Actors Dataset/Elliptic++_Actors_Classification.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Elliptic++ Actors (Wallet Addresses) Dataset**\n",
        "\n",
        "***Note: \"Actor\" refers to a Bitcoin wallet address***\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "\n",
        "Released by: Youssef Elmougy, Ling Liu\n",
        "\n",
        "\n",
        "\n",
        "School of Computer Science, Georgia Institute of Technology\n",
        "\n",
        "Contact: yelmougy3@gatech.edu\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Github Repository: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)\n",
        "\n",
        "\n",
        "If you use our dataset in your work, please cite our paper:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ">> Youssef Elmougy and Ling Liu. 2023. Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "O34u-DVsX4jx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [SETUP] Import libraries and csv files "
      ],
      "metadata": {
        "id": "ReHrhaPiaiI-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download dataset from: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)"
      ],
      "metadata": {
        "id": "TLi0Zc7j6Rb6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUbJT_J-A1Mw",
        "outputId": "4020ff04-efa0-4b11-be41-a8507f0bf55e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/wallets_features.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/wallets_classes.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/AddrAddr_edgelist.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/AddrTx_edgelist.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/TxAddr_edgelist.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/wallets_features_classes_combined.csv ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz7WtWhG6MtI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "076dbd94-a606-4648-b1c7-c1839a9b5e79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import networkx as nx\n",
        "import plotly.graph_objs as go \n",
        "import plotly.offline as py \n",
        "import math\n",
        "from collections import Counter\n",
        "\n",
        "!pip install -U ipython \n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "InteractiveShell.ast_node_interactivity = 'all'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn.base import clone \n",
        "\n",
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "TKJFAkVLp34j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install eli5\n",
        "import eli5\n",
        "from eli5.sklearn import PermutationImportance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRW1hh3S4pbS",
        "outputId": "5eb48ea7-b0dd-4804-aaf3-6e438b3eef65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: eli5\n",
            "  Building wheel for eli5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for eli5: filename=eli5-0.13.0-py2.py3-none-any.whl size=107748 sha256=4ad64e3b8b283172c1ae01f750cdea27fcc42eeef533a5a0428c77fa474f08d2\n",
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            "Successfully built eli5\n",
            "Installing collected packages: jinja2, eli5\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 2.11.3\n",
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            "      Successfully uninstalled Jinja2-2.11.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed eli5-0.13.0 jinja2-3.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Actors Dataset Overview\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section loads the 5 csv files (wallets_features, wallets_classes, AddrAddr_edgelist, AddrTx_edgelist, TxAddr_edgelist) and provides a quick overview of the dataset structure and features."
      ],
      "metadata": {
        "id": "y3JLmL3SfJqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load saved actors dataset csv files:"
      ],
      "metadata": {
        "id": "ZcdjXmV8gr8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nActors features: \\n\")\n",
        "df_wallets_features = pd.read_csv(\"wallets_features.csv\")\n",
        "df_wallets_features\n",
        "\n",
        "print(\"\\nActors Classes: \\n\")\n",
        "df_wallets_classes = pd.read_csv(\"wallets_classes.csv\")\n",
        "df_wallets_classes\n",
        "\n",
        "print(\"\\nAddress-Address edgelist: \\n\") #Actor Interaction graph edgelist\n",
        "df_AddrAddr_edgelist = pd.read_csv(\"AddrAddr_edgelist.csv\")\n",
        "df_AddrAddr_edgelist\n",
        "\n",
        "print(\"\\nAddress-Transaction edgelist: \\n\") #Address-Transaction graph edgelist\n",
        "df_AddrTx_edgelist = pd.read_csv(\"AddrTx_edgelist.csv\")\n",
        "df_AddrTx_edgelist\n",
        "\n",
        "print(\"\\nTransaction-Address edgelist: \\n\") #Address-Transaction graph edgelist\n",
        "df_TxAddr_edgelist = pd.read_csv(\"TxAddr_edgelist.csv\")\n",
        "df_TxAddr_edgelist\n",
        "\n",
        "# combined features and classes csv files for convenience\n",
        "df_wallets_features_classes_combined = pd.read_csv(\"wallets_features_classes_combined.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dNNEwGmae2Eo",
        "outputId": "fe1af67e-d31b-49bf-e19b-7f93dd6fdb98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Actors features: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    address  Time step  num_txs_as_sender  \\\n",
              "0         111112TykSw72ztDN2WJger4cynzWYC5w         25                0.0   \n",
              "1         1111DAYXhoxZx2tsRnzimfozo783x1yC2         25                0.0   \n",
              "2         1111DAYXhoxZx2tsRnzimfozo783x1yC2         29                0.0   \n",
              "3         1111DAYXhoxZx2tsRnzimfozo783x1yC2         39                0.0   \n",
              "4         1111DAYXhoxZx2tsRnzimfozo783x1yC2         39                0.0   \n",
              "...                                     ...        ...                ...   \n",
              "1268255  3R2Uw5MRdSSigp8AjfT7K5es6Hupm4qLSq         31                1.0   \n",
              "1268256  3R2VBFbqHGC4bQ7b4ixN4jZTdv7RMbEYtf         44                0.0   \n",
              "1268257  3R2WFmRwbDeo3rMVVu5J3jjMxAuQYYWAid          9                0.0   \n",
              "1268258  3R2WTZGYLmbJQyoDSBftJsPRvF1mSEtkh6          3                0.0   \n",
              "1268259  3R2bm6iqbzcVpEeFCSFarS56b56QfjyiSW          9                1.0   \n",
              "\n",
              "         num_txs_as receiver  first_block_appeared_in  last_block_appeared_in  \\\n",
              "0                        1.0                 439586.0                439586.0   \n",
              "1                        8.0                 439589.0                485959.0   \n",
              "2                        8.0                 439589.0                485959.0   \n",
              "3                        8.0                 439589.0                485959.0   \n",
              "4                        8.0                 439589.0                485959.0   \n",
              "...                      ...                      ...                     ...   \n",
              "1268255                  1.0                 451684.0                451685.0   \n",
              "1268256                  1.0                 477895.0                477895.0   \n",
              "1268257                  1.0                 407342.0                407342.0   \n",
              "1268258                  1.0                 395235.0                395235.0   \n",
              "1268259                  0.0                 407333.0                407333.0   \n",
              "\n",
              "         lifetime_in_blocks  total_txs  first_sent_block  \\\n",
              "0                       0.0        1.0               0.0   \n",
              "1                   46370.0        8.0               0.0   \n",
              "2                   46370.0        8.0               0.0   \n",
              "3                   46370.0        8.0               0.0   \n",
              "4                   46370.0        8.0               0.0   \n",
              "...                     ...        ...               ...   \n",
              "1268255                 1.0        2.0          451685.0   \n",
              "1268256                 0.0        1.0               0.0   \n",
              "1268257                 0.0        1.0               0.0   \n",
              "1268258                 0.0        1.0               0.0   \n",
              "1268259                 0.0        1.0          407333.0   \n",
              "\n",
              "         first_received_block  ...  blocks_btwn_output_txs_min  \\\n",
              "0                    439586.0  ...                         0.0   \n",
              "1                    439589.0  ...                         0.0   \n",
              "2                    439589.0  ...                         0.0   \n",
              "3                    439589.0  ...                         0.0   \n",
              "4                    439589.0  ...                         0.0   \n",
              "...                       ...  ...                         ...   \n",
              "1268255              451684.0  ...                         0.0   \n",
              "1268256              477895.0  ...                         0.0   \n",
              "1268257              407342.0  ...                         0.0   \n",
              "1268258              395235.0  ...                         0.0   \n",
              "1268259                   0.0  ...                         0.0   \n",
              "\n",
              "         blocks_btwn_output_txs_max  blocks_btwn_output_txs_mean  \\\n",
              "0                               0.0                     0.000000   \n",
              "1                           20164.0                  6624.285714   \n",
              "2                           20164.0                  6624.285714   \n",
              "3                           20164.0                  6624.285714   \n",
              "4                           20164.0                  6624.285714   \n",
              "...                             ...                          ...   \n",
              "1268255                         0.0                     0.000000   \n",
              "1268256                         0.0                     0.000000   \n",
              "1268257                         0.0                     0.000000   \n",
              "1268258                         0.0                     0.000000   \n",
              "1268259                         0.0                     0.000000   \n",
              "\n",
              "         blocks_btwn_output_txs_median  num_addr_transacted_multiple  \\\n",
              "0                                  0.0                           0.0   \n",
              "1                               8060.0                           0.0   \n",
              "2                               8060.0                           0.0   \n",
              "3                               8060.0                           0.0   \n",
              "4                               8060.0                           0.0   \n",
              "...                                ...                           ...   \n",
              "1268255                            0.0                           0.0   \n",
              "1268256                            0.0                           0.0   \n",
              "1268257                            0.0                           0.0   \n",
              "1268258                            0.0                           0.0   \n",
              "1268259                            0.0                           0.0   \n",
              "\n",
              "         transacted_w_address_total  transacted_w_address_min  \\\n",
              "0                              24.0                       1.0   \n",
              "1                               8.0                       1.0   \n",
              "2                               8.0                       1.0   \n",
              "3                               8.0                       1.0   \n",
              "4                               8.0                       1.0   \n",
              "...                             ...                       ...   \n",
              "1268255                         3.0                       1.0   \n",
              "1268256                         1.0                       1.0   \n",
              "1268257                         1.0                       1.0   \n",
              "1268258                         1.0                       1.0   \n",
              "1268259                         1.0                       1.0   \n",
              "\n",
              "         transacted_w_address_max  transacted_w_address_mean  \\\n",
              "0                             1.0                        1.0   \n",
              "1                             1.0                        1.0   \n",
              "2                             1.0                        1.0   \n",
              "3                             1.0                        1.0   \n",
              "4                             1.0                        1.0   \n",
              "...                           ...                        ...   \n",
              "1268255                       1.0                        1.0   \n",
              "1268256                       1.0                        1.0   \n",
              "1268257                       1.0                        1.0   \n",
              "1268258                       1.0                        1.0   \n",
              "1268259                       1.0                        1.0   \n",
              "\n",
              "         transacted_w_address_median  \n",
              "0                                1.0  \n",
              "1                                1.0  \n",
              "2                                1.0  \n",
              "3                                1.0  \n",
              "4                                1.0  \n",
              "...                              ...  \n",
              "1268255                          1.0  \n",
              "1268256                          1.0  \n",
              "1268257                          1.0  \n",
              "1268258                          1.0  \n",
              "1268259                          1.0  \n",
              "\n",
              "[1268260 rows x 57 columns]"
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              "      <th>address</th>\n",
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              "      <th>num_addr_transacted_multiple</th>\n",
              "      <th>transacted_w_address_total</th>\n",
              "      <th>transacted_w_address_min</th>\n",
              "      <th>transacted_w_address_max</th>\n",
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          "metadata": {},
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              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Transaction-Address edgelist: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             txId                      output_address\n",
              "0       230325127  1GASxu5nMntiRKdVtTVRvEbP965G51bhHH\n",
              "1       230325127  14YRXHHof4BY1TVxN5FqYPcEdpmXiYT78a\n",
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              "4        86875675  1Kk1NVYnCE8ALXDhgMM6HqTt1jDSvi6QBA\n",
              "...           ...                                 ...\n",
              "837119  157659306  3MfN5to5K5be2RupWE8rjJHQ6V9L8ypWeh\n",
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              "837122  157670868  1JERHCgwHG2Z7T3KjNpEwj3fJNX8vSfCX2\n",
              "837123  157670868  15T4xJj6XxyPZWGLPR3S72hD9f9YzUQf36\n",
              "\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data structure for an example actor (address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K):"
      ],
      "metadata": {
        "id": "5Qw43a6xe9rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nwallets_features.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\\n\")\n",
        "df_wallets_features[df_wallets_features['address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K']\n",
        "\n",
        "print(\"\\nwallets_classes.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\\n\")\n",
        "df_wallets_classes[df_wallets_classes['address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K']\n",
        "\n",
        "print(\"\\nAddrAddr_edgelist.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K0\\n\")\n",
        "df_AddrAddr_edgelist[(df_AddrAddr_edgelist['input_address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K') | (df_AddrAddr_edgelist['output_address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K')]\n",
        "\n",
        "print(\"\\nAddrTx_edgelist.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K0\\n\")\n",
        "df_AddrTx_edgelist[df_AddrTx_edgelist['input_address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K']\n",
        "\n",
        "print(\"\\nTxAddr_edgelist.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K0\\n\")\n",
        "df_TxAddr_edgelist[df_TxAddr_edgelist['output_address']=='39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BHp9b7S1e1-F",
        "outputId": "0fd2f605-9078-4941-ef43-e98e6a67d535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "wallets_features.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    address  Time step  num_txs_as_sender  \\\n",
              "1149250  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         23              420.0   \n",
              "1149251  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         23              420.0   \n",
              "1149252  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         23              420.0   \n",
              "1149253  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         23              420.0   \n",
              "1149254  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         24              420.0   \n",
              "...                                     ...        ...                ...   \n",
              "1149666  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         32              420.0   \n",
              "1149667  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         32              420.0   \n",
              "1149668  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         32              420.0   \n",
              "1149669  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         32              420.0   \n",
              "1149670  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K         32              420.0   \n",
              "\n",
              "         num_txs_as receiver  first_block_appeared_in  last_block_appeared_in  \\\n",
              "1149250                  1.0                 435559.0                453704.0   \n",
              "1149251                  1.0                 435559.0                453704.0   \n",
              "1149252                  1.0                 435559.0                453704.0   \n",
              "1149253                  1.0                 435559.0                453704.0   \n",
              "1149254                  1.0                 435559.0                453704.0   \n",
              "...                      ...                      ...                     ...   \n",
              "1149666                  1.0                 435559.0                453704.0   \n",
              "1149667                  1.0                 435559.0                453704.0   \n",
              "1149668                  1.0                 435559.0                453704.0   \n",
              "1149669                  1.0                 435559.0                453704.0   \n",
              "1149670                  1.0                 435559.0                453704.0   \n",
              "\n",
              "         lifetime_in_blocks  total_txs  first_sent_block  \\\n",
              "1149250             18145.0      421.0          435559.0   \n",
              "1149251             18145.0      421.0          435559.0   \n",
              "1149252             18145.0      421.0          435559.0   \n",
              "1149253             18145.0      421.0          435559.0   \n",
              "1149254             18145.0      421.0          435559.0   \n",
              "...                     ...        ...               ...   \n",
              "1149666             18145.0      421.0          435559.0   \n",
              "1149667             18145.0      421.0          435559.0   \n",
              "1149668             18145.0      421.0          435559.0   \n",
              "1149669             18145.0      421.0          435559.0   \n",
              "1149670             18145.0      421.0          435559.0   \n",
              "\n",
              "         first_received_block  ...  blocks_btwn_output_txs_min  \\\n",
              "1149250              451682.0  ...                         0.0   \n",
              "1149251              451682.0  ...                         0.0   \n",
              "1149252              451682.0  ...                         0.0   \n",
              "1149253              451682.0  ...                         0.0   \n",
              "1149254              451682.0  ...                         0.0   \n",
              "...                       ...  ...                         ...   \n",
              "1149666              451682.0  ...                         0.0   \n",
              "1149667              451682.0  ...                         0.0   \n",
              "1149668              451682.0  ...                         0.0   \n",
              "1149669              451682.0  ...                         0.0   \n",
              "1149670              451682.0  ...                         0.0   \n",
              "\n",
              "         blocks_btwn_output_txs_max  blocks_btwn_output_txs_mean  \\\n",
              "1149250                         0.0                          0.0   \n",
              "1149251                         0.0                          0.0   \n",
              "1149252                         0.0                          0.0   \n",
              "1149253                         0.0                          0.0   \n",
              "1149254                         0.0                          0.0   \n",
              "...                             ...                          ...   \n",
              "1149666                         0.0                          0.0   \n",
              "1149667                         0.0                          0.0   \n",
              "1149668                         0.0                          0.0   \n",
              "1149669                         0.0                          0.0   \n",
              "1149670                         0.0                          0.0   \n",
              "\n",
              "         blocks_btwn_output_txs_median  num_addr_transacted_multiple  \\\n",
              "1149250                            0.0                          17.0   \n",
              "1149251                            0.0                          17.0   \n",
              "1149252                            0.0                          17.0   \n",
              "1149253                            0.0                          17.0   \n",
              "1149254                            0.0                          17.0   \n",
              "...                                ...                           ...   \n",
              "1149666                            0.0                          17.0   \n",
              "1149667                            0.0                          17.0   \n",
              "1149668                            0.0                          17.0   \n",
              "1149669                            0.0                          17.0   \n",
              "1149670                            0.0                          17.0   \n",
              "\n",
              "         transacted_w_address_total  transacted_w_address_min  \\\n",
              "1149250                       495.0                       1.0   \n",
              "1149251                       495.0                       1.0   \n",
              "1149252                       495.0                       1.0   \n",
              "1149253                       495.0                       1.0   \n",
              "1149254                       495.0                       1.0   \n",
              "...                             ...                       ...   \n",
              "1149666                       495.0                       1.0   \n",
              "1149667                       495.0                       1.0   \n",
              "1149668                       495.0                       1.0   \n",
              "1149669                       495.0                       1.0   \n",
              "1149670                       495.0                       1.0   \n",
              "\n",
              "         transacted_w_address_max  transacted_w_address_mean  \\\n",
              "1149250                       3.0                   1.046512   \n",
              "1149251                       3.0                   1.046512   \n",
              "1149252                       3.0                   1.046512   \n",
              "1149253                       3.0                   1.046512   \n",
              "1149254                       3.0                   1.046512   \n",
              "...                           ...                        ...   \n",
              "1149666                       3.0                   1.046512   \n",
              "1149667                       3.0                   1.046512   \n",
              "1149668                       3.0                   1.046512   \n",
              "1149669                       3.0                   1.046512   \n",
              "1149670                       3.0                   1.046512   \n",
              "\n",
              "         transacted_w_address_median  \n",
              "1149250                          1.0  \n",
              "1149251                          1.0  \n",
              "1149252                          1.0  \n",
              "1149253                          1.0  \n",
              "1149254                          1.0  \n",
              "...                              ...  \n",
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              "1149668                          1.0  \n",
              "1149669                          1.0  \n",
              "1149670                          1.0  \n",
              "\n",
              "[421 rows x 57 columns]"
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              "      <th>Time step</th>\n",
              "      <th>num_txs_as_sender</th>\n",
              "      <th>num_txs_as receiver</th>\n",
              "      <th>first_block_appeared_in</th>\n",
              "      <th>last_block_appeared_in</th>\n",
              "      <th>lifetime_in_blocks</th>\n",
              "      <th>total_txs</th>\n",
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              "      <th>blocks_btwn_output_txs_min</th>\n",
              "      <th>blocks_btwn_output_txs_max</th>\n",
              "      <th>blocks_btwn_output_txs_mean</th>\n",
              "      <th>blocks_btwn_output_txs_median</th>\n",
              "      <th>num_addr_transacted_multiple</th>\n",
              "      <th>transacted_w_address_total</th>\n",
              "      <th>transacted_w_address_min</th>\n",
              "      <th>transacted_w_address_max</th>\n",
              "      <th>transacted_w_address_mean</th>\n",
              "      <th>transacted_w_address_median</th>\n",
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              "      <th>1149250</th>\n",
              "      <td>39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K</td>\n",
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              "      <td>39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K</td>\n",
              "      <td>23</td>\n",
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              "      <td>39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K</td>\n",
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              "      <td>39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K</td>\n",
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              "  </tbody>\n",
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              "<p>421 rows × 57 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f201a3b-9a7a-41b3-9830-a75d1c614310')\"\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "wallets_classes.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
            "\n"
          ]
        },
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          "data": {
            "text/plain": [
              "                                   address  class\n",
              "745591  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K      1"
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              "      <th>745591</th>\n",
              "      <td>39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K</td>\n",
              "      <td>1</td>\n",
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          },
          "metadata": {},
          "execution_count": 6
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        {
          "output_type": "stream",
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          "text": [
            "\n",
            "AddrAddr_edgelist.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K0\n",
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              "1317522  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K   \n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "TxAddr_edgelist.csv for address = 39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K0\n",
            "\n"
          ]
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              "             txId                      output_address\n",
              "431088  291338524  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "431200   88250674  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "431209  331553508  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
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              "436269  289228146  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "...           ...                                 ...\n",
              "539069  355110135  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "539071  355110139  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
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              "539074  355110144  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "539076   69064953  39sfuA8pY4UfybgEZi7uvA13jkGzZpsg5K\n",
              "\n",
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          },
          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Actors features --- class, 28 transaction-related features, 27 time-related features:\n",
        "\n",
        "![actors_features.png](data:image/png;base64,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)\n"
      ],
      "metadata": {
        "id": "moS6bxoLg1Pk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_wallets_features.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpljxgT7k49T",
        "outputId": "ee920f81-831e-411b-e840-5aaae0c6e018"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['address', 'Time step', 'num_txs_as_sender', 'num_txs_as receiver',\n",
              "       'first_block_appeared_in', 'last_block_appeared_in',\n",
              "       'lifetime_in_blocks', 'total_txs', 'first_sent_block',\n",
              "       'first_received_block', 'num_timesteps_appeared_in',\n",
              "       'btc_transacted_total', 'btc_transacted_min', 'btc_transacted_max',\n",
              "       'btc_transacted_mean', 'btc_transacted_median', 'btc_sent_total',\n",
              "       'btc_sent_min', 'btc_sent_max', 'btc_sent_mean', 'btc_sent_median',\n",
              "       'btc_received_total', 'btc_received_min', 'btc_received_max',\n",
              "       'btc_received_mean', 'btc_received_median', 'fees_total', 'fees_min',\n",
              "       'fees_max', 'fees_mean', 'fees_median', 'fees_as_share_total',\n",
              "       'fees_as_share_min', 'fees_as_share_max', 'fees_as_share_mean',\n",
              "       'fees_as_share_median', 'blocks_btwn_txs_total', 'blocks_btwn_txs_min',\n",
              "       'blocks_btwn_txs_max', 'blocks_btwn_txs_mean', 'blocks_btwn_txs_median',\n",
              "       'blocks_btwn_input_txs_total', 'blocks_btwn_input_txs_min',\n",
              "       'blocks_btwn_input_txs_max', 'blocks_btwn_input_txs_mean',\n",
              "       'blocks_btwn_input_txs_median', 'blocks_btwn_output_txs_total',\n",
              "       'blocks_btwn_output_txs_min', 'blocks_btwn_output_txs_max',\n",
              "       'blocks_btwn_output_txs_mean', 'blocks_btwn_output_txs_median',\n",
              "       'num_addr_transacted_multiple', 'transacted_w_address_total',\n",
              "       'transacted_w_address_min', 'transacted_w_address_max',\n",
              "       'transacted_w_address_mean', 'transacted_w_address_median'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LLgHsC-abwGa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Machine Learning Model Classification\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section does data preprocessing, creates the training and testing sets, and runs the Logistic Regression, Random Forest, Multilayer Perceptrons, and XGBoost models as well as the ensembles on the dataset.\n",
        "<br>\n",
        "\n",
        "It's important to note that 2 types of classifications can be done: (i) classify addresses using their features, (ii) classify addresses across time steps using their features and temporal interactions. The former is more intuitive, thus, shown in this tutorial. \n"
      ],
      "metadata": {
        "id": "_3cwbmq8oi6-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create set of addresses, their class label, and all their features for classification of (i) from above:"
      ],
      "metadata": {
        "id": "9g-EEKP0H7tp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_wallets_classification = df_wallets_features_classes_combined\n",
        "df_wallets_classification = df_wallets_classification.drop(columns=['Time step']).drop_duplicates()\n",
        "df_wallets_classification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "I73DkK_MH7QY",
        "outputId": "afda0a81-246a-4ebb-94a7-773d406b0bc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    address  class  num_txs_as_sender  \\\n",
              "0         111112TykSw72ztDN2WJger4cynzWYC5w      2                0.0   \n",
              "1         1111DAYXhoxZx2tsRnzimfozo783x1yC2      3                0.0   \n",
              "9          1111VHuXEzHaRCgXbVwojtaP7Co3QABb      2                0.0   \n",
              "10       111218KKkh1JJFRHbwM16AwCiVCc4m7he1      3                1.0   \n",
              "12        1115LWW3xsD9jT9VRY7viCN9S34RVAAuA      2                0.0   \n",
              "...                                     ...    ...                ...   \n",
              "1268254  3R2Uw5MRdSSigp8AjfT7K5es6Hupm4qLSq      3                1.0   \n",
              "1268256  3R2VBFbqHGC4bQ7b4ixN4jZTdv7RMbEYtf      3                0.0   \n",
              "1268257  3R2WFmRwbDeo3rMVVu5J3jjMxAuQYYWAid      3                0.0   \n",
              "1268258  3R2WTZGYLmbJQyoDSBftJsPRvF1mSEtkh6      3                0.0   \n",
              "1268259  3R2bm6iqbzcVpEeFCSFarS56b56QfjyiSW      3                1.0   \n",
              "\n",
              "         num_txs_as receiver  first_block_appeared_in  last_block_appeared_in  \\\n",
              "0                        1.0                 439586.0                439586.0   \n",
              "1                        8.0                 439589.0                485959.0   \n",
              "9                        1.0                 431522.0                431522.0   \n",
              "10                       1.0                 423456.0                423456.0   \n",
              "12                       1.0                 429513.0                429513.0   \n",
              "...                      ...                      ...                     ...   \n",
              "1268254                  1.0                 451684.0                451685.0   \n",
              "1268256                  1.0                 477895.0                477895.0   \n",
              "1268257                  1.0                 407342.0                407342.0   \n",
              "1268258                  1.0                 395235.0                395235.0   \n",
              "1268259                  0.0                 407333.0                407333.0   \n",
              "\n",
              "         lifetime_in_blocks  total_txs  first_sent_block  \\\n",
              "0                       0.0        1.0               0.0   \n",
              "1                   46370.0        8.0               0.0   \n",
              "9                       0.0        1.0               0.0   \n",
              "10                      0.0        2.0          423456.0   \n",
              "12                      0.0        1.0               0.0   \n",
              "...                     ...        ...               ...   \n",
              "1268254                 1.0        2.0          451685.0   \n",
              "1268256                 0.0        1.0               0.0   \n",
              "1268257                 0.0        1.0               0.0   \n",
              "1268258                 0.0        1.0               0.0   \n",
              "1268259                 0.0        1.0          407333.0   \n",
              "\n",
              "         first_received_block  ...  blocks_btwn_output_txs_min  \\\n",
              "0                    439586.0  ...                         0.0   \n",
              "1                    439589.0  ...                         0.0   \n",
              "9                    431522.0  ...                         0.0   \n",
              "10                   423456.0  ...                         0.0   \n",
              "12                   429513.0  ...                         0.0   \n",
              "...                       ...  ...                         ...   \n",
              "1268254              451684.0  ...                         0.0   \n",
              "1268256              477895.0  ...                         0.0   \n",
              "1268257              407342.0  ...                         0.0   \n",
              "1268258              395235.0  ...                         0.0   \n",
              "1268259                   0.0  ...                         0.0   \n",
              "\n",
              "         blocks_btwn_output_txs_max  blocks_btwn_output_txs_mean  \\\n",
              "0                               0.0                     0.000000   \n",
              "1                           20164.0                  6624.285714   \n",
              "9                               0.0                     0.000000   \n",
              "10                              0.0                     0.000000   \n",
              "12                              0.0                     0.000000   \n",
              "...                             ...                          ...   \n",
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    },
    {
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      "source": [
        "Data transformation on the features using MinMaxScaler:"
      ],
      "metadata": {
        "id": "r2jcou5LpnWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df_wallets_classification.columns[2:]:\n",
        "    feature = np.array(df_wallets_classification[column]).reshape(-1,1)\n",
        "    scaler = MinMaxScaler()\n",
        "    scaler.fit(feature)\n",
        "    feature_scaled = scaler.transform(feature)\n",
        "    df_wallets_classification[column] = feature_scaled.reshape(1,-1)[0]\n",
        "\n",
        "df_wallets_classification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "CI_cLQf1nM9d",
        "outputId": "e9bfced3-88f9-4e12-9b96-0699eef5a515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
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          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
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              "                                    address  class  num_txs_as_sender  \\\n",
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              "1268259  3R2bm6iqbzcVpEeFCSFarS56b56QfjyiSW      3           0.000688   \n",
              "\n",
              "         num_txs_as receiver  first_block_appeared_in  last_block_appeared_in  \\\n",
              "0                   0.001825                 0.499985                0.499985   \n",
              "1                   0.014599                 0.500015                0.979168   \n",
              "9                   0.001825                 0.416657                0.416657   \n",
              "10                  0.001825                 0.333309                0.333309   \n",
              "12                  0.001825                 0.395898                0.395898   \n",
              "...                      ...                      ...                     ...   \n",
              "1268254             0.001825                 0.624996                0.625006   \n",
              "1268256             0.001825                 0.895841                0.895841   \n",
              "1268257             0.001825                 0.166799                0.166799   \n",
              "1268258             0.001825                 0.041695                0.041695   \n",
              "1268259             0.000000                 0.166706                0.166706   \n",
              "\n",
              "         lifetime_in_blocks  total_txs  first_sent_block  \\\n",
              "0                  0.000000   0.000000          0.000000   \n",
              "1                  0.479153   0.004762          0.000000   \n",
              "9                  0.000000   0.000000          0.000000   \n",
              "10                 0.000000   0.000680          0.867782   \n",
              "12                 0.000000   0.000000          0.000000   \n",
              "...                     ...        ...               ...   \n",
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              "1268258            0.000000   0.000000          0.000000   \n",
              "1268259            0.000000   0.000000          0.834742   \n",
              "\n",
              "         first_received_block  ...  blocks_btwn_output_txs_min  \\\n",
              "0                    0.900837  ...                         0.0   \n",
              "1                    0.900843  ...                         0.0   \n",
              "9                    0.884312  ...                         0.0   \n",
              "10                   0.867782  ...                         0.0   \n",
              "12                   0.880195  ...                         0.0   \n",
              "...                       ...  ...                         ...   \n",
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              "1268256              0.979343  ...                         0.0   \n",
              "1268257              0.834760  ...                         0.0   \n",
              "1268258              0.809949  ...                         0.0   \n",
              "1268259              0.000000  ...                         0.0   \n",
              "\n",
              "         blocks_btwn_output_txs_max  blocks_btwn_output_txs_mean  \\\n",
              "0                          0.000000                     0.000000   \n",
              "1                          0.208377                     0.068456   \n",
              "9                          0.000000                     0.000000   \n",
              "10                         0.000000                     0.000000   \n",
              "12                         0.000000                     0.000000   \n",
              "...                             ...                          ...   \n",
              "1268254                    0.000000                     0.000000   \n",
              "1268256                    0.000000                     0.000000   \n",
              "1268257                    0.000000                     0.000000   \n",
              "1268258                    0.000000                     0.000000   \n",
              "1268259                    0.000000                     0.000000   \n",
              "\n",
              "         blocks_btwn_output_txs_median  num_addr_transacted_multiple  \\\n",
              "0                             0.000000                           0.0   \n",
              "1                             0.083293                           0.0   \n",
              "9                             0.000000                           0.0   \n",
              "10                            0.000000                           0.0   \n",
              "12                            0.000000                           0.0   \n",
              "...                                ...                           ...   \n",
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              "1268257                       0.000000                           0.0   \n",
              "1268258                       0.000000                           0.0   \n",
              "1268259                       0.000000                           0.0   \n",
              "\n",
              "         transacted_w_address_total  transacted_w_address_min  \\\n",
              "0                          0.000608                       0.0   \n",
              "1                          0.000185                       0.0   \n",
              "9                          0.000000                       0.0   \n",
              "10                         0.000079                       0.0   \n",
              "12                         0.000106                       0.0   \n",
              "...                             ...                       ...   \n",
              "1268254                    0.000053                       0.0   \n",
              "1268256                    0.000000                       0.0   \n",
              "1268257                    0.000000                       0.0   \n",
              "1268258                    0.000000                       0.0   \n",
              "1268259                    0.000000                       0.0   \n",
              "\n",
              "         transacted_w_address_max  transacted_w_address_mean  \\\n",
              "0                             0.0                        0.0   \n",
              "1                             0.0                        0.0   \n",
              "9                             0.0                        0.0   \n",
              "10                            0.0                        0.0   \n",
              "12                            0.0                        0.0   \n",
              "...                           ...                        ...   \n",
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              "1268258                       0.0                        0.0   \n",
              "1268259                       0.0                        0.0   \n",
              "\n",
              "         transacted_w_address_median  \n",
              "0                                0.0  \n",
              "1                                0.0  \n",
              "9                                0.0  \n",
              "10                               0.0  \n",
              "12                               0.0  \n",
              "...                              ...  \n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1111DAYXhoxZx2tsRnzimfozo783x1yC2</td>\n",
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              "      <th>9</th>\n",
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          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove 'unknown' actors\n",
        "data = df_wallets_classification.loc[(df_wallets_classification['class'] != 3), 'address']\n",
        "df_wallets_feature_selected = df_wallets_classification.loc[df_wallets_classification['address'].isin(data)]\n",
        "df_wallets_feature_selected"
      ],
      "metadata": {
        "colab": {
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          "height": 505
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        "id": "ogpHA22sI6eM",
        "outputId": "4f9b6f92-e4b1-451b-f107-a633fb25b6dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    address  class  num_txs_as_sender  \\\n",
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              "...                      ...                      ...                     ...   \n",
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              "         first_received_block  ...  blocks_btwn_output_txs_min  \\\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
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              "  </tbody>\n",
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              "<p>265354 rows × 57 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hqoZEG47J-QD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the data into training and testing sets with 70/30 split:"
      ],
      "metadata": {
        "id": "1yHvfgghreFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Goal: binary classification of 0,1\n",
        "# 0: licit, 1: illicit\n",
        "\n",
        "# change illicit (class-2) to '0' for classification\n",
        "y = df_wallets_feature_selected[['class']]\n",
        "y = y['class'].apply(lambda x: 0 if x == 2 else 1 )\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_wallets_feature_selected,y,test_size=0.30,random_state=15, shuffle=False)\n",
        "X_train = X_train.drop(columns=['address', 'class'])\n",
        "X_test = X_test.drop(columns=['address', 'class'])"
      ],
      "metadata": {
        "id": "UQ8yTO0rI6XV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run classifiers (LR, RF, MLP, XGB):"
      ],
      "metadata": {
        "id": "px1WEceJs0jm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2c05db2-bba8-4eb2-d9b4-d58ac62cdd48",
        "id": "7PfhVySoKcu7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression\n",
            "Precision: 0.477 \n",
            "Recall: 0.046 \n",
            "F1 Score: 0.083\n",
            "Micro-Average F1 Score: 0.964\n"
          ]
        }
      ],
      "source": [
        "# LOGISTIC REGRESSION (LR)\n",
        "cLR = LogisticRegression(max_iter=1000).fit(X_train.values,y_train.values)\n",
        "y_preds_LR = cLR.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_LR)\n",
        "\n",
        "print(\"Logistic Regression\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_LR, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUNTtTvdHObf",
        "outputId": "3237362a-66d2-4756-b19b-6640500c2265"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest 50 trees\n",
            "Precision: 0.911 \n",
            "Recall: 0.789 \n",
            "F1 Score: 0.845\n",
            "Micro-Average F1 Score: 0.990\n"
          ]
        }
      ],
      "source": [
        "# RANDOM FOREST (RF)\n",
        "cRF = RandomForestClassifier(n_estimators=50).fit(X_train.values,y_train.values)\n",
        "y_preds_RF = cRF.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_RF)\n",
        "\n",
        "print(\"Random Forest 50 trees\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_RF, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71pdefvzH-pj",
        "outputId": "7a048c4c-a321-41bf-9691-8429aa84830f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Multilayer Perceptron (MLP)\n",
            "Precision: 0.747 \n",
            "Recall: 0.413 \n",
            "F1 Score: 0.532\n",
            "Micro-Average F1 Score: 0.974\n"
          ]
        }
      ],
      "source": [
        "# MULTILAYER PERCEPTRON (MLP)\n",
        "cMLP = MLPClassifier(solver='adam', learning_rate_init=0.001, max_iter=500).fit(X_train.values,y_train.values)\n",
        "y_preds_MLP = cMLP.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_MLP)\n",
        "\n",
        "print(\"Multilayer Perceptron (MLP)\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_MLP, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# XGBOOST (XGB)\n",
        "cXGB = xgb.XGBClassifier(objective=\"multi:softmax\", num_class=2, random_state=42)\n",
        "cXGB.fit(X_train.values, y_train.values)\n",
        "y_preds_XGB = cXGB.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_XGB)\n",
        "\n",
        "print(\"XGBOOST\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_XGB, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-TF75xyHVMm",
        "outputId": "a55aac54-2837-48d9-8abe-06660796959a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBOOST\n",
            "Precision: 0.869 \n",
            "Recall: 0.534 \n",
            "F1 Score: 0.662\n",
            "Micro-Average F1 Score: 0.980\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run ensemble classifiers (RF+MLP, RF+XGB, MLP+XGB, RF+MLP+XGB):"
      ],
      "metadata": {
        "id": "tHTu-iSgyf-u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dictionary of our models\n",
        "estimatorsRFMLP=[('MLP', cMLP), ('RF', cRF)]\n",
        "#create our voting classifier, inputting our models\n",
        "ensembleRFMLP = VotingClassifier(estimatorsRFMLP, voting='hard')\n",
        "ensembleRFMLP.fit(X_train.values, y_train.values)\n",
        "y_preds_RFMLP = ensembleRFMLP.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_RFMLP)\n",
        "\n",
        "print(\"Ensemble: Random Forest (RF) + Multilayer Perceptron (MLP)\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_RFMLP, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8u6s6vOmZLn",
        "outputId": "779e3ae6-9276-4650-ec69-27f5f52f8cd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble: Random Forest (RF) + Multilayer Perceptron (MLP)\n",
            "Precision: 0.967 \n",
            "Recall: 0.403 \n",
            "F1 Score: 0.568\n",
            "Micro-Average F1 Score: 0.978\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dictionary of our models\n",
        "estimatorsXGBRF=[('RF', cRF), ('XGB', cXGB)]\n",
        "#create our voting classifier, inputting our models\n",
        "ensembleXGBRF = VotingClassifier(estimatorsXGBRF, voting='hard')\n",
        "ensembleXGBRF.fit(X_train.values, y_train.values)\n",
        "y_preds_XGBRF = ensembleXGBRF.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_XGBRF)\n",
        "\n",
        "print(\"Ensemble: XGBoost (XGB) + Random Forest (RF)\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_XGBRF, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rE-LCNAUv2bG",
        "outputId": "1706e369-19be-4690-b150-138976a478e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble: XGBoost (XGB) + Random Forest (RF)\n",
            "Precision: 0.959 \n",
            "Recall: 0.530 \n",
            "F1 Score: 0.682\n",
            "Micro-Average F1 Score: 0.982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dictionary of our models\n",
        "estimatorsMLPXGB=[('MLP', cMLP), ('XGB', cXGB)]\n",
        "#create our voting classifier, inputting our models\n",
        "ensembleMLPXGB = VotingClassifier(estimatorsMLPXGB, voting='hard')\n",
        "ensembleMLPXGB.fit(X_train.values, y_train.values)\n",
        "y_preds_MLPXGB = ensembleMLPXGB.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_MLPXGB)\n",
        "\n",
        "print(\"Ensemble: Multilayer Perceptron (MLP) + XGBoost\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_MLPXGB, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DNeYG7kG4qS",
        "outputId": "6746eb98-8867-487b-b052-1aa904299c16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble: Multilayer Perceptron (MLP) + XGBoost\n",
            "Precision: 0.929 \n",
            "Recall: 0.324 \n",
            "F1 Score: 0.481\n",
            "Micro-Average F1 Score: 0.975\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dictionary of our models\n",
        "estimatorsXGBRFMLP=[('XGB', cXGB_selected), ('MLP', cMLP), ('RF', cRF)]\n",
        "#create our voting classifier, inputting our models\n",
        "ensembleXGBRFMLP = VotingClassifier(estimatorsXGBRFMLP, voting='hard')\n",
        "ensembleXGBRFMLP.fit(X_train.values, y_train.values)\n",
        "y_preds_XGBRFMLP = ensembleXGBRFMLP.predict(X_test.values)\n",
        "prec,rec,f1,num = precision_recall_fscore_support(y_test.values, y_preds_XGBRFMLP)\n",
        "\n",
        "print(\"Ensemble (all): XGBoost + Random Forest (RF) + Multilayer Perceptron (MLP)\")\n",
        "print(\"Precision: %.3f \\nRecall: %.3f \\nF1 Score: %.3f\"%(prec[1],rec[1],f1[1]))\n",
        "micro_f1 = f1_score(y_test, y_preds_XGBRFMLP, average='micro')\n",
        "print(\"Micro-Average F1 Score: %.3f\"%(micro_f1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3XzOoWcm1jZ",
        "outputId": "127568ba-84ab-40b9-8e72-61a3126e9986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ensemble (all): XGBoost + Random Forest (RF) + Multilayer Perceptron (MLP)\n",
            "Precision: 0.932 \n",
            "Recall: 0.571 \n",
            "F1 Score: 0.709\n",
            "Micro-Average F1 Score: 0.983\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(LSTM not included)"
      ],
      "metadata": {
        "id": "dNR2aTas1Ej3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LRcjdSBwqaZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Acknowledgements**\n",
        "\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "\n",
        "Released by: Youssef Elmougy, Ling Liu\n",
        "\n",
        "\n",
        "\n",
        "School of Computer Science, Georgia Institute of Technology\n",
        "\n",
        "Contact: yelmougy3@gatech.edu\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Github Repository: [https://www.github.com/git-disl/EllipticPlusPlus](https://www.github.com/git-disl/EllipticPlusPlus)\n",
        "\n",
        "\n",
        "If you use our dataset in your work, please cite our paper:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ">> Youssef Elmougy and Ling Liu. 2023. Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "BwrFHYfy5hrz"
      }
    }
  ]
}