MSS_Xmas_Trees/ml_seg_try1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Contest entry by Wouter Kimman \n",
    "\n",
    "\n",
    "Strategy: \n",
    "----------------------------------------------\n",
    "\n",
    "--------------------------------------------------------------------------------------------\n",
    "Trying some pre-processing with simple random forest..hopefully preprocessing as important as type of classifier\n",
    "\n",
    "The problem has a smell of descision trees to me, since most predictions of the neighboring \n",
    "facies are very accurate. \n",
    "\n",
    "--------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.fft import rfft\n",
    "from scipy import signal\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import timeit\n",
    "from sqlalchemy.sql import text\n",
    "from sklearn import tree\n",
    "from sklearn import cross_validation\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#import sherlock.filesystem as sfs\n",
    "#import sherlock.database as sdb\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First steps, reading in and exploring the data are the same as Brendon's steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1344,
   "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>Facies</th>\n",
       "      <th>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>77.45</td>\n",
       "      <td>0.664</td>\n",
       "      <td>9.9</td>\n",
       "      <td>11.915</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.5</td>\n",
       "      <td>78.26</td>\n",
       "      <td>0.661</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.565</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>79.05</td>\n",
       "      <td>0.658</td>\n",
       "      <td>14.8</td>\n",
       "      <td>13.050</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.5</td>\n",
       "      <td>86.10</td>\n",
       "      <td>0.655</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.115</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>74.58</td>\n",
       "      <td>0.647</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.300</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Facies Formation  Well Name   Depth     GR  ILD_log10  DeltaPHI   PHIND  \\\n",
       "0       3     A1 SH  SHRIMPLIN  2793.0  77.45      0.664       9.9  11.915   \n",
       "1       3     A1 SH  SHRIMPLIN  2793.5  78.26      0.661      14.2  12.565   \n",
       "2       3     A1 SH  SHRIMPLIN  2794.0  79.05      0.658      14.8  13.050   \n",
       "3       3     A1 SH  SHRIMPLIN  2794.5  86.10      0.655      13.9  13.115   \n",
       "4       3     A1 SH  SHRIMPLIN  2795.0  74.58      0.647      13.5  13.300   \n",
       "\n",
       "    PE  NM_M  RELPOS  \n",
       "0  4.6     1   1.000  \n",
       "1  4.1     1   0.979  \n",
       "2  3.6     1   0.957  \n",
       "3  3.5     1   0.936  \n",
       "4  3.4     1   0.915  "
      ]
     },
     "execution_count": 1344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'training_data.csv'\n",
    "training_data0 = pd.read_csv(filename)\n",
    "training_data0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Prediction from training set using all wells\n",
    "-------------------------------------------------------\n",
    "Let's do a first shot with random forests. \n",
    "First we cheat and see how awesome we would do if the test data was not from an independent well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1350,
   "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>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.135769</td>\n",
       "      <td>0.642719</td>\n",
       "      <td>3.559642</td>\n",
       "      <td>13.483213</td>\n",
       "      <td>3.725014</td>\n",
       "      <td>1.498453</td>\n",
       "      <td>0.520287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.854826</td>\n",
       "      <td>0.241845</td>\n",
       "      <td>5.228948</td>\n",
       "      <td>7.698980</td>\n",
       "      <td>0.896152</td>\n",
       "      <td>0.500075</td>\n",
       "      <td>0.286792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.250000</td>\n",
       "      <td>-0.025949</td>\n",
       "      <td>-21.832000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46.918750</td>\n",
       "      <td>0.492750</td>\n",
       "      <td>1.163750</td>\n",
       "      <td>8.346750</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.273000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.721500</td>\n",
       "      <td>0.624437</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>12.150000</td>\n",
       "      <td>3.551500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.626250</td>\n",
       "      <td>0.812735</td>\n",
       "      <td>6.432500</td>\n",
       "      <td>16.453750</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.767250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>361.150000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>84.400000</td>\n",
       "      <td>8.094000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GR    ILD_log10     DeltaPHI        PHIND           PE  \\\n",
       "count  3232.000000  3232.000000  3232.000000  3232.000000  3232.000000   \n",
       "mean     66.135769     0.642719     3.559642    13.483213     3.725014   \n",
       "std      30.854826     0.241845     5.228948     7.698980     0.896152   \n",
       "min      13.250000    -0.025949   -21.832000     0.550000     0.200000   \n",
       "25%      46.918750     0.492750     1.163750     8.346750     3.100000   \n",
       "50%      65.721500     0.624437     3.500000    12.150000     3.551500   \n",
       "75%      79.626250     0.812735     6.432500    16.453750     4.300000   \n",
       "max     361.150000     1.480000    18.600000    84.400000     8.094000   \n",
       "\n",
       "              NM_M       RELPOS  \n",
       "count  3232.000000  3232.000000  \n",
       "mean      1.498453     0.520287  \n",
       "std       0.500075     0.286792  \n",
       "min       1.000000     0.010000  \n",
       "25%       1.000000     0.273000  \n",
       "50%       1.000000     0.526000  \n",
       "75%       2.000000     0.767250  \n",
       "max       2.000000     1.000000  "
      ]
     },
     "execution_count": 1350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_facies_labels = training_data0['Facies'].values\n",
    "\n",
    "feature_vectors = training_data0.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "feature_vectors.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1351,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "scaled_features = scaler.transform(feature_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,  correct_facies_labels, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction from random forest:\n",
      "0.724884080371\n",
      "f1 score:\n",
      "0.725714762122\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth = 15,n_estimators=200,max_features=None)\n",
    "#rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "predicted_random_forest = rf.predict(X_test)\n",
    "\n",
    "print \"prediction from random forest:\"\n",
    "print metrics.accuracy_score(list(y_test), predicted_random_forest)\n",
    "\n",
    "print \"f1 score:\"\n",
    "print metrics.f1_score(list(y_test), predicted_random_forest,average = 'weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1356,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_data=training_data0.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "2) Prediction of Blind well\n",
    "---------------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#remove 1 well\n",
    "blind = training_data[training_data['Well Name'] == 'SHANKLE']\n",
    "training_data = training_data[training_data['Well Name'] != 'SHANKLE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1358,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_facies_labels = training_data['Facies'].values\n",
    "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "scaled_features = scaler.transform(feature_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=15, max_features=None, max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 1359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, dum1, y_train, dum2 = train_test_split(scaled_features,  correct_facies_labels, test_size=0.2, random_state=0)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1360,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get the blind well\n",
    "correct_facies_labels = blind['Facies'].values\n",
    "feature_vectors = blind.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "scaled_features = scaler.transform(feature_vectors)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All training data different from test well\n",
      "prediction from random forest:\n",
      "0.456570155902\n",
      "f1 score:\n",
      "0.443892955065\n"
     ]
    }
   ],
   "source": [
    "predicted_random_forest = rf.predict(scaled_features)\n",
    "print \"All training data different from test well\"\n",
    "print \"prediction from random forest:\"\n",
    "print metrics.accuracy_score(correct_facies_labels, predicted_random_forest)\n",
    "\n",
    "print \"f1 score:\"\n",
    "print metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction performs much much beter if the all data is included in the training,\n",
    "compared to blind wells. Shouldn't be that much a surprise but doesn't this suggest some wells\n",
    "are not representative of the others\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    15    73     1                                        89\n",
      "     CSiS     9    65    15                                        89\n",
      "     FSiS     1    61    55                                       117\n",
      "     SiSh                                   6           1           7\n",
      "       MS                       6           8           5          19\n",
      "       WS                 2     1     2    43          23          71\n",
      "        D                                         7    10          17\n",
      "       PS                                  14     3    20     3    40\n",
      "       BS                                                           0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from classification_utilities import display_cm, display_adj_cm\n",
    "\n",
    "#conf = confusion_matrix(correct_facies_labels, predicted_gradboost)\n",
    "conf = confusion_matrix(correct_facies_labels, predicted_random_forest)\n",
    "\n",
    "display_cm(conf, facies_labels, hide_zeros=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the benchmark to beat : 0.44 using rf, (slightly higher for gradient boost)\n",
    "\n",
    "------------------------------------------\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Data exploration\n",
    "-----------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic statistics by facies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1668,
   "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>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2749.503861</td>\n",
       "      <td>64.497761</td>\n",
       "      <td>0.371312</td>\n",
       "      <td>3.506463</td>\n",
       "      <td>14.818996</td>\n",
       "      <td>2.913313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.452486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2837.130081</td>\n",
       "      <td>74.605205</td>\n",
       "      <td>0.548171</td>\n",
       "      <td>5.309705</td>\n",
       "      <td>15.189019</td>\n",
       "      <td>3.215267</td>\n",
       "      <td>1.008130</td>\n",
       "      <td>0.491150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2875.241463</td>\n",
       "      <td>79.915083</td>\n",
       "      <td>0.547504</td>\n",
       "      <td>3.797480</td>\n",
       "      <td>20.619698</td>\n",
       "      <td>3.132340</td>\n",
       "      <td>1.026016</td>\n",
       "      <td>0.560979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2867.282609</td>\n",
       "      <td>92.169957</td>\n",
       "      <td>0.776840</td>\n",
       "      <td>5.579728</td>\n",
       "      <td>11.152821</td>\n",
       "      <td>3.802152</td>\n",
       "      <td>1.972826</td>\n",
       "      <td>0.421908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2957.559908</td>\n",
       "      <td>60.612788</td>\n",
       "      <td>0.820655</td>\n",
       "      <td>3.735212</td>\n",
       "      <td>9.562668</td>\n",
       "      <td>3.995184</td>\n",
       "      <td>1.949309</td>\n",
       "      <td>0.590733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2892.073593</td>\n",
       "      <td>55.554532</td>\n",
       "      <td>0.884502</td>\n",
       "      <td>2.728424</td>\n",
       "      <td>7.442779</td>\n",
       "      <td>4.234136</td>\n",
       "      <td>1.995671</td>\n",
       "      <td>0.493753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2926.974490</td>\n",
       "      <td>64.614643</td>\n",
       "      <td>0.465649</td>\n",
       "      <td>5.034133</td>\n",
       "      <td>14.080418</td>\n",
       "      <td>3.671286</td>\n",
       "      <td>1.989796</td>\n",
       "      <td>0.600520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2891.041165</td>\n",
       "      <td>47.195886</td>\n",
       "      <td>0.747021</td>\n",
       "      <td>1.444823</td>\n",
       "      <td>9.585972</td>\n",
       "      <td>4.518653</td>\n",
       "      <td>1.975904</td>\n",
       "      <td>0.585058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3033.400621</td>\n",
       "      <td>44.877280</td>\n",
       "      <td>0.574677</td>\n",
       "      <td>0.198547</td>\n",
       "      <td>13.226944</td>\n",
       "      <td>5.295925</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.451925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Depth         GR  ILD_log10  DeltaPHI      PHIND        PE  \\\n",
       "Facies                                                                     \n",
       "1       2749.503861  64.497761   0.371312  3.506463  14.818996  2.913313   \n",
       "2       2837.130081  74.605205   0.548171  5.309705  15.189019  3.215267   \n",
       "3       2875.241463  79.915083   0.547504  3.797480  20.619698  3.132340   \n",
       "4       2867.282609  92.169957   0.776840  5.579728  11.152821  3.802152   \n",
       "5       2957.559908  60.612788   0.820655  3.735212   9.562668  3.995184   \n",
       "6       2892.073593  55.554532   0.884502  2.728424   7.442779  4.234136   \n",
       "7       2926.974490  64.614643   0.465649  5.034133  14.080418  3.671286   \n",
       "8       2891.041165  47.195886   0.747021  1.444823   9.585972  4.518653   \n",
       "9       3033.400621  44.877280   0.574677  0.198547  13.226944  5.295925   \n",
       "\n",
       "            NM_M    RELPOS  \n",
       "Facies                      \n",
       "1       1.000000  0.452486  \n",
       "2       1.008130  0.491150  \n",
       "3       1.026016  0.560979  \n",
       "4       1.972826  0.421908  \n",
       "5       1.949309  0.590733  \n",
       "6       1.995671  0.493753  \n",
       "7       1.989796  0.600520  \n",
       "8       1.975904  0.585058  \n",
       "9       2.000000  0.451925  "
      ]
     },
     "execution_count": 1668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_1=training_data.groupby('Formation').mean()\n",
    "temp_2=training_data.groupby('Facies').mean()\n",
    "#temp_3=training_data.groupby('Facies').count()\n",
    "temp_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic statistics by well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1364,
   "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>Facies</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHURCHMAN BIBLE</th>\n",
       "      <td>5.655941</td>\n",
       "      <td>3019.482673</td>\n",
       "      <td>63.683453</td>\n",
       "      <td>0.758757</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>14.011116</td>\n",
       "      <td>3.789926</td>\n",
       "      <td>1.722772</td>\n",
       "      <td>0.544317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CROSS H CATTLE</th>\n",
       "      <td>3.127745</td>\n",
       "      <td>2702.360279</td>\n",
       "      <td>72.699285</td>\n",
       "      <td>0.568829</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>15.574850</td>\n",
       "      <td>3.325852</td>\n",
       "      <td>1.297405</td>\n",
       "      <td>0.506182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUKE G U</th>\n",
       "      <td>4.353579</td>\n",
       "      <td>2725.708243</td>\n",
       "      <td>64.777223</td>\n",
       "      <td>0.639690</td>\n",
       "      <td>4.218438</td>\n",
       "      <td>12.953905</td>\n",
       "      <td>3.660705</td>\n",
       "      <td>1.466377</td>\n",
       "      <td>0.514095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEWBY</th>\n",
       "      <td>4.801296</td>\n",
       "      <td>2941.500000</td>\n",
       "      <td>60.733045</td>\n",
       "      <td>0.674812</td>\n",
       "      <td>3.477970</td>\n",
       "      <td>11.625378</td>\n",
       "      <td>3.771706</td>\n",
       "      <td>1.615551</td>\n",
       "      <td>0.528138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOLAN</th>\n",
       "      <td>4.643373</td>\n",
       "      <td>2957.000000</td>\n",
       "      <td>68.693940</td>\n",
       "      <td>0.592400</td>\n",
       "      <td>3.134070</td>\n",
       "      <td>12.197361</td>\n",
       "      <td>3.857947</td>\n",
       "      <td>1.527711</td>\n",
       "      <td>0.549007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recruit F9</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3038.367647</td>\n",
       "      <td>44.712853</td>\n",
       "      <td>0.567853</td>\n",
       "      <td>1.436515</td>\n",
       "      <td>13.183368</td>\n",
       "      <td>5.460735</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.448279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHRIMPLIN</th>\n",
       "      <td>4.384289</td>\n",
       "      <td>2910.501062</td>\n",
       "      <td>69.408896</td>\n",
       "      <td>0.659679</td>\n",
       "      <td>7.708493</td>\n",
       "      <td>12.173705</td>\n",
       "      <td>4.219958</td>\n",
       "      <td>1.488323</td>\n",
       "      <td>0.514482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Facies        Depth         GR  ILD_log10  DeltaPHI  \\\n",
       "Well Name                                                                \n",
       "CHURCHMAN BIBLE  5.655941  3019.482673  63.683453   0.758757  1.560000   \n",
       "CROSS H CATTLE   3.127745  2702.360279  72.699285   0.568829  2.466667   \n",
       "LUKE G U         4.353579  2725.708243  64.777223   0.639690  4.218438   \n",
       "NEWBY            4.801296  2941.500000  60.733045   0.674812  3.477970   \n",
       "NOLAN            4.643373  2957.000000  68.693940   0.592400  3.134070   \n",
       "Recruit F9       9.000000  3038.367647  44.712853   0.567853  1.436515   \n",
       "SHRIMPLIN        4.384289  2910.501062  69.408896   0.659679  7.708493   \n",
       "\n",
       "                     PHIND        PE      NM_M    RELPOS  \n",
       "Well Name                                                 \n",
       "CHURCHMAN BIBLE  14.011116  3.789926  1.722772  0.544317  \n",
       "CROSS H CATTLE   15.574850  3.325852  1.297405  0.506182  \n",
       "LUKE G U         12.953905  3.660705  1.466377  0.514095  \n",
       "NEWBY            11.625378  3.771706  1.615551  0.528138  \n",
       "NOLAN            12.197361  3.857947  1.527711  0.549007  \n",
       "Recruit F9       13.183368  5.460735  2.000000  0.448279  \n",
       "SHRIMPLIN        12.173705  4.219958  1.488323  0.514482  "
      ]
     },
     "execution_count": 1364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_4=training_data.groupby('Well Name')\n",
    "#temp_4.describe()\n",
    "#temp_5=training_data.groupby('Well Name').count()\n",
    "#temp_5=training_data.groupby('Well Name').max()\n",
    "temp_5=training_data.groupby('Well Name').mean()\n",
    "temp_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 ) Select a feature from 1 well and play with this\n",
    "--------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1373,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xx0 = list(training_data0.Facies)\n",
    "#xx1 = list(training_data0.DeltaPHI)\n",
    "xx1 = list(training_data0.GR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1374,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NAAAg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\nymSS3n9fKi6WJk0yOFwlREdHy9PTU5s2bTI6i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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4418bd0090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min1=np.roll(xx1, 1)\n",
    "x_min2=np.roll(xx1, 2)\n",
    "x_min3=np.roll(xx1, 3)\n",
    "scale=0.5\n",
    "#b, a = signal.butter(2, 0.125, analog=False)\n",
    "b, a = signal.butter(2, 0.09, btype='low', analog=False)\n",
    "b, a = signal.butter(2, 0.2, btype='high', analog=False)\n",
    "\n",
    "xx1=xx1-np.mean(xx1) \n",
    "xx_fil = signal.filtfilt(b, a, xx1)\n",
    "xx_mf= signal.medfilt(xx1,15)\n",
    "\n",
    "xx_grad=np.gradient(xx1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30, 20))\n",
    "\n",
    "plt.plot(scale*xx1, color='black', label='Original Delta PHI')\n",
    "\n",
    "#plt.plot(scale*xx_grad, color='blue', label='derivative')\n",
    "#plt.plot(scale*xx_fil, color='red', label='low pass filter')\n",
    "#plt.plot(scale*xx_fil, color='red', label='high pass filter')\n",
    "plt.plot(scale*xx_mf, color='blue', label='median filter')\n",
    "#plt.plot(x_min1, color='yellow', label='1 sample shift')\n",
    "#xlim([500 800])\n",
    "plt.plot(xx0, color='green', label='Facies')\n",
    "ax.set_xlim(400,700)\n",
    "#plt.plot(sig_lf, color='#cc0000', label='lfilter')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1608,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def magic(df):\n",
    "    df1=df.copy()\n",
    "    b, a = signal.butter(2, 0.2, btype='high', analog=False)\n",
    "    feats00=['GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n",
    "    feats01=['GR','DeltaPHI','PHIND']\n",
    "    for ii in feats0:\n",
    "        df1[ii]=df[ii]\n",
    "        name1=ii + '_1'\n",
    "        name2=ii + '_2'\n",
    "        name3=ii + '_3'\n",
    "        name4=ii + '_4'\n",
    "        xx1 = list(df[ii])\n",
    "        xx_mf= signal.medfilt(xx1,9)\n",
    "        x_min3=np.roll(xx_mf, 3)    \n",
    "        xx1a=xx1-np.mean(xx1)\n",
    "        xx_fil = signal.filtfilt(b, a, xx1)\n",
    "        xx_grad=np.gradient(xx1a) \n",
    "        if ii in feats01: \n",
    "            df1[name1]=x_min3\n",
    "            df1[name2]=xx_fil\n",
    "            df1[name3]=xx_grad\n",
    "            df1[name4]=xx_mf     \n",
    "    return df1\n",
    "\n",
    "        \n",
    "#del training_data1\n",
    "df=training_data0.copy()\n",
    "training_data1=magic(df)\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1412,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "11\n"
     ]
    },
    {
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ojnjXAWbFVVXS7J3ya5T8by5/ft7n+c2371jD7Fn3b3AdNrp+t7wEZUVU/KeLb17oz+xH/ObbtB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\nvmRiZs7MzB83Gsu9ldJVa3FgJ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ElSr0TEKpSBQ4AZwEIAww0ctpbXAcSHKN0dpgKrAW+v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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44192ae950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=rf.feature_importances_\n",
    "kolummen = feature_vectors.columns.tolist()\n",
    "mask=x>0.025\n",
    "mask=x>0.035\n",
    "#mask=x>0.025\n",
    "x1=x[mask]\n",
    "\n",
    "#kols=kolummen[mask]\n",
    "kols=[]\n",
    "kols_out=[]\n",
    "count=0\n",
    "for name in kolummen:\n",
    "    if mask[count]==True:\n",
    "        kols.append(name)\n",
    "    else:\n",
    "        kols_out.append(name)\n",
    "    count+=1        \n",
    "fig, ax = plt.subplots(figsize=(30, 20))\n",
    "\n",
    "\n",
    "## the data\n",
    "N = len(kols)\n",
    "#N = len(kolummen)-18\n",
    "\n",
    "#X=gradboost.feature_importances_\n",
    "#X=rf.feature_importances_\n",
    "X=x1\n",
    "\n",
    "## necessary variables\n",
    "ind = np.arange(N)                # the x locations for the groups\n",
    "width = 0.30                      # the width of the bars\n",
    "fsize=16\n",
    "## the bars\n",
    "rects1 = ax.bar(ind, X, width,\n",
    "                color='black')\n",
    "\n",
    "# axes and labels\n",
    "ax.set_xlim(-width,len(ind)+width)\n",
    "#ax.set_ylim(0,45)\n",
    "ax.set_xlabel('feature', fontsize=fsize)\n",
    "ax.set_ylabel('importance', fontsize=fsize)\n",
    "ax.set_title('feature importance', fontsize=fsize)\n",
    "#xTickMarks = ['Group'+str(i) for i in range(1,6)]\n",
    "xTickMarks = kols\n",
    "ax.set_xticks(ind+width)\n",
    "xtickNames = ax.set_xticklabels(xTickMarks, fontsize=fsize)\n",
    "plt.setp(xtickNames, rotation=45, fontsize=fsize)\n",
    "\n",
    "## add a legend\n",
    "#ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
    "print count\n",
    "print N\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1413,
   "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>Facies</th>\n",
       "      <th>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "      <th>FaciesLabels</th>\n",
       "      <th>DeltaPHI_1</th>\n",
       "      <th>DeltaPHI_2</th>\n",
       "      <th>DeltaPHI_6</th>\n",
       "      <th>DeltaPHI_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>77.45</td>\n",
       "      <td>0.664</td>\n",
       "      <td>9.9</td>\n",
       "      <td>11.915</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>FSiS</td>\n",
       "      <td>2.241</td>\n",
       "      <td>2.241</td>\n",
       "      <td>9.9</td>\n",
       "      <td>2.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.5</td>\n",
       "      <td>78.26</td>\n",
       "      <td>0.661</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.565</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979</td>\n",
       "      <td>FSiS</td>\n",
       "      <td>2.241</td>\n",
       "      <td>9.900</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>79.05</td>\n",
       "      <td>0.658</td>\n",
       "      <td>14.8</td>\n",
       "      <td>13.050</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.957</td>\n",
       "      <td>FSiS</td>\n",
       "      <td>2.241</td>\n",
       "      <td>13.500</td>\n",
       "      <td>13.9</td>\n",
       "      <td>9.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.5</td>\n",
       "      <td>86.10</td>\n",
       "      <td>0.655</td>\n",
       "      <td>13.9</td>\n",
       "      <td>13.115</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.936</td>\n",
       "      <td>FSiS</td>\n",
       "      <td>9.900</td>\n",
       "      <td>13.900</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>74.58</td>\n",
       "      <td>0.647</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.300</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915</td>\n",
       "      <td>FSiS</td>\n",
       "      <td>13.500</td>\n",
       "      <td>14.000</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Facies Formation  Well Name   Depth     GR  ILD_log10  DeltaPHI   PHIND  \\\n",
       "0       3     A1 SH  SHRIMPLIN  2793.0  77.45      0.664       9.9  11.915   \n",
       "1       3     A1 SH  SHRIMPLIN  2793.5  78.26      0.661      14.2  12.565   \n",
       "2       3     A1 SH  SHRIMPLIN  2794.0  79.05      0.658      14.8  13.050   \n",
       "3       3     A1 SH  SHRIMPLIN  2794.5  86.10      0.655      13.9  13.115   \n",
       "4       3     A1 SH  SHRIMPLIN  2795.0  74.58      0.647      13.5  13.300   \n",
       "\n",
       "    PE  NM_M  RELPOS FaciesLabels  DeltaPHI_1  DeltaPHI_2  DeltaPHI_6  \\\n",
       "0  4.6     1   1.000         FSiS       2.241       2.241         9.9   \n",
       "1  4.1     1   0.979         FSiS       2.241       9.900        13.5   \n",
       "2  3.6     1   0.957         FSiS       2.241      13.500        13.9   \n",
       "3  3.5     1   0.936         FSiS       9.900      13.900        14.0   \n",
       "4  3.4     1   0.915         FSiS      13.500      14.000        14.2   \n",
       "\n",
       "   DeltaPHI_7  \n",
       "0       2.241  \n",
       "1       2.241  \n",
       "2       9.900  \n",
       "3      13.500  \n",
       "4      13.900  "
      ]
     },
     "execution_count": 1413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data1a = training_data1.drop(kols_out, axis=1)\n",
    "training_data1a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1614,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "def run_test(remove_well, df_train):\n",
    "    #df_test=training_data0\n",
    "    df_test=training_data1\n",
    "    #---------------------------------\n",
    "    #df_train=training_data1a\n",
    "    #df_train=training_data2\n",
    "    #df_test=df_test.drop(kols_out, axis=1)\n",
    "    #---------------------------------\n",
    "    #df_train=training_data0\n",
    "    #df_train=training_data1\n",
    "\n",
    "    #df_train=df_train.drop(kols_out, axis=1)\n",
    "\n",
    "    #training_data1a = training_data1.drop(kols_out, axis=1)\n",
    "\n",
    "\n",
    "    blind = df_test[df_test['Well Name'] == remove_well]      \n",
    "    training_data = df_train[df_train['Well Name'] != remove_well]  \n",
    "\n",
    "    correct_facies_labels_train = training_data['Facies'].values\n",
    "    feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "    scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "    #scaled_features_train = scaler.transform(feature_vectors)\n",
    "    scaled_features_train = feature_vectors\n",
    "    \n",
    "    rf = RandomForestClassifier(max_depth = 15, n_estimators=600) \n",
    "    #rf = RandomForestClassifier()\n",
    "    rf.fit(scaled_features_train, correct_facies_labels_train)\n",
    "\n",
    "\n",
    "    # get the blind well\n",
    "    correct_facies_labels = blind['Facies'].values\n",
    "    feature_vectors = blind.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "    scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "    #scaled_features = scaler.transform(feature_vectors)\n",
    "    scaled_features =feature_vectors\n",
    "\n",
    "    predicted_random_forest = rf.predict(scaled_features)\n",
    "    #print \"All training data different from test well\"\n",
    "    #print \"prediction from random forest:\"\n",
    "    #print metrics.accuracy_score(correct_facies_labels, predicted_random_forest)\n",
    "\n",
    "    #printnt \"f1 score:\"\n",
    "    #print metrics.f1_score(correct_facies_labels, predicted_random_forest,average = None)\n",
    "    #print \"average\"\n",
    "    out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')\n",
    "    return out_f1\n",
    "#print \n",
    "\n",
    "# 5-Fold Cross validation\n",
    "#print \"3-Fold Cross validation\"\n",
    "#cv_scores = cross_val_score(rf, scaled_features, correct_facies_labels, cv=4, scoring='f1_macro')\n",
    "#avg_cv_score = np.mean(cv_scores)\n",
    "\n",
    "#print cv_scores\n",
    "#avg_cv_score\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1615,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "well : CHURCHMAN BIBLE, f1 for different runs:\n",
      "average f1 is 0.577558, 2*std is 0.023684\n",
      "well : SHANKLE, f1 for different runs:\n",
      "average f1 is 0.510765, 2*std is 0.009153\n",
      "well : NOLAN, f1 for different runs:\n",
      "average f1 is 0.500402, 2*std is 0.018596\n",
      "well : NEWBY, f1 for different runs:\n",
      "average f1 is 0.506120, 2*std is 0.008876\n",
      "well : Recruit F9, f1 for different runs:\n",
      "average f1 is 0.803922, 2*std is 0.013865\n",
      "well : CROSS H CATTLE, f1 for different runs:\n",
      "average f1 is 0.379907, 2*std is 0.013173\n",
      "well : LUKE G U, f1 for different runs:\n",
      "average f1 is 0.613160, 2*std is 0.012440\n",
      "well : SHRIMPLIN, f1 for different runs:\n",
      "average f1 is 0.592357, 2*std is 0.015113\n",
      "overall average f1 is 0.560524\n"
     ]
    }
   ],
   "source": [
    "#df_train=training_data1a\n",
    "df_train=training_data1\n",
    "wells=['CHURCHMAN BIBLE','SHANKLE','NOLAN','NEWBY','Recruit F9' ,'CROSS H CATTLE','LUKE G U','SHRIMPLIN']\n",
    "av_all=[]\n",
    "for remove_well in wells:\n",
    "    all=[]\n",
    "    print(\"well : %s, f1 for different runs:\" % (remove_well))\n",
    "    for ii in range(3):\n",
    "        out_f1=run_test(remove_well,df_train)                \n",
    "        all.append(out_f1)\n",
    "    av1=np.mean(all) \n",
    "    av_all.append(av1)\n",
    "    print(\"average f1 is %f, 2*std is %f\" % (av1, 2*np.std(all)) )\n",
    "\n",
    "print(\"overall average f1 is %f\" % (np.mean(av_all)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1572,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 350, 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "#rf = RandomForestClassifier(max_depth = 1, max_features= 'sqrt', n_estimators=50, oob_score = True) \n",
    "rfc = RandomForestClassifier(max_depth = 9, max_features= 'sqrt', n_estimators=250) \n",
    "#rf = RandomForestClassifier()\n",
    "#rf.fit(scaled_features_train, correct_facies_labels_train)\n",
    "param_grid = { \n",
    "    'max_depth'   : [5,6,7,8,9],\n",
    "    'n_estimators': [150, 250, 350, 600]\n",
    "}    \n",
    "#    'max_features': ['auto', 'sqrt', 'log2']\n",
    "#}\n",
    "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
    "#CV_rfc.fit(X, y)\n",
    "CV_rfc.fit(scaled_features_train, correct_facies_labels_train)\n",
    "print CV_rfc.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train for the test data\n",
    "---------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1633,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'training_data.csv'\n",
    "training_data = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1642,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = 'validation_data_nofacies.csv'\n",
    "test_data = pd.read_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1643,
   "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>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>66.276</td>\n",
       "      <td>0.630</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.65</td>\n",
       "      <td>3.591</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2808.5</td>\n",
       "      <td>77.252</td>\n",
       "      <td>0.585</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11.95</td>\n",
       "      <td>3.341</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>82.899</td>\n",
       "      <td>0.566</td>\n",
       "      <td>9.4</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.064</td>\n",
       "      <td>1</td>\n",
       "      <td>0.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2809.5</td>\n",
       "      <td>80.671</td>\n",
       "      <td>0.593</td>\n",
       "      <td>9.5</td>\n",
       "      <td>13.25</td>\n",
       "      <td>2.977</td>\n",
       "      <td>1</td>\n",
       "      <td>0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.971</td>\n",
       "      <td>0.638</td>\n",
       "      <td>8.7</td>\n",
       "      <td>12.35</td>\n",
       "      <td>3.020</td>\n",
       "      <td>1</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Formation Well Name   Depth      GR  ILD_log10  DeltaPHI  PHIND     PE  \\\n",
       "0     A1 SH    STUART  2808.0  66.276      0.630       3.3  10.65  3.591   \n",
       "1     A1 SH    STUART  2808.5  77.252      0.585       6.5  11.95  3.341   \n",
       "2     A1 SH    STUART  2809.0  82.899      0.566       9.4  13.60  3.064   \n",
       "3     A1 SH    STUART  2809.5  80.671      0.593       9.5  13.25  2.977   \n",
       "4     A1 SH    STUART  2810.0  75.971      0.638       8.7  12.35  3.020   \n",
       "\n",
       "   NM_M  RELPOS  \n",
       "0     1   1.000  \n",
       "1     1   0.978  \n",
       "2     1   0.956  \n",
       "3     1   0.933  \n",
       "4     1   0.911  "
      ]
     },
     "execution_count": 1643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1635,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SHRIMPLIN, SHANKLE, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
       "Categories (8, object): [SHRIMPLIN, SHANKLE, LUKE G U, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]"
      ]
     },
     "execution_count": 1635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
    "training_data['Formation'] = training_data['Formation'].astype('category')\n",
    "training_data['Well Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1636,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS', 'WS', 'D','PS', 'BS']    \n",
    "training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1657,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preprocessing\n",
    "test_data1=magic(test_data)\n",
    "training_data1=magic(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1665,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def predict_final(test_well, training_data,test_data):\n",
    "    blind = test_data[test_data['Well Name'] == test_well]      \n",
    "    correct_facies_labels_train = training_data['Facies'].values\n",
    "    feature_vectors_train = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
    "    rf = RandomForestClassifier(max_depth = 15, n_estimators=600) \n",
    "    rf.fit(feature_vectors_train, correct_facies_labels_train)\n",
    "\n",
    "    # the blind well\n",
    "    feature_vectors_blind = blind.drop(['Formation', 'Well Name', 'Depth'], axis=1)\n",
    "    predicted_random_forest = rf.predict(feature_vectors_blind)\n",
    "    #out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')\n",
    "    return predicted_random_forest\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1666,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_well='STUART'\n",
    "predicted_stu=predict_final(test_well, training_data1, test_data1)\n",
    "test_well='CRAWFORD'\n",
    "predicted_craw=predict_final(test_well, training_data1, test_data1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1669,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       1, 2, 2, 1, 2, 2, 1, 2, 2, 3, 2, 2, 2, 2, 3, 2, 3, 3, 2, 2, 8, 8, 8,\n",
       "       8, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6,\n",
       "       6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6,\n",
       "       6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 6, 8, 6, 6, 6, 6, 6, 6, 6,\n",
       "       6, 6, 6, 6, 6, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
       "       8, 8, 5, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 8, 2, 2,\n",
       "       2, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 8, 8, 8, 8, 8, 8, 8,\n",
       "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2, 2, 3,\n",
       "       3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 8, 8, 8, 8,\n",
       "       8, 8, 8, 8, 8, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n",
       "       3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 3, 3, 3, 3, 8, 8, 8, 8,\n",
       "       8, 8, 8, 8, 8, 8, 8, 7, 4, 7, 7, 8, 8, 8, 8, 6, 6, 8, 8, 8, 8, 9, 9,\n",
       "       9, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       3, 3, 3, 3, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
       "       8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6,\n",
       "       4, 4, 4, 4, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 8, 8, 4, 8, 4, 8, 8, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])"
      ]
     },
     "execution_count": 1669,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "predicted_stu"
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  {
   "cell_type": "code",
   "execution_count": 1670,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 5, 5, 7, 7, 7, 7, 7, 4, 4, 4, 4,\n",
       "       4, 7, 4, 4, 4, 4, 4, 6, 4, 4, 4, 8, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 6,\n",
       "       8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
       "       4, 4, 4, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 6,\n",
       "       6, 6, 6, 6, 6, 6, 8, 8, 8, 3, 3, 8, 8, 8, 8, 8, 8, 8, 5, 5, 8, 8, 8,\n",
       "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8,\n",
       "       6, 8, 8, 8, 6, 6, 6, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 1, 2, 3, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 8, 8, 8, 8, 7, 7,\n",
       "       7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 8, 8, 8, 8,\n",
       "       8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 8, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,\n",
       "       3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
       "       7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
       "       7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 2, 3, 3, 3, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3])"
      ]
     },
     "execution_count": 1670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_craw"
   ]
  }
 ],
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