train-test-val.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>...</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PF</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PTS</th>\n",
       "      <th>YR</th>\n",
       "      <th>POS</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "      <th>NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>191</td>\n",
       "      <td>Tyshawn Taylor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>432</td>\n",
       "      <td>278</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.76</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.97</td>\n",
       "      <td>...</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.58</td>\n",
       "      <td>13.64</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>201</td>\n",
       "      <td>Ron Mercer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>786</td>\n",
       "      <td>168</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2.39</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.49</td>\n",
       "      <td>6.51</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>80</td>\n",
       "      <td>188</td>\n",
       "      <td>Howard Eisley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1.30</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>188</td>\n",
       "      <td>Nolan Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>116</td>\n",
       "      <td>206</td>\n",
       "      <td>Chris Richard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    GP   GS   MIN   FGM    FGA    FG%   3PM   3PA   3P%   FTM       ...        \\\n",
       "0   61    3   8.0  1.03   2.92  0.354  0.15  0.41  0.36  0.64       ...         \n",
       "1  432  278  31.0  5.76  13.43  0.429  0.14  0.56  0.25  1.97       ...         \n",
       "2  786  168  20.4  2.39   5.87  0.407  0.66  1.88  0.35  1.07       ...         \n",
       "3   84    4   9.9  1.30   3.50  0.371  0.23  0.87  0.26  0.48       ...         \n",
       "4   70    3  11.1  0.80   1.66  0.483  0.00  0.00  0.00  0.33       ...         \n",
       "\n",
       "    STL   BLK    PF   TOV    PTS  YR  POS    W    H            NAME  \n",
       "0  0.41  0.02  0.89  0.92   2.85   2   12   84  191  Tyshawn Taylor  \n",
       "1  1.06  0.26  2.04  1.58  13.64   8    2   95  201      Ron Mercer  \n",
       "2  0.69  0.11  2.16  1.49   6.51   1   12   80  188   Howard Eisley  \n",
       "3  0.30  0.05  0.96  0.85   3.30   2   12   86  188     Nolan Smith  \n",
       "4  0.26  0.23  1.90  0.54   1.93   2   34  116  206   Chris Richard  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data3/shuffeled.txt', sep='\\t')\n",
    "# ucitavam ceo dataset\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1429, 26)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'GP', u'GS', u'MIN', u'FGM', u'FGA', u'FG%', u'3PM', u'3PA', u'3P%',\n",
       "       u'FTM', u'FTA', u'FT%', u'OFF', u'DEF', u'TRB', u'AST', u'STL', u'BLK',\n",
       "       u'PF', u'TOV', u'PTS', u'YR', u'POS', u'W', u'H', u'NAME'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This method splits the dataset into three subsets:\n",
    "Training set\n",
    "Test set\n",
    "Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_test_val(dataframe,end):\n",
    "    num_rows = dataframe.shape[0]\n",
    "    #print num_rows\n",
    "    row_n = num_rows*6/10\n",
    "    #print row_n\n",
    "    # training data dataframe\n",
    "    train_data = dataframe.ix[:row_n,:end]\n",
    "    print \"Training set has %s rows and %s columns\" %(train_data.shape[0], train_data.shape[1])\n",
    "    row_m = num_rows*8/10\n",
    "    #print row_m\n",
    "    test_data = dataframe.ix[row_n+1:,:end]\n",
    "    test_data = test_data.ix[:row_m,:end]\n",
    "    print \"Test set has %s rows and %s columns\" %(test_data.shape[0], test_data.shape[1])\n",
    "    valid_data = dataframe.ix[row_m+1:,:end]\n",
    "    print \"Validation set has %s rows and %s columns\" %(valid_data.shape[0], valid_data.shape[1])\n",
    "\n",
    "    return train_data, test_data, valid_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 858 rows and 25 columns\n",
      "Test set has 286 rows and 25 columns\n",
      "Validation set has 285 rows and 25 columns\n"
     ]
    }
   ],
   "source": [
    "train_data, test_data, valid_data = train_test_val(df,25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimenzije train seta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(858, 25)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>...</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PF</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PTS</th>\n",
       "      <th>YR</th>\n",
       "      <th>POS</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1.03</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   GP  GS  MIN   FGM   FGA    FG%   3PM   3PA   3P%   FTM ...    AST   STL  \\\n",
       "0  61   3    8  1.03  2.92  0.354  0.15  0.41  0.36  0.64 ...   0.93  0.41   \n",
       "\n",
       "    BLK    PF   TOV   PTS  YR  POS   W    H  \n",
       "0  0.02  0.89  0.92  2.85   2   12  84  191  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train_data = train_data.ix[:,:25]\n",
    "train_data[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Set file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data.to_csv(\"data/train_data.txt\", sep=\"\\t\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_labels = train_data.ix[:,22:]\n",
    "#train_labels[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train_labels.to_csv(\"train_labels.txt\", sep=\"\\t\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>...</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PF</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PTS</th>\n",
       "      <th>YR</th>\n",
       "      <th>POS</th>\n",
       "      <th>W</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>887</td>\n",
       "      <td>331</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.81</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>...</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.62</td>\n",
       "      <td>0.83</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>107</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GP   GS   MIN   FGM   FGA    FG%  3PM   3PA  3P%   FTM ...    AST   STL  \\\n",
       "858  887  331  18.4  1.57  3.81  0.412    0  0.01    0  0.78 ...   0.42  0.38   \n",
       "\n",
       "      BLK    PF   TOV   PTS  YR  POS    W    H  \n",
       "858  1.16  2.62  0.83  3.92   1    5  107  211  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimenzije test seta: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 25)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_data.to_csv(\"data/test_data.txt\", sep=\"\\t\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#test_labels = test_data.ix[:,22:]\n",
    "#test_labels[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#test_labels.to_csv(\"test_labels.txt\", sep=\"\\t\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'val_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-cd4ce43981f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#val_data = df.ix[row_m+1:,:23]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mval_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'val_data' is not defined"
     ]
    }
   ],
   "source": [
    "#val_data = df.ix[row_m+1:,:23]\n",
    "val_data[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimenzije validation seta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data.to_csv(\"data/val_data.txt\", sep=\"\\t\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Sets for Classification with 5 positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df5 = pd.read_csv('data5/cleaned.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df5.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data5, test_data5, val_data5 = train_test_val(df5,25)\n",
    "#712*6/10\n",
    "#test_data5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data5.to_csv('data5/train_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data5.to_csv('data5/test_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val_data5.to_csv('data5/valid_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sets for Classification with 3 positions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3 = pd.read_csv('data3/cleaned.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data3, test_data3, valid_data3 = train_test_val(df3,25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data3.to_csv('data3/train_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data3.to_csv('data3/test_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation set file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_data3.to_csv('data3/valid_data.txt', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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