td>Topic 0</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>walk forward group entrepreneur seek second lo...</td>\n",
       "      <td>F</td>\n",
       "      <td>1975</td>\n",
       "      <td>lender</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "      <td>0.335674</td>\n",
       "      <td>0.416064</td>\n",
       "      <td>0.001612</td>\n",
       "      <td>0.246650</td>\n",
       "      <td>Topic 1</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>group people hope start business group look re...</td>\n",
       "      <td>F</td>\n",
       "      <td>2175</td>\n",
       "      <td>partner</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "      <td>0.568597</td>\n",
       "      <td>0.153035</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>0.275999</td>\n",
       "      <td>Topic 0</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>live group woman look receive first loan young...</td>\n",
       "      <td>F</td>\n",
       "      <td>1425</td>\n",
       "      <td>partner</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>0</td>\n",
       "      <td>0.285612</td>\n",
       "      <td>0.225983</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>0.486547</td>\n",
       "      <td>Topic 3</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>vive large group people hope take loan many se...</td>\n",
       "      <td>F</td>\n",
       "      <td>4025</td>\n",
       "      <td>partner</td>\n",
       "      <td>Food</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383666</td>\n",
       "      <td>0.345023</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>0.269472</td>\n",
       "      <td>Topic 0</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country                                                 en  \\\n",
       "0  Dominican Republic  group woman look receive small loan take small...   \n",
       "1  Dominican Republic  walk forward group entrepreneur seek second lo...   \n",
       "2  Dominican Republic  group people hope start business group look re...   \n",
       "3  Dominican Republic  live group woman look receive first loan young...   \n",
       "4  Dominican Republic  vive large group people hope take loan many se...   \n",
       "\n",
       "  gender  loan_amount nonpayment    sector  status   Topic_0   Topic_1  \\\n",
       "0      F         1225    partner    Retail       0  0.443424  0.170630   \n",
       "1      F         1975     lender  Clothing       0  0.335674  0.416064   \n",
       "2      F         2175    partner  Clothing       0  0.568597  0.153035   \n",
       "3      F         1425    partner  Clothing       0  0.285612  0.225983   \n",
       "4      F         4025    partner      Food       0  0.383666  0.345023   \n",
       "\n",
       "    Topic_2   Topic_3 Dominant_Topic  Perc_Dominant_Topic  \n",
       "0  0.001865  0.384082        Topic 0                 0.44  \n",
       "1  0.001612  0.246650        Topic 1                 0.42  \n",
       "2  0.002369  0.275999        Topic 0                 0.57  \n",
       "3  0.001858  0.486547        Topic 3                 0.49  \n",
       "4  0.001839  0.269472        Topic 0                 0.38  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_results = assign_model(lda)\n",
    "lda_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e9a8f7b4-c9dd-4298-999a-610d952a55e0');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(lda, plot = 'tsne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cebb33643dd34925a3f6cf851b9f347c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Frequency Plot', 'freque…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
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