{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d2a08ef6",
   "metadata": {},
   "source": [
    "# LIME explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "767b003e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import trustyai\n",
    "\n",
    "trustyai.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e194eb56",
   "metadata": {},
   "source": [
    "## Simple example\n",
    "\n",
    "We start by defining our black-box model, typically represented by\n",
    "\n",
    "$$\n",
    "f(\\mathbf{x}) = \\mathbf{y}\n",
    "$$\n",
    "\n",
    "Where $\\mathbf{x}=\\{x_1, x_2, \\dots,x_m\\}$ and $\\mathbf{y}=\\{y_1, y_2, \\dots,y_n\\}$.\n",
    "\n",
    "Our example toy model, in this case, takes an all-numerical input $\\mathbf{x}$ and return a $\\mathbf{y}$ of either `true` or `false` if the sum of the $\\mathbf{x}$ components is within a threshold $\\epsilon$ of a point $\\mathbf{C}$, that is:\n",
    "\n",
    "$$\n",
    "f(\\mathbf{x}, \\epsilon, \\mathbf{C})=\\begin{cases}\n",
    "\\text{true},\\qquad \\text{if}\\ \\mathbf{C}-\\epsilon<\\sum_{i=1}^m x_i <\\mathbf{C}+\\epsilon \\\\\n",
    "\\text{false},\\qquad \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "This model is provided in the `TestUtils` module. We instantiate with a $\\mathbf{C}=500$ and $\\epsilon=1.0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd02e320",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.utils import TestUtils\n",
    "\n",
    "center = 10.0\n",
    "epsilon = 2.0\n",
    "\n",
    "model = TestUtils.getSumThresholdModel(center, epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4a15f8b",
   "metadata": {},
   "source": [
    "Next we need to define a **goal**.\n",
    "If our model is $f(\\mathbf{x'})=\\mathbf{y'}$ we are then defining our $\\mathbf{y'}$ and the counterfactual result will be the $\\mathbf{x'}$ which satisfies $f(\\mathbf{x'})=\\mathbf{y'}$.\n",
    "\n",
    "We will define our goal as `true`, that is, the sum is withing the vicinity of a (to be defined) point $\\mathbf{C}$. The goal is a list of `Output` which take the following parameters\n",
    "\n",
    "- The feature name\n",
    "- The feature type\n",
    "- The feature value (wrapped in `Value`)\n",
    "- A confidence threshold, which we will leave at zero (no threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf3f4232",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import output\n",
    "\n",
    "decision = \"inside\"\n",
    "goal = [output(name=decision, dtype=\"bool\", value=True, score=0.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64349c3e",
   "metadata": {},
   "source": [
    "We will now define our initial features, $\\mathbf{x}$. Each feature can be instantiated by using `FeatureFactory` and in this case we want to use numerical features, so we'll use `FeatureFactory.newNumericalFeature`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d688a7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from trustyai.model import feature\n",
    "\n",
    "features = [feature(name=f\"x{i+1}\", dtype=\"number\", value=random.random()*10.0) for i in range(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a562ef68",
   "metadata": {},
   "source": [
    "As we can see, the sum of of the features will not be within $\\epsilon$ (1.0) of $\\mathbf{C}$ (500.0). As such the model prediction will be `false`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48212d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature x1 has value 8.824930054798465\n",
      "Feature x2 has value 4.246070534263824\n",
      "Feature x3 has value 5.512117711918773\n",
      "\n",
      "Features sum is 18.583118300981063\n"
     ]
    }
   ],
   "source": [
    "feature_sum = 0.0\n",
    "for f in features:\n",
    "    value = f.value.as_number()\n",
    "    print(f\"Feature {f.name} has value {value}\")\n",
    "    feature_sum += value\n",
    "print(f\"\\nFeatures sum is {feature_sum}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13001554",
   "metadata": {},
   "source": [
    "We execute the model on the generated input and collect the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a45c0e0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from org.kie.kogito.explainability.model import PredictionInput, PredictionOutput\n",
    "\n",
    "goals = model.predictAsync([PredictionInput(features)]).get()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324cefdf",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We wrap these quantities in a `SimplePrediction`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bb2aac1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from trustyai.model import simple_prediction\n",
    "\n",
    "\n",
    "prediction = simple_prediction(input_features=features, outputs=goals[0].outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb631f9",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can now instantiate the **explainer** itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "115fa89c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\n",
      "SLF4J: Defaulting to no-operation (NOP) logger implementation\n",
      "SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\n"
     ]
    }
   ],
   "source": [
    "from trustyai.explainers import LimeExplainer\n",
    "\n",
    "explainer = LimeExplainer(samples=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd8b2b4",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We generate the **explanation** as a _dict : decision --> saliency_.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b34e26d7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "explanation = explainer.explain(prediction, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f8671f0",
   "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>inside_features</th>\n",
       "      <th>inside_score</th>\n",
       "      <th>inside_value</th>\n",
       "      <th>inside_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x1</td>\n",
       "      <td>0.583312</td>\n",
       "      <td>8.824930</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.246071</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x3</td>\n",
       "      <td>0.804062</td>\n",
       "      <td>5.512118</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  inside_features  inside_score  inside_value  inside_confidence\n",
       "0              x1      0.583312      8.824930                0.0\n",
       "1              x2      0.000000      4.246071                0.0\n",
       "2              x3      0.804062      5.512118                0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanation.as_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32e4272",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We inspect the saliency scores assigned by LIME to each feature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c53fcc5",
   "metadata": {},
   "source": [
    "We generate the saliency graph with the builtin method `plot(decision)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59ddb1d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation.plot(decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169d975c",
   "metadata": {},
   "source": [
    "## Using Python models\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5511214d",
   "metadata": {},
   "source": [
    "We will now show how to use a custom Python model with TrustyAI LIME implementation.\n",
    "\n",
    "The model will be an XGBoost one trained with the `credit-bias` dataset.\n",
    "\n",
    "For convenience, the model is pre-trained and serialised with `joblib` so that for this example we simply need to deserialised it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "754a7737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.07, max_delta_step=0, max_depth=8,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=200, n_jobs=12, num_parallel_tree=1, random_state=27,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=0.9861206227457426,\n",
      "              seed=27, subsample=1, tree_method='exact', validate_parameters=1,\n",
      "              verbosity=None)\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "xg_model = joblib.load(\"models/credit-bias-xgboost.joblib\")\n",
    "print(xg_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986b8eb7",
   "metadata": {},
   "source": [
    "This model has as a single **output** a boolean `PaidLoan`, which will predict whether a certain loan applicant will repay the loan in time or not. The model is slightly more complex than the previous examples, with **input** features:\n",
    "\n",
    "|Input feature         | Type    | Note        |\n",
    "|----------------------|---------|-------------|\n",
    "|`NewCreditCustomer`   |boolean  ||\n",
    "|`Amount`              |numerical||\n",
    "|`Interest`            |numerical||\n",
    "|`LoanDuration`        |numerical|In months|\n",
    "|`Education`           |numerical|Level (1, 2, 3..)|\n",
    "|`NrOfDependants`      |numerical|Integer|\n",
    "|`EmploymentDurationCurrentEmployer`|numerical|Integer (years)|\n",
    "|`IncomeFromPrincipalEmployer`|numerical||\n",
    "|`IncomeFromPension`   |numerical||\n",
    "|`IncomeFromFamilyAllowance`|numerical||\n",
    "|`IncomeFromSocialWelfare`|numerical||\n",
    "|`IncomeFromLeavePay`|numerical||\n",
    "|`IncomeFromChildSupport`|numerical||\n",
    "|`IncomeOther`|numerical||\n",
    "|`ExistingLiabilities`|numerical|integer|\n",
    "|`RefinanceLiabilities`|numerical|integer|\n",
    "|`DebtToIncome`|numerical||\n",
    "|`FreeCash`|numerical||\n",
    "|`CreditScoreEeMini`|numerical|integer|\n",
    "|`NoOfPreviousLoansBeforeLoan`|numerical|integer|\n",
    "|`AmountOfPreviousLoansBeforeLoan`|numerical||\n",
    "|`PreviousRepaymentsBeforeLoan`|numerical||\n",
    "|`PreviousEarlyRepaymentsBefoleLoan`|numerical||\n",
    "|`PreviousEarlyRepaymentsCountBeforeLoan`|numerical|integer|\n",
    "|`Council_house`|boolean||\n",
    "|`Homeless`|boolean||\n",
    "|`Joint_ownership`|boolean||\n",
    "|`Joint_tenant`|boolean||\n",
    "|`Living_with_parents`|boolean||\n",
    "|`Mortgage`|boolean||\n",
    "|`Other`|boolean||\n",
    "|`Owner`|boolean||\n",
    "|`Owner_with_encumbrance`|boolean||\n",
    "|`Tenant`|boolean||\n",
    "|`Entrepreneur`|boolean||\n",
    "|`Fully`|boolean||\n",
    "|`Partially`|boolean||\n",
    "|`Retiree`|boolean||\n",
    "|`Self_employed`|boolean||\n",
    "\n",
    "We will start by testing the model with an input we are quite sure (from the original data) that will be predicted as `false`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33ed8c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [[False,2125.0,20.97,60,4.0,0.0,6.0,0.0,301.0,0.0,53.0,0.0,0.0,0.0,8,6,26.29,10.92,1000.0,1.0,500.0,590.95,0.0,0.0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806f119b",
   "metadata": {},
   "source": [
    "We can see that this application will be rejected with a probability of $\\sim77\\%$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ebf11020",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.7770493  0.22295067]]\n",
      "Paid loan is predicted as: [False]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(xg_model.predict_proba(np.array(x)))\n",
    "print(f\"Paid loan is predicted as: {xg_model.predict(np.array(x))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a09b584",
   "metadata": {},
   "source": [
    "We will now prepare the XGBoost model to be used from the TrustyAI counterfactual engine.\n",
    "\n",
    "To do so, we simply need to first create a prediction function which takes:\n",
    "\n",
    "- A list of `PredictionInput` as inputs\n",
    "- A list of `PredictionOutput` as outputs\n",
    "\n",
    "If these two conditions are met, the actual inner working of this method can be anything (including calling a XGBoost Python model for prediction as in our case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "995adddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.kie.kogito.explainability.model import PredictionInput, PredictionOutput\n",
    "\n",
    "def predict(inputs):\n",
    "    values = [_feature.value.as_obj() for _feature in inputs[0].features]\n",
    "    result = xg_model.predict_proba(np.array([values]))\n",
    "    false_prob, true_prob = result[0]\n",
    "    if false_prob > true_prob:\n",
    "        _prediction = (False, false_prob)\n",
    "    else:\n",
    "        _prediction = (True, true_prob)\n",
    "    _output = output(name=\"PaidLoan\", dtype=\"bool\", value=_prediction[0], score=_prediction[1])\n",
    "    return [PredictionOutput([_output])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811d2e6c",
   "metadata": {},
   "source": [
    "Once the prediction method is created, we wrap in a `PredictionProvider` class.\n",
    "\n",
    "This class takes care of all the JVM's asynchronous plumbing for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5dfde4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import Model\n",
    "\n",
    "cb_model = Model(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ddc56a",
   "metadata": {},
   "source": [
    "We will now express the previous inputs (`x`) in terms of `Feature`s, so that we might use it for the counterfactual search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "358ce263",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_feature(name, _value):\n",
    "    if isinstance(_value, bool):\n",
    "        return feature(name=name, dtype=\"bool\", value=_value)\n",
    "    else:\n",
    "        return feature(name=name, dtype=\"number\", value=_value)\n",
    "\n",
    "features = [make_feature(p[0], p[1]) for p in [(\"NewCreditCustomer\", False),\n",
    "        (\"Amount\", 2125.0),\n",
    "        (\"Interest\", 20.97),\n",
    "        (\"LoanDuration\", 60.0),\n",
    "        (\"Education\", 4.0),\n",
    "        (\"NrOfDependants\", 0.0),\n",
    "        (\"EmploymentDurationCurrentEmployer\", 6.0),\n",
    "        (\"IncomeFromPrincipalEmployer\", 0.0),\n",
    "        (\"IncomeFromPension\", 301.0),\n",
    "        (\"IncomeFromFamilyAllowance\", 0.0),\n",
    "        (\"IncomeFromSocialWelfare\", 53.0),\n",
    "        (\"IncomeFromLeavePay\", 0.0),\n",
    "        (\"IncomeFromChildSupport\", 0.0),\n",
    "        (\"IncomeOther\", 0.0),\n",
    "        (\"ExistingLiabilities\", 8.0),\n",
    "        (\"RefinanceLiabilities\", 6.0),\n",
    "        (\"DebtToIncome\", 26.29),\n",
    "        (\"FreeCash\", 10.92),\n",
    "        (\"CreditScoreEeMini\", 1000.0),\n",
    "        (\"NoOfPreviousLoansBeforeLoan\", 1.0),\n",
    "        (\"AmountOfPreviousLoansBeforeLoan\", 500.0),\n",
    "        (\"PreviousRepaymentsBeforeLoan\", 590.95),\n",
    "        (\"PreviousEarlyRepaymentsBefoleLoan\", 0.0),\n",
    "        (\"PreviousEarlyRepaymentsCountBeforeLoan\", 0.0),\n",
    "        (\"Council_house\", False),\n",
    "        (\"Homeless\", False),\n",
    "        (\"Joint_ownership\", False),\n",
    "        (\"Joint_tenant\", False),\n",
    "        (\"Living_with_parents\", False),\n",
    "        (\"Mortgage\", False),\n",
    "        (\"Other\", False),\n",
    "        (\"Owner\", False),\n",
    "        (\"Owner_with_encumbrance\", True),\n",
    "        (\"Tenant\", True),\n",
    "        (\"Entrepreneur\", False),\n",
    "        (\"Fully\", False),\n",
    "        (\"Partially\", False),\n",
    "        (\"Retiree\", True),\n",
    "        (\"Self_employed\", False)]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82cb625",
   "metadata": {},
   "source": [
    "We can confirm now, with the newly created `PredictionProvider` model that this input will lead to a `false` `PaidLoan` prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a370f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Output{value=false, type=boolean, score=0.7835956811904907, name='PaidLoan'}'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = cb_model.predictAsync([PredictionInput(features)]).get()\n",
    "prediction[0].outputs[0].toString()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61f25e5",
   "metadata": {},
   "source": [
    "We generate a prediction to be passed to the LIME explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3bb1be05",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_obj = simple_prediction(input_features=features, outputs=prediction[0].outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1cf07f",
   "metadata": {},
   "source": [
    "We execute the LIME explainer on the XGBoost model and prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4918cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_explainer = LimeExplainer(samples=100, perturbations=2, seed=23, normalise_weights=False)\n",
    "cb_explanation = cb_explainer.explain(prediction_obj, cb_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "549406d8",
   "metadata": {},
   "source": [
    "We output the top 2 most important features for the prediction outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc585f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FeatureImportance{feature=Feature{name='Education', type=number, value=4.0}, score=3.282586466080571, confidence= +/-0.0}\n",
      "FeatureImportance{feature=Feature{name='NrOfDependants', type=number, value=0.0}, score=-1.864816175288724, confidence= +/-0.0}\n"
     ]
    }
   ],
   "source": [
    "for f in cb_explanation.map()['PaidLoan'].getTopFeatures(2):\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49f49b52",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>PaidLoan_features</th>\n",
       "      <th>PaidLoan_score</th>\n",
       "      <th>PaidLoan_value</th>\n",
       "      <th>PaidLoan_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NewCreditCustomer</td>\n",
       "      <td>-1.605224</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amount</td>\n",
       "      <td>0.484565</td>\n",
       "      <td>2125.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Interest</td>\n",
       "      <td>0.165306</td>\n",
       "      <td>20.97</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LoanDuration</td>\n",
       "      <td>-0.768934</td>\n",
       "      <td>60.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Education</td>\n",
       "      <td>3.282586</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NrOfDependants</td>\n",
       "      <td>-1.864816</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EmploymentDurationCurrentEmployer</td>\n",
       "      <td>1.196610</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IncomeFromPrincipalEmployer</td>\n",
       "      <td>0.812697</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IncomeFromPension</td>\n",
       "      <td>-0.300951</td>\n",
       "      <td>301.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IncomeFromFamilyAllowance</td>\n",
       "      <td>-1.560764</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IncomeFromSocialWelfare</td>\n",
       "      <td>-0.010848</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IncomeFromLeavePay</td>\n",
       "      <td>-0.129406</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IncomeFromChildSupport</td>\n",
       "      <td>1.094789</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IncomeOther</td>\n",
       "      <td>0.945468</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ExistingLiabilities</td>\n",
       "      <td>-0.147790</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RefinanceLiabilities</td>\n",
       "      <td>0.578735</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DebtToIncome</td>\n",
       "      <td>0.562596</td>\n",
       "      <td>26.29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FreeCash</td>\n",
       "      <td>-1.241136</td>\n",
       "      <td>10.92</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CreditScoreEeMini</td>\n",
       "      <td>1.528180</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NoOfPreviousLoansBeforeLoan</td>\n",
       "      <td>0.443888</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AmountOfPreviousLoansBeforeLoan</td>\n",
       "      <td>-0.881605</td>\n",
       "      <td>500.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PreviousRepaymentsBeforeLoan</td>\n",
       "      <td>-1.143805</td>\n",
       "      <td>590.95</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PreviousEarlyRepaymentsBefoleLoan</td>\n",
       "      <td>0.162419</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PreviousEarlyRepaymentsCountBeforeLoan</td>\n",
       "      <td>1.095744</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Council_house</td>\n",
       "      <td>-0.724558</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Homeless</td>\n",
       "      <td>-0.492904</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Joint_ownership</td>\n",
       "      <td>1.584465</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Joint_tenant</td>\n",
       "      <td>0.420845</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Living_with_parents</td>\n",
       "      <td>-0.875883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mortgage</td>\n",
       "      <td>-1.583980</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Other</td>\n",
       "      <td>-0.253131</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0.159048</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Owner_with_encumbrance</td>\n",
       "      <td>-0.080871</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Tenant</td>\n",
       "      <td>0.844265</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>1.417670</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Fully</td>\n",
       "      <td>-0.036086</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Partially</td>\n",
       "      <td>-0.815799</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Retiree</td>\n",
       "      <td>-0.386546</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Self_employed</td>\n",
       "      <td>-1.528299</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         PaidLoan_features  PaidLoan_score  PaidLoan_value  \\\n",
       "0                        NewCreditCustomer       -1.605224            0.00   \n",
       "1                                   Amount        0.484565         2125.00   \n",
       "2                                 Interest        0.165306           20.97   \n",
       "3                             LoanDuration       -0.768934           60.00   \n",
       "4                                Education        3.282586            4.00   \n",
       "5                           NrOfDependants       -1.864816            0.00   \n",
       "6        EmploymentDurationCurrentEmployer        1.196610            6.00   \n",
       "7              IncomeFromPrincipalEmployer        0.812697            0.00   \n",
       "8                        IncomeFromPension       -0.300951          301.00   \n",
       "9                IncomeFromFamilyAllowance       -1.560764            0.00   \n",
       "10                 IncomeFromSocialWelfare       -0.010848           53.00   \n",
       "11                      IncomeFromLeavePay       -0.129406            0.00   \n",
       "12                  IncomeFromChildSupport        1.094789            0.00   \n",
       "13                             IncomeOther        0.945468            0.00   \n",
       "14                     ExistingLiabilities       -0.147790            8.00   \n",
       "15                    RefinanceLiabilities        0.578735            6.00   \n",
       "16                            DebtToIncome        0.562596           26.29   \n",
       "17                                FreeCash       -1.241136           10.92   \n",
       "18                       CreditScoreEeMini        1.528180         1000.00   \n",
       "19             NoOfPreviousLoansBeforeLoan        0.443888            1.00   \n",
       "20         AmountOfPreviousLoansBeforeLoan       -0.881605          500.00   \n",
       "21            PreviousRepaymentsBeforeLoan       -1.143805          590.95   \n",
       "22       PreviousEarlyRepaymentsBefoleLoan        0.162419            0.00   \n",
       "23  PreviousEarlyRepaymentsCountBeforeLoan        1.095744            0.00   \n",
       "24                           Council_house       -0.724558            0.00   \n",
       "25                                Homeless       -0.492904            0.00   \n",
       "26                         Joint_ownership        1.584465            0.00   \n",
       "27                            Joint_tenant        0.420845            0.00   \n",
       "28                     Living_with_parents       -0.875883            0.00   \n",
       "29                                Mortgage       -1.583980            0.00   \n",
       "30                                   Other       -0.253131            0.00   \n",
       "31                                   Owner        0.159048            0.00   \n",
       "32                  Owner_with_encumbrance       -0.080871            1.00   \n",
       "33                                  Tenant        0.844265            1.00   \n",
       "34                            Entrepreneur        1.417670            0.00   \n",
       "35                                   Fully       -0.036086            0.00   \n",
       "36                               Partially       -0.815799            0.00   \n",
       "37                                 Retiree       -0.386546            1.00   \n",
       "38                           Self_employed       -1.528299            0.00   \n",
       "\n",
       "    PaidLoan_confidence  \n",
       "0                   0.0  \n",
       "1                   0.0  \n",
       "2                   0.0  \n",
       "3                   0.0  \n",
       "4                   0.0  \n",
       "5                   0.0  \n",
       "6                   0.0  \n",
       "7                   0.0  \n",
       "8                   0.0  \n",
       "9                   0.0  \n",
       "10                  0.0  \n",
       "11                  0.0  \n",
       "12                  0.0  \n",
       "13                  0.0  \n",
       "14                  0.0  \n",
       "15                  0.0  \n",
       "16                  0.0  \n",
       "17                  0.0  \n",
       "18                  0.0  \n",
       "19                  0.0  \n",
       "20                  0.0  \n",
       "21                  0.0  \n",
       "22                  0.0  \n",
       "23                  0.0  \n",
       "24                  0.0  \n",
       "25                  0.0  \n",
       "26                  0.0  \n",
       "27                  0.0  \n",
       "28                  0.0  \n",
       "29                  0.0  \n",
       "30                  0.0  \n",
       "31                  0.0  \n",
       "32                  0.0  \n",
       "33                  0.0  \n",
       "34                  0.0  \n",
       "35                  0.0  \n",
       "36                  0.0  \n",
       "37                  0.0  \n",
       "38                  0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_explanation.as_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7052b9f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cb_explanation.plot('PaidLoan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6466635d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "trustyai-python-module",
   "language": "python",
   "name": "trustyai-python-module"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}