examples/tutorials/HELOC.ipynb

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    "# Credit Approval Tutorial\n",
    "This tutorial illustrates the use of several methods in the AI Explainability 360 Toolkit  to provide different kinds of explanations suited to different users in the context of a credit approval process enabled by machine learning. We use data from the [FICO Explainable Machine Learning Challenge](https://community.fico.com/s/explainable-machine-learning-challenge) as [described below](#intro). The three types of users (a.k.a. consumers) that we consider are a data scientist, who evaluates the machine learning model before deployment, a loan officer, who makes the final decision based on the model's output, and a bank customer, who wants to understand the reasons for their application result. \n",
    "\n",
    "For the [data scientist](#rule-based-models), we present two directly interpretable rule-based models that provide global understanding of their behavior. These models are produced by the [Boolean Rule Column Generation](#BRCG) (BRCG, class `BooleanRuleCG`) and [Logistic Rule Regression](#LogRR) (LogRR, class `LogisticRuleRegression`) algorithms in AIX360. The former yields very simple OR-of-ANDs classification rules while the latter gives weighted combinations of rules that are more accurate and still interpretable.\n",
    "\n",
    "For the [loan officer](#prototypes), we demonstrate a different way of explaining machine learning predictions by showing examples, specifically _prototypes_ or representatives in the training data that are similar to a given loan applicant and receive the same class label. We use the ProtoDash method (class `ProtodashExplainer`) to find these prototypes.\n",
    "\n",
    "For the [bank customer](#contrastive), we consider the Contrastive Explanations Method (CEM, class `CEMExplainer`) for explaining the predictions of black box models to end users. CEM builds upon the popular approach of highlighting features present in the input instance that are responsible for the model's classification. In addition to these, CEM also identifies features that are (minimally) absent in the input instance, but whose presence would have altered the classification.\n",
    "\n",
    "The tutorial is organized around these three types of consumers, following an introduction to the dataset.\n",
    "1. [Introduction to FICO HELOC Dataset](#intro)\n",
    "2. [Data Scientist: Boolean Rules and Logistic Rule Regression models](#rule-based-models)\n",
    "3. [Loan Officer: Similar samples as explanations for predictions based on HELOC Dataset](#prototypes)\n",
    "4. [Customer: Contrastive Explanations for predictions based on HELOC Dataset](#contrastive)"
   ]
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    "<a name=\"intro\"></a>\n",
    "## 1. Introduction to FICO HELOC Dataset\n",
    "\n",
    "The FICO HELOC dataset contains anonymized information about home equity line of credit (HELOC) applications made by real homeowners. A HELOC is a line of credit typically offered by a US bank as a percentage of home equity (the difference between the current market value of a home and the outstanding balance of all liens, e.g. mortgages). The customers in this dataset have requested a credit line in the range of USD 5,000 - 150,000. The machine learning task we are considering is to use the information about the applicant in their credit report to predict whether they will make timely payments over a two year period. The machine learning prediction can then be used to decide whether the homeowner qualifies for a line of credit and, if so, how much credit should be extended. \n",
    "\n",
    "The HELOC dataset and more information about it, including instructions to download, can be found [here](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-3158a=2).\n",
    "\n",
    "The table below reproduces part of the data dictionary that comes with the HELOC dataset, explaining the predictor variables and target variable. For example, NumSatisfactoryTrades is a predictor variable that counts the number of past credit agreements with the applicant, which resulted in on-time payments. The target variable to predict is a binary variable called RiskPerformance. The value “Bad” indicates that an applicant was 90 days past due or worse at least once over a period of 24 months from when the credit account was opened. The value “Good” indicates that they have made their payments without ever being more than 90 days overdue. The relationship between a predictor variable and the target is indicated in the last column of the table. If a predictor variable is monotonically decreasing with respect to probability of bad = 1, it \n",
    "means that as the value of the variable increases, the probability of the loan application being \"Bad\" decreases, i.e. it becomes more \"good\". For example, ExternalRiskEstimate and NumSatisfactoryTrades are shown as monotonically decreasing. Monotonically increasing has the opposite meaning.\n",
    "\n",
    "\n",
    "|Field | Meaning |Monotonicity Constraint (with respect to probability of bad = 1)|\n",
    "|------|---------|----------------------------------------------------------------|\n",
    "|ExternalRiskEstimate |\tConsolidated version of risk markers |Monotonically Decreasing| \n",
    "|MSinceOldestTradeOpen\t| Months Since Oldest Trade Open | Monotonically Decreasing|\n",
    "|MSinceMostRecentTradeOpen | Months Since Most Recent Trade Open |Monotonically Decreasing\n",
    "|AverageMInFile\t| Average Months in File |Monotonically Decreasing|\n",
    "|NumSatisfactoryTrades |\tNumber Satisfactory Trades |Monotonically Decreasing|\n",
    "|NumTrades60Ever2DerogPubRec |\tNumber Trades 60+ Ever |Monotonically Decreasing|\n",
    "|NumTrades90Ever2DerogPubRec | Number Trades 90+ Ever |Monotonically Decreasing| \n",
    "|PercentTradesNeverDelq\t| Percent Trades Never Delinquent|Monotonically Decreasing|\n",
    "|MSinceMostRecentDelq\t| Months Since Most Recent Delinquency|Monotonically Decreasing|\n",
    "|MaxDelq2PublicRecLast12M |\tMax Delq/Public Records Last 12 Months. See tab \"MaxDelq\" for each category|Values 0-7 are monotonically decreasing|\n",
    "|MaxDelqEver |\tMax Delinquency Ever. See tab \"MaxDelq\" for each category|Values 2-8 are monotonically decreasing|\n",
    "|NumTotalTrades\t| Number of Total Trades (total number of credit accounts)|No constraint|\n",
    "|NumTradesOpeninLast12M\t| Number of Trades Open in Last 12 Months|Monotonically Increasing| \n",
    "|PercentInstallTrades\t| Percent Installment Trades|No constraint|\n",
    "|MSinceMostRecentInqexcl7days |\tMonths Since Most Recent Inq excl 7days|Monotonically Decreasing| \n",
    "|NumInqLast6M\t| Number of Inq Last 6 Months|Monotonically Increasing|\n",
    "|NumInqLast6Mexcl7days\t| Number of Inq Last 6 Months excl 7days. Excluding the last 7 days removes inquiries that are likely due to price comparision shopping. |Monotonically Increasing|\n",
    "|NetFractionRevolvingBurden\t| Net Fraction Revolving Burden. This is revolving balance divided by credit limit |Monotonically Increasing|\n",
    "|NetFractionInstallBurden\t| Net Fraction Installment Burden. This is installment balance divided by original loan amount |Monotonically Increasing| \n",
    "|NumRevolvingTradesWBalance\t| Number Revolving Trades with Balance |No constraint|\n",
    "|NumInstallTradesWBalance\t| Number Installment Trades with Balance |No constraint|\n",
    "|NumBank2NatlTradesWHighUtilization\t| Number Bank/Natl Trades w high utilization ratio |Monotonically Increasing|\n",
    "|PercentTradesWBalance\t| Percent Trades with Balance |No constraint\n",
    "|RiskPerformance\t| Paid as negotiated flag (12-36 Months). String of Good and Bad | Target |\n",
    "\n",
    "\n",
    "#### Storing HELOC dataset to run this notebook\n",
    "- In this notebook, we assume that the HELOC dataset is saved as `./aix360/data/heloc_data/heloc_dataset.csv`, where \".\" is the root directory of the Git repository before running a pip install of aix360 library. \n",
    "- If the data is downloaded after installation, please place the file within the respective folder under site-packages of your virtual environment `path-to-your-virtual-env/lib/python3.6/site-packages/aix360/data/heloc_data/heloc_dataset.csv`\n"
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    "<a name=\"rule-based-models\"></a>\n",
    "## 2. Data scientist: Boolean Rule and Logistic Rule Regression models\n",
    "In evaluating a machine learning model for deployment, a data scientist would ideally like to understand the behavior of the model as a whole, not just in specific instances (e.g. specific loan applicants). This is especially true in regulated industries such as banking where higher standards of explainability may be required. For example, the data scientist may have to present the model to: 1) technical and business managers for review before deployment, 2) a lending expert to compare the model to the expert's knowledge, or 3) a regulator to check for compliance. Furthermore, it is common for a model to be deployed in a different geography than the one it was trained on. A global view of the model may uncover problems with overfitting and poor generalization to other geographies before deployment.\n",
    "\n",
    "Directly interpretable models can provide such global understanding because they have a sufficiently simple form for their workings to be transparent. Below we present two directly interpretable models in the form of a [Boolean rule (BR)](#BRCG) and a [logistic rule regression (LogRR)](#LogRR) model. The former is produced by the Boolean Rule Column Generation (BRCG) algorithm while the latter is a generalized linear rule model (GLRM), both implemented in AIX360. While both models are interpretable, they provide different trade-offs between model simplicity and accuracy in predicting loan repayment. BRCG yields a very simple set of rules that has reasonable accuracy. LogRR achieves higher accuracy, higher even than some uninterpretable models, while retaining the form of a linear model. Its interpretation is enhanced by [plots as demonstrated below](#visualize)."
   ]
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    "### 2.1. Load and process data for BRCG and LogRR\n",
    "We use the `HELOCDataset` class in AIX360 to load the FICO HELOC data as a DataFrame. The setting `custom_preprocessing=nan_preprocessing` converts special values in the data (coded as negative integers) to `np.nan`, which can be handled properly by BRCG and LogRR, as opposed to replacing them with zeros or mean values. The data is then split into training and test sets using a fixed random seed."
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Heloc dataset:  /Users/vijay/DEVJAN22/AIX360/aix360/datasets/../data/heloc_data/heloc_dataset.csv\n"
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       "      <th>8960</th>\n",
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       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
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       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>175.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>117.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <th>AverageMInFile</th>\n",
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       "      <td>69.0</td>\n",
       "      <td>48.0</td>\n",
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       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>63.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>78.0</td>\n",
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       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>63.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>56.0</td>\n",
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       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>16.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
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       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>94.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
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       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>50.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>83.0</td>\n",
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       "                                     8960  8403   1949   4886   4998\n",
       "ExternalRiskEstimate                 64.0  57.0   59.0   65.0   65.0\n",
       "MSinceOldestTradeOpen               175.0  47.0  168.0  228.0  117.0\n",
       "MSinceMostRecentTradeOpen             6.0   9.0    3.0    5.0    7.0\n",
       "AverageMInFile                       97.0  35.0   38.0   69.0   48.0\n",
       "NumSatisfactoryTrades                29.0   5.0   21.0   24.0    7.0\n",
       "NumTrades60Ever2DerogPubRec           9.0   1.0    0.0    3.0    1.0\n",
       "NumTrades90Ever2DerogPubRec           9.0   0.0    0.0    2.0    1.0\n",
       "PercentTradesNeverDelq               63.0  50.0  100.0   85.0   78.0\n",
       "MSinceMostRecentDelq                  2.0  16.0    NaN    3.0   36.0\n",
       "MaxDelq2PublicRecLast12M              4.0   6.0    7.0    0.0    6.0\n",
       "MaxDelqEver                           4.0   5.0    8.0    2.0    4.0\n",
       "NumTotalTrades                       41.0  10.0   21.0   27.0    9.0\n",
       "NumTradesOpeninLast12M                1.0   1.0   12.0    1.0    2.0\n",
       "PercentInstallTrades                 63.0  30.0   38.0   31.0   56.0\n",
       "MSinceMostRecentInqexcl7days          0.0   0.0    0.0    7.0    7.0\n",
       "NumInqLast6M                          1.0   2.0    1.0    0.0    0.0\n",
       "NumInqLast6Mexcl7days                 1.0   2.0    1.0    0.0    0.0\n",
       "NetFractionRevolvingBurden           16.0  66.0   85.0   13.0   54.0\n",
       "NetFractionInstallBurden             94.0  70.0   90.0   66.0   69.0\n",
       "NumRevolvingTradesWBalance            1.0   2.0   10.0    3.0    2.0\n",
       "NumInstallTradesWBalance              1.0   2.0    5.0    2.0    3.0\n",
       "NumBank2NatlTradesWHighUtilization    NaN   0.0    4.0    0.0    1.0\n",
       "PercentTradesWBalance                50.0  57.0   94.0   46.0   83.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Load FICO HELOC data with special values converted to np.nan\n",
    "from aix360.datasets.heloc_dataset import HELOCDataset, nan_preprocessing\n",
    "data = HELOCDataset(custom_preprocessing=nan_preprocessing).data()\n",
    "# Separate target variable\n",
    "y = data.pop('RiskPerformance')\n",
    "\n",
    "# Split data into training and test sets using fixed random seed\n",
    "from sklearn.model_selection import train_test_split\n",
    "dfTrain, dfTest, yTrain, yTest = train_test_split(data, y, random_state=0, stratify=y)\n",
    "dfTrain.head().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BRCG and LogRR require non-binary features to be binarized using the provided `FeatureBinarizer` class. We use the default of nine quantile thresholds (i.e. 10 bins) to binarize ordinal (including continuous-valued) features, include all negations (e.g. '>' comparisons as well as '<='), and also return standardized versions of the original unbinarized ordinal features, which are used by LogRR but not BRCG. Below is the result of binarizing the first 'ExternalRiskEstimate' feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8403</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation   <=                                            >                 \\\n",
       "value     59.0 63.0 66.0 69.0 72.0 75.0 78.0 82.0 86.0 59.0 63.0 66.0 69.0   \n",
       "8960         0    0    1    1    1    1    1    1    1    1    1    0    0   \n",
       "8403         1    1    1    1    1    1    1    1    1    0    0    0    0   \n",
       "1949         1    1    1    1    1    1    1    1    1    0    0    0    0   \n",
       "4886         0    0    1    1    1    1    1    1    1    1    1    0    0   \n",
       "4998         0    0    1    1    1    1    1    1    1    1    1    0    0   \n",
       "\n",
       "operation                           ==  !=  \n",
       "value     72.0 75.0 78.0 82.0 86.0 NaN NaN  \n",
       "8960         0    0    0    0    0   0   1  \n",
       "8403         0    0    0    0    0   0   1  \n",
       "1949         0    0    0    0    0   0   1  \n",
       "4886         0    0    0    0    0   0   1  \n",
       "4998         0    0    0    0    0   0   1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binarize data and also return standardized ordinal features\n",
    "from aix360.algorithms.rbm import FeatureBinarizer\n",
    "fb = FeatureBinarizer(negations=True, returnOrd=True)\n",
    "dfTrain, dfTrainStd = fb.fit_transform(dfTrain)\n",
    "dfTest, dfTestStd = fb.transform(dfTest)\n",
    "dfTrain['ExternalRiskEstimate'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"BRCG\"></a>\n",
    "### 2.2. Run Boolean Rule Column Generation (BRCG)\n",
    "First we consider BRCG, which is designed to produce a very simple OR-of-ANDs rule (known more formally as disjunctive normal form, DNF) or alternatively an AND-of-ORs rule (conjunctive normal form, CNF) to predict whether an applicant will repay the loan on time (Y = 1). For a binary classification problem such as we have here, a DNF rule is equivalent to a *rule set*, where AND clauses in the DNF correspond to individual rules in the rule set. Furthermore, it can be shown that a CNF rule for Y = 1 is equivalent to a DNF rule for Y = 0 [[1]](https://ieeexplore.ieee.org/document/7738856). BRCG is distinguished by its use of the optimization technique of column generation to search the space of possible clauses, which is exponential in size. To learn more about column generation, please see our NeurIPS paper [[2]](http://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation). \n",
    "\n",
    "For this dataset, we find that a CNF rule for Y = 1 (i.e. a DNF for Y = 0, enabled by setting `CNF=True`) is slightly better than a DNF rule for Y = 1. The model complexity parameters `lambda0` and `lambda1` penalize the number of clauses in the rule and the number of conditions in each clause. We use the default values of 1e-3 for `lambda0` and `lambda1` (decreasing them did not increase accuracy here) and leave other parameters at their defaults as well. The model is then trained, evaluated, and printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning CNF rule with complexity parameters lambda0=0.001, lambda1=0.001\n",
      "Initial LP solved\n",
      "Iteration: 1, Objective: 0.2895\n",
      "Iteration: 2, Objective: 0.2895\n",
      "Iteration: 3, Objective: 0.2895\n",
      "Iteration: 4, Objective: 0.2895\n",
      "Iteration: 5, Objective: 0.2864\n",
      "Iteration: 6, Objective: 0.2864\n",
      "Iteration: 7, Objective: 0.2864\n",
      "Training accuracy: 0.719573146021883\n",
      "Test accuracy: 0.696515397082658\n",
      "Predict Y=0 if ANY of the following rules are satisfied, otherwise Y=1:\n",
      "['ExternalRiskEstimate <= 75.00 AND NumSatisfactoryTrades <= 17.00', 'ExternalRiskEstimate <= 72.00 AND NumSatisfactoryTrades > 17.00']\n"
     ]
    }
   ],
   "source": [
    "# Instantiate BRCG with small complexity penalty and large beam search width\n",
    "from aix360.algorithms.rbm import BooleanRuleCG\n",
    "br = BooleanRuleCG(lambda0=1e-3, lambda1=1e-3, CNF=True)\n",
    "\n",
    "# Train, print, and evaluate model\n",
    "br.fit(dfTrain, yTrain)\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Training accuracy:', accuracy_score(yTrain, br.predict(dfTrain)))\n",
    "print('Test accuracy:', accuracy_score(yTest, br.predict(dfTest)))\n",
    "print('Predict Y=0 if ANY of the following rules are satisfied, otherwise Y=1:')\n",
    "print(br.explain()['rules'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned DNF rule for Y = 0 is indeed very simple with only two clauses, each involving the same two features. It is interesting to see that such a rule can already achieve 69.7% accuracy. 'ExternalRiskEstimate' is a consolidated version of some risk markers (higher is better), while 'NumSatisfactoryTrades' is the number of satisfactory credit accounts. It makes sense therefore that for applicants with more than 17 satisfactory accounts, the ExternalRiskEstimate threshold dividing good (Y = 1) and bad (Y = 0) credit risk is slightly lower (more lenient) than for applicants with fewer satisfactory accounts.\n",
    "\n",
    "We note that AIX360 includes only a heuristic beam search version of BRCG. The published version of BRCG [[2]](http://papers.nips.cc/paper/7716-boolean-decision-rules-via-column-generation) (not implemented in AIX360) uses integer programming to yield slightly more complex rules that are also more accurate (close to 72% test accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"LogRR\"></a>\n",
    "### 2.3. Run Logistic Rule Regression (LogRR)\n",
    "Next we consider a LogRR model, which can improve accuracy at the cost of a more complex but still interpretable model. Specifically, LogRR fits a logistic regression model using rule-based features, where column generation is again used to generate promising candidates from the space of all possible rules. Here we are also including unbinarized ordinal features (`useOrd=True`) in addition to rules. Similar to BRCG, the complexity parameters `lambda0`, `lambda1` penalize the number of rules included in the model and the number of conditions in each rule. the The values for `lambda0`, `lambda1` below strike a good balance between accuracy and model complexity, based on our published experience with the FICO HELOC dataset [[3]](http://proceedings.mlr.press/v97/wei19a.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.7426718897744158\n",
      "Test accuracy: 0.7260940032414911\n",
      "Probability of Y=1 is predicted as logistic(z) = 1 / (1 + exp(-z))\n",
      "where z is a linear combination of the following rules/numerical features:\n"
     ]
    },
    {
     "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>rule/numerical feature</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(intercept)</td>\n",
       "      <td>-0.129693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSinceMostRecentInqexcl7days &gt; 0.00</td>\n",
       "      <td>0.680256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ExternalRiskEstimate</td>\n",
       "      <td>0.654176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NetFractionRevolvingBurden</td>\n",
       "      <td>-0.554147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NumSatisfactoryTrades</td>\n",
       "      <td>0.551635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NumInqLast6M</td>\n",
       "      <td>-0.463194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NumBank2NatlTradesWHighUtilization</td>\n",
       "      <td>-0.448368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AverageMInFile &lt;= 52.00</td>\n",
       "      <td>-0.43437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NumRevolvingTradesWBalance &lt;= 5.00</td>\n",
       "      <td>0.421518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MaxDelq2PublicRecLast12M &lt;= 5.00</td>\n",
       "      <td>-0.418161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PercentInstallTrades &gt; 50.00</td>\n",
       "      <td>-0.317599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NumSatisfactoryTrades &lt;= 12.00</td>\n",
       "      <td>-0.312496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MSinceMostRecentDelq &lt;= 21.00</td>\n",
       "      <td>-0.301578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PercentTradesNeverDelq &lt;= 95.00</td>\n",
       "      <td>-0.273951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ExternalRiskEstimate &gt; 75.00</td>\n",
       "      <td>0.263442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AverageMInFile &lt;= 84.00</td>\n",
       "      <td>-0.18216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PercentInstallTrades &gt; 42.00</td>\n",
       "      <td>-0.174236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PercentTradesNeverDelq</td>\n",
       "      <td>0.166512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AverageMInFile</td>\n",
       "      <td>0.150658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NumBank2NatlTradesWHighUtilization &lt;= 0.00</td>\n",
       "      <td>0.135371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MSinceOldestTradeOpen &lt;= 122.00</td>\n",
       "      <td>-0.132653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PercentTradesNeverDelq &lt;= 91.00</td>\n",
       "      <td>-0.117718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NumSatisfactoryTrades &lt;= 17.00</td>\n",
       "      <td>-0.110236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ExternalRiskEstimate &gt; 72.00</td>\n",
       "      <td>0.107625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NumInqLast6M &lt;= 0.00</td>\n",
       "      <td>0.0994006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MSinceOldestTradeOpen &lt;= 146.00</td>\n",
       "      <td>-0.0967865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MSinceMostRecentInqexcl7days &lt;= 0.00</td>\n",
       "      <td>-0.0900478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AverageMInFile &lt;= 61.00</td>\n",
       "      <td>-0.0794819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AverageMInFile &lt;= 76.00</td>\n",
       "      <td>-0.0722781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PercentInstallTrades &lt;= 42.00</td>\n",
       "      <td>0.0661581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NetFractionRevolvingBurden &lt;= 39.00</td>\n",
       "      <td>0.0627333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MSinceOldestTradeOpen &gt; 122.00</td>\n",
       "      <td>0.0602286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NetFractionRevolvingBurden &lt;= 50.00</td>\n",
       "      <td>0.0455194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>MSinceOldestTradeOpen</td>\n",
       "      <td>0.0421194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ExternalRiskEstimate &gt; 69.00</td>\n",
       "      <td>0.0354084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PercentTradesWBalance &lt;= 73.00</td>\n",
       "      <td>-0.0345556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MSinceOldestTradeOpen &gt; 146.00</td>\n",
       "      <td>0.0243619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        rule/numerical feature coefficient\n",
       "0                                  (intercept)   -0.129693\n",
       "1          MSinceMostRecentInqexcl7days > 0.00    0.680256\n",
       "2                         ExternalRiskEstimate    0.654176\n",
       "3                   NetFractionRevolvingBurden   -0.554147\n",
       "4                        NumSatisfactoryTrades    0.551635\n",
       "5                                 NumInqLast6M   -0.463194\n",
       "6           NumBank2NatlTradesWHighUtilization   -0.448368\n",
       "7                      AverageMInFile <= 52.00    -0.43437\n",
       "8           NumRevolvingTradesWBalance <= 5.00    0.421518\n",
       "9             MaxDelq2PublicRecLast12M <= 5.00   -0.418161\n",
       "10                PercentInstallTrades > 50.00   -0.317599\n",
       "11              NumSatisfactoryTrades <= 12.00   -0.312496\n",
       "12               MSinceMostRecentDelq <= 21.00   -0.301578\n",
       "13             PercentTradesNeverDelq <= 95.00   -0.273951\n",
       "14                ExternalRiskEstimate > 75.00    0.263442\n",
       "15                     AverageMInFile <= 84.00    -0.18216\n",
       "16                PercentInstallTrades > 42.00   -0.174236\n",
       "17                      PercentTradesNeverDelq    0.166512\n",
       "18                              AverageMInFile    0.150658\n",
       "19  NumBank2NatlTradesWHighUtilization <= 0.00    0.135371\n",
       "20             MSinceOldestTradeOpen <= 122.00   -0.132653\n",
       "21             PercentTradesNeverDelq <= 91.00   -0.117718\n",
       "22              NumSatisfactoryTrades <= 17.00   -0.110236\n",
       "23                ExternalRiskEstimate > 72.00    0.107625\n",
       "24                        NumInqLast6M <= 0.00   0.0994006\n",
       "25             MSinceOldestTradeOpen <= 146.00  -0.0967865\n",
       "26        MSinceMostRecentInqexcl7days <= 0.00  -0.0900478\n",
       "27                     AverageMInFile <= 61.00  -0.0794819\n",
       "28                     AverageMInFile <= 76.00  -0.0722781\n",
       "29               PercentInstallTrades <= 42.00   0.0661581\n",
       "30         NetFractionRevolvingBurden <= 39.00   0.0627333\n",
       "31              MSinceOldestTradeOpen > 122.00   0.0602286\n",
       "32         NetFractionRevolvingBurden <= 50.00   0.0455194\n",
       "33                       MSinceOldestTradeOpen   0.0421194\n",
       "34                ExternalRiskEstimate > 69.00   0.0354084\n",
       "35              PercentTradesWBalance <= 73.00  -0.0345556\n",
       "36              MSinceOldestTradeOpen > 146.00   0.0243619"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate LRR with good complexity penalties and numerical features\n",
    "from aix360.algorithms.rbm import LogisticRuleRegression\n",
    "lrr = LogisticRuleRegression(lambda0=0.005, lambda1=0.001, useOrd=True)\n",
    "\n",
    "# Train, print, and evaluate model\n",
    "lrr.fit(dfTrain, yTrain, dfTrainStd)\n",
    "print('Training accuracy:', accuracy_score(yTrain, lrr.predict(dfTrain, dfTrainStd)))\n",
    "print('Test accuracy:', accuracy_score(yTest, lrr.predict(dfTest, dfTestStd)))\n",
    "print('Probability of Y=1 is predicted as logistic(z) = 1 / (1 + exp(-z))')\n",
    "print('where z is a linear combination of the following rules/numerical features:')\n",
    "lrr.explain()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test accuracy of LogRR is significantly better than that of BRCG and even better than the neural network in the [Loan Officer](#c2) and [Customer](#contrastive) sections. The LogRR model remains directly interpretable as it is a logistic regression model that uses the 36 rule-based and ordinal features shown above (in addition to an intercept term). Rules are distinguished by having one or more conditions on feature values (e.g. AverageMInFile <= 52.0) while ordinal features are marked by just the feature name without conditions (e.g. ExternalRiskEstimate). Being a linear model, feature importance is naturally given by the model coefficients and thus the list is sorted in order of decreasing coefficient magnitude. The list can be truncated if the user wishes to display fewer features.\n",
    "\n",
    "Since the rules in this LogRR model happen to all be single conditions on individual features, the model contains no interactions between features. It is therefore a kind of [generalized additive model (GAM)](https://en.wikipedia.org/wiki/Generalized_additive_model), i.e. a sum of functions of individual features, where these functions are themselves sums of step function components from rules and linear components from unbinarized ordinal features. Thus a better way to visualize the model is by plotting the univariate functions that make up the GAM, as we do next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"visualize\"></a>\n",
    "### 2.4. Visualize LogRR model as a Generalized Additive Model (GAM)\n",
    "We use the `visualize()` method of `LogisticRuleRegression` to plot the functions in the GAM that corresponds to the LogRR model (more generally, `visualize()` plots the GAM part of a LogRR model, excluding higher-degree rules). The plots show the sizes and shapes of the model's dependences on individual features. These can then be compared to a lending expert's knowledge. In the present case, all plots indicate that the model behaves as we would expect with some interesting nuances. \n",
    "\n",
    "The 36 features shown above involve only 14 of the original features in the data (not including the intercept), as verified below. For example, ExternalRiskEstimate appears in its unbinarized form in row 2 above and also in 3 rules (rows 14, 23, 34)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx = lrr.explain()\n",
    "# Separate 1st-degree rules into (feature, operation, value) to count unique features\n",
    "dfx2 = dfx['rule/numerical feature'].str.split(' ', expand=True)\n",
    "dfx2.columns = ['feature','operation','value']\n",
    "dfx2['feature'].nunique() # includes intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It follows that there are 14 functions to plot, which we organize into semantic groups below to ease interpretation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ExternalRiskEstimate\n",
    "As expected from the BRCG Boolean rule above, 'ExternalRiskEstimate' is an important feature positively correlated with good credit risk. The jumps in the plot indicate that applicants with above average 'ExternalRiskEstimate' (the mean is 72) get an additional boost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['ExternalRiskEstimate']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Credit inquiries\n",
    "The next two plots illustrate the dependence on the applicant's credit inquiries. The first plot shows a significant penalty for having less than one month since the most recent inquiry ('MSinceMostRecentInqexcl7days' = 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['MSinceMostRecentInqexcl7days']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second shows that predicted risk increases with the number of inquiries in the last six months ('NumInqLast6M')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['NumInqLast6M']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Debt level\n",
    "The following four plots relate to the applicant's debt level. 'NetFractionRevolvingBurden' is the ratio of revolving debt (e.g. credit card) balance to credit limit, expressed as a percentage, and has a large negative impact on the probability of good credit. A small fraction of applicants (less than 1%) actually have NetFractionRevolvingBurden greater than 100%, i.e. more revolving debt than their credit limit. This might be investigated further by the data scientist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['NetFractionRevolvingBurden']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second 'NumBank2NatlTradesWHighUtilization' plot shows that the number of accounts (\"trades\") with high utilization (high balance relative to credit limit for each account) also has a large impact, with a drop as soon as one account has high utilization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['NumBank2NatlTradesWHighUtilization']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The third plot shows that the model gives a bonus to applicants who carry balances on no more than five revolving debt accounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['NumRevolvingTradesWBalance']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fourth shows an effect from the percentage of accounts with a balance that is much smaller than those from other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['PercentTradesWBalance']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number and type of accounts\n",
    "The number of \"satisfactory\" accounts (\"trades\") has a significant positive effect on the predicted probability of good credit, with jumps at 12 and 17 accounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['NumSatisfactoryTrades']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, having more than 40% as installment debt accounts (e.g. car loans) is seen as a negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['PercentInstallTrades']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Length of credit history\n",
    "The 'AverageMInFile' plot shows that most of the benefit of having a longer average credit history accrues between average ages of 52 and 84 months (four to seven years). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['AverageMInFile']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar but smaller gains come when the age of the oldest account ('MSinceOldestTradeOpen') exceeds 122 and 146 months (10-12 years)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['MSinceOldestTradeOpen']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delinquencies\n",
    "The last set of plots looks at the effect of delinquencies. The first plot shows that much of the change due to the percentage of accounts that were never delinquent ('PercentTradesNeverDelq') occurs between 90% and 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['PercentTradesNeverDelq']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'MaxDelq2PublicRecLast12M' measures the severity of the applicant's worst delinquency from the last 12 months of the public record. A value of 5 or below indicates that some delinquency has occurred, whether of unknown duration, 30/60/90/120 days delinquent, or a derogatory comment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['MaxDelq2PublicRecLast12M']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the last 'MSinceMostRecentDelq' plot, the effect of the most recent delinquency wears off after 21 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrr.visualize(data, fb, ['MSinceMostRecentDelq']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prototypes\"></a>\n",
    "## 3. Loan Officer: Prototypical explanations for HELOC use case\n",
    "\n",
    "We now show how to generate explanations in the form of selecting prototypical or similar user profiles to an applicant in question that a bank employee such as a loan officer may be interested in. This may help the employee understand the decision of an applicant's HELOC application being accepted or rejected in the context of other similar applications. Note that the selected prototypical applications are profiles that are part of the training set that has been used to train an AI model that predicts good or bad i.e. approved or rejected for these applications. In fact, the method used in this notebook can work even if we are given not just one but a set of user profiles for which we want to find similar profiles from a training dataset. Additionally, the method computes weights for each prototype showcasing its similarity to the user(s) in question.\n",
    "\n",
    "The prototypical explanations in AIX360 are obtained using the Protodash algorithm developed in the following work: [ProtoDash: Fast Interpretable Prototype Selection](https://arxiv.org/abs/1707.01212)\n",
    "\n",
    "We now provide a brief overview of the method. The method takes as input a datapoint (or group of datapoints) that we want to explain with respect to instances in a training set belonging to the same feature space. The method then tries to minimize the maximum mean discrepancy (MMD metric) between the datapoints we want to explain and a prespecified number of instances from the training set that it will select. In other words, it will try to select training instances that have the same distribution as the datapoints we want to explain. The method does greedy selection and has quality guarantees with it also returning importance weights for the chosen prototypical training instances indicative of how similar/representative they are.\n",
    "\n",
    "In this tutorial, we will see two examples of obtaining prototypes, one for a user whose HELOC application was approved and another for a user whose HELOC application was rejected. In each case, we showcase the top five prototypes from the training data along with how similar the feature values were for these prototypes.\n",
    "\n",
    "[Example 1. Obtaining similar samples as explanations for a HELOC applicant predicted as \"Good\"](#good)<br>\n",
    "[Example 2. Obtaining similar samples as explanations for a HELOC applicant predicted as \"Bad\"](#bad)<br>\n",
    "\n",
    "\n",
    "###### Why Protodash?\n",
    "Before we showcase the two examples we provide some motivation for using this method. The method selects applications from the training set that are similar in different ways to the user application we want to explain. For example, a users loan may be rejected justifiably because the number of satisfactory trades he performed were low similar to another rejected user, or because his/her debts were too high similar to a different rejected user. Either of these reasons in isolation may be sufficient for rejection and the method is able to surface a variety of such reasons through the selected prototypes. This is not the case using standard nearest neighbor techniques which use metrics such as euclidean distance, cosine similarity amongst others, where one might get the same type of explanation (i.e. applications with only low number of satisfactory trades). Protodash thus is able to provide a much more well rounded and comprehensive view of why the decision for the applicant may be justifiable.\n",
    "\n",
    "Another benefit of the method is that — since it does distribution matching between the user/users in question and those available in the training set — it could, in principle, be applied also in non-iid settings such as for time series data. Other approaches which find similar profiles using standard distance measures (viz. euclidean, cosine) do not have this property. Additionally, we can also highlight important features for the different prototypes that made them similar to the user/users in question.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import statements\n",
    "\n",
    "Import necessary libraries, frameworks and algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential, Model, load_model, model_from_json\n",
    "from keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "from aix360.algorithms.contrastive import CEMExplainer, KerasClassifier\n",
    "from aix360.algorithms.protodash import ProtodashExplainer\n",
    "from aix360.datasets.heloc_dataset import HELOCDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load HELOC dataset and show sample applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Heloc dataset:  /Users/vijay/DEVJAN22/AIX360/aix360/datasets/../data/heloc_data/heloc_dataset.csv\n",
      "Size of HELOC dataset: (10459, 24)\n",
      "Number of \"Good\" applicants: 5000\n",
      "Number of \"Bad\" applicants: 5459\n",
      "Sample Applicants:\n"
     ]
    },
    {
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       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>55</td>\n",
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       "      <td>73</td>\n",
       "      <td>132</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>138</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
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       "      <td>93</td>\n",
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       "      <td>2</td>\n",
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       "      <td>76</td>\n",
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       "      <td>3</td>\n",
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       "      <td>43</td>\n",
       "      <td>67</td>\n",
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       "      <td>57</td>\n",
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       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>72</td>\n",
       "      <td>51</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>28</td>\n",
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       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>-8</td>\n",
       "      <td>-8</td>\n",
       "      <td>66</td>\n",
       "      <td>83</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>-8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
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       "      <th>NumInstallTradesWBalance</th>\n",
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       "      <td>-8</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>1</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0    1    2    3    4    5     6     7    8    9\n",
       "ExternalRiskEstimate                 55   61   67   66   81   59    54    68   59   61\n",
       "MSinceOldestTradeOpen               144   58   66  169  333  137    88   148  324   79\n",
       "MSinceMostRecentTradeOpen             4   15    5    1   27   11     7     7    2    4\n",
       "AverageMInFile                       84   41   24   73  132   78    37    65  138   36\n",
       "NumSatisfactoryTrades                20    2    9   28   12   31    25    17   24   19\n",
       "NumTrades60Ever2DerogPubRec           3    4    0    1    0    0     0     0    0    0\n",
       "NumTrades90Ever2DerogPubRec           0    4    0    1    0    0     0     0    0    0\n",
       "PercentTradesNeverDelq               83  100  100   93  100   91    92    83   85   95\n",
       "MSinceMostRecentDelq                  2   -7   -7   76   -7    1     9    31    5    5\n",
       "MaxDelq2PublicRecLast12M              3    0    7    6    7    4     4     6    4    4\n",
       "MaxDelqEver                           5    8    8    6    8    6     6     6    6    6\n",
       "NumTotalTrades                       23    7    9   30   12   32    26    18   27   19\n",
       "NumTradesOpeninLast12M                1    0    4    3    0    1     3     1    1    3\n",
       "PercentInstallTrades                 43   67   44   57   25   47    58    44   26   26\n",
       "MSinceMostRecentInqexcl7days          0    0    0    0    0    0     0     0    0    0\n",
       "NumInqLast6M                          0    0    4    5    1    0     4     0    1    6\n",
       "NumInqLast6Mexcl7days                 0    0    4    4    1    0     4     0    1    6\n",
       "NetFractionRevolvingBurden           33    0   53   72   51   62    89    28   68   31\n",
       "NetFractionInstallBurden             -8   -8   66   83   89   93    76    48   -8   86\n",
       "NumRevolvingTradesWBalance            8    0    4    6    3   12     7     2    7    5\n",
       "NumInstallTradesWBalance              1   -8    2    4    1    4     7     2    1    3\n",
       "NumBank2NatlTradesWHighUtilization    1   -8    1    3    0    3     2     2    3    1\n",
       "PercentTradesWBalance                69    0   86   91   80   94   100    40   90   62\n",
       "RiskPerformance                     Bad  Bad  Bad  Bad  Bad  Bad  Good  Good  Bad  Bad"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heloc = HELOCDataset()\n",
    "df = heloc.dataframe()\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 24)\n",
    "pd.set_option('display.width', 1000)\n",
    "print(\"Size of HELOC dataset:\", df.shape)\n",
    "print(\"Number of \\\"Good\\\" applicants:\", np.sum(df['RiskPerformance']=='Good'))\n",
    "print(\"Number of \\\"Bad\\\" applicants:\", np.sum(df['RiskPerformance']=='Bad'))\n",
    "print(\"Sample Applicants:\")\n",
    "df.head(10).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of ExternalRiskEstimate and NumSatisfactoryTrades columns:\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot (example) distributions for two features\n",
    "print(\"Distribution of ExternalRiskEstimate and NumSatisfactoryTrades columns:\")\n",
    "hist = df.hist(column=['ExternalRiskEstimate', 'NumSatisfactoryTrades'], bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"c1\"></a>\n",
    "### Step 1: Process and Normalize HELOC dataset for training\n",
    "\n",
    "We will first process the HELOC dataset before using it to train an NN model that can predict the\n",
    "target variable RiskPerformance. The HELOC dataset is a tabular dataset with numerical values. However, some of the values are negative and need to be filtered. The processed data is stored in the file heloc.npz for easy access. The dataset is also normalized for training.\n",
    "\n",
    "The data processing and the type of model built in this case is different from the Data Scientist persona described above where rule based methods are showcased. This is the reason for going through these steps again for the Loan Officer persona."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Process the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data and split dataset into train/test\n",
    "(Data, x_train, x_test, y_train_b, y_test_b) = heloc.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### b. Normalize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.vstack((x_train, x_test))\n",
    "Zmax = np.max(Z, axis=0)\n",
    "Zmin = np.min(Z, axis=0)\n",
    "\n",
    "#normalize an array of samples to range [-0.5, 0.5]\n",
    "def normalize(V):\n",
    "    VN = (V - Zmin)/(Zmax - Zmin)\n",
    "    VN = VN - 0.5\n",
    "    return(VN)\n",
    "    \n",
    "# rescale a sample to recover original values for normalized values. \n",
    "def rescale(X):\n",
    "    return(np.multiply ( X + 0.5, (Zmax - Zmin) ) + Zmin)\n",
    "\n",
    "N = normalize(Z)\n",
    "xn_train = N[0:x_train.shape[0], :]\n",
    "xn_test  = N[x_train.shape[0]:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"c2\"></a>\n",
    "### Step 2. Define and train a NN classifier\n",
    "\n",
    "Let us now build a loan approval model based on the HELOC dataset.\n",
    "\n",
    "#### a. Define NN architecture\n",
    "We now define the architecture of a 2-layer neural network classifier whose predictions we will try to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn with no softmax\n",
    "def nn_small():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=23, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(2, kernel_initializer='normal'))    \n",
    "    return model    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Train the NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-26-96ecdbbb04d6>:9: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 10)                240       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 262\n",
      "Trainable params: 262\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:From /Users/vijay/opt/anaconda3/envs/aix360env/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train accuracy: 0.7387545704841614\n",
      "Test accuracy: 0.7224473357200623\n"
     ]
    }
   ],
   "source": [
    "# Set random seeds for repeatability\n",
    "np.random.seed(1) \n",
    "tf.set_random_seed(2) \n",
    "\n",
    "class_names = ['Bad', 'Good']\n",
    "\n",
    "# loss function\n",
    "def fn(correct, predicted):\n",
    "    return tf.nn.softmax_cross_entropy_with_logits(labels=correct, logits=predicted)\n",
    "\n",
    "# compile and print model summary\n",
    "nn = nn_small()\n",
    "nn.compile(loss=fn, optimizer='adam', metrics=['accuracy'])\n",
    "nn.summary()\n",
    "\n",
    "\n",
    "# train model or load a trained model\n",
    "TRAIN_MODEL = False\n",
    "\n",
    "if (TRAIN_MODEL): \n",
    "    nn.fit(xn_train, y_train_b, batch_size=128, epochs=500, verbose=1, shuffle=False)\n",
    "    nn.save_weights(\"heloc_nnsmall.h5\")     \n",
    "else:    \n",
    "    nn.load_weights(\"heloc_nnsmall.h5\")\n",
    "        \n",
    "\n",
    "# evaluate model accuracy        \n",
    "score = nn.evaluate(xn_train, y_train_b, verbose=0) #Compute training set accuracy\n",
    "#print('Train loss:', score[0])\n",
    "print('Train accuracy:', score[1])\n",
    "\n",
    "score = nn.evaluate(xn_test, y_test_b, verbose=0) #Compute test set accuracy\n",
    "#print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"good\"></a>\n",
    "### Step 3: Obtain similar samples as explanations for a HELOC applicant predicted as \"Good\" (Example 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Normalize the data and chose a particular applicant, whose profile is displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_train = nn.predict_classes(xn_train) # Use trained neural network to predict train points\n",
    "p_train = p_train.reshape((p_train.shape[0],1))\n",
    "\n",
    "z_train = np.hstack((xn_train, p_train)) # Store (normalized) instances that were predicted as Good\n",
    "z_train_good = z_train[z_train[:,-1]==1, :]\n",
    "\n",
    "zun_train = np.hstack((x_train, p_train)) # Store (unnormalized) instances that were predicted as Good \n",
    "zun_train_good = zun_train[zun_train[:,-1]==1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now consider applicant 8 whose loan was approved. Note that this applicant was also considered for the contrastive explainer, however, we now justify the approved status in a different manner using prototypical examples, which is arguably a better explanation for a bank employee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen Sample: 8\n",
      "Prediction made by the model: Good\n",
      "Prediction probabilities: [[-0.1889221   0.29527372]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0\n",
       "ExternalRiskEstimate                  82\n",
       "MSinceOldestTradeOpen                280\n",
       "MSinceMostRecentTradeOpen             13\n",
       "AverageMInFile                       102\n",
       "NumSatisfactoryTrades                 22\n",
       "NumTrades60Ever2DerogPubRec            0\n",
       "NumTrades90Ever2DerogPubRec            0\n",
       "PercentTradesNeverDelq                91\n",
       "MSinceMostRecentDelq                  26\n",
       "MaxDelq2PublicRecLast12M               6\n",
       "MaxDelqEver                            6\n",
       "NumTotalTrades                        23\n",
       "NumTradesOpeninLast12M                 0\n",
       "PercentInstallTrades                   9\n",
       "MSinceMostRecentInqexcl7days           0\n",
       "NumInqLast6M                           0\n",
       "NumInqLast6Mexcl7days                  0\n",
       "NetFractionRevolvingBurden             3\n",
       "NetFractionInstallBurden               0\n",
       "NumRevolvingTradesWBalance             4\n",
       "NumInstallTradesWBalance               1\n",
       "NumBank2NatlTradesWHighUtilization     1\n",
       "PercentTradesWBalance                 42\n",
       "RiskPerformance                     Good"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 8\n",
    "\n",
    "X = xn_test[idx].reshape((1,) + xn_test[idx].shape)\n",
    "\n",
    "print(\"Chosen Sample:\", idx)\n",
    "print(\"Prediction made by the model:\", class_names[np.argmax(nn.predict_proba(X))])\n",
    "print(\"Prediction probabilities:\", nn.predict_proba(X))\n",
    "print(\"\")\n",
    "\n",
    "# attach the prediction made by the model to X\n",
    "X = np.hstack((X, nn.predict_classes(X).reshape((1,1))))\n",
    "\n",
    "Xun = x_test[idx].reshape((1,) + x_test[idx].shape) \n",
    "dfx = pd.DataFrame.from_records(Xun.astype('double')) # Create dataframe with original feature values\n",
    "dfx[23] = class_names[int(X[0, -1])]\n",
    "dfx.columns = df.columns\n",
    "dfx.transpose()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Find similar applicants predicted as \"good\" using the protodash explainer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -2.0000e+04  4e+00  1e+00  1e+00\n",
      " 1:  1.8207e+01 -2.2985e+05  5e+01  1e+00  1e+00\n",
      " 2: -1.6771e+00 -1.4132e+06  3e+02  1e+00  1e+00\n",
      " 3:  6.4653e-01 -7.7669e+06  2e+03  1e+00  1e+00\n",
      " 4:  9.0963e-01 -1.6930e+08  3e+04  1e+00  1e+00\n",
      " 5:  6.8400e-01 -8.7461e+10  2e+07  1e+00  1e+00\n",
      " 6:  2.1065e+08 -1.7700e+18  2e+18  3e-13  8e-03\n",
      " 7:  2.1065e+08 -1.7700e+16  2e+16  3e-15  1e-03\n",
      " 8:  2.1065e+08 -1.7700e+14  2e+14  1e-16  3e-05\n",
      " 9:  2.1065e+08 -1.7706e+12  2e+12  6e-17  1e-07\n",
      "10:  2.1059e+08 -1.8270e+10  2e+10  3e-16  3e-09\n",
      "11:  2.0548e+08 -7.3263e+08  9e+08  1e-16  4e-09\n",
      "12:  5.4547e+06 -5.0769e+08  5e+08  2e-16  4e-10\n",
      "13:  2.4579e+06 -1.0151e+07  1e+07  3e-16  1e-11\n",
      "14:  3.9731e+05 -4.8259e+05  9e+05  8e-17  6e-13\n",
      "15:  5.6807e+04 -6.2926e+04  1e+05  1e-16  2e-13\n",
      "16:  8.0641e+03 -9.1700e+03  2e+04  3e-16  1e-14\n",
      "17:  1.1237e+03 -1.3430e+03  2e+03  4e-16  1e-14\n",
      "18:  1.4817e+02 -2.0491e+02  4e+02  2e-16  8e-15\n",
      "19:  1.5650e+01 -3.4597e+01  5e+01  2e-16  2e-15\n",
      "20: -6.5180e-01 -7.5158e+00  7e+00  3e-16  6e-16\n",
      "21: -2.1215e+00 -2.8262e+00  7e-01  2e-16  1e-16\n",
      "22: -2.2224e+00 -2.3257e+00  1e-01  2e-16  3e-17\n",
      "23: -2.2551e+00 -2.2713e+00  2e-02  9e-17  7e-17\n",
      "24: -2.2583e+00 -2.2599e+00  2e-03  4e-16  7e-17\n",
      "25: -2.2584e+00 -2.2585e+00  5e-05  6e-17  1e-16\n",
      "26: -2.2584e+00 -2.2584e+00  5e-07  2e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -3.0000e+04  6e+00  1e+00  1e+00\n",
      " 1:  3.0722e+01 -4.4267e+05  9e+01  1e+00  1e+00\n",
      " 2: -1.6074e+00 -1.6114e+06  3e+02  1e+00  1e+00\n",
      " 3:  1.4698e+00 -5.8978e+06  1e+03  1e+00  1e+00\n",
      " 4:  5.1359e+00 -5.6757e+07  1e+04  1e+00  1e+00\n",
      " 5:  8.8032e+00 -6.9908e+09  1e+06  1e+00  1e+00\n",
      " 6:  1.8944e+08 -1.6526e+17  2e+17  7e-13  3e-04\n",
      " 7:  1.8944e+08 -1.6526e+15  2e+15  7e-15  8e-05\n",
      " 8:  1.8944e+08 -1.6526e+13  2e+13  7e-17  1e-06\n",
      " 9:  1.8943e+08 -1.6612e+11  2e+11  2e-16  2e-08\n",
      "10:  1.8825e+08 -2.5115e+09  3e+09  7e-17  4e-10\n",
      "11:  1.2280e+08 -5.4548e+08  7e+08  2e-16  4e-08\n",
      "12:  1.8756e+07 -9.4847e+07  1e+08  2e-16  3e-12\n",
      "13:  3.6747e+06 -5.2922e+06  9e+06  2e-16  3e-13\n",
      "14:  5.3007e+05 -5.8496e+05  1e+06  2e-16  3e-13\n",
      "15:  7.5911e+04 -8.5173e+04  2e+05  2e-16  7e-14\n",
      "16:  1.0792e+04 -1.2212e+04  2e+04  3e-16  2e-14\n",
      "17:  1.5104e+03 -1.7838e+03  3e+03  4e-17  9e-15\n",
      "18:  2.0196e+02 -2.6962e+02  5e+02  2e-16  6e-15\n",
      "19:  2.2751e+01 -4.4473e+01  7e+01  3e-16  1e-15\n",
      "20:  1.6485e-01 -9.1332e+00  9e+00  2e-16  1e-15\n",
      "21: -2.0349e+00 -3.0759e+00  1e+00  1e-16  2e-16\n",
      "22: -2.1758e+00 -2.4046e+00  2e-01  6e-17  5e-17\n",
      "23: -2.2521e+00 -2.3210e+00  7e-02  1e-16  2e-16\n",
      "24: -2.2594e+00 -2.2652e+00  6e-03  1e-16  1e-16\n",
      "25: -2.2601e+00 -2.2604e+00  3e-04  6e-17  6e-17\n",
      "26: -2.2601e+00 -2.2601e+00  3e-06  5e-17  1e-16\n",
      "27: -2.2601e+00 -2.2601e+00  3e-08  2e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -0.0000e+00 -4.0000e+04  8e+00  1e+00  1e+00\n",
      " 1:  4.4367e+01 -7.1824e+05  1e+02  1e+00  1e+00\n",
      " 2: -2.0468e+00 -3.1903e+06  7e+02  1e+00  1e+00\n",
      " 3:  1.2538e+01 -1.4991e+07  3e+03  1e+00  1e+00\n",
      " 4:  1.8503e+01 -3.6431e+08  7e+04  1e+00  1e+00\n",
      " 5:  1.4872e+01 -4.6590e+11  1e+08  1e+00  1e+00\n",
      " 6:  1.8484e+08 -7.2574e+18  7e+18  3e-13  1e-02\n",
      " 7:  1.8484e+08 -7.2574e+16  7e+16  3e-15  8e-03\n",
      " 8:  1.8484e+08 -7.2574e+14  7e+14  1e-16  1e-04\n",
      " 9:  1.8484e+08 -7.2586e+12  7e+12  2e-16  1e-06\n",
      "10:  1.8482e+08 -7.3749e+10  7e+10  1e-16  2e-08\n",
      "11:  1.8327e+08 -1.8914e+09  2e+09  1e-16  5e-10\n",
      "12:  6.6101e+07 -3.5884e+08  4e+08  1e-16  1e-08\n",
      "13:  1.4131e+07 -2.4415e+07  4e+07  3e-16  5e-10\n",
      "14:  2.0607e+06 -2.3295e+06  4e+06  2e-16  5e-13\n",
      "15:  2.9628e+05 -3.3354e+05  6e+05  1e-16  1e-13\n",
      "16:  4.2328e+04 -4.7437e+04  9e+04  2e-16  6e-14\n",
      "17:  5.9948e+03 -6.8520e+03  1e+04  2e-16  2e-14\n",
      "18:  8.3044e+02 -1.0092e+03  2e+03  2e-16  1e-14\n",
      "19:  1.0739e+02 -1.5582e+02  3e+02  2e-16  3e-15\n",
      "20:  1.0233e+01 -2.7124e+01  4e+01  2e-16  9e-16\n",
      "21: -1.3211e+00 -6.3304e+00  5e+00  1e-16  4e-16\n",
      "22: -2.2395e+00 -2.6971e+00  5e-01  1e-16  1e-16\n",
      "23: -2.2596e+00 -2.2756e+00  2e-02  1e-16  1e-16\n",
      "24: -2.2616e+00 -2.2630e+00  1e-03  1e-16  9e-17\n",
      "25: -2.2617e+00 -2.2617e+00  2e-05  2e-16  5e-17\n",
      "26: -2.2617e+00 -2.2617e+00  2e-07  1e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -5.0000e+04  1e+01  1e+00  1e+00\n",
      " 1:  6.4530e+01 -1.0740e+06  2e+02  1e+00  1e+00\n",
      " 2: -1.0387e+00 -3.8672e+06  8e+02  1e+00  1e+00\n",
      " 3:  1.7593e+01 -1.4129e+07  3e+03  1e+00  1e+00\n",
      " 4:  2.4754e+01 -1.7515e+08  4e+04  1e+00  1e+00\n",
      " 5:  2.8355e+01 -4.2312e+10  9e+06  1e+00  1e+00\n",
      " 6:  2.6599e+08 -9.5334e+17  1e+18  4e-13  1e-03\n",
      " 7:  2.6599e+08 -9.5334e+15  1e+16  4e-15  4e-04\n",
      " 8:  2.6599e+08 -9.5336e+13  1e+14  1e-16  5e-06\n",
      " 9:  2.6599e+08 -9.5545e+11  1e+12  1e-16  1e-07\n",
      "10:  2.6558e+08 -1.1640e+10  1e+10  2e-16  2e-09\n",
      "11:  2.4039e+08 -2.0164e+09  2e+09  2e-16  3e-08\n",
      "12:  2.9390e+07 -1.5952e+09  2e+09  3e-16  1e-08\n",
      "13:  1.0754e+07 -3.7180e+07  5e+07  1e-16  3e-10\n",
      "14:  1.6461e+06 -1.9697e+06  4e+06  3e-16  4e-12\n",
      "15:  2.3560e+05 -2.6053e+05  5e+05  2e-16  2e-13\n",
      "16:  3.3615e+04 -3.7633e+04  7e+04  2e-16  4e-14\n",
      "17:  4.7521e+03 -5.4485e+03  1e+04  2e-16  2e-14\n",
      "18:  6.5532e+02 -8.0583e+02  1e+03  2e-16  1e-14\n",
      "19:  8.3449e+01 -1.2556e+02  2e+02  3e-16  3e-15\n",
      "20:  7.2389e+00 -2.2354e+01  3e+01  4e-16  2e-15\n",
      "21: -1.5947e+00 -5.4973e+00  4e+00  2e-16  4e-16\n",
      "22: -2.2383e+00 -2.5578e+00  3e-01  2e-16  7e-17\n",
      "23: -2.2526e+00 -2.2903e+00  4e-02  2e-16  1e-16\n",
      "24: -2.2616e+00 -2.2685e+00  7e-03  3e-16  2e-16\n",
      "25: -2.2622e+00 -2.2630e+00  8e-04  1e-16  1e-16\n",
      "26: -2.2622e+00 -2.2622e+00  2e-05  1e-16  1e-16\n",
      "27: -2.2622e+00 -2.2622e+00  2e-07  9e-17  2e-16\n",
      "Optimal solution found.\n"
     ]
    }
   ],
   "source": [
    "explainer = ProtodashExplainer()\n",
    "(W, S, setValues) = explainer.explain(X, z_train_good, m=5) # Return weights W, Prototypes S and objective function values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. Display similar applicant user profiles and the extent to which they are similar to the chosen applicant as indicated by the last row in the table below labelled as \"Weight\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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>ExternalRiskEstimate</th>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>223</td>\n",
       "      <td>379</td>\n",
       "      <td>338</td>\n",
       "      <td>789</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>13</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>87</td>\n",
       "      <td>257</td>\n",
       "      <td>109</td>\n",
       "      <td>102</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>91</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.730222</td>\n",
       "      <td>0.0690562</td>\n",
       "      <td>0.0978593</td>\n",
       "      <td>0.0498047</td>\n",
       "      <td>0.0530578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0          1          2          3          4\n",
       "ExternalRiskEstimate                      85         89         77         83         73\n",
       "MSinceOldestTradeOpen                    223        379        338        789        230\n",
       "MSinceMostRecentTradeOpen                 13        156          2          6          5\n",
       "AverageMInFile                            87        257        109        102         89\n",
       "NumSatisfactoryTrades                     23          3         16         41         61\n",
       "NumTrades60Ever2DerogPubRec                0          0          2          0          0\n",
       "NumTrades90Ever2DerogPubRec                0          0          2          0          0\n",
       "PercentTradesNeverDelq                    91        100         90        100        100\n",
       "MSinceMostRecentDelq                      26          0         65          0          0\n",
       "MaxDelq2PublicRecLast12M                   6          7          6          7          6\n",
       "MaxDelqEver                                6          8          2          8          7\n",
       "NumTotalTrades                            26          3         21         41         37\n",
       "NumTradesOpeninLast12M                     0          0          1          1          3\n",
       "PercentInstallTrades                       9         33         14         17         18\n",
       "MSinceMostRecentInqexcl7days               1          0          0          0          0\n",
       "NumInqLast6M                               1          0          1          1          2\n",
       "NumInqLast6Mexcl7days                      1          0          1          0          2\n",
       "NetFractionRevolvingBurden                 4          0          2          1         59\n",
       "NetFractionInstallBurden                   0          0          0          0         72\n",
       "NumRevolvingTradesWBalance                 4          0          1          3          9\n",
       "NumInstallTradesWBalance                   1          0          1          0          1\n",
       "NumBank2NatlTradesWHighUtilization         0          0          0          1          7\n",
       "PercentTradesWBalance                     50          0         22         23         53\n",
       "RiskPerformance                         Good       Good       Good       Good       Good\n",
       "Weight                              0.730222  0.0690562  0.0978593  0.0498047  0.0530578"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = pd.DataFrame.from_records(zun_train_good[S, 0:-1].astype('double'))\n",
    "RP=[]\n",
    "for i in range(S.shape[0]):\n",
    "    RP.append(class_names[int(z_train_good[S[i], -1])]) # Append class names\n",
    "dfs[23] = RP\n",
    "dfs.columns = df.columns  \n",
    "dfs[\"Weight\"] = np.around(W, 5)/np.sum(np.around(W, 5)) # Calculate normalized importance weights\n",
    "dfs.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d. Compute how similar a feature of a prototypical user is to the chosen applicant.\n",
    "The more similar the feature of prototypical user is to the applicant, the closer its weight is to 1. We can see below that several features for prototypes are quite similar to the chosen applicant. A human friendly explanation is provided thereafter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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>ExternalRiskEstimate</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>0.26</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0     1     2     3     4\n",
       "ExternalRiskEstimate                0.59  0.29  0.42  0.84  0.21\n",
       "MSinceOldestTradeOpen               0.76  0.62  0.76  0.09  0.79\n",
       "MSinceMostRecentTradeOpen           1.00  0.09  0.83  0.89  0.87\n",
       "AverageMInFile                      0.79  0.09  0.90  1.00  0.82\n",
       "NumSatisfactoryTrades               0.95  0.39  0.74  0.39  0.15\n",
       "NumTrades60Ever2DerogPubRec         1.00  1.00  0.08  1.00  1.00\n",
       "NumTrades90Ever2DerogPubRec         1.00  1.00  0.08  1.00  1.00\n",
       "PercentTradesNeverDelq              1.00  0.15  0.81  0.15  0.15\n",
       "MSinceMostRecentDelq                1.00  0.36  0.22  0.36  0.36\n",
       "MaxDelq2PublicRecLast12M            1.00  0.13  1.00  0.13  1.00\n",
       "MaxDelqEver                         1.00  0.41  0.17  0.41  0.64\n",
       "NumTotalTrades                      0.80  0.23  0.86  0.26  0.35\n",
       "NumTradesOpeninLast12M              1.00  1.00  0.40  0.40  0.06\n",
       "PercentInstallTrades                1.00  0.05  0.54  0.37  0.33\n",
       "MSinceMostRecentInqexcl7days        0.08  1.00  1.00  1.00  1.00\n",
       "NumInqLast6M                        0.21  1.00  0.21  0.21  0.04\n",
       "NumInqLast6Mexcl7days               0.26  1.00  0.26  1.00  0.07\n",
       "NetFractionRevolvingBurden          0.96  0.88  0.96  0.92  0.09\n",
       "NetFractionInstallBurden            1.00  1.00  1.00  1.00  0.08\n",
       "NumRevolvingTradesWBalance          1.00  0.28  0.38  0.73  0.20\n",
       "NumInstallTradesWBalance            1.00  0.13  1.00  0.13  1.00\n",
       "NumBank2NatlTradesWHighUtilization  0.69  0.69  0.69  1.00  0.11\n",
       "PercentTradesWBalance               0.67  0.12  0.36  0.38  0.57"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = z_train_good[S, 0:-1] # Store chosen prototypes\n",
    "eps = 1e-10 # Small constant defined to eliminate divide-by-zero errors\n",
    "fwt = np.zeros(z.shape)\n",
    "for i in range (z.shape[0]):\n",
    "    for j in range(z.shape[1]):\n",
    "        fwt[i, j] = np.exp(-1 * abs(X[0, j] - z[i,j])/(np.std(z[:, j])+eps)) # Compute feature similarity in [0,1]\n",
    "                \n",
    "# move wts to a dataframe to display\n",
    "dfw = pd.DataFrame.from_records(np.around(fwt.astype('double'), 2))\n",
    "dfw.columns = df.columns[:-1]\n",
    "dfw.transpose()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation:\n",
    "The above table depicts the five closest user profiles to the chosen applicant. Based on importance weight outputted by the method, we see that the prototype under column zero is the most representative user profile by far. This is (intuitively) confirmed from the feature similarity table above where more than 50% of the features (12 out of 23) of this prototype are identical to that of the chosen user whose prediction we want to explain. Also, the bank employee looking at the prototypical users and their features surmises that the approved applicant belongs to a group of approved users that have practically no debt (NetFractionInstallBurden). This justification gives the employee more confidence in approving the users application.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"bad\"></a>\n",
    "### Example 2. Obtaining similar samples as explanations for a HELOC applicant predicted as \"Bad\". \n",
    "We now consider a user 1272 whose loan was denied. We obtained a contrastive explanation for this user before. Similar to user 8, we now obtain exemplar based explanations for this user to help the bank employee understand the reasons for the rejection. Steps similar to example 1 are followed in this case too, where we first process the data, obtain prototypes and their importance weights, and finally showcase how similar the features are of these prototypes to the user we want to explain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Normalize the data and chose a particular applicant, whose profile is displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_train_bad = z_train[z_train[:,-1]==0, :]\n",
    "zun_train_bad = zun_train[zun_train[:,-1]==0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen Sample: 1272\n",
      "Prediction made by the model: Bad\n",
      "Prediction probabilities: [[ 0.40682057 -0.391679  ]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0\n",
       "ExternalRiskEstimate                 65\n",
       "MSinceOldestTradeOpen               256\n",
       "MSinceMostRecentTradeOpen            15\n",
       "AverageMInFile                       52\n",
       "NumSatisfactoryTrades                17\n",
       "NumTrades60Ever2DerogPubRec           0\n",
       "NumTrades90Ever2DerogPubRec           0\n",
       "PercentTradesNeverDelq              100\n",
       "MSinceMostRecentDelq                  0\n",
       "MaxDelq2PublicRecLast12M              7\n",
       "MaxDelqEver                           8\n",
       "NumTotalTrades                       19\n",
       "NumTradesOpeninLast12M                0\n",
       "PercentInstallTrades                 29\n",
       "MSinceMostRecentInqexcl7days          2\n",
       "NumInqLast6M                          5\n",
       "NumInqLast6Mexcl7days                 5\n",
       "NetFractionRevolvingBurden           57\n",
       "NetFractionInstallBurden             79\n",
       "NumRevolvingTradesWBalance            2\n",
       "NumInstallTradesWBalance              4\n",
       "NumBank2NatlTradesWHighUtilization    2\n",
       "PercentTradesWBalance                60\n",
       "RiskPerformance                     Bad"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 1272 #another user to try 2385\n",
    "\n",
    "X = xn_test[idx].reshape((1,) + xn_test[idx].shape)\n",
    "print(\"Chosen Sample:\", idx)\n",
    "print(\"Prediction made by the model:\", class_names[np.argmax(nn.predict_proba(X))])\n",
    "print(\"Prediction probabilities:\", nn.predict_proba(X))\n",
    "print(\"\")\n",
    "\n",
    "X = np.hstack((X, nn.predict_classes(X).reshape((1,1))))\n",
    "\n",
    "# move samples to a dataframe to display\n",
    "Xun = x_test[idx].reshape((1,) + x_test[idx].shape)\n",
    "dfx = pd.DataFrame.from_records(Xun.astype('double'))\n",
    "dfx[23] = class_names[int(X[0, -1])]\n",
    "dfx.columns = df.columns\n",
    "dfx.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Find similar applicants predicted as \"bad\" using the protodash explainer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -2.0000e+04  4e+00  1e+00  1e+00\n",
      " 1:  1.3951e+01 -1.8757e+05  4e+01  1e+00  1e+00\n",
      " 2: -1.0452e+00 -2.4808e+06  5e+02  1e+00  1e+00\n",
      " 3:  9.9094e-01 -3.7820e+07  8e+03  1e+00  1e+00\n",
      " 4:  1.2044e+00 -5.7710e+09  1e+06  1e+00  1e+00\n",
      " 5:  1.6105e+08 -1.3427e+17  1e+17  6e-13  3e-04\n",
      " 6:  1.6105e+08 -1.3427e+15  1e+15  6e-15  2e-04\n",
      " 7:  1.6105e+08 -1.3427e+13  1e+13  2e-16  2e-06\n",
      " 8:  1.6104e+08 -1.3473e+11  1e+11  1e-16  6e-08\n",
      " 9:  1.6063e+08 -1.8053e+09  2e+09  6e-18  1e-09\n",
      "10:  1.2950e+08 -3.8084e+08  5e+08  5e-17  7e-10\n",
      "11:  6.9589e+06 -2.2750e+08  2e+08  2e-16  4e-12\n",
      "12:  2.4924e+06 -4.9489e+06  7e+06  1e-16  8e-13\n",
      "13:  3.7960e+05 -4.1688e+05  8e+05  2e-16  0e+00\n",
      "14:  5.4362e+04 -6.0989e+04  1e+05  1e-16  1e-13\n",
      "15:  7.7281e+03 -8.7442e+03  2e+04  1e-16  5e-14\n",
      "16:  1.0814e+03 -1.2777e+03  2e+03  2e-16  2e-14\n",
      "17:  1.4452e+02 -1.9320e+02  3e+02  2e-16  6e-15\n",
      "18:  1.6236e+01 -3.1901e+01  5e+01  2e-16  3e-15\n",
      "19:  7.9417e-02 -6.5709e+00  7e+00  4e-16  8e-16\n",
      "20: -1.5034e+00 -2.2383e+00  7e-01  3e-16  2e-16\n",
      "21: -1.6411e+00 -1.7669e+00  1e-01  2e-16  4e-17\n",
      "22: -1.6773e+00 -1.6963e+00  2e-02  1e-16  5e-17\n",
      "23: -1.6801e+00 -1.6816e+00  1e-03  3e-16  6e-17\n",
      "24: -1.6802e+00 -1.6802e+00  2e-05  2e-16  7e-17\n",
      "25: -1.6802e+00 -1.6802e+00  2e-07  2e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -3.0000e+04  6e+00  1e+00  1e+00\n",
      " 1:  2.2652e+01 -3.4494e+05  7e+01  1e+00  1e+00\n",
      " 2: -9.7166e-01 -1.5165e+06  3e+02  1e+00  1e+00\n",
      " 3:  8.8732e-01 -6.4545e+06  1e+03  1e+00  1e+00\n",
      " 4:  4.0619e+00 -9.5492e+07  2e+04  1e+00  1e+00\n",
      " 5:  6.8342e+00 -3.0797e+10  7e+06  1e+00  1e+00\n",
      " 6:  1.4054e+08 -6.7268e+17  7e+17  5e-13  2e-03\n",
      " 7:  1.4054e+08 -6.7268e+15  7e+15  5e-15  1e-03\n",
      " 8:  1.4054e+08 -6.7269e+13  7e+13  2e-16  1e-05\n",
      " 9:  1.4054e+08 -6.7332e+11  7e+11  4e-16  1e-07\n",
      "10:  1.4040e+08 -7.3721e+09  8e+09  2e-16  2e-09\n",
      "11:  1.2803e+08 -6.5498e+08  8e+08  2e-16  1e-10\n",
      "12:  1.0414e+07 -2.6146e+08  3e+08  1e-16  1e-10\n",
      "13:  3.1711e+06 -6.3976e+06  1e+07  4e-16  3e-12\n",
      "14:  4.8148e+05 -5.3517e+05  1e+06  1e-16  4e-13\n",
      "15:  6.8958e+04 -7.7139e+04  1e+05  3e-16  6e-14\n",
      "16:  9.8128e+03 -1.1070e+04  2e+04  2e-16  3e-14\n",
      "17:  1.3771e+03 -1.6135e+03  3e+03  2e-16  1e-14\n",
      "18:  1.8573e+02 -2.4246e+02  4e+02  6e-17  6e-16\n",
      "19:  2.1713e+01 -3.9393e+01  6e+01  1e-16  3e-15\n",
      "20:  7.1800e-01 -7.7941e+00  9e+00  1e-16  8e-16\n",
      "21: -1.4375e+00 -2.4351e+00  1e+00  2e-16  2e-16\n",
      "22: -1.6114e+00 -1.8294e+00  2e-01  4e-17  8e-17\n",
      "23: -1.6765e+00 -1.7151e+00  4e-02  3e-16  5e-17\n",
      "24: -1.6822e+00 -1.6854e+00  3e-03  2e-16  2e-17\n",
      "25: -1.6824e+00 -1.6824e+00  6e-05  6e-17  6e-17\n",
      "26: -1.6824e+00 -1.6824e+00  6e-07  1e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -0.0000e+00 -4.0000e+04  8e+00  1e+00  1e+00\n",
      " 1:  3.1518e+01 -5.1960e+05  1e+02  1e+00  1e+00\n",
      " 2: -5.3941e-01 -2.2190e+06  5e+02  1e+00  1e+00\n",
      " 3: -5.5212e-01 -8.2651e+06  2e+03  1e+00  1e+00\n",
      " 4:  8.1060e-01 -7.5932e+07  2e+04  1e+00  1e+00\n",
      " 5:  2.5556e+00 -6.5290e+09  1e+06  1e+00  1e+00\n",
      " 6:  3.5093e+08 -1.5662e+17  2e+17  4e-13  4e-04\n",
      " 7:  3.5093e+08 -1.5662e+15  2e+15  4e-15  5e-05\n",
      " 8:  3.5093e+08 -1.5663e+13  2e+13  1e-16  2e-06\n",
      " 9:  3.5088e+08 -1.5782e+11  2e+11  1e-16  2e-08\n",
      "10:  3.4591e+08 -2.7463e+09  3e+09  9e-17  5e-10\n",
      "11:  1.5618e+08 -4.5360e+08  6e+08  2e-16  7e-08\n",
      "12:  2.5039e+07 -5.2066e+07  8e+07  1e-16  1e-12\n",
      "13:  3.9788e+06 -4.5353e+06  9e+06  2e-16  8e-13\n",
      "14:  5.7260e+05 -6.4450e+05  1e+06  2e-16  3e-13\n",
      "15:  8.1978e+04 -9.1397e+04  2e+05  1e-16  1e-13\n",
      "16:  1.1669e+04 -1.3145e+04  2e+04  2e-16  3e-14\n",
      "17:  1.6394e+03 -1.9143e+03  4e+03  1e-16  1e-14\n",
      "18:  2.2187e+02 -2.8701e+02  5e+02  3e-16  6e-15\n",
      "19:  2.6316e+01 -4.6341e+01  7e+01  3e-16  4e-15\n",
      "20:  1.1314e+00 -9.0235e+00  1e+01  3e-16  3e-16\n",
      "21: -1.4923e+00 -2.7146e+00  1e+00  3e-16  3e-16\n",
      "22: -1.6504e+00 -1.7666e+00  1e-01  2e-16  6e-17\n",
      "23: -1.6819e+00 -1.6990e+00  2e-02  2e-16  1e-16\n",
      "24: -1.6844e+00 -1.6860e+00  2e-03  2e-16  1e-16\n",
      "25: -1.6845e+00 -1.6845e+00  5e-05  1e-16  2e-16\n",
      "26: -1.6845e+00 -1.6845e+00  5e-07  2e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  0.0000e+00 -5.0000e+04  1e+01  1e+00  1e+00\n",
      " 1:  4.2777e+01 -7.4034e+05  1e+02  1e+00  1e+00\n",
      " 2: -4.0907e-01 -3.0255e+06  6e+02  1e+00  1e+00\n",
      " 3:  3.2489e+00 -1.1962e+07  2e+03  1e+00  1e+00\n",
      " 4:  1.3141e+01 -1.5965e+08  3e+04  1e+00  1e+00\n",
      " 5:  2.0375e+01 -5.8520e+10  1e+07  1e+00  1e+00\n",
      " 6:  1.4807e+08 -1.2576e+18  1e+18  7e-13  3e-03\n",
      " 7:  1.4807e+08 -1.2576e+16  1e+16  7e-15  9e-04\n",
      " 8:  1.4807e+08 -1.2576e+14  1e+14  2e-16  1e-05\n",
      " 9:  1.4807e+08 -1.2588e+12  1e+12  2e-16  1e-07\n",
      "10:  1.4795e+08 -1.3762e+10  1e+10  8e-17  2e-09\n",
      "11:  1.3813e+08 -1.2350e+09  1e+09  2e-16  2e-09\n",
      "12:  1.4061e+07 -5.6498e+08  6e+08  4e-16  3e-10\n",
      "13:  3.8779e+06 -1.2907e+07  2e+07  1e-16  6e-12\n",
      "14:  5.8014e+05 -6.7599e+05  1e+06  2e-16  3e-13\n",
      "15:  8.2943e+04 -9.1922e+04  2e+05  2e-16  8e-14\n",
      "16:  1.1808e+04 -1.3283e+04  3e+04  3e-16  4e-14\n",
      "17:  1.6603e+03 -1.9330e+03  4e+03  1e-16  2e-14\n",
      "18:  2.2530e+02 -2.8932e+02  5e+02  2e-16  8e-15\n",
      "19:  2.7008e+01 -4.6485e+01  7e+01  2e-16  4e-15\n",
      "20:  1.3453e+00 -8.9445e+00  1e+01  3e-16  6e-16\n",
      "21: -1.3724e+00 -2.6264e+00  1e+00  2e-16  1e-16\n",
      "22: -1.5360e+00 -1.9467e+00  4e-01  2e-16  1e-16\n",
      "23: -1.6708e+00 -1.8638e+00  2e-01  1e-16  8e-17\n",
      "24: -1.6830e+00 -1.6970e+00  1e-02  2e-16  9e-17\n",
      "25: -1.6847e+00 -1.6856e+00  9e-04  2e-16  1e-16\n",
      "26: -1.6848e+00 -1.6848e+00  4e-05  2e-16  1e-16\n",
      "27: -1.6848e+00 -1.6848e+00  6e-07  2e-16  1e-16\n",
      "Optimal solution found.\n"
     ]
    }
   ],
   "source": [
    "(W, S, setValues) = explainer.explain(X, z_train_bad, m=5) # Return weights W, Prototypes S and objective function values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. Display similar applicant user profiles and the extent to which they are similar to the chosen applicant as indicated by the last row in the table below labelled as \"Weight\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>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>ExternalRiskEstimate</th>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>64</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>191</td>\n",
       "      <td>125</td>\n",
       "      <td>85</td>\n",
       "      <td>194</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>53</td>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>84</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>31</td>\n",
       "      <td>232</td>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>54</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.781763</td>\n",
       "      <td>0.0822525</td>\n",
       "      <td>0.0573946</td>\n",
       "      <td>0.0642844</td>\n",
       "      <td>0.0143057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0          1          2          3          4\n",
       "ExternalRiskEstimate                      73         61         64         55          0\n",
       "MSinceOldestTradeOpen                    191        125         85        194        383\n",
       "MSinceMostRecentTradeOpen                 17          7          0         26        383\n",
       "AverageMInFile                            53         32         13        100        383\n",
       "NumSatisfactoryTrades                     19          5          2         18          1\n",
       "NumTrades60Ever2DerogPubRec                0          1          0          0          1\n",
       "NumTrades90Ever2DerogPubRec                0          1          0          0          1\n",
       "PercentTradesNeverDelq                   100        100        100         84        100\n",
       "MSinceMostRecentDelq                       0          0          0          1          0\n",
       "MaxDelq2PublicRecLast12M                   7          7          7          4          6\n",
       "MaxDelqEver                                8          8          8          6          8\n",
       "NumTotalTrades                            20          6          9         11          1\n",
       "NumTradesOpeninLast12M                     0          3          8          0          0\n",
       "PercentInstallTrades                      25         60         33         42        100\n",
       "MSinceMostRecentInqexcl7days               0          0          0         23          0\n",
       "NumInqLast6M                               0          1         66          0          1\n",
       "NumInqLast6Mexcl7days                      0          1         66          0          1\n",
       "NetFractionRevolvingBurden                31        232         65         84          0\n",
       "NetFractionInstallBurden                  78         83          0         48          0\n",
       "NumRevolvingTradesWBalance                 4          1          2          5          0\n",
       "NumInstallTradesWBalance                   3          3          3          3          0\n",
       "NumBank2NatlTradesWHighUtilization         1          1          1          3          0\n",
       "PercentTradesWBalance                     54        100         71        100          0\n",
       "RiskPerformance                          Bad        Bad        Bad        Bad        Bad\n",
       "Weight                              0.781763  0.0822525  0.0573946  0.0642844  0.0143057"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# move samples to a dataframe to display\n",
    "dfs = pd.DataFrame.from_records(zun_train_bad[S, 0:-1].astype('double'))\n",
    "RP=[]\n",
    "for i in range(S.shape[0]):\n",
    "    RP.append(class_names[int(z_train_bad[S[i], -1])]) # Append class names\n",
    "dfs[23] = RP\n",
    "dfs.columns = df.columns  \n",
    "dfs[\"Weight\"] = np.around(W, 5)/np.sum(np.around(W, 5)) # Compute normalized importance weights for prototypes\n",
    "dfs.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d. Compute how similar a feature of a prototypical user is to the chosen applicant.\n",
    "The more similar the feature of prototypical user is to the applicant, the closer its weight is to 1. We can see below that several features for prototypes are quite similar to the chosen applicant. Following this table we provide human friendly explanation based on this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>ExternalRiskEstimate</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0     1     2     3     4\n",
       "ExternalRiskEstimate                0.73  0.86  0.96  0.68  0.08\n",
       "MSinceOldestTradeOpen               0.53  0.28  0.19  0.55  0.29\n",
       "MSinceMostRecentTradeOpen           0.99  0.95  0.90  0.93  0.08\n",
       "AverageMInFile                      0.99  0.86  0.75  0.70  0.09\n",
       "NumSatisfactoryTrades               0.78  0.22  0.15  0.88  0.13\n",
       "NumTrades60Ever2DerogPubRec         1.00  0.13  1.00  1.00  0.13\n",
       "NumTrades90Ever2DerogPubRec         1.00  0.13  1.00  1.00  0.13\n",
       "PercentTradesNeverDelq              1.00  1.00  1.00  0.08  1.00\n",
       "MSinceMostRecentDelq                1.00  1.00  1.00  0.08  1.00\n",
       "MaxDelq2PublicRecLast12M            1.00  1.00  1.00  0.08  0.42\n",
       "MaxDelqEver                         1.00  1.00  1.00  0.08  1.00\n",
       "NumTotalTrades                      0.85  0.13  0.20  0.28  0.06\n",
       "NumTradesOpeninLast12M              1.00  0.38  0.08  1.00  1.00\n",
       "PercentInstallTrades                0.86  0.31  0.86  0.61  0.07\n",
       "MSinceMostRecentInqexcl7days        0.80  0.80  0.80  0.10  0.80\n",
       "NumInqLast6M                        0.83  0.86  0.10  0.83  0.86\n",
       "NumInqLast6Mexcl7days               0.83  0.86  0.10  0.83  0.86\n",
       "NetFractionRevolvingBurden          0.72  0.11  0.91  0.71  0.49\n",
       "NetFractionInstallBurden            0.97  0.90  0.11  0.42  0.11\n",
       "NumRevolvingTradesWBalance          0.34  0.58  1.00  0.20  0.34\n",
       "NumInstallTradesWBalance            0.43  0.43  0.43  0.43  0.04\n",
       "NumBank2NatlTradesWHighUtilization  0.36  0.36  0.36  0.36  0.13\n",
       "PercentTradesWBalance               0.85  0.34  0.74  0.34  0.20"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = z_train_bad[S, 0:-1] # Store the prototypes\n",
    "eps = 1e-10 # Small constant to guard against divide by zero errors\n",
    "fwt = np.zeros(z.shape)\n",
    "for i in range (z.shape[0]): # Compute feature similarity for each prototype\n",
    "    for j in range(z.shape[1]):\n",
    "        fwt[i, j] = np.exp(-1 * abs(X[0, j] - z[i,j])/(np.std(z[:, j])+eps))\n",
    "                \n",
    "# move wts to a dataframe to display\n",
    "dfw = pd.DataFrame.from_records(np.around(fwt.astype('double'), 2))\n",
    "dfw.columns = df.columns[:-1]\n",
    "dfw.transpose()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation:\n",
    "Here again, the above table depicts the five closest user profiles to the chosen applicant. Based on importance weight outputted by the method we see that the prototype under column zero is the most representative user profile by far. This is (intuitively) confirmed from the feature similarity table above where 10 features out of 23 of this prototype are highly similar (>0.9) to that of the user we want to explain. Also the bank employee can see that the applicant belongs to a group of rejected applicants with similar deliquency behavior. Realizing that the user also poses similar risk as these other applicants whose loan was rejected, the employee takes the more conservative decision of rejecting the users application as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"contrastive\"></a>\n",
    "## 4. Customer: Contrastive explanations for HELOC Use Case\n",
    "\n",
    "We now demonstrate how to compute contrastive explanations using AIX360 and how such explanations can help home owners understand the decisions made by AI models that approve or reject their HELOC applications. \n",
    "\n",
    "Typically, home owners would like to understand why they do not qualify for a line of credit and if so what changes in their application would qualify them. On the other hand, if they qualified, they might want to know what factors led to the approval of their application. \n",
    "\n",
    "In this context, contrastive explanations provide information to applicants about what minimal changes to their profile would have changed the decision of the AI model from reject to accept or vice-versa (_pertinent negatives_). For example, increasing the number of satisfactory trades to a certain value may have led to the acceptance of the application everything else being the same. \n",
    "\n",
    "The method presented here also highlights a minimal set of features and their values that would still maintain the original decision (_pertinent positives_). For example, for an applicant whose HELOC application was approved, the \n",
    "explanation may say that even if the number of satisfactory trades was reduced to a lower number, the loan would have still gotten through.\n",
    "\n",
    "Additionally, organizations (Banks, financial institutions, etc.) would like to understand trends in the behavior of their AI models in approving loan applications, which could be done by studying contrastive explanations for individuals whose loans were either accepted or rejected. Looking at the aggregate statistics of pertinent positives for approved applicants the organization can get insight into what minimal set of features and their values play an important role in acceptances. While studying the aggregate statistics of pertinent negatives the organization can get insight into features that could change the status of rejected applicants and potentially uncover ways that an applicant may game the system by changing potentially non-important features that could alter the models outcome. \n",
    "\n",
    "The contrastive explanations in AIX360 are implemented using the algorithm developed in the following work:\n",
    "###### [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://arxiv.org/abs/1802.07623)\n",
    "\n",
    "We now provide a brief overview of the method. As mentioned above the algorithm outputs a contrastive explanation which consists of two parts: a) pertinent negatives (PNs) and b) pertinent positives (PPs). PNs identify a minimal set of features which if altered would change the classification of the original input. For example, in the loan case if a person's credit score is increased their loan application status may change from reject to accept. The manner in which the method accomplishes this is by optimizing a change in the prediction probability loss while enforcing an elastic norm constraint that results in minimal change of features and their values. Optionally, an auto-encoder may also be used to force these minimal changes to produce realistic PNs. PPs on the other hand identify a minimal set of features and their values that are sufficient to yield the original input's classification. For example, an individual's loan may still be accepted if the salary was 50K as opposed to 100K. Here again we have an elastic norm term so that the amount of information needed is minimal, however, the first loss term in this case tries to make the original input's class to be the winning class. For a more in-depth discussion, please refer to the above work.\n",
    "\n",
    "\n",
    "The three main steps to obtain a contrastive explanation are shown below. The first two steps are more about processing the data and building an AI model while the third step computes the actual explanation. \n",
    "\n",
    " [Step 1. Process and Normalize HELOC dataset for training](#c1)<br>\n",
    " [Step 2. Define and train a NN classifier](#c2)<br>\n",
    " [Step 3. Compute contrastive explanations for a few applicants](#c3)<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load HELOC dataset and show sample applicants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Heloc dataset:  /Users/vijay/DEVJAN22/AIX360/aix360/datasets/../data/heloc_data/heloc_dataset.csv\n",
      "Size of HELOC dataset: (10459, 24)\n",
      "Number of \"Good\" applicants: 5000\n",
      "Number of \"Bad\" applicants: 5459\n",
      "Sample Applicants:\n"
     ]
    },
    {
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       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>55</td>\n",
       "      <td>61</td>\n",
       "      <td>67</td>\n",
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       "      <th>MSinceOldestTradeOpen</th>\n",
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       "      <td>169</td>\n",
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       "      <td>132</td>\n",
       "      <td>78</td>\n",
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       "      <td>36</td>\n",
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       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>20</td>\n",
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       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
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       "      <td>93</td>\n",
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       "      <th>NetFractionInstallBurden</th>\n",
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       "      <td>83</td>\n",
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       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
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       "      <td>86</td>\n",
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       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>8</td>\n",
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       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
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       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>1</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>PercentTradesWBalance</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
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       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                      0    1    2    3    4    5     6     7    8    9\n",
       "ExternalRiskEstimate                 55   61   67   66   81   59    54    68   59   61\n",
       "MSinceOldestTradeOpen               144   58   66  169  333  137    88   148  324   79\n",
       "MSinceMostRecentTradeOpen             4   15    5    1   27   11     7     7    2    4\n",
       "AverageMInFile                       84   41   24   73  132   78    37    65  138   36\n",
       "NumSatisfactoryTrades                20    2    9   28   12   31    25    17   24   19\n",
       "NumTrades60Ever2DerogPubRec           3    4    0    1    0    0     0     0    0    0\n",
       "NumTrades90Ever2DerogPubRec           0    4    0    1    0    0     0     0    0    0\n",
       "PercentTradesNeverDelq               83  100  100   93  100   91    92    83   85   95\n",
       "MSinceMostRecentDelq                  2   -7   -7   76   -7    1     9    31    5    5\n",
       "MaxDelq2PublicRecLast12M              3    0    7    6    7    4     4     6    4    4\n",
       "MaxDelqEver                           5    8    8    6    8    6     6     6    6    6\n",
       "NumTotalTrades                       23    7    9   30   12   32    26    18   27   19\n",
       "NumTradesOpeninLast12M                1    0    4    3    0    1     3     1    1    3\n",
       "PercentInstallTrades                 43   67   44   57   25   47    58    44   26   26\n",
       "MSinceMostRecentInqexcl7days          0    0    0    0    0    0     0     0    0    0\n",
       "NumInqLast6M                          0    0    4    5    1    0     4     0    1    6\n",
       "NumInqLast6Mexcl7days                 0    0    4    4    1    0     4     0    1    6\n",
       "NetFractionRevolvingBurden           33    0   53   72   51   62    89    28   68   31\n",
       "NetFractionInstallBurden             -8   -8   66   83   89   93    76    48   -8   86\n",
       "NumRevolvingTradesWBalance            8    0    4    6    3   12     7     2    7    5\n",
       "NumInstallTradesWBalance              1   -8    2    4    1    4     7     2    1    3\n",
       "NumBank2NatlTradesWHighUtilization    1   -8    1    3    0    3     2     2    3    1\n",
       "PercentTradesWBalance                69    0   86   91   80   94   100    40   90   62\n",
       "RiskPerformance                     Bad  Bad  Bad  Bad  Bad  Bad  Good  Good  Bad  Bad"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "heloc = HELOCDataset()\n",
    "df = heloc.dataframe()\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 24)\n",
    "pd.set_option('display.width', 1000)\n",
    "print(\"Size of HELOC dataset:\", df.shape)\n",
    "print(\"Number of \\\"Good\\\" applicants:\", np.sum(df['RiskPerformance']=='Good'))\n",
    "print(\"Number of \\\"Bad\\\" applicants:\", np.sum(df['RiskPerformance']=='Bad'))\n",
    "print(\"Sample Applicants:\")\n",
    "df.head(10).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of ExternalRiskEstimate and NumSatisfactoryTrades columns:\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot (example) distributions for two features\n",
    "print(\"Distribution of ExternalRiskEstimate and NumSatisfactoryTrades columns:\")\n",
    "hist = df.hist(column=['ExternalRiskEstimate', 'NumSatisfactoryTrades'], bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"c1\"></a>\n",
    "### Step 1. Process and Normalize HELOC dataset for training\n",
    "\n",
    "We will first process the HELOC dataset before using it to train an NN model that can predict the\n",
    "target variable RiskPerformance. The HELOC dataset is a tabular dataset with numerical values. However, some of the values are negative and need to be filtered. The processed data is stored in the file heloc.npz for easy access. The dataset is also normalized for training.\n",
    "\n",
    "The data processing and model building is very similar to the Loan Officer persona above, where ProtoDash was the method of choice. We repeat these steps here so that both the use cases can be run independently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Process the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data and split dataset into train/test\n",
    "PROCESS_DATA = False\n",
    "\n",
    "if (PROCESS_DATA): \n",
    "    (Data, x_train, x_test, y_train_b, y_test_b) = heloc.split()\n",
    "    np.savez('heloc.npz', Data=Data, x_train=x_train, x_test=x_test, y_train_b=y_train_b, y_test_b=y_test_b)\n",
    "else:\n",
    "    heloc = np.load('heloc.npz', allow_pickle = True)\n",
    "    Data = heloc['Data']\n",
    "    x_train = heloc['x_train']\n",
    "    x_test  = heloc['x_test']\n",
    "    y_train_b = heloc['y_train_b']\n",
    "    y_test_b  = heloc['y_test_b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### b. Normalize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.vstack((x_train, x_test))\n",
    "Zmax = np.max(Z, axis=0)\n",
    "Zmin = np.min(Z, axis=0)\n",
    "\n",
    "#normalize an array of samples to range [-0.5, 0.5]\n",
    "def normalize(V):\n",
    "    VN = (V - Zmin)/(Zmax - Zmin)\n",
    "    VN = VN - 0.5\n",
    "    return(VN)\n",
    "    \n",
    "# rescale a sample to recover original values for normalized values. \n",
    "def rescale(X):\n",
    "    return(np.multiply ( X + 0.5, (Zmax - Zmin) ) + Zmin)\n",
    "\n",
    "N = normalize(Z)\n",
    "xn_train = N[0:x_train.shape[0], :]\n",
    "xn_test  = N[x_train.shape[0]:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"c2\"></a>\n",
    "### Step 2. Define and train a NN classifier\n",
    "\n",
    "Let us now build a loan approval model based on the HELOC dataset.\n",
    "\n",
    "#### a. Define NN architecture\n",
    "We now define the architecture of a 2-layer neural network classifier whose predictions we will try to interpret. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn with no softmax\n",
    "def nn_small():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=23, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(2, kernel_initializer='normal'))    \n",
    "    return model    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Train the NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 10)                240       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 262\n",
      "Trainable params: 262\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train accuracy: 0.7387545704841614\n",
      "Test accuracy: 0.7224473357200623\n"
     ]
    }
   ],
   "source": [
    "# Set random seeds for repeatability\n",
    "np.random.seed(1) \n",
    "tf.set_random_seed(2) \n",
    "\n",
    "class_names = ['Bad', 'Good']\n",
    "\n",
    "# loss function\n",
    "def fn(correct, predicted):\n",
    "    return tf.nn.softmax_cross_entropy_with_logits(labels=correct, logits=predicted)\n",
    "\n",
    "# compile and print model summary\n",
    "nn = nn_small()\n",
    "nn.compile(loss=fn, optimizer='adam', metrics=['accuracy'])\n",
    "nn.summary()\n",
    "\n",
    "\n",
    "# train model or load a trained model\n",
    "TRAIN_MODEL = False\n",
    "\n",
    "if (TRAIN_MODEL):             \n",
    "    nn.fit(xn_train, y_train_b, batch_size=128, epochs=500, verbose=1, shuffle=False)\n",
    "    nn.save_weights(\"heloc_nnsmall.h5\")     \n",
    "else:    \n",
    "    nn.load_weights(\"heloc_nnsmall.h5\")\n",
    "        \n",
    "\n",
    "# evaluate model accuracy        \n",
    "score = nn.evaluate(xn_train, y_train_b, verbose=0) #Compute training set accuracy\n",
    "#print('Train loss:', score[0])\n",
    "print('Train accuracy:', score[1])\n",
    "\n",
    "score = nn.evaluate(xn_test, y_test_b, verbose=0) #Compute test set accuracy\n",
    "#print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"c3\"></a>\n",
    "### Step 3. Compute contrastive explanations for a few applicants\n",
    "\n",
    "Given the trained NN model to decide on loan approvals, let us first examine an applicant whose application was denied and what (minimal) changes to his/her application would lead to approval (i.e. finding pertinent negatives). We will then look at another applicant whose loan was approved and ascertain features that would minimally suffice in him/her still getting a positive outcome (i.e. finding pertinent positives).\n",
    "\n",
    "#### a. Compute Pertinent Negatives (PN): \n",
    "\n",
    "In order to compute pertinent negatives, the CEM explainer computes a user profile that is close to the original applicant but for whom the decision of HELOC application is different. The explainer alters a minimal set of features by a minimal (positive) amount. This will help the user whose loan application was initially rejected say, to ascertain how to get it accepted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing PN for Sample: 1272\n",
      "Prediction made by the model: [[ 0.40682057 -0.391679  ]]\n",
      "Prediction probabilities: Bad\n",
      "\n",
      "WARNING:tensorflow:From /Users/vijay/DEVJAN22/AIX360/aix360/algorithms/contrastive/CEM_aen.py:54: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/vijay/DEVJAN22/AIX360/aix360/algorithms/contrastive/CEM_aen.py:86: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/vijay/DEVJAN22/AIX360/aix360/algorithms/contrastive/CEM_aen.py:150: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/vijay/opt/anaconda3/envs/aix360env/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py:409: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "WARNING:tensorflow:From /Users/vijay/DEVJAN22/AIX360/aix360/algorithms/contrastive/CEM_aen.py:153: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/vijay/opt/anaconda3/envs/aix360env/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /Users/vijay/DEVJAN22/AIX360/aix360/algorithms/contrastive/CEM_aen.py:167: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "iter:0 const:[10.]\n",
      "Loss_Overall:0.2935, Loss_Attack:0.0000\n",
      "Loss_L2Dist:0.2065, Loss_L1Dist:0.8703, AE_loss:0.0\n",
      "target_lab_score:-1.1559, max_nontarget_lab_score:1.3184\n",
      "\n",
      "iter:500 const:[10.]\n",
      "Loss_Overall:0.1687, Loss_Attack:0.0000\n",
      "Loss_L2Dist:0.1139, Loss_L1Dist:0.5485, AE_loss:0.0\n",
      "target_lab_score:-0.7303, max_nontarget_lab_score:0.8571\n",
      "\n",
      "iter:0 const:[5.]\n",
      "Loss_Overall:0.0668, Loss_Attack:0.0000\n",
      "Loss_L2Dist:0.0368, Loss_L1Dist:0.3000, AE_loss:0.0\n",
      "target_lab_score:-0.2295, max_nontarget_lab_score:0.3076\n",
      "\n",
      "iter:500 const:[5.]\n",
      "Loss_Overall:1.5487, Loss_Attack:1.5277\n",
      "Loss_L2Dist:0.0085, Loss_L1Dist:0.1243, AE_loss:0.0\n",
      "target_lab_score:0.1245, max_nontarget_lab_score:-0.0810\n",
      "\n",
      "iter:0 const:[2.5]\n",
      "Loss_Overall:1.8033, Loss_Attack:1.7989\n",
      "Loss_L2Dist:0.0011, Loss_L1Dist:0.0335, AE_loss:0.0\n",
      "target_lab_score:0.3218, max_nontarget_lab_score:-0.2978\n",
      "\n",
      "iter:500 const:[2.5]\n",
      "Loss_Overall:2.2462, Loss_Attack:2.2462\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[1.25]\n",
      "Loss_Overall:1.1231, Loss_Attack:1.1231\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:500 const:[1.25]\n",
      "Loss_Overall:1.1231, Loss_Attack:1.1231\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[1.875]\n",
      "Loss_Overall:1.6834, Loss_Attack:1.6834\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0001, AE_loss:0.0\n",
      "target_lab_score:0.4065, max_nontarget_lab_score:-0.3913\n",
      "\n",
      "iter:500 const:[1.875]\n",
      "Loss_Overall:1.6847, Loss_Attack:1.6847\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[2.1875]\n",
      "Loss_Overall:1.7709, Loss_Attack:1.7690\n",
      "Loss_L2Dist:0.0003, Loss_L1Dist:0.0168, AE_loss:0.0\n",
      "target_lab_score:0.3641, max_nontarget_lab_score:-0.3445\n",
      "\n",
      "iter:500 const:[2.1875]\n",
      "Loss_Overall:1.9655, Loss_Attack:1.9655\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[2.03125]\n",
      "Loss_Overall:1.7340, Loss_Attack:1.7331\n",
      "Loss_L2Dist:0.0001, Loss_L1Dist:0.0085, AE_loss:0.0\n",
      "target_lab_score:0.3853, max_nontarget_lab_score:-0.3679\n",
      "\n",
      "iter:500 const:[2.03125]\n",
      "Loss_Overall:1.8251, Loss_Attack:1.8251\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[1.953125]\n",
      "Loss_Overall:1.7104, Loss_Attack:1.7100\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0043, AE_loss:0.0\n",
      "target_lab_score:0.3959, max_nontarget_lab_score:-0.3796\n",
      "\n",
      "iter:500 const:[1.953125]\n",
      "Loss_Overall:1.7549, Loss_Attack:1.7549\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n",
      "iter:0 const:[1.9921875]\n",
      "Loss_Overall:1.7227, Loss_Attack:1.7220\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0064, AE_loss:0.0\n",
      "target_lab_score:0.3906, max_nontarget_lab_score:-0.3738\n",
      "\n",
      "iter:500 const:[1.9921875]\n",
      "Loss_Overall:1.7900, Loss_Attack:1.7900\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:0.4068, max_nontarget_lab_score:-0.3917\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Some interesting user samples to try: 2344 449 1168 1272\n",
    "idx = 1272\n",
    "\n",
    "X = xn_test[idx].reshape((1,) + xn_test[idx].shape)\n",
    "print(\"Computing PN for Sample:\", idx)\n",
    "print(\"Prediction made by the model:\", nn.predict_proba(X))\n",
    "print(\"Prediction probabilities:\", class_names[np.argmax(nn.predict_proba(X))])\n",
    "print(\"\")\n",
    "\n",
    "mymodel = KerasClassifier(nn)\n",
    "explainer = CEMExplainer(mymodel)\n",
    "\n",
    "arg_mode = 'PN' # Find pertinent negatives\n",
    "arg_max_iter = 1000 # Maximum number of iterations to search for the optimal PN for given parameter settings\n",
    "arg_init_const = 10.0 # Initial coefficient value for main loss term that encourages class change\n",
    "arg_b = 9 # No. of updates to the coefficient of the main loss term\n",
    "arg_kappa = 0.1 # Minimum confidence gap between the PNs (changed) class probability and original class' probability\n",
    "arg_beta = 1e-1 # Controls sparsity of the solution (L1 loss)\n",
    "arg_gamma = 100 # Controls how much to adhere to a (optionally trained) auto-encoder\n",
    "my_AE_model = None # Pointer to an auto-encoder\n",
    "\n",
    "# Find PN for applicant 1272\n",
    "(adv_pn, delta_pn, info_pn) = explainer.explain_instance(X, arg_mode, my_AE_model, arg_kappa, arg_b,\n",
    "                                                         arg_max_iter, arg_init_const, arg_beta, arg_gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us start by examining one particular loan application that was denied for applicant 1272. We showcase below how the decision could have been different through minimal changes to the profile conveyed by the pertinent negative. We also indicate the importance of different features to produce the change in the application status. The column delta in the table below indicates the necessary deviations for each of the features to produce this change. A human friendly explanation is then provided based on these deviations following the feature importance plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample: 1272\n",
      "prediction(X) [[ 0.40682057 -0.391679  ]] Bad\n",
      "prediction(Xpn) [[-0.02118406  0.07892033]] Good\n"
     ]
    },
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       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row0_col0\" class=\"data row0 col0\" >65</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row0_col1\" class=\"data row0 col1\" >76.37</td>\n",
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       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row1_col0\" class=\"data row1 col0\" >256</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row1_col1\" class=\"data row1 col1\" >256</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row1_col2\" class=\"data row1 col2\" >0</td>\n",
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       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row2_col1\" class=\"data row2 col1\" >15</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row2_col2\" class=\"data row2 col2\" >0</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row3\" class=\"row_heading level0 row3\" >AverageMInFile</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row3_col0\" class=\"data row3 col0\" >52</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row3_col1\" class=\"data row3 col1\" >64.81</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row3_col2\" class=\"data row3 col2\" >12.81</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row4\" class=\"row_heading level0 row4\" >NumSatisfactoryTrades</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row4_col0\" class=\"data row4 col0\" >17</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row4_col1\" class=\"data row4 col1\" >20.3</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row4_col2\" class=\"data row4 col2\" >3.3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row5\" class=\"row_heading level0 row5\" >NumTrades60Ever2DerogPubRec</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row6\" class=\"row_heading level0 row6\" >NumTrades90Ever2DerogPubRec</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row7\" class=\"row_heading level0 row7\" >PercentTradesNeverDelq</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row7_col0\" class=\"data row7 col0\" >100</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row7_col1\" class=\"data row7 col1\" >100</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row8\" class=\"row_heading level0 row8\" >MSinceMostRecentDelq</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row9\" class=\"row_heading level0 row9\" >MaxDelq2PublicRecLast12M</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row9_col0\" class=\"data row9 col0\" >7</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row9_col1\" class=\"data row9 col1\" >7</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row9_col2\" class=\"data row9 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row10\" class=\"row_heading level0 row10\" >MaxDelqEver</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row10_col0\" class=\"data row10 col0\" >8</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row10_col1\" class=\"data row10 col1\" >8</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row10_col2\" class=\"data row10 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row11\" class=\"row_heading level0 row11\" >NumTotalTrades</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row11_col0\" class=\"data row11 col0\" >19</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row11_col1\" class=\"data row11 col1\" >19</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row11_col2\" class=\"data row11 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row12\" class=\"row_heading level0 row12\" >NumTradesOpeninLast12M</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row12_col0\" class=\"data row12 col0\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row12_col1\" class=\"data row12 col1\" >0</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row12_col2\" class=\"data row12 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row13\" class=\"row_heading level0 row13\" >PercentInstallTrades</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row13_col0\" class=\"data row13 col0\" >29</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row13_col1\" class=\"data row13 col1\" >29</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row13_col2\" class=\"data row13 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row14\" class=\"row_heading level0 row14\" >MSinceMostRecentInqexcl7days</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row14_col0\" class=\"data row14 col0\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row14_col1\" class=\"data row14 col1\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row14_col2\" class=\"data row14 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row15\" class=\"row_heading level0 row15\" >NumInqLast6M</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row15_col0\" class=\"data row15 col0\" >5</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row15_col1\" class=\"data row15 col1\" >5</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row15_col2\" class=\"data row15 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row16\" class=\"row_heading level0 row16\" >NumInqLast6Mexcl7days</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row16_col0\" class=\"data row16 col0\" >5</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row16_col1\" class=\"data row16 col1\" >5</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row16_col2\" class=\"data row16 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row17\" class=\"row_heading level0 row17\" >NetFractionRevolvingBurden</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row17_col0\" class=\"data row17 col0\" >57</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row17_col1\" class=\"data row17 col1\" >57</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row17_col2\" class=\"data row17 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row18\" class=\"row_heading level0 row18\" >NetFractionInstallBurden</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row18_col0\" class=\"data row18 col0\" >79</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row18_col1\" class=\"data row18 col1\" >79</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row18_col2\" class=\"data row18 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row19\" class=\"row_heading level0 row19\" >NumRevolvingTradesWBalance</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row19_col0\" class=\"data row19 col0\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row19_col1\" class=\"data row19 col1\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row19_col2\" class=\"data row19 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row20\" class=\"row_heading level0 row20\" >NumInstallTradesWBalance</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row20_col0\" class=\"data row20 col0\" >4</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row20_col1\" class=\"data row20 col1\" >4</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row20_col2\" class=\"data row20 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row21\" class=\"row_heading level0 row21\" >NumBank2NatlTradesWHighUtilization</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row21_col0\" class=\"data row21 col0\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row21_col1\" class=\"data row21 col1\" >2</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row21_col2\" class=\"data row21 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row22\" class=\"row_heading level0 row22\" >PercentTradesWBalance</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row22_col0\" class=\"data row22 col0\" >60</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row22_col1\" class=\"data row22 col1\" >60</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row22_col2\" class=\"data row22 col2\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4632658c_3cfc_11ea_8596_acde48001122level0_row23\" class=\"row_heading level0 row23\" >RiskPerformance</th>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row23_col0\" class=\"data row23 col0\" >Bad</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row23_col1\" class=\"data row23 col1\" >Good</td>\n",
       "                        <td id=\"T_4632658c_3cfc_11ea_8596_acde48001122row23_col2\" class=\"data row23 col2\" >NIL</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16c359390>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xpn = adv_pn\n",
    "classes = [ class_names[np.argmax(nn.predict_proba(X))], class_names[np.argmax(nn.predict_proba(Xpn))], 'NIL' ]\n",
    "\n",
    "print(\"Sample:\", idx)\n",
    "print(\"prediction(X)\", nn.predict_proba(X), class_names[np.argmax(nn.predict_proba(X))])\n",
    "print(\"prediction(Xpn)\", nn.predict_proba(Xpn), class_names[np.argmax(nn.predict_proba(Xpn))] )\n",
    "\n",
    "\n",
    "X_re = rescale(X) # Convert values back to original scale from normalized\n",
    "Xpn_re = rescale(Xpn)\n",
    "Xpn_re = np.around(Xpn_re.astype(np.double), 2)\n",
    "\n",
    "delta_re = Xpn_re - X_re\n",
    "delta_re = np.around(delta_re.astype(np.double), 2)\n",
    "delta_re[np.absolute(delta_re) < 1e-4] = 0\n",
    "\n",
    "X3 = np.vstack((X_re, Xpn_re, delta_re))\n",
    "\n",
    "dfre = pd.DataFrame.from_records(X3) # Create dataframe to display original point, PN and difference (delta)\n",
    "dfre[23] = classes\n",
    "\n",
    "dfre.columns = df.columns\n",
    "dfre.rename(index={0:'X',1:'X_PN', 2:'(X_PN - X)'}, inplace=True)\n",
    "dfret = dfre.transpose()\n",
    "\n",
    "\n",
    "def highlight_ce(s, col, ncols):\n",
    "    if (type(s[col]) != str):\n",
    "        if (s[col] > 0):\n",
    "            return(['background-color: yellow']*ncols)    \n",
    "    return(['background-color: white']*ncols)\n",
    "\n",
    "dfret.style.apply(highlight_ce, col='(X_PN - X)', ncols=3, axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us compute the importance of different PN features that would be instrumental in 1272 receiving a favorable outcome and display below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fi = abs((X-Xpn).astype('double'))/np.std(xn_train.astype('double'), axis=0) # Compute PN feature importance\n",
    "objects = df.columns[-2::-1]\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = fi[0, -1::-1]\n",
    "\n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5) # bar chart\n",
    "plt.yticks(y_pos, objects) # Display features on y-axis\n",
    "plt.xlabel('weight') # x-label\n",
    "plt.title('PN (feature importance)') # Heading\n",
    "\n",
    "plt.show() # Display PN feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation:  \n",
    "We observe that the applicant 1272's loan application would have been accepted if the consolidated risk marker score  (i.e. ExternalRiskEstimate) increased from 65 to 76, the loan application was on file (i.e. AverageMlnFile) for about 65 months and if the number of satisfactory trades (i.e. NumSatisfactoryTrades) increased to little over 20.\n",
    "\n",
    "_The above changes to the three suggested factors are also intuitively consistent in improving the chances of acceptance of an application, since all three are monotonic with probability of acceptance (refer HELOC description table). \n",
    "However, one must realize that the above explanation is for the particular applicant based on what the model would do and does not necessarily have to agree with their intuitive meaning. In fact, if the explanation is deemed unacceptable then its an indication that perhaps the model should be debugged/updated_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Pertinent Positives (PP):\n",
    "In order to compute pertinent positives, the CEM explainer identifies a minimal set of features along with their values (as close to 0) that would still maintain the predicted loan application status of the applicant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing PP for Sample: 8\n",
      "Prediction made by the model: Good\n",
      "Prediction probabilities: [[-0.1889221   0.29527372]]\n",
      "\n",
      "iter:0 const:[10.]\n",
      "Loss_Overall:8.1419, Loss_Attack:7.9649\n",
      "Loss_L2Dist:0.1243, Loss_L1Dist:0.5266, AE_loss:0.0\n",
      "target_lab_score:-0.3578, max_nontarget_lab_score:0.3387\n",
      "\n",
      "iter:500 const:[10.]\n",
      "Loss_Overall:0.8676, Loss_Attack:0.0000\n",
      "Loss_L2Dist:0.7509, Loss_L1Dist:1.1667, AE_loss:0.0\n",
      "target_lab_score:0.6813, max_nontarget_lab_score:-0.6306\n",
      "\n",
      "iter:0 const:[5.]\n",
      "Loss_Overall:8.4407, Loss_Attack:8.3992\n",
      "Loss_L2Dist:0.0216, Loss_L1Dist:0.1987, AE_loss:0.0\n",
      "target_lab_score:-0.8223, max_nontarget_lab_score:0.7575\n",
      "\n",
      "iter:500 const:[5.]\n",
      "Loss_Overall:2.9148, Loss_Attack:2.6934\n",
      "Loss_L2Dist:0.1709, Loss_L1Dist:0.5053, AE_loss:0.0\n",
      "target_lab_score:-0.2224, max_nontarget_lab_score:0.2163\n",
      "\n",
      "iter:0 const:[2.5]\n",
      "Loss_Overall:6.1030, Loss_Attack:6.1013\n",
      "Loss_L2Dist:0.0002, Loss_L1Dist:0.0149, AE_loss:0.0\n",
      "target_lab_score:-1.2149, max_nontarget_lab_score:1.1256\n",
      "\n",
      "iter:500 const:[2.5]\n",
      "Loss_Overall:6.2723, Loss_Attack:6.2723\n",
      "Loss_L2Dist:0.0000, Loss_L1Dist:0.0000, AE_loss:0.0\n",
      "target_lab_score:-1.2499, max_nontarget_lab_score:1.1590\n",
      "\n",
      "iter:0 const:[3.75]\n",
      "Loss_Overall:7.8400, Loss_Attack:7.8242\n",
      "Loss_L2Dist:0.0059, Loss_L1Dist:0.0990, AE_loss:0.0\n",
      "target_lab_score:-1.0324, max_nontarget_lab_score:0.9541\n",
      "\n",
      "iter:500 const:[3.75]\n",
      "Loss_Overall:5.9820, Loss_Attack:5.9208\n",
      "Loss_L2Dist:0.0410, Loss_L1Dist:0.2024, AE_loss:0.0\n",
      "target_lab_score:-0.7734, max_nontarget_lab_score:0.7054\n",
      "\n",
      "iter:0 const:[4.375]\n",
      "Loss_Overall:8.2661, Loss_Attack:8.2388\n",
      "Loss_L2Dist:0.0125, Loss_L1Dist:0.1488, AE_loss:0.0\n",
      "target_lab_score:-0.9274, max_nontarget_lab_score:0.8558\n",
      "\n",
      "iter:500 const:[4.375]\n",
      "Loss_Overall:6.4978, Loss_Attack:6.4204\n",
      "Loss_L2Dist:0.0538, Loss_L1Dist:0.2363, AE_loss:0.0\n",
      "target_lab_score:-0.7153, max_nontarget_lab_score:0.6522\n",
      "\n",
      "iter:0 const:[4.0625]\n",
      "Loss_Overall:8.0845, Loss_Attack:8.0632\n",
      "Loss_L2Dist:0.0089, Loss_L1Dist:0.1239, AE_loss:0.0\n",
      "target_lab_score:-0.9799, max_nontarget_lab_score:0.9049\n",
      "\n",
      "iter:500 const:[4.0625]\n",
      "Loss_Overall:6.2721, Loss_Attack:6.2050\n",
      "Loss_L2Dist:0.0457, Loss_L1Dist:0.2137, AE_loss:0.0\n",
      "target_lab_score:-0.7470, max_nontarget_lab_score:0.6803\n",
      "\n",
      "iter:0 const:[3.90625]\n",
      "Loss_Overall:7.9701, Loss_Attack:7.9516\n",
      "Loss_L2Dist:0.0073, Loss_L1Dist:0.1115, AE_loss:0.0\n",
      "target_lab_score:-1.0061, max_nontarget_lab_score:0.9295\n",
      "\n",
      "iter:500 const:[3.90625]\n",
      "Loss_Overall:6.3834, Loss_Attack:6.3266\n",
      "Loss_L2Dist:0.0375, Loss_L1Dist:0.1935, AE_loss:0.0\n",
      "target_lab_score:-0.7943, max_nontarget_lab_score:0.7253\n",
      "\n",
      "iter:0 const:[3.828125]\n",
      "Loss_Overall:7.9070, Loss_Attack:7.8899\n",
      "Loss_L2Dist:0.0066, Loss_L1Dist:0.1052, AE_loss:0.0\n",
      "target_lab_score:-1.0193, max_nontarget_lab_score:0.9418\n",
      "\n",
      "iter:500 const:[3.828125]\n",
      "Loss_Overall:6.2849, Loss_Attack:6.2289\n",
      "Loss_L2Dist:0.0368, Loss_L1Dist:0.1919, AE_loss:0.0\n",
      "target_lab_score:-0.7982, max_nontarget_lab_score:0.7290\n",
      "\n",
      "iter:0 const:[3.8671875]\n",
      "Loss_Overall:7.9391, Loss_Attack:7.9213\n",
      "Loss_L2Dist:0.0070, Loss_L1Dist:0.1083, AE_loss:0.0\n",
      "target_lab_score:-1.0127, max_nontarget_lab_score:0.9356\n",
      "\n",
      "iter:500 const:[3.8671875]\n",
      "Loss_Overall:6.4549, Loss_Attack:6.4018\n",
      "Loss_L2Dist:0.0345, Loss_L1Dist:0.1857, AE_loss:0.0\n",
      "target_lab_score:-0.8127, max_nontarget_lab_score:0.7428\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Some interesting user samples to try: 8 9 11\n",
    "idx = 8\n",
    "\n",
    "X = xn_test[idx].reshape((1,) + xn_test[idx].shape)\n",
    "print(\"Computing PP for Sample:\", idx)\n",
    "print(\"Prediction made by the model:\", class_names[np.argmax(nn.predict_proba(X))])\n",
    "print(\"Prediction probabilities:\", nn.predict_proba(X))\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "mymodel = KerasClassifier(nn)\n",
    "explainer = CEMExplainer(mymodel)\n",
    "\n",
    "arg_mode = 'PP' # Find pertinent positives\n",
    "arg_max_iter = 1000 # Maximum number of iterations to search for the optimal PN for given parameter settings\n",
    "arg_init_const = 10.0 # Initial coefficient value for main loss term that encourages class change\n",
    "arg_b = 9 # No. of updates to the coefficient of the main loss term\n",
    "arg_kappa = 0.1 # Minimum confidence gap between the PNs (changed) class probability and original class' probability\n",
    "arg_beta = 1e-1 # Controls sparsity of the solution (L1 loss)\n",
    "arg_gamma = 100 # Controls how much to adhere to a (optionally trained) auto-encoder\n",
    "my_AE_model = None # Pointer to an auto-encoder\n",
    "\n",
    "(adv_pp, delta_pp, info_pp) = explainer.explain_instance(X, arg_mode, my_AE_model, arg_kappa, arg_b,\n",
    "                                                         arg_max_iter, arg_init_const, arg_beta, arg_gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the pertinent positives, we look at a different applicant 8 whose loan application was approved. We want to ascertain here what minimal values for this profile would still have lead to acceptance. Below, we showcase the pertinent positive as well as the important features in maintaining the approved status. The 0s in the PP column indicate that those features were not important. The 0s in the PP column indicate that those features were not important. Here too, we provide a human friendly explanation following the feature importance plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PP for Sample: 8\n",
      "Prediction(Xpp) : Good\n",
      "Prediction probabilities for Xpp: [[-0.04739372  0.06548987]]\n",
      "\n"
     ]
    },
    {
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       "        }</style><table id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >X</th>        <th class=\"col_heading level0 col1\" >X_PP</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row0\" class=\"row_heading level0 row0\" >ExternalRiskEstimate</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row0_col0\" class=\"data row0 col0\" >82</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row0_col1\" class=\"data row0 col1\" >43.38</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row1\" class=\"row_heading level0 row1\" >MSinceOldestTradeOpen</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row1_col0\" class=\"data row1 col0\" >280</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row2\" class=\"row_heading level0 row2\" >MSinceMostRecentTradeOpen</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row2_col0\" class=\"data row2 col0\" >13</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row3\" class=\"row_heading level0 row3\" >AverageMInFile</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row3_col0\" class=\"data row3 col0\" >102</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row3_col1\" class=\"data row3 col1\" >38.24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row4\" class=\"row_heading level0 row4\" >NumSatisfactoryTrades</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row4_col0\" class=\"data row4 col0\" >22</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row4_col1\" class=\"data row4 col1\" >7.32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row5\" class=\"row_heading level0 row5\" >NumTrades60Ever2DerogPubRec</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row6\" class=\"row_heading level0 row6\" >NumTrades90Ever2DerogPubRec</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row7\" class=\"row_heading level0 row7\" >PercentTradesNeverDelq</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row7_col0\" class=\"data row7 col0\" >91</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row8\" class=\"row_heading level0 row8\" >MSinceMostRecentDelq</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row8_col0\" class=\"data row8 col0\" >26</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row9\" class=\"row_heading level0 row9\" >MaxDelq2PublicRecLast12M</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row9_col0\" class=\"data row9 col0\" >6</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row9_col1\" class=\"data row9 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row10\" class=\"row_heading level0 row10\" >MaxDelqEver</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row10_col0\" class=\"data row10 col0\" >6</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row10_col1\" class=\"data row10 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row11\" class=\"row_heading level0 row11\" >NumTotalTrades</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row11_col0\" class=\"data row11 col0\" >23</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row11_col1\" class=\"data row11 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row12\" class=\"row_heading level0 row12\" >NumTradesOpeninLast12M</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row12_col0\" class=\"data row12 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row12_col1\" class=\"data row12 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row13\" class=\"row_heading level0 row13\" >PercentInstallTrades</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row13_col0\" class=\"data row13 col0\" >9</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row13_col1\" class=\"data row13 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row14\" class=\"row_heading level0 row14\" >MSinceMostRecentInqexcl7days</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row14_col0\" class=\"data row14 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row14_col1\" class=\"data row14 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row15\" class=\"row_heading level0 row15\" >NumInqLast6M</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row15_col0\" class=\"data row15 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row15_col1\" class=\"data row15 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row16\" class=\"row_heading level0 row16\" >NumInqLast6Mexcl7days</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row16_col0\" class=\"data row16 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row16_col1\" class=\"data row16 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row17\" class=\"row_heading level0 row17\" >NetFractionRevolvingBurden</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row17_col0\" class=\"data row17 col0\" >3</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row17_col1\" class=\"data row17 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row18\" class=\"row_heading level0 row18\" >NetFractionInstallBurden</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row18_col0\" class=\"data row18 col0\" >0</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row18_col1\" class=\"data row18 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row19\" class=\"row_heading level0 row19\" >NumRevolvingTradesWBalance</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row19_col0\" class=\"data row19 col0\" >4</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row19_col1\" class=\"data row19 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row20\" class=\"row_heading level0 row20\" >NumInstallTradesWBalance</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row20_col0\" class=\"data row20 col0\" >1</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row20_col1\" class=\"data row20 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row21\" class=\"row_heading level0 row21\" >NumBank2NatlTradesWHighUtilization</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row21_col0\" class=\"data row21 col0\" >1</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row21_col1\" class=\"data row21 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row22\" class=\"row_heading level0 row22\" >PercentTradesWBalance</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row22_col0\" class=\"data row22 col0\" >42</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row22_col1\" class=\"data row22 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122level0_row23\" class=\"row_heading level0 row23\" >RiskPerformance</th>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row23_col0\" class=\"data row23 col0\" >Good</td>\n",
       "                        <td id=\"T_4b4f4834_3cfc_11ea_8596_acde48001122row23_col1\" class=\"data row23 col1\" >Good</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16a4975c0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xpp = delta_pp\n",
    "classes = [ class_names[np.argmax(nn.predict_proba(X))], class_names[np.argmax(nn.predict_proba(Xpp))]]\n",
    "\n",
    "print(\"PP for Sample:\", idx)\n",
    "print(\"Prediction(Xpp) :\", class_names[np.argmax(nn.predict_proba(Xpp))])\n",
    "print(\"Prediction probabilities for Xpp:\", nn.predict_proba(Xpp))\n",
    "print(\"\")\n",
    "\n",
    "X_re = rescale(X) # Convert values back to original scale from normalized\n",
    "adv_pp_re = rescale(adv_pp)\n",
    "Xpp_re = X_re - adv_pp_re\n",
    "Xpp_re = np.around(Xpp_re.astype(np.double), 2)\n",
    "Xpp_re[Xpp_re < 1e-4] = 0\n",
    "\n",
    "X2 = np.vstack((X_re, Xpp_re))\n",
    "\n",
    "dfpp = pd.DataFrame.from_records(X2.astype('double')) # Showcase a dataframe for the original point and PP\n",
    "dfpp[23] = classes\n",
    "dfpp.columns = df.columns\n",
    "dfpp.rename(index={0:'X',1:'X_PP'}, inplace=True)\n",
    "dfppt = dfpp.transpose()\n",
    "\n",
    "dfppt.style.apply(highlight_ce, col='X_PP', ncols=2, axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fi = abs(Xpp_re.astype('double'))/np.std(x_train.astype('double'), axis=0) # Compute PP feature importance\n",
    "    \n",
    "objects = df.columns[-2::-1]\n",
    "y_pos = np.arange(len(objects)) # Get input feature names\n",
    "performance = fi[0, -1::-1]\n",
    "\n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5) # Bar chart\n",
    "plt.yticks(y_pos, objects) # Plot feature names on y-axis\n",
    "plt.xlabel('weight') #x-label\n",
    "plt.title('PP (feature importance)') # Figure heading\n",
    "\n",
    "plt.show()    # Display the feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation:  \n",
    "We observe that the applicant 8's loan application would still have been accepted even if the consolidated risk marker score (i.e. ExternalRiskEstimate) reduced from 82 to around 40, application was on file (i.e. AverageMlnFile) for close to 70 months and number of satisfactory trades (i.e. NumSatisfactoryTrades) reduced from 22 to almost single digits.\n",
    "\n",
    "_Note that explanations may change a bit based on equivalent values in a local minima._"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}