Safe Drive Predic_DL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import all necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('E:/Machine Learning/NED_Machine_Learning/Kaggle_Data/train.csv',nrows=50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "      <th>ps_ind_01</th>\n",
       "      <th>ps_ind_02_cat</th>\n",
       "      <th>ps_ind_03</th>\n",
       "      <th>ps_ind_04_cat</th>\n",
       "      <th>ps_ind_05_cat</th>\n",
       "      <th>ps_ind_06_bin</th>\n",
       "      <th>ps_ind_07_bin</th>\n",
       "      <th>ps_ind_08_bin</th>\n",
       "      <th>...</th>\n",
       "      <th>ps_calc_11</th>\n",
       "      <th>ps_calc_12</th>\n",
       "      <th>ps_calc_13</th>\n",
       "      <th>ps_calc_14</th>\n",
       "      <th>ps_calc_15_bin</th>\n",
       "      <th>ps_calc_16_bin</th>\n",
       "      <th>ps_calc_17_bin</th>\n",
       "      <th>ps_calc_18_bin</th>\n",
       "      <th>ps_calc_19_bin</th>\n",
       "      <th>ps_calc_20_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>9</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
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      "text/plain": [
       "   id  target  ps_ind_01  ps_ind_02_cat  ps_ind_03  ps_ind_04_cat  \\\n",
       "0   7       0          2              2          5              1   \n",
       "1   9       0          1              1          7              0   \n",
       "2  13       0          5              4          9              1   \n",
       "3  16       0          0              1          2              0   \n",
       "4  17       0          0              2          0              1   \n",
       "\n",
       "   ps_ind_05_cat  ps_ind_06_bin  ps_ind_07_bin  ps_ind_08_bin       ...        \\\n",
       "0              0              0              1              0       ...         \n",
       "1              0              0              0              1       ...         \n",
       "2              0              0              0              1       ...         \n",
       "3              0              1              0              0       ...         \n",
       "4              0              1              0              0       ...         \n",
       "\n",
       "   ps_calc_11  ps_calc_12  ps_calc_13  ps_calc_14  ps_calc_15_bin  \\\n",
       "0           9           1           5           8               0   \n",
       "1           3           1           1           9               0   \n",
       "2           4           2           7           7               0   \n",
       "3           2           2           4           9               0   \n",
       "4           3           1           1           3               0   \n",
       "\n",
       "   ps_calc_16_bin  ps_calc_17_bin  ps_calc_18_bin  ps_calc_19_bin  \\\n",
       "0               1               1               0               0   \n",
       "1               1               1               0               1   \n",
       "2               1               1               0               1   \n",
       "3               0               0               0               0   \n",
       "4               0               0               1               1   \n",
       "\n",
       "   ps_calc_20_bin  \n",
       "0               1  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Number of Rows: ', 50000)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "      <th>ps_ind_01</th>\n",
       "      <th>ps_ind_02_cat</th>\n",
       "      <th>ps_ind_03</th>\n",
       "      <th>ps_ind_04_cat</th>\n",
       "      <th>ps_ind_05_cat</th>\n",
       "      <th>ps_ind_06_bin</th>\n",
       "      <th>ps_ind_07_bin</th>\n",
       "      <th>ps_ind_08_bin</th>\n",
       "      <th>...</th>\n",
       "      <th>ps_calc_11</th>\n",
       "      <th>ps_calc_12</th>\n",
       "      <th>ps_calc_13</th>\n",
       "      <th>ps_calc_14</th>\n",
       "      <th>ps_calc_15_bin</th>\n",
       "      <th>ps_calc_16_bin</th>\n",
       "      <th>ps_calc_17_bin</th>\n",
       "      <th>ps_calc_18_bin</th>\n",
       "      <th>ps_calc_19_bin</th>\n",
       "      <th>ps_calc_20_bin</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  target  ps_ind_01  ps_ind_02_cat  ps_ind_03  ps_ind_04_cat  \\\n",
       "0   7       0          2              2          5              1   \n",
       "1   9       0          1              1          7              0   \n",
       "2  13       0          5              4          9              1   \n",
       "3  16       0          0              1          2              0   \n",
       "4  17       0          0              2          0              1   \n",
       "\n",
       "   ps_ind_05_cat  ps_ind_06_bin  ps_ind_07_bin  ps_ind_08_bin       ...        \\\n",
       "0              0              0              1              0       ...         \n",
       "1              0              0              0              1       ...         \n",
       "2              0              0              0              1       ...         \n",
       "3              0              1              0              0       ...         \n",
       "4              0              1              0              0       ...         \n",
       "\n",
       "   ps_calc_11  ps_calc_12  ps_calc_13  ps_calc_14  ps_calc_15_bin  \\\n",
       "0           9           1           5           8               0   \n",
       "1           3           1           1           9               0   \n",
       "2           4           2           7           7               0   \n",
       "3           2           2           4           9               0   \n",
       "4           3           1           1           3               0   \n",
       "\n",
       "   ps_calc_16_bin  ps_calc_17_bin  ps_calc_18_bin  ps_calc_19_bin  \\\n",
       "0               1               1               0               0   \n",
       "1               1               1               0               1   \n",
       "2               1               1               0               1   \n",
       "3               0               0               0               0   \n",
       "4               0               0               1               1   \n",
       "\n",
       "   ps_calc_20_bin  \n",
       "0               1  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows\n",
    "print('Number of Rows: ', len(df_train['target']))\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "; Java HotSpot(TM) 64-Bit Server VM (build 25.152-b16, mixed mode)\n",
      "  Starting server from C:\\Anaconda2\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: c:\\users\\mudass~1\\appdata\\local\\temp\\tmpvs2bms\n",
      "  JVM stdout: c:\\users\\mudass~1\\appdata\\local\\temp\\tmpvs2bms\\h2o_Mudassar_Umar_started_from_python.out\n",
      "  JVM stderr: c:\\users\\mudass~1\\appdata\\local\\temp\\tmpvs2bms\\h2o_Mudassar_Umar_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321... successful.\n",
      "Warning: Your H2O cluster version is too old (5 months and 28 days)! Please download and install the latest version from http://h2o.ai/download/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>05 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.4.8</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>5 months and 28 days !!!</td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_Mudassar_Umar_nc700c</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>5.333 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.11 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------------\n",
       "H2O cluster uptime:         05 secs\n",
       "H2O cluster version:        3.10.4.8\n",
       "H2O cluster version age:    5 months and 28 days !!!\n",
       "H2O cluster name:           H2O_from_python_Mudassar_Umar_nc700c\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    5.333 Gb\n",
       "H2O cluster total cores:    4\n",
       "H2O cluster allowed cores:  4\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "Python version:             2.7.11 final\n",
       "--------------------------  ------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In case you get error while importing h2o,run it again until you get cluster running\n",
    "import h2o\n",
    "h2o.init(max_mem_size='6g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# The data is imported to h2o data frame from pandas data frame\n",
    "hdf = h2o.H2OFrame(df_train)\n",
    "\n",
    "# Class labels is converted to binary values\n",
    "hdf['target']= hdf['target'].asfactor()\n",
    "# split data in training, testing and validation set with 60,20,20\n",
    "hdf_train, hdf_validate, hdf_test = hdf.split_frame([0.60,0.20], seed=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'id', u'ps_ind_01', u'ps_ind_02_cat', u'ps_ind_03', u'ps_ind_04_cat', u'ps_ind_05_cat', u'ps_ind_06_bin', u'ps_ind_07_bin', u'ps_ind_08_bin', u'ps_ind_09_bin', u'ps_ind_10_bin', u'ps_ind_11_bin', u'ps_ind_12_bin', u'ps_ind_13_bin', u'ps_ind_14', u'ps_ind_15', u'ps_ind_16_bin', u'ps_ind_17_bin', u'ps_ind_18_bin', u'ps_reg_01', u'ps_reg_02', u'ps_reg_03', u'ps_car_01_cat', u'ps_car_02_cat', u'ps_car_03_cat', u'ps_car_04_cat', u'ps_car_05_cat', u'ps_car_06_cat', u'ps_car_07_cat', u'ps_car_08_cat', u'ps_car_09_cat', u'ps_car_10_cat', u'ps_car_11_cat', u'ps_car_11', u'ps_car_12', u'ps_car_13', u'ps_car_14', u'ps_car_15', u'ps_calc_01', u'ps_calc_02', u'ps_calc_03', u'ps_calc_04', u'ps_calc_05', u'ps_calc_06', u'ps_calc_07', u'ps_calc_08', u'ps_calc_09', u'ps_calc_10', u'ps_calc_11', u'ps_calc_12', u'ps_calc_13', u'ps_calc_14', u'ps_calc_15_bin', u'ps_calc_16_bin', u'ps_calc_17_bin', u'ps_calc_18_bin', u'ps_calc_19_bin', u'ps_calc_20_bin']\n"
     ]
    }
   ],
   "source": [
    "# feature set to be used while training Neural network\n",
    "input_col = hdf.col_names\n",
    "input_col.remove('target')\n",
    "print(input_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "dl = H2ODeepLearningEstimator(seed =1,  hidden=[10,10], epochs =11, l2= 0.7, \n",
    "                              activation = 'MaxoutWithDropout', distribution = 'AUTO')\n",
    "dl.train(x =input_col, y = 'target', training_frame=hdf_train, validation_frame=hdf_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Train accuracy:', 0.5)\n",
      "('valid accuracy:', 0.5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128d1090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def model_performance(model):\n",
    "    print('Train accuracy:', model.auc(train=True))\n",
    "    print('valid accuracy:', model.auc(valid=True))\n",
    "\n",
    "    roc_valid = model.roc(valid=True)\n",
    "    roc_train= model.roc()\n",
    "\n",
    "    plt.plot(roc_valid[0],roc_valid[1])\n",
    "    plt.plot(roc_train[0],roc_train[1])\n",
    "    plt.plot(np.arange(0.0,1.1,0.1),np.arange(0.0,1.1,0.1))\n",
    "    \n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(['Valid','Train'])\n",
    "    plt.show()\n",
    "\n",
    "model_performance(dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x128f7370>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dl.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}