tg5354_HW4-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.matlib\n",
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Variables for Q1. It includes learning rate, momentum and iterations for reporting accuracy for 10 runs\n",
    "np.random.seed(12)\n",
    "num_observations = 5000\n",
    "\n",
    "x1 = np.random.multivariate_normal([0, 0], [[1, .75],[.75, 1]], num_observations)\n",
    "x2 = np.random.multivariate_normal([1, 4], [[1, .75],[.75, 1]], num_observations)\n",
    "\n",
    "simulated_separableish_features = np.vstack((x1, x2)).astype(np.float32)\n",
    "simulated_labels = np.hstack((np.zeros(num_observations),\n",
    "                              np.ones(num_observations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x218b04e7388>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(simulated_separableish_features[:, 0], simulated_separableish_features[:, 1],\n",
    "            c = simulated_labels, alpha = .4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>6</th>\n",
       "      <th>15</th>\n",
       "      <th>12</th>\n",
       "      <th>1.1</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>0.21</th>\n",
       "      <th>0.22</th>\n",
       "      <th>0.23</th>\n",
       "      <th>6.3</th>\n",
       "      <th>14.1</th>\n",
       "      <th>7.4</th>\n",
       "      <th>1.3</th>\n",
       "      <th>0.24</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3817</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3818</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3819</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3820</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3821</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3822 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1   6  15  12  1.1  0.1  0.2  0.3  7  ...  0.21  0.22  0.23  6.3  \\\n",
       "0     0  0  10  16   6    0    0    0    0  7  ...     0     0     0   10   \n",
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       "4     0  0  11  16  10    1    0    0    0  4  ...     3     0     0   10   \n",
       "...  .. ..  ..  ..  ..  ...  ...  ...  ... ..  ...   ...   ...   ...  ...   \n",
       "3817  0  0   5  13  11    2    0    0    0  2  ...     0     0     0    8   \n",
       "3818  0  0   0   1  12    1    0    0    0  0  ...     0     0     0    0   \n",
       "3819  0  0   3  15   0    0    0    0    0  0  ...     0     0     0    4   \n",
       "3820  0  0   6  16   2    0    0    0    0  0  ...     0     0     0    5   \n",
       "3821  0  0   2  15  16   13    1    0    0  0  ...     0     0     0    4   \n",
       "\n",
       "      14.1  7.4  1.3  0.24  0.25  0.26  \n",
       "0       16   15    3     0     0     0  \n",
       "1       14    0    0     0     0     7  \n",
       "2        1   15    2     0     0     4  \n",
       "3       12   14    7     0     0     6  \n",
       "4       16   16   16    16     6     2  \n",
       "...    ...  ...  ...   ...   ...   ...  \n",
       "3817    13   15   10     1     0     9  \n",
       "3818     4    9    0     0     0     4  \n",
       "3819    14   16    9     0     0     6  \n",
       "3820    16   16   16     5     0     6  \n",
       "3821    14    1    0     0     0     7  \n",
       "\n",
       "[3822 rows x 65 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('optdigits.tra', sep=\",\")\n",
    "train.shape\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3822 entries, 0 to 3821\n",
      "Data columns (total 65 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
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      " 32  0.12    3822 non-null   int64\n",
      " 33  7.2     3822 non-null   int64\n",
      " 34  13      3822 non-null   int64\n",
      " 35  3.1     3822 non-null   int64\n",
      " 36  0.13    3822 non-null   int64\n",
      " 37  8.1     3822 non-null   int64\n",
      " 38  7.3     3822 non-null   int64\n",
      " 39  0.14    3822 non-null   int64\n",
      " 40  0.15    3822 non-null   int64\n",
      " 41  4       3822 non-null   int64\n",
      " 42  12.1    3822 non-null   int64\n",
      " 43  0.16    3822 non-null   int64\n",
      " 44  1.2     3822 non-null   int64\n",
      " 45  13.1    3822 non-null   int64\n",
      " 46  5.2     3822 non-null   int64\n",
      " 47  0.17    3822 non-null   int64\n",
      " 48  0.18    3822 non-null   int64\n",
      " 49  0.19    3822 non-null   int64\n",
      " 50  14      3822 non-null   int64\n",
      " 51  9       3822 non-null   int64\n",
      " 52  15.1    3822 non-null   int64\n",
      " 53  9.1     3822 non-null   int64\n",
      " 54  0.20    3822 non-null   int64\n",
      " 55  0.21    3822 non-null   int64\n",
      " 56  0.22    3822 non-null   int64\n",
      " 57  0.23    3822 non-null   int64\n",
      " 58  6.3     3822 non-null   int64\n",
      " 59  14.1    3822 non-null   int64\n",
      " 60  7.4     3822 non-null   int64\n",
      " 61  1.3     3822 non-null   int64\n",
      " 62  0.24    3822 non-null   int64\n",
      " 63  0.25    3822 non-null   int64\n",
      " 64  0.26    3822 non-null   int64\n",
      "dtypes: int64(65)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
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       "[5 rows x 65 columns]"
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     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       int64\n",
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     "metadata": {},
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   "source": [
    "train.dtypes"
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  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>...</th>\n",
       "      <th>0.21</th>\n",
       "      <th>0.22</th>\n",
       "      <th>0.23</th>\n",
       "      <th>6.3</th>\n",
       "      <th>14.1</th>\n",
       "      <th>7.4</th>\n",
       "      <th>1.3</th>\n",
       "      <th>0.24</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3822.0</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.000000</td>\n",
       "      <td>3822.00000</td>\n",
       "      <td>3822.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.301151</td>\n",
       "      <td>5.481685</td>\n",
       "      <td>11.805076</td>\n",
       "      <td>11.451334</td>\n",
       "      <td>5.506541</td>\n",
       "      <td>1.387755</td>\n",
       "      <td>0.142334</td>\n",
       "      <td>0.002093</td>\n",
       "      <td>1.959184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148352</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.283098</td>\n",
       "      <td>5.855835</td>\n",
       "      <td>11.942439</td>\n",
       "      <td>11.462323</td>\n",
       "      <td>6.701988</td>\n",
       "      <td>2.106227</td>\n",
       "      <td>0.20225</td>\n",
       "      <td>4.498430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.867026</td>\n",
       "      <td>4.632199</td>\n",
       "      <td>4.260055</td>\n",
       "      <td>4.538141</td>\n",
       "      <td>5.613322</td>\n",
       "      <td>3.371810</td>\n",
       "      <td>1.051733</td>\n",
       "      <td>0.088583</td>\n",
       "      <td>3.051663</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767858</td>\n",
       "      <td>0.016175</td>\n",
       "      <td>0.928156</td>\n",
       "      <td>4.980663</td>\n",
       "      <td>4.334947</td>\n",
       "      <td>4.992066</td>\n",
       "      <td>5.775834</td>\n",
       "      <td>4.028649</td>\n",
       "      <td>1.15084</td>\n",
       "      <td>2.869284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0            1            6           15           12  \\\n",
       "count  3822.0  3822.000000  3822.000000  3822.000000  3822.000000   \n",
       "mean      0.0     0.301151     5.481685    11.805076    11.451334   \n",
       "std       0.0     0.867026     4.632199     4.260055     4.538141   \n",
       "min       0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.0     0.000000     1.000000    10.000000     9.000000   \n",
       "50%       0.0     0.000000     5.000000    13.000000    13.000000   \n",
       "75%       0.0     0.000000     9.000000    15.000000    15.000000   \n",
       "max       0.0     8.000000    16.000000    16.000000    16.000000   \n",
       "\n",
       "               1.1          0.1          0.2          0.3            7  ...  \\\n",
       "count  3822.000000  3822.000000  3822.000000  3822.000000  3822.000000  ...   \n",
       "mean      5.506541     1.387755     0.142334     0.002093     1.959184  ...   \n",
       "std       5.613322     3.371810     1.051733     0.088583     3.051663  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       4.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%      10.000000     0.000000     0.000000     0.000000     3.000000  ...   \n",
       "max      16.000000    16.000000    16.000000     5.000000    15.000000  ...   \n",
       "\n",
       "              0.21         0.22         0.23          6.3         14.1  \\\n",
       "count  3822.000000  3822.000000  3822.000000  3822.000000  3822.000000   \n",
       "mean      0.148352     0.000262     0.283098     5.855835    11.942439   \n",
       "std       0.767858     0.016175     0.928156     4.980663     4.334947   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     1.000000    10.000000   \n",
       "50%       0.000000     0.000000     0.000000     5.000000    13.000000   \n",
       "75%       0.000000     0.000000     0.000000    10.000000    15.000000   \n",
       "max      12.000000     1.000000    10.000000    16.000000    16.000000   \n",
       "\n",
       "               7.4          1.3         0.24        0.25         0.26  \n",
       "count  3822.000000  3822.000000  3822.000000  3822.00000  3822.000000  \n",
       "mean     11.462323     6.701988     2.106227     0.20225     4.498430  \n",
       "std       4.992066     5.775834     4.028649     1.15084     2.869284  \n",
       "min       0.000000     0.000000     0.000000     0.00000     0.000000  \n",
       "25%       9.000000     0.000000     0.000000     0.00000     2.000000  \n",
       "50%      13.000000     6.000000     0.000000     0.00000     4.000000  \n",
       "75%      16.000000    12.000000     2.000000     0.00000     7.000000  \n",
       "max      16.000000    16.000000    16.000000    16.00000     9.000000  \n",
       "\n",
       "[8 rows x 65 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['0', '1', '6', '15', '12', '1.1', '0.1', '0.2', '0.3', '7', '16', '6.1',\n",
      "       '6.2', '10', '0.4', '0.5', '0.6', '8', '16.1', '2', '0.7', '11', '2.1',\n",
      "       '0.8', '0.9', '5', '16.2', '3', '0.10', '5.1', '7.1', '0.11', '0.12',\n",
      "       '7.2', '13', '3.1', '0.13', '8.1', '7.3', '0.14', '0.15', '4', '12.1',\n",
      "       '0.16', '1.2', '13.1', '5.2', '0.17', '0.18', '0.19', '14', '9', '15.1',\n",
      "       '9.1', '0.20', '0.21', '0.22', '0.23', '6.3', '14.1', '7.4', '1.3',\n",
      "       '0.24', '0.25', '0.26'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********This is my Testing set**********\n",
      "   0  0.1   5  13   9   1  0.2  0.3  0.4  0.5  ...  0.23  0.24  0.25  6  13.2  \\\n",
      "0  0    0   0  12  13   5    0    0    0    0  ...     0     0     0  0    11   \n",
      "1  0    0   0   4  15  12    0    0    0    0  ...     0     0     0  0     3   \n",
      "2  0    0   7  15  13   1    0    0    0    8  ...     0     0     0  7    13   \n",
      "3  0    0   0   1  11   0    0    0    0    0  ...     0     0     0  0     2   \n",
      "4  0    0  12  10   0   0    0    0    0    0  ...     0     0     0  9    16   \n",
      "\n",
      "   10.2  0.26  0.27  0.28  0.29  \n",
      "0    16    10     0     0     1  \n",
      "1    11    16     9     0     2  \n",
      "2    13     9     0     0     3  \n",
      "3    16     4     0     0     4  \n",
      "4    16    10     0     0     5  \n",
      "\n",
      "[5 rows x 65 columns]\n",
      "(1796, 65)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>5</th>\n",
       "      <th>13</th>\n",
       "      <th>9</th>\n",
       "      <th>1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>...</th>\n",
       "      <th>0.23</th>\n",
       "      <th>0.24</th>\n",
       "      <th>0.25</th>\n",
       "      <th>6</th>\n",
       "      <th>13.2</th>\n",
       "      <th>10.2</th>\n",
       "      <th>0.26</th>\n",
       "      <th>0.27</th>\n",
       "      <th>0.28</th>\n",
       "      <th>0.29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1796.0</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.00000</td>\n",
       "      <td>1796.00000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "      <td>1796.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.304009</td>\n",
       "      <td>5.204900</td>\n",
       "      <td>11.835189</td>\n",
       "      <td>11.849666</td>\n",
       "      <td>5.784521</td>\n",
       "      <td>1.363029</td>\n",
       "      <td>0.129733</td>\n",
       "      <td>0.005568</td>\n",
       "      <td>1.994989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206570</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>0.279510</td>\n",
       "      <td>5.55735</td>\n",
       "      <td>12.08853</td>\n",
       "      <td>11.810134</td>\n",
       "      <td>6.767817</td>\n",
       "      <td>2.069042</td>\n",
       "      <td>0.364699</td>\n",
       "      <td>4.493318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.907416</td>\n",
       "      <td>4.756148</td>\n",
       "      <td>4.249936</td>\n",
       "      <td>4.288055</td>\n",
       "      <td>5.666871</td>\n",
       "      <td>3.326546</td>\n",
       "      <td>1.037667</td>\n",
       "      <td>0.094248</td>\n",
       "      <td>3.196704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984663</td>\n",
       "      <td>0.023596</td>\n",
       "      <td>0.934539</td>\n",
       "      <td>5.10443</td>\n",
       "      <td>4.37586</td>\n",
       "      <td>4.935137</td>\n",
       "      <td>5.900105</td>\n",
       "      <td>4.091396</td>\n",
       "      <td>1.860620</td>\n",
       "      <td>2.864140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          0.1            5           13            9  \\\n",
       "count  1796.0  1796.000000  1796.000000  1796.000000  1796.000000   \n",
       "mean      0.0     0.304009     5.204900    11.835189    11.849666   \n",
       "std       0.0     0.907416     4.756148     4.249936     4.288055   \n",
       "min       0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.0     0.000000     1.000000    10.000000    10.000000   \n",
       "50%       0.0     0.000000     4.000000    13.000000    13.000000   \n",
       "75%       0.0     0.000000     9.000000    15.000000    15.000000   \n",
       "max       0.0     8.000000    16.000000    16.000000    16.000000   \n",
       "\n",
       "                 1          0.2          0.3          0.4          0.5  ...  \\\n",
       "count  1796.000000  1796.000000  1796.000000  1796.000000  1796.000000  ...   \n",
       "mean      5.784521     1.363029     0.129733     0.005568     1.994989  ...   \n",
       "std       5.666871     3.326546     1.037667     0.094248     3.196704  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       4.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%      11.000000     0.000000     0.000000     0.000000     3.000000  ...   \n",
       "max      16.000000    16.000000    15.000000     2.000000    16.000000  ...   \n",
       "\n",
       "              0.23         0.24         0.25           6        13.2  \\\n",
       "count  1796.000000  1796.000000  1796.000000  1796.00000  1796.00000   \n",
       "mean      0.206570     0.000557     0.279510     5.55735    12.08853   \n",
       "std       0.984663     0.023596     0.934539     5.10443     4.37586   \n",
       "min       0.000000     0.000000     0.000000     0.00000     0.00000   \n",
       "25%       0.000000     0.000000     0.000000     1.00000    11.00000   \n",
       "50%       0.000000     0.000000     0.000000     4.00000    13.00000   \n",
       "75%       0.000000     0.000000     0.000000    10.00000    16.00000   \n",
       "max      13.000000     1.000000     9.000000    16.00000    16.00000   \n",
       "\n",
       "              10.2         0.26         0.27         0.28         0.29  \n",
       "count  1796.000000  1796.000000  1796.000000  1796.000000  1796.000000  \n",
       "mean     11.810134     6.767817     2.069042     0.364699     4.493318  \n",
       "std       4.935137     5.900105     4.091396     1.860620     2.864140  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%      10.000000     0.000000     0.000000     0.000000     2.000000  \n",
       "50%      14.000000     6.000000     0.000000     0.000000     4.000000  \n",
       "75%      16.000000    12.000000     2.000000     0.000000     7.000000  \n",
       "max      16.000000    16.000000    16.000000    16.000000     9.000000  \n",
       "\n",
       "[8 rows x 65 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('optdigits.tes', sep=\",\")\n",
    "print(\"**********This is my Testing set**********\")\n",
    "print(test.head())\n",
    "print(test.shape)\n",
    "test.info\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    389\n",
       "1    389\n",
       "7    387\n",
       "4    387\n",
       "9    382\n",
       "8    380\n",
       "2    380\n",
       "6    377\n",
       "5    376\n",
       "0    375\n",
       "Name: 0.26, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['0.26'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     486\n",
       "16    188\n",
       "12    103\n",
       "15    101\n",
       "8      90\n",
       "1      83\n",
       "3      81\n",
       "5      74\n",
       "14     72\n",
       "11     70\n",
       "9      68\n",
       "6      67\n",
       "4      66\n",
       "2      66\n",
       "7      63\n",
       "10     61\n",
       "13     57\n",
       "Name: 0.26, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['0.26'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train index:  [ 383  384  385 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382] \n",
      "\n",
      "[[ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  1 ...  0  0  0]\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  2 ...  0  0  0]\n",
      " [ 0  0  6 ...  7  0  0]\n",
      " [ 0  0  3 ... 14  1  0]] 383     6\n",
      "384     1\n",
      "385     4\n",
      "386     9\n",
      "387     1\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3439, dtype: int64 0      0\n",
      "1      7\n",
      "2      4\n",
      "3      6\n",
      "4      2\n",
      "      ..\n",
      "378    8\n",
      "379    2\n",
      "380    0\n",
      "381    5\n",
      "382    7\n",
      "Name: 0.26, Length: 383, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400\n",
      " 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418\n",
      " 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436\n",
      " 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454\n",
      " 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472\n",
      " 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490\n",
      " 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508\n",
      " 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526\n",
      " 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544\n",
      " 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562\n",
      " 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580\n",
      " 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598\n",
      " 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616\n",
      " 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634\n",
      " 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652\n",
      " 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670\n",
      " 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688\n",
      " 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706\n",
      " 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724\n",
      " 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742\n",
      " 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760\n",
      " 761 762 763 764 765] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  1 ...  0  0  0]\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 0  2 16 ... 16  5  0]\n",
      " [ 0  1 14 ...  0  0  0]\n",
      " [ 0  0  0 ...  2  0  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3439, dtype: int64 383    6\n",
      "384    1\n",
      "385    4\n",
      "386    9\n",
      "387    1\n",
      "      ..\n",
      "761    1\n",
      "762    7\n",
      "763    7\n",
      "764    2\n",
      "765    6\n",
      "Name: 0.26, Length: 383, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [ 766  767  768  769  770  771  772  773  774  775  776  777  778  779\n",
      "  780  781  782  783  784  785  786  787  788  789  790  791  792  793\n",
      "  794  795  796  797  798  799  800  801  802  803  804  805  806  807\n",
      "  808  809  810  811  812  813  814  815  816  817  818  819  820  821\n",
      "  822  823  824  825  826  827  828  829  830  831  832  833  834  835\n",
      "  836  837  838  839  840  841  842  843  844  845  846  847  848  849\n",
      "  850  851  852  853  854  855  856  857  858  859  860  861  862  863\n",
      "  864  865  866  867  868  869  870  871  872  873  874  875  876  877\n",
      "  878  879  880  881  882  883  884  885  886  887  888  889  890  891\n",
      "  892  893  894  895  896  897  898  899  900  901  902  903  904  905\n",
      "  906  907  908  909  910  911  912  913  914  915  916  917  918  919\n",
      "  920  921  922  923  924  925  926  927  928  929  930  931  932  933\n",
      "  934  935  936  937  938  939  940  941  942  943  944  945  946  947\n",
      "  948  949  950  951  952  953  954  955  956  957  958  959  960  961\n",
      "  962  963  964  965  966  967  968  969  970  971  972  973  974  975\n",
      "  976  977  978  979  980  981  982  983  984  985  986  987  988  989\n",
      "  990  991  992  993  994  995  996  997  998  999 1000 1001 1002 1003\n",
      " 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017\n",
      " 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031\n",
      " 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045\n",
      " 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059\n",
      " 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073\n",
      " 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087\n",
      " 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101\n",
      " 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115\n",
      " 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129\n",
      " 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143\n",
      " 1144 1145 1146 1147] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  1 12 ... 11  0  0]\n",
      " [ 0  0 11 ... 15  2  0]\n",
      " [ 0  3 16 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  2 ...  6  0  0]\n",
      " [ 0  0  0 ...  1  0  0]\n",
      " [ 0  0  0 ...  0  0  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 766     3\n",
      "767     5\n",
      "768     5\n",
      "769     9\n",
      "770     2\n",
      "       ..\n",
      "1143    6\n",
      "1144    8\n",
      "1145    6\n",
      "1146    4\n",
      "1147    4\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161\n",
      " 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175\n",
      " 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189\n",
      " 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203\n",
      " 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217\n",
      " 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231\n",
      " 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245\n",
      " 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259\n",
      " 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273\n",
      " 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287\n",
      " 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301\n",
      " 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315\n",
      " 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329\n",
      " 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343\n",
      " 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357\n",
      " 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371\n",
      " 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385\n",
      " 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399\n",
      " 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413\n",
      " 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427\n",
      " 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441\n",
      " 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455\n",
      " 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469\n",
      " 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483\n",
      " 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497\n",
      " 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511\n",
      " 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525\n",
      " 1526 1527 1528 1529] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0 10 ...  8  0  0]\n",
      " [ 0  0  5 ...  2  0  0]\n",
      " [ 0  0  4 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  3 ... 12  1  0]\n",
      " [ 0  0  8 ...  3  0  0]\n",
      " [ 0  0  0 ...  1  0  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 1148    5\n",
      "1149    8\n",
      "1150    0\n",
      "1151    1\n",
      "1152    0\n",
      "       ..\n",
      "1525    1\n",
      "1526    2\n",
      "1527    8\n",
      "1528    7\n",
      "1529    4\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543\n",
      " 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557\n",
      " 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571\n",
      " 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585\n",
      " 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599\n",
      " 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613\n",
      " 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627\n",
      " 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641\n",
      " 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655\n",
      " 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669\n",
      " 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683\n",
      " 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697\n",
      " 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711\n",
      " 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725\n",
      " 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739\n",
      " 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753\n",
      " 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767\n",
      " 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781\n",
      " 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795\n",
      " 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809\n",
      " 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823\n",
      " 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837\n",
      " 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851\n",
      " 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865\n",
      " 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879\n",
      " 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893\n",
      " 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907\n",
      " 1908 1909 1910 1911] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0  0 ...  7  0  0]\n",
      " [ 0  0  6 ... 15  2  0]\n",
      " [ 0  0  6 ...  9  0  0]\n",
      " ...\n",
      " [ 0  0  4 ...  8  0  0]\n",
      " [ 0  0 14 ...  1  0  0]\n",
      " [ 0  1 14 ... 16  5  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 1530    1\n",
      "1531    5\n",
      "1532    9\n",
      "1533    2\n",
      "1534    2\n",
      "       ..\n",
      "1907    8\n",
      "1908    2\n",
      "1909    8\n",
      "1910    7\n",
      "1911    5\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925\n",
      " 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939\n",
      " 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953\n",
      " 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967\n",
      " 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981\n",
      " 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995\n",
      " 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009\n",
      " 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n",
      " 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037\n",
      " 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051\n",
      " 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065\n",
      " 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079\n",
      " 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093\n",
      " 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107\n",
      " 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121\n",
      " 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135\n",
      " 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149\n",
      " 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163\n",
      " 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177\n",
      " 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191\n",
      " 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205\n",
      " 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219\n",
      " 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233\n",
      " 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247\n",
      " 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261\n",
      " 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275\n",
      " 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289\n",
      " 2290 2291 2292 2293] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0  2 ...  0  0  0]\n",
      " [ 0  2 15 ...  3  0  0]\n",
      " [ 0  0  8 ...  9  0  0]\n",
      " ...\n",
      " [ 0  1 10 ... 15  1  0]\n",
      " [ 0  0  5 ... 12  0  0]\n",
      " [ 0  0  5 ... 13  2  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 1912    6\n",
      "1913    3\n",
      "1914    9\n",
      "1915    4\n",
      "1916    1\n",
      "       ..\n",
      "2289    4\n",
      "2290    0\n",
      "2291    5\n",
      "2292    8\n",
      "2293    3\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307\n",
      " 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321\n",
      " 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335\n",
      " 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349\n",
      " 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363\n",
      " 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377\n",
      " 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391\n",
      " 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405\n",
      " 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419\n",
      " 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433\n",
      " 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447\n",
      " 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461\n",
      " 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475\n",
      " 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489\n",
      " 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503\n",
      " 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517\n",
      " 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531\n",
      " 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545\n",
      " 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559\n",
      " 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573\n",
      " 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587\n",
      " 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601\n",
      " 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615\n",
      " 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629\n",
      " 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643\n",
      " 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657\n",
      " 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671\n",
      " 2672 2673 2674 2675] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[0 0 4 ... 1 0 0]\n",
      " [0 0 6 ... 0 0 0]\n",
      " [0 0 0 ... 2 0 0]\n",
      " ...\n",
      " [0 0 4 ... 6 0 0]\n",
      " [0 0 9 ... 6 0 0]\n",
      " [0 0 8 ... 0 0 0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 2294    9\n",
      "2295    4\n",
      "2296    4\n",
      "2297    2\n",
      "2298    9\n",
      "       ..\n",
      "2671    0\n",
      "2672    1\n",
      "2673    5\n",
      "2674    3\n",
      "2675    8\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689\n",
      " 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703\n",
      " 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717\n",
      " 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731\n",
      " 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745\n",
      " 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759\n",
      " 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787\n",
      " 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801\n",
      " 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815\n",
      " 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829\n",
      " 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843\n",
      " 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857\n",
      " 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871\n",
      " 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885\n",
      " 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899\n",
      " 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913\n",
      " 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927\n",
      " 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941\n",
      " 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955\n",
      " 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969\n",
      " 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983\n",
      " 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997\n",
      " 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011\n",
      " 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025\n",
      " 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039\n",
      " 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053\n",
      " 3054 3055 3056 3057] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0  0 ... 11  0  0]\n",
      " [ 0  0 10 ... 15  0  0]\n",
      " [ 0  0  9 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  2 ...  3  0  0]\n",
      " [ 0  0  5 ...  3  0  0]\n",
      " [ 0  0  0 ...  1  0  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 2676    4\n",
      "2677    5\n",
      "2678    9\n",
      "2679    5\n",
      "2680    4\n",
      "       ..\n",
      "3053    2\n",
      "3054    1\n",
      "3055    2\n",
      "3056    0\n",
      "3057    0\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3819 3820 3821] \n",
      "\n",
      "Test index:  [3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071\n",
      " 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085\n",
      " 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099\n",
      " 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113\n",
      " 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127\n",
      " 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141\n",
      " 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155\n",
      " 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169\n",
      " 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183\n",
      " 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197\n",
      " 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211\n",
      " 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225\n",
      " 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239\n",
      " 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253\n",
      " 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267\n",
      " 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281\n",
      " 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295\n",
      " 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309\n",
      " 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323\n",
      " 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337\n",
      " 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351\n",
      " 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365\n",
      " 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379\n",
      " 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393\n",
      " 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407\n",
      " 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421\n",
      " 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435\n",
      " 3436 3437 3438 3439] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] [[ 0  0  9 ... 12  1  0]\n",
      " [ 0  2 14 ...  0  0  0]\n",
      " [ 0  0  0 ...  2  0  0]\n",
      " ...\n",
      " [ 0  0  4 ...  4  0  0]\n",
      " [ 0  0  6 ... 10  1  0]\n",
      " [ 0  0  8 ...  5  0  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 3440, dtype: int64 3058    5\n",
      "3059    8\n",
      "3060    4\n",
      "3061    4\n",
      "3062    3\n",
      "       ..\n",
      "3435    9\n",
      "3436    5\n",
      "3437    0\n",
      "3438    8\n",
      "3439    0\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "Train index:  [   0    1    2 ... 3437 3438 3439] \n",
      "\n",
      "Test index:  [3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453\n",
      " 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467\n",
      " 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481\n",
      " 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495\n",
      " 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509\n",
      " 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523\n",
      " 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537\n",
      " 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551\n",
      " 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565\n",
      " 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579\n",
      " 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593\n",
      " 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607\n",
      " 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621\n",
      " 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635\n",
      " 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649\n",
      " 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663\n",
      " 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677\n",
      " 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691\n",
      " 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705\n",
      " 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719\n",
      " 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733\n",
      " 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747\n",
      " 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761\n",
      " 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775\n",
      " 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789\n",
      " 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803\n",
      " 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817\n",
      " 3818 3819 3820 3821] \n",
      "\n",
      "[[ 0  0 10 ...  0  0  0]\n",
      " [ 0  0  8 ... 13  0  0]\n",
      " [ 0  0  0 ... 16  0  0]\n",
      " ...\n",
      " [ 0  0  4 ...  4  0  0]\n",
      " [ 0  0  6 ... 10  1  0]\n",
      " [ 0  0  8 ...  5  0  0]] [[ 0  0  0 ...  0  0  0]\n",
      " [ 0  0  4 ...  2  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  3 ...  0  0  0]\n",
      " [ 0  0  6 ...  0  0  0]\n",
      " [ 0  0  2 ... 13  1  0]] 0       0\n",
      "1       7\n",
      "2       4\n",
      "3       6\n",
      "4       2\n",
      "       ..\n",
      "3435    9\n",
      "3436    5\n",
      "3437    0\n",
      "3438    8\n",
      "3439    0\n",
      "Name: 0.26, Length: 3440, dtype: int64 3440    4\n",
      "3441    9\n",
      "3442    2\n",
      "3443    0\n",
      "3444    3\n",
      "       ..\n",
      "3817    9\n",
      "3818    4\n",
      "3819    6\n",
      "3820    6\n",
      "3821    7\n",
      "Name: 0.26, Length: 382, dtype: int64\n",
      "KFold(n_splits=10, random_state=42, shuffle=False)\n"
     ]
    }
   ],
   "source": [
    "#Defining certain functions for performing the logistic regression(without its sklearn library) on test data for classes 1 and 7\n",
    "#and calculating errors and for deleting the classes as mentioned\n",
    "\n",
    "features = train.values[:,:8]\n",
    "target = train['0.26']\n",
    "kf = KFold(n_splits=10, random_state = 42)\n",
    "kf.get_n_splits(features)\n",
    "\n",
    "for train_index, test_index in kf.split(features):\n",
    "    print(\"Train index: \", train_index, \"\\n\")\n",
    "    print(\"Test index: \", test_index, \"\\n\")\n",
    "    features_train, features_test = features[train_index], features[test_index]\n",
    "    target_train, target_test = target[train_index], target[test_index]\n",
    "    print(features_train, features_test, target_train, target_test)\n",
    "\n",
    "features_train, features_test, target_train, target_test = train_test_split(features,\n",
    "                                                                            target, test_size = 0.80, random_state = 10)\n",
    "def calc_train_error(features_train, target_train, df):\n",
    "    #returns in-sample error for already fit model\n",
    "    predictor = df.predict(features_train)\n",
    "    mse = mean_squared_error(target_train, predictor)\n",
    "    rmse = np.sqrt(mse)\n",
    "    return mse\n",
    "\n",
    "def calc_validation_error(features_test, target_test, df):\n",
    "    #returns out-of-sample error for already fit model\n",
    "    predictor = df.predict(features_test)\n",
    "    mse = mean_squared_error(target_test, predictor)\n",
    "    rmse = np.sqrt(mse)\n",
    "    return mse\n",
    "\n",
    "def calc_metrics(features_train, target_train, features_test, target_test, df):\n",
    "    #fits model and returns the RMSE for in-sample error and out-of-sample error\n",
    "    df.fit(features_train, target_train)\n",
    "    train_error = calc_train_error(features_train, target_train, df)\n",
    "    validation_error = calc_validation_error(features_test, target_test, model)\n",
    "    return train_error, validation_error\n",
    "\n",
    "def sigmoid(scores):\n",
    "    return 1 / (1 + np.exp(-scores))\n",
    "\n",
    "def log_likelihood(features, target, weights):\n",
    "    scores = np.dot(features, weights)\n",
    "    ll = np.sum( target*scores - np.log(1 + np.exp(scores)) )\n",
    "    return ll\n",
    "\n",
    "print(kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 7, 4, 6, 2, 5, 8, 1, 9, 3], dtype=int64)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['0.26'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calling logistic regression function with respective parameters(learning rate, log likelihood, etc.)\n",
    "\n",
    "def logistic_regression(features, target, num_steps, learning_rate, add_intercept = False):\n",
    "    if add_intercept:\n",
    "        intercept = np.ones((features.shape[0], 1))\n",
    "        features = np.hstack((intercept, features))\n",
    "        \n",
    "    weights = np.zeros(features.shape[1])\n",
    "    \n",
    "    for step in range(num_steps):\n",
    "        scores = np.dot(features, weights)\n",
    "        predictions = sigmoid(scores)\n",
    "\n",
    "        # Update weights with gradient\n",
    "        output_error_signal = target - predictions\n",
    "        gradient = np.dot(features.T, output_error_signal)\n",
    "        weights += learning_rate * gradient\n",
    "        \n",
    "        # Print log-likelihood every so often\n",
    "        if step % 10000 == 0:\n",
    "            print(log_likelihood(features, target, weights))\n",
    "        \n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-120417.7113284467\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n",
      "-140.72542135269305\n"
     ]
    }
   ],
   "source": [
    "#Parameters for the above function.It mentions the values for iterations, learning rate and momentum.\n",
    "\n",
    "weights = logistic_regression(simulated_separableish_features, simulated_labels,\n",
    "                     num_steps = 300000, learning_rate = 0.005, add_intercept=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.09231079] [[-5.05902155  8.28959202]]\n",
      "[-14.09225541  -5.05899648   8.28955762]\n"
     ]
    }
   ],
   "source": [
    "#Comparing it with logistic regression function using sci-kit learn Neural Network.\n",
    "#This is Q3 of the assignment. I am implementing this by taking a new value of C to increase the test error!\n",
    "\n",
    "clf = LogisticRegression(fit_intercept=True, C = 1e15)\n",
    "clf.fit(simulated_separableish_features, simulated_labels)\n",
    "\n",
    "print(clf.intercept_, clf.coef_)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from scratch: 0.9948\n",
      "Accuracy from sk-learn: 0.9948\n"
     ]
    }
   ],
   "source": [
    "#Calculating the error/accuracy for this algorithm\n",
    "data_with_intercept = np.hstack((np.ones((simulated_separableish_features.shape[0], 1)),\n",
    "                                 simulated_separableish_features))\n",
    "final_scores = np.dot(data_with_intercept, weights)\n",
    "preds = np.round(sigmoid(final_scores))\n",
    "\n",
    "print('Accuracy from scratch: {0}'.format((preds == simulated_labels).sum().astype(float) / len(preds)))\n",
    "print('Accuracy from sk-learn: {0}'.format(clf.score(simulated_separableish_features, simulated_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x218b2bfbdc8>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(simulated_separableish_features[:, 0], simulated_separableish_features[:, 1],\n",
    "            c = preds == simulated_labels - 1, alpha = .8, s = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000218B0DDBA88>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('0.26')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(classification_report(target_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3822 entries, 0 to 3821\n",
      "Data columns (total 65 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   0       3822 non-null   int64\n",
      " 1   1       3822 non-null   int64\n",
      " 2   6       3822 non-null   int64\n",
      " 3   15      3822 non-null   int64\n",
      " 4   12      3822 non-null   int64\n",
      " 5   1.1     3822 non-null   int64\n",
      " 6   0.1     3822 non-null   int64\n",
      " 7   0.2     3822 non-null   int64\n",
      " 8   0.3     3822 non-null   int64\n",
      " 9   7       3822 non-null   int64\n",
      " 10  16      3822 non-null   int64\n",
      " 11  6.1     3822 non-null   int64\n",
      " 12  6.2     3822 non-null   int64\n",
      " 13  10      3822 non-null   int64\n",
      " 14  0.4     3822 non-null   int64\n",
      " 15  0.5     3822 non-null   int64\n",
      " 16  0.6     3822 non-null   int64\n",
      " 17  8       3822 non-null   int64\n",
      " 18  16.1    3822 non-null   int64\n",
      " 19  2       3822 non-null   int64\n",
      " 20  0.7     3822 non-null   int64\n",
      " 21  11      3822 non-null   int64\n",
      " 22  2.1     3822 non-null   int64\n",
      " 23  0.8     3822 non-null   int64\n",
      " 24  0.9     3822 non-null   int64\n",
      " 25  5       3822 non-null   int64\n",
      " 26  16.2    3822 non-null   int64\n",
      " 27  3       3822 non-null   int64\n",
      " 28  0.10    3822 non-null   int64\n",
      " 29  5.1     3822 non-null   int64\n",
      " 30  7.1     3822 non-null   int64\n",
      " 31  0.11    3822 non-null   int64\n",
      " 32  0.12    3822 non-null   int64\n",
      " 33  7.2     3822 non-null   int64\n",
      " 34  13      3822 non-null   int64\n",
      " 35  3.1     3822 non-null   int64\n",
      " 36  0.13    3822 non-null   int64\n",
      " 37  8.1     3822 non-null   int64\n",
      " 38  7.3     3822 non-null   int64\n",
      " 39  0.14    3822 non-null   int64\n",
      " 40  0.15    3822 non-null   int64\n",
      " 41  4       3822 non-null   int64\n",
      " 42  12.1    3822 non-null   int64\n",
      " 43  0.16    3822 non-null   int64\n",
      " 44  1.2     3822 non-null   int64\n",
      " 45  13.1    3822 non-null   int64\n",
      " 46  5.2     3822 non-null   int64\n",
      " 47  0.17    3822 non-null   int64\n",
      " 48  0.18    3822 non-null   int64\n",
      " 49  0.19    3822 non-null   int64\n",
      " 50  14      3822 non-null   int64\n",
      " 51  9       3822 non-null   int64\n",
      " 52  15.1    3822 non-null   int64\n",
      " 53  9.1     3822 non-null   int64\n",
      " 54  0.20    3822 non-null   int64\n",
      " 55  0.21    3822 non-null   int64\n",
      " 56  0.22    3822 non-null   int64\n",
      " 57  0.23    3822 non-null   int64\n",
      " 58  6.3     3822 non-null   int64\n",
      " 59  14.1    3822 non-null   int64\n",
      " 60  7.4     3822 non-null   int64\n",
      " 61  1.3     3822 non-null   int64\n",
      " 62  0.24    3822 non-null   int64\n",
      " 63  0.25    3822 non-null   int64\n",
      " 64  0.26    3822 non-null   int64\n",
      "dtypes: int64(65)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "#Looking at features to decide which ones should be eliminated. This is Q2 of the assignment\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.sum of 0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "3817    False\n",
       "3818    False\n",
       "3819    False\n",
       "3820    False\n",
       "3821    False\n",
       "Name: 0.10, Length: 3822, dtype: bool>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['0.10'].isnull().sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.03954653 0.23059534 0.22266144 0.20978762 0.20187284\n",
      " 0.08637917 0.00915706]\n"
     ]
    }
   ],
   "source": [
    "#Performing feature importance, however, this is not included with Logistic regression. This is for my understanding.\n",
    "model = ExtraTreesClassifier(n_estimators=10)\n",
    "model.fit(features, target)\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Features: 3\n",
      "Selected Features: [False  True False False False False  True  True]\n",
      "Feature Ranking: [6 1 2 4 5 3 1 1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\juhik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "#Performing feature importance with logistic regression that uses RFE\n",
    "model1 = LogisticRegression(solver='lbfgs')\n",
    "rfe = RFE(model1, 3)\n",
    "fit = rfe.fit(features, target)\n",
    "print(\"Num Features: %s\" % (fit.n_features_))\n",
    "print(\"Selected Features: %s\" % (fit.support_))\n",
    "print(\"Feature Ranking: %s\" % (fit.ranking_))\n",
    "\n",
    "#notice, the number of iterations reached limit. In this case, we can ignore this, because we care about the selected features\n",
    "#however, it is not a good idea to ignore such a warning, specially while using logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The above operation gave me top 3 features that should not be eliminated. So, I will eliminate feature columns 4, 7 and 16 here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0  1   6  15  1.1  0.1  0.2  0.3  7  16  ...  0.21  0.22  0.23  6.3  \\\n",
      "0     0  0  10  16    0    0    0    0  7  16  ...     0     0     0   10   \n",
      "1     0  0   8  15   13    0    0    0  1  11  ...     0     0     0    9   \n",
      "2     0  0   0   3   16    0    0    0  0   5  ...     0     0     0    0   \n",
      "3     0  0   5  14    0    0    0    0  0  13  ...     0     0     0    4   \n",
      "4     0  0  11  16    1    0    0    0  4  16  ...     3     0     0   10   \n",
      "...  .. ..  ..  ..  ...  ...  ...  ... ..  ..  ...   ...   ...   ...  ...   \n",
      "3817  0  0   5  13    2    0    0    0  2  15  ...     0     0     0    8   \n",
      "3818  0  0   0   1    1    0    0    0  0   0  ...     0     0     0    0   \n",
      "3819  0  0   3  15    0    0    0    0  0  11  ...     0     0     0    4   \n",
      "3820  0  0   6  16    0    0    0    0  0  15  ...     0     0     0    5   \n",
      "3821  0  0   2  15   13    1    0    0  0   3  ...     0     0     0    4   \n",
      "\n",
      "      14.1  7.4  1.3  0.24  0.25  0.26  \n",
      "0       16   15    3     0     0     0  \n",
      "1       14    0    0     0     0     7  \n",
      "2        1   15    2     0     0     4  \n",
      "3       12   14    7     0     0     6  \n",
      "4       16   16   16    16     6     2  \n",
      "...    ...  ...  ...   ...   ...   ...  \n",
      "3817    13   15   10     1     0     9  \n",
      "3818     4    9    0     0     0     4  \n",
      "3819    14   16    9     0     0     6  \n",
      "3820    16   16   16     5     0     6  \n",
      "3821    14    1    0     0     0     7  \n",
      "\n",
      "[3822 rows x 64 columns]\n"
     ]
    }
   ],
   "source": [
    "a = train.drop(train.columns[4],axis=1)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from scratch: 0.9948\n",
      "Accuracy from sk-learn: 0.9948\n"
     ]
    }
   ],
   "source": [
    "data_with_intercept = np.hstack((np.ones((simulated_separableish_features.shape[0], 1)),\n",
    "                                 simulated_separableish_features))\n",
    "final_scores = np.dot(data_with_intercept, weights)\n",
    "preds = np.round(sigmoid(final_scores))\n",
    "\n",
    "print('Accuracy from scratch: {0}'.format((preds == simulated_labels).sum().astype(float) / len(preds)))\n",
    "print('Accuracy from sk-learn: {0}'.format(clf.score(simulated_separableish_features, simulated_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.09231079] [[-5.05902155  8.28959202]]\n",
      "[-14.09229958  -5.05901203   8.2895831 ]\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(fit_intercept=True, C = 1e15)\n",
    "clf.fit(simulated_separableish_features, simulated_labels)\n",
    "\n",
    "print(clf.intercept_, clf.coef_)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0  1   6  15  12  1.1  0.1  0.3  7  16  ...  0.21  0.22  0.23  6.3  \\\n",
      "0     0  0  10  16   6    0    0    0  7  16  ...     0     0     0   10   \n",
      "1     0  0   8  15  16   13    0    0  1  11  ...     0     0     0    9   \n",
      "2     0  0   0   3  11   16    0    0  0   5  ...     0     0     0    0   \n",
      "3     0  0   5  14   4    0    0    0  0  13  ...     0     0     0    4   \n",
      "4     0  0  11  16  10    1    0    0  4  16  ...     3     0     0   10   \n",
      "...  .. ..  ..  ..  ..  ...  ...  ... ..  ..  ...   ...   ...   ...  ...   \n",
      "3817  0  0   5  13  11    2    0    0  2  15  ...     0     0     0    8   \n",
      "3818  0  0   0   1  12    1    0    0  0   0  ...     0     0     0    0   \n",
      "3819  0  0   3  15   0    0    0    0  0  11  ...     0     0     0    4   \n",
      "3820  0  0   6  16   2    0    0    0  0  15  ...     0     0     0    5   \n",
      "3821  0  0   2  15  16   13    1    0  0   3  ...     0     0     0    4   \n",
      "\n",
      "      14.1  7.4  1.3  0.24  0.25  0.26  \n",
      "0       16   15    3     0     0     0  \n",
      "1       14    0    0     0     0     7  \n",
      "2        1   15    2     0     0     4  \n",
      "3       12   14    7     0     0     6  \n",
      "4       16   16   16    16     6     2  \n",
      "...    ...  ...  ...   ...   ...   ...  \n",
      "3817    13   15   10     1     0     9  \n",
      "3818     4    9    0     0     0     4  \n",
      "3819    14   16    9     0     0     6  \n",
      "3820    16   16   16     5     0     6  \n",
      "3821    14    1    0     0     0     7  \n",
      "\n",
      "[3822 rows x 64 columns]\n"
     ]
    }
   ],
   "source": [
    "b = train.drop(train.columns[7],axis=1)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.09231079] [[-5.05902155  8.28959202]]\n",
      "[-14.09229958  -5.05901203   8.2895831 ]\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(fit_intercept=True, C = 1e15)\n",
    "clf.fit(simulated_separableish_features, simulated_labels)\n",
    "\n",
    "print(clf.intercept_, clf.coef_)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from scratch: 0.9948\n",
      "Accuracy from sk-learn: 0.9948\n"
     ]
    }
   ],
   "source": [
    "data_with_intercept = np.hstack((np.ones((simulated_separableish_features.shape[0], 1)),\n",
    "                                 simulated_separableish_features))\n",
    "final_scores = np.dot(data_with_intercept, weights)\n",
    "preds = np.round(sigmoid(final_scores))\n",
    "\n",
    "print('Accuracy from scratch: {0}'.format((preds == simulated_labels).sum().astype(float) / len(preds)))\n",
    "print('Accuracy from sk-learn: {0}'.format(clf.score(simulated_separableish_features, simulated_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0  1   6  15  12  1.1  0.1  0.2  0.3  7  ...  0.21  0.22  0.23  6.3  \\\n",
      "0     0  0  10  16   6    0    0    0    0  7  ...     0     0     0   10   \n",
      "1     0  0   8  15  16   13    0    0    0  1  ...     0     0     0    9   \n",
      "2     0  0   0   3  11   16    0    0    0  0  ...     0     0     0    0   \n",
      "3     0  0   5  14   4    0    0    0    0  0  ...     0     0     0    4   \n",
      "4     0  0  11  16  10    1    0    0    0  4  ...     3     0     0   10   \n",
      "...  .. ..  ..  ..  ..  ...  ...  ...  ... ..  ...   ...   ...   ...  ...   \n",
      "3817  0  0   5  13  11    2    0    0    0  2  ...     0     0     0    8   \n",
      "3818  0  0   0   1  12    1    0    0    0  0  ...     0     0     0    0   \n",
      "3819  0  0   3  15   0    0    0    0    0  0  ...     0     0     0    4   \n",
      "3820  0  0   6  16   2    0    0    0    0  0  ...     0     0     0    5   \n",
      "3821  0  0   2  15  16   13    1    0    0  0  ...     0     0     0    4   \n",
      "\n",
      "      14.1  7.4  1.3  0.24  0.25  0.26  \n",
      "0       16   15    3     0     0     0  \n",
      "1       14    0    0     0     0     7  \n",
      "2        1   15    2     0     0     4  \n",
      "3       12   14    7     0     0     6  \n",
      "4       16   16   16    16     6     2  \n",
      "...    ...  ...  ...   ...   ...   ...  \n",
      "3817    13   15   10     1     0     9  \n",
      "3818     4    9    0     0     0     4  \n",
      "3819    14   16    9     0     0     6  \n",
      "3820    16   16   16     5     0     6  \n",
      "3821    14    1    0     0     0     7  \n",
      "\n",
      "[3822 rows x 64 columns]\n"
     ]
    }
   ],
   "source": [
    "c = train.drop(train.columns[16],axis=1)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.09231079] [[-5.05902155  8.28959202]]\n",
      "[-14.09229958  -5.05901203   8.2895831 ]\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(fit_intercept=True, C = 1e15)\n",
    "clf.fit(simulated_separableish_features, simulated_labels)\n",
    "\n",
    "print(clf.intercept_, clf.coef_)\n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from scratch: 0.9948\n",
      "Accuracy from sk-learn: 0.9948\n"
     ]
    }
   ],
   "source": [
    "data_with_intercept = np.hstack((np.ones((simulated_separableish_features.shape[0], 1)),\n",
    "                                 simulated_separableish_features))\n",
    "final_scores = np.dot(data_with_intercept, weights)\n",
    "preds = np.round(sigmoid(final_scores))\n",
    "\n",
    "print('Accuracy from scratch: {0}'.format((preds == simulated_labels).sum().astype(float) / len(preds)))\n",
    "print('Accuracy from sk-learn: {0}'.format(clf.score(simulated_separableish_features, simulated_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If we look at the accuracy in all the 3 cases, it is coming out to be nearly the same. So, I don't think feature selection \n",
    "#helped. Feature selection was actually based on elimination. Since, this dataset did not have any missing values, therefore\n",
    "#figuring out the best feature to eliminate was not an easy task. Even with feature selection, the accuracy is coming out to be \n",
    "#nearly equal to 99%. "
   ]
  }
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