Dask-ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Necessary Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn import ensemble\n",
    "from dask.distributed import Client\n",
    "from dask_ml import preprocessing as pre\n",
    "import dask\n",
    "import joblib\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.19 s, sys: 42.5 ms, total: 1.24 s\n",
      "Wall time: 1.59 s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bin_0</th>\n",
       "      <th>bin_1</th>\n",
       "      <th>bin_2</th>\n",
       "      <th>bin_3</th>\n",
       "      <th>bin_4</th>\n",
       "      <th>nom_0</th>\n",
       "      <th>nom_1</th>\n",
       "      <th>nom_2</th>\n",
       "      <th>nom_3</th>\n",
       "      <th>...</th>\n",
       "      <th>nom_9</th>\n",
       "      <th>ord_0</th>\n",
       "      <th>ord_1</th>\n",
       "      <th>ord_2</th>\n",
       "      <th>ord_3</th>\n",
       "      <th>ord_4</th>\n",
       "      <th>ord_5</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>T</td>\n",
       "      <td>Y</td>\n",
       "      <td>Green</td>\n",
       "      <td>Triangle</td>\n",
       "      <td>Snake</td>\n",
       "      <td>Finland</td>\n",
       "      <td>...</td>\n",
       "      <td>2f4cb3d51</td>\n",
       "      <td>2</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Cold</td>\n",
       "      <td>h</td>\n",
       "      <td>D</td>\n",
       "      <td>kr</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>T</td>\n",
       "      <td>Y</td>\n",
       "      <td>Green</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Hamster</td>\n",
       "      <td>Russia</td>\n",
       "      <td>...</td>\n",
       "      <td>f83c56c21</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Hot</td>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
       "      <td>bF</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Russia</td>\n",
       "      <td>...</td>\n",
       "      <td>ae6800dd0</td>\n",
       "      <td>1</td>\n",
       "      <td>Expert</td>\n",
       "      <td>Lava Hot</td>\n",
       "      <td>h</td>\n",
       "      <td>R</td>\n",
       "      <td>Jc</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Red</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Snake</td>\n",
       "      <td>Canada</td>\n",
       "      <td>...</td>\n",
       "      <td>8270f0d71</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Boiling Hot</td>\n",
       "      <td>i</td>\n",
       "      <td>D</td>\n",
       "      <td>kW</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Red</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Canada</td>\n",
       "      <td>...</td>\n",
       "      <td>b164b72a7</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Freezing</td>\n",
       "      <td>a</td>\n",
       "      <td>R</td>\n",
       "      <td>qP</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  bin_0  bin_1  bin_2 bin_3 bin_4  nom_0      nom_1    nom_2    nom_3  \\\n",
       "0   0      0      0      0     T     Y  Green   Triangle    Snake  Finland   \n",
       "1   1      0      1      0     T     Y  Green  Trapezoid  Hamster   Russia   \n",
       "2   2      0      0      0     F     Y   Blue  Trapezoid     Lion   Russia   \n",
       "3   3      0      1      0     F     Y    Red  Trapezoid    Snake   Canada   \n",
       "4   4      0      0      0     F     N    Red  Trapezoid     Lion   Canada   \n",
       "\n",
       "   ...      nom_9 ord_0        ord_1        ord_2 ord_3 ord_4  ord_5 day  \\\n",
       "0  ...  2f4cb3d51     2  Grandmaster         Cold     h     D     kr   2   \n",
       "1  ...  f83c56c21     1  Grandmaster          Hot     a     A     bF   7   \n",
       "2  ...  ae6800dd0     1       Expert     Lava Hot     h     R     Jc   7   \n",
       "3  ...  8270f0d71     1  Grandmaster  Boiling Hot     i     D     kW   2   \n",
       "4  ...  b164b72a7     1  Grandmaster     Freezing     a     R     qP   7   \n",
       "\n",
       "  month target  \n",
       "0     2      0  \n",
       "1     8      0  \n",
       "2     2      0  \n",
       "3     1      1  \n",
       "4     8      0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas dataframe takes around 1.12 s to load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 275 ms, sys: 56.7 ms, total: 332 ms\n",
      "Wall time: 2.37 s\n"
     ]
    },
    {
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       "      <th>ord_5</th>\n",
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       "      <td>...</td>\n",
       "      <td>2f4cb3d51</td>\n",
       "      <td>2</td>\n",
       "      <td>Grandmaster</td>\n",
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       "      <td>Trapezoid</td>\n",
       "      <td>Hamster</td>\n",
       "      <td>Russia</td>\n",
       "      <td>...</td>\n",
       "      <td>f83c56c21</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Hot</td>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
       "      <td>bF</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>F</td>\n",
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       "      <td>Blue</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Russia</td>\n",
       "      <td>...</td>\n",
       "      <td>ae6800dd0</td>\n",
       "      <td>1</td>\n",
       "      <td>Expert</td>\n",
       "      <td>Lava Hot</td>\n",
       "      <td>h</td>\n",
       "      <td>R</td>\n",
       "      <td>Jc</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Red</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Snake</td>\n",
       "      <td>Canada</td>\n",
       "      <td>...</td>\n",
       "      <td>8270f0d71</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Boiling Hot</td>\n",
       "      <td>i</td>\n",
       "      <td>D</td>\n",
       "      <td>kW</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Red</td>\n",
       "      <td>Trapezoid</td>\n",
       "      <td>Lion</td>\n",
       "      <td>Canada</td>\n",
       "      <td>...</td>\n",
       "      <td>b164b72a7</td>\n",
       "      <td>1</td>\n",
       "      <td>Grandmaster</td>\n",
       "      <td>Freezing</td>\n",
       "      <td>a</td>\n",
       "      <td>R</td>\n",
       "      <td>qP</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  bin_0  bin_1  bin_2 bin_3 bin_4  nom_0      nom_1    nom_2    nom_3  \\\n",
       "0   0      0      0      0     T     Y  Green   Triangle    Snake  Finland   \n",
       "1   1      0      1      0     T     Y  Green  Trapezoid  Hamster   Russia   \n",
       "2   2      0      0      0     F     Y   Blue  Trapezoid     Lion   Russia   \n",
       "3   3      0      1      0     F     Y    Red  Trapezoid    Snake   Canada   \n",
       "4   4      0      0      0     F     N    Red  Trapezoid     Lion   Canada   \n",
       "\n",
       "   ...      nom_9 ord_0        ord_1        ord_2 ord_3 ord_4  ord_5 day  \\\n",
       "0  ...  2f4cb3d51     2  Grandmaster         Cold     h     D     kr   2   \n",
       "1  ...  f83c56c21     1  Grandmaster          Hot     a     A     bF   7   \n",
       "2  ...  ae6800dd0     1       Expert     Lava Hot     h     R     Jc   7   \n",
       "3  ...  8270f0d71     1  Grandmaster  Boiling Hot     i     D     kW   2   \n",
       "4  ...  b164b72a7     1  Grandmaster     Freezing     a     R     qP   7   \n",
       "\n",
       "  month target  \n",
       "0     2      0  \n",
       "1     8      0  \n",
       "2     2      0  \n",
       "3     1      1  \n",
       "4     8      0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "import dask.dataframe as dd\n",
    "dask_df=dd.read_csv(\"train.csv\")\n",
    "type(dask_df)\n",
    "dask_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask dataframe takes around 91.5 ms to load the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Cat Features into Numerical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5']\n"
     ]
    }
   ],
   "source": [
    "categorical_feature_mask = df.dtypes==object\n",
    "categorical_cols = df.columns[categorical_feature_mask].tolist()\n",
    "print(categorical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin_3</th>\n",
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       "      <th>nom_0</th>\n",
       "      <th>nom_1</th>\n",
       "      <th>nom_2</th>\n",
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       "      <th>ord_1</th>\n",
       "      <th>ord_2</th>\n",
       "      <th>ord_3</th>\n",
       "      <th>ord_4</th>\n",
       "      <th>ord_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <td>78</td>\n",
       "      <td>120</td>\n",
       "      <td>491</td>\n",
       "      <td>1686</td>\n",
       "      <td>2175</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>136</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>510</td>\n",
       "      <td>260</td>\n",
       "      <td>650</td>\n",
       "      <td>11635</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>766</td>\n",
       "      <td>1932</td>\n",
       "      <td>8078</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>209</td>\n",
       "      <td>165</td>\n",
       "      <td>1121</td>\n",
       "      <td>629</td>\n",
       "      <td>6056</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>61</td>\n",
       "      <td>34</td>\n",
       "      <td>1760</td>\n",
       "      <td>8231</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>86</td>\n",
       "      <td>1213</td>\n",
       "      <td>1574</td>\n",
       "      <td>3832</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>110</td>\n",
       "      <td>928</td>\n",
       "      <td>65</td>\n",
       "      <td>5860</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>177</td>\n",
       "      <td>188</td>\n",
       "      <td>1318</td>\n",
       "      <td>11922</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>131</td>\n",
       "      <td>272</td>\n",
       "      <td>1416</td>\n",
       "      <td>2445</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>282</td>\n",
       "      <td>353</td>\n",
       "      <td>385</td>\n",
       "      <td>693</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bin_3  bin_4  nom_0  nom_1  nom_2  nom_3  nom_4  nom_5  nom_6  nom_7  \\\n",
       "0      1      1      1      5      5      3      0     78    120    491   \n",
       "1      1      1      1      4      3      5      2    159    510    260   \n",
       "2      0      1      0      4      4      5      3     44     14    766   \n",
       "3      0      1      2      4      5      0      1    209    165   1121   \n",
       "4      0      0      2      4      4      0      1     90     61     34   \n",
       "5      1      0      0      1      4      2      1     65     86   1213   \n",
       "6      1      0      1      4      1      1      2    169    110    928   \n",
       "7      1      1      2      5      2      5      1    106    177    188   \n",
       "8      1      1      0      2      3      0      0     63    131    272   \n",
       "9      0      1      2      4      4      1      2    154    282    353   \n",
       "\n",
       "   nom_8  nom_9  ord_1  ord_2  ord_3  ord_4  ord_5  \n",
       "0   1686   2175      2      1      7      3    136  \n",
       "1    650  11635      2      3      0      0     93  \n",
       "2   1932   8078      1      4      7     17     31  \n",
       "3    629   6056      2      0      8      3    134  \n",
       "4   1760   8231      2      2      0     17    158  \n",
       "5   1574   3832      4      2      9      4     53  \n",
       "6     65   5860      2      4      6     15    185  \n",
       "7   1318  11922      4      4      9     10     17  \n",
       "8   1416   2445      4      0      4     21    160  \n",
       "9    385    693      1      2      7     16     11  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb = preprocessing.LabelEncoder()\n",
    "df[categorical_cols] = df[categorical_cols].apply(lambda col: lb.fit_transform(col))\n",
    "df[categorical_cols].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"target\"]\n",
    "x = df.drop([\"target\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting into Train and Test or Train and Valid split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Random Forest model: 0.7288686868686869\n",
      "CPU times: user 58.2 s, sys: 236 ms, total: 58.4 s\n",
      "Wall time: 58.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random_forest = ensemble.RandomForestClassifier()\n",
    "random_forest.fit(X_train,y_train)\n",
    "y_pred_random_forest = random_forest.predict(X_test)\n",
    "acc = metrics.accuracy_score(y_test,y_pred_random_forest)\n",
    "print(\"The accuracy of Random Forest model:\",acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.1 s ± 621 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nEstimator = [1,2,3,4,5]\n",
    "depth = [1,2,3,4,5]\n",
    "RF =ensemble.RandomForestClassifier()\n",
    "hyperParam = {'n_estimators':nEstimator,'max_depth': depth}\n",
    "grid_search = model_selection.GridSearchCV(RF,hyperParam,n_jobs=-1)\n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dask Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pcuser/.local/lib/python3.6/site-packages/distributed/node.py:155: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 35715 instead\n",
      "  http_address[\"port\"], self.http_server.port\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:37459</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:35715/status' target='_blank'>http://127.0.0.1:35715/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>8.27 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:37459' processes=4 threads=4, memory=8.27 GB>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This shows us the current working configuration which is going to utilize by dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Random Forest model: 0.7286262626262626\n",
      "CPU times: user 11.8 s, sys: 1.85 s, total: 13.6 s\n",
      "Wall time: 38.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf = ensemble.RandomForestClassifier()\n",
    "with joblib.parallel_backend('dask',scatter=[X_train,y_train]):\n",
    "    rf.fit(X_train,y_train)\n",
    "nomalization(df,['nom_9'])\n",
    "print(\"---\")y_pred = rf.predict(X_test)\n",
    "acc = metrics.accuracy_score(y_test,y_pred)\n",
    "print(\"The accuracy of Random Forest model:\",acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.89 s, sys: 754 ms, total: 7.65 s\n",
      "Wall time: 30.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nEstimator = [1,2,3,4,5]\n",
    "depth = [1,2,3,4,5]\n",
    "RF = ensemble.RandomForestClassifier()\n",
    "hyperParam = [{'n_estimators':nEstimator,'max_depth': depth}]\n",
    "grid_search = model_selection.GridSearchCV(RF,hyperParam,scoring='f1_weighted')\n",
    "\n",
    "with joblib.parallel_backend('dask',scatter=[X_train, y_train]):\n",
    "    grid_search.fit(X_train, y_train)"
   ]
  }
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