H2o Implementation v0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import sklearn.metrics\n",
    "from scipy.misc import imread, imsave\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "\n",
    "import h2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6414012, 5)\n",
      "Index([u'Unnamed: 0', u'Red', u'Green', u'Blue', u'Mask'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "dfTrain = pd.read_csv('Train.csv')\n",
    "print dfTrain.shape\n",
    "print dfTrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTrain = dfTrain.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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 25.91-b15, mixed mode)\n",
      "  Starting server from D:\\Anaconda2\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: c:\\users\\aakashg\\appdata\\local\\temp\\tmpjsrfo3\n",
      "  JVM stdout: c:\\users\\aakashg\\appdata\\local\\temp\\tmpjsrfo3\\h2o_AakashG_started_from_python.out\n",
      "  JVM stderr: c:\\users\\aakashg\\appdata\\local\\temp\\tmpjsrfo3\\h2o_AakashG_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"
     ]
    },
    {
     "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>3.10.2.1</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>16 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_AakashG_6gohan</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>3.531 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>Python version:</td>\n",
       "<td>2.7.12 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------\n",
       "H2O cluster uptime:         05 secs\n",
       "H2O cluster version:        3.10.2.1\n",
       "H2O cluster version age:    16 days\n",
       "H2O cluster name:           H2O_from_python_AakashG_6gohan\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.531 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",
       "Python version:             2.7.12 final\n",
       "--------------------------  ------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to H2O server at http://localhost:54321... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>37 secs</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.10.2.1</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>16 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_AakashG_6gohan</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>3.531 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://localhost:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.12 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ------------------------------\n",
       "H2O cluster uptime:         37 secs\n",
       "H2O cluster version:        3.10.2.1\n",
       "H2O cluster version age:    16 days\n",
       "H2O cluster name:           H2O_from_python_AakashG_6gohan\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.531 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://localhost:54321\n",
       "H2O connection proxy:\n",
       "Python version:             2.7.12 final\n",
       "--------------------------  ------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "h2o.connect()\n",
    "train = h2o.H2OFrame(dfTrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deeplearning Model Build progress: |██████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "train[3] = train[3].asfactor()\n",
    "\n",
    "from h2o.estimators.deeplearning import H2ODeepLearningEstimator\n",
    "model = H2ODeepLearningEstimator()\n",
    "model.train(x=range(3), y=3, training_frame=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2ODeepLearningEstimator :  Deep Learning\n",
      "Model Key:  DeepLearning_model_python_1483860648623_1\n",
      "\n",
      "\n",
      "ModelMetricsBinomial: deeplearning\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.0238169429963\n",
      "RMSE: 0.154327389003\n",
      "LogLoss: 0.0985650502885\n",
      "Mean Per-Class Error: 0.0733210047316\n",
      "AUC: 0.976477631488\n",
      "Gini: 0.952955262976\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.216569993386: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>0</b></td>\n",
       "<td><b>1</b></td>\n",
       "<td><b>Error</b></td>\n",
       "<td><b>Rate</b></td></tr>\n",
       "<tr><td>0</td>\n",
       "<td>9041.0</td>\n",
       "<td>86.0</td>\n",
       "<td>0.0094</td>\n",
       "<td> (86.0/9127.0)</td></tr>\n",
       "<tr><td>1</td>\n",
       "<td>187.0</td>\n",
       "<td>677.0</td>\n",
       "<td>0.2164</td>\n",
       "<td> (187.0/864.0)</td></tr>\n",
       "<tr><td>Total</td>\n",
       "<td>9228.0</td>\n",
       "<td>763.0</td>\n",
       "<td>0.0273</td>\n",
       "<td> (273.0/9991.0)</td></tr></table></div>"
      ],
      "text/plain": [
       "       0     1    Error    Rate\n",
       "-----  ----  ---  -------  --------------\n",
       "0      9041  86   0.0094   (86.0/9127.0)\n",
       "1      187   677  0.2164   (187.0/864.0)\n",
       "Total  9228  763  0.0273   (273.0/9991.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Metrics: Maximum metrics at their respective thresholds\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>metric</b></td>\n",
       "<td><b>threshold</b></td>\n",
       "<td><b>value</b></td>\n",
       "<td><b>idx</b></td></tr>\n",
       "<tr><td>max f1</td>\n",
       "<td>0.2165700</td>\n",
       "<td>0.8322065</td>\n",
       "<td>229.0</td></tr>\n",
       "<tr><td>max f2</td>\n",
       "<td>0.0502025</td>\n",
       "<td>0.8412875</td>\n",
       "<td>315.0</td></tr>\n",
       "<tr><td>max f0point5</td>\n",
       "<td>0.4774781</td>\n",
       "<td>0.8954112</td>\n",
       "<td>153.0</td></tr>\n",
       "<tr><td>max accuracy</td>\n",
       "<td>0.2990078</td>\n",
       "<td>0.9732759</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max precision</td>\n",
       "<td>0.9998925</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max recall</td>\n",
       "<td>0.0000187</td>\n",
       "<td>1.0</td>\n",
       "<td>399.0</td></tr>\n",
       "<tr><td>max specificity</td>\n",
       "<td>0.9998925</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td></tr>\n",
       "<tr><td>max absolute_mcc</td>\n",
       "<td>0.2990078</td>\n",
       "<td>0.8201719</td>\n",
       "<td>200.0</td></tr>\n",
       "<tr><td>max min_per_class_accuracy</td>\n",
       "<td>0.0144338</td>\n",
       "<td>0.9212963</td>\n",
       "<td>357.0</td></tr>\n",
       "<tr><td>max mean_per_class_accuracy</td>\n",
       "<td>0.0214935</td>\n",
       "<td>0.9266790</td>\n",
       "<td>346.0</td></tr></table></div>"
      ],
      "text/plain": [
       "metric                       threshold    value     idx\n",
       "---------------------------  -----------  --------  -----\n",
       "max f1                       0.21657      0.832207  229\n",
       "max f2                       0.0502025    0.841287  315\n",
       "max f0point5                 0.477478     0.895411  153\n",
       "max accuracy                 0.299008     0.973276  200\n",
       "max precision                0.999893     1         0\n",
       "max recall                   1.87337e-05  1         399\n",
       "max specificity              0.999893     1         0\n",
       "max absolute_mcc             0.299008     0.820172  200\n",
       "max min_per_class_accuracy   0.0144338    0.921296  357\n",
       "max mean_per_class_accuracy  0.0214935    0.926679  346"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gains/Lift Table: Avg response rate:  8.65 %\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>group</b></td>\n",
       "<td><b>cumulative_data_fraction</b></td>\n",
       "<td><b>lower_threshold</b></td>\n",
       "<td><b>lift</b></td>\n",
       "<td><b>cumulative_lift</b></td>\n",
       "<td><b>response_rate</b></td>\n",
       "<td><b>cumulative_response_rate</b></td>\n",
       "<td><b>capture_rate</b></td>\n",
       "<td><b>cumulative_capture_rate</b></td>\n",
       "<td><b>gain</b></td>\n",
       "<td><b>cumulative_gain</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>1</td>\n",
       "<td>0.0100090</td>\n",
       "<td>0.9996285</td>\n",
       "<td>11.5636574</td>\n",
       "<td>11.5636574</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1157407</td>\n",
       "<td>0.1157407</td>\n",
       "<td>1056.3657407</td>\n",
       "<td>1056.3657407</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2</td>\n",
       "<td>0.0200180</td>\n",
       "<td>0.9990061</td>\n",
       "<td>11.5636574</td>\n",
       "<td>11.5636574</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1157407</td>\n",
       "<td>0.2314815</td>\n",
       "<td>1056.3657407</td>\n",
       "<td>1056.3657407</td></tr>\n",
       "<tr><td></td>\n",
       "<td>3</td>\n",
       "<td>0.0300270</td>\n",
       "<td>0.9956673</td>\n",
       "<td>11.5636574</td>\n",
       "<td>11.5636574</td>\n",
       "<td>1.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.1157407</td>\n",
       "<td>0.3472222</td>\n",
       "<td>1056.3657407</td>\n",
       "<td>1056.3657407</td></tr>\n",
       "<tr><td></td>\n",
       "<td>4</td>\n",
       "<td>0.0400360</td>\n",
       "<td>0.9752422</td>\n",
       "<td>11.3323843</td>\n",
       "<td>11.5058391</td>\n",
       "<td>0.98</td>\n",
       "<td>0.995</td>\n",
       "<td>0.1134259</td>\n",
       "<td>0.4606481</td>\n",
       "<td>1033.2384259</td>\n",
       "<td>1050.5839120</td></tr>\n",
       "<tr><td></td>\n",
       "<td>5</td>\n",
       "<td>0.0500450</td>\n",
       "<td>0.8287596</td>\n",
       "<td>11.2167477</td>\n",
       "<td>11.4480208</td>\n",
       "<td>0.97</td>\n",
       "<td>0.99</td>\n",
       "<td>0.1122685</td>\n",
       "<td>0.5729167</td>\n",
       "<td>1021.6747685</td>\n",
       "<td>1044.8020833</td></tr>\n",
       "<tr><td></td>\n",
       "<td>6</td>\n",
       "<td>0.1000901</td>\n",
       "<td>0.0618243</td>\n",
       "<td>5.7587014</td>\n",
       "<td>8.6033611</td>\n",
       "<td>0.498</td>\n",
       "<td>0.744</td>\n",
       "<td>0.2881944</td>\n",
       "<td>0.8611111</td>\n",
       "<td>475.8701389</td>\n",
       "<td>760.3361111</td></tr>\n",
       "<tr><td></td>\n",
       "<td>7</td>\n",
       "<td>0.1500350</td>\n",
       "<td>0.0143017</td>\n",
       "<td>1.1818568</td>\n",
       "<td>6.1328270</td>\n",
       "<td>0.1022044</td>\n",
       "<td>0.5303536</td>\n",
       "<td>0.0590278</td>\n",
       "<td>0.9201389</td>\n",
       "<td>18.1856769</td>\n",
       "<td>513.2826977</td></tr>\n",
       "<tr><td></td>\n",
       "<td>8</td>\n",
       "<td>0.2000801</td>\n",
       "<td>0.0058259</td>\n",
       "<td>0.6244375</td>\n",
       "<td>4.7550407</td>\n",
       "<td>0.054</td>\n",
       "<td>0.4112056</td>\n",
       "<td>0.03125</td>\n",
       "<td>0.9513889</td>\n",
       "<td>-37.55625</td>\n",
       "<td>375.5040715</td></tr>\n",
       "<tr><td></td>\n",
       "<td>9</td>\n",
       "<td>0.3000701</td>\n",
       "<td>0.0016163</td>\n",
       "<td>0.1852037</td>\n",
       "<td>3.2322698</td>\n",
       "<td>0.0160160</td>\n",
       "<td>0.2795197</td>\n",
       "<td>0.0185185</td>\n",
       "<td>0.9699074</td>\n",
       "<td>-81.4796278</td>\n",
       "<td>223.2269816</td></tr>\n",
       "<tr><td></td>\n",
       "<td>10</td>\n",
       "<td>0.4001601</td>\n",
       "<td>0.0005222</td>\n",
       "<td>0.1734549</td>\n",
       "<td>2.4671835</td>\n",
       "<td>0.015</td>\n",
       "<td>0.2133567</td>\n",
       "<td>0.0173611</td>\n",
       "<td>0.9872685</td>\n",
       "<td>-82.6545139</td>\n",
       "<td>146.7183534</td></tr>\n",
       "<tr><td></td>\n",
       "<td>11</td>\n",
       "<td>0.5003503</td>\n",
       "<td>0.0001825</td>\n",
       "<td>0.0693126</td>\n",
       "<td>1.9870337</td>\n",
       "<td>0.0059940</td>\n",
       "<td>0.1718344</td>\n",
       "<td>0.0069444</td>\n",
       "<td>0.9942130</td>\n",
       "<td>-93.0687368</td>\n",
       "<td>98.7033749</td></tr>\n",
       "<tr><td></td>\n",
       "<td>12</td>\n",
       "<td>0.6008408</td>\n",
       "<td>0.0000815</td>\n",
       "<td>0.0115176</td>\n",
       "<td>1.6566292</td>\n",
       "<td>0.0009960</td>\n",
       "<td>0.1432617</td>\n",
       "<td>0.0011574</td>\n",
       "<td>0.9953704</td>\n",
       "<td>-98.8482413</td>\n",
       "<td>65.6629247</td></tr>\n",
       "<tr><td></td>\n",
       "<td>13</td>\n",
       "<td>0.7000300</td>\n",
       "<td>0.0000415</td>\n",
       "<td>0.0116687</td>\n",
       "<td>1.4235500</td>\n",
       "<td>0.0010091</td>\n",
       "<td>0.1231055</td>\n",
       "<td>0.0011574</td>\n",
       "<td>0.9965278</td>\n",
       "<td>-98.8331325</td>\n",
       "<td>42.3550047</td></tr>\n",
       "<tr><td></td>\n",
       "<td>14</td>\n",
       "<td>0.8000200</td>\n",
       "<td>0.0000192</td>\n",
       "<td>0.0347257</td>\n",
       "<td>1.2499687</td>\n",
       "<td>0.0030030</td>\n",
       "<td>0.1080946</td>\n",
       "<td>0.0034722</td>\n",
       "<td>1.0</td>\n",
       "<td>-96.5274302</td>\n",
       "<td>24.9968723</td></tr>\n",
       "<tr><td></td>\n",
       "<td>15</td>\n",
       "<td>0.9000100</td>\n",
       "<td>0.0000070</td>\n",
       "<td>0.0</td>\n",
       "<td>1.1110988</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0960854</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>11.1098754</td></tr>\n",
       "<tr><td></td>\n",
       "<td>16</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0000000</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>0.0</td>\n",
       "<td>0.0864778</td>\n",
       "<td>0.0</td>\n",
       "<td>1.0</td>\n",
       "<td>-100.0</td>\n",
       "<td>0.0</td></tr></table></div>"
      ],
      "text/plain": [
       "    group    cumulative_data_fraction    lower_threshold    lift       cumulative_lift    response_rate    cumulative_response_rate    capture_rate    cumulative_capture_rate    gain      cumulative_gain\n",
       "--  -------  --------------------------  -----------------  ---------  -----------------  ---------------  --------------------------  --------------  -------------------------  --------  -----------------\n",
       "    1        0.010009                    0.999628           11.5637    11.5637            1                1                           0.115741        0.115741                   1056.37   1056.37\n",
       "    2        0.020018                    0.999006           11.5637    11.5637            1                1                           0.115741        0.231481                   1056.37   1056.37\n",
       "    3        0.030027                    0.995667           11.5637    11.5637            1                1                           0.115741        0.347222                   1056.37   1056.37\n",
       "    4        0.040036                    0.975242           11.3324    11.5058            0.98             0.995                       0.113426        0.460648                   1033.24   1050.58\n",
       "    5        0.050045                    0.82876            11.2167    11.448             0.97             0.99                        0.112269        0.572917                   1021.67   1044.8\n",
       "    6        0.10009                     0.0618243          5.7587     8.60336            0.498            0.744                       0.288194        0.861111                   475.87    760.336\n",
       "    7        0.150035                    0.0143017          1.18186    6.13283            0.102204         0.530354                    0.0590278       0.920139                   18.1857   513.283\n",
       "    8        0.20008                     0.00582592         0.624437   4.75504            0.054            0.411206                    0.03125         0.951389                   -37.5563  375.504\n",
       "    9        0.30007                     0.00161626         0.185204   3.23227            0.016016         0.27952                     0.0185185       0.969907                   -81.4796  223.227\n",
       "    10       0.40016                     0.000522166        0.173455   2.46718            0.015            0.213357                    0.0173611       0.987269                   -82.6545  146.718\n",
       "    11       0.50035                     0.000182475        0.0693126  1.98703            0.00599401       0.171834                    0.00694444      0.994213                   -93.0687  98.7034\n",
       "    12       0.600841                    8.15188e-05        0.0115176  1.65663            0.000996016      0.143262                    0.00115741      0.99537                    -98.8482  65.6629\n",
       "    13       0.70003                     4.14858e-05        0.0116687  1.42355            0.00100908       0.123106                    0.00115741      0.996528                   -98.8331  42.355\n",
       "    14       0.80002                     1.9221e-05         0.0347257  1.24997            0.003003         0.108095                    0.00347222      1                          -96.5274  24.9969\n",
       "    15       0.90001                     6.98894e-06        0          1.1111             0                0.0960854                   0               1                          -100      11.1099\n",
       "    16       1                           1.53955e-23        0          1                  0                0.0864778                   0               1                          -100      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scoring History: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b></b></td>\n",
       "<td><b>timestamp</b></td>\n",
       "<td><b>duration</b></td>\n",
       "<td><b>training_speed</b></td>\n",
       "<td><b>epochs</b></td>\n",
       "<td><b>iterations</b></td>\n",
       "<td><b>samples</b></td>\n",
       "<td><b>training_rmse</b></td>\n",
       "<td><b>training_logloss</b></td>\n",
       "<td><b>training_auc</b></td>\n",
       "<td><b>training_lift</b></td>\n",
       "<td><b>training_classification_error</b></td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:05:06</td>\n",
       "<td> 0.000 sec</td>\n",
       "<td>None</td>\n",
       "<td>0.0</td>\n",
       "<td>0</td>\n",
       "<td>0.0</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td>\n",
       "<td>nan</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:05:25</td>\n",
       "<td>20.159 sec</td>\n",
       "<td>5323 obs/sec</td>\n",
       "<td>0.0156016</td>\n",
       "<td>1</td>\n",
       "<td>100069.0</td>\n",
       "<td>0.1995171</td>\n",
       "<td>0.3050348</td>\n",
       "<td>0.9443951</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0307277</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:05:39</td>\n",
       "<td>34.334 sec</td>\n",
       "<td>6122 obs/sec</td>\n",
       "<td>0.0312408</td>\n",
       "<td>2</td>\n",
       "<td>200379.0</td>\n",
       "<td>0.1942252</td>\n",
       "<td>0.1692469</td>\n",
       "<td>0.9608259</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0305275</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:05:51</td>\n",
       "<td>46.247 sec</td>\n",
       "<td>6780 obs/sec</td>\n",
       "<td>0.0468788</td>\n",
       "<td>3</td>\n",
       "<td>300681.0</td>\n",
       "<td>0.1682343</td>\n",
       "<td>0.1113085</td>\n",
       "<td>0.9726685</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0305275</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:00</td>\n",
       "<td>54.880 sec</td>\n",
       "<td>7600 obs/sec</td>\n",
       "<td>0.0625018</td>\n",
       "<td>4</td>\n",
       "<td>400887.0</td>\n",
       "<td>0.2069815</td>\n",
       "<td>0.3295087</td>\n",
       "<td>0.9459215</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0328295</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:09</td>\n",
       "<td> 1 min  3.376 sec</td>\n",
       "<td>8221 obs/sec</td>\n",
       "<td>0.0781933</td>\n",
       "<td>5</td>\n",
       "<td>501533.0</td>\n",
       "<td>0.1756284</td>\n",
       "<td>0.1422356</td>\n",
       "<td>0.9762770</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0285257</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:16</td>\n",
       "<td> 1 min 10.841 sec</td>\n",
       "<td>8817 obs/sec</td>\n",
       "<td>0.0938438</td>\n",
       "<td>6</td>\n",
       "<td>601915.0</td>\n",
       "<td>0.1675466</td>\n",
       "<td>0.1376042</td>\n",
       "<td>0.9749558</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0283255</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:23</td>\n",
       "<td> 1 min 17.485 sec</td>\n",
       "<td>9388 obs/sec</td>\n",
       "<td>0.1093704</td>\n",
       "<td>7</td>\n",
       "<td>701503.0</td>\n",
       "<td>0.1982830</td>\n",
       "<td>0.1423854</td>\n",
       "<td>0.9721144</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0291262</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:29</td>\n",
       "<td> 1 min 23.864 sec</td>\n",
       "<td>9909 obs/sec</td>\n",
       "<td>0.1249377</td>\n",
       "<td>8</td>\n",
       "<td>801352.0</td>\n",
       "<td>0.1667021</td>\n",
       "<td>0.1102559</td>\n",
       "<td>0.9773568</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0277250</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:36</td>\n",
       "<td> 1 min 30.510 sec</td>\n",
       "<td>10326 obs/sec</td>\n",
       "<td>0.1405384</td>\n",
       "<td>9</td>\n",
       "<td>901415.0</td>\n",
       "<td>0.2268248</td>\n",
       "<td>0.3976215</td>\n",
       "<td>0.9518224</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0272245</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:42</td>\n",
       "<td> 1 min 37.128 sec</td>\n",
       "<td>10686 obs/sec</td>\n",
       "<td>0.1561380</td>\n",
       "<td>10</td>\n",
       "<td>1001471.0</td>\n",
       "<td>0.1662710</td>\n",
       "<td>0.1167393</td>\n",
       "<td>0.9767510</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0265239</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:49</td>\n",
       "<td> 1 min 43.674 sec</td>\n",
       "<td>11009 obs/sec</td>\n",
       "<td>0.1717625</td>\n",
       "<td>11</td>\n",
       "<td>1101687.0</td>\n",
       "<td>0.2236053</td>\n",
       "<td>0.3380492</td>\n",
       "<td>0.9571063</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0312281</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:06:55</td>\n",
       "<td> 1 min 49.817 sec</td>\n",
       "<td>11329 obs/sec</td>\n",
       "<td>0.1872753</td>\n",
       "<td>12</td>\n",
       "<td>1201186.0</td>\n",
       "<td>0.1616578</td>\n",
       "<td>0.1601786</td>\n",
       "<td>0.9608786</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0294265</td></tr>\n",
       "<tr><td></td>\n",
       "<td>2017-01-08 13:07:01</td>\n",
       "<td> 1 min 56.239 sec</td>\n",
       "<td>11587 obs/sec</td>\n",
       "<td>0.2028188</td>\n",
       "<td>13</td>\n",
       "<td>1300882.0</td>\n",
       "<td>0.1543274</td>\n",
       "<td>0.0985651</td>\n",
       "<td>0.9764776</td>\n",
       "<td>11.5636574</td>\n",
       "<td>0.0273246</td></tr></table></div>"
      ],
      "text/plain": [
       "    timestamp            duration          training_speed    epochs     iterations    samples      training_rmse    training_logloss    training_auc    training_lift    training_classification_error\n",
       "--  -------------------  ----------------  ----------------  ---------  ------------  -----------  ---------------  ------------------  --------------  ---------------  -------------------------------\n",
       "    2017-01-08 13:05:06  0.000 sec                           0          0             0            nan              nan                 nan             nan              nan\n",
       "    2017-01-08 13:05:25  20.159 sec        5323 obs/sec      0.0156016  1             100069       0.199517         0.305035            0.944395        11.5637          0.0307277\n",
       "    2017-01-08 13:05:39  34.334 sec        6122 obs/sec      0.0312408  2             200379       0.194225         0.169247            0.960826        11.5637          0.0305275\n",
       "    2017-01-08 13:05:51  46.247 sec        6780 obs/sec      0.0468788  3             300681       0.168234         0.111309            0.972669        11.5637          0.0305275\n",
       "    2017-01-08 13:06:00  54.880 sec        7600 obs/sec      0.0625018  4             400887       0.206982         0.329509            0.945921        11.5637          0.0328295\n",
       "    2017-01-08 13:06:09  1 min  3.376 sec  8221 obs/sec      0.0781933  5             501533       0.175628         0.142236            0.976277        11.5637          0.0285257\n",
       "    2017-01-08 13:06:16  1 min 10.841 sec  8817 obs/sec      0.0938438  6             601915       0.167547         0.137604            0.974956        11.5637          0.0283255\n",
       "    2017-01-08 13:06:23  1 min 17.485 sec  9388 obs/sec      0.10937    7             701503       0.198283         0.142385            0.972114        11.5637          0.0291262\n",
       "    2017-01-08 13:06:29  1 min 23.864 sec  9909 obs/sec      0.124938   8             801352       0.166702         0.110256            0.977357        11.5637          0.027725\n",
       "    2017-01-08 13:06:36  1 min 30.510 sec  10326 obs/sec     0.140538   9             901415       0.226825         0.397621            0.951822        11.5637          0.0272245\n",
       "    2017-01-08 13:06:42  1 min 37.128 sec  10686 obs/sec     0.156138   10            1.00147e+06  0.166271         0.116739            0.976751        11.5637          0.0265239\n",
       "    2017-01-08 13:06:49  1 min 43.674 sec  11009 obs/sec     0.171763   11            1.10169e+06  0.223605         0.338049            0.957106        11.5637          0.0312281\n",
       "    2017-01-08 13:06:55  1 min 49.817 sec  11329 obs/sec     0.187275   12            1.20119e+06  0.161658         0.160179            0.960879        11.5637          0.0294265\n",
       "    2017-01-08 13:07:01  1 min 56.239 sec  11587 obs/sec     0.202819   13            1.30088e+06  0.154327         0.0985651           0.976478        11.5637          0.0273246"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['017532875DDF.jpg',\n",
       " '1467973104.jpg',\n",
       " '1467973122.jpg',\n",
       " '1467973178.jpg',\n",
       " '54A84627F362.jpg',\n",
       " 'train-0.jpg',\n",
       " 'train-1.jpg',\n",
       " 'train-10.jpg',\n",
       " 'train-100.jpg',\n",
       " 'train-101.jpg',\n",
       " 'train-102.jpg',\n",
       " 'train-103.jpg',\n",
       " 'train-104.jpg',\n",
       " 'train-105.jpg',\n",
       " 'train-106.jpg',\n",
       " 'train-107.jpg',\n",
       " 'train-108.jpg',\n",
       " 'train-109.jpg',\n",
       " 'train-11.jpg',\n",
       " 'train-110.jpg',\n",
       " 'train-111.jpg',\n",
       " 'train-112.jpg',\n",
       " 'train-113.jpg',\n",
       " 'train-114.jpg',\n",
       " 'train-115.jpg',\n",
       " 'train-116.jpg',\n",
       " 'train-117.jpg',\n",
       " 'train-118.jpg',\n",
       " 'train-119.jpg',\n",
       " 'train-12.jpg',\n",
       " 'train-120.jpg',\n",
       " 'train-121.jpg',\n",
       " 'train-122.jpg',\n",
       " 'train-123.jpg',\n",
       " 'train-124.jpg',\n",
       " 'train-125.jpg',\n",
       " 'train-126.jpg',\n",
       " 'train-127.jpg',\n",
       " 'train-128.jpg',\n",
       " 'train-129.jpg',\n",
       " 'train-13.jpg',\n",
       " 'train-130.jpg',\n",
       " 'train-131.jpg',\n",
       " 'train-132.jpg',\n",
       " 'train-133.jpg',\n",
       " 'train-134.jpg',\n",
       " 'train-135.jpg',\n",
       " 'train-136.jpg',\n",
       " 'train-137.jpg',\n",
       " 'train-138.jpg',\n",
       " 'train-139.jpg',\n",
       " 'train-14.jpg',\n",
       " 'train-140.jpg',\n",
       " 'train-141.jpg',\n",
       " 'train-142.jpg',\n",
       " 'train-143.jpg',\n",
       " 'train-144.jpg',\n",
       " 'train-145.jpg',\n",
       " 'train-146.jpg',\n",
       " 'train-147.jpg',\n",
       " 'train-148.jpg',\n",
       " 'train-149.jpg',\n",
       " 'train-15.jpg',\n",
       " 'train-150.jpg',\n",
       " 'train-151.jpg',\n",
       " 'train-152.jpg',\n",
       " 'train-153.jpg',\n",
       " 'train-154.jpg',\n",
       " 'train-155.jpg',\n",
       " 'train-156.jpg',\n",
       " 'train-157.jpg',\n",
       " 'train-158.jpg',\n",
       " 'train-159.jpg',\n",
       " 'train-16.jpg',\n",
       " 'train-160.jpg',\n",
       " 'train-161.jpg',\n",
       " 'train-162.jpg',\n",
       " 'train-163.jpg',\n",
       " 'train-17.jpg',\n",
       " 'train-18.jpg',\n",
       " 'train-19.jpg',\n",
       " 'train-2.jpg',\n",
       " 'train-20.jpg',\n",
       " 'train-21.jpg',\n",
       " 'train-22.jpg',\n",
       " 'train-23.jpg',\n",
       " 'train-24.jpg',\n",
       " 'train-25.jpg',\n",
       " 'train-26.jpg',\n",
       " 'train-27.jpg',\n",
       " 'train-28.jpg',\n",
       " 'train-29.jpg',\n",
       " 'train-3.jpg',\n",
       " 'train-30.jpg',\n",
       " 'train-31.jpg',\n",
       " 'train-32.jpg',\n",
       " 'train-33.jpg',\n",
       " 'train-34.jpg',\n",
       " 'train-35.jpg',\n",
       " 'train-36.jpg',\n",
       " 'train-37.jpg',\n",
       " 'train-38.jpg',\n",
       " 'train-39.jpg',\n",
       " 'train-4.jpg',\n",
       " 'train-40.jpg',\n",
       " 'train-41.jpg',\n",
       " 'train-42.jpg',\n",
       " 'train-43.jpg',\n",
       " 'train-44.jpg',\n",
       " 'train-45.jpg',\n",
       " 'train-46.jpg',\n",
       " 'train-47.jpg',\n",
       " 'train-48.jpg',\n",
       " 'train-49.jpg',\n",
       " 'train-5.jpg',\n",
       " 'train-50.jpg',\n",
       " 'train-51.jpg',\n",
       " 'train-52.jpg',\n",
       " 'train-53.jpg',\n",
       " 'train-54.jpg',\n",
       " 'train-55.jpg',\n",
       " 'train-56.jpg',\n",
       " 'train-57.jpg',\n",
       " 'train-58.jpg',\n",
       " 'train-59.jpg',\n",
       " 'train-6.jpg',\n",
       " 'train-60.jpg',\n",
       " 'train-61.jpg',\n",
       " 'train-62.jpg',\n",
       " 'train-63.jpg',\n",
       " 'train-64.jpg',\n",
       " 'train-65.jpg',\n",
       " 'train-66.jpg',\n",
       " 'train-67.jpg',\n",
       " 'train-68.jpg',\n",
       " 'train-69.jpg',\n",
       " 'train-7.jpg',\n",
       " 'train-70.jpg',\n",
       " 'train-71.jpg',\n",
       " 'train-72.jpg',\n",
       " 'train-73.jpg',\n",
       " 'train-74.jpg',\n",
       " 'train-75.jpg',\n",
       " 'train-76.jpg',\n",
       " 'train-77.jpg',\n",
       " 'train-78.jpg',\n",
       " 'train-79.jpg',\n",
       " 'train-8.jpg',\n",
       " 'train-80.jpg',\n",
       " 'train-81.jpg',\n",
       " 'train-82.jpg',\n",
       " 'train-83.jpg',\n",
       " 'train-84.jpg',\n",
       " 'train-85.jpg',\n",
       " 'train-86.jpg',\n",
       " 'train-87.jpg',\n",
       " 'train-88.jpg',\n",
       " 'train-89.jpg',\n",
       " 'train-9.jpg',\n",
       " 'train-90.jpg',\n",
       " 'train-91.jpg',\n",
       " 'train-92.jpg',\n",
       " 'train-93.jpg',\n",
       " 'train-94.jpg',\n",
       " 'train-95.jpg',\n",
       " 'train-96.jpg',\n",
       " 'train-97.jpg',\n",
       " 'train-98.jpg',\n",
       " 'train-99.jpg']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read the test files\n",
    "ip_files = []\n",
    "test_files = os.listdir('E:\\\\SigTuple_Hack\\\\Train_Data\\\\')\n",
    "ip_files += [f for f in test_files if f.endswith('.jpg') and 'mask' not in f]\n",
    "\n",
    "ip_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E:\\SigTuple_Hack\\Train_Data\\017532875DDF.jpg\n",
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      "(16384, 1)\n",
      "(16384, 3)\n",
      "0.999938964844\n",
      "0.0\n",
      "0.999938964844\n",
      "[[16383     1]\n",
      " [    0     0]]\n",
      "E:\\SigTuple_Hack\\Train_Data\\train-96.jpg\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n",
      "['predict']\n",
      "(16384, 1)\n",
      "(16384, 3)\n",
      "0.999938964844\n",
      "0.0\n",
      "0.999938964844\n",
      "[[16383     1]\n",
      " [    0     0]]\n",
      "E:\\SigTuple_Hack\\Train_Data\\train-97.jpg\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n",
      "['predict']\n",
      "(16384, 1)\n",
      "(16384, 3)\n",
      "0.999938964844\n",
      "0.0\n",
      "0.999938964844\n",
      "[[16383     1]\n",
      " [    0     0]]\n",
      "E:\\SigTuple_Hack\\Train_Data\\train-98.jpg\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n",
      "['predict']\n",
      "(16384, 1)\n",
      "(16384, 3)\n",
      "0.999938964844\n",
      "0.0\n",
      "0.999938964844\n",
      "[[16383     1]\n",
      " [    0     0]]\n",
      "E:\\SigTuple_Hack\\Train_Data\\train-99.jpg\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n",
      "['predict']\n",
      "(16384, 1)\n",
      "(16384, 3)\n",
      "0.999938964844\n",
      "0.0\n",
      "0.999938964844\n",
      "[[16383     1]\n",
      " [    0     0]]\n"
     ]
    }
   ],
   "source": [
    "#valList = ['54A84627F362.jpg', '1467973178.jpg', '017532875DDF.jpg', '1467973122.jpg']\n",
    "grid = joblib.load('E:\\\\SigTuple_Hack\\\\Models\\\\grid_linearSVC_v0.pkl')\n",
    "\n",
    "for img_file in ip_files:\n",
    "    input_file = 'E:\\\\SigTuple_Hack\\\\Train_Data\\\\' + img_file\n",
    "    mask_file = 'E:\\\\SigTuple_Hack\\\\Train_Data\\\\' + img_file[:-4] + '-mask.jpg'\n",
    "    mask_file2 = 'E:\\\\SigTuple_Hack\\\\Train_Data\\\\' + img_file[:-4] + '-mask2.jpg'\n",
    "    \n",
    "    print input_file\n",
    "    \n",
    "    # Load the image & flatten 3d to 2d .\n",
    "    imgcolor = imread(input_file)/255.0\n",
    "    imgshape = imgcolor.shape\n",
    "    imgdf = pd.DataFrame(imgcolor.transpose(2,0,1).reshape(3,-1).transpose(1,0))\n",
    "    imgdf.columns = ['Red', 'Green', 'Blue']\n",
    "    #imgdf.drop('Unnamed: 0', 1, inplace = True)\n",
    "    test = h2o.H2OFrame(imgdf)\n",
    "    \n",
    "    # Create mask, using LinearSVC model\n",
    "    Pred = model.predict(test)['predict']\n",
    "    print Pred.columns\n",
    "    print Pred.shape\n",
    "    print imgdf.shape\n",
    "    imgdf.loc[:,'Predict'] = tmp['predict']. as_data_frame()\n",
    "    \n",
    "    imgmask = imread(mask_file)/255.0\n",
    "    maskShape = imgmask.shape\n",
    "    imgdf['Mask'] = imgmask.reshape(maskShape[0]*maskShape[1])\n",
    "    \n",
    "    # Update the mask column\n",
    "    imgdf.Mask[dfTrain.Mask < 0.1] = 0\n",
    "    imgdf.Mask[dfTrain.Mask > 0.9] = 1\n",
    "\n",
    "    # Calculate vanity figures\n",
    "    print sklearn.metrics.accuracy_score(imgdf.Mask, imgdf.Predict)\n",
    "    print sklearn.metrics.f1_score(imgdf.Mask, imgdf.Predict)\n",
    "    print sklearn.metrics.jaccard_similarity_score(imgdf.Mask, imgdf.Predict)\n",
    "    print sklearn.metrics.confusion_matrix(imgdf.Mask, imgdf.Predict)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Save mask in directory\n",
    "    imgmask2= imgdf['Predict'].reshape(maskShape[0], maskShape[1])\n",
    "    imsave(mask_file2, imgmask2)\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'predict', u'p0', u'p1']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1144832, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1144832, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.011689924809928443]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " tmp['predict'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgdf.loc[:,'Predict'] = tmp['predict']. as_data_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "      <th>Blue</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.901961</td>\n",
       "      <td>0.819608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.913725</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.890196</td>\n",
       "      <td>0.909804</td>\n",
       "      <td>0.831373</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "        Red     Green      Blue  Predict\n",
       "0  0.894118  0.901961  0.819608        0\n",
       "1  0.894118  0.905882  0.823529        0\n",
       "2  0.894118  0.905882  0.823529        0\n",
       "3  0.894118  0.913725  0.835294        0\n",
       "4  0.890196  0.909804  0.831373        0"
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "imgdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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