data_completeness_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load training data (from the starter notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this bit thanks to Brendon Hall\n",
    "s3_train_csv = 's3://zarr-depot/wells/FORCE: Machine Predicted Lithology/train.csv'\n",
    "data = pd.read_csv(s3_train_csv, sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am using the library `missingno` to make a quick visual summary plot of the dataset completion, log by log (all wells together)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = data.drop(['WELL', 'DEPTH_MD','X_LOC','Y_LOC','Z_LOC','GROUP','FORMATION',\n",
    "                       'FORCE_2020_LITHOFACIES_LITHOLOGY','FORCE_2020_LITHOFACIES_CONFIDENCE'], \n",
    "                      axis=1)\n",
    "msno.matrix(w, color=(0., 0., 0.45))   \n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20, 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the library's documentation, the sparkline at the right of the plot summarizes the general shape of the data completeness and points out the rows with the maximum and minimum nullity in the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explore in more depth the data completion, below I am also adding below `missingno` 's dendrogram plot.\n",
    "\n",
    "As explained in the library's documentation, _The dendrogram uses a hierarchical clustering algorithm (courtesy of scipy) to bin variables against one another by their nullity correlation (measured in terms of binary distance). At each step of the tree the variables are split up based on which combination minimizes the distance of the remaining clusters. The more monotone the set of variables, the closer their total distance is to zero, and the closer their average distance (the y-axis) is to zero._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.dendrogram(data.drop(['WELL', 'DEPTH_MD','X_LOC','Y_LOC','Z_LOC','GROUP','FORMATION',\n",
    "                         'FORCE_2020_LITHOFACIES_LITHOLOGY','FORCE_2020_LITHOFACIES_CONFIDENCE'], axis=1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two plots provide in my opinion a very compelling and informative way to inspect data completeness, and I am wondering if they could inform and guide the strategy to deal with missing data, together with domain knowledge from petrophysics.\n",
    "\n",
    "Interpreting the dendrogram in a top-down fashion, as suggested in the library documentation, my first thoguths are that this may suggest trying to predict missing values in a sequential fashion rather than for all logs at once.  For example, looking at the largest cluster on the left, and starting from top right, I am thinking of testing use of GR to first predict missing values in RDEP, then both to predict missing values in RMED, then DTC. Then add CALI and use all logs completed so far to predict RHOB, and so on. \n",
    "\n",
    "Naturally, this strategy will need to be tested against alternative strategies using lithology prediction accuracy. I would do that in the context of learning curves: I am imagining comparing the training and crossvalidation error first using only non NaN rows, then replace all NANs with mean, then compare separately this sequential log completing strategy with an all-in one strategy.\n",
    "\n",
    "Stay tuned...."
   ]
  }
 ],
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