{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## <b>KGLiDS APIs</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to Stardog: https://cloud.stardog.com/\n"
     ]
    }
   ],
   "source": [
    "from api.api import KGLiDS\n",
    "import pandas as pd\n",
    "kglids = KGLiDS(endpoint='localhost', port=5821)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information captured: \n"
     ]
    },
    {
     "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>Datasets</th>\n",
       "      <th>Pipelines</th>\n",
       "      <th>Tables</th>\n",
       "      <th>Columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>969</td>\n",
       "      <td>418</td>\n",
       "      <td>9502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Datasets  Pipelines  Tables  Columns\n",
       "0       101        969     418     9502"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.show_graph_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<b> Running Adhoc-queries on our graph!</b><br><br> \n",
    "<b>1.  Customized queries </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kaggle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source\n",
       "0  kaggle"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\" \n",
    "SELECT ?source {\n",
    "?source_id rdf:type    kglids:Source    ;\n",
    "           schema:name ?source          . } \"\"\"\n",
    "kglids.query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<b>Not comfortable with Sparql? No worries!</b><br> \n",
    "<b> You can use pre-defined KGLiDS APIs to discover useful insights that remain locked inside your Data</b>\n",
    "\n",
    "Note: \n",
    "- All query result(s) are returned in a form of <code>pandas.DataFrame</code> or plots in some cases \n",
    "- KGLiDS is a transparent system, you can see the queries used by the system by setting <code>show_query = True</code> in case of every API you wish to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>2.  Retrieving dataset(s) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Dataset</th>\n",
       "      <th>Number_of_tables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COVID-19 Dataset</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COVID-19 Healthy Diet Dataset</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COVID-19 Indonesia Dataset</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COVID-19 World Vaccination Progress</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>unitednations.international-greenhouse-gas-emi...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>upadorprofzs.testes</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>vitaliymalcev.russian-passenger-air-service-20...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Dataset  Number_of_tables\n",
       "0                  COVID-19 Corona Virus India Dataset                 8\n",
       "1                                     COVID-19 Dataset                 6\n",
       "2                        COVID-19 Healthy Diet Dataset                 5\n",
       "3                           COVID-19 Indonesia Dataset                 1\n",
       "4                  COVID-19 World Vaccination Progress                 2\n",
       "..                                                 ...               ...\n",
       "96            uciml.red-wine-quality-cortez-et-al-2009                22\n",
       "97   unitednations.international-greenhouse-gas-emi...                 3\n",
       "98                                 upadorprofzs.testes                 8\n",
       "99   vitaliymalcev.russian-passenger-air-service-20...                14\n",
       "100               ylchang.coffee-shop-sample-data-1113                10\n",
       "\n",
       "[101 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_datasets_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>3.  Retrieving table(s) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing all available table(s): \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Table</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>state_level_daily.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/shubham/datasets/data_lake/COVID-19 Coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>state_level_latest.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/shubham/datasets/data_lake/COVID-19 Coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>patients_data.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/shubham/datasets/data_lake/COVID-19 Coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tests_day_wise.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/shubham/datasets/data_lake/COVID-19 Coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nation_level_daily.csv</td>\n",
       "      <td>COVID-19 Corona Virus India Dataset</td>\n",
       "      <td>/data/shubham/datasets/data_lake/COVID-19 Coro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>pastry inventory.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/shubham/datasets/data_lake/ylchang.coffe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>201904 sales reciepts.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/shubham/datasets/data_lake/ylchang.coffe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>sales_outlet.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/shubham/datasets/data_lake/ylchang.coffe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>product.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/shubham/datasets/data_lake/ylchang.coffe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Dates.csv</td>\n",
       "      <td>ylchang.coffee-shop-sample-data-1113</td>\n",
       "      <td>/data/shubham/datasets/data_lake/ylchang.coffe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Table                               Dataset  \\\n",
       "0        state_level_daily.csv   COVID-19 Corona Virus India Dataset   \n",
       "1       state_level_latest.csv   COVID-19 Corona Virus India Dataset   \n",
       "2            patients_data.csv   COVID-19 Corona Virus India Dataset   \n",
       "3           tests_day_wise.csv   COVID-19 Corona Virus India Dataset   \n",
       "4       nation_level_daily.csv   COVID-19 Corona Virus India Dataset   \n",
       "..                         ...                                   ...   \n",
       "413       pastry inventory.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "414  201904 sales reciepts.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "415           sales_outlet.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "416                product.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "417                  Dates.csv  ylchang.coffee-shop-sample-data-1113   \n",
       "\n",
       "                                         Path_to_table  \n",
       "0    /data/shubham/datasets/data_lake/COVID-19 Coro...  \n",
       "1    /data/shubham/datasets/data_lake/COVID-19 Coro...  \n",
       "2    /data/shubham/datasets/data_lake/COVID-19 Coro...  \n",
       "3    /data/shubham/datasets/data_lake/COVID-19 Coro...  \n",
       "4    /data/shubham/datasets/data_lake/COVID-19 Coro...  \n",
       "..                                                 ...  \n",
       "413  /data/shubham/datasets/data_lake/ylchang.coffe...  \n",
       "414  /data/shubham/datasets/data_lake/ylchang.coffe...  \n",
       "415  /data/shubham/datasets/data_lake/ylchang.coffe...  \n",
       "416  /data/shubham/datasets/data_lake/ylchang.coffe...  \n",
       "417  /data/shubham/datasets/data_lake/ylchang.coffe...  \n",
       "\n",
       "[418 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_tables_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing table(s) for 'UK COVID-19 Data' dataset: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Table</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UK_Devolved_Nations_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK_Local_Authority_UTLA_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>England_Regions_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UK_National_Total_COVID_Dataset.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NEW_Official_Population_Data_ONS_mid-2019.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Populations_for_UK_and_Devolved_Nations.csv</td>\n",
       "      <td>UK COVID-19 Data</td>\n",
       "      <td>/data/shubham/datasets/data_lake/UK COVID-19 D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Table           Dataset  \\\n",
       "0          UK_Devolved_Nations_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "1      UK_Local_Authority_UTLA_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "2              England_Regions_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "3            UK_National_Total_COVID_Dataset.csv  UK COVID-19 Data   \n",
       "4  NEW_Official_Population_Data_ONS_mid-2019.csv  UK COVID-19 Data   \n",
       "5    Populations_for_UK_and_Devolved_Nations.csv  UK COVID-19 Data   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/shubham/datasets/data_lake/UK COVID-19 D...  \n",
       "1  /data/shubham/datasets/data_lake/UK COVID-19 D...  \n",
       "2  /data/shubham/datasets/data_lake/UK COVID-19 D...  \n",
       "3  /data/shubham/datasets/data_lake/UK COVID-19 D...  \n",
       "4  /data/shubham/datasets/data_lake/UK COVID-19 D...  \n",
       "5  /data/shubham/datasets/data_lake/UK COVID-19 D...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_tables_info(dataset='UK COVID-19 Data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>Discovering enrichment possibilities using KGLiDS's recommendations </b><br><br>\n",
    "<b>4. Recommending tables by column name search</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing recommendations as per the following conditions:\n",
      "Condition =  [['player', 'club']]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Table</th>\n",
       "      <th>Number_of_columns</th>\n",
       "      <th>Number_of_rows</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_21.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18944</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18483</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>18278</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_19.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>18085</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_18.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>17954</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA19_official_data.csv</td>\n",
       "      <td>64</td>\n",
       "      <td>17943</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA18_official_data.csv</td>\n",
       "      <td>64</td>\n",
       "      <td>17927</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_19.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17770</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_17.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>17597</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_18.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17592</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA17_official_data.csv</td>\n",
       "      <td>63</td>\n",
       "      <td>17560</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA21_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>17108</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA20_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>17104</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_17.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>17009</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA22_official_data.csv</td>\n",
       "      <td>65</td>\n",
       "      <td>16710</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_15.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>16155</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FIFA 21 complete player dataset</td>\n",
       "      <td>players_16.csv</td>\n",
       "      <td>106</td>\n",
       "      <td>15623</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 21 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_15.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>15465</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_16.csv</td>\n",
       "      <td>104</td>\n",
       "      <td>14881</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_draft.csv</td>\n",
       "      <td>11</td>\n",
       "      <td>9003</td>\n",
       "      <td>/data/shubham/datasets/data_lake/open-source-s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_awards_players.csv</td>\n",
       "      <td>6</td>\n",
       "      <td>1719</td>\n",
       "      <td>/data/shubham/datasets/data_lake/open-source-s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>open-source-sports.mens-professional-basketball</td>\n",
       "      <td>basketball_player_allstar.csv</td>\n",
       "      <td>23</td>\n",
       "      <td>1609</td>\n",
       "      <td>/data/shubham/datasets/data_lake/open-source-s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Dataset  \\\n",
       "0                   FIFA 21 complete player dataset   \n",
       "1                   FIFA 21 complete player dataset   \n",
       "2                   FIFA 20 complete player dataset   \n",
       "3                   FIFA 21 complete player dataset   \n",
       "4                   FIFA 21 complete player dataset   \n",
       "5                           FIFA22 OFFICIAL DATASET   \n",
       "6                           FIFA22 OFFICIAL DATASET   \n",
       "7                   FIFA 20 complete player dataset   \n",
       "8                   FIFA 21 complete player dataset   \n",
       "9                   FIFA 20 complete player dataset   \n",
       "10                          FIFA22 OFFICIAL DATASET   \n",
       "11                          FIFA22 OFFICIAL DATASET   \n",
       "12                          FIFA22 OFFICIAL DATASET   \n",
       "13                  FIFA 20 complete player dataset   \n",
       "14                          FIFA22 OFFICIAL DATASET   \n",
       "15                  FIFA 21 complete player dataset   \n",
       "16                  FIFA 21 complete player dataset   \n",
       "17                  FIFA 20 complete player dataset   \n",
       "18                  FIFA 20 complete player dataset   \n",
       "19  open-source-sports.mens-professional-basketball   \n",
       "20  open-source-sports.mens-professional-basketball   \n",
       "21  open-source-sports.mens-professional-basketball   \n",
       "\n",
       "                            Table  Number_of_columns  Number_of_rows  \\\n",
       "0                  players_21.csv                106           18944   \n",
       "1                  players_20.csv                106           18483   \n",
       "2                  players_20.csv                104           18278   \n",
       "3                  players_19.csv                106           18085   \n",
       "4                  players_18.csv                106           17954   \n",
       "5        FIFA19_official_data.csv                 64           17943   \n",
       "6        FIFA18_official_data.csv                 64           17927   \n",
       "7                  players_19.csv                104           17770   \n",
       "8                  players_17.csv                106           17597   \n",
       "9                  players_18.csv                104           17592   \n",
       "10       FIFA17_official_data.csv                 63           17560   \n",
       "11       FIFA21_official_data.csv                 65           17108   \n",
       "12       FIFA20_official_data.csv                 65           17104   \n",
       "13                 players_17.csv                104           17009   \n",
       "14       FIFA22_official_data.csv                 65           16710   \n",
       "15                 players_15.csv                106           16155   \n",
       "16                 players_16.csv                106           15623   \n",
       "17                 players_15.csv                104           15465   \n",
       "18                 players_16.csv                104           14881   \n",
       "19           basketball_draft.csv                 11            9003   \n",
       "20  basketball_awards_players.csv                  6            1719   \n",
       "21  basketball_player_allstar.csv                 23            1609   \n",
       "\n",
       "                                        Path_to_table  \n",
       "0   /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "1   /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "2   /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "3   /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "4   /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "5   /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "6   /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "7   /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "8   /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "9   /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "10  /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "11  /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "12  /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "13  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "14  /data/shubham/datasets/data_lake/FIFA22 OFFICI...  \n",
       "15  /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "16  /data/shubham/datasets/data_lake/FIFA 21 compl...  \n",
       "17  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "18  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "19  /data/shubham/datasets/data_lake/open-source-s...  \n",
       "20  /data/shubham/datasets/data_lake/open-source-s...  \n",
       "21  /data/shubham/datasets/data_lake/open-source-s...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = kglids.search_tables_on(conditions=[['player', 'club']])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    " <b>5. Recommending unionable tables</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing the top-5 unionable table recommendations:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Recommended_table</th>\n",
       "      <th>Score</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>1.00</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_19.csv</td>\n",
       "      <td>0.85</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_18.csv</td>\n",
       "      <td>0.85</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_17.csv</td>\n",
       "      <td>0.85</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_15.csv</td>\n",
       "      <td>0.84</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Dataset Recommended_table  Score  \\\n",
       "0  FIFA 20 complete player dataset    players_20.csv   1.00   \n",
       "1  FIFA 20 complete player dataset    players_19.csv   0.85   \n",
       "2  FIFA 20 complete player dataset    players_18.csv   0.85   \n",
       "3  FIFA 20 complete player dataset    players_17.csv   0.85   \n",
       "4  FIFA 20 complete player dataset    players_15.csv   0.84   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "1  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "2  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "3  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "4  /data/shubham/datasets/data_lake/FIFA 20 compl...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_union = kglids.recommend_k_unionable_tables(df.iloc[0], k = 5)\n",
    "recommendations_union"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>6. Recommending joinable tables</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing the top-2 joinable table recommendations:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Recommended_table</th>\n",
       "      <th>Score</th>\n",
       "      <th>Path_to_table</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIFA 20 complete player dataset</td>\n",
       "      <td>players_20.csv</td>\n",
       "      <td>1.0</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA 20 compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIFA22 OFFICIAL DATASET</td>\n",
       "      <td>FIFA22_official_data.csv</td>\n",
       "      <td>0.5</td>\n",
       "      <td>/data/shubham/datasets/data_lake/FIFA22 OFFICI...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Dataset         Recommended_table  Score  \\\n",
       "0  FIFA 20 complete player dataset            players_20.csv    1.0   \n",
       "1          FIFA22 OFFICIAL DATASET  FIFA22_official_data.csv    0.5   \n",
       "\n",
       "                                       Path_to_table  \n",
       "0  /data/shubham/datasets/data_lake/FIFA 20 compl...  \n",
       "1  /data/shubham/datasets/data_lake/FIFA22 OFFICI...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_join = kglids.recommend_k_joinable_tables(df.iloc[0], k = 2)\n",
    "recommendations_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>7. Visualizing the path between two joinable tables</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_path_between_tables(df.iloc[0], recommendations_join.iloc[1], hops=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# df1 = pd.read_csv(df.iloc[0].Path_to_table)\n",
    "# df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# df2 = pd.read_csv(recommendations_join.iloc[1].Path_to_table)\n",
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# result_join = df1.merge(df2, left_on='sofifa_id', right_on='ID')\n",
    "# result_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>8. Search pipelines</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <td>0.895508</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.816314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Neural nets VS KNN</td>\n",
       "      <td>idoyo92.epl-stats-20192020</td>\n",
       "      <td>Karthiks061992</td>\n",
       "      <td>2020-05-14 21:32:25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514592</td>\n",
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       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>Project-EDA EPL2020</td>\n",
       "      <td>idoyo92.epl-stats-20192020</td>\n",
       "      <td>Ishdeep Chadha</td>\n",
       "      <td>2020-05-24 07:48:36</td>\n",
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       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>Hurricanes</td>\n",
       "      <td>noaa.hurricane-database</td>\n",
       "      <td>Eric Song</td>\n",
       "      <td>2020-04-11 19:47:02</td>\n",
       "      <td>0</td>\n",
       "      <td>0.504264</td>\n",
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      "text/plain": [
       "                                    Pipeline  \\\n",
       "0                            How Models Work   \n",
       "1          Your First Machine Learning Model   \n",
       "2                           Model Validation   \n",
       "3               Underfitting and Overfitting   \n",
       "4    Machine Learning Tutorial for Beginners   \n",
       "..                                       ...   \n",
       "964       Predicting EPL table for 2019-2020   \n",
       "965                       notebookc8a19c85bb   \n",
       "966                       Neural nets VS KNN   \n",
       "967                      Project-EDA EPL2020   \n",
       "968                               Hurricanes   \n",
       "\n",
       "                                               Dataset            Author  \\\n",
       "0        iabhishekofficial.mobile-price-classification              DanB   \n",
       "1        iabhishekofficial.mobile-price-classification              DanB   \n",
       "2        iabhishekofficial.mobile-price-classification              DanB   \n",
       "3        iabhishekofficial.mobile-price-classification              DanB   \n",
       "4    uciml.biomechanical-features-of-orthopedic-pat...             DATAI   \n",
       "..                                                 ...               ...   \n",
       "964                         idoyo92.epl-stats-20192020          anupriyo   \n",
       "965                    fedesoriano.hepatitis-c-dataset  Moschopoulos Apo   \n",
       "966                         idoyo92.epl-stats-20192020    Karthiks061992   \n",
       "967                         idoyo92.epl-stats-20192020    Ishdeep Chadha   \n",
       "968                            noaa.hurricane-database         Eric Song   \n",
       "\n",
       "              Written_on  Number_of_votes     Score  \n",
       "0    2022-01-05 13:51:41             5315  0.834509  \n",
       "1    2021-11-09 00:01:04             2909  0.634321  \n",
       "2    2021-11-09 00:00:51             2659  0.765677  \n",
       "3    2021-11-09 00:01:33             2502  0.644229  \n",
       "4    2018-07-24 14:43:45             2129  0.895508  \n",
       "..                   ...              ...       ...  \n",
       "964  2020-07-28 10:16:17                0  0.987387  \n",
       "965  2021-07-15 14:32:57                0  0.816314  \n",
       "966  2020-05-14 21:32:25                0  0.514592  \n",
       "967  2020-05-24 07:48:36                0  0.600902  \n",
       "968  2020-04-11 19:47:02                0  0.504264  \n",
       "\n",
       "[969 rows x 6 columns]"
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     },
     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "kglids.get_pipelines_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <td>1710</td>\n",
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       "      <th>5</th>\n",
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       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2021-11-09 00:01:19</td>\n",
       "      <td>1551</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Explore Your Data</td>\n",
       "      <td>dansbecker.home-data-for-ml-course</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-01-23 01:07:33</td>\n",
       "      <td>1328</td>\n",
       "      <td>0.695214</td>\n",
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       "      <th>7</th>\n",
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       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-08-24 02:41:50</td>\n",
       "      <td>95</td>\n",
       "      <td>0.616984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>How Models Work Daily</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-08-24 02:42:02</td>\n",
       "      <td>71</td>\n",
       "      <td>0.947072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Model Validation Daily</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-08-24 02:41:47</td>\n",
       "      <td>61</td>\n",
       "      <td>0.918014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Underfitting and Overfitting Daily</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-08-24 02:41:52</td>\n",
       "      <td>37</td>\n",
       "      <td>0.779103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Exercise: Explore Your Data</td>\n",
       "      <td>dansbecker.home-data-for-ml-course</td>\n",
       "      <td>DanB</td>\n",
       "      <td>2019-01-23 01:07:42</td>\n",
       "      <td>14</td>\n",
       "      <td>0.864991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                              Pipeline  \\\n",
       "0                      How Models Work   \n",
       "1    Your First Machine Learning Model   \n",
       "2                     Model Validation   \n",
       "3         Underfitting and Overfitting   \n",
       "4                       Random Forests   \n",
       "5               Basic Data Exploration   \n",
       "6                    Explore Your Data   \n",
       "7         Basic Data Exploration Daily   \n",
       "8                How Models Work Daily   \n",
       "9               Model Validation Daily   \n",
       "10  Underfitting and Overfitting Daily   \n",
       "11         Exercise: Explore Your Data   \n",
       "\n",
       "                                          Dataset Author           Written_on  \\\n",
       "0   iabhishekofficial.mobile-price-classification   DanB  2022-01-05 13:51:41   \n",
       "1   iabhishekofficial.mobile-price-classification   DanB  2021-11-09 00:01:04   \n",
       "2   iabhishekofficial.mobile-price-classification   DanB  2021-11-09 00:00:51   \n",
       "3   iabhishekofficial.mobile-price-classification   DanB  2021-11-09 00:01:33   \n",
       "4   iabhishekofficial.mobile-price-classification   DanB  2021-11-09 00:03:02   \n",
       "5   iabhishekofficial.mobile-price-classification   DanB  2021-11-09 00:01:19   \n",
       "6              dansbecker.home-data-for-ml-course   DanB  2019-01-23 01:07:33   \n",
       "7   iabhishekofficial.mobile-price-classification   DanB  2019-08-24 02:41:50   \n",
       "8   iabhishekofficial.mobile-price-classification   DanB  2019-08-24 02:42:02   \n",
       "9   iabhishekofficial.mobile-price-classification   DanB  2019-08-24 02:41:47   \n",
       "10  iabhishekofficial.mobile-price-classification   DanB  2019-08-24 02:41:52   \n",
       "11             dansbecker.home-data-for-ml-course   DanB  2019-01-23 01:07:42   \n",
       "\n",
       "    Number_of_votes     Score  \n",
       "0              5315  0.834509  \n",
       "1              2909  0.634321  \n",
       "2              2659  0.765677  \n",
       "3              2502  0.644229  \n",
       "4              1710  0.618779  \n",
       "5              1551  0.535365  \n",
       "6              1328  0.695214  \n",
       "7                95  0.616984  \n",
       "8                71  0.947072  \n",
       "9                61  0.918014  \n",
       "10               37  0.779103  \n",
       "11               14  0.864991  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_pipelines_info(author='DanB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>9. Most recent pipeline</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Written_on</th>\n",
       "      <th>Number_of_votes</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Twitch Streamers Analytics using Python</td>\n",
       "      <td>aayushmishra1512.twitchdata</td>\n",
       "      <td>Aleix Castellvi</td>\n",
       "      <td>2022-02-16 14:05:16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.986247</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                  Pipeline                      Dataset  \\\n",
       "0  Twitch Streamers Analytics using Python  aayushmishra1512.twitchdata   \n",
       "\n",
       "            Author           Written_on  Number_of_votes     Score  \n",
       "0  Aleix Castellvi  2022-02-16 14:05:16                2  0.986247  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_most_recent_pipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OP...</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>caleb reigada</td>\n",
       "      <td>2022-02-14 12:51:35</td>\n",
       "      <td>13</td>\n",
       "      <td>0.581313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Pipeline  \\\n",
       "0  Liver Disease Analysis: EDA➡️SMOTE➡️OP...   \n",
       "\n",
       "                           Dataset         Author           Written_on  \\\n",
       "0  fedesoriano.hepatitis-c-dataset  caleb reigada  2022-02-14 12:51:35   \n",
       "\n",
       "   Number_of_votes     Score  \n",
       "0               13  0.581313  "
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_most_recent_pipeline(dataset='fedesoriano.hepatitis-c-dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>10.<i> Top-k</i> scoring pipelines</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hepatitis c</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>RATNADEEP GAWADE</td>\n",
       "      <td>2021-07-13 19:50:43</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comprehensive EDA + Predicting Subscriber Count</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "      <td>Sayar Banerjee</td>\n",
       "      <td>2020-05-23 15:23:34</td>\n",
       "      <td>22</td>\n",
       "      <td>0.999729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>notebooke72847ec86</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "      <td>semih çınar</td>\n",
       "      <td>2021-07-19 22:23:00</td>\n",
       "      <td>11</td>\n",
       "      <td>0.999695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Topic Modeling with LDA</td>\n",
       "      <td>kulgen.elon-musks-tweets</td>\n",
       "      <td>mohamed elbeih</td>\n",
       "      <td>2021-02-21 16:22:46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India Import and Export</td>\n",
       "      <td>lakshyaag.india-trade-data</td>\n",
       "      <td>Mysterious9912</td>\n",
       "      <td>2019-10-13 13:08:48</td>\n",
       "      <td>3</td>\n",
       "      <td>0.998829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                                          Pipeline  \\\n",
       "0                                      hepatitis c   \n",
       "1  Comprehensive EDA + Predicting Subscriber Count   \n",
       "2                               notebooke72847ec86   \n",
       "3                          Topic Modeling with LDA   \n",
       "4                         India Import and Export    \n",
       "\n",
       "                           Dataset            Author           Written_on  \\\n",
       "0  fedesoriano.hepatitis-c-dataset  RATNADEEP GAWADE  2021-07-13 19:50:43   \n",
       "1          andrewmvd.udemy-courses    Sayar Banerjee  2020-05-23 15:23:34   \n",
       "2          andrewmvd.udemy-courses     semih çınar  2021-07-19 22:23:00   \n",
       "3         kulgen.elon-musks-tweets    mohamed elbeih  2021-02-21 16:22:46   \n",
       "4       lakshyaag.india-trade-data    Mysterious9912  2019-10-13 13:08:48   \n",
       "\n",
       "   Number_of_votes     Score  \n",
       "0                1  0.999835  \n",
       "1               22  0.999729  \n",
       "2               11  0.999695  \n",
       "3                0  0.999219  \n",
       "4                3  0.998829  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_k_scoring_pipelines_for_dataset(k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th>Pipeline</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>hepatitis c</td>\n",
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       "      <td>RATNADEEP GAWADE</td>\n",
       "      <td>2021-07-13 19:50:43</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Starter: Hepatitis C Dataset c674c472-8</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Kaggle Kerneler</td>\n",
       "      <td>2020-12-21 17:02:06</td>\n",
       "      <td>2</td>\n",
       "      <td>0.985005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Modelo Preditivo Doenças Hepáticas</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Ives Santos</td>\n",
       "      <td>2021-09-05 21:00:40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hepatitis_c_pred</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Sanket Sharma</td>\n",
       "      <td>2021-07-23 09:02:09</td>\n",
       "      <td>2</td>\n",
       "      <td>0.954447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liver Disorders Storytelling</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Mohammed Omda</td>\n",
       "      <td>2022-01-19 21:26:46</td>\n",
       "      <td>24</td>\n",
       "      <td>0.928420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Pipeline                          Dataset  \\\n",
       "0                              hepatitis c  fedesoriano.hepatitis-c-dataset   \n",
       "1  Starter: Hepatitis C Dataset c674c472-8  fedesoriano.hepatitis-c-dataset   \n",
       "2    Modelo Preditivo Doenças Hepáticas   fedesoriano.hepatitis-c-dataset   \n",
       "3                         Hepatitis_c_pred  fedesoriano.hepatitis-c-dataset   \n",
       "4             Liver Disorders Storytelling  fedesoriano.hepatitis-c-dataset   \n",
       "\n",
       "             Author           Written_on  Number_of_votes     Score  \n",
       "0  RATNADEEP GAWADE  2021-07-13 19:50:43                1  0.999835  \n",
       "1   Kaggle Kerneler  2020-12-21 17:02:06                2  0.985005  \n",
       "2       Ives Santos  2021-09-05 21:00:40                1  0.968082  \n",
       "3     Sanket Sharma  2021-07-23 09:02:09                2  0.954447  \n",
       "4     Mohammed Omda  2022-01-19 21:26:46               24  0.928420  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_k_scoring_pipelines_for_dataset(dataset='fedesoriano.hepatitis-c-dataset', k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>11. Search classifiers used in a dataset</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
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       "      <td>0.968082</td>\n",
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       "      <th>1</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
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       "      <td>0.968082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Modelo Preditivo Doenças Hepáticas</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.968082</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
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       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.968082</td>\n",
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       "      <th>4</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
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       "      <td>0.954447</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
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       "      <td>0.954447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis_c_pred</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.954447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis_c_pred</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.954447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>EDA and hepatitis C prediction using RFs</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.848565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>notebookc8a19c85bb</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.816314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction+EDA+Validation</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.788348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction+EDA+Validation</td>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.788348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction+EDA+Validation</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.788348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction+EDA+Validation</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.788348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis Prediction+EDA+Validation</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.788348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.774037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.774037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.774037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis C Predictions 🩺</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.774037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>0.671879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.671879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.671879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.671879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis dataset study</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.611311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis dataset study</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.611311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis dataset study</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.611311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis dataset study</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.611311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OP...</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.581313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OP...</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.581313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OP...</td>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>0.581313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>RidgeClassifier</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.558427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Gradient Boosting Prediction - 97.74% Accuracy</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.501611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Gradient Boosting Prediction - 97.74% Accuracy</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.501611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Gradient Boosting Prediction - 97.74% Accuracy</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.501611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Dataset  \\\n",
       "0   fedesoriano.hepatitis-c-dataset   \n",
       "1   fedesoriano.hepatitis-c-dataset   \n",
       "2   fedesoriano.hepatitis-c-dataset   \n",
       "3   fedesoriano.hepatitis-c-dataset   \n",
       "4   fedesoriano.hepatitis-c-dataset   \n",
       "5   fedesoriano.hepatitis-c-dataset   \n",
       "6   fedesoriano.hepatitis-c-dataset   \n",
       "7   fedesoriano.hepatitis-c-dataset   \n",
       "8   fedesoriano.hepatitis-c-dataset   \n",
       "9   fedesoriano.hepatitis-c-dataset   \n",
       "10  fedesoriano.hepatitis-c-dataset   \n",
       "11  fedesoriano.hepatitis-c-dataset   \n",
       "12  fedesoriano.hepatitis-c-dataset   \n",
       "13  fedesoriano.hepatitis-c-dataset   \n",
       "14  fedesoriano.hepatitis-c-dataset   \n",
       "15  fedesoriano.hepatitis-c-dataset   \n",
       "16  fedesoriano.hepatitis-c-dataset   \n",
       "17  fedesoriano.hepatitis-c-dataset   \n",
       "18  fedesoriano.hepatitis-c-dataset   \n",
       "19  fedesoriano.hepatitis-c-dataset   \n",
       "20  fedesoriano.hepatitis-c-dataset   \n",
       "21  fedesoriano.hepatitis-c-dataset   \n",
       "22  fedesoriano.hepatitis-c-dataset   \n",
       "23  fedesoriano.hepatitis-c-dataset   \n",
       "24  fedesoriano.hepatitis-c-dataset   \n",
       "25  fedesoriano.hepatitis-c-dataset   \n",
       "26  fedesoriano.hepatitis-c-dataset   \n",
       "27  fedesoriano.hepatitis-c-dataset   \n",
       "28  fedesoriano.hepatitis-c-dataset   \n",
       "29  fedesoriano.hepatitis-c-dataset   \n",
       "30  fedesoriano.hepatitis-c-dataset   \n",
       "31  fedesoriano.hepatitis-c-dataset   \n",
       "32  fedesoriano.hepatitis-c-dataset   \n",
       "33  fedesoriano.hepatitis-c-dataset   \n",
       "34  fedesoriano.hepatitis-c-dataset   \n",
       "35  fedesoriano.hepatitis-c-dataset   \n",
       "36  fedesoriano.hepatitis-c-dataset   \n",
       "37  fedesoriano.hepatitis-c-dataset   \n",
       "38  fedesoriano.hepatitis-c-dataset   \n",
       "\n",
       "                                             Pipeline  \\\n",
       "0               Modelo Preditivo Doenças Hepáticas    \n",
       "1               Modelo Preditivo Doenças Hepáticas    \n",
       "2               Modelo Preditivo Doenças Hepáticas    \n",
       "3               Modelo Preditivo Doenças Hepáticas    \n",
       "4                                    Hepatitis_c_pred   \n",
       "5                                    Hepatitis_c_pred   \n",
       "6                                    Hepatitis_c_pred   \n",
       "7                                    Hepatitis_c_pred   \n",
       "8            EDA and hepatitis C prediction using RFs   \n",
       "9                                  notebookc8a19c85bb   \n",
       "10                Hepatitis Prediction+EDA+Validation   \n",
       "11                Hepatitis Prediction+EDA+Validation   \n",
       "12                Hepatitis Prediction+EDA+Validation   \n",
       "13                Hepatitis Prediction+EDA+Validation   \n",
       "14                Hepatitis Prediction+EDA+Validation   \n",
       "15                       Hepatitis C Predictions 🩺   \n",
       "16                       Hepatitis C Predictions 🩺   \n",
       "17                       Hepatitis C Predictions 🩺   \n",
       "18                       Hepatitis C Predictions 🩺   \n",
       "19             Easy Hepatitis C Prediction ACC=98%!!!   \n",
       "20             Easy Hepatitis C Prediction ACC=98%!!!   \n",
       "21             Easy Hepatitis C Prediction ACC=98%!!!   \n",
       "22             Easy Hepatitis C Prediction ACC=98%!!!   \n",
       "23                            Hepatitis dataset study   \n",
       "24                            Hepatitis dataset study   \n",
       "25                            Hepatitis dataset study   \n",
       "26                            Hepatitis dataset study   \n",
       "27  Liver Disease Analysis: EDA➡️SMOTE➡️OP...   \n",
       "28  Liver Disease Analysis: EDA➡️SMOTE➡️OP...   \n",
       "29  Liver Disease Analysis: EDA➡️SMOTE➡️OP...   \n",
       "30                             Hepatitis-C Prediction   \n",
       "31                             Hepatitis-C Prediction   \n",
       "32                             Hepatitis-C Prediction   \n",
       "33                             Hepatitis-C Prediction   \n",
       "34                             Hepatitis-C Prediction   \n",
       "35                             Hepatitis-C Prediction   \n",
       "36     Gradient Boosting Prediction - 97.74% Accuracy   \n",
       "37     Gradient Boosting Prediction - 97.74% Accuracy   \n",
       "38     Gradient Boosting Prediction - 97.74% Accuracy   \n",
       "\n",
       "                    Classifier     Score  \n",
       "0       DecisionTreeClassifier  0.968082  \n",
       "1       RandomForestClassifier  0.968082  \n",
       "2           LogisticRegression  0.968082  \n",
       "3   GradientBoostingClassifier  0.968082  \n",
       "4           LogisticRegression  0.954447  \n",
       "5                          SVC  0.954447  \n",
       "6       RandomForestClassifier  0.954447  \n",
       "7       DecisionTreeClassifier  0.954447  \n",
       "8       RandomForestClassifier  0.848565  \n",
       "9           LogisticRegression  0.816314  \n",
       "10          LogisticRegression  0.788348  \n",
       "11        ExtraTreesClassifier  0.788348  \n",
       "12      RandomForestClassifier  0.788348  \n",
       "13          AdaBoostClassifier  0.788348  \n",
       "14      DecisionTreeClassifier  0.788348  \n",
       "15          LogisticRegression  0.774037  \n",
       "16      DecisionTreeClassifier  0.774037  \n",
       "17      RandomForestClassifier  0.774037  \n",
       "18  GradientBoostingClassifier  0.774037  \n",
       "19               MLPClassifier  0.671879  \n",
       "20      DecisionTreeClassifier  0.671879  \n",
       "21               XGBClassifier  0.671879  \n",
       "22      RandomForestClassifier  0.671879  \n",
       "23      DecisionTreeClassifier  0.611311  \n",
       "24          LogisticRegression  0.611311  \n",
       "25      RandomForestClassifier  0.611311  \n",
       "26                         SVC  0.611311  \n",
       "27          LogisticRegression  0.581313  \n",
       "28                         SVC  0.581313  \n",
       "29            VotingClassifier  0.581313  \n",
       "30      RandomForestClassifier  0.558427  \n",
       "31             RidgeClassifier  0.558427  \n",
       "32                         SVC  0.558427  \n",
       "33        ExtraTreesClassifier  0.558427  \n",
       "34               MLPClassifier  0.558427  \n",
       "35  GradientBoostingClassifier  0.558427  \n",
       "36      RandomForestClassifier  0.501611  \n",
       "37          LogisticRegression  0.501611  \n",
       "38  GradientBoostingClassifier  0.501611  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_info = kglids.search_classifier(dataset='fedesoriano.hepatitis-c-dataset')\n",
    "classifier_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>12. Fetch hyper-parameters </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>RandomForestClassifier_hyperparameter</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>class_weight</td>\n",
       "      <td>balanced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n_jobs</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>random_state</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n_estimators</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RandomForestClassifier_hyperparameter     Value\n",
       "0                          class_weight  balanced\n",
       "1                                n_jobs      None\n",
       "2                          random_state       100\n",
       "3                          n_estimators       100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_hyperparameters(classifier_info.iloc[8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>13. Visualize library utilization</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.get_top_k_library_used(k=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.get_top_k_library_used(dataset='fedesoriano.hepatitis-c-dataset', k=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>14. Library discovery</b><br> \n",
    "tasks supported: classification, regression, visualization and clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 107/107 [00:00<00:00, 125150.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 4 libraries were found for classification: \n",
      "sklearn, yellowbrick, transformers, xgboost\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.RandomForestClassifier</td>\n",
       "      <td>Final table predictions using XGb and VAR fore...</td>\n",
       "      <td>idoyo92.epl-stats-20192020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.classification_report</td>\n",
       "      <td>Glass - Classification</td>\n",
       "      <td>uciml.glass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.classification_report</td>\n",
       "      <td>Predict the Winning Horse(100% on small test d...</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.RandomForestClassifier</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.GradientBoostingClassifier</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>transformers</td>\n",
       "      <td>TFDistilBertForSequenceClassification</td>\n",
       "      <td>Cell Phones Reviews Sentiment Analysis - Body</td>\n",
       "      <td>grikomsn.amazon-cell-phones-reviews</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>Tutorial: Machine Learning Interpretability</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>yellowbrick</td>\n",
       "      <td>classifier.DiscriminationThreshold</td>\n",
       "      <td>Catboost classifier for astronomical objects</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>yellowbrick</td>\n",
       "      <td>classifier.ROCAUC</td>\n",
       "      <td>Catboost classifier for astronomical objects</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>yellowbrick</td>\n",
       "      <td>classifier.classification_report</td>\n",
       "      <td>Catboost classifier for astronomical objects</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Library                                 Module  \\\n",
       "0         sklearn        ensemble.RandomForestClassifier   \n",
       "1         sklearn          metrics.classification_report   \n",
       "2         sklearn          metrics.classification_report   \n",
       "3         sklearn        ensemble.RandomForestClassifier   \n",
       "4         sklearn    ensemble.GradientBoostingClassifier   \n",
       "..            ...                                    ...   \n",
       "102  transformers  TFDistilBertForSequenceClassification   \n",
       "103       xgboost                          XGBClassifier   \n",
       "104   yellowbrick     classifier.DiscriminationThreshold   \n",
       "105   yellowbrick                      classifier.ROCAUC   \n",
       "106   yellowbrick       classifier.classification_report   \n",
       "\n",
       "                                              Pipeline  \\\n",
       "0    Final table predictions using XGb and VAR fore...   \n",
       "1                               Glass - Classification   \n",
       "2    Predict the Winning Horse(100% on small test d...   \n",
       "3                               Hepatitis-C Prediction   \n",
       "4                               Hepatitis-C Prediction   \n",
       "..                                                 ...   \n",
       "102      Cell Phones Reviews Sentiment Analysis - Body   \n",
       "103        Tutorial: Machine Learning Interpretability   \n",
       "104       Catboost classifier for astronomical objects   \n",
       "105       Catboost classifier for astronomical objects   \n",
       "106       Catboost classifier for astronomical objects   \n",
       "\n",
       "                                        Dataset  \n",
       "0                    idoyo92.epl-stats-20192020  \n",
       "1                                   uciml.glass  \n",
       "2                               gdaley.hkracing  \n",
       "3               fedesoriano.hepatitis-c-dataset  \n",
       "4               fedesoriano.hepatitis-c-dataset  \n",
       "..                                          ...  \n",
       "102         grikomsn.amazon-cell-phones-reviews  \n",
       "103    uciml.red-wine-quality-cortez-et-al-2009  \n",
       "104  muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "105  muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "106  muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "\n",
       "[107 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18/18 [00:00<00:00, 58254.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 2 libraries were found for regression: \n",
      "sklearn, lightgbm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lightgbm</td>\n",
       "      <td>LGBMRegressor</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LogisticRegression</td>\n",
       "      <td>Best of 14 Alg. to classify Mushrooms (100% acc.)</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>tree.ExtraTreeRegressor</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LinearRegression</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.SGDRegressor</td>\n",
       "      <td>prediction energetique 3 - modelisation simple</td>\n",
       "      <td>city-of-seattle.sea-building-energy-benchmarking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LinearRegression</td>\n",
       "      <td>Marcos - Aprendizado de Máquinas - Treinament...</td>\n",
       "      <td>agajorte.zoo-animals-extended-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LogisticRegression</td>\n",
       "      <td>Marcos - Aprendizado de Máquinas - Treinament...</td>\n",
       "      <td>agajorte.zoo-animals-extended-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.GradientBoostingRegressor</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LinearRegression</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.RandomForestRegressor</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.AdaBoostRegressor</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LogisticRegression</td>\n",
       "      <td>Hepatitis-C Prediction</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LogisticRegression</td>\n",
       "      <td>sky-survey-dr16 classifier</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.GradientBoostingRegressor</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LinearRegression</td>\n",
       "      <td>Energy Price Prediction [ML]</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.LogisticRegression</td>\n",
       "      <td>Mobile Price Prediction 🏷️📱</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>ensemble.RandomForestRegressor</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>linear_model.SGDRegressor</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Library                              Module  \\\n",
       "0   lightgbm                       LGBMRegressor   \n",
       "1    sklearn     linear_model.LogisticRegression   \n",
       "2    sklearn             tree.ExtraTreeRegressor   \n",
       "3    sklearn       linear_model.LinearRegression   \n",
       "4    sklearn           linear_model.SGDRegressor   \n",
       "5    sklearn       linear_model.LinearRegression   \n",
       "6    sklearn     linear_model.LogisticRegression   \n",
       "7    sklearn  ensemble.GradientBoostingRegressor   \n",
       "8    sklearn       linear_model.LinearRegression   \n",
       "9    sklearn      ensemble.RandomForestRegressor   \n",
       "10   sklearn          ensemble.AdaBoostRegressor   \n",
       "11   sklearn     linear_model.LogisticRegression   \n",
       "12   sklearn     linear_model.LogisticRegression   \n",
       "13   sklearn  ensemble.GradientBoostingRegressor   \n",
       "14   sklearn       linear_model.LinearRegression   \n",
       "15   sklearn     linear_model.LogisticRegression   \n",
       "16   sklearn      ensemble.RandomForestRegressor   \n",
       "17   sklearn           linear_model.SGDRegressor   \n",
       "\n",
       "                                             Pipeline  \\\n",
       "0                Electric Price Prediction - LightGBM   \n",
       "1   Best of 14 Alg. to classify Mushrooms (100% acc.)   \n",
       "2          U-Courses Exploration + Reviews Prediction   \n",
       "3          U-Courses Exploration + Reviews Prediction   \n",
       "4      prediction energetique 3 - modelisation simple   \n",
       "5   Marcos - Aprendizado de Máquinas - Treinament...   \n",
       "6   Marcos - Aprendizado de Máquinas - Treinament...   \n",
       "7   Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "8   Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "9   Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "10  Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "11                             Hepatitis-C Prediction   \n",
       "12                         sky-survey-dr16 classifier   \n",
       "13               Electric Price Prediction - LightGBM   \n",
       "14                       Energy Price Prediction [ML]   \n",
       "15                Mobile Price Prediction 🏷️📱   \n",
       "16         U-Courses Exploration + Reviews Prediction   \n",
       "17         U-Courses Exploration + Reviews Prediction   \n",
       "\n",
       "                                              Dataset  \n",
       "0   nicholasjhana.energy-consumption-generation-pr...  \n",
       "1                       uciml.mushroom-classification  \n",
       "2                             andrewmvd.udemy-courses  \n",
       "3                             andrewmvd.udemy-courses  \n",
       "4    city-of-seattle.sea-building-energy-benchmarking  \n",
       "5               agajorte.zoo-animals-extended-dataset  \n",
       "6               agajorte.zoo-animals-extended-dataset  \n",
       "7            uciml.red-wine-quality-cortez-et-al-2009  \n",
       "8            uciml.red-wine-quality-cortez-et-al-2009  \n",
       "9            uciml.red-wine-quality-cortez-et-al-2009  \n",
       "10           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "11                    fedesoriano.hepatitis-c-dataset  \n",
       "12         muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "13  nicholasjhana.energy-consumption-generation-pr...  \n",
       "14  nicholasjhana.energy-consumption-generation-pr...  \n",
       "15      iabhishekofficial.mobile-price-classification  \n",
       "16                            andrewmvd.udemy-courses  \n",
       "17                            andrewmvd.udemy-courses  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 112/112 [00:00<00:00, 10737.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing the top 5 libraries for visualization: \n",
      "plotly, sklearn, bokeh, fbprophet, matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bokeh</td>\n",
       "      <td>plotting.figure</td>\n",
       "      <td>Exploring EPL</td>\n",
       "      <td>thefc17.epl-results-19932018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bokeh</td>\n",
       "      <td>plotting.show</td>\n",
       "      <td>Space Missions: EDA Plotly</td>\n",
       "      <td>agirlcoding.all-space-missions-from-1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bokeh</td>\n",
       "      <td>plotting.show</td>\n",
       "      <td>📊 Data Visualization Analysis</td>\n",
       "      <td>iabhishekofficial.mobile-price-classification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bokeh</td>\n",
       "      <td>plotting.show</td>\n",
       "      <td>What's this? Chai and DataScience?</td>\n",
       "      <td>rohanrao.chai-time-data-science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bokeh</td>\n",
       "      <td>plotting.show</td>\n",
       "      <td>Exploring EPL</td>\n",
       "      <td>thefc17.epl-results-19932018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.plot_precision_recall_curve</td>\n",
       "      <td>Nueral Network Regression VS Sklearn Algorithms</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.plot_confusion_matrix</td>\n",
       "      <td>Log_KNN</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.plot_roc_curve</td>\n",
       "      <td>Energy Price Prediction [ML]</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.plot_confusion_matrix</td>\n",
       "      <td>ML models Feature Importance</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>metrics.plot_precision_recall_curve</td>\n",
       "      <td>Gender Classification-II</td>\n",
       "      <td>hb20007.gender-classification</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Library                               Module  \\\n",
       "0     bokeh                      plotting.figure   \n",
       "1     bokeh                        plotting.show   \n",
       "2     bokeh                        plotting.show   \n",
       "3     bokeh                        plotting.show   \n",
       "4     bokeh                        plotting.show   \n",
       "..      ...                                  ...   \n",
       "90  sklearn  metrics.plot_precision_recall_curve   \n",
       "91  sklearn        metrics.plot_confusion_matrix   \n",
       "92  sklearn               metrics.plot_roc_curve   \n",
       "93  sklearn        metrics.plot_confusion_matrix   \n",
       "94  sklearn  metrics.plot_precision_recall_curve   \n",
       "\n",
       "                                           Pipeline  \\\n",
       "0                                     Exploring EPL   \n",
       "1                        Space Missions: EDA Plotly   \n",
       "2                  📊 Data Visualization Analysis   \n",
       "3                What's this? Chai and DataScience?   \n",
       "4                                     Exploring EPL   \n",
       "..                                              ...   \n",
       "90  Nueral Network Regression VS Sklearn Algorithms   \n",
       "91                                          Log_KNN   \n",
       "92                     Energy Price Prediction [ML]   \n",
       "93                     ML models Feature Importance   \n",
       "94                         Gender Classification-II   \n",
       "\n",
       "                                              Dataset  \n",
       "0                        thefc17.epl-results-19932018  \n",
       "1            agirlcoding.all-space-missions-from-1957  \n",
       "2       iabhishekofficial.mobile-price-classification  \n",
       "3                     rohanrao.chai-time-data-science  \n",
       "4                        thefc17.epl-results-19932018  \n",
       "..                                                ...  \n",
       "90  alexteboul.heart-disease-health-indicators-dat...  \n",
       "91  alexteboul.heart-disease-health-indicators-dat...  \n",
       "92  nicholasjhana.energy-consumption-generation-pr...  \n",
       "93                 becksddf.churn-in-telecoms-dataset  \n",
       "94                      hb20007.gender-classification  \n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'visualization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:00<00:00, 42153.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 2 libraries were found for clustering: \n",
      "scipy, yellowbrick\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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>Library</th>\n",
       "      <th>Module</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.hierarchy.linkage</td>\n",
       "      <td>Machine Learning Tutorial for Beginners</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.hierarchy.dendrogram</td>\n",
       "      <td>Machine Learning Tutorial for Beginners</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.hierarchy.dendrogram</td>\n",
       "      <td>Unsupervised Learning KMeans and Hierarchical</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.hierarchy.linkage</td>\n",
       "      <td>EDA and Machine Learning</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.vq.vq</td>\n",
       "      <td>Super Hero Recommender based on Powers</td>\n",
       "      <td>claudiodavi.superhero-set</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>scipy</td>\n",
       "      <td>cluster.hierarchy.linkage</td>\n",
       "      <td>Clustering Methods on Stars Dataset</td>\n",
       "      <td>mariopasquato.star-cluster-simulations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>yellowbrick</td>\n",
       "      <td>cluster.SilhouetteVisualizer</td>\n",
       "      <td>Catboost classifier for astronomical objects</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>yellowbrick</td>\n",
       "      <td>cluster.InterclusterDistance</td>\n",
       "      <td>Catboost classifier for astronomical objects</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Library                        Module  \\\n",
       "0        scipy     cluster.hierarchy.linkage   \n",
       "1        scipy  cluster.hierarchy.dendrogram   \n",
       "2        scipy  cluster.hierarchy.dendrogram   \n",
       "3        scipy     cluster.hierarchy.linkage   \n",
       "4        scipy                 cluster.vq.vq   \n",
       "5        scipy     cluster.hierarchy.linkage   \n",
       "6  yellowbrick  cluster.SilhouetteVisualizer   \n",
       "7  yellowbrick  cluster.InterclusterDistance   \n",
       "\n",
       "                                         Pipeline  \\\n",
       "0         Machine Learning Tutorial for Beginners   \n",
       "1         Machine Learning Tutorial for Beginners   \n",
       "2  Unsupervised Learning KMeans and Hierarchical    \n",
       "3                       EDA and Machine Learning    \n",
       "4          Super Hero Recommender based on Powers   \n",
       "5             Clustering Methods on Stars Dataset   \n",
       "6    Catboost classifier for astronomical objects   \n",
       "7    Catboost classifier for astronomical objects   \n",
       "\n",
       "                                             Dataset  \n",
       "0  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "1  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "2  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "3  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "4                          claudiodavi.superhero-set  \n",
       "5             mariopasquato.star-cluster-simulations  \n",
       "6         muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "7         muhakabartay.sloan-digital-sky-survey-dr16  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_top_used_libraries(k = 5, task = 'clustering')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>15. Pipeline Discovery</b> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Score</th>\n",
       "      <th>Number_of_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ML models Feature Importance</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "      <td>Mellissa Valle</td>\n",
       "      <td>0.815066</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Easy Hepatitis C Prediction ACC=98%!!!</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "      <td>Fran Valuch</td>\n",
       "      <td>0.671879</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Pipeline                             Dataset  \\\n",
       "0            ML models Feature Importance  becksddf.churn-in-telecoms-dataset   \n",
       "1  Easy Hepatitis C Prediction ACC=98%!!!     fedesoriano.hepatitis-c-dataset   \n",
       "\n",
       "           Author     Score  Number_of_votes  \n",
       "0  Mellissa Valle  0.815066                1  \n",
       "1     Fran Valuch  0.671879                7  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipelines_info = kglids.get_pipelines_calling_libraries(['pandas.read_csv', \n",
    "                                                         'xgboost.XGBClassifier', \n",
    "                                                         'sklearn.metrics.f1_score'])\n",
    "pipelines_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>16. Search deep learning pipelines</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Author</th>\n",
       "      <th>Written_on</th>\n",
       "      <th>Score</th>\n",
       "      <th>Number_of_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K-Nearest Neighbors (KNN) Classification (75.2...</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>Furkan Gulsen</td>\n",
       "      <td>2020-05-15 00:20:26</td>\n",
       "      <td>0.973140</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-Supervised Beer Similarity API</td>\n",
       "      <td>nickhould.craft-cans</td>\n",
       "      <td>Dionisio Chiuratto Agourakis</td>\n",
       "      <td>2021-01-07 17:08:25</td>\n",
       "      <td>0.941796</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cell Phones Reviews Sentiment Analysis - Title</td>\n",
       "      <td>grikomsn.amazon-cell-phones-reviews</td>\n",
       "      <td>Gaurav Dutta</td>\n",
       "      <td>2021-09-03 07:50:20</td>\n",
       "      <td>0.921559</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "      <td>Dimitrios Roussis</td>\n",
       "      <td>2021-03-25 16:02:26</td>\n",
       "      <td>0.886413</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VGG16-Honey Bee Health Classification</td>\n",
       "      <td>jenny18.honey-bee-annotated-images</td>\n",
       "      <td>xan</td>\n",
       "      <td>2018-12-03 13:18:17</td>\n",
       "      <td>0.865201</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Biomechanical features - 20 popular models</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "      <td>Vitalii Mokin</td>\n",
       "      <td>2020-10-11 19:52:55</td>\n",
       "      <td>0.841577</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cell Phones Reviews Sentiment Analysis - Body</td>\n",
       "      <td>grikomsn.amazon-cell-phones-reviews</td>\n",
       "      <td>Gaurav Dutta</td>\n",
       "      <td>2021-09-03 12:31:00</td>\n",
       "      <td>0.715935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Twitter Sentiment Analysis (MultinomialNB+LSTM)</td>\n",
       "      <td>arkhoshghalb.twitter-sentiment-analysis-hatred...</td>\n",
       "      <td>Dev Chauhan</td>\n",
       "      <td>2021-07-07 23:15:26</td>\n",
       "      <td>0.677666</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A Story about Unsupervised Learning ✨</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "      <td>🌵</td>\n",
       "      <td>2021-11-18 21:31:03</td>\n",
       "      <td>0.675113</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SDSS Classification with Deep Neural Networks</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "      <td>Kevin Trinh</td>\n",
       "      <td>2020-05-22 23:15:09</td>\n",
       "      <td>0.661107</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Univariate Time Series Forecasting With Keras</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "      <td>Nicholas Jhana</td>\n",
       "      <td>2020-03-26 18:27:49</td>\n",
       "      <td>0.622930</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Classification_of_temp_reading_using_LR_and</td>\n",
       "      <td>atulanandjha.temperature-readings-iot-devices</td>\n",
       "      <td>Bhushan Rajput</td>\n",
       "      <td>2019-12-30 19:37:51</td>\n",
       "      <td>0.622849</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Who said this line[EDA/Classification/Keras/ANN]</td>\n",
       "      <td>thec03u5.seinfeld-chronicles</td>\n",
       "      <td>Siddharth Yadav</td>\n",
       "      <td>2018-06-09 05:28:57</td>\n",
       "      <td>0.613280</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Logistic Regression &amp; KNN Online Ads Purchases</td>\n",
       "      <td>rohanrao.chai-time-data-science</td>\n",
       "      <td>Umerkk12</td>\n",
       "      <td>2020-07-24 22:38:07</td>\n",
       "      <td>0.594176</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Trends in 2020 with Advice from Top Kagglers</td>\n",
       "      <td>rohanrao.chai-time-data-science</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>2020-12-20 21:06:57</td>\n",
       "      <td>0.571417</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mushroom Classification using Genetic Algorithm</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "      <td>Alin Cijov</td>\n",
       "      <td>2021-03-07 15:51:55</td>\n",
       "      <td>0.511241</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Pipeline  \\\n",
       "0   K-Nearest Neighbors (KNN) Classification (75.2...   \n",
       "1                 Self-Supervised Beer Similarity API   \n",
       "2      Cell Phones Reviews Sentiment Analysis - Title   \n",
       "3     Electricity price forecasting with DNNs (+ EDA)   \n",
       "4               VGG16-Honey Bee Health Classification   \n",
       "5          Biomechanical features - 20 popular models   \n",
       "6       Cell Phones Reviews Sentiment Analysis - Body   \n",
       "7     Twitter Sentiment Analysis (MultinomialNB+LSTM)   \n",
       "8             A Story about Unsupervised Learning ✨   \n",
       "9       SDSS Classification with Deep Neural Networks   \n",
       "10      Univariate Time Series Forecasting With Keras   \n",
       "11        Classification_of_temp_reading_using_LR_and   \n",
       "12   Who said this line[EDA/Classification/Keras/ANN]   \n",
       "13     Logistic Regression & KNN Online Ads Purchases   \n",
       "14       Trends in 2020 with Advice from Top Kagglers   \n",
       "15    Mushroom Classification using Genetic Algorithm   \n",
       "\n",
       "                                              Dataset  \\\n",
       "0   uciml.biomechanical-features-of-orthopedic-pat...   \n",
       "1                                nickhould.craft-cans   \n",
       "2                 grikomsn.amazon-cell-phones-reviews   \n",
       "3   nicholasjhana.energy-consumption-generation-pr...   \n",
       "4                  jenny18.honey-bee-annotated-images   \n",
       "5   uciml.biomechanical-features-of-orthopedic-pat...   \n",
       "6                 grikomsn.amazon-cell-phones-reviews   \n",
       "7   arkhoshghalb.twitter-sentiment-analysis-hatred...   \n",
       "8            uciml.red-wine-quality-cortez-et-al-2009   \n",
       "9          muhakabartay.sloan-digital-sky-survey-dr16   \n",
       "10  nicholasjhana.energy-consumption-generation-pr...   \n",
       "11      atulanandjha.temperature-readings-iot-devices   \n",
       "12                       thec03u5.seinfeld-chronicles   \n",
       "13                    rohanrao.chai-time-data-science   \n",
       "14                    rohanrao.chai-time-data-science   \n",
       "15                      uciml.mushroom-classification   \n",
       "\n",
       "                          Author           Written_on     Score  \\\n",
       "0                  Furkan Gulsen  2020-05-15 00:20:26  0.973140   \n",
       "1   Dionisio Chiuratto Agourakis  2021-01-07 17:08:25  0.941796   \n",
       "2                   Gaurav Dutta  2021-09-03 07:50:20  0.921559   \n",
       "3              Dimitrios Roussis  2021-03-25 16:02:26  0.886413   \n",
       "4                            xan  2018-12-03 13:18:17  0.865201   \n",
       "5                  Vitalii Mokin  2020-10-11 19:52:55  0.841577   \n",
       "6                   Gaurav Dutta  2021-09-03 12:31:00  0.715935   \n",
       "7                    Dev Chauhan  2021-07-07 23:15:26  0.677666   \n",
       "8                           🌵  2021-11-18 21:31:03  0.675113   \n",
       "9                    Kevin Trinh  2020-05-22 23:15:09  0.661107   \n",
       "10                Nicholas Jhana  2020-03-26 18:27:49  0.622930   \n",
       "11                Bhushan Rajput  2019-12-30 19:37:51  0.622849   \n",
       "12               Siddharth Yadav  2018-06-09 05:28:57  0.613280   \n",
       "13                      Umerkk12  2020-07-24 22:38:07  0.594176   \n",
       "14                        Leonie  2020-12-20 21:06:57  0.571417   \n",
       "15                    Alin Cijov  2021-03-07 15:51:55  0.511241   \n",
       "\n",
       "    Number_of_votes  \n",
       "0                24  \n",
       "1                 3  \n",
       "2                 3  \n",
       "3               112  \n",
       "4                 2  \n",
       "5                58  \n",
       "6                 1  \n",
       "7                10  \n",
       "8                50  \n",
       "9                 3  \n",
       "10               58  \n",
       "11                5  \n",
       "12               75  \n",
       "13               12  \n",
       "14               77  \n",
       "15               68  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.get_pipelines_for_deep_learning()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<b>17. Search for possible transformations</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transformation</th>\n",
       "      <th>Pipeline</th>\n",
       "      <th>Table</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Predict the winner - Data Visualization and Model</td>\n",
       "      <td>runs.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Predict the winner - Data Visualization and Model</td>\n",
       "      <td>races.csv</td>\n",
       "      <td>gdaley.hkracing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>P2_Seattle - Nettoyage</td>\n",
       "      <td>Xs_no_es.csv</td>\n",
       "      <td>city-of-seattle.sea-building-energy-benchmarking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>P2_Seattle - Nettoyage</td>\n",
       "      <td>2016-building-energy-benchmarking.csv</td>\n",
       "      <td>city-of-seattle.sea-building-energy-benchmarking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>P2_Seattle - Nettoyage</td>\n",
       "      <td>Ys_no_es.csv</td>\n",
       "      <td>city-of-seattle.sea-building-energy-benchmarking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>P2_Seattle - Nettoyage</td>\n",
       "      <td>2015-building-energy-benchmarking.csv</td>\n",
       "      <td>city-of-seattle.sea-building-energy-benchmarking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Premier league fantasy point prediction</td>\n",
       "      <td>players_1920_fin.csv</td>\n",
       "      <td>idoyo92.epl-stats-20192020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>IoT Temperature Forecasting</td>\n",
       "      <td>IOT-temp.csv</td>\n",
       "      <td>atulanandjha.temperature-readings-iot-devices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>SDSS Classification with Random Forests (99.2%)</td>\n",
       "      <td>Skyserver_12_30_2019%204_49_58%20PM.csv</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sklearn.preprocessing.LabelBinarizer</td>\n",
       "      <td>Liver Disorders Storytelling</td>\n",
       "      <td>HepatitisCdata.csv</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Altı Özelliğe Göre Hastaların Sınıfland...</td>\n",
       "      <td>column_3C_weka.csv</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Altı Özelliğe Göre Hastaların Sınıfland...</td>\n",
       "      <td>column_2C_weka.csv</td>\n",
       "      <td>uciml.biomechanical-features-of-orthopedic-pat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>What kind of Bee am I?</td>\n",
       "      <td>bee_data.csv</td>\n",
       "      <td>jenny18.honey-bee-annotated-images</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>weather_features.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>energy_dataset.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>weather_features.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Electricity price forecasting with DNNs (+ EDA)</td>\n",
       "      <td>energy_dataset.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Demand forecasting RMSLE 49.24</td>\n",
       "      <td>submission.csv</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Demand forecasting RMSLE 49.24</td>\n",
       "      <td>meal_info.csv</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Demand forecasting RMSLE 49.24</td>\n",
       "      <td>test.csv</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Demand forecasting RMSLE 49.24</td>\n",
       "      <td>fulfilment_center_info.csv</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Demand forecasting RMSLE 49.24</td>\n",
       "      <td>train.csv</td>\n",
       "      <td>kannanaikkal.food-demand-forecasting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>winequality-red.csv</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>winequality-red.csv</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Intro to Parameter Tuning in Scikit [Acc :0.9175]</td>\n",
       "      <td>winequality-red.csv</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sklearn.preprocessing.OneHotEncoder</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>udemy_courses.csv</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>udemy_courses.csv</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>U-Courses Exploration + Reviews Prediction</td>\n",
       "      <td>udemy_courses.csv</td>\n",
       "      <td>andrewmvd.udemy-courses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sklearn.preprocessing.MaxAbsScaler</td>\n",
       "      <td>ML models Feature Importance</td>\n",
       "      <td>bigml_59c28831336c6604c800002a.csv</td>\n",
       "      <td>becksddf.churn-in-telecoms-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Kırmızı Şarap Kalite Tahmini(Sınıflandı...</td>\n",
       "      <td>winequality-red.csv</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Prediction of quality of Wine</td>\n",
       "      <td>winequality-red.csv</td>\n",
       "      <td>uciml.red-wine-quality-cortez-et-al-2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>DL_Learning</td>\n",
       "      <td>hotel.csv</td>\n",
       "      <td>piyushgoyal443.red-wine-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>DL_Learning</td>\n",
       "      <td>fuel.csv</td>\n",
       "      <td>piyushgoyal443.red-wine-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>SDSS Classification with Deep Neural Networks</td>\n",
       "      <td>Skyserver_12_30_2019%204_49_58%20PM.csv</td>\n",
       "      <td>muhakabartay.sloan-digital-sky-survey-dr16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>sklearn.preprocessing.OneHotEncoder</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>weather_features.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>sklearn.preprocessing.OneHotEncoder</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>energy_dataset.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>energy_dataset.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>sklearn.preprocessing.RobustScaler</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>weather_features.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>sklearn.preprocessing.RobustScaler</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>energy_dataset.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Electric Price Prediction - LightGBM</td>\n",
       "      <td>weather_features.csv</td>\n",
       "      <td>nicholasjhana.energy-consumption-generation-pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sklearn.preprocessing.OneHotEncoder</td>\n",
       "      <td>Who said this line[EDA/Classification/Keras/ANN]</td>\n",
       "      <td>scripts.csv</td>\n",
       "      <td>thec03u5.seinfeld-chronicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>PNAD: Income Prediction</td>\n",
       "      <td>pnad_2015_clean.csv</td>\n",
       "      <td>upadorprofzs.testes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>sklearn.preprocessing.StandardScaler</td>\n",
       "      <td>Liver Disease Analysis: EDA➡️SMOTE➡️OP...</td>\n",
       "      <td>HepatitisCdata.csv</td>\n",
       "      <td>fedesoriano.hepatitis-c-dataset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>sklearn.preprocessing.MinMaxScaler</td>\n",
       "      <td>Heart Disease - Binary Classification</td>\n",
       "      <td>heart_disease_health_indicators_BRFSS2015.csv</td>\n",
       "      <td>alexteboul.heart-disease-health-indicators-dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>sklearn.preprocessing.LabelEncoder</td>\n",
       "      <td>Mushroom Classification using Genetic Algorithm</td>\n",
       "      <td>mushrooms.csv</td>\n",
       "      <td>uciml.mushroom-classification</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Transformation  \\\n",
       "0   sklearn.preprocessing.StandardScaler   \n",
       "1   sklearn.preprocessing.StandardScaler   \n",
       "2   sklearn.preprocessing.StandardScaler   \n",
       "3   sklearn.preprocessing.StandardScaler   \n",
       "4   sklearn.preprocessing.StandardScaler   \n",
       "5   sklearn.preprocessing.StandardScaler   \n",
       "6     sklearn.preprocessing.LabelEncoder   \n",
       "7   sklearn.preprocessing.StandardScaler   \n",
       "8     sklearn.preprocessing.LabelEncoder   \n",
       "9   sklearn.preprocessing.LabelBinarizer   \n",
       "10  sklearn.preprocessing.StandardScaler   \n",
       "11  sklearn.preprocessing.StandardScaler   \n",
       "12    sklearn.preprocessing.LabelEncoder   \n",
       "13    sklearn.preprocessing.MinMaxScaler   \n",
       "14    sklearn.preprocessing.MinMaxScaler   \n",
       "15    sklearn.preprocessing.LabelEncoder   \n",
       "16    sklearn.preprocessing.LabelEncoder   \n",
       "17  sklearn.preprocessing.StandardScaler   \n",
       "18  sklearn.preprocessing.StandardScaler   \n",
       "19  sklearn.preprocessing.StandardScaler   \n",
       "20  sklearn.preprocessing.StandardScaler   \n",
       "21  sklearn.preprocessing.StandardScaler   \n",
       "22    sklearn.preprocessing.LabelEncoder   \n",
       "23    sklearn.preprocessing.MinMaxScaler   \n",
       "24  sklearn.preprocessing.StandardScaler   \n",
       "25   sklearn.preprocessing.OneHotEncoder   \n",
       "26    sklearn.preprocessing.MinMaxScaler   \n",
       "27    sklearn.preprocessing.LabelEncoder   \n",
       "28    sklearn.preprocessing.MaxAbsScaler   \n",
       "29  sklearn.preprocessing.StandardScaler   \n",
       "30    sklearn.preprocessing.LabelEncoder   \n",
       "31  sklearn.preprocessing.StandardScaler   \n",
       "32  sklearn.preprocessing.StandardScaler   \n",
       "33  sklearn.preprocessing.StandardScaler   \n",
       "34   sklearn.preprocessing.OneHotEncoder   \n",
       "35   sklearn.preprocessing.OneHotEncoder   \n",
       "36    sklearn.preprocessing.LabelEncoder   \n",
       "37    sklearn.preprocessing.RobustScaler   \n",
       "38    sklearn.preprocessing.RobustScaler   \n",
       "39    sklearn.preprocessing.LabelEncoder   \n",
       "40   sklearn.preprocessing.OneHotEncoder   \n",
       "41    sklearn.preprocessing.MinMaxScaler   \n",
       "42  sklearn.preprocessing.StandardScaler   \n",
       "43    sklearn.preprocessing.MinMaxScaler   \n",
       "44    sklearn.preprocessing.LabelEncoder   \n",
       "\n",
       "                                             Pipeline  \\\n",
       "0   Predict the winner - Data Visualization and Model   \n",
       "1   Predict the winner - Data Visualization and Model   \n",
       "2                              P2_Seattle - Nettoyage   \n",
       "3                              P2_Seattle - Nettoyage   \n",
       "4                              P2_Seattle - Nettoyage   \n",
       "5                              P2_Seattle - Nettoyage   \n",
       "6             Premier league fantasy point prediction   \n",
       "7                         IoT Temperature Forecasting   \n",
       "8     SDSS Classification with Random Forests (99.2%)   \n",
       "9                        Liver Disorders Storytelling   \n",
       "10  Altı Özelliğe Göre Hastaların Sınıfland...   \n",
       "11  Altı Özelliğe Göre Hastaların Sınıfland...   \n",
       "12                             What kind of Bee am I?   \n",
       "13    Electricity price forecasting with DNNs (+ EDA)   \n",
       "14    Electricity price forecasting with DNNs (+ EDA)   \n",
       "15    Electricity price forecasting with DNNs (+ EDA)   \n",
       "16    Electricity price forecasting with DNNs (+ EDA)   \n",
       "17                     Demand forecasting RMSLE 49.24   \n",
       "18                     Demand forecasting RMSLE 49.24   \n",
       "19                     Demand forecasting RMSLE 49.24   \n",
       "20                     Demand forecasting RMSLE 49.24   \n",
       "21                     Demand forecasting RMSLE 49.24   \n",
       "22  Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "23  Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "24  Intro to Parameter Tuning in Scikit [Acc :0.9175]   \n",
       "25         U-Courses Exploration + Reviews Prediction   \n",
       "26         U-Courses Exploration + Reviews Prediction   \n",
       "27         U-Courses Exploration + Reviews Prediction   \n",
       "28                       ML models Feature Importance   \n",
       "29  Kırmızı Şarap Kalite Tahmini(Sınıflandı...   \n",
       "30                      Prediction of quality of Wine   \n",
       "31                                        DL_Learning   \n",
       "32                                        DL_Learning   \n",
       "33      SDSS Classification with Deep Neural Networks   \n",
       "34               Electric Price Prediction - LightGBM   \n",
       "35               Electric Price Prediction - LightGBM   \n",
       "36               Electric Price Prediction - LightGBM   \n",
       "37               Electric Price Prediction - LightGBM   \n",
       "38               Electric Price Prediction - LightGBM   \n",
       "39               Electric Price Prediction - LightGBM   \n",
       "40   Who said this line[EDA/Classification/Keras/ANN]   \n",
       "41                            PNAD: Income Prediction   \n",
       "42  Liver Disease Analysis: EDA➡️SMOTE➡️OP...   \n",
       "43              Heart Disease - Binary Classification   \n",
       "44    Mushroom Classification using Genetic Algorithm   \n",
       "\n",
       "                                            Table  \\\n",
       "0                                        runs.csv   \n",
       "1                                       races.csv   \n",
       "2                                    Xs_no_es.csv   \n",
       "3           2016-building-energy-benchmarking.csv   \n",
       "4                                    Ys_no_es.csv   \n",
       "5           2015-building-energy-benchmarking.csv   \n",
       "6                            players_1920_fin.csv   \n",
       "7                                    IOT-temp.csv   \n",
       "8         Skyserver_12_30_2019%204_49_58%20PM.csv   \n",
       "9                              HepatitisCdata.csv   \n",
       "10                             column_3C_weka.csv   \n",
       "11                             column_2C_weka.csv   \n",
       "12                                   bee_data.csv   \n",
       "13                           weather_features.csv   \n",
       "14                             energy_dataset.csv   \n",
       "15                           weather_features.csv   \n",
       "16                             energy_dataset.csv   \n",
       "17                                 submission.csv   \n",
       "18                                  meal_info.csv   \n",
       "19                                       test.csv   \n",
       "20                     fulfilment_center_info.csv   \n",
       "21                                      train.csv   \n",
       "22                            winequality-red.csv   \n",
       "23                            winequality-red.csv   \n",
       "24                            winequality-red.csv   \n",
       "25                              udemy_courses.csv   \n",
       "26                              udemy_courses.csv   \n",
       "27                              udemy_courses.csv   \n",
       "28             bigml_59c28831336c6604c800002a.csv   \n",
       "29                            winequality-red.csv   \n",
       "30                            winequality-red.csv   \n",
       "31                                      hotel.csv   \n",
       "32                                       fuel.csv   \n",
       "33        Skyserver_12_30_2019%204_49_58%20PM.csv   \n",
       "34                           weather_features.csv   \n",
       "35                             energy_dataset.csv   \n",
       "36                             energy_dataset.csv   \n",
       "37                           weather_features.csv   \n",
       "38                             energy_dataset.csv   \n",
       "39                           weather_features.csv   \n",
       "40                                    scripts.csv   \n",
       "41                            pnad_2015_clean.csv   \n",
       "42                             HepatitisCdata.csv   \n",
       "43  heart_disease_health_indicators_BRFSS2015.csv   \n",
       "44                                  mushrooms.csv   \n",
       "\n",
       "                                              Dataset  \n",
       "0                                     gdaley.hkracing  \n",
       "1                                     gdaley.hkracing  \n",
       "2    city-of-seattle.sea-building-energy-benchmarking  \n",
       "3    city-of-seattle.sea-building-energy-benchmarking  \n",
       "4    city-of-seattle.sea-building-energy-benchmarking  \n",
       "5    city-of-seattle.sea-building-energy-benchmarking  \n",
       "6                          idoyo92.epl-stats-20192020  \n",
       "7       atulanandjha.temperature-readings-iot-devices  \n",
       "8          muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "9                     fedesoriano.hepatitis-c-dataset  \n",
       "10  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "11  uciml.biomechanical-features-of-orthopedic-pat...  \n",
       "12                 jenny18.honey-bee-annotated-images  \n",
       "13  nicholasjhana.energy-consumption-generation-pr...  \n",
       "14  nicholasjhana.energy-consumption-generation-pr...  \n",
       "15  nicholasjhana.energy-consumption-generation-pr...  \n",
       "16  nicholasjhana.energy-consumption-generation-pr...  \n",
       "17               kannanaikkal.food-demand-forecasting  \n",
       "18               kannanaikkal.food-demand-forecasting  \n",
       "19               kannanaikkal.food-demand-forecasting  \n",
       "20               kannanaikkal.food-demand-forecasting  \n",
       "21               kannanaikkal.food-demand-forecasting  \n",
       "22           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "23           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "24           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "25                            andrewmvd.udemy-courses  \n",
       "26                            andrewmvd.udemy-courses  \n",
       "27                            andrewmvd.udemy-courses  \n",
       "28                 becksddf.churn-in-telecoms-dataset  \n",
       "29           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "30           uciml.red-wine-quality-cortez-et-al-2009  \n",
       "31                    piyushgoyal443.red-wine-dataset  \n",
       "32                    piyushgoyal443.red-wine-dataset  \n",
       "33         muhakabartay.sloan-digital-sky-survey-dr16  \n",
       "34  nicholasjhana.energy-consumption-generation-pr...  \n",
       "35  nicholasjhana.energy-consumption-generation-pr...  \n",
       "36  nicholasjhana.energy-consumption-generation-pr...  \n",
       "37  nicholasjhana.energy-consumption-generation-pr...  \n",
       "38  nicholasjhana.energy-consumption-generation-pr...  \n",
       "39  nicholasjhana.energy-consumption-generation-pr...  \n",
       "40                       thec03u5.seinfeld-chronicles  \n",
       "41                                upadorprofzs.testes  \n",
       "42                    fedesoriano.hepatitis-c-dataset  \n",
       "43  alexteboul.heart-disease-health-indicators-dat...  \n",
       "44                      uciml.mushroom-classification  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kglids.recommend_transformations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 18. Visualize pipeline usage by tasks</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_pipeline_usage_by_task()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 19. Most used ML models by tasks</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_top_k_models_by_task(k=10, task='classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum 8 regressor(s) were found\n",
      "Showing top-8 regressors\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kglids.show_top_k_models_by_task(k=10, task='regression')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}