FNMA_Multi_Home.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FNMA_Multi_Home",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMDONAVx6xarB7az5l+hY58",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srujanreddy09/Mortgage-Default-Prediction-using-Deep-Learning/blob/main/FNMA_Multi_Home.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RWBT1_I-V3v"
      },
      "source": [
        "## **Importing Modules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2nTzs18lp6q"
      },
      "source": [
        "!pip install tensorflow==2.0.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opCYN_vDtTyG"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import random\n",
        "RANDOM_SEED = 42\n",
        "TRAINING_SAMPLE = 0.95\n",
        "BATCH_SIZE = 64\n",
        "LATENT_DIMENSION = 8\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
        "from scipy import sparse\n",
        "\n",
        "np.random.seed(RANDOM_SEED)\n",
        "tf.random.set_seed(RANDOM_SEED)\n",
        "random.seed(RANDOM_SEED)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDOLfW67lcOS",
        "outputId": "43044073-f531-4839-945f-ba0e05a3bbae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.0.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI_3dPLTX5DL"
      },
      "source": [
        "## **Loading the datasets**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYO_H69gxCoD"
      },
      "source": [
        "# Choosing Fannie Mae Multifamily dataset to build a preliminary model.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhFBkmci-lQB",
        "outputId": "8b4a0458-d3ee-4786-d8ed-e786c9ac4351",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Mounting the google drive where the data is present\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "root_dir = \"/content/gdrive/My Drive/\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mo-Mfiaxg_a6"
      },
      "source": [
        "# Choosing only relevant features. These can be adjusted\n",
        "relevant_features = [\"Loan Number\",\n",
        "                     \"Note Date\",\n",
        "                     \"Maturity Date - Current\",\n",
        "                     \"Reporting Period Date\",\n",
        "                    \"Loan Acquisition UPB\", \n",
        "                     \"UPB - Current\",\n",
        "                    \"Amortization Type\", \n",
        "                    \"Interest Type\", \n",
        "                    \"Loan Product Type\",\n",
        "                    \"Amortization Term\", \n",
        "                    \"Original Interest Rate\", \n",
        "                    \"Lien Position\", \n",
        "                    \"Loan Acquisition LTV\",\n",
        "                    \"Underwritten DSCR\",\n",
        "                    \"Underwritten DSCR Type\",\n",
        "                    \"Original Term\",\n",
        "                    \"Loan Ever 60+ Days Delinquent\",\n",
        "                    \"SDQ Indicator\",\n",
        "                    \"Number of Properties at Acquisition\",\n",
        "                    \"Property Acquisition Total Unit Count\",\n",
        "                    \"Specific Property Type\",\n",
        "                    \"Property State\",\n",
        "                    \"Physical Occupancy %\",\n",
        "                    \"Loan Payment Status\",\n",
        "                    \"Liquidation/Prepayment Code\",\n",
        "                    ]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9bUK9yn-xGm"
      },
      "source": [
        "# Path to the data directory\n",
        "mf_data_path = root_dir + \"FNMA/Multi_Family\"\n",
        "# Loading the csv into a dataframe\n",
        "df = pd.read_csv(mf_data_path + \"/FNMA_MF_Loan_Performance_Data_202003.csv\", usecols = relevant_features)\n",
        "# Choosing only the final status of the loans\n",
        "df = df.loc[df[\"Liquidation/Prepayment Code\"].notnull(), relevant_features]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUgdpnmhHBx_"
      },
      "source": [
        "## **Data Processing and Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJLOnjEiYtbs",
        "outputId": "5ebeb23f-afe8-4b5e-ac47-948336e5d0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "# Data Shape and Data Info\n",
        "print(df.shape)\n",
        "print(df.info())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(27895, 25)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 27895 entries, 3 to 3439320\n",
            "Data columns (total 25 columns):\n",
            " #   Column                                 Non-Null Count  Dtype  \n",
            "---  ------                                 --------------  -----  \n",
            " 0   Loan Number                            27895 non-null  int64  \n",
            " 1   Note Date                              27895 non-null  object \n",
            " 2   Maturity Date - Current                27895 non-null  object \n",
            " 3   Reporting Period Date                  27895 non-null  object \n",
            " 4   Loan Acquisition UPB                   27895 non-null  object \n",
            " 5   UPB - Current                          27895 non-null  object \n",
            " 6   Amortization Type                      27893 non-null  object \n",
            " 7   Interest Type                          27895 non-null  object \n",
            " 8   Loan Product Type                      27895 non-null  object \n",
            " 9   Amortization Term                      26868 non-null  float64\n",
            " 10  Original Interest Rate                 27895 non-null  float64\n",
            " 11  Lien Position                          27894 non-null  object \n",
            " 12  Loan Acquisition LTV                   27892 non-null  float64\n",
            " 13  Underwritten DSCR                      27886 non-null  float64\n",
            " 14  Underwritten DSCR Type                 27886 non-null  object \n",
            " 15  Original Term                          27895 non-null  int64  \n",
            " 16  Loan Ever 60+ Days Delinquent          27895 non-null  object \n",
            " 17  SDQ Indicator                          27895 non-null  object \n",
            " 18  Number of Properties at Acquisition    27558 non-null  float64\n",
            " 19  Property Acquisition Total Unit Count  27352 non-null  float64\n",
            " 20  Specific Property Type                 27895 non-null  object \n",
            " 21  Property State                         27895 non-null  object \n",
            " 22  Physical Occupancy %                   27352 non-null  float64\n",
            " 23  Loan Payment Status                    27895 non-null  object \n",
            " 24  Liquidation/Prepayment Code            27895 non-null  object \n",
            "dtypes: float64(7), int64(2), object(16)\n",
            "memory usage: 5.5+ MB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwiTe41FY57g",
        "outputId": "2f41ba13-5ec5-42ea-99d1-f04f683912f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "# Checking for missing data\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loan Number                                 0\n",
            "Note Date                                   0\n",
            "Maturity Date - Current                     0\n",
            "Reporting Period Date                       0\n",
            "Loan Acquisition UPB                        0\n",
            "UPB - Current                               0\n",
            "Amortization Type                           2\n",
            "Interest Type                               0\n",
            "Loan Product Type                           0\n",
            "Amortization Term                        1027\n",
            "Original Interest Rate                      0\n",
            "Lien Position                               1\n",
            "Loan Acquisition LTV                        3\n",
            "Underwritten DSCR                           9\n",
            "Underwritten DSCR Type                      9\n",
            "Original Term                               0\n",
            "Loan Ever 60+ Days Delinquent               0\n",
            "SDQ Indicator                               0\n",
            "Number of Properties at Acquisition       337\n",
            "Property Acquisition Total Unit Count     543\n",
            "Specific Property Type                      0\n",
            "Property State                              0\n",
            "Physical Occupancy %                      543\n",
            "Loan Payment Status                         0\n",
            "Liquidation/Prepayment Code                 0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gv-6XcsG2RfE"
      },
      "source": [
        "# Changing the data type of date features\n",
        "df[[\"Note Date\", \"Maturity Date - Current\", \"Reporting Period Date\"]] = df[[\"Note Date\", \"Maturity Date - Current\", \"Reporting Period Date\"]].astype(\"datetime64\")\n",
        "# Creating a new delta column (Maturity date - Note Date)\n",
        "df[\"Delta Maturity\"] = (df[\"Maturity Date - Current\"] - df[\"Note Date\"]).apply(lambda x: x.days)\n",
        "df.drop(columns=[\"Maturity Date - Current\", \"Note Date\", \"Reporting Period Date\"], inplace = True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXlU6iT0HmSn"
      },
      "source": [
        "# Cleaning the features corresponding to amount in USD \n",
        "df[\"Loan Acquisition UPB\"] = df[\"Loan Acquisition UPB\"].apply(lambda x: x.replace('$', '').replace(',', '')).astype('float')\n",
        "df[\"UPB - Current\"] = df[\"UPB - Current\"].apply(lambda x: x.replace('$', '').replace(',', '')).astype('float')\n",
        "# Creating a new delta column for Unpaid Principal Balance\n",
        "df[\"Delta UPB\"] = df[\"Loan Acquisition UPB\"] - df[\"UPB - Current\"]\n",
        "df.drop(columns=[\"Loan Acquisition UPB\", \"UPB - Current\"], inplace = True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgkTwfGXsjz2"
      },
      "source": [
        "# Dropping NA values from categorical features. Missing data imputation can be explored further\n",
        "df.dropna(subset=[\"Amortization Type\", \"Lien Position\", \"Underwritten DSCR Type\"], inplace=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UllB0vbl3oZE"
      },
      "source": [
        "# Filling missing data of continous feature with either mean of mode\n",
        "df[\"Amortization Term\"].fillna(df[\"Amortization Term\"].mode().iloc[0], inplace = True)\n",
        "df[\"Number of Properties at Acquisition\"].fillna(df[\"Number of Properties at Acquisition\"].mode().iloc[0], inplace = True)\n",
        "df[\"Loan Acquisition LTV\"].fillna(df[\"Loan Acquisition LTV\"].mean(), inplace = True)\n",
        "df[\"Property Acquisition Total Unit Count\"].fillna(df[\"Property Acquisition Total Unit Count\"].mean(), inplace = True)\n",
        "df[\"Physical Occupancy %\"].fillna(100.0, inplace = True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYUKFmzbn3xV"
      },
      "source": [
        "## Encoding the target variable\n",
        "# 0 - Defaulted, 1 - Not Defaulted\n",
        "df[\"Liquidation/Prepayment Code\"].replace({\n",
        "    \"Deed-in-Lieu\": 0,\n",
        "    \"Discounted Payoff\": 1,\n",
        "    \"Dissolution\": 1,\n",
        "    \"Foreclosure\": 0,\n",
        "    \"Fully Paid, Matured\": 1,\n",
        "    \"Fully Paid, Prepaid\": 1,\n",
        "    \"Fully Paid, Refinanced\": 1,\n",
        "    \"Repurchase\": 1,\n",
        "    \"Substitution\": 1,\n",
        "    \"Third Party Sale\": 0,\n",
        "    \"Other Liquidation\": 1\n",
        "}, inplace = True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EijPtdZzpzPV",
        "outputId": "9fbb2d62-0df5-4a55-de41-b4c2a70d95f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "#Check if the data is imbalanced\n",
        "sns.distplot(LabelEncoder().fit_transform(df[\"Liquidation/Prepayment Code\"]))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ec1dbe940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFzV3UgFq7Yc",
        "outputId": "baff2382-befa-47d0-dc6c-3c0245b9f371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "df[\"Liquidation/Prepayment Code\"].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    27221\n",
              "0      664\n",
              "Name: Liquidation/Prepayment Code, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nC4HO_HgQRN8"
      },
      "source": [
        "## **EDA** ##"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s98fSFZaQTxA",
        "outputId": "8068d13d-9403-41cf-9a89-df96a992f479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "# Checking multicolinearity between continous features\n",
        "#correlation matrix\n",
        "corrmat = df.corr()\n",
        "f, ax = plt.subplots(figsize=(12, 9))\n",
        "sns.heatmap(corrmat, vmax=.8, square=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ebee585c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Z1z29TARb7F"
      },
      "source": [
        "## From the above heatmap, it is clear that original term and delta maturity have a high colinearity. Hence, dropping one of them.\n",
        "df.drop(columns=[\"Delta Maturity\"], inplace=True)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HkGY0x3Q1Zt3"
      },
      "source": [
        "## **Preparing train and test splits**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54nTVSLBMXwz"
      },
      "source": [
        "## Split the data into closed and defaulted \n",
        "paid_df = df[df[\"Liquidation/Prepayment Code\"] == 1]\n",
        "default_df = df[df[\"Liquidation/Prepayment Code\"] == 0]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18G7EuytOYJH"
      },
      "source": [
        "## Split into train test\n",
        "# Shuffle the dataset \n",
        "paid_df = paid_df.sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "train_size = int(len(paid_df)*TRAINING_SAMPLE)\n",
        "\n",
        "train_df = paid_df.iloc[:train_size].drop(\"Liquidation/Prepayment Code\", axis=1)\n",
        "test_df_paid = paid_df.iloc[train_size:].sample(frac=1)\n",
        "test_df_default = default_df.sample(frac=1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZHtPNb7QWhr",
        "outputId": "5af1b278-900e-47ad-fa44-6f1a885747e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "test_df_paid[\"Liquidation/Prepayment Code\"].value_counts()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1362\n",
              "Name: Liquidation/Prepayment Code, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYBm0VOK9jW2",
        "outputId": "4a59e364-009a-48f7-91ae-5ae5d5fb8d6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "test_df_default[\"Liquidation/Prepayment Code\"].value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    664\n",
              "Name: Liquidation/Prepayment Code, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9B0vqW_zz76"
      },
      "source": [
        "test_df_paid.drop(\"Liquidation/Prepayment Code\", axis=1, inplace=True)\n",
        "test_df_default.drop(\"Liquidation/Prepayment Code\", axis=1, inplace= True)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30xvf2vFXa13"
      },
      "source": [
        "## **Encoding the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnFDc3WNYEML"
      },
      "source": [
        "categorical_variables = [\"Amortization Type\", \"Interest Type\", \"Loan Product Type\", \"Lien Position\", \"Underwritten DSCR Type\", \"Loan Ever 60+ Days Delinquent\", \"SDQ Indicator\", \"Specific Property Type\", \"Property State\", \"Loan Payment Status\"]\n",
        "target_variable = [\"Liquidation/Prepayment Code\"]\n",
        "numerical_variables = [feature for feature in df.columns if feature not in (categorical_variables + target_variable)]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IFJAohAAo6E"
      },
      "source": [
        "#One hot encoding categorical variables\n",
        "one_cat = OneHotEncoder(handle_unknown=\"ignore\")\n",
        "one_cat.fit(train_df[categorical_variables])\n",
        "\n",
        "train_cat_variables_encoded = one_cat.transform(train_df[categorical_variables])\n",
        "test_cat_paid = one_cat.transform(test_df_paid[categorical_variables])\n",
        "test_cat_default = one_cat.transform(test_df_default[categorical_variables])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zrj82DSTaCOw"
      },
      "source": [
        "# Standardizing numerical features\n",
        "sc_num = StandardScaler()\n",
        "sc_num.fit(train_df[numerical_variables])\n",
        "\n",
        "train_num_variables_encoded = sc_num.transform(train_df[numerical_variables])\n",
        "test_num_paid = sc_num.transform(test_df_paid[numerical_variables])\n",
        "test_num_default = sc_num.transform(test_df_default[numerical_variables])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvvx8fMeaj-r"
      },
      "source": [
        "## **Autoencoder**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLBu_DekaqFF"
      },
      "source": [
        "# Create Input\n",
        "train_input = sparse.hstack((train_cat_variables_encoded, train_num_variables_encoded)).toarray()\n",
        "input_shape = train_input.shape"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMefinIObui3"
      },
      "source": [
        "# Model\n",
        "autoencoder = tf.keras.models.Sequential()\n",
        "# Encoding Layers\n",
        "autoencoder.add(tf.keras.layers.Dense(input_shape[1], activation=\"relu\", input_shape = (input_shape[1], )))\n",
        "autoencoder.add(tf.keras.layers.GaussianNoise(0.05))\n",
        "autoencoder.add(tf.keras.layers.Dense(32, activation=\"relu\"))\n",
        "# Compressed representation\n",
        "autoencoder.add(tf.keras.layers.Dense(LATENT_DIMENSION, activation=\"relu\"))\n",
        "# Decoding layers\n",
        "autoencoder.add(tf.keras.layers.Dense(32, activation=\"relu\"))\n",
        "autoencoder.add(tf.keras.layers.Dense(input_shape[1], activation=\"relu\"))\n",
        "\n",
        "# Compiling Model\n",
        "autoencoder.compile(optimizer = tf.keras.optimizers.Adam(), loss = \"mae\", metrics = [\"mae\"])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsGNrY1wdH0S",
        "outputId": "98f02aba-1e73-480a-b88d-99a01589dfc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "autoencoder.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 99)                9900      \n",
            "_________________________________________________________________\n",
            "gaussian_noise (GaussianNois (None, 99)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                3200      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 8)                 264       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 32)                288       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 99)                3267      \n",
            "=================================================================\n",
            "Total params: 16,919\n",
            "Trainable params: 16,919\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMfo50so2ncr",
        "outputId": "517e0945-60ae-4331-ce77-28884b5c1e03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "source": [
        "tf.keras.utils.plot_model(autoencoder, show_shapes=True, show_layer_names=True, rankdir = \"LR\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ykdmE3rcDgP"
      },
      "source": [
        "callback=[tf.keras.callbacks.EarlyStopping(patience=5, monitor=\"val_loss\", restore_best_weights=True)]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXMmDKd2cYAW",
        "outputId": "c705fc40-b6ff-4a00-d55a-062b2e40646c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = autoencoder.fit(train_input, train_input, epochs = 100, batch_size = BATCH_SIZE, callbacks=callback, validation_split=0.2)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 20687 samples, validate on 5172 samples\n",
            "Epoch 1/100\n",
            "20687/20687 [==============================] - 3s 124us/sample - loss: 0.1084 - mae: 0.1084 - val_loss: 0.0814 - val_mae: 0.0814\n",
            "Epoch 2/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0790 - mae: 0.0790 - val_loss: 0.0757 - val_mae: 0.0757\n",
            "Epoch 3/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0752 - mae: 0.0752 - val_loss: 0.0753 - val_mae: 0.0753\n",
            "Epoch 4/100\n",
            "20687/20687 [==============================] - 2s 76us/sample - loss: 0.0745 - mae: 0.0745 - val_loss: 0.0732 - val_mae: 0.0732\n",
            "Epoch 5/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0724 - mae: 0.0724 - val_loss: 0.0720 - val_mae: 0.0720\n",
            "Epoch 6/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0716 - mae: 0.0716 - val_loss: 0.0717 - val_mae: 0.0717\n",
            "Epoch 7/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0713 - mae: 0.0713 - val_loss: 0.0715 - val_mae: 0.0715\n",
            "Epoch 8/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0712 - mae: 0.0712 - val_loss: 0.0714 - val_mae: 0.0714\n",
            "Epoch 9/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0711 - mae: 0.0711 - val_loss: 0.0713 - val_mae: 0.0713\n",
            "Epoch 10/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0709 - mae: 0.0709 - val_loss: 0.0711 - val_mae: 0.0711\n",
            "Epoch 11/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0708 - mae: 0.0708 - val_loss: 0.0712 - val_mae: 0.0712\n",
            "Epoch 12/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0693 - mae: 0.0693 - val_loss: 0.0684 - val_mae: 0.0684\n",
            "Epoch 13/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0680 - mae: 0.0680 - val_loss: 0.0683 - val_mae: 0.0683\n",
            "Epoch 14/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0679 - mae: 0.0679 - val_loss: 0.0684 - val_mae: 0.0684\n",
            "Epoch 15/100\n",
            "20687/20687 [==============================] - 1s 66us/sample - loss: 0.0678 - mae: 0.0678 - val_loss: 0.0682 - val_mae: 0.0682\n",
            "Epoch 16/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0676 - mae: 0.0676 - val_loss: 0.0675 - val_mae: 0.0675\n",
            "Epoch 17/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0669 - mae: 0.0669 - val_loss: 0.0675 - val_mae: 0.0675\n",
            "Epoch 18/100\n",
            "20687/20687 [==============================] - 1s 72us/sample - loss: 0.0663 - mae: 0.0663 - val_loss: 0.0666 - val_mae: 0.0666\n",
            "Epoch 19/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0661 - mae: 0.0661 - val_loss: 0.0666 - val_mae: 0.0666\n",
            "Epoch 20/100\n",
            "20687/20687 [==============================] - 1s 70us/sample - loss: 0.0660 - mae: 0.0660 - val_loss: 0.0665 - val_mae: 0.0665\n",
            "Epoch 21/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0659 - mae: 0.0659 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 22/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0659 - mae: 0.0659 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 23/100\n",
            "20687/20687 [==============================] - 1s 66us/sample - loss: 0.0659 - mae: 0.0659 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 24/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 25/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 26/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 27/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 28/100\n",
            "20687/20687 [==============================] - 1s 72us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0665 - val_mae: 0.0665\n",
            "Epoch 29/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0658 - mae: 0.0658 - val_loss: 0.0663 - val_mae: 0.0663\n",
            "Epoch 30/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0657 - mae: 0.0657 - val_loss: 0.0663 - val_mae: 0.0663\n",
            "Epoch 31/100\n",
            "20687/20687 [==============================] - 1s 70us/sample - loss: 0.0657 - mae: 0.0657 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 32/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0657 - mae: 0.0657 - val_loss: 0.0663 - val_mae: 0.0663\n",
            "Epoch 33/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0657 - mae: 0.0657 - val_loss: 0.0664 - val_mae: 0.0664\n",
            "Epoch 34/100\n",
            "20687/20687 [==============================] - 1s 67us/sample - loss: 0.0657 - mae: 0.0657 - val_loss: 0.0662 - val_mae: 0.0662\n",
            "Epoch 35/100\n",
            "20687/20687 [==============================] - 1s 70us/sample - loss: 0.0655 - mae: 0.0655 - val_loss: 0.0661 - val_mae: 0.0661\n",
            "Epoch 36/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0652 - mae: 0.0652 - val_loss: 0.0656 - val_mae: 0.0656\n",
            "Epoch 37/100\n",
            "20687/20687 [==============================] - 1s 72us/sample - loss: 0.0644 - mae: 0.0644 - val_loss: 0.0647 - val_mae: 0.0647\n",
            "Epoch 38/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0641 - mae: 0.0641 - val_loss: 0.0647 - val_mae: 0.0647\n",
            "Epoch 39/100\n",
            "20687/20687 [==============================] - 1s 70us/sample - loss: 0.0640 - mae: 0.0640 - val_loss: 0.0645 - val_mae: 0.0645\n",
            "Epoch 40/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0639 - mae: 0.0639 - val_loss: 0.0645 - val_mae: 0.0645\n",
            "Epoch 41/100\n",
            "20687/20687 [==============================] - 2s 74us/sample - loss: 0.0638 - mae: 0.0638 - val_loss: 0.0645 - val_mae: 0.0645\n",
            "Epoch 42/100\n",
            "20687/20687 [==============================] - 1s 70us/sample - loss: 0.0638 - mae: 0.0638 - val_loss: 0.0644 - val_mae: 0.0644\n",
            "Epoch 43/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0637 - mae: 0.0637 - val_loss: 0.0643 - val_mae: 0.0643\n",
            "Epoch 44/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0637 - mae: 0.0637 - val_loss: 0.0642 - val_mae: 0.0642\n",
            "Epoch 45/100\n",
            "20687/20687 [==============================] - 1s 71us/sample - loss: 0.0636 - mae: 0.0636 - val_loss: 0.0642 - val_mae: 0.0642\n",
            "Epoch 46/100\n",
            "20687/20687 [==============================] - 2s 73us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0642 - val_mae: 0.0642\n",
            "Epoch 47/100\n",
            "20687/20687 [==============================] - 2s 74us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0642 - val_mae: 0.0642\n",
            "Epoch 48/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0640 - val_mae: 0.0640\n",
            "Epoch 49/100\n",
            "20687/20687 [==============================] - 1s 69us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0641 - val_mae: 0.0641\n",
            "Epoch 50/100\n",
            "20687/20687 [==============================] - 1s 66us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0640 - val_mae: 0.0640\n",
            "Epoch 51/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0635 - mae: 0.0635 - val_loss: 0.0641 - val_mae: 0.0641\n",
            "Epoch 52/100\n",
            "20687/20687 [==============================] - 1s 68us/sample - loss: 0.0634 - mae: 0.0634 - val_loss: 0.0642 - val_mae: 0.0642\n",
            "Epoch 53/100\n",
            "20687/20687 [==============================] - 1s 66us/sample - loss: 0.0634 - mae: 0.0634 - val_loss: 0.0640 - val_mae: 0.0640\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u92nk7OX7GNU"
      },
      "source": [
        "## **Reconstructing Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7t5ILbp8xtOS"
      },
      "source": [
        "test_input_paid = sparse.hstack((test_cat_paid, test_num_paid)).toarray()\n",
        "reconstructions_paid = autoencoder.predict(test_input_paid)\n",
        "\n",
        "test_input_default = sparse.hstack((test_cat_default, test_num_default)).toarray()\n",
        "reconstructions_default = autoencoder.predict(test_input_default)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hE6XtE-BPYv"
      },
      "source": [
        "mae_paid = np.mean(np.abs(test_input_paid - reconstructions_paid), axis=1)\n",
        "mae_default = np.mean(np.abs(test_input_default - reconstructions_default), axis=1)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEx63CshB4AS",
        "outputId": "1937577d-eeeb-40f2-ceba-56c99b5e9d3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mae_paid.mean()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06436724424654929"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGZkjaSYE7el",
        "outputId": "5ccbb090-35d6-4f17-f302-0c24e60dfa96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mae_default.mean()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10563797903040247"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5d3mPic-gUi"
      },
      "source": [
        "# Future Plan of Action\n",
        "Determine Thresholds\n",
        "\n",
        "Scale this model to larger dataset(Single Family mortgages). Will increase the imbalance which will improve the autoencoder\n",
        "\n",
        "Incorporate external macroeconomic factors\n",
        "\n",
        "Try other architecutres\n",
        "\n",
        "HyperParameter Tuning, Feature Engineering\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhZuQ_uFJnJs"
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    }
  ]
}