{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/html"
   },
   "source": [
    "<img src=\"../_images/Gamma_Facet_Logo_RGB_LB.svg\" width=\"500\" style=\"padding-bottom: 70px; padding-top: 70px; margin: auto; display: block\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to FACET\n",
    "\n",
    "FACET is composed of the following key components:\n",
    "\n",
    "- **Model Inspection**\n",
    "\n",
    "    FACET introduces a new algorithm to quantify dependencies and interactions between features in ML models. This new tool for human-explainable AI adds a new, global perspective to the observation-level explanations provided by the popular [SHAP](https://shap.readthedocs.io/en/latest/) approach. To learn more about FACET's model inspection capabilities, see the getting started example below.\n",
    "\n",
    "\n",
    "- **Model Simulation**\n",
    "\n",
    "    FACET's model simulation algorithms use ML models for *virtual experiments* to help identify scenarios that optimise predicted  outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below.    \n",
    "    \n",
    "    \n",
    "- **Enhanced Machine Learning Workflow**  \n",
    "\n",
    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
    "\n",
    "***\n",
    "\n",
    "**Context**\n",
    "\n",
    "Drilling a water well is dangerous and costly. Costs are driven by the time it takes to finalize a well in order to start pumping water from it. In order to reduce those costs, drillers are usually incentivised to drill at a faster pace. However, drilling faster increases risks of incident which is the reason why the Rate of Penetration (ROP) is a measure constantly monitored.\n",
    "\n",
    "Utilizing FACET, we will:\n",
    "\n",
    "1. Apply use machine learning to prevent a water well drilling operation from an incident.\n",
    "2. Quantify how the ROP impacts the estimated risk.  \n",
    "\n",
    "***\n",
    "\n",
    "**Tutorial outline**\n",
    "\n",
    "1. [Required imports](#Required-imports)\n",
    "2. [Data and initial feature selection](#Data-and-initial-feature-selection)\n",
    "3. [Selecting a learner using FACET selector](#Selecting-a-learner-using-FACET-selector)\n",
    "4. [Using FACET for advanced model inspection](#Using-FACET-for-advanced-model-inspection)\n",
    "5. [FACET univariate simulator: the impact of rate of penetration](#FACET-univariate-simulator:-the-impact-of-rate-of-penetration)\n",
    "6. [Appendix: generating the dataset](#Appendix:-generating-the-dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:28.594834Z",
     "start_time": "2020-08-31T08:33:28.410363Z"
    },
    "delete_for_interactive": true,
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "# this cell's metadata contains\n",
    "# \"nbsphinx\": \"hidden\" so it is hidden by nbsphinx\n",
    "\n",
    "\n",
    "# ignore irrelevant warnings that would affect the output of this tutorial notebook\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=r\".*Xcode_8\\.3\\.3\")\n",
    "warnings.filterwarnings(\"ignore\", message=r\".*`should_run_async` will not call `transform_cell`\")\n",
    "warnings.filterwarnings(\"ignore\", message=r\".*`np\\..*` is a deprecated alias\")\n",
    "warnings.filterwarnings(\"ignore\", message=r\"Importing display from IPython.core.display is deprecated.*\")\n",
    "\n",
    "\n",
    "# set global options for matplotlib\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (12.0, 6.0)\n",
    "matplotlib.rcParams[\"figure.dpi\"] = 96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T17:21:45.452088Z",
     "start_time": "2020-08-28T17:21:45.450036Z"
    },
    "tags": []
   },
   "source": [
    "# Required imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to run this notebook, we will import not only the FACET package, but also other packages useful to solve this task. Overall, we can break down the imports into three categories: \n",
    "\n",
    "1. Common packages (pandas, matplotlib, etc.)\n",
    "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-28T17:21:38.623408Z",
     "start_time": "2020-08-28T17:21:38.620085Z"
    }
   },
   "source": [
    "**Common package imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:34:10.269188Z",
     "start_time": "2020-08-31T08:34:02.013Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import RandomizedSearchCV, RepeatedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**FACET imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:30.464262Z",
     "start_time": "2020-08-31T08:33:30.101989Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from facet.data import Sample\n",
    "from facet.inspection import LearnerInspector\n",
    "from facet.selection import LearnerSelector, ParameterSpace\n",
    "from facet.validation import BootstrapCV\n",
    "from facet.data.partition import ContinuousRangePartitioner\n",
    "from facet.simulation import UnivariateProbabilitySimulator\n",
    "from facet.simulation.viz import SimulationDrawer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-18T14:15:20.686543Z",
     "start_time": "2020-08-18T14:15:20.683573Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**sklearndf imports**\n",
    "\n",
    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
    "\n",
    "`from sklearn.pipeline import Pipeline`\n",
    "\n",
    "becomes:\n",
    "\n",
    "`from sklearndf.pipeline import PipelineDF`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:30.620441Z",
     "start_time": "2020-08-31T08:33:30.465741Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearndf.pipeline import PipelineDF, ClassifierPipelineDF\n",
    "from sklearndf.classification import RandomForestClassifierDF\n",
    "from sklearndf.classification.extra import LGBMClassifierDF\n",
    "from sklearndf.transformation.extra import BorutaDF\n",
    "from sklearndf.transformation import SimpleImputerDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "**pytools imports**\n",
    "\n",
    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:30.629194Z",
     "start_time": "2020-08-31T08:33:30.622223Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pytools.viz.dendrogram import DendrogramDrawer, DendrogramReportStyle\n",
    "from pytools.viz.distribution import ECDFDrawer\n",
    "from pytools.viz.matrix import MatrixDrawer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Data and initial feature selection\n",
    "\n",
    "For the sake of simplicity, we use a simplified artificial dataset, it contains 500 observations, each row representing a drilling operation of the past, the target is the occurrence of drill breakdown (incident). Details and the code used to simulate this dataset can be found in the [Appendix](#Appendix:-generating-the-dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:30.656685Z",
     "start_time": "2020-08-31T08:33:30.639398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight on bit (kg)</th>\n",
       "      <td>289.201651</td>\n",
       "      <td>341.949835</td>\n",
       "      <td>266.831213</td>\n",
       "      <td>267.340585</td>\n",
       "      <td>305.977342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rotation speed (rpm)</th>\n",
       "      <td>10594.222670</td>\n",
       "      <td>6962.659505</td>\n",
       "      <td>11065.697315</td>\n",
       "      <td>7890.678632</td>\n",
       "      <td>12017.344224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depth of operation (m)</th>\n",
       "      <td>790.947541</td>\n",
       "      <td>811.833996</td>\n",
       "      <td>619.497649</td>\n",
       "      <td>1048.481202</td>\n",
       "      <td>613.434303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mud density (kg/L)</th>\n",
       "      <td>2.898840</td>\n",
       "      <td>1.677378</td>\n",
       "      <td>2.213403</td>\n",
       "      <td>2.683010</td>\n",
       "      <td>2.360972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rate of Penetration (ft/h)</th>\n",
       "      <td>28.403279</td>\n",
       "      <td>27.066685</td>\n",
       "      <td>30.556081</td>\n",
       "      <td>23.735377</td>\n",
       "      <td>28.502248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperature (C)</th>\n",
       "      <td>39.539919</td>\n",
       "      <td>74.050548</td>\n",
       "      <td>45.194728</td>\n",
       "      <td>55.135234</td>\n",
       "      <td>60.585239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mud Flow in (m3/s)</th>\n",
       "      <td>50.299606</td>\n",
       "      <td>72.140061</td>\n",
       "      <td>10.908230</td>\n",
       "      <td>51.029350</td>\n",
       "      <td>44.159394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hole diameter (m)</th>\n",
       "      <td>5.369813</td>\n",
       "      <td>5.580490</td>\n",
       "      <td>4.374240</td>\n",
       "      <td>6.981177</td>\n",
       "      <td>4.217036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Incident</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inverse Rate of Penetration (h/ft)</th>\n",
       "      <td>0.035207</td>\n",
       "      <td>0.036946</td>\n",
       "      <td>0.032727</td>\n",
       "      <td>0.042131</td>\n",
       "      <td>0.035085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               0            1             2  \\\n",
       "Weight on bit (kg)                    289.201651   341.949835    266.831213   \n",
       "Rotation speed (rpm)                10594.222670  6962.659505  11065.697315   \n",
       "Depth of operation (m)                790.947541   811.833996    619.497649   \n",
       "Mud density (kg/L)                      2.898840     1.677378      2.213403   \n",
       "Rate of Penetration (ft/h)             28.403279    27.066685     30.556081   \n",
       "Temperature (C)                        39.539919    74.050548     45.194728   \n",
       "Mud Flow in (m3/s)                     50.299606    72.140061     10.908230   \n",
       "Hole diameter (m)                       5.369813     5.580490      4.374240   \n",
       "Incident                                0.000000     1.000000      0.000000   \n",
       "Inverse Rate of Penetration (h/ft)      0.035207     0.036946      0.032727   \n",
       "\n",
       "                                              3             4  \n",
       "Weight on bit (kg)                   267.340585    305.977342  \n",
       "Rotation speed (rpm)                7890.678632  12017.344224  \n",
       "Depth of operation (m)              1048.481202    613.434303  \n",
       "Mud density (kg/L)                     2.683010      2.360972  \n",
       "Rate of Penetration (ft/h)            23.735377     28.502248  \n",
       "Temperature (C)                       55.135234     60.585239  \n",
       "Mud Flow in (m3/s)                    51.029350     44.159394  \n",
       "Hole diameter (m)                      6.981177      4.217036  \n",
       "Incident                               0.000000      1.000000  \n",
       "Inverse Rate of Penetration (h/ft)     0.042131      0.035085  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the prepared dataframe\n",
    "df = pd.read_csv(\n",
    "    \"water_drilling_classification_data.csv\",\n",
    "    sep=\";\",\n",
    "    encoding=\"utf-8\",\n",
    ")\n",
    "\n",
    "# quick look\n",
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:30.665214Z",
     "start_time": "2020-08-31T08:33:30.659809Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create a FACET sample object\n",
    "drilling_obs = Sample(observations=df, target_name=\"Incident\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Next, we perform some initial feature selection using Boruta, a recent approach shown to have quite good performance. The Boruta algorithm seeks to identify and remove features that are no more predictive than random noise. If you are interested further, please see this  [article](https://www.jstatsoft.org/article/view/v036i11).\n",
    "\n",
    "The `BorutaDF` transformer in our sklearndf package provides easy access to this method. The approach relies on a tree-based learner, usually a random forest. For settings, a `max_depth` of between 3 and 7 is typically recommended, and here we utilise the default setting of 5. However, as this depends on the number of features and the complexity of interactions, one could also explore the sensitivity of feature selection to this parameter. The number of trees is automatically managed by the Boruta feature selector argument `n_estimators=\"auto\"`.\n",
    "\n",
    "We also use parallelization for the random forest using `n_jobs` to accelerate the Boruta iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Weight on bit (kg)', 'Rotation speed (rpm)', 'Depth of operation (m)',\n",
       "       'Mud density (kg/L)', 'Rate of Penetration (ft/h)', 'Temperature (C)',\n",
       "       'Mud Flow in (m3/s)', 'Hole diameter (m)', 'Incident',\n",
       "       'Inverse Rate of Penetration (h/ft)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:53:50.515286Z",
     "start_time": "2020-08-31T08:53:26.621355Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features: ['Weight on bit (kg)', 'Rotation speed (rpm)', 'Depth of operation (m)', 'Mud density (kg/L)', 'Rate of Penetration (ft/h)', 'Hole diameter (m)', 'Inverse Rate of Penetration (h/ft)']\n"
     ]
    }
   ],
   "source": [
    "# wrapper class to implement Boruta feature selection\n",
    "feature_selector = BorutaDF(\n",
    "    estimator=RandomForestClassifierDF(max_depth=5, random_state=42, n_jobs=-3),\n",
    "    n_estimators=\"auto\",\n",
    "    random_state=42,\n",
    "    verbose=0,\n",
    "    max_iter=200,\n",
    ")\n",
    "\n",
    "# create a pipeline that includes some simple preprocessing (imputation) and Boruta\n",
    "feature_preprocessing = PipelineDF(\n",
    "    steps=[(\"impute\", SimpleImputerDF()), (\"feature selection\", feature_selector)]\n",
    ")\n",
    "\n",
    "# run feature selection using Boruta and report those selected\n",
    "feature_preprocessing.fit(X=drilling_obs.features, y=drilling_obs.target)\n",
    "print(f\"Selected features: {list(feature_preprocessing.feature_names_original_.unique())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see that the key features that we would expect to impact the safety of the operation are included after the feature selection. A working hypothesis of how each influences the target is: \n",
    "\n",
    "- **Weight on bit**: we expect higher weight to increase the likelihood of a failure due to heavier equipment wear\n",
    "\n",
    "- **Rotation speed**: Too fast rotation speed can lead to overheating and breaking the material, too low rotation renders drilling more difficult and is not economical\n",
    "\n",
    "- **Depth of operation**: As a simplification we will take for granted that the deeper we dig, the denser the soil will be, increasing the likelihood of either a collapse or breaking equipment wear\n",
    "\n",
    "- **Mud density**: Mud density needs to match soil density to avoid well collapse (formation falling in well and blocking pipe) or mud loss (mud flowing in the formation)\n",
    "\n",
    "- **Rate of Penetration**: A higher ROP leads to more wear & tear of the equipment and thus we expect a positive effect\n",
    "\n",
    "- **Hole diameter**: Thinner wholes are used in deeper sections of the well hence usually relate to more dangerous zones\n",
    "\n",
    "- **Inverse Rate of Penetration**: As described by its name, this feature is the inverse of the ROP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Weight on bit (kg)', 'Rotation speed (rpm)',\n",
       "       'Depth of operation (m)', 'Mud density (kg/L)',\n",
       "       'Rate of Penetration (ft/h)', 'Hole diameter (m)',\n",
       "       'Inverse Rate of Penetration (h/ft)'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get original feature names\n",
    "feature_preprocessing.feature_names_original_.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create a FACET sample object with features selected by Boruta\n",
    "drilling_obs_reduced_featset = drilling_obs.keep(\n",
    "    feature_names=feature_preprocessing.feature_names_original_.unique()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-08-05T10:53:26.316Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Selecting a learner using FACET selector\n",
    "\n",
    "FACET implements several additional useful wrappers which further simplify comparing and tuning a larger number of models and configurations: \n",
    "\n",
    "- `ParameterSpace`: allows you to pass a learner pipeline (i.e., classifier + any preprocessing) and a set of hyperparameters\n",
    "- `LearnerSelector`: multiple ParameterSpaces can be passed into this class as MultiEstimatorClassifierParameterSpace - this allows tuning hyperparameters both across different types of learners in a single step and ranks the resulting models accordingly\n",
    "\n",
    "The following learners and hyperparameter ranges will be assessed using 5 repeated 5-fold cross-validation:\n",
    "\n",
    "\n",
    "1. **Random forest**: with hyperparameters\n",
    "    - min_samples_leaf: [8, 11, 15]\n",
    "\n",
    "  \n",
    "2. **Light gradient boosting**: with hyperparameters\n",
    "    - min_child_samples: [8, 11, 15]\n",
    "\n",
    "Note if you want to see a list of hyperparameters you can use `classifier_name().get_params().keys()` where `classifier_name` could be for example `RandomForestClassifierDF` and if you want to see the default values, just use `classifier_name().get_params()`.\n",
    "\n",
    "Finally, for this exercise we will use accuracy which is the default performance metric for scoring and ranking our classifiers.\n",
    "\n",
    "First, we specify the classifiers we want to train using `ClassifierPipelineDF` from sklearndf. Note here we also include feature preprocessing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:33:54.961942Z",
     "start_time": "2020-08-31T08:33:54.958552Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# random forest learner\n",
    "rforest_clf = ClassifierPipelineDF(\n",
    "    classifier=RandomForestClassifierDF(n_estimators=500, random_state=42),\n",
    ")\n",
    "\n",
    "# light gradient boosting learner\n",
    "lgbm_clf = ClassifierPipelineDF(\n",
    "    classifier=LGBMClassifierDF(random_state=42),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Then we create parameter spaces with `ParameterSpace` for each classifier and specify set of hyperparameters for each one of them. Contrary to standard `sklearn` workflow, in this approach setting wrong hyperparameter will throw an exception as setting an attribute comes with a proper check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rforest_ps = ParameterSpace(rforest_clf)\n",
    "\n",
    "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n",
    "rforest_ps.classifier.min_samples_leaf = stats.zipfian(a=1, n=12, loc=7)\n",
    "\n",
    "lgbm_ps = ParameterSpace(lgbm_clf)\n",
    "\n",
    "# random ints 8 <= x <= 19; smaller ints are more frequent (zipfian distribution)\n",
    "lgbm_ps.classifier.min_child_samples = stats.zipfian(a=1, n=12, loc=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We now the `LearnerSelector` using the parameter spaces defined above, which will run a gridsearch using 10 repeated 5-fold cross-validation on our selected set of features from Boruta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:34:04.796507Z",
     "start_time": "2020-08-31T08:33:54.964092Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create cv iterator 5 repeated 5-fold\n",
    "cv_approach = RepeatedKFold(n_splits=5, n_repeats=5, random_state=42)\n",
    "\n",
    "# fit selector\n",
    "model_selector = LearnerSelector(\n",
    "    searcher_type=RandomizedSearchCV,\n",
    "    parameter_space=[rforest_ps, lgbm_ps],\n",
    "    cv=cv_approach,\n",
    "    n_jobs=-3,\n",
    "    scoring=\"accuracy\",\n",
    "    random_state=42,\n",
    ").fit(\n",
    "    drilling_obs_reduced_featset\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To see the configuration of the best selected model, we can access the `best_estimator_` property of the fitted `LearnerSelector` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierPipelineDF(\n",
       "    classifier=LGBMClassifierDF(min_child_samples=17, random_state=42)\n",
       ")</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClassifierPipelineDF</label><div class=\"sk-toggleable__content\"><pre>ClassifierPipelineDF(\n",
       "    classifier=LGBMClassifierDF(min_child_samples=17, random_state=42)\n",
       ")</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">classifier: LGBMClassifierDF</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifierDF(min_child_samples=17, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifierDF</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifierDF(min_child_samples=17, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ClassifierPipelineDF(classifier=LGBMClassifierDF(min_child_samples=17, random_state=42))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_selector.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can see how each model scored using the `summary_report()` method of the `LearnerSelector`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">score</th>\n",
       "      <th>candidate</th>\n",
       "      <th colspan=\"2\" halign=\"left\">param</th>\n",
       "      <th colspan=\"4\" halign=\"left\">time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">test</th>\n",
       "      <th>-</th>\n",
       "      <th colspan=\"2\" halign=\"left\">classifier</th>\n",
       "      <th colspan=\"2\" halign=\"left\">fit</th>\n",
       "      <th colspan=\"2\" halign=\"left\">score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>-</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>min_child_samples</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.8544</td>\n",
       "      <td>0.036341</td>\n",
       "      <td>LGBMClassifierDF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>0.000078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>0.8452</td>\n",
       "      <td>0.044732</td>\n",
       "      <td>LGBMClassifierDF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>0.021619</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.8316</td>\n",
       "      <td>0.044960</td>\n",
       "      <td>LGBMClassifierDF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0.023706</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.001507</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0.8316</td>\n",
       "      <td>0.044960</td>\n",
       "      <td>LGBMClassifierDF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>0.022872</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.001427</td>\n",
       "      <td>0.000067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.7648</td>\n",
       "      <td>0.032634</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.281148</td>\n",
       "      <td>0.012409</td>\n",
       "      <td>0.016739</td>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0.7552</td>\n",
       "      <td>0.034190</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.280517</td>\n",
       "      <td>0.013183</td>\n",
       "      <td>0.017294</td>\n",
       "      <td>0.001392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.7552</td>\n",
       "      <td>0.034190</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275385</td>\n",
       "      <td>0.012340</td>\n",
       "      <td>0.016589</td>\n",
       "      <td>0.000584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.7508</td>\n",
       "      <td>0.032486</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.264602</td>\n",
       "      <td>0.005580</td>\n",
       "      <td>0.016063</td>\n",
       "      <td>0.000214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0.7488</td>\n",
       "      <td>0.035589</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.268346</td>\n",
       "      <td>0.011811</td>\n",
       "      <td>0.016440</td>\n",
       "      <td>0.000448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0.7404</td>\n",
       "      <td>0.035268</td>\n",
       "      <td>RandomForestClassifierDF</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.266838</td>\n",
       "      <td>0.012662</td>\n",
       "      <td>0.016058</td>\n",
       "      <td>0.000363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  score                                   candidate            param  \\\n",
       "   test                                           -       classifier   \n",
       "   rank    mean       std                         - min_samples_leaf   \n",
       "8     1  0.8544  0.036341          LGBMClassifierDF              NaN   \n",
       "6     2  0.8452  0.044732          LGBMClassifierDF              NaN   \n",
       "3     3  0.8316  0.044960          LGBMClassifierDF              NaN   \n",
       "9     3  0.8316  0.044960          LGBMClassifierDF              NaN   \n",
       "4     5  0.7648  0.032634  RandomForestClassifierDF                9   \n",
       "2     6  0.7552  0.034190  RandomForestClassifierDF               11   \n",
       "5     6  0.7552  0.034190  RandomForestClassifierDF               11   \n",
       "1     8  0.7508  0.032486  RandomForestClassifierDF               12   \n",
       "0     9  0.7488  0.035589  RandomForestClassifierDF               14   \n",
       "7    10  0.7404  0.035268  RandomForestClassifierDF               18   \n",
       "\n",
       "                         time                                \n",
       "                          fit               score            \n",
       "  min_child_samples      mean       std      mean       std  \n",
       "8                17  0.015063  0.000635  0.001186  0.000078  \n",
       "6                11  0.021619  0.001111  0.001393  0.000095  \n",
       "3                 8  0.023706  0.001044  0.001507  0.000059  \n",
       "9                 8  0.022872  0.000284  0.001427  0.000067  \n",
       "4               NaN  0.281148  0.012409  0.016739  0.000678  \n",
       "2               NaN  0.280517  0.013183  0.017294  0.001392  \n",
       "5               NaN  0.275385  0.012340  0.016589  0.000584  \n",
       "1               NaN  0.264602  0.005580  0.016063  0.000214  \n",
       "0               NaN  0.268346  0.011811  0.016440  0.000448  \n",
       "7               NaN  0.266838  0.012662  0.016058  0.000363  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at performance for the top ranked classifiers\n",
    "model_selector.summary_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-05T11:42:06.425585Z",
     "start_time": "2020-08-05T11:42:06.423740Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Using FACET for advanced model inspection\n",
    "\n",
    "The [SHAP approach](http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions) has become the standard method for model inspection. SHAP values are used to explain the additive contribution of each feature to the prediction for each observation (i.e., explain **individual** predictions).\n",
    "\n",
    "The FACET `LearnerInspector` computes SHAP values using the best model identified by the `LearnerSelector`. The FACET `LearnerInspector` then provides advanced model inspection through new SHAP-based summary metrics for understanding pairwise feature redundancy and synergy. Redundancy and synergy are calculated using a new algorithm to understand model predictions from a **global perspective** to complement local SHAP.\n",
    "\n",
    "The definitions of synergy and redundancy are as follows:\n",
    "\n",
    "\n",
    "- **Synergy**\n",
    "\n",
    "  The degree to which the model combines information from one feature with \n",
    "  another to predict the target. For example, let's assume we are predicting \n",
    "  cardiovascular health using age and gender and the fitted model includes \n",
    "  a complex interaction between them. This means these two features are \n",
    "  synergistic for predicting cardiovascular health. Further, both features \n",
    "  are important to the model and removing either one would significantly \n",
    "  impact performance. Let's assume age brings more information to the joint\n",
    "  contribution than gender. This asymmetric contribution means the synergy for\n",
    "  (age, gender) is less than the synergy for (gender, age). To think about it\n",
    "  another way, imagine the prediction is a coordinate you are trying to reach.\n",
    "  From your starting point, age gets you much closer to this point than \n",
    "  gender, however, you need both to get there. Synergy reflects the fact \n",
    "  that gender gets more help from age (higher synergy from the perspective \n",
    "  of gender) than age does from gender (lower synergy from the perspective of\n",
    "  age) to reach the prediction. *This leads to an important point: synergy \n",
    "  is a naturally asymmetric property of the global information two interacting \n",
    "  features contribute to the model predictions.* Synergy is expressed as a \n",
    "  percentage ranging from 0% (full autonomy) to 100% (full synergy).\n",
    "\n",
    "\n",
    "- **Redundancy**\n",
    "\n",
    "  The degree to which a feature in a model duplicates the information of a \n",
    "  second feature to predict the target. For example, let's assume we had \n",
    "  house size and number of bedrooms for predicting house price. These \n",
    "  features capture similar information as the more bedrooms the larger \n",
    "  the house and likely a higher price on average. The redundancy for \n",
    "  (number of bedrooms, house size) will be greater than the redundancy \n",
    "  for (house size, number of bedrooms). This is because house size \n",
    "  \"knows\" more of what number of bedrooms does for predicting house price \n",
    "  than vice-versa. Hence, there is greater redundancy from the perspective \n",
    "  of number of bedrooms. Another way to think about it is removing house \n",
    "  size will be more detrimental to model performance than removing number \n",
    "  of bedrooms, as house size can better compensate for the absence of \n",
    "  number of bedrooms. This also implies that house size would be a more \n",
    "  important feature than number of bedrooms in the model. *The important \n",
    "  point here is that like synergy, redundancy is a naturally asymmetric \n",
    "  property of the global information feature pairs have for predicting \n",
    "  an outcome.* Redundancy is expressed as a percentage ranging from 0% \n",
    "  (full uniqueness) to 100% (full redundancy).\n",
    "\n",
    "\n",
    "Note that cases can apply at the same time so a feature pair can use some information synergistically and some information redundantly.\n",
    "\n",
    "The FACET `LearnerInspector` can calculate all of this with a single method call, but also offers methods to access the intermediate results of each step. A lightweight visualization framework is available to render the results in different styles.\n",
    "\n",
    "SHAP values from the `LearnerInspector` can also be used with the SHAP package plotting functions for sample and observation level SHAP visualizations, such as SHAP distribution plots, dependency plots, force plots and waterfall plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-31T08:34:09.495621Z",
     "start_time": "2020-08-31T08:34:04.816216Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "model_inspector = LearnerInspector(\n",
    "    pipeline=model_selector.best_estimator_,\n",
    "    n_jobs=-3\n",
    ").fit(\n",
    "    drilling_obs_reduced_featset\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature\n",
       "Rate of Penetration (ft/h)            0.184227\n",
       "Weight on bit (kg)                    0.164185\n",
       "Inverse Rate of Penetration (h/ft)    0.163715\n",
       "Rotation speed (rpm)                  0.147971\n",
       "Mud density (kg/L)                    0.145815\n",
       "Hole diameter (m)                     0.104211\n",
       "Depth of operation (m)                0.089876\n",
       "Name: 0.0, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick look at feature importance\n",
    "model_inspector.feature_importance().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Synergy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "synergy_matrix = model_inspector.feature_synergy_matrix()\n",
    "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To interpret the synergy matrix, the first feature in a pair is the row (\"perspective from\"), and the second feature the column. For example, let's take the highest synergy value of 81% for the feature pair rotation speed and weight on the bit. From the perspective of rotation speed we find that 81% of the information is combined with weight on the bit to predict failure. This seems sensible in context, as drilling with both a high bit weight and a high rotation can have a disproportionately large impact on the wear of the equipment, and so drastically increase the likelihood of failure. It is understandable that the synergy is also high from the perspective of weight on the bit (70%). This also means if we want to reduce the impact of either of these factors on the likelihood of failure, we should consider them both together and not independently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Redundancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "redundancy_matrix = model_inspector.feature_redundancy_matrix()\n",
    "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with synergy, the matrix row is the \"perspective from\" feature in the row-column feature pair. First let's consider the feature pair (ROP, Inverse ROP). The redundancy here is similar from the perspective of either (85%) and this is because one feature is the inverse of the other and so can substitute one another in the model for predicting failure. Next let's consider the feature pair (depth of the operation, hole diameter) which have the highest redundancies after (ROP, Inverse ROP). From the perspective of hole diameter 51% of the information is duplicated with depth of the operation to predict failure. Intuitively, we can see why, as the depth of operation and the hole diameter are highly connected as drillers use thinner drilling bits as they drill deeper into the earth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature clustering\n",
    "\n",
    "As detailed above redundancy and synergy for a feature pair is from the \"perspective\" of one of the features in the pair, and so yields two distinct values. However, a symmetric version can also be computed that provides not only a simplified perspective but allows the use of (1 - metric) as a feature distance. With this distance hierarchical, single linkage clustering is applied to create a dendrogram visualization. This helps to identify groups of low distance, features which activate \"in tandem\" to predict the outcome. Such information can then be used to either reduce clusters of highly redundant features to a subset or highlight clusters of highly synergistic features that should always be considered together.\n",
    "\n",
    "For this example, let's apply clustering to redundancy to see how the apparent grouping observed in the heatmap appears in the dendrogram. Ideally, we want to see features only start to cluster as close to the right-hand side of the dendrogram as possible. This implies all features in the model are contributing uniquely to our predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "redundancy = model_inspector.feature_redundancy_linkage()\n",
    "DendrogramDrawer().draw(title=\"Redundancy linkage\", data=redundancy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the dendrogram shows a high redundancy (left-most feature cluster) between the ROP and the Inverse ROP, as both features compete in terms of feature importance. The dendrogram below shows that we should remove one to help orthogonalise the feature set before simulation. We could also consider removing one of Hole diameter and Depth of Operation. For the purpose of this tutorial, we will remove Inverse ROP and retain ROP, which is more interpretable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The reason we want to engineer an orthogonal set of features is so we can use the univariate simulator. An orthogonal feature set of is needed so that the artificially created samples stay plausible. Indeed, not removing the Inverse ROP feature from the set would lead to unrealistic artificial observations while using the univariate simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove redundant feature Inverse ROP\n",
    "redundant_features = [\"Inverse Rate of Penetration (h/ft)\"]\n",
    "drilling_obs_not_redundant = drilling_obs_reduced_featset.drop(feature_names=redundant_features)\n",
    "\n",
    "model_selector_2 = LearnerSelector(\n",
    "    searcher_type=RandomizedSearchCV,\n",
    "    parameter_space=[rforest_ps, lgbm_ps],\n",
    "    cv=cv_approach, \n",
    "    n_jobs=-3,\n",
    "    scoring=\"accuracy\"\n",
    ").fit(\n",
    "    drilling_obs_not_redundant\n",
    ")\n",
    "\n",
    "model_inspector_2 = LearnerInspector(\n",
    "    pipeline=model_selector_2.best_estimator_,\n",
    "    n_jobs=-3\n",
    ").fit(\n",
    "    drilling_obs_not_redundant\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "redundancy = model_inspector_2.feature_redundancy_linkage()\n",
    "DendrogramDrawer().draw(title=\"Redundancy linkage\", data=redundancy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now that our feature set is looking more linearly independent, we can start making simulations to gain knowledge into how ROP will impact failure likelihood.\n",
    "\n",
    "Note that removing the Inverse ROP has given more feature importance to the ROP, which is now the most important feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# FACET univariate simulator: the impact of rate of penetration\n",
    "\n",
    "The ROP is a parameter very much monitored while drilling a well as it is a tradeoff between safety and economy, it is safer to drill at a low pace but much costlier as it takes more time. It has also the highest feature importance in our model (see dendrogram above). Let's use a simulation to get a sense of how the failure likelihood behaves if we simulate changes in the ROP applied.\n",
    "\n",
    "As the basis for the simulation, we divide the feature into relevant partitions: \n",
    "\n",
    "- We use FACET's `ContinuousRangePartitioner` to split the range of observed values of ROP into intervals of equal size. Each partition is represented by the central value of that partition. \n",
    "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best estimator acquired from the selector, the simulator now re-predicts all targets using the models trained on full sample and determines the mean predicted probability of the target variable resulting from this, as well as a confidence interval derived from the standard error of the mean predicted probability.\n",
    "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# set-up and run a simulation\n",
    "SIM_FEATURE = \"Rate of Penetration (ft/h)\"\n",
    "rop_bins = ContinuousRangePartitioner()\n",
    "rop_simulator = UnivariateProbabilitySimulator(\n",
    "    model=model_selector_2.best_estimator_,\n",
    "    sample=drilling_obs_not_redundant,\n",
    "    n_jobs=-3\n",
    ")\n",
    "rop_simulation = rop_simulator.simulate_feature(feature_name=SIM_FEATURE, partitioner=rop_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SimulationDrawer().draw(data=rop_simulation, title=SIM_FEATURE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation can be used to obtain insight on failure likelihood changes depending on the ROP applied. As an example, the simulation suggests that operating with an ROP above 30ft/h can lead to an incident likelihood above 70%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix: generating the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of simplicity, we use a simplified artificial dataset, it contains 500 observations, each row representing a drilling operation of the past, the target is the occurrence of drill breakdown (incident)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# additional imports\n",
    "from scipy.linalg import toeplitz\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from typing import Union\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def drilling_data_sim():\n",
    "    \n",
    "    # set sample size\n",
    "    n=500\n",
    "\n",
    "    # set seed\n",
    "    np.random.seed(seed=4763546)\n",
    "\n",
    "    # add 6 uncorrelated N(0,1) features, U(-1,1) for non-linear feature and a single surrogate linear feature\n",
    "    col_names = ['TwoFactor1', 'TwoFactor2', 'Linear1', 'Linear2', 'Linear3', 'Noise1']\n",
    "    tmp_data = pd.DataFrame(np.random.normal(size=(n, 6)), columns=col_names)\n",
    "    tmp_data['Nonlinear1'] = pd.Series(np.random.uniform(low=-1.0, high=1.0, size=n))\n",
    "    tmp_data['Linear1_prime'] = tmp_data['Linear1'] + np.random.normal(0, 0.05, size=n)\n",
    "\n",
    "    # generate linear predictor\n",
    "    lp = 8 * tmp_data.TwoFactor1 * tmp_data.TwoFactor2 \\\n",
    "         + tmp_data.Nonlinear1 ** 3 + 2 * np.exp(-6 * (tmp_data.Nonlinear1 - 0.3) ** 2) + \\\n",
    "         2.5 * tmp_data.Linear1 + -1.75 * tmp_data.Linear2 + 4.0 * tmp_data.Linear3\n",
    "\n",
    "    # convert to probability\n",
    "    prob = 1 / (1 + np.exp(-lp))\n",
    "\n",
    "    # generate target\n",
    "    tmp_data['target'] = np.where(prob <= np.random.uniform(size=n), 0, 1)\n",
    "\n",
    "    return tmp_data\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def scale_var(df: pd.DataFrame, \n",
    "              feature_name: str, \n",
    "              min_: Union[int, float]=0, \n",
    "              max_: Union[int, float]=1) -> np.array: \n",
    "    \"\"\"\n",
    "    Takes in a data frame and applies a min-max scaler to given bounds for a single column\n",
    "    \"\"\"\n",
    "    \n",
    "    scaler = MinMaxScaler(feature_range=(min_, max_))\n",
    "    scaled_arr = scaler.fit_transform(df[[feature_name]]).reshape(1, -1)[0]\n",
    "    \n",
    "    return scaled_arr\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def refactor_dataset(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    df.rename({ \n",
    "        \"TwoFactor1\": \"Weight on bit (kg)\", # higher weight --> higher weight will increase risks of danger \n",
    "        \"TwoFactor2\": \"Rotation speed (rpm)\", # Rotation speed of the drilling bit (too fast rotation can lead to overheating, too low rotation renders drilling mnore difficult)         \n",
    "        \"Linear1\": \"Depth of operation (m)\", # lower point of the well\n",
    "        \"Linear1_prime\": \"Hole diameter (m)\", # Diameter of the hole (diameter diminishes as depth increases)\n",
    "        \"Nonlinear1\": \"Mud Flow in (m3/s)\", # Speed of mud circulation\n",
    "        \"Linear2\": \"Mud density (kg/L)\", # need to have equal mud and soil density to avoid well collapse (formation falling in well and blocking pipe) or mud loss (mud flowing in the formation)\n",
    "        \"Linear3\": \"Rate of Penetration (ft/h)\", # higher RoP will provide less time for drilling engineers to observe real time data and adjust drilling parameter set up -> leading to a higher risk of incident (but more economic to drill faster)\n",
    "        \"Noise1\": \"Temperature (C)\", # Temperature at the drilling bit \n",
    "        \"target\": \"Incident\"\n",
    "    }, axis=1, inplace=True)\n",
    "    \n",
    "    scaling_dict = { \n",
    "        'Weight on bit (kg)': [100, 500], \n",
    "        'Rotation speed (rpm)': [900, 15000],\n",
    "        'Rate of Penetration (ft/h)': [10, 40],\n",
    "        'Mud density (kg/L)': [0.5, 4],\n",
    "        'Hole diameter (m)': [0.5, 10], \n",
    "        'Temperature (C)': [0, 100], \n",
    "        'Depth of operation (m)': [0, 1500], \n",
    "        'Mud Flow in (m3/s)': [0, 100],\n",
    "        'Incident': [0, 1]\n",
    "    }\n",
    "\n",
    "    for k,v in scaling_dict.items(): \n",
    "        df.loc[:, k] = scale_var(df, k, v[0], v[1])\n",
    "        \n",
    "    df[\"Inverse Rate of Penetration (h/ft)\"] = 1/df[\"Rate of Penetration (ft/h)\"]\n",
    "        \n",
    "    return df\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# generate and save the data for the example\n",
    "df = drilling_data_sim()\n",
    "df = refactor_dataset(df)\n",
    "df.to_csv(\"sphinx/source/tutorial/water_drilling_classification_data.csv\", sep=\";\", encoding=\"utf-8\", index=False)\n",
    "```"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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