classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import joblib\n",
    "from sklearn import preprocessing\n",
    "from imblearn.datasets import make_imbalance\n",
    "from collections import Counter\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import inspection\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from mlens.visualization import corrmat\n",
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the sea ice velocity has been classified into three categories based on their magnitude. Predicting the extreme high or low values can be a common probelm in regression. So the idea is to first clssify them into two or three categories based on their intensity and then doing regression analysis on them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['day_no', 'year', 'month', 'doy', 'x_EASE', 'y_EASE', 'u_buoy',\n",
      "       'v_buoy', 'val_mag', 'vel_dir', 'ice_quadrant', 'u_ERA5', 'v_ERA5',\n",
      "       'wind_val_mag', 'wind_quadrant', 'wind_dir', 'sic_CDR', 'h_piomas',\n",
      "       'd2c'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel('modified_trainset.xlsx')\n",
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_no</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>doy</th>\n",
       "      <th>x_EASE</th>\n",
       "      <th>y_EASE</th>\n",
       "      <th>u_buoy</th>\n",
       "      <th>v_buoy</th>\n",
       "      <th>val_mag</th>\n",
       "      <th>vel_dir</th>\n",
       "      <th>ice_quadrant</th>\n",
       "      <th>u_ERA5</th>\n",
       "      <th>v_ERA5</th>\n",
       "      <th>wind_val_mag</th>\n",
       "      <th>wind_quadrant</th>\n",
       "      <th>wind_dir</th>\n",
       "      <th>sic_CDR</th>\n",
       "      <th>h_piomas</th>\n",
       "      <th>d2c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
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       "      <td>49</td>\n",
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       "      <td>0.741408</td>\n",
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       "      <td>1</td>\n",
       "      <td>-6.818630</td>\n",
       "      <td>-0.674205</td>\n",
       "      <td>6.851881</td>\n",
       "      <td>3</td>\n",
       "      <td>185.646875</td>\n",
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       "      <td>2.484009</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.243717</td>\n",
       "      <td>1.187695</td>\n",
       "      <td>168.158690</td>\n",
       "      <td>2</td>\n",
       "      <td>-8.825469</td>\n",
       "      <td>1.123955</td>\n",
       "      <td>8.896751</td>\n",
       "      <td>2</td>\n",
       "      <td>172.742248</td>\n",
       "      <td>0.996022</td>\n",
       "      <td>2.474106</td>\n",
       "      <td>362.547379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>147.524719</td>\n",
       "      <td>157.382492</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3.740522</td>\n",
       "      <td>381.025629</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>147.470963</td>\n",
       "      <td>138.599823</td>\n",
       "      <td>0.380940</td>\n",
       "      <td>1.243485</td>\n",
       "      <td>1.300527</td>\n",
       "      <td>72.967623</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.169171</td>\n",
       "      <td>2.537787</td>\n",
       "      <td>3.338513</td>\n",
       "      <td>2</td>\n",
       "      <td>130.522141</td>\n",
       "      <td>0.990302</td>\n",
       "      <td>3.188522</td>\n",
       "      <td>521.535334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   day_no  year  month  doy      x_EASE      y_EASE    u_buoy    v_buoy  \\\n",
       "0       0  1979      2   49  147.506958  138.582672 -0.797554  1.114740   \n",
       "1       0  1979      2   49  146.834778  120.509880  0.643200  0.368754   \n",
       "2       0  1979      2   49  130.993561  129.623672 -1.162420  0.243717   \n",
       "3       0  1979      2   49  147.524719  157.382492  0.919766  0.025784   \n",
       "4       1  1979      2   50  147.470963  138.599823  0.380940  1.243485   \n",
       "\n",
       "    val_mag     vel_dir  ice_quadrant    u_ERA5    v_ERA5  wind_val_mag  \\\n",
       "0  1.370671  125.582277             2 -6.704156 -0.321260      6.711849   \n",
       "1  0.741408   29.826147             1 -6.818630 -0.674205      6.851881   \n",
       "2  1.187695  168.158690             2 -8.825469  1.123955      8.896751   \n",
       "3  0.920127    1.605796             1 -1.079951 -1.035410      1.496117   \n",
       "4  1.300527   72.967623             1 -2.169171  2.537787      3.338513   \n",
       "\n",
       "   wind_quadrant    wind_dir   sic_CDR  h_piomas         d2c  \n",
       "0              3  182.743485  0.990195  3.189743  522.523298  \n",
       "1              3  185.646875  0.966372  2.484009  412.767669  \n",
       "2              2  172.742248  0.996022  2.474106  362.547379  \n",
       "3              3  223.793757  0.982681  3.740522  381.025629  \n",
       "4              2  130.522141  0.990302  3.188522  521.535334  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var=df.copy()\n",
    "var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>day_no</th>\n",
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       "      <td>-8.825469</td>\n",
       "      <td>1.123955</td>\n",
       "      <td>8.896751</td>\n",
       "      <td>2</td>\n",
       "      <td>172.742248</td>\n",
       "      <td>0.996022</td>\n",
       "      <td>2.474106</td>\n",
       "      <td>362.547379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>147.524719</td>\n",
       "      <td>157.382492</td>\n",
       "      <td>0.919766</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1.079951</td>\n",
       "      <td>-1.035410</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.982681</td>\n",
       "      <td>3.740522</td>\n",
       "      <td>381.025629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1979</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>147.470963</td>\n",
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       "      <td>1</td>\n",
       "      <td>-2.169171</td>\n",
       "      <td>2.537787</td>\n",
       "      <td>3.338513</td>\n",
       "      <td>2</td>\n",
       "      <td>130.522141</td>\n",
       "      <td>0.990302</td>\n",
       "      <td>3.188522</td>\n",
       "      <td>521.535334</td>\n",
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      "text/plain": [
       "   day_no  year  month  doy      x_EASE      y_EASE    u_buoy    v_buoy  \\\n",
       "0       0  1979      2   49  147.506958  138.582672 -0.797554  1.114740   \n",
       "1       0  1979      2   49  146.834778  120.509880  0.643200  0.368754   \n",
       "2       0  1979      2   49  130.993561  129.623672 -1.162420  0.243717   \n",
       "3       0  1979      2   49  147.524719  157.382492  0.919766  0.025784   \n",
       "4       1  1979      2   50  147.470963  138.599823  0.380940  1.243485   \n",
       "\n",
       "    val_mag     vel_dir  ice_quadrant    u_ERA5    v_ERA5  wind_val_mag  \\\n",
       "0  1.370671  125.582277             2 -6.704156 -0.321260      6.711849   \n",
       "1  0.741408   29.826147             1 -6.818630 -0.674205      6.851881   \n",
       "2  1.187695  168.158690             2 -8.825469  1.123955      8.896751   \n",
       "3  0.920127    1.605796             1 -1.079951 -1.035410      1.496117   \n",
       "4  1.300527   72.967623             1 -2.169171  2.537787      3.338513   \n",
       "\n",
       "   wind_quadrant    wind_dir   sic_CDR  h_piomas         d2c  \n",
       "0              3  182.743485  0.990195  3.189743  522.523298  \n",
       "1              3  185.646875  0.966372  2.484009  412.767669  \n",
       "2              2  172.742248  0.996022  2.474106  362.547379  \n",
       "3              3  223.793757  0.982681  3.740522  381.025629  \n",
       "4              2  130.522141  0.990302  3.188522  521.535334  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "var = var[['day_no', 'year', 'month', 'doy', 'x_EASE', 'y_EASE', 'u_buoy',\n",
    "       'v_buoy', 'val_mag', 'u_ERA5', 'v_ERA5','sic_CDR', 'h_piomas','d2c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339203, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day_no', 'year', 'month', 'doy', 'x_EASE', 'y_EASE', 'u_buoy',\n",
       "       'v_buoy', 'val_mag', 'u_ERA5', 'v_ERA5', 'sic_CDR', 'h_piomas', 'd2c'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_no        int64\n",
       "year          int64\n",
       "month         int64\n",
       "doy           int64\n",
       "x_EASE      float64\n",
       "y_EASE      float64\n",
       "u_buoy      float64\n",
       "v_buoy      float64\n",
       "val_mag     float64\n",
       "u_ERA5      float64\n",
       "v_ERA5      float64\n",
       "sic_CDR     float64\n",
       "h_piomas    float64\n",
       "d2c         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_no</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>doy</th>\n",
       "      <th>x_EASE</th>\n",
       "      <th>y_EASE</th>\n",
       "      <th>u_buoy</th>\n",
       "      <th>v_buoy</th>\n",
       "      <th>val_mag</th>\n",
       "      <th>u_ERA5</th>\n",
       "      <th>v_ERA5</th>\n",
       "      <th>sic_CDR</th>\n",
       "      <th>h_piomas</th>\n",
       "      <th>d2c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "      <td>339203.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10374.519264</td>\n",
       "      <td>2007.017715</td>\n",
       "      <td>6.746862</td>\n",
       "      <td>189.943291</td>\n",
       "      <td>163.580333</td>\n",
       "      <td>156.494980</td>\n",
       "      <td>-0.333268</td>\n",
       "      <td>1.360217</td>\n",
       "      <td>8.354355</td>\n",
       "      <td>-0.096073</td>\n",
       "      <td>0.335635</td>\n",
       "      <td>0.955251</td>\n",
       "      <td>2.069756</td>\n",
       "      <td>418.241205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3587.901557</td>\n",
       "      <td>9.817846</td>\n",
       "      <td>3.453604</td>\n",
       "      <td>105.545448</td>\n",
       "      <td>24.443882</td>\n",
       "      <td>34.704966</td>\n",
       "      <td>8.075045</td>\n",
       "      <td>7.860813</td>\n",
       "      <td>7.691850</td>\n",
       "      <td>4.320043</td>\n",
       "      <td>4.123823</td>\n",
       "      <td>0.124664</td>\n",
       "      <td>0.922228</td>\n",
       "      <td>259.875226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.168983</td>\n",
       "      <td>36.870293</td>\n",
       "      <td>-74.682899</td>\n",
       "      <td>-66.781601</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-23.084634</td>\n",
       "      <td>-20.146717</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.359009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7860.500000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>146.797035</td>\n",
       "      <td>129.199318</td>\n",
       "      <td>-3.725245</td>\n",
       "      <td>-2.324845</td>\n",
       "      <td>2.888850</td>\n",
       "      <td>-3.149338</td>\n",
       "      <td>-2.617492</td>\n",
       "      <td>0.976678</td>\n",
       "      <td>1.478115</td>\n",
       "      <td>210.261503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11106.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>161.220627</td>\n",
       "      <td>152.304901</td>\n",
       "      <td>-0.024725</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>6.665244</td>\n",
       "      <td>-0.165271</td>\n",
       "      <td>0.289756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.041318</td>\n",
       "      <td>389.715241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13293.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>285.000000</td>\n",
       "      <td>181.103218</td>\n",
       "      <td>179.585617</td>\n",
       "      <td>2.943056</td>\n",
       "      <td>4.967892</td>\n",
       "      <td>11.675371</td>\n",
       "      <td>2.933966</td>\n",
       "      <td>3.203830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.619862</td>\n",
       "      <td>599.311791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14925.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>279.391541</td>\n",
       "      <td>279.562073</td>\n",
       "      <td>68.396103</td>\n",
       "      <td>68.639801</td>\n",
       "      <td>75.609704</td>\n",
       "      <td>17.911558</td>\n",
       "      <td>22.862028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.284669</td>\n",
       "      <td>1120.364872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              day_no           year          month            doy  \\\n",
       "count  339203.000000  339203.000000  339203.000000  339203.000000   \n",
       "mean    10374.519264    2007.017715       6.746862     189.943291   \n",
       "std      3587.901557       9.817846       3.453604     105.545448   \n",
       "min         0.000000    1979.000000       1.000000       1.000000   \n",
       "25%      7860.500000    2000.000000       4.000000     101.000000   \n",
       "50%     11106.000000    2009.000000       7.000000     190.000000   \n",
       "75%     13293.000000    2015.000000      10.000000     285.000000   \n",
       "max     14925.000000    2019.000000      12.000000     364.000000   \n",
       "\n",
       "              x_EASE         y_EASE         u_buoy         v_buoy  \\\n",
       "count  339203.000000  339203.000000  339203.000000  339203.000000   \n",
       "mean      163.580333     156.494980      -0.333268       1.360217   \n",
       "std        24.443882      34.704966       8.075045       7.860813   \n",
       "min        39.168983      36.870293     -74.682899     -66.781601   \n",
       "25%       146.797035     129.199318      -3.725245      -2.324845   \n",
       "50%       161.220627     152.304901      -0.024725       0.321954   \n",
       "75%       181.103218     179.585617       2.943056       4.967892   \n",
       "max       279.391541     279.562073      68.396103      68.639801   \n",
       "\n",
       "             val_mag         u_ERA5         v_ERA5        sic_CDR  \\\n",
       "count  339203.000000  339203.000000  339203.000000  339203.000000   \n",
       "mean        8.354355      -0.096073       0.335635       0.955251   \n",
       "std         7.691850       4.320043       4.123823       0.124664   \n",
       "min         0.000000     -23.084634     -20.146717       0.000027   \n",
       "25%         2.888850      -3.149338      -2.617492       0.976678   \n",
       "50%         6.665244      -0.165271       0.289756       1.000000   \n",
       "75%        11.675371       2.933966       3.203830       1.000000   \n",
       "max        75.609704      17.911558      22.862028       1.000000   \n",
       "\n",
       "            h_piomas            d2c  \n",
       "count  339203.000000  339203.000000  \n",
       "mean        2.069756     418.241205  \n",
       "std         0.922228     259.875226  \n",
       "min         0.000000       0.359009  \n",
       "25%         1.478115     210.261503  \n",
       "50%         2.041318     389.715241  \n",
       "75%         2.619862     599.311791  \n",
       "max         6.284669    1120.364872  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classifying the velocity magnitudes into 3 categories based on their intensity and number of values in that range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "range1 = list(np.arange(0,1,.1)) + list(np.arange(.91,.99,.01)) + list(np.arange(.99,1.0001,.001))\n",
    "p = var.val_mag.quantile(range1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>val_mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.502237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>2.107865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>3.636044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>5.110915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      val_mag\n",
       "0.0  0.000000\n",
       "0.1  0.502237\n",
       "0.2  2.107865\n",
       "0.3  3.636044\n",
       "0.4  5.110915"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(p)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "for i in df1.iterrows():\n",
    "    percentile_value = i[1]['val_mag']\n",
    "    size = var[var['val_mag']<=percentile_value].shape[0]\n",
    "    a.append(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=[0]+a\n",
    "del b[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "[0, 9104, 33921, 67841, 101761, 135681, 169602, 203522, 237442, 271362, 305282, 308674, 312066, 315458, 318850, 322242, 325634, 329026, 332418, 335810, 336150, 336489, 336828, 337167, 337506, 337846, 338185, 338524, 338863]\n"
     ]
    }
   ],
   "source": [
    "print(len(b))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "[9104, 33921, 67841, 101761, 135681, 169602, 203522, 237442, 271362, 305282, 308674, 312066, 315458, 318850, 322242, 325634, 329026, 332418, 335810, 336150, 336489, 336828, 337167, 337506, 337846, 338185, 338524, 338863, 339203]\n"
     ]
    }
   ],
   "source": [
    "print(len(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9104 24817 33920 33920 33920 33921 33920 33920 33920 33920  3392  3392\n",
      "  3392  3392  3392  3392  3392  3392  3392   340   339   339   339   339\n",
      "   340   339   339   339   340]\n"
     ]
    }
   ],
   "source": [
    "c = np.array(a) - np.array(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['count'] = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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>val_mag</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>0.502237</td>\n",
       "      <td>24817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.200</th>\n",
       "      <td>2.107865</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.300</th>\n",
       "      <td>3.636044</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.400</th>\n",
       "      <td>5.110915</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500</th>\n",
       "      <td>6.665244</td>\n",
       "      <td>33921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.600</th>\n",
       "      <td>8.390036</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.700</th>\n",
       "      <td>10.439153</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.800</th>\n",
       "      <td>13.151437</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.900</th>\n",
       "      <td>17.649177</td>\n",
       "      <td>33920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.910</th>\n",
       "      <td>18.319683</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.920</th>\n",
       "      <td>19.099461</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.930</th>\n",
       "      <td>19.994742</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.940</th>\n",
       "      <td>21.061995</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.950</th>\n",
       "      <td>22.279127</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.960</th>\n",
       "      <td>23.907943</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.970</th>\n",
       "      <td>26.067315</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.980</th>\n",
       "      <td>29.404999</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.990</th>\n",
       "      <td>36.205260</td>\n",
       "      <td>3392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.991</th>\n",
       "      <td>37.435597</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.992</th>\n",
       "      <td>38.734981</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.993</th>\n",
       "      <td>40.196313</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.994</th>\n",
       "      <td>41.903515</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.995</th>\n",
       "      <td>43.787915</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.996</th>\n",
       "      <td>46.335222</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.997</th>\n",
       "      <td>49.519703</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.998</th>\n",
       "      <td>53.777639</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.999</th>\n",
       "      <td>59.310440</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000</th>\n",
       "      <td>75.609704</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         val_mag  count\n",
       "0.000   0.000000   9104\n",
       "0.100   0.502237  24817\n",
       "0.200   2.107865  33920\n",
       "0.300   3.636044  33920\n",
       "0.400   5.110915  33920\n",
       "0.500   6.665244  33921\n",
       "0.600   8.390036  33920\n",
       "0.700  10.439153  33920\n",
       "0.800  13.151437  33920\n",
       "0.900  17.649177  33920\n",
       "0.910  18.319683   3392\n",
       "0.920  19.099461   3392\n",
       "0.930  19.994742   3392\n",
       "0.940  21.061995   3392\n",
       "0.950  22.279127   3392\n",
       "0.960  23.907943   3392\n",
       "0.970  26.067315   3392\n",
       "0.980  29.404999   3392\n",
       "0.990  36.205260   3392\n",
       "0.991  37.435597    340\n",
       "0.992  38.734981    339\n",
       "0.993  40.196313    339\n",
       "0.994  41.903515    339\n",
       "0.995  43.787915    339\n",
       "0.996  46.335222    340\n",
       "0.997  49.519703    339\n",
       "0.998  53.777639    339\n",
       "0.999  59.310440    339\n",
       "1.000  75.609704    340"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "var['type'] = var.val_mag.map(lambda x: 0 if x == 0 else 2 if x > 30 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    323755\n",
      "0      9104\n",
      "2      6344\n",
      "Name: type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(var['type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339203, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dummy variables for multiclass predictor\n",
    "cov_dummy = pd.get_dummies(var['type'])\n",
    "cov_dummy.head()\n",
    "cov_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339203, 18)\n"
     ]
    },
    {
     "data": {
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>day_no</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>doy</th>\n",
       "      <th>x_EASE</th>\n",
       "      <th>y_EASE</th>\n",
       "      <th>u_buoy</th>\n",
       "      <th>v_buoy</th>\n",
       "      <th>val_mag</th>\n",
       "      <th>u_ERA5</th>\n",
       "      <th>v_ERA5</th>\n",
       "      <th>sic_CDR</th>\n",
       "      <th>h_piomas</th>\n",
       "      <th>d2c</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  day_no  year  month  doy      x_EASE      y_EASE    u_buoy  \\\n",
       "0  0  1  0       0  1979      2   49  147.506958  138.582672 -0.797554   \n",
       "1  0  1  0       0  1979      2   49  146.834778  120.509880  0.643200   \n",
       "2  0  1  0       0  1979      2   49  130.993561  129.623672 -1.162420   \n",
       "3  0  1  0       0  1979      2   49  147.524719  157.382492  0.919766   \n",
       "4  0  1  0       1  1979      2   50  147.470963  138.599823  0.380940   \n",
       "\n",
       "     v_buoy   val_mag    u_ERA5    v_ERA5   sic_CDR  h_piomas         d2c  \\\n",
       "0  1.114740  1.370671 -6.704156 -0.321260  0.990195  3.189743  522.523298   \n",
       "1  0.368754  0.741408 -6.818630 -0.674205  0.966372  2.484009  412.767669   \n",
       "2  0.243717  1.187695 -8.825469  1.123955  0.996022  2.474106  362.547379   \n",
       "3  0.025784  0.920127 -1.079951 -1.035410  0.982681  3.740522  381.025629   \n",
       "4  1.243485  1.300527 -2.169171  2.537787  0.990302  3.188522  521.535334   \n",
       "\n",
       "   type  \n",
       "0     1  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.concat([cov_dummy, var], axis = 1)\n",
    "print(df2.shape)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 1,\n",
       " 2,\n",
       " 'day_no',\n",
       " 'year',\n",
       " 'month',\n",
       " 'doy',\n",
       " 'x_EASE',\n",
       " 'y_EASE',\n",
       " 'u_buoy',\n",
       " 'v_buoy',\n",
       " 'val_mag',\n",
       " 'u_ERA5',\n",
       " 'v_ERA5',\n",
       " 'sic_CDR',\n",
       " 'h_piomas',\n",
       " 'd2c']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_column_names = list(df2.columns)\n",
    "df2_column_names.remove('type')\n",
    "df2_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize all columns\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_normalized = pd.DataFrame(data=x_scaled, columns=df2_column_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339203, 17)\n",
      "(339203, 18)\n"
     ]
    }
   ],
   "source": [
    "print(df_normalized.shape)\n",
    "print(df2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>339203 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1    2    day_no  year     month       doy    x_EASE    y_EASE  \\\n",
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       "...     ...  ...  ...       ...   ...       ...       ...       ...       ...   \n",
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       "339202  1.0  0.0  0.0  1.000000   1.0  1.000000  1.000000  0.254926  0.506398   \n",
       "\n",
       "          u_buoy    v_buoy   val_mag    u_ERA5    v_ERA5   sic_CDR  h_piomas  \\\n",
       "0       0.516395  0.501371  0.018128  0.399561  0.460963  0.990195  0.507543   \n",
       "1       0.526465  0.495862  0.009806  0.396769  0.452757  0.966371  0.395249   \n",
       "2       0.513845  0.494939  0.015708  0.347817  0.494566  0.996022  0.393673   \n",
       "3       0.528398  0.493330  0.012169  0.536749  0.444359  0.982681  0.595182   \n",
       "4       0.524632  0.502322  0.017201  0.510181  0.527439  0.990301  0.507349   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
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       "339199  0.471313  0.454077  0.118676  0.450518  0.459557  1.000000  0.205095   \n",
       "339200  0.515583  0.509253  0.031289  0.610437  0.494453  1.000000  0.311926   \n",
       "339201  0.521261  0.406065  0.155961  0.502881  0.343724  1.000000  0.256321   \n",
       "339202  0.521970  0.493139  0.000000  0.596570  0.638411  0.949973  0.213090   \n",
       "\n",
       "             d2c  \n",
       "0       0.466216  \n",
       "1       0.368220  \n",
       "2       0.323381  \n",
       "3       0.339879  \n",
       "4       0.465334  \n",
       "...          ...  \n",
       "339198  0.626741  \n",
       "339199  0.321545  \n",
       "339200  0.351284  \n",
       "339201  0.606871  \n",
       "339202  0.010146  \n",
       "\n",
       "[339203 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "          0    1    2  day_no  year  month  doy    x_EASE    y_EASE    u_buoy  \\\n",
       "339198  0.0  1.0  0.0     1.0   1.0    1.0  1.0  0.641335  0.559853  0.515112   \n",
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       "\n",
       "          v_buoy   val_mag    u_ERA5    v_ERA5   sic_CDR  h_piomas       d2c  \\\n",
       "339198  0.406754  0.155264  0.501463  0.328627  1.000000  0.257773  0.626741   \n",
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       "\n",
       "        type  \n",
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       "339199     1  \n",
       "339200     1  \n",
       "339201     1  \n",
       "339202     0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#append Y axis back to this \n",
    "df_normalized_w_target = pd.concat([df_normalized, df2['type']], axis=1)\n",
    "df_normalized_w_target.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339203, 14)\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "     day_no  year     month       doy    x_EASE    y_EASE    u_buoy    v_buoy  \\\n",
       "0  0.000000   0.0  0.090909  0.132231  0.450990  0.419101  0.516395  0.501371   \n",
       "1  0.000000   0.0  0.090909  0.132231  0.448192  0.344633  0.526465  0.495862   \n",
       "2  0.000000   0.0  0.090909  0.132231  0.382248  0.382186  0.513845  0.494939   \n",
       "3  0.000000   0.0  0.090909  0.132231  0.451064  0.496565  0.528398  0.493330   \n",
       "4  0.000067   0.0  0.090909  0.134986  0.450840  0.419172  0.524632  0.502322   \n",
       "\n",
       "    val_mag    u_ERA5    v_ERA5   sic_CDR  h_piomas       d2c  \n",
       "0  0.018128  0.399561  0.460963  0.990195  0.507543  0.466216  \n",
       "1  0.009806  0.396769  0.452757  0.966371  0.395249  0.368220  \n",
       "2  0.015708  0.347817  0.494566  0.996022  0.393673  0.323381  \n",
       "3  0.012169  0.536749  0.444359  0.982681  0.595182  0.339879  \n",
       "4  0.017201  0.510181  0.527439  0.990301  0.507349  0.465334  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set target data  and x values\n",
    "X=df_normalized_w_target[list(df_normalized_w_target.columns)[3:-1]]\n",
    "print(X.shape)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339203, 14)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(['u_buoy', 'v_buoy','val_mag'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339203, 11)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day_no', 'year', 'month', 'doy', 'x_EASE', 'y_EASE', 'u_ERA5',\n",
       "       'v_ERA5', 'sic_CDR', 'h_piomas', 'd2c'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339203,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df_normalized_w_target[list(df_normalized_w_target.columns)[-1]]\n",
    "print(Y.shape)\n",
    "Y.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339203, 11)\n",
      "(339203,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sample the data, 6300 per output\n",
    "x, y = make_imbalance(X, Y,\n",
    "                      sampling_strategy={0: 6300, 1: 6300, 2: 6300},\n",
    "                      random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into training and testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    x, y, random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training target statistics: Counter({2: 4736, 0: 4733, 1: 4706})\n",
      "Testing target statistics: Counter({1: 1594, 0: 1567, 2: 1564})\n"
     ]
    }
   ],
   "source": [
    "print('Training target statistics: {}'.format(Counter(y_train)))\n",
    "print('Testing target statistics: {}'.format(Counter(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "cols2 = ['type', 'u_buoy', 'v_buoy']\n",
    "sns.pairplot(df_normalized_w_target[cols2], height = 2.5)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier( random_state=42)\n",
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Train Accuracy: 0.9998589065255732\n",
      "Decision Tree Test Accuracy: 0.8747089947089947\n"
     ]
    }
   ],
   "source": [
    "print (\"Decision Tree Train Accuracy:\", metrics.accuracy_score(y_train, dtree.predict(X_train)))\n",
    "print (\"Decision Tree Test Accuracy:\", metrics.accuracy_score(y_test, dtree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.92      0.92      1567\n",
      "           1       0.82      0.84      0.83      1594\n",
      "           2       0.89      0.86      0.88      1564\n",
      "\n",
      "    accuracy                           0.87      4725\n",
      "   macro avg       0.88      0.87      0.88      4725\n",
      "weighted avg       0.88      0.87      0.87      4725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>day_no</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>doy</th>\n",
       "      <th>x_EASE</th>\n",
       "      <th>y_EASE</th>\n",
       "      <th>u_ERA5</th>\n",
       "      <th>v_ERA5</th>\n",
       "      <th>sic_CDR</th>\n",
       "      <th>h_piomas</th>\n",
       "      <th>d2c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13723</th>\n",
       "      <td>0.968643</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.716253</td>\n",
       "      <td>0.687178</td>\n",
       "      <td>0.464266</td>\n",
       "      <td>0.587272</td>\n",
       "      <td>0.522515</td>\n",
       "      <td>0.162757</td>\n",
       "      <td>0.016351</td>\n",
       "      <td>0.494926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>0.810787</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.264463</td>\n",
       "      <td>0.418170</td>\n",
       "      <td>0.385224</td>\n",
       "      <td>0.687469</td>\n",
       "      <td>0.365709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.406380</td>\n",
       "      <td>0.459169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5549</th>\n",
       "      <td>0.906600</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.179063</td>\n",
       "      <td>0.822569</td>\n",
       "      <td>0.592322</td>\n",
       "      <td>0.575139</td>\n",
       "      <td>0.255510</td>\n",
       "      <td>0.962714</td>\n",
       "      <td>0.176069</td>\n",
       "      <td>0.141705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>0.808777</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.471470</td>\n",
       "      <td>0.624858</td>\n",
       "      <td>0.543142</td>\n",
       "      <td>0.466057</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.488096</td>\n",
       "      <td>0.014663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10294</th>\n",
       "      <td>0.654472</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.560221</td>\n",
       "      <td>0.521393</td>\n",
       "      <td>0.587147</td>\n",
       "      <td>0.337018</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.312133</td>\n",
       "      <td>0.732446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11744</th>\n",
       "      <td>0.753568</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928375</td>\n",
       "      <td>0.567032</td>\n",
       "      <td>0.392819</td>\n",
       "      <td>0.481061</td>\n",
       "      <td>0.705515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.202081</td>\n",
       "      <td>0.745225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15978</th>\n",
       "      <td>0.211256</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.768595</td>\n",
       "      <td>0.521094</td>\n",
       "      <td>0.839908</td>\n",
       "      <td>0.502362</td>\n",
       "      <td>0.760485</td>\n",
       "      <td>0.967299</td>\n",
       "      <td>0.148617</td>\n",
       "      <td>0.097660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>0.649581</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.677686</td>\n",
       "      <td>0.592524</td>\n",
       "      <td>0.706087</td>\n",
       "      <td>0.598211</td>\n",
       "      <td>0.472945</td>\n",
       "      <td>0.999134</td>\n",
       "      <td>0.243234</td>\n",
       "      <td>0.272368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17824</th>\n",
       "      <td>0.973802</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928375</td>\n",
       "      <td>0.511375</td>\n",
       "      <td>0.276499</td>\n",
       "      <td>0.364072</td>\n",
       "      <td>0.539611</td>\n",
       "      <td>0.940313</td>\n",
       "      <td>0.086677</td>\n",
       "      <td>0.222779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18875</th>\n",
       "      <td>0.997856</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.911846</td>\n",
       "      <td>0.508095</td>\n",
       "      <td>0.386222</td>\n",
       "      <td>0.852998</td>\n",
       "      <td>0.455320</td>\n",
       "      <td>0.980950</td>\n",
       "      <td>0.172443</td>\n",
       "      <td>0.662823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4725 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         day_no   year     month       doy    x_EASE    y_EASE    u_ERA5  \\\n",
       "13723  0.968643  0.975  0.727273  0.716253  0.687178  0.464266  0.587272   \n",
       "1447   0.810787  0.825  0.272727  0.264463  0.418170  0.385224  0.687469   \n",
       "5549   0.906600  0.925  0.181818  0.179063  0.822569  0.592322  0.575139   \n",
       "1634   0.808777  0.825  0.181818  0.181818  0.471470  0.624858  0.543142   \n",
       "10294  0.654472  0.650  0.909091  0.878788  0.560221  0.521393  0.587147   \n",
       "...         ...    ...       ...       ...       ...       ...       ...   \n",
       "11744  0.753568  0.750  1.000000  0.928375  0.567032  0.392819  0.481061   \n",
       "15978  0.211256  0.200  0.818182  0.768595  0.521094  0.839908  0.502362   \n",
       "4371   0.649581  0.650  0.727273  0.677686  0.592524  0.706087  0.598211   \n",
       "17824  0.973802  0.975  1.000000  0.928375  0.511375  0.276499  0.364072   \n",
       "18875  0.997856  1.000  0.909091  0.911846  0.508095  0.386222  0.852998   \n",
       "\n",
       "         v_ERA5   sic_CDR  h_piomas       d2c  \n",
       "13723  0.522515  0.162757  0.016351  0.494926  \n",
       "1447   0.365709  1.000000  0.406380  0.459169  \n",
       "5549   0.255510  0.962714  0.176069  0.141705  \n",
       "1634   0.466057  1.000000  0.488096  0.014663  \n",
       "10294  0.337018  1.000000  0.312133  0.732446  \n",
       "...         ...       ...       ...       ...  \n",
       "11744  0.705515  1.000000  0.202081  0.745225  \n",
       "15978  0.760485  0.967299  0.148617  0.097660  \n",
       "4371   0.472945  0.999134  0.243234  0.272368  \n",
       "17824  0.539611  0.940313  0.086677  0.222779  \n",
       "18875  0.455320  0.980950  0.172443  0.662823  \n",
       "\n",
       "[4725 rows x 11 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1443  108   16]\n",
      " [ 101 1340  153]\n",
      " [  37  177 1350]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy Rate')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_rate = []\n",
    "training_acc = []\n",
    "\n",
    "for i in range(1,21):   \n",
    "    dtree = DecisionTreeClassifier(max_depth=i)\n",
    "    dtree.fit(X_train, y_train) \n",
    "    accuracy_rate.append(dtree.score(X_test, y_test))\n",
    "    training_acc.append(dtree.score(X_train, y_train))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.plot(np.arange(1,21), accuracy_rate,color='red', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.plot(np.arange(1,21), training_acc,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.title('Accuracy Rate vs. Tree Depth')\n",
    "plt.xlabel('Tree Depth')\n",
    "plt.ylabel('Accuracy Rate')\n",
    "\n",
    "#blue - trianing accuracy \n",
    "# red - testing accuracyy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Train Accuracy Baseline: 0.9997178130511464\n",
      "Random Forest Test Accuracy Baselsine: 0.9185185185185185\n"
     ]
    }
   ],
   "source": [
    "y_pred =  rfc.predict(X_test)\n",
    "print (\"Random Forest Train Accuracy Baseline:\", metrics.accuracy_score(y_train, rfc.predict(X_train)))\n",
    "print (\"Random Forest Test Accuracy Baselsine:\", metrics.accuracy_score(y_test, rfc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1470   87   10]\n",
      " [  61 1413  120]\n",
      " [  18   89 1457]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.94      1567\n",
      "           1       0.89      0.89      0.89      1594\n",
      "           2       0.92      0.93      0.92      1564\n",
      "\n",
      "    accuracy                           0.92      4725\n",
      "   macro avg       0.92      0.92      0.92      4725\n",
      "weighted avg       0.92      0.92      0.92      4725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sample the data, 6300 per output\n",
    "x, y = make_imbalance(X, Y,\n",
    "                      sampling_strategy={0: 6300, 1: 6300, 2: 6300},\n",
    "                      random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into training and testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    x, y, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training target statistics: Counter({0: 4774, 2: 4713, 1: 4688})\n",
      "Testing target statistics: Counter({1: 1612, 2: 1587, 0: 1526})\n"
     ]
    }
   ],
   "source": [
    "print('Training target statistics: {}'.format(Counter(y_train)))\n",
    "print('Testing target statistics: {}'.format(Counter(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random forest baseline performance\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': [True],\n",
       " 'max_depth': [10, 15, 20, None],\n",
       " 'min_samples_leaf': [1, 10, 20, 50, 100, 500],\n",
       " 'min_samples_split': [2, 30, 40, 100]}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [10,15, 20, None],\n",
    "    'min_samples_leaf': [1, 10, 20, 50, 100,500],\n",
    "    'min_samples_split': [2, 30, 40,100],\n",
    "}\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a based model\n",
    "rfc_gs = RandomForestClassifier()\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rfc_gs, param_grid = param_grid, \n",
    "                          cv = 10, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 96 candidates, totalling 960 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   21.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  5.5min\n",
      "[Parallel(n_jobs=-1)]: Done 960 out of 960 | elapsed:  8.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True], 'max_depth': [10, 15, 20, None],\n",
       "                         'min_samples_leaf': [1, 10, 20, 50, 100, 500],\n",
       "                         'min_samples_split': [2, 30, 40, 100]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_grid = grid_search.best_estimator_\n",
    "best_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'min_samples_leaf': [1],\n",
    "    'min_samples_split': [2],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator = rfc, param_grid = optimal_param_grid, \n",
    "                          cv = 25, n_jobs = -1, verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 25 folds for each of 1 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:   22.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=25, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'bootstrap': [True], 'min_samples_leaf': [1],\n",
       "                         'min_samples_split': [2]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Train Accuracy Baseline After Grid Search: 0.9997883597883598\n",
      "Random Forest Test Accuracy Baseline After Grid Search: 0.9223280423280423\n"
     ]
    }
   ],
   "source": [
    "rfc_pred_gs = grid_search.predict(X_test)\n",
    "\n",
    "y_pred_gs =  grid_search.predict(X_test)\n",
    "print (\"Random Forest Train Accuracy Baseline After Grid Search:\", metrics.accuracy_score(y_train, grid_search.predict(X_train)))\n",
    "print (\"Random Forest Test Accuracy Baseline After Grid Search:\", metrics.accuracy_score(y_test, grid_search.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy Rate')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_rate = []\n",
    "training_acc = []\n",
    "\n",
    "for i in range(1,100):   \n",
    "    rfc_plot = RandomForestClassifier(bootstrap=True,\n",
    "                       max_depth=20, \n",
    "                       min_samples_leaf=1, min_samples_split=2,\n",
    "                       n_estimators=i)\n",
    "    rfc_plot.fit(X_train, y_train) \n",
    "    accuracy_rate.append(rfc_plot.score(X_test, y_test))\n",
    "    training_acc.append(rfc_plot.score(X_train, y_train))\n",
    "    \n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(np.arange(1,100), accuracy_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.plot(np.arange(1,100), training_acc,color='red', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "\n",
    "#training > testing\n",
    "plt.title('Accuracy Rate vs. N-Estimators')\n",
    "plt.xlabel('N-Estimators')\n",
    "plt.ylabel('Accuracy Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}