SubsurfaceDataAnalytics_NaiveBayes.ipynb

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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Naive Bayes Classification for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PGE 383 Exercise: Naive Bayes Classification for Subsurface Data Analytics in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of naive Bayes classification for subsurface modeling workflows. This should help you get started with building subsurface models that with predictions based on multiple sources of information.\n",
    "\n",
    "This method is great as it builds directly on our knowledge Bayesian statistics to provide a simple, but flexible classification method.\n",
    "\n",
    "#### Bayesian Updating\n",
    "\n",
    "The naive Bayes classifier is based on the conditional probability of a category, $k$, given $n$ features, $x_1, \\dots , x_n$.  \n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n)\n",
    "\\end{equation}\n",
    "\n",
    "we can solve this with Bayesian updating:\n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n) = \\frac{p(x_1, \\dots , x_n | C_k) p(C_k)}{p(x_1, \\dots , x_n)}\n",
    "\\end{equation}\n",
    "\n",
    "let's combine the likelihood and prior for the momment:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1, \\dots , x_n | C_k) p(C_k) = p(x_1, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "we can exand the full joint distribution recursively as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "expansion of the joint with the conditional and prior\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "continue recursively expanding\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2 | x_3, \\dots , x_n, C_k) p(x_3, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "we can generalize as\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2 | x_3, \\dots , x_n, C_k) p(x_3 | x_4, \\dots , x_n, C_k) \\ldots (x_{n-1} | x_n, C_k)  (x_{n} | C_k) p(C_k)\n",
    "\\end{equation}\n",
    "\n",
    "#### Naive Bayes Approach\n",
    "\n",
    "The likelihood, conditional probability with the joint conditional is difficult to calculate.  It requires information about the joint relationship between $x_1, \\dots , x_n$ features. As $n$ increases this requires a lot of data to inform the joint distribution.\n",
    "\n",
    "With the naive bayes approach we make the 'naive' assumption that the features are all **conditionally independent**.  This entails:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_i | x_{i+1}, \\ldots , x_n, C_k) = p(x_i | C_k)\n",
    "\\end{equation}\n",
    "\n",
    "for all $i = 1, \\ldots, n$ features.\n",
    "\n",
    "We can now solve for the needed conditional probability as:\n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n) = \\frac{p(C_k) \\prod_{i=1}^{n} p(x_i | C_k)}{p(x_1, \\dots , x_n)}\n",
    "\\end{equation}\n",
    "\n",
    "We only need the prior, $p(C_k)$, and a set of conditionals, $p(x_i | C_k)$, for all predictor features, $i = 1,\\ldots,n$ and all categories, $k = 1,\\ldots,K$.\n",
    "\n",
    "The evidence term, $p(x_1, \\dots , x_n)$, is only based on the features $x_1, \\dots , x_n$; therefore, is a constant over the categories $k = 1,\\ldots,n$. \n",
    "\n",
    "* it ensures closure - probabilities over all categories sum to one \n",
    "\n",
    "* we simply standardize the numerators to sum to one over the categories.  \n",
    "\n",
    "The naive Bayes approach is:\n",
    "\n",
    "* simple to understand, builds on fundamental Bayesian statistics\n",
    "\n",
    "* pratical even with small datasets since with the conditional independence we only need to estimate simple conditional distributions \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - [unconv_MV_v4.csv](https://git.io/fhHLT).\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "Let's import the GeostatsPy package.  I actually don't use it in this workflow, but just incase.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                            # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                      # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                          # ndarrys for gridded data\n",
    "import pandas as pd                                         # DataFrames for tabular data\n",
    "import os                                                   # set working directory, run executables\n",
    "import matplotlib.pyplot as plt                             # for plotting\n",
    "from scipy import stats                                     # summary statistics\n",
    "import math                                                 # trig etc.\n",
    "from sklearn.model_selection import train_test_split        # train and test split\n",
    "from sklearn.naive_bayes import GaussianNB                  # naive Bayes model and prediction\n",
    "from sklearn import metrics                                 # measures to check our models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Let's define a couple of functions to streamline plotting correlation matrices and visualization of a decision tree regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(dataframe,size=10):                               # plots a graphical correlation matrix \n",
    "    corr = dataframe.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    im = ax.matshow(corr,vmin = -1.0, vmax = 1.0)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.colorbar(im, orientation = 'vertical')\n",
    "    plt.title('Correlation Matrix')\n",
    "    \n",
    "def visualize_model(model,xfeature,x_min,x_max,yfeature,y_min,y_max,response,z_min,z_max,title,):# plots the data points and the decision tree prediction \n",
    "    cmap = plt.cm.plasma\n",
    "    xplot_step = (x_max - x_min)/300.0; yplot_step = (y_max - y_min)/300.0 # resolution of the model visualization\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, xplot_step), # set up the mesh\n",
    "                     np.arange(y_min, y_max, yplot_step))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])      # predict with our trained model over the mesh\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    cs = plt.contourf(xx, yy, Z, cmap=cmap,vmin=z_min, vmax=z_max, levels = 50) # plot the predictions\n",
    "    # add the data values as a colored by response feature scatter plot\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=cmap, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)                                       # add the labels\n",
    "    plt.xlabel(xfeature.name); plt.ylabel(yfeature.name)\n",
    "    plt.xlim([x_min,x_max]); plt.ylim([y_min,y_max])\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')      # add the color bar\n",
    "    cbar.set_label(response.name, rotation=270, labelpad=20)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "    return(plt)\n",
    "    \n",
    "def visualize_model_prob(model,xfeature,x_min,x_max,yfeature,y_min,y_max,response,title,):# plots the data points and the prediction probabilities \n",
    "    n_classes = 10\n",
    "    cmap = plt.cm.plasma\n",
    "    xplot_step = (x_max - x_min)/300.0; yplot_step = (y_max - y_min)/300.0 # resolution of the model visualization\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, xplot_step), # set up the mesh\n",
    "                     np.arange(y_min, y_max, yplot_step))\n",
    "    \n",
    "    z_min = 0.0; z_max = 1.0\n",
    "    Z = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z1 = Z[:,0].reshape(xx.shape); Z2 = Z[:,1].reshape(xx.shape)\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    cs1 = plt.contourf(xx, yy, Z1, cmap=cmap,vmin=z_min, vmax=z_max, levels=np.linspace(z_min, z_max, 100))\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=plt.cm.Greys, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title + ' Probability of Low Production')\n",
    "    plt.xlabel(xfeature.name)\n",
    "    plt.ylabel(yfeature.name)\n",
    "    cbar = plt.colorbar(cs1, orientation = 'vertical')\n",
    "    cbar.set_label('Probability', rotation=270, labelpad=20)\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    cs2 = plt.contourf(xx, yy, Z2, cmap=cmap,vmin=z_min, vmax=z_max, levels=np.linspace(z_min, z_max, 100))\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=plt.cm.Greys, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title + ' Probability of High Production')\n",
    "    plt.xlabel(xfeature.name)\n",
    "    plt.ylabel(yfeature.name)\n",
    "    cbar = plt.colorbar(cs2, orientation = 'vertical')\n",
    "    cbar.set_label('Probability', rotation=270, labelpad=20)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "    plt.show()\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                                       # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\").  \n",
    "\n",
    "#### Read the data table\n",
    "\n",
    "First copy the \"unconv_MV.csv\" comma delimited file from https://github.com/GeostatsGuy/GeoDataSets to your working directory, then run this command to read the file into a DataFrame object (part of Pandas package).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = pd.read_csv(\"unconv_MV_v4.csv\")                   # load the comma delimited data file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the first several rows of our data stored in a DataFrame so we can make sure we successfully loaded the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12.08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1695.360819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>12.38</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.22</td>\n",
       "      <td>46.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3007.096063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>14.02</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.01</td>\n",
       "      <td>72.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2531.938259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17.67</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.63</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5288.514854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17.52</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.18</td>\n",
       "      <td>10.94</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2859.469624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>14.53</td>\n",
       "      <td>4.81</td>\n",
       "      <td>2.69</td>\n",
       "      <td>53.60</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4017.374438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>13.49</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.93</td>\n",
       "      <td>63.71</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2952.812773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>11.58</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.25</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2670.933846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>12.52</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.43</td>\n",
       "      <td>65.77</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2474.048178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.25</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.71</td>\n",
       "      <td>66.20</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2722.893266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>15.04</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.22</td>\n",
       "      <td>61.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3828.247174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>16.19</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2.29</td>\n",
       "      <td>49.10</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5095.810104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>16.82</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2.80</td>\n",
       "      <td>66.65</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4091.637316</td>\n",
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      "text/plain": [
       "    Well    Por  Perm    AI  Brittle   TOC    VR         Prod\n",
       "0      1  12.08  2.92  2.80    81.40  1.16  2.31  1695.360819\n",
       "1      2  12.38  3.53  3.22    46.17  0.89  1.88  3007.096063\n",
       "2      3  14.02  2.59  4.01    72.80  0.89  2.72  2531.938259\n",
       "3      4  17.67  6.75  2.63    39.81  1.08  1.88  5288.514854\n",
       "4      5  17.52  4.57  3.18    10.94  1.51  1.90  2859.469624\n",
       "5      6  14.53  4.81  2.69    53.60  0.94  1.67  4017.374438\n",
       "6      7  13.49  3.60  2.93    63.71  0.80  1.85  2952.812773\n",
       "7      8  11.58  3.03  3.25    53.00  0.69  1.93  2670.933846\n",
       "8      9  12.52  2.72  2.43    65.77  0.95  1.98  2474.048178\n",
       "9     10  13.25  3.94  3.71    66.20  1.14  2.65  2722.893266\n",
       "10    11  15.04  4.39  2.22    61.11  1.08  1.77  3828.247174\n",
       "11    12  16.19  6.30  2.29    49.10  1.53  1.86  5095.810104\n",
       "12    13  16.82  5.42  2.80    66.65  1.17  1.98  4091.637316"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head(n=13)                                          # preview the first n rows of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the well index and check the summary summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>200.0</td>\n",
       "      <td>14.991150</td>\n",
       "      <td>2.971176</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>12.912500</td>\n",
       "      <td>15.070000</td>\n",
       "      <td>17.402500</td>\n",
       "      <td>23.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>200.0</td>\n",
       "      <td>4.330750</td>\n",
       "      <td>1.731014</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>3.122500</td>\n",
       "      <td>4.035000</td>\n",
       "      <td>5.287500</td>\n",
       "      <td>9.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2.968850</td>\n",
       "      <td>0.566885</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>2.547500</td>\n",
       "      <td>2.955000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>4.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>200.0</td>\n",
       "      <td>48.161950</td>\n",
       "      <td>14.129455</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>37.755000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>58.262500</td>\n",
       "      <td>84.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.990450</td>\n",
       "      <td>0.481588</td>\n",
       "      <td>-0.190000</td>\n",
       "      <td>0.617500</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>2.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>200.0</td>\n",
       "      <td>1.964300</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>2.142500</td>\n",
       "      <td>2.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod</th>\n",
       "      <td>200.0</td>\n",
       "      <td>3864.407081</td>\n",
       "      <td>1553.277558</td>\n",
       "      <td>839.822063</td>\n",
       "      <td>2686.227611</td>\n",
       "      <td>3604.303507</td>\n",
       "      <td>4752.637556</td>\n",
       "      <td>8590.384044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std         min          25%  \\\n",
       "Por      200.0    14.991150     2.971176    6.550000    12.912500   \n",
       "Perm     200.0     4.330750     1.731014    1.130000     3.122500   \n",
       "AI       200.0     2.968850     0.566885    1.280000     2.547500   \n",
       "Brittle  200.0    48.161950    14.129455   10.940000    37.755000   \n",
       "TOC      200.0     0.990450     0.481588   -0.190000     0.617500   \n",
       "VR       200.0     1.964300     0.300827    0.930000     1.770000   \n",
       "Prod     200.0  3864.407081  1553.277558  839.822063  2686.227611   \n",
       "\n",
       "                 50%          75%          max  \n",
       "Por        15.070000    17.402500    23.550000  \n",
       "Perm        4.035000     5.287500     9.870000  \n",
       "AI          2.955000     3.345000     4.630000  \n",
       "Brittle    49.510000    58.262500    84.330000  \n",
       "TOC         1.030000     1.350000     2.180000  \n",
       "VR          1.960000     2.142500     2.870000  \n",
       "Prod     3604.303507  4752.637556  8590.384044  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = my_data.iloc[:,1:]                                # remove the well index\n",
    "my_data.describe().transpose()                              # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is good that we checked the summary statistics, because we have some negative values for brittleness and total organic carbon. The is physically imposible.  The values must be in error. We know the lowest possible values are 0.0, so we will truncate on 0.0.  We use the *get_numerical_data()* DataFrame member function to get a shallow copy of the data from the DataFrame.  Since it is a shallow copy, any changes we make to the copy are made to the data in the original DataFrame.  This allows us to apply this simple conditional statement to all the data values in the DataFrame all at once.\n",
    "\n",
    "Let's also make a categorical variable for production, based on a threshold of 4,000 MCFPD.\n",
    "\n",
    "* high production > 4,000 MCFPD, cprod = 1\n",
    "* low production <= 4,000 MCFPD, cprod = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>200.0</td>\n",
       "      <td>14.991150</td>\n",
       "      <td>2.971176</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>12.912500</td>\n",
       "      <td>15.070000</td>\n",
       "      <td>17.402500</td>\n",
       "      <td>23.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>200.0</td>\n",
       "      <td>4.330750</td>\n",
       "      <td>1.731014</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>3.122500</td>\n",
       "      <td>4.035000</td>\n",
       "      <td>5.287500</td>\n",
       "      <td>9.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2.968850</td>\n",
       "      <td>0.566885</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>2.547500</td>\n",
       "      <td>2.955000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>4.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>200.0</td>\n",
       "      <td>48.161950</td>\n",
       "      <td>14.129455</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>37.755000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>58.262500</td>\n",
       "      <td>84.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.991950</td>\n",
       "      <td>0.478264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.617500</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>2.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>200.0</td>\n",
       "      <td>1.964300</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>2.142500</td>\n",
       "      <td>2.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod</th>\n",
       "      <td>200.0</td>\n",
       "      <td>3864.407081</td>\n",
       "      <td>1553.277558</td>\n",
       "      <td>839.822063</td>\n",
       "      <td>2686.227611</td>\n",
       "      <td>3604.303507</td>\n",
       "      <td>4752.637556</td>\n",
       "      <td>8590.384044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cProd</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.497001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std         min          25%  \\\n",
       "Por      200.0    14.991150     2.971176    6.550000    12.912500   \n",
       "Perm     200.0     4.330750     1.731014    1.130000     3.122500   \n",
       "AI       200.0     2.968850     0.566885    1.280000     2.547500   \n",
       "Brittle  200.0    48.161950    14.129455   10.940000    37.755000   \n",
       "TOC      200.0     0.991950     0.478264    0.000000     0.617500   \n",
       "VR       200.0     1.964300     0.300827    0.930000     1.770000   \n",
       "Prod     200.0  3864.407081  1553.277558  839.822063  2686.227611   \n",
       "cProd    200.0     0.435000     0.497001    0.000000     0.000000   \n",
       "\n",
       "                 50%          75%          max  \n",
       "Por        15.070000    17.402500    23.550000  \n",
       "Perm        4.035000     5.287500     9.870000  \n",
       "AI          2.955000     3.345000     4.630000  \n",
       "Brittle    49.510000    58.262500    84.330000  \n",
       "TOC         1.030000     1.350000     2.180000  \n",
       "VR          1.960000     2.142500     2.870000  \n",
       "Prod     3604.303507  4752.637556  8590.384044  \n",
       "cProd       0.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = my_data._get_numeric_data()                           # get shallow copy of the numerical values from the DataFrame\n",
    "num[num < 0] = 0                                            # truncate negative values to 0.0\n",
    "my_data['cProd'] = np.where(my_data['Prod']>=4000, 1, 0)    # conditional statement assign a new feature\n",
    "my_data.describe().transpose()                              # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure that we have the new categorical feature for production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Prod</th>\n",
       "      <th>cProd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1695.360819</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.38</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.22</td>\n",
       "      <td>46.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3007.096063</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.02</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.01</td>\n",
       "      <td>72.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2531.938259</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.67</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.63</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5288.514854</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.52</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.18</td>\n",
       "      <td>10.94</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2859.469624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Por  Perm    AI  Brittle   TOC    VR         Prod  cProd\n",
       "0  12.08  2.92  2.80    81.40  1.16  2.31  1695.360819      0\n",
       "1  12.38  3.53  3.22    46.17  0.89  1.88  3007.096063      0\n",
       "2  14.02  2.59  4.01    72.80  0.89  2.72  2531.938259      0\n",
       "3  17.67  6.75  2.63    39.81  1.08  1.88  5288.514854      1\n",
       "4  17.52  4.57  3.18    10.94  1.51  1.90  2859.469624      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head()                                              # preview the first n rows of the updated DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has variables from 200 unconventional wells including well average porosity, log transform of permeability (to linearize the relationships with other variables), accoustic impedance (kg/m2s*10^6), brittness ratio (%), total organic carbon (%), vitrinite reflectance (%), and initial production 90 day average (MCFPD).  Note, the dataset is synthetic.\n",
    "\n",
    "#### Calculate the correlation matrix \n",
    "\n",
    "For multivariate analysis it is a good idea to check the correlation matrix.  We can calculate it and view it in the console with these commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    0.76 -0.46 -0.22  0.71  0.11  0.88]\n",
      " [ 0.76  1.   -0.24 -0.12  0.47  0.05  0.71]\n",
      " [-0.46 -0.24  1.    0.13 -0.53  0.5  -0.37]\n",
      " [-0.22 -0.12  0.13  1.   -0.21  0.32 -0.02]\n",
      " [ 0.71  0.47 -0.53 -0.21  1.    0.3   0.64]\n",
      " [ 0.11  0.05  0.5   0.32  0.3   1.    0.23]\n",
      " [ 0.88  0.71 -0.37 -0.02  0.64  0.23  1.  ]]\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = np.corrcoef(my_data.iloc[:,:7], rowvar = False)  # correlation matrix without the categorical value\n",
    "print(np.around(corr_matrix,2))                           # print the correlation matrix to 2 decimals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the 1.0 diagonal resulting from the correlation of each variable with themselves.  \n",
    "\n",
    "Let's use our function declared above to make a graphical correlation matrix visualization.  This may inprove our ability to spot features.  It relies on the built in correlation matrix method with Numpy DataFrames and MatPlotLib for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(my_data.iloc[:,:7],10)                            # using our correlation matrix visualization function\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with Only Two Features\n",
    "\n",
    "Let's simplify the problem to 2 feature), Porosity and Brittleness to predict Production rate.  By working with only 2 features, it is very easy to visualize the segmentation of the feature space (it is only 2D and can be shown compleltely on a single plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_subset = my_data.iloc[:,[0,3,7]]                # extract just por, brittle and prod with 100 samples\n",
    "X_train, X_test, y_train, y_test = train_test_split(my_data_subset.iloc[:,[0,1]], my_data_subset.iloc[:,2], test_size=0.25, random_state=73073)\n",
    "y_train = pd.DataFrame({'cprod':y_train.values})\n",
    "y_test = pd.DataFrame({'cprod':y_test.values})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Min and Max for Plotting \n",
    "\n",
    "x1 and x2 are the predictor features\n",
    "\n",
    "* x1 on x axis and x2 on the y axis for the plots below\n",
    "\n",
    "y is the response feature\n",
    "\n",
    "* used for the color bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1min = 5.0; x1max = 25.0\n",
    "x2min = 0.0; x2max = 100.0\n",
    "ymin = 0.0; ymax = 9000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check the univariate statistics of Porosity, Brittleness and Producton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>150.0</td>\n",
       "      <td>15.005267</td>\n",
       "      <td>2.971274</td>\n",
       "      <td>6.55</td>\n",
       "      <td>12.8975</td>\n",
       "      <td>15.055</td>\n",
       "      <td>17.500</td>\n",
       "      <td>23.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>150.0</td>\n",
       "      <td>47.857067</td>\n",
       "      <td>13.886701</td>\n",
       "      <td>10.94</td>\n",
       "      <td>37.8400</td>\n",
       "      <td>49.150</td>\n",
       "      <td>57.985</td>\n",
       "      <td>81.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count       mean        std    min      25%     50%     75%    max\n",
       "Por      150.0  15.005267   2.971274   6.55  12.8975  15.055  17.500  23.55\n",
       "Brittle  150.0  47.857067  13.886701  10.94  37.8400  49.150  57.985  81.40"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe().transpose()                         # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>50.0</td>\n",
       "      <td>14.9488</td>\n",
       "      <td>3.00064</td>\n",
       "      <td>7.38</td>\n",
       "      <td>13.3525</td>\n",
       "      <td>15.195</td>\n",
       "      <td>16.9325</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0766</td>\n",
       "      <td>14.94183</td>\n",
       "      <td>15.68</td>\n",
       "      <td>37.3550</td>\n",
       "      <td>52.545</td>\n",
       "      <td>60.0575</td>\n",
       "      <td>84.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count     mean       std    min      25%     50%      75%    max\n",
       "Por       50.0  14.9488   3.00064   7.38  13.3525  15.195  16.9325  20.96\n",
       "Brittle   50.0  49.0766  14.94183  15.68  37.3550  52.545  60.0575  84.33"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.describe().transpose()                         # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cprod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cprod\n",
       "count  150.000000\n",
       "mean     0.433333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.describe()[:2]                                    # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cprod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cprod\n",
       "count  50.00\n",
       "mean    0.44"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.describe()[:2]                                    # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check the univariate distributions of Porosity, Brittleness and Producton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(231)\n",
    "plt.hist(X_train[\"Por\"], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Porosity Training Data (%)')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.hist(X_train[\"Brittle\"], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Britteness Training Data (%)')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.hist(y_train['cprod'], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Production Training Data (MCFPD)')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.hist(X_test[\"Por\"], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Porosity Testing Data (%)')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.hist(X_test[\"Brittle\"], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Britteness Testing Data (%)')\n",
    "\n",
    "plt.subplot(236)\n",
    "plt.hist(y_test['cprod'], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "plt.title('Production Testing Data (MCFPD)')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=3.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions are well behaved, we cannot observe obvious gaps nor truncations.  Let's look at a scatter plot of Porosity vs. Brittleness with points colored by Production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "im = plt.scatter(X_train[\"Por\"],X_train[\"Brittle\"],s=None, c=y_train['cprod'], marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "plt.title('Training Production vs. Brittleness and Porosity'); plt.xlabel('Porosity (%)'); plt.ylabel('Brittleness (%)')\n",
    "cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "cbar.set_label(\"Production\", rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplot(122)\n",
    "im = plt.scatter(X_test[\"Por\"],X_test[\"Brittle\"],s=None, c=y_test['cprod'], marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=0.3, verts=None, edgecolors=\"black\")\n",
    "plt.title('Testing Production vs. Brittleness and Porosity'); plt.xlabel('Porosity (%)'); plt.ylabel('Brittleness (%)')\n",
    "cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "cbar.set_label(\"Production\", rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate, Fit and Predict with Gausian Naive Bayes\n",
    "\n",
    "Let's build a Gaussian naive Bayes model.\n",
    "\n",
    "We select the Gaussian model as it simplifies the inference problem to just a set of conditional means and variances given each feature.\n",
    "\n",
    "Recall we can set a prior probability of each response category\n",
    "\n",
    "* We will use the proportions from the training dataset.\n",
    "\n",
    "* 0.43 for high production (the mean of the binary dataset is the proportion of 1's)\n",
    "\n",
    "* 0.57 for low production (1 - proportion of high production)\n",
    "\n",
    "Another option would be to assume a naive, uniform prior, substitute the following:\n",
    "\n",
    "```python\n",
    "priors = (0.5,0.5)   # naive prior\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "priors = (1.0,0.0)                                        # set the prior probabilities of low and high production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build our Gaussian naive Bayes model.\n",
    "\n",
    "* instantiate it with the priors \n",
    "\n",
    "* train with the training data, we use the standard fit function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB(priors = priors)                           # instantiate the Gaussian naive Bayes model\n",
    "GaussianNB_fit = gnb.fit(X_train,y_train['cprod'].values)   # train with the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's predict with our new model over the testing dataset.\n",
    "\n",
    "* test by predicting with the testing data, we use the standard prediction function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\17137\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:449: RuntimeWarning: divide by zero encountered in log\n",
      "  jointi = np.log(self.class_prior_[i])\n"
     ]
    }
   ],
   "source": [
    "y_pred = GaussianNB_fit.predict(np.c_[X_test['Por'].values,X_test['Brittle'].values]) # predict over the testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Checking\n",
    "\n",
    "Let's check our model. With scikit learn we have great built in tools to evaluate our classification model. Let's try the classification report first.\n",
    "\n",
    "```python\n",
    "classification_report(truth, predicted)                # build a classification report to check our classification model\n",
    "```\n",
    "\n",
    "We get a table with summary metrics for model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      1.00      0.72        28\n",
      "           1       0.00      0.00      0.00        22\n",
      "\n",
      "    accuracy                           0.56        50\n",
      "   macro avg       0.28      0.50      0.36        50\n",
      "weighted avg       0.31      0.56      0.40        50\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\17137\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test['cprod'].values, y_pred, labels=[0,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metrics include:\n",
    "    \n",
    "    * recall - the ratio of true positives divided by all cases of the category in the testing dataset\n",
    "    \n",
    "    * precision - the ratio of true positives divided by all positives (true positives + false positives)\n",
    "    \n",
    "    * f1-score - the harmonic mean of recall and precision\n",
    "    \n",
    "    * support - the number of samples of each category in the testing data\n",
    "    \n",
    "I also like to look at the confusion matrix.  \n",
    "\n",
    "* the x axis is the prediction - category 0 or 1\n",
    "\n",
    "* the y axis is the truth - category 0 or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28  0]\n",
      " [22  0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test['cprod'].values, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above we can observe:\n",
    "\n",
    "* 26 low production wells classified correctly as low production\n",
    "\n",
    "* 1 high production well misclassified as low production\n",
    "\n",
    "* 2 low production wells misclassified as high production\n",
    "\n",
    "* 21 high production wells classified correctly as high production\n",
    "\n",
    "#### Visualizing the Classification Model\n",
    "\n",
    "Let's visualize the model over the entire feature space.\n",
    "\n",
    "* here's the training data with the classification over the full range of predictor features.\n",
    "\n",
    "* blue for low production and yellow for high production\n",
    "\n",
    "Note: naive Bayes provides the posterior probability of high and low production\n",
    "\n",
    "* the classifications below are based on maximum apriori selection (MAPS), selecting the category with the highest probability\n",
    "\n",
    "Let's visualize the classification model (blue - low production, yellow - high production) over the predictor feature space with the training data plotted (white - low production, black - high production)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\17137\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:449: RuntimeWarning: divide by zero encountered in log\n",
      "  jointi = np.log(self.class_prior_[i])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\17137\\\\anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(GaussianNB_fit,X_train[\"Por\"],x1min,x1max,X_train[\"Brittle\"],x2min,x2max,y_train['cprod'],0.0,1.0,'Training Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also visualize the posterior probabilities of low and high production.\n",
    "\n",
    "* here's the posterior probability of low and high production over the predictor feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model_prob(GaussianNB_fit,X_train[\"Por\"],x1min,x1max,X_train[\"Brittle\"],x2min,x2max,y_train['cprod'],'Training Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's look at the classification model over the predictor feature space (blue - low production, yellow - high production) with the testing data plotted (white - low production, black - high production)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\17137\\\\anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(GaussianNB_fit,X_test[\"Por\"],x1min,x1max,X_test[\"Brittle\"],x2min,x2max,y_test['cprod'],0.0,1.0,'Testing Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a reasonable model to predict well production from porosity and brittleness for an unconventional reservoir.\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of naive Bayes for prediction. A lot more could be done, for example, we could have applied variants such as:\n",
    "\n",
    "* multinomial naive Bayes\n",
    "\n",
    "* compliment naive Bayes\n",
    "\n",
    "* Bernoulli naive Bayes\n",
    "\n",
    "We could have worked with more predictor features, but for learning the method, it is nice to be able to visualize the entire classification in one plot!\n",
    "\n",
    "If you struggled with the basic Python used here check out my other basic demonstrations for DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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