GLM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 25.161-b12, mixed mode)\n",
      "  Starting server from C:\\Users\\Viswas\\Anaconda2\\envs\\py27\\lib\\site-packages\\h2o\\backend\\bin\\h2o.jar\n",
      "  Ice root: c:\\users\\viswas\\appdata\\local\\temp\\tmptrwqje\n",
      "  JVM stdout: c:\\users\\viswas\\appdata\\local\\temp\\tmptrwqje\\h2o_Viswas_started_from_python.out\n",
      "  JVM stderr: c:\\users\\viswas\\appdata\\local\\temp\\tmptrwqje\\h2o_Viswas_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>03 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>Asia/Kolkata</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.18.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>7 days, 22 hours and 54 minutes </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_Viswas_jedf3e</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>3.533 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</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>H2O API Extensions:</td>\n",
       "<td>Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>2.7.14 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  -------------------------------\n",
       "H2O cluster uptime:         03 secs\n",
       "H2O cluster timezone:       Asia/Kolkata\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.18.0.2\n",
       "H2O cluster version age:    7 days, 22 hours and 54 minutes\n",
       "H2O cluster name:           H2O_from_python_Viswas_jedf3e\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    3.533 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\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",
       "H2O API Extensions:         Algos, AutoML, Core V3, Core V4\n",
       "Python version:             2.7.14 final\n",
       "--------------------------  -------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "prostate = h2o.import_file(\"https://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv\")\n",
    "prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()\n",
    "prostate['RACE'] = prostate['RACE'].asfactor()\n",
    "prostate['DCAPS'] = prostate['DCAPS'].asfactor()\n",
    "prostate['DPROS'] = prostate['DPROS'].asfactor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  ID</th><th style=\"text-align: right;\">  CAPSULE</th><th style=\"text-align: right;\">  AGE</th><th style=\"text-align: right;\">  RACE</th><th style=\"text-align: right;\">  DPROS</th><th style=\"text-align: right;\">  DCAPS</th><th style=\"text-align: right;\">  PSA</th><th style=\"text-align: right;\">  VOL</th><th style=\"text-align: right;\">  GLEASON</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\">   1</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   65</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">  1.4</td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   2</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   72</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  6.7</td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   3</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   70</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  4.9</td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   4</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   76</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\"> 51.2</td><td style=\"text-align: right;\"> 20  </td><td style=\"text-align: right;\">        7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   5</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   69</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\"> 12.3</td><td style=\"text-align: right;\"> 55.9</td><td style=\"text-align: right;\">        6</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   6</td><td style=\"text-align: right;\">        1</td><td style=\"text-align: right;\">   71</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      3</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">  3.3</td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        8</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   7</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   68</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\"> 31.9</td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   8</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   61</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\"> 66.7</td><td style=\"text-align: right;\"> 27.2</td><td style=\"text-align: right;\">        7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">   9</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   69</td><td style=\"text-align: right;\">     1</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">  3.9</td><td style=\"text-align: right;\"> 24  </td><td style=\"text-align: right;\">        7</td></tr>\n",
       "<tr><td style=\"text-align: right;\">  10</td><td style=\"text-align: right;\">        0</td><td style=\"text-align: right;\">   68</td><td style=\"text-align: right;\">     2</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\"> 13  </td><td style=\"text-align: right;\">  0  </td><td style=\"text-align: right;\">        6</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prostate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"AGE\", \"RACE\", \"VOL\", \"GLEASON\"]\n",
    "response_col = \"CAPSULE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glm Model Build progress: |███████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "glm_model = H2OGeneralizedLinearEstimator(family= \"binomial\", lambda_ = 0, compute_p_values = True)\n",
    "glm_model.train(predictors, response_col, training_frame= prostate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'GLEASON': 1.2503593867263172, u'VOL': -0.012783348665664449, u'AGE': -0.017888697161812357, u'Intercept': -6.675155394082711, u'RACE.2': -0.5899232636956414, u'RACE.1': -0.442787516808813}\n"
     ]
    }
   ],
   "source": [
    "print(glm_model.coef())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{u'GLEASON': 1.3653341515811626, u'VOL': -0.2345440232267344, u'AGE': -0.11676080128780758, u'Intercept': -0.07610006436753279, u'RACE.2': -0.5899232636956414, u'RACE.1': -0.442787516808813}\n"
     ]
    }
   ],
   "source": [
    "print(glm_model.coef_norm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: glm coefficients\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td><b>names</b></td>\n",
       "<td><b>coefficients</b></td>\n",
       "<td><b>std_error</b></td>\n",
       "<td><b>z_value</b></td>\n",
       "<td><b>p_value</b></td>\n",
       "<td><b>standardized_coefficients</b></td></tr>\n",
       "<tr><td>Intercept</td>\n",
       "<td>-6.6751554</td>\n",
       "<td>1.9317604</td>\n",
       "<td>-3.4554780</td>\n",
       "<td>0.0005493</td>\n",
       "<td>-0.0761001</td></tr>\n",
       "<tr><td>RACE.1</td>\n",
       "<td>-0.4427875</td>\n",
       "<td>1.3242308</td>\n",
       "<td>-0.3343734</td>\n",
       "<td>0.7380978</td>\n",
       "<td>-0.4427875</td></tr>\n",
       "<tr><td>RACE.2</td>\n",
       "<td>-0.5899233</td>\n",
       "<td>1.3734658</td>\n",
       "<td>-0.4295143</td>\n",
       "<td>0.6675490</td>\n",
       "<td>-0.5899233</td></tr>\n",
       "<tr><td>AGE</td>\n",
       "<td>-0.0178887</td>\n",
       "<td>0.0187019</td>\n",
       "<td>-0.9565159</td>\n",
       "<td>0.3388116</td>\n",
       "<td>-0.1167608</td></tr>\n",
       "<tr><td>VOL</td>\n",
       "<td>-0.0127833</td>\n",
       "<td>0.0075144</td>\n",
       "<td>-1.7011907</td>\n",
       "<td>0.0889072</td>\n",
       "<td>-0.2345440</td></tr>\n",
       "<tr><td>GLEASON</td>\n",
       "<td>1.2503594</td>\n",
       "<td>0.1561563</td>\n",
       "<td>8.0071033</td>\n",
       "<td>0.0000000</td>\n",
       "<td>1.3653342</td></tr></table></div>"
      ],
      "text/plain": [
       "names      coefficients    std_error    z_value    p_value      standardized_coefficients\n",
       "---------  --------------  -----------  ---------  -----------  ---------------------------\n",
       "Intercept  -6.67516        1.93176      -3.45548   0.000549318  -0.0761001\n",
       "RACE.1     -0.442788       1.32423      -0.334373  0.738098     -0.442788\n",
       "RACE.2     -0.589923       1.37347      -0.429514  0.667549     -0.589923\n",
       "AGE        -0.0178887      0.0187019    -0.956516  0.338812     -0.116761\n",
       "VOL        -0.0127833      0.00751435   -1.70119   0.0889072    -0.234544\n",
       "GLEASON    1.25036         0.156156     8.0071     1.22125e-15  1.36533"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glm_model._model_json['output']['coefficients_table']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.9317603626604154, 1.3242308316851048, 1.3734657932878145, 0.01870193337051069, 0.007514353657915353, 0.15615627100849927]\n"
     ]
    }
   ],
   "source": [
    "print(glm_model._model_json['output']['coefficients_table']['std_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0005493180609458248, 0.7380978369202372, 0.6675489550762541, 0.33881164088847104, 0.08890718096586625, 1.2212453270876722e-15]\n"
     ]
    }
   ],
   "source": [
    "print(glm_model._model_json['output']['coefficients_table']['p_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.4554779791059094, -0.3343733631736688, -0.4295143472655973, -0.9565159284557903, -1.701190714147307, 8.007103260414452]\n"
     ]
    }
   ],
   "source": [
    "print(glm_model._model_json['output']['coefficients_table']['z_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99cfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm_model.std_coef_plot()"
   ]
  }
 ],
 "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}