Bird_Team/Facies_classification_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facies classification using Machine Learning\n",
    "\n",
    "#### Bird Team: PG+AC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold, cross_val_score,LeavePGroupsOut, LeaveOneGroupOut, cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, make_scorer, f1_score, accuracy_score, recall_score, precision_score\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel, RFECV\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['SHRIMPLIN', 'Recruit F9', 'ALEXANDER D', 'SHANKLE', 'CHURCHMAN BIBLE', 'NOLAN', 'KIMZEY A', 'NEWBY', 'LUKE G U', 'CROSS H CATTLE'])\n"
     ]
    },
    {
     "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": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '../facies_vectors.csv'\n",
    "training_data = pd.read_csv(filename)\n",
    "print(set(training_data[\"Well Name\"]))\n",
    "training_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set(['CRAWFORD', 'STUART'])\n",
      "(830, 10)\n"
     ]
    },
    {
     "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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_data = pd.read_csv('./../validation_data_nofacies.csv')\n",
    "print(set(well_data[\"Well Name\"]))\n",
    "print(well_data.shape)\n",
    "well_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\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",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\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",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\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",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\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",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\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",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2810.5</td>\n",
       "      <td>73.955</td>\n",
       "      <td>0.667</td>\n",
       "      <td>6.9</td>\n",
       "      <td>12.25</td>\n",
       "      <td>3.086</td>\n",
       "      <td>1</td>\n",
       "      <td>0.889</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>77.962</td>\n",
       "      <td>0.674</td>\n",
       "      <td>6.5</td>\n",
       "      <td>12.45</td>\n",
       "      <td>3.092</td>\n",
       "      <td>1</td>\n",
       "      <td>0.867</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2811.5</td>\n",
       "      <td>83.894</td>\n",
       "      <td>0.667</td>\n",
       "      <td>6.3</td>\n",
       "      <td>12.65</td>\n",
       "      <td>3.123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.844</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2812.0</td>\n",
       "      <td>84.424</td>\n",
       "      <td>0.653</td>\n",
       "      <td>6.7</td>\n",
       "      <td>13.05</td>\n",
       "      <td>3.121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.822</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>STUART</td>\n",
       "      <td>2812.5</td>\n",
       "      <td>83.160</td>\n",
       "      <td>0.642</td>\n",
       "      <td>7.3</td>\n",
       "      <td>12.95</td>\n",
       "      <td>3.127</td>\n",
       "      <td>1</td>\n",
       "      <td>0.800</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Facies Formation Well Name   Depth      GR  ILD_log10  DeltaPHI  PHIND  \\\n",
       "0     NaN     A1 SH    STUART  2808.0  66.276      0.630       3.3  10.65   \n",
       "1     NaN     A1 SH    STUART  2808.5  77.252      0.585       6.5  11.95   \n",
       "2     NaN     A1 SH    STUART  2809.0  82.899      0.566       9.4  13.60   \n",
       "3     NaN     A1 SH    STUART  2809.5  80.671      0.593       9.5  13.25   \n",
       "4     NaN     A1 SH    STUART  2810.0  75.971      0.638       8.7  12.35   \n",
       "5     NaN     A1 SH    STUART  2810.5  73.955      0.667       6.9  12.25   \n",
       "6     NaN     A1 SH    STUART  2811.0  77.962      0.674       6.5  12.45   \n",
       "7     NaN     A1 SH    STUART  2811.5  83.894      0.667       6.3  12.65   \n",
       "8     NaN     A1 SH    STUART  2812.0  84.424      0.653       6.7  13.05   \n",
       "9     NaN     A1 SH    STUART  2812.5  83.160      0.642       7.3  12.95   \n",
       "\n",
       "      PE  NM_M  RELPOS origin  \n",
       "0  3.591     1   1.000   test  \n",
       "1  3.341     1   0.978   test  \n",
       "2  3.064     1   0.956   test  \n",
       "3  2.977     1   0.933   test  \n",
       "4  3.020     1   0.911   test  \n",
       "5  3.086     1   0.889   test  \n",
       "6  3.092     1   0.867   test  \n",
       "7  3.123     1   0.844   test  \n",
       "8  3.121     1   0.822   test  \n",
       "9  3.127     1   0.800   test  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat train and test for processing \n",
    "well_data[\"origin\"] = 'test'\n",
    "training_data[\"origin\"] = 'train'\n",
    "df = pd.concat([well_data,training_data],axis=0,ignore_index=True)[list(training_data.columns)]\n",
    "df['Well Name'] = df['Well Name'].astype('category')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "session\n",
      "depth\n",
      "add avgs of feat\n",
      "add distances feat.\n",
      "lag lead\n",
      "rolling\n",
      "special window features for NM_M\n",
      "filling na\n",
      "Vectorizing Formation text data\n",
      "Finished preparing data. Now ready for ML ignition!\n"
     ]
    }
   ],
   "source": [
    "# add some features based on the well data. \n",
    "\n",
    "# nb points : can be correlated with how soft soil is ? \n",
    "print(\"session\")\n",
    "sessionsize = df.groupby([\"Well Name\",'Formation']).size().reset_index()\n",
    "sessionsize.columns =  [\"Well Name\",'Formation','formation_size']\n",
    "df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
    "\n",
    "# depth : \n",
    "print(\"depth\")\n",
    "sessionsize = df.groupby([\"Well Name\",'Formation'])[\"Depth\"].min().reset_index()\n",
    "sessionsize.columns =  [\"Well Name\",'Formation','minimum_depth']\n",
    "df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
    "\n",
    "sessionsize = df.groupby([\"Well Name\",'Formation'])[\"Depth\"].max().reset_index()\n",
    "sessionsize.columns =  [\"Well Name\",'Formation','maximum_depth']\n",
    "df = pd.merge(df,sessionsize,how='left',on = [\"Well Name\",'Formation'])\n",
    "\n",
    "df['formation_depth'] = df[\"maximum_depth\"] - df[\"minimum_depth\"]\n",
    "\n",
    "df[\"soft_indic\"] = df['formation_depth'] / df[\"formation_size\"]\n",
    "\n",
    "# add avgs of feat\n",
    "print(\"add avgs of feat\")\n",
    "list_to_avg = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']\n",
    "for val in list_to_avg : \n",
    "    df[val + \"_min\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.min)\n",
    "    df[val + \"_max\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.max)\n",
    "    df[val + \"_mean\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.mean)\n",
    "    df[val + \"_var\"] = df.groupby([\"Well Name\",'Formation'])[val].transform(np.var) \n",
    "\n",
    "# add distances feat. = an attempt at regulariation.\n",
    "print(\"add distances feat.\")\n",
    "for val in list_to_avg : \n",
    "    df[val + \"_min_dist\"] = df[val] -df[val + \"_min\"]\n",
    "    df[val + \"_max_dist\"] =  df[val] -df[val + \"_max\"]\n",
    "    df[val + \"_mean_dist\"] =  df[val] -df[val + \"_mean\"]\n",
    "    \n",
    "# add lag and lead !\n",
    "print(\"lag lead\")\n",
    "list_to_lag = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']\n",
    "for val in list_to_lag:\n",
    "    for lag in range(1,11):\n",
    "        df[val+'_lag_'+str(lag)]=df[val]-df.groupby(\"Well Name\")[val].shift(periods=lag)\n",
    "        df[val+'_lead_'+str(lag)]=df[val]-df.groupby(\"Well Name\")[val].shift(periods=-lag)\n",
    "\n",
    "# adding some Formation lag and lead. \n",
    "for lag in range(1,3):\n",
    "    df['Formation'+'_lag_'+str(lag)]=df.groupby(\"Well Name\")['Formation'].shift(periods=lag)\n",
    "    df['Formation'+'_lead_'+str(lag)]=df.groupby(\"Well Name\")['Formation'].shift(periods=-lag)\n",
    "    df['Formation'+'_lag_'+str(lag) + 'equal'] = (df['Formation'+'_lag_'+str(lag)] == df[\"Formation\"]).astype(int)\n",
    "    df['Formation'+'_lead_'+str(lag) + 'equal'] = (df['Formation'+'_lead_'+str(lag)] == df[\"Formation\"]).astype(int) \n",
    "\n",
    "print(\"rolling\")\n",
    "#Add rolling features\n",
    "list_to_roll = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M','RELPOS']\n",
    "window_size = [5,10,15,20,50]\n",
    "for w in window_size:\n",
    "    for val in list_to_roll:\n",
    "        df[val+'_rollingmean_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
    "            lambda x:x.rolling(window=w,center=True).mean())\n",
    "        df[val+'_rollingmax_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
    "            lambda x:x.rolling(window=w,center=True).max())\n",
    "        df[val+'_rollingmin_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
    "            lambda x:x.rolling(window=w,center=True).min())\n",
    "        df[val+'_rollingstd_'+str(w)]=df.groupby(\"Well Name\")[val].apply(\n",
    "            lambda x:x.rolling(window=w,center=True).std())\n",
    "        \n",
    "print(\"special window features for NM_M\")\n",
    "def NM_M_distance(x,how,target):\n",
    "    length = len(x)\n",
    "    rank = np.empty(length)\n",
    "    count = -1\n",
    "    NM_M = x[\"NM_M\"].values\n",
    "    if how==\"up\":\n",
    "        order = range(length)\n",
    "    elif how==\"down\":\n",
    "        order = range(length-1,-1,-1)\n",
    "    for i in order:\n",
    "        if ((NM_M[i] != target) & (count>-1)):\n",
    "            count+=1\n",
    "            rank[i] += count\n",
    "        elif NM_M[i] == target:\n",
    "            count=0\n",
    "        else:\n",
    "            rank[i] = count\n",
    "    rank = pd.DataFrame(rank.astype(int), columns=[\"NM_M_Rank_Target_+\"+str(target)+\"_\"+how], index = x.index)\n",
    "    return(rank)\n",
    "df[\"NM_M_Rank_Target_1_up\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"up\",target=1)\n",
    "df[\"NM_M_Rank_Target_2_up\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"up\",target=2)\n",
    "df[\"NM_M_Rank_Target_1_down\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"down\",target=1)\n",
    "df[\"NM_M_Rank_Target_2_down\"]=df.groupby([\"Well Name\"]).apply(NM_M_distance,how=\"down\",target=2)\n",
    "\n",
    "print(\"filling na\")\n",
    "df = df.groupby([\"Well Name\"], as_index=False).apply(lambda group: group.bfill())\n",
    "df = df.groupby([\"Well Name\"], as_index=False).apply(lambda group: group.ffill())\n",
    "df = df.fillna(df.mean())\n",
    "\n",
    "print(\"Vectorizing Formation text data\")\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "list_formation = ['Formation',\n",
    " 'Formation_lag_1',\n",
    " 'Formation_lead_1',\n",
    " 'Formation_lag_2',\n",
    " 'Formation_lead_2']\n",
    "for l in list_formation:\n",
    "    cv = CountVectorizer()\n",
    "    counts = cv.fit_transform(df[l].values)\n",
    "    cols = [c+\"_\"+l for c in cv.get_feature_names()]\n",
    "    counts = pd.DataFrame(counts.toarray(),columns = cols)\n",
    "    df = df.drop(l,axis = 1)\n",
    "    df = pd.concat([df,counts],axis=1)\n",
    "\n",
    "print(\"Finished preparing data. Now ready for ML ignition!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#tokeep =['Facies','origin','Formation','Formation_lag_1','Formation_lead_1','Formation_lag_2','Formation_lead_2',\n",
    "#         'Well Name','Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n",
    "#nums = ['Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']\n",
    "#tokeep = tokeep + [x+'_lag_1' for x in nums] +[x+'_lead_1' for x in nums]\n",
    "#df = df[tokeep]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CV performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f1_micro = make_scorer(f1_score, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this time let's use all the training set \n",
    "groups = df[(df['origin']=='train')][\"Well Name\"]\n",
    "ytrain = df[(df['origin']=='train')]['Facies']\n",
    "yvalid = df[(df['origin']=='test')]['Facies']\n",
    "xtrain = df[(df['origin']=='train')].drop(['Well Name','origin','Facies'],axis=1)\n",
    "xvalid = df[(df['origin']=='test')].drop(['Well Name','origin','Facies'],axis=1)\n",
    "custom_cv=LeaveOneGroupOut().split(xtrain, ytrain, groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf_rfe = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    criterion=\"entropy\",\n",
    "    class_weight='balanced',\n",
    ") \n",
    "clf_final = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    criterion=\"entropy\",\n",
    "    class_weight='balanced',\n",
    "    min_samples_leaf=5,\n",
    "    min_samples_split=25,\n",
    "    max_features=10,\n",
    "    n_jobs=-1\n",
    ")\n",
    "pipe = Pipeline([\n",
    "  ('fs', RFECV(clf_rfe,\n",
    "               cv=custom_cv,\n",
    "               scoring=f1_micro,\n",
    "               step=0.1,\n",
    "               verbose=2)),\n",
    "  ('cl', clf_final)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 437 features.\n",
      "Fitting estimator with 394 features.\n",
      "Fitting estimator with 351 features.\n",
      "Fitting estimator with 308 features.\n",
      "Fitting estimator with 265 features.\n",
      "Fitting estimator with 222 features.\n",
      "Fitting estimator with 179 features.\n",
      "Fitting estimator with 136 features.\n",
      "Fitting estimator with 93 features.\n",
      "Fitting estimator with 50 features.\n",
      "Fitting estimator with 7 features.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('fs', RFECV(cv=<generator object split at 0x1044c18c0>,\n",
       "   estimator=RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "            criterion='entropy', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "            min_samples_leaf=1..._jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        1.0       0.88      1.00      0.93       268\n",
      "        2.0       0.95      0.91      0.93       940\n",
      "        3.0       0.94      0.93      0.93       780\n",
      "        4.0       0.79      0.94      0.86       271\n",
      "        5.0       0.87      0.86      0.87       296\n",
      "        6.0       0.90      0.79      0.84       582\n",
      "        7.0       0.76      0.98      0.85       141\n",
      "        8.0       0.91      0.87      0.89       686\n",
      "        9.0       0.94      0.98      0.96       185\n",
      "\n",
      "avg / total       0.91      0.90      0.90      4149\n",
      "\n",
      "0.902868161003\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipe.predict(xtrain)\n",
    "print(classification_report(ytrain, y_pred))\n",
    "print(f1_score(ytrain, y_pred, average=\"micro\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 9 NM_M_mean (0.085054)\n",
      "2. feature 23 NM_M_rollingmean_5 (0.078424)\n",
      "3. feature 5 NM_M (0.058173)\n",
      "4. feature 42 PE_rollingmean_50 (0.031362)\n",
      "5. feature 43 PE_rollingmax_50 (0.031326)\n",
      "6. feature 48 sh_Formation (0.029579)\n",
      "7. feature 35 NM_M_rollingmean_15 (0.025371)\n",
      "8. feature 13 GR_rollingmin_5 (0.023771)\n",
      "9. feature 1 GR (0.023272)\n",
      "10. feature 40 RELPOS_rollingstd_20 (0.022150)\n",
      "11. feature 32 RELPOS_rollingstd_10 (0.021844)\n",
      "12. feature 29 PE_rollingmean_10 (0.020928)\n",
      "13. feature 36 RELPOS_rollingstd_15 (0.020512)\n",
      "14. feature 28 DeltaPHI_rollingmean_10 (0.019988)\n",
      "15. feature 47 lm_Formation (0.019870)\n",
      "16. feature 2 ILD_log10 (0.019583)\n",
      "17. feature 8 PHIND_mean (0.019222)\n",
      "18. feature 15 ILD_log10_rollingmin_5 (0.018798)\n",
      "19. feature 14 ILD_log10_rollingmean_5 (0.018413)\n",
      "20. feature 6 GR_min (0.017810)\n",
      "21. feature 38 PE_rollingmax_20 (0.017757)\n",
      "22. feature 11 GR_min_dist (0.017583)\n",
      "23. feature 27 GR_rollingmin_10 (0.017055)\n",
      "24. feature 18 PHIND_rollingmean_5 (0.016203)\n",
      "25. feature 45 NM_M_rollingmean_50 (0.015900)\n",
      "26. feature 44 PE_rollingmin_50 (0.015898)\n",
      "27. feature 30 PE_rollingmin_10 (0.015659)\n",
      "28. feature 19 PHIND_rollingmax_5 (0.015139)\n",
      "29. feature 3 PHIND (0.015066)\n",
      "30. feature 20 PHIND_rollingmin_5 (0.014670)\n",
      "31. feature 25 RELPOS_rollingstd_5 (0.014481)\n",
      "32. feature 46 NM_M_Rank_Target_1_up (0.014468)\n",
      "33. feature 33 Depth_rollingmax_15 (0.014306)\n",
      "34. feature 41 Depth_rollingmax_50 (0.013709)\n",
      "35. feature 10 Depth_min_dist (0.013705)\n",
      "36. feature 16 DeltaPHI_rollingmean_5 (0.013677)\n",
      "37. feature 37 Depth_rollingmax_20 (0.013369)\n",
      "38. feature 34 DeltaPHI_rollingmean_15 (0.013297)\n",
      "39. feature 17 DeltaPHI_rollingmin_5 (0.013126)\n",
      "40. feature 7 ILD_log10_mean (0.012856)\n",
      "41. feature 21 PE_rollingmean_5 (0.012784)\n",
      "42. feature 26 Depth_rollingmax_10 (0.012233)\n",
      "43. feature 12 GR_rollingmean_5 (0.012187)\n",
      "44. feature 24 NM_M_rollingmax_5 (0.012033)\n",
      "45. feature 0 Depth (0.011084)\n",
      "46. feature 22 PE_rollingmax_5 (0.010701)\n",
      "47. feature 39 NM_M_rollingmean_20 (0.009658)\n",
      "48. feature 4 PE (0.008100)\n",
      "49. feature 31 NM_M_rollingmean_10 (0.007713)\n",
      "50. feature 49 lm_Formation_lag_1 (0.000130)\n"
     ]
    }
   ],
   "source": [
    "importances = pipe.named_steps[\"cl\"].feature_importances_\n",
    "support = pipe.named_steps[\"fs\"].support_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "print(\"Feature ranking:\")\n",
    "feature = pd.Series(xtrain.columns.values)\n",
    "selected_features = list(feature[support])\n",
    "for f in range(len(selected_features)):\n",
    "    print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], \n",
    "                                      selected_features[indices[f]], \n",
    "                                      importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply to test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this time let's use all the training set \n",
    "ytrain = df[(df['origin']=='train')]['Facies']\n",
    "yvalid = df[(df['origin']=='test')]['Facies']\n",
    "xtrain = df[(df['origin']=='train')].drop(['Well Name','origin','Facies'],axis=1)\n",
    "xvalid = df[(df['origin']=='test')].drop(['Well Name','origin','Facies'],axis=1)\n",
    "well_name_valid = df.loc[(df['origin']=='test'),\"Well Name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pipe.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = pipe.predict(xvalid.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DOfvjJaOjwUBMGgi+lrs1dY49i8d+yy3Z+xANxYer3Bnjb/JcU0LxerrbJArF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\n4SEwvoTi63Ruu0QeuxA9Rmsovk4oFD8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8xpgZxpizgQXAzcaY5xDcqf7vcBtr7VPArQS/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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116abb610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "well = \"CRAWFORD\"\n",
    "depth = xvalid.loc[well_name_valid== well ,\"Depth\"]\n",
    "predictions = pd.Series(preds).loc[well_name_valid==well]\n",
    "plt.plot(depth,predictions)\n",
    "plt.axis([2950,3175, 1, 9])\n",
    "plt.grid(b=True, which='major', color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PiiC4sdXLro8RhKiA8QFFEEQUCQQHKAehN00RCFXKQRjGREmuHoTKQRgsykEQ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11594b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "well = \"STUART\"\n",
    "depth = xvalid.loc[well_name_valid== well ,\"Depth\"]\n",
    "predictions = pd.Series(preds).loc[well_name_valid==well]\n",
    "plt.plot(depth,predictions)\n",
    "plt.axis([2800,3050, 1, 9])\n",
    "plt.grid(b=True, which='major', color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xvalid['Facies']=preds\n",
    "xvalid.to_csv('XmasPreds_4.csv')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}