Indian Liver Patient Records.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.gridspec as gridspec \n",
    "import seaborn as sns\n",
    "import sys\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC  \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from lightning.classification import FistaClassifier\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('indian_liver_patient.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Albumin_and_Globulin_Ratio'].fillna(value=0.947538, inplace=True)\n",
    "from sklearn. preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "df.iloc[:,2:-1] = sc.fit_transform(df.iloc[:,2:-1])\n",
    "\n",
    "#df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      "Age                           583 non-null int64\n",
      "Gender                        583 non-null object\n",
      "Total_Bilirubin               583 non-null float64\n",
      "Direct_Bilirubin              583 non-null float64\n",
      "Alkaline_Phosphotase          583 non-null float64\n",
      "Alamine_Aminotransferase      583 non-null float64\n",
      "Aspartate_Aminotransferase    583 non-null float64\n",
      "Total_Protiens                583 non-null float64\n",
      "Albumin                       583 non-null float64\n",
      "Albumin_and_Globulin_Ratio    583 non-null float64\n",
      "Dataset                       583 non-null int64\n",
      "dtypes: float64(8), int64(2), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.418878</td>\n",
       "      <td>-0.493964</td>\n",
       "      <td>-0.426715</td>\n",
       "      <td>-0.354665</td>\n",
       "      <td>-0.318393</td>\n",
       "      <td>0.292120</td>\n",
       "      <td>0.198969</td>\n",
       "      <td>-0.147908</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1.225171</td>\n",
       "      <td>1.430423</td>\n",
       "      <td>1.682629</td>\n",
       "      <td>-0.091599</td>\n",
       "      <td>-0.034333</td>\n",
       "      <td>0.937566</td>\n",
       "      <td>0.073157</td>\n",
       "      <td>-0.650707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>0.644919</td>\n",
       "      <td>0.931508</td>\n",
       "      <td>0.821588</td>\n",
       "      <td>-0.113522</td>\n",
       "      <td>-0.145186</td>\n",
       "      <td>0.476533</td>\n",
       "      <td>0.198969</td>\n",
       "      <td>-0.179333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.370523</td>\n",
       "      <td>-0.387054</td>\n",
       "      <td>-0.447314</td>\n",
       "      <td>-0.365626</td>\n",
       "      <td>-0.311465</td>\n",
       "      <td>0.292120</td>\n",
       "      <td>0.324781</td>\n",
       "      <td>0.166341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096902</td>\n",
       "      <td>0.183135</td>\n",
       "      <td>-0.393756</td>\n",
       "      <td>-0.294379</td>\n",
       "      <td>-0.176363</td>\n",
       "      <td>0.753153</td>\n",
       "      <td>-0.933340</td>\n",
       "      <td>-1.719155</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65       0        -0.418878         -0.493964             -0.426715   \n",
       "1   62       1         1.225171          1.430423              1.682629   \n",
       "2   62       1         0.644919          0.931508              0.821588   \n",
       "3   58       1        -0.370523         -0.387054             -0.447314   \n",
       "4   72       1         0.096902          0.183135             -0.393756   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                 -0.354665                   -0.318393        0.292120   \n",
       "1                 -0.091599                   -0.034333        0.937566   \n",
       "2                 -0.113522                   -0.145186        0.476533   \n",
       "3                 -0.365626                   -0.311465        0.292120   \n",
       "4                 -0.294379                   -0.176363        0.753153   \n",
       "\n",
       "    Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0  0.198969                   -0.147908        1  \n",
       "1  0.073157                   -0.650707        1  \n",
       "2  0.198969                   -0.179333        1  \n",
       "3  0.324781                    0.166341        1  \n",
       "4 -0.933340                   -1.719155        1  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df['Gender'] = df['Gender'].map({'Male':1,'Female':0})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset\n",
      "1    416\n",
      "2    167\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e1df60780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.groupby('Dataset').size())\n",
    "sns.countplot(df['Dataset'],label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test size: 59\n",
      "Dataset\n",
      "1    372\n",
      "2    152\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "test_per = int(len(df.index)*0.9) #actual is 0.1-0.9\n",
    "print(\"test size:\", int(len(df.index)- test_per))\n",
    "testdf = df[test_per:]\n",
    "df = df[:test_per]\n",
    "df.shape\n",
    "print(df.groupby('Dataset').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python35\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "c:\\python35\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.418878</td>\n",
       "      <td>-0.493964</td>\n",
       "      <td>-0.426715</td>\n",
       "      <td>-0.354665</td>\n",
       "      <td>-0.318393</td>\n",
       "      <td>0.292120</td>\n",
       "      <td>0.198969</td>\n",
       "      <td>-0.147908</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1.225171</td>\n",
       "      <td>1.430423</td>\n",
       "      <td>1.682629</td>\n",
       "      <td>-0.091599</td>\n",
       "      <td>-0.034333</td>\n",
       "      <td>0.937566</td>\n",
       "      <td>0.073157</td>\n",
       "      <td>-0.650707</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>0.644919</td>\n",
       "      <td>0.931508</td>\n",
       "      <td>0.821588</td>\n",
       "      <td>-0.113522</td>\n",
       "      <td>-0.145186</td>\n",
       "      <td>0.476533</td>\n",
       "      <td>0.198969</td>\n",
       "      <td>-0.179333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.370523</td>\n",
       "      <td>-0.387054</td>\n",
       "      <td>-0.447314</td>\n",
       "      <td>-0.365626</td>\n",
       "      <td>-0.311465</td>\n",
       "      <td>0.292120</td>\n",
       "      <td>0.324781</td>\n",
       "      <td>0.166341</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096902</td>\n",
       "      <td>0.183135</td>\n",
       "      <td>-0.393756</td>\n",
       "      <td>-0.294379</td>\n",
       "      <td>-0.176363</td>\n",
       "      <td>0.753153</td>\n",
       "      <td>-0.933340</td>\n",
       "      <td>-1.719155</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65       0        -0.418878         -0.493964             -0.426715   \n",
       "1   62       1         1.225171          1.430423              1.682629   \n",
       "2   62       1         0.644919          0.931508              0.821588   \n",
       "3   58       1        -0.370523         -0.387054             -0.447314   \n",
       "4   72       1         0.096902          0.183135             -0.393756   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                 -0.354665                   -0.318393        0.292120   \n",
       "1                 -0.091599                   -0.034333        0.937566   \n",
       "2                 -0.113522                   -0.145186        0.476533   \n",
       "3                 -0.365626                   -0.311465        0.292120   \n",
       "4                 -0.294379                   -0.176363        0.753153   \n",
       "\n",
       "    Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0  0.198969                   -0.147908        2  \n",
       "1  0.073157                   -0.650707        2  \n",
       "2  0.198969                   -0.179333        2  \n",
       "3  0.324781                    0.166341        2  \n",
       "4 -0.933340                   -1.719155        2  "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_labelnoise(noise_level, seed=None):\n",
    "    np.random.seed(seed) \n",
    "   # train_p = int(noise_level * len(traindf.index))\n",
    "   # validate_p = int(noise_level * len(valdf.index)) \n",
    "   # x = np.random.randint(low = 0, high = len(traindf)-train_p)\n",
    "  #  y = np.random.randint(low = 0, high = len(valdf)-validate_p)\n",
    "   # print(y)\n",
    "    l = int(noise_level*len(df.index))\n",
    "    for i in range(l):\n",
    "        if df['Dataset'][i]==1:\n",
    "            df['Dataset'][i]=2\n",
    "        elif df['Dataset'][i]==2:\n",
    "            df['Dataset'][i]=1\n",
    "      \n",
    "        \n",
    "    #return traindf, valdf\n",
    "\n",
    "#t.head(20)\n",
    "#t['Outcome'][:10]\n",
    "\n",
    "add_labelnoise(0.30)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset\n",
      "1    303\n",
      "2    221\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('Dataset').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6aqZONbsYIYSlnWF2ASNhp0CeV3jHGaibbGYhQgjLkw65yMJAP4DTXz3F5FqEENY2K9TYbLucsEUg5+cfzwXijoqAR7krasyuSQhhebbbX9MWgQwE8segZ/K8ybL0sRDiOMw2u4DhsksgTwZyAK7qyfUm1yKEsAfbLTxml0CeAjgBHB5/pcm1CCHsYbrZBQyXnQI5DaA8Pr/JtQgh7EECuUhqKASy22vLSyKFEONOArlIqskHssPtlQ5ZCHE8bHcBmV0CuQrIACiXRwJZCHE8KswuYLjsFMhpABwuj7mlCCFswm12AcPlMruAY8lfFBIAYgDoXNbUgoTlKJ3VQdeD7f2urF0aDFEkWuNUiqyrumI7wPz/+5Jjy398Pmd2XcfL8oGc5yQ/D5lcLmNuKcJq3p389f70hCe9j4Qqp5ldi7CM2QBtq9tsE8ZggyGLxOYWjTF+7ADQWgJZHKJ0mo/5/lR3e7Z7qk7K74Z4g7TZBQyX5QM5L02h1lxWXnTioHcm7ukL+TKeaodWqzqju82uR1iKBHKRHOqQJZBFnkNn9L/6Hjk46+a2dHQ6g7l+M2sSltJtdgHDZZdAHqQQyNnBAZNrERZxfeq+XbP82YNn0oMK18r90U4zaxKWst3sAobLLoF8cMgil4z1mlyLsACHzuibfQ8Fj/y8dMniMBLIRdJHfk5hNt5juz9DxNi7PnXfrjpvdsi62NIli8NIIBfJboydZMlEO3pMrkWY7K264wLpkkVeu9kFDJedAtkDkOndK4Fc5t6qOy6QLlnkSYdcJN2ABsjGuxM6m0mZXI8wybG64wLpkgUSyEVzMJABcgPxAybWIkx0rO64QLrkshcFNptdxHDZJZB7yO8YApCJHdhlYi3CJMfbHRdIl1zWnmxb3Wa7dW/sEsj9GDMtvADp7t07zS1HmOF4u+MC6ZLL2uNmFzAStgjk/HoWr2AsVE9q1yu2GxsSozPc7rhAuuSyJYFcZC+TX3A6G+vsz8o4clm5PnXvsLrjgqDCtUq65HLTDzxvdhEjYadAfkNXnOnbv9WsQsT4Mrrjh4fdHRfclu6TLrm8PN22us12CwuBvQJ5D5Akf8Xe4J6Nm8wtR4yXkXbHBQGlXKs6pEsuI4+ZXcBI2SaQE5tbckAbUAuQ3LZ+Wy6diptblSi20XbHBbcNSpdcJjRwr9lFjJRtAjnvH+QvoUbndLpr58vmliOKbbTdcYF0yWVjXdvqtm1mFzFSdgvkVzDWRnYBDGx/8SVzyxHFNFbdcYF0yWXhLrMLGA1bBXJic8sA8AxQD5Da9fKeXKpfVn8rUWPVHRdIl1zyksCvzS5iNGwVyHlPk19oCGDwwPY2E2sRRTLW3XGBdMkl7f621W1Rs4sYDTsG8iYgQT6UExvXrdc6Z6udZcWxjXV3XCBdckmz9XAF2DCQE5tbMsATwESATO++aLprl4wll5BidccF0iWXpC3AI2YXMVq2C+S8JzDmIyuA/lf//qTW+uj3ELZRrO64QLrkkvQlOy4mdCRbBnJic8se4DlgMkC6Y2tnpm//RnOrEmOh2N1xgXTJJWULcLfZRYwFl9kFjMKfgNMKHyQ2PfVE9RnXLDCxHjEGbkjds7OuJjur2I+T75J3r51RU1nsxxqOwa5Bdv9wN5loBoDaC2qpv7SeTDzDzu/uJN2Zxl3vZtbHZuGsdA65f8+6Hg78wVjmZeIVE6k9t5ZcOseO/91BuidN3Yo6Jlw0AYDdP9lN3YV1+EK+8XuCxVES3THYtEPO2wa8RmEK3M4NuzOxrnZTKxKj4tAZ/S++h6vH6/Gs2CUrp2LKtVMIfznM3Fvn0v1oNwO7B+hs7iSwMMAJXzmBwMIAB5qHrq2ViWfoeKCDubfOZd4X5tHxQAfZ/izxDXF8IR/zvzifnr8ZO6AldyTROV0KYVwy3THYOJDzS3I+CAQKn+t/+a8PaxlMtq0bUvfsrPXmxi2QrTiW7K5xHwxJp8+Jd5qXTE+GaGuUmnONYfWac2uIrh86uyu+IU5gUQBXwIWz0klgUYBYWwzlVOTSOXT20Euj43cdTL5m8vg8qeIqme4YbBzIea9hrAJXB5Da/eredOf2VnNLEiMx3t1xQb5LtuSaKIMHBhnYPoBvno9MXwZ3jRsAV7WLTF9myO0zPRncde6DH7tr3WR6MgQWBcj0Zdj6xa3UX15PtDVKxewK3LXuId/DZjZRQt0x2DyQ8wsO/QKoIj/jIrb+j4/qbHrA1MLEsI13d1wQUMp12f5o13g/7rFkB7Ls+PYOpvzTFJy+N44VK6Xyv+3HRzkVMz86k/n/OZ/q06vperiL+lX17L13Lzu+vYNoq22vpbiplLpjsHkgAyQ2t2zCuHpvKhi7Ug9sf+lvphYlhsWs7rjgC2lrdck6o9n57Z3UnFVD9WnGj8VV7SLdayzxm+5N46oaej7eVesi3X1oGeB0TxpX7Rtv1/XXLmrOriH5etII6o/N5MCDttzr4Sdtq9v+anYRY832gZz3G4yewQMQe3Hts7KjiH2Y1R0XWKlL1lqz+8e78U71Ur+q/uDnq06tonddLwC963qpWlo15L6BxQHiG+Jk+7MHT+YFFh88xUK2P0vsxRg159SQG8wdfPXn0va60FVr3QF82uw6ikGVyjkwfzhyOfBu8juLVMxeMiu4/MrrlVLD+ONOjDeHzujnHddHzQxkgLjWmbNmzBzA4wgc+9bF07+pn21f3oZ3hpfCr+7kd03GN8/Hzv+3k3R3GvcENzM/NhNXwEVyW5Lux7qZfsN0AHr+3sOBPx427e282oPfe+89ewkuDRJYGCA3mGP7/24n05Oh7sI6JlwyYfyf7Mi9r211231mF1EMpRTIXuDLGJ1yFKD6zPes8E4/8TxTCxNH9aGBn+34PzVriz7v+Hh81lW1/c8za2abXYd4a1rrP224fkOD2XUUS6kMWZDY3JICfgxMAJwAfc/+7m/ZRN8eUwsTb8nsseMjWW0sWbyR1jqulLrJ7DqKqWQCGSCxueVlYC0wA4BsJhd99v7f6lzGlhseljqzx46PZKWxZDGUUuqjbavbdphdRzGVVCDn/Q5jQ9R6gHTn9u7k68+tNbckcSSnTluqOy6QLtmatNbfalvd9guz6yi2kgvk/NDF9zD23vMAxF96eH26Z++rphYm3mBN6p5dVuqOC6RLth6d1S1KqVvMrmM8lFwgAyQ2t+wE7gGmFz7X++Q992eT0f3mVSUKnDqt/8X/l6HztixCumTr0DndqZzqHW2r28pi2LEkAznvMeBF8qGsU/2DfU//6t5cOiUvNJOtSd2zq9Zjve64QLpka9BaZ5VDXdO2um2v2bWMl5IN5Pxl1T8AusiPJ2d69vTF1v/xPp3LDl0IQIwLq3fHBdIlW4DmM22r254wu4zxVLKBDJDY3BIHvoExDS4IkNr18u7ExnX3l8r8a7uxendcIF2yuXRGf3/Dmg1fN7uO8VbSgQyQ2NyyDyOUJwBegP5XHn85teuVv5lZVzmyS3dcIF2yOXKp3G+Uq7TnG7+Vkg9kgMTmlo3AnRjjyU6A6D9++3hq/+stphZWZuzSHRcElHJdLl3yuMoms486vI5r21a3leWfsGURyHnrgD8As8k/7751v1g72LH1WVOrKhN2644LbhvsmyFd8vjIJrMtTp/z8lJbUnM4yiaQ8zuM/A74KxAiv6Js7xN3/2mwY9vzJpZWFuzWHRf4HcopXXLxZQeybU6fc0Xb6rZBs2sxU8ksLnS8/OGIE7geOB9oBzRAzfmrr/RMnL3UvMpKl1On9XPONVE7BjJAIqezkZkzk2avBFeqcqncFofXcVrb6rY+s2sxW9l0yAWJzS1Z4C6MIYwQBzvln/1hsHPHCyaWVrKOtzu+4YEkk/4rxuLvHBoh6E5qLvl5P+Fvxbnk5/30JN+8gbjrhUHC34oT/lacu14wmqxURrPq7n4WfyfOd5491Hjd+Ick6/ce/1/F0iUXTzaRfQ3FGRLGhrILZIDE5pYM8BPgKQqhrLXuffynD6T2bn7a1OJKzHDGjq8/1c3a6/xv+FzTuhQXzXGx+eMBLprjomldasj9upOaOx5P0fKhSv7xoUrueDxFT1Lz0OsZlk918tJNlfzgeSOQX9yXJZuDZVOdQ77P0chY8tjL9GVeAJa//OGXe8yuxSrKMpDhYCjfCTyDEcrGib6n7n04ufX5P8vu1WPjhtQ9O493qOL82S7qfG/cT+CBjRlWLzE241y9xM39G4de0/PQlgyXzDXuW+tTXDLXxdotGdwOSGYgfVgzfOtjKb64wjvs5yFd8thKd6efSHWkznjlplcSZtdiJWUbyHAwlH/EoRN9LoBYa/M/+l9+7FdyRd/oOHU69zH/X0Y1brw/nmNq0Pg1nRJQ7I8P3W5odyzHzOpDv8ozqhzsjuW4ZJ6LffEcZ97Zz2fP8fLgxjTLpjqYFhzZr710yWMjtT91X/+m/rdt/dLWslifYjjKOpABEptb0sDPgF8Bs4AKgMTGda9Fn3vgpzozKP+Dj9AHU78Y05kVSimGsyGXy6G4551+Wj8S4N0nufjGM4PccpaXTz00wLt+leDBjcPLA+mSR0fntE7uSH5l02c3vW/nd3ce91+gSqnHlFIrj/jcJ5RS3z3KfWz5H2fZBzIYU+ISm1v+CHwXmEzhMuudG3b3rrvnR7lUv7wIh8mp07mb/I+MOownBxzsjRld8d5YjkmVQ39lpwcd7Ow71DnviuaYfkQX/J1nB/nnJW6e2ZXF44RfvsvHl/4+dDz6WKRLHpncYC6V3Jr8yJZbtzSO4O73Atce8blr858vKRLIh0lsbnka+CoQAOoA0l07erof/cEP0737XjO1OJsZq+74yhNc3PWi0cne9WKaqxa4htxm5XwXD2/N0JPU9CQ1D2/NsHL+odv1JDV/3Jzhn5e4SaQ1DgVKwcAIBqSkSx6+dG96X+zF2AWvf/H1H47wW/wGaFBKeQCUUiFgGtCqlHpUKbVeKdWmlLpqbCo2T9nNQz4e/nBkBvApoBI4uPRf1WlXn+uddfIK2cn66Jw6nXvOuSY23EB+328T/K09S2dCM7lScccFXq4+0cV7fpNkR59mdrXiV+/2U+dTPLcny/eeG+RHV/oA+HHrIF9+wuh4/+M8L2uWeg5+30+uHeCqE11cEHIxkNFceW+C3THNR5d7+HjE86a1HI3MSz5+yfZkS+/TvVd1ru0c1VrkSqk/Aj/UWj+glGrEWMGxEfBrraNKqXqME/RhrbVWSsW11rb795FAfgv+cKQG+DCwGNgBZAEq5iwLBU6+5J0Ot9d2/9jj5caBn+7495qHLbGTdLF8zlW1/U+yQ/VbyqVz6fiG+Pd6Hu/5TLQ1OvyxoSMopd4PvF1r/T6l1AvAB4GXgK9jXOSVAxYAc7TW+ySQS5A/HHEBV+WP/UA/gDMwwV991nuucVVNnGdmfVY00u7YbqRLfmuZvkxXdH30xtiLsd9HW6NjEjBKqQCwFVgF3Ke1PkEpdT1wGXCd1jqtlGoHLtBat9s1kGUM+SgSm1syic0tvwX+G2NceTJANt6V6P7Ld+9Otrc+LFPj3misZ1ZYlYwlv7nkjuT6zoc6z9j9092/G6swBtBaxzF2Afoxh07mVQMd+TC+EGPhMFuTDvk4+cOReuAjQBjYBWQA3BNDE6qWXXGVM1A708z6rKBcuuOCRE5nIzNmJvFKl5xNZOPR1uj3o89Gb422RpPFeAyl1NXA74GFWuvX8uPGfwDcwAvAOcBldu6QJZCHwR+OuIEGjCGMOMb2UKCUCi5tiFTMXrJCOZxuE0s0VTmMHR+p0RXc3jyz1vad2UhprXViS+Ll3id7P5XpzTwyll1xOZJAHgF/OBLCOOE3ncO75frZdcHlV1zpCtSV3Qu03LrjgnLukjOxTG/vU71397/a/6Voq+zoPhYkkEfIH454ME4oXI1xsq+z8LXg0svPqJi9ZIVyuoe/aIJNlWN3XFBuXbLO6Vz/a/2tPet6Pp1L5P4ebY0OvZ5djIgE8ij5w5HZwIeAmcBuIA3PMMScAAAJ6klEQVTGTIzgsoaL3PWzl5b6vGWnTueed66J1ZRZd1xQTl3yYOfg/r6Wvp8kNif+K9oa7Ta7nlIjgTwG8mPLK4F3YAxf7CO/8L1n2oIpgZMvWVXKwxjl3B0XlHqXnIln+qLPRZ+KvRC7DXhOxoqLQwJ5DPnDkcnAu4AzgF7g4Dqv/oXnn+SfH7nU4fGVVBdZ7t1xQal2ybnBXDL2Yuylvpa+O3VG/zLaGo2aXVMpk0AeY/5wRGFcMfQBjJN++4EkgHJ7XcFTLz/LO/3Es5XTXWFimWNGuuNDSqlL1lmd7d/U/2rvk72/zsazP4u2RtvNrqkcSCAXSf4qv7MwVqWqwFgTIwPg8AW9gVMuPdM79YSz7HziT7rjNyqFLllrzcDOga2963ofGuwY/C6wQYYnxo8EcpH5w5EAxtzlSzGut99Hfl0Mh6+qwgjm8Jl2DGbpjoeya5esczo3sHPg9b5/9P0jtTv1feDpaGtUrkIdZxLI4yR/pd9KYAUlEMzSHb85u3XJOqvTia2JTX3P9LWlu9L3AY9EW6P9ZtdVriSQx9mxgrly0YXLvVMXnOHwVBzXxqBmke74rdmhS86lcwOJzYnXep/ufSkbyz4IPCbT2MwngWySfDCvAi7ECOYO8nOYcTgd/gXnnOibteRMK66RId3x0Vm5S86lcvH4q/FX+1r6Xswlc78Hnoi2RmNm1yUMEsgm84cjEzG65YswNlntIr/MJ4B3xknT/PMjEVft9EXK4Rje3vVFIt3xsVmpS9Za63RXent8Q3xbrC3WRpb7gWeKtQiQGDkJZIvwhyOVwOnA24EJGKHcRf4CE2fVxEDlwvOXeybNPdXh8dWYVad0x8fHCl1yLpWLJ7YlNkafi25Ld6a3YqyU9ny0NSq7PVuUBLLF+MMRJ3AScDlwIsYwRgf5KXNg7FpSMeuUJe7aaScpp2v4exCNgnTHx+/zruD2P45zl6y11unO9LZ8N7yTHM8DfwU2Rluj2fGsRQyfBLKF5ff2uxA4D2PN1xjG1X8aQHn8bv8JZ53knXbiEmegbk6xl8yQ7nh4EjmdPXPGjKT2OoveJWf7sweS7cn26Ppoe7orvQtYCzwbbY32HOu+wjokkG3AH474MPb2W4HRNWuOGGt21U6r9s+PnOKun7XQ6a+eWow6Pjrw4x2NNY9IdzwMxeySs/3ZjuSO5JZ4W3xfak+qF3geY1eNTdIN25MEss3kZ2csBy7BGGtOYyz9OVi4jat2WrUvtPRE98TQic5A3eyxWG3OqdO59c418WpPztLT8axmrLvkTDyzf2DnwJb4S/F9qb2pKMZw1qMY3XDvWDyGMI8Esk35wxEHMAfj8uyzAB/G9Lku8mtnADgra32+ucsXuCfNO9FVVT9POZyukTyedMcjN5ouWed0LhPL7E7tTm2PvRTbP7hvMIYxd/3vwIvAXrm0uXRIIJeA/LoZIeBU4GygMAujG2OrKcAYc66YdfJsz6S5c101k+c4KoJTjqd5lu54dIbbJWcT2a7BA4OvJ9uTe/tf64/lkrkMxloojwNtSAiXLAnkEpPvnKcDJ2Ns+jgNY8w5gbEk6MHZGs5And87c3HIUz97jqt60lyHt7Luzb6ndMejd7QuOTeYS6S701sHdg7s6H+tvyfdlS4MP+0GnsAI4X0SwqVPArmE5ZcCnQTMB5YBizBmayiMGRt9GMMcgDH27J2+MOSunTbDXVk9V/mq65wqJ93xGDi8S84ms93pnvTOwY7BPcltyb6B7QMDGP9pRoFW4CVga7Q12mdq0WLcSSCXkfzQxgxgHsaJwRMwwhmMgI4BWdCE2XVZvWdwV+30OX03TXllxqxqNW1ywDGtyqtqzanenrpTenDPAO2b4uz5dsyzp3VPNpGNZZ0YAZwBXsaYHfE6sF+64PImgVzG/OGIF5iNEcyLMDppR4BkzUK1fUEViYeUIk5+3jNAvV9VnDbNOXVurWPSlICaMNGv6mt9qt7vVkFznoU1pDI62TOgO7sSunNfXHdu78t1Pb07m/pLyhXpcTqf00oNYsyE2QhsALYCO2SJS3E4CWRxUL6DnlxF/4JFqn1RpUpNxhiDzgFOjEBJ5I83XH5bW4Fn8SRn/ZxaR/30oKqfVOmor/Op+qCXGpdDucf7uRRDNqez/WmiPUndeSChO/fEcp3tvbnOlzty0b1xDeDHWI9E54+dHU5H90aPpyWr1HbggOzQLI5GAlkc1ZUL3F5gKkYwzwFmYQx7FKbZFYY8CkFdGA89qN6vKmZVq+CUgCM4qVIF63wqWFOhqqq8KhjwEKx0q2CFi4DToRzj9bwOl9NapzIk+tM6Fh8kGkvpWF9Kx7qTOnagX8f2xXV0ZzQX3xvTWhuh68vfVQMOjKGencB2oB1jRsT+BzemZc0IMSwSyGLYrlzgVkAlUJ8/pmJMu5uZ/7jQISqMwMpgdNep/DHIEaEN4HfjqqlQnmqv8lZ5lSfgwVvpUZ5Kt/JWevD6XMpT4cLrcuBUCqVAKeOqF6UUaI3Oaa21sRORzmpyAxlSybQeTKRJJdJ6sD+tU/FBBqMpnYqm9GDvgE5FU2QBD+DNHx6Mvwhyhz0PMPZH3IERuvsxLsjpfHBjWlZNE2NCAlmMqSsXuN1ALRA87JiYP+qBOox50orDZnhwKPQKXXL2sCPDoXDkiLfqiPsrjDAtHK7DvmfuiPsXHi+HsUZIZ/44gHGBTeywo+fBjWm5HFkUlQSyGHdXLnA7MP70D2JsAFvoSgtvKzA6cP9hb70Y4engUOetMMI1d9ihMa5UTGKs9VE4Cp354W8HMKaaJR/cmJYXgjCdBLIQQliEKSdRhBBCDDWihWaEGA2l1ASMFcoApmCMEx/If3yG1nrwTe849PvcAPxJa71v7KsUYvzJkIUwlVLqdiCutf7aCO67DrhZa/3CmBcmhAmkQxaWopRaDfwLxsm9p4CbMYbWfoKxmp0CfoAx7exU4JdKqSTD6KyFsCoJZGEZSqnFwDuAs7XWGaXUD4BrMdZ5qNdan5y/XY3Wulcp9XGkQxYlRAJZWMnFGDtvP5dfp9mHcQXcQ8ACpdQ3gWbgYdMqFKKIJJCFlSjgx1rrW4d8QalTgMuAfwXeCdw4zrUJUXQy7U1YySPAe5RS9WDMxlBKzVJKTcQ4Af1r4DaMtZ3BuIKurFeZE6VFOmRhGVrrNqXUHcAjSikHxopyH8WYFndnfrNWDXwuf5efAD+Sk3qiVMi0NyGEsAgZshBCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIuQQBZCCIv4/6TYAbjoOG3EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23e1dea6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set (419, 11)\n",
      "Test set (59, 11)\n",
      "Validation set (105, 11)\n",
      "Dataset\n",
      "1    241\n",
      "2    178\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#split our data into train and test\n",
    "#traindf, testdf = train_test_split(df, test_size = 0.3)\n",
    "#traindf, valdf, testdf = train_validate_test_split(df, train_percent=.7, validate_percent=.2, seed=None)\n",
    "traindf, valdf = train_test_split(df, test_size = 0.2)\n",
    "labels = 'Train', 'Test', 'Val'\n",
    "plt.pie([70, 10, 20], labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "plt.show()\n",
    "print(\"Train set\", traindf.shape)\n",
    "print(\"Test set\", testdf.shape)\n",
    "print(\"Validation set\", valdf.shape)\n",
    "\n",
    "print(traindf.groupby('Dataset').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X=traindf[traindf.columns[:-1]]\n",
    "val_X=valdf[testdf.columns[:-1]]\n",
    "train_Y=traindf['Dataset']\n",
    "val_Y=valdf['Dataset']\n",
    "test_X=testdf[testdf.columns[:-1]]\n",
    "test_Y=testdf['Dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7457627118644068\n",
      "Cross_val score : 0.5115584415584415\n"
     ]
    }
   ],
   "source": [
    "X = train_X\n",
    "y = train_Y\n",
    "clf = LinearSVC(penalty='l1', loss='squared_hinge', dual=False, tol=1e-3, C=1.0,\n",
    "               multi_class='ovr', fit_intercept=True, intercept_scaling=1,\n",
    "               class_weight=None, verbose=0, random_state=None, max_iter=1000)\n",
    "clf.fit(X,y)\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.711864406779661\n",
      "Cross_val score : 0.5633766233766233\n"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC(penalty='l2', loss='hinge', dual=True, tol=1e-3, C=1.0,\n",
    "               multi_class='ovr', fit_intercept=True, intercept_scaling=1,\n",
    "               class_weight=None, verbose=0, random_state=None, max_iter=1000)\n",
    "clf.fit(X,y)\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.711864406779661\n",
      "Cross_val score : 0.5220779220779221\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(loss='hinge', penalty='l1', alpha=0.0001, l1_ratio=0.15,\n",
    "                    fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,\n",
    "                    verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, \n",
    "                    class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "clf.fit(X,y)\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7457627118644068\n",
      "Cross_val score : 0.5142857142857142\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(loss='squared_hinge', penalty='l1', alpha=0.0001, l1_ratio=0.15,\n",
    "                    fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,\n",
    "                    verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, \n",
    "                    class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "clf.fit(X,y)\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7457627118644068\n",
      "Cross_val score : 0.5542857142857143\n"
     ]
    }
   ],
   "source": [
    "clf = SGDClassifier(loss='modified_huber', penalty='l1', alpha=0.0001, l1_ratio=0.15,\n",
    "                    fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,\n",
    "                    verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, \n",
    "                    class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "clf.fit(X,y)\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FISTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Set classifier options.\n",
    "clf = FistaClassifier(C=1.0, alpha=1.0, loss='squared_hinge', penalty='l1',\n",
    "                      multiclass=True, max_iter=1000, max_steps=30, eta=2.0,\n",
    "                      sigma=1e-05, callback=None, verbose=0  )\n",
    "\n",
    "# Train the model.\n",
    "clf.fit(X, y)\n",
    "\n",
    "\n",
    "# Accuracy\n",
    "print(\"Accuracy :\", clf.score(test_X, test_Y))\n",
    "\n",
    "# Percentage of selected features\n",
    "print(clf.n_nonzero(percentage=True))\n",
    "\n",
    "scores = cross_val_score(clf, val_X, val_Y, cv=5, scoring='accuracy')\n",
    "print(\"Cross_val score :\", scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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