Transactions Dataset/Elliptic++_Transactions_Feature_Analysis.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Elliptic++ Transactions Dataset**\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": "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": "6e64f92e-3bac-4dbc-b4f2-d57489140473"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_features.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_classes.csv ./\n",
        "!cp drive/My\\ Drive/Elliptic++\\ Dataset/txs_edgelist.csv ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz7WtWhG6MtI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba02acc2-6f29-4d71-c5df-2d6bc5c61453"
      },
      "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",
        "\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": "af64ff37-eb12-4dd8-faba-628b9b695aec"
      },
      "execution_count": null,
      "outputs": [
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          "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=138c7b7afc731dc3a39e6bd4e82b7a9fa2f965be699cbe7c19820bc09cf8bfa4\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/ac/25/ffcd87ef8f9b1eec324fdf339359be71f22612459d8c75d89c\n",
            "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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            "\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": [
        "## Transactions Dataset Overview\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section loads the 3 csv files (txs_features, txs_classes, txs_edgelist) and provides a quick overview of the dataset structure and features."
      ],
      "metadata": {
        "id": "y3JLmL3SfJqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load saved transactions dataset csv files:"
      ],
      "metadata": {
        "id": "ZcdjXmV8gr8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTransaction features: \\n\")\n",
        "df_txs_features = pd.read_csv(\"txs_features.csv\")\n",
        "df_txs_features\n",
        "\n",
        "print(\"\\nTransaction classes: \\n\")\n",
        "df_txs_classes = pd.read_csv(\"txs_classes.csv\")\n",
        "df_txs_classes\n",
        "\n",
        "print(\"\\nTransaction-Transaction edgelist: \\n\")\n",
        "df_txs_edgelist = pd.read_csv(\"txs_edgelist.csv\")\n",
        "df_txs_edgelist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dNNEwGmae2Eo",
        "outputId": "3ed76d40-095b-42dc-8362-5eb54958b88a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Transaction features: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             txId  Time step  Local_feature_1  Local_feature_2  \\\n",
              "0            3321          1        -0.169615        -0.184668   \n",
              "1           11108          1        -0.137586        -0.184668   \n",
              "2           51816          1        -0.170103        -0.184668   \n",
              "3           68869          1        -0.114267        -0.184668   \n",
              "4           89273          1         5.202107        -0.210553   \n",
              "...           ...        ...              ...              ...   \n",
              "203764  158304003         49        -0.165622        -0.139563   \n",
              "203765  158303998         49        -0.167040        -0.139563   \n",
              "203766  158303966         49        -0.167040        -0.139563   \n",
              "203767  161526077         49        -0.172212        -0.139573   \n",
              "203768  194103537         49        -0.172212        -0.139573   \n",
              "\n",
              "        Local_feature_3  Local_feature_4  Local_feature_5  Local_feature_6  \\\n",
              "0             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "1             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "2             -1.201369        -0.121970        -0.043875        -0.113002   \n",
              "3             -1.201369         0.028105        -0.043875        -0.113002   \n",
              "4             -1.756361        -0.121970       260.090707        -0.113002   \n",
              "...                 ...              ...              ...              ...   \n",
              "203764         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203765         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203766         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203767         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "203768         1.018602        -0.121970        -0.043875        -0.113002   \n",
              "\n",
              "        Local_feature_7  Local_feature_8  ...  in_BTC_min  in_BTC_max  \\\n",
              "0             -0.061584        -0.160199  ...    0.534072    0.534072   \n",
              "1             -0.061584        -0.127429  ...    5.611878    5.611878   \n",
              "2             -0.061584        -0.160699  ...    0.456608    0.456608   \n",
              "3              0.547008        -0.161652  ...    0.308900    8.000000   \n",
              "4             -0.061584         5.335864  ...  852.164680  852.164680   \n",
              "...                 ...              ...  ...         ...         ...   \n",
              "203764        -0.061584        -0.156113  ...         NaN         NaN   \n",
              "203765        -0.061584        -0.157564  ...         NaN         NaN   \n",
              "203766        -0.061584        -0.157564  ...         NaN         NaN   \n",
              "203767        -0.061584        -0.162856  ...         NaN         NaN   \n",
              "203768        -0.061584        -0.162856  ...         NaN         NaN   \n",
              "\n",
              "        in_BTC_mean  in_BTC_median  in_BTC_total   out_BTC_min  out_BTC_max  \\\n",
              "0          0.534072       0.534072      0.534072  1.668990e-01     0.367074   \n",
              "1          5.611878       5.611878      5.611878  5.861940e-01     5.025584   \n",
              "2          0.456608       0.456608      0.456608  2.279902e-01     0.228518   \n",
              "3          3.102967       1.000000      9.308900  1.229000e+00     8.079800   \n",
              "4        852.164680     852.164680    852.164680  1.300000e-07    41.264036   \n",
              "...             ...            ...           ...           ...          ...   \n",
              "203764          NaN            NaN           NaN           NaN          NaN   \n",
              "203765          NaN            NaN           NaN           NaN          NaN   \n",
              "203766          NaN            NaN           NaN           NaN          NaN   \n",
              "203767          NaN            NaN           NaN           NaN          NaN   \n",
              "203768          NaN            NaN           NaN           NaN          NaN   \n",
              "\n",
              "        out_BTC_mean  out_BTC_median  out_BTC_total  \n",
              "0           0.266986        0.266986       0.533972  \n",
              "1           2.805889        2.805889       5.611778  \n",
              "2           0.228254        0.228254       0.456508  \n",
              "3           4.654400        4.654400       9.308800  \n",
              "4           0.065016        0.000441     852.164680  \n",
              "...              ...             ...            ...  \n",
              "203764           NaN             NaN            NaN  \n",
              "203765           NaN             NaN            NaN  \n",
              "203766           NaN             NaN            NaN  \n",
              "203767           NaN             NaN            NaN  \n",
              "203768           NaN             NaN            NaN  \n",
              "\n",
              "[203769 rows x 184 columns]"
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              "      <th></th>\n",
              "      <th>txId</th>\n",
              "      <th>Time step</th>\n",
              "      <th>Local_feature_1</th>\n",
              "      <th>Local_feature_2</th>\n",
              "      <th>Local_feature_3</th>\n",
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              "      <th>Local_feature_8</th>\n",
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              "      <th>out_BTC_max</th>\n",
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              "      <td>-0.160199</td>\n",
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              "      <td>1.668990e-01</td>\n",
              "      <td>0.367074</td>\n",
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              "      <td>11108</td>\n",
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              "      <td>5.861940e-01</td>\n",
              "      <td>5.025584</td>\n",
              "      <td>2.805889</td>\n",
              "      <td>2.805889</td>\n",
              "      <td>5.611778</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>51816</td>\n",
              "      <td>1</td>\n",
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              "      <td>-0.184668</td>\n",
              "      <td>-1.201369</td>\n",
              "      <td>-0.121970</td>\n",
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              "      <td>-0.113002</td>\n",
              "      <td>-0.061584</td>\n",
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              "      <td>...</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>0.456608</td>\n",
              "      <td>2.279902e-01</td>\n",
              "      <td>0.228518</td>\n",
              "      <td>0.228254</td>\n",
              "      <td>0.228254</td>\n",
              "      <td>0.456508</td>\n",
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              "      <td>-0.114267</td>\n",
              "      <td>-0.184668</td>\n",
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              "      <td>0.547008</td>\n",
              "      <td>-0.161652</td>\n",
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              "      <td>0.308900</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.102967</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.308900</td>\n",
              "      <td>1.229000e+00</td>\n",
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              "      <td>4.654400</td>\n",
              "      <td>4.654400</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data structure for an example transaction (txId = 272145560):"
      ],
      "metadata": {
        "id": "5Qw43a6xe9rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\ntxs_features.csv for txId = 272145560\\n\")\n",
        "df_txs_features[df_txs_features['txId']==272145560]\n",
        "\n",
        "print(\"\\ntxs_classes.csv for txId = 272145560\\n\")\n",
        "df_txs_classes[df_txs_classes['txId']==272145560]\n",
        "\n",
        "print(\"\\ntxs_edgelist.csv for txId = 272145560\\n\")\n",
        "df_txs_edgelist[(df_txs_edgelist['txId1']==272145560) | (df_txs_edgelist['txId2']==272145560)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "BHp9b7S1e1-F",
        "outputId": "757d6c2d-3c51-49f6-d0eb-2d701b52fc78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "txs_features.csv for txId=272145560\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             txId  Time step  Local_feature_1  Local_feature_2  \\\n",
              "105573  272145560         24        -0.155493        -0.107012   \n",
              "\n",
              "        Local_feature_3  Local_feature_4  Local_feature_5  Local_feature_6  \\\n",
              "105573        -1.201369         -0.12197        -0.043875        -0.113002   \n",
              "\n",
              "        Local_feature_7  Local_feature_8  ...  in_BTC_min  in_BTC_max  \\\n",
              "105573        -0.061584        -0.145749  ...      2.7732      2.7732   \n",
              "\n",
              "        in_BTC_mean  in_BTC_median  in_BTC_total  out_BTC_min  out_BTC_max  \\\n",
              "105573       2.7732         2.7732        2.7732     0.001917     2.770883   \n",
              "\n",
              "        out_BTC_mean  out_BTC_median  out_BTC_total  \n",
              "105573        1.3864          1.3864         2.7728  \n",
              "\n",
              "[1 rows x 184 columns]"
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            "\n",
            "txs_classes.csv for txId=272145560\n",
            "\n"
          ]
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              "             txId  class\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cb80618-7e36-43e1-bc3a-7916574c7b0d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "txs_edgelist.csv for txId=272145560\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            txId1      txId2\n",
              "123072  272145560  296926618\n",
              "123272  272145560  272145556\n",
              "125873  299475624  272145560"
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              "      <th>txId2</th>\n",
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              "    <tr>\n",
              "      <th>123072</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>125873</th>\n",
              "      <td>299475624</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80dae61b-ee8e-4b72-a243-d2516eb56b84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Transaction features --- 94 local features, 72 aggregate features, 17 augmented features:\n"
      ],
      "metadata": {
        "id": "moS6bxoLg1Pk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list(df_txs_features.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpljxgT7k49T",
        "outputId": "916b4dda-11d6-4f92-f10d-3d7040f10ea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['txId',\n",
              " 'Time step',\n",
              " 'class',\n",
              " 'Local_feature_1',\n",
              " 'Local_feature_2',\n",
              " 'Local_feature_3',\n",
              " 'Local_feature_4',\n",
              " 'Local_feature_5',\n",
              " 'Local_feature_6',\n",
              " 'Local_feature_7',\n",
              " 'Local_feature_8',\n",
              " 'Local_feature_9',\n",
              " 'Local_feature_10',\n",
              " 'Local_feature_11',\n",
              " 'Local_feature_12',\n",
              " 'Local_feature_13',\n",
              " 'Local_feature_14',\n",
              " 'Local_feature_15',\n",
              " 'Local_feature_16',\n",
              " 'Local_feature_17',\n",
              " 'Local_feature_18',\n",
              " 'Local_feature_19',\n",
              " 'Local_feature_20',\n",
              " 'Local_feature_21',\n",
              " 'Local_feature_22',\n",
              " 'Local_feature_23',\n",
              " 'Local_feature_24',\n",
              " 'Local_feature_25',\n",
              " 'Local_feature_26',\n",
              " 'Local_feature_27',\n",
              " 'Local_feature_28',\n",
              " 'Local_feature_29',\n",
              " 'Local_feature_30',\n",
              " 'Local_feature_31',\n",
              " 'Local_feature_32',\n",
              " 'Local_feature_33',\n",
              " 'Local_feature_34',\n",
              " 'Local_feature_35',\n",
              " 'Local_feature_36',\n",
              " 'Local_feature_37',\n",
              " 'Local_feature_38',\n",
              " 'Local_feature_39',\n",
              " 'Local_feature_40',\n",
              " 'Local_feature_41',\n",
              " 'Local_feature_42',\n",
              " 'Local_feature_43',\n",
              " 'Local_feature_44',\n",
              " 'Local_feature_45',\n",
              " 'Local_feature_46',\n",
              " 'Local_feature_47',\n",
              " 'Local_feature_48',\n",
              " 'Local_feature_49',\n",
              " 'Local_feature_50',\n",
              " 'Local_feature_51',\n",
              " 'Local_feature_52',\n",
              " 'Local_feature_53',\n",
              " 'Local_feature_54',\n",
              " 'Local_feature_55',\n",
              " 'Local_feature_56',\n",
              " 'Local_feature_57',\n",
              " 'Local_feature_58',\n",
              " 'Local_feature_59',\n",
              " 'Local_feature_60',\n",
              " 'Local_feature_61',\n",
              " 'Local_feature_62',\n",
              " 'Local_feature_63',\n",
              " 'Local_feature_64',\n",
              " 'Local_feature_65',\n",
              " 'Local_feature_66',\n",
              " 'Local_feature_67',\n",
              " 'Local_feature_68',\n",
              " 'Local_feature_69',\n",
              " 'Local_feature_70',\n",
              " 'Local_feature_71',\n",
              " 'Local_feature_72',\n",
              " 'Local_feature_73',\n",
              " 'Local_feature_74',\n",
              " 'Local_feature_75',\n",
              " 'Local_feature_76',\n",
              " 'Local_feature_77',\n",
              " 'Local_feature_78',\n",
              " 'Local_feature_79',\n",
              " 'Local_feature_80',\n",
              " 'Local_feature_81',\n",
              " 'Local_feature_82',\n",
              " 'Local_feature_83',\n",
              " 'Local_feature_84',\n",
              " 'Local_feature_85',\n",
              " 'Local_feature_86',\n",
              " 'Local_feature_87',\n",
              " 'Local_feature_88',\n",
              " 'Local_feature_89',\n",
              " 'Local_feature_90',\n",
              " 'Local_feature_91',\n",
              " 'Local_feature_92',\n",
              " 'Local_feature_93',\n",
              " 'Aggregate_feature_1',\n",
              " 'Aggregate_feature_2',\n",
              " 'Aggregate_feature_3',\n",
              " 'Aggregate_feature_4',\n",
              " 'Aggregate_feature_5',\n",
              " 'Aggregate_feature_6',\n",
              " 'Aggregate_feature_7',\n",
              " 'Aggregate_feature_8',\n",
              " 'Aggregate_feature_9',\n",
              " 'Aggregate_feature_10',\n",
              " 'Aggregate_feature_11',\n",
              " 'Aggregate_feature_12',\n",
              " 'Aggregate_feature_13',\n",
              " 'Aggregate_feature_14',\n",
              " 'Aggregate_feature_15',\n",
              " 'Aggregate_feature_16',\n",
              " 'Aggregate_feature_17',\n",
              " 'Aggregate_feature_18',\n",
              " 'Aggregate_feature_19',\n",
              " 'Aggregate_feature_20',\n",
              " 'Aggregate_feature_21',\n",
              " 'Aggregate_feature_22',\n",
              " 'Aggregate_feature_23',\n",
              " 'Aggregate_feature_24',\n",
              " 'Aggregate_feature_25',\n",
              " 'Aggregate_feature_26',\n",
              " 'Aggregate_feature_27',\n",
              " 'Aggregate_feature_28',\n",
              " 'Aggregate_feature_29',\n",
              " 'Aggregate_feature_30',\n",
              " 'Aggregate_feature_31',\n",
              " 'Aggregate_feature_32',\n",
              " 'Aggregate_feature_33',\n",
              " 'Aggregate_feature_34',\n",
              " 'Aggregate_feature_35',\n",
              " 'Aggregate_feature_36',\n",
              " 'Aggregate_feature_37',\n",
              " 'Aggregate_feature_38',\n",
              " 'Aggregate_feature_39',\n",
              " 'Aggregate_feature_40',\n",
              " 'Aggregate_feature_41',\n",
              " 'Aggregate_feature_42',\n",
              " 'Aggregate_feature_43',\n",
              " 'Aggregate_feature_44',\n",
              " 'Aggregate_feature_45',\n",
              " 'Aggregate_feature_46',\n",
              " 'Aggregate_feature_47',\n",
              " 'Aggregate_feature_48',\n",
              " 'Aggregate_feature_49',\n",
              " 'Aggregate_feature_50',\n",
              " 'Aggregate_feature_51',\n",
              " 'Aggregate_feature_52',\n",
              " 'Aggregate_feature_53',\n",
              " 'Aggregate_feature_54',\n",
              " 'Aggregate_feature_55',\n",
              " 'Aggregate_feature_56',\n",
              " 'Aggregate_feature_57',\n",
              " 'Aggregate_feature_58',\n",
              " 'Aggregate_feature_59',\n",
              " 'Aggregate_feature_60',\n",
              " 'Aggregate_feature_61',\n",
              " 'Aggregate_feature_62',\n",
              " 'Aggregate_feature_63',\n",
              " 'Aggregate_feature_64',\n",
              " 'Aggregate_feature_65',\n",
              " 'Aggregate_feature_66',\n",
              " 'Aggregate_feature_67',\n",
              " 'Aggregate_feature_68',\n",
              " 'Aggregate_feature_69',\n",
              " 'Aggregate_feature_70',\n",
              " 'Aggregate_feature_71',\n",
              " 'Aggregate_feature_72',\n",
              " 'in_txs_degree',\n",
              " 'out_txs_degree',\n",
              " 'total_BTC',\n",
              " 'fees',\n",
              " 'size',\n",
              " 'num_input_addresses',\n",
              " 'num_output_addresses',\n",
              " 'in_BTC_min',\n",
              " 'in_BTC_max',\n",
              " 'in_BTC_mean',\n",
              " 'in_BTC_median',\n",
              " 'in_BTC_total',\n",
              " 'out_BTC_min',\n",
              " 'out_BTC_max',\n",
              " 'out_BTC_mean',\n",
              " 'out_BTC_median',\n",
              " 'out_BTC_total']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1-J38vk9fKfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Analysis\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section analyzes features using scikit learn feature importances, permutation feature importance, and drop column feature importance."
      ],
      "metadata": {
        "id": "0p7g5U6R3xGp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "RF.feature_importances_:"
      ],
      "metadata": {
        "id": "7WQUMNaB4FrF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imp_df = pd.DataFrame({\n",
        "    \"Feature\": X_train.columns,\n",
        "    \"Imp\": cRF.feature_importances_\n",
        "})\n",
        "imp_df.sort_values(by=\"Imp\", ascending=False)\n",
        "imp_df_sorted = imp_df.sort_values(by=\"Imp\", ascending=False)\n",
        "imp_df_sorted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "fb194d3a-94d3-410b-abf7-368708bbcced",
        "id": "3jQTFSfTrAfF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Feature       Imp\n",
              "52   Local_feature_53  0.057675\n",
              "54   Local_feature_55  0.041891\n",
              "169              size  0.041139\n",
              "46   Local_feature_47  0.035689\n",
              "75   Local_feature_76  0.034637\n",
              "..                ...       ...\n",
              "37   Local_feature_38  0.000128\n",
              "38   Local_feature_39  0.000124\n",
              "6     Local_feature_7  0.000080\n",
              "69   Local_feature_70  0.000067\n",
              "14   Local_feature_15  0.000006\n",
              "\n",
              "[182 rows x 2 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Imp</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Local_feature_53</td>\n",
              "      <td>0.057675</td>\n",
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              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Local_feature_55</td>\n",
              "      <td>0.041891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>size</td>\n",
              "      <td>0.041139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Local_feature_47</td>\n",
              "      <td>0.035689</td>\n",
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              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Local_feature_76</td>\n",
              "      <td>0.034637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Local_feature_38</td>\n",
              "      <td>0.000128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Local_feature_39</td>\n",
              "      <td>0.000124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Local_feature_7</td>\n",
              "      <td>0.000080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Local_feature_70</td>\n",
              "      <td>0.000067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Local_feature_15</td>\n",
              "      <td>0.000006</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>182 rows × 2 columns</p>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98814eff-f9a7-464c-b5a5-8c3fda126f83 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98814eff-f9a7-464c-b5a5-8c3fda126f83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 20 and Bottom 20 features:"
      ],
      "metadata": {
        "id": "OJknTiei4P2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# top 20 features\n",
        "plt.figure(figsize=(10,10))\n",
        "imp_df_sorted[:20].iloc[::-1].plot(kind='barh',y='Imp',x='Feature',color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "85dd6432-2345-4bf3-e76f-c471b7190043",
        "id": "ESkHTa11rAfH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f626fabbc70>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bottom 20 features\n",
        "plt.figure(figsize=(10,10))\n",
        "imp_df_sorted[162:].iloc[::-1].plot(kind='barh',y='Imp',x='Feature',color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "ThNvUWZor_bh",
        "outputId": "0a8b615e-7be5-4618-bbb3-0e47d4988e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6270193520>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Permutation feature importance:"
      ],
      "metadata": {
        "id": "GJ9GWms94bzl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "perm = PermutationImportance(cRF, random_state=1).fit(X_train.values, y_train.values)\n",
        "eli5.show_weights(perm, feature_names = X_train.columns.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "TBpICSAZvFly",
        "outputId": "3270ad6a-2465-42dd-c804-a70cf1cdb294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <style>\n",
              "    table.eli5-weights tr:hover {\n",
              "        filter: brightness(85%);\n",
              "    }\n",
              "</style>\n",
              "\n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
              "    <thead>\n",
              "    <tr style=\"border: none;\">\n",
              "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
              "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tbody>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0276\n",
              "                \n",
              "                    &plusmn; 0.0013\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_53\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 89.63%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0108\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_55\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 93.19%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0059\n",
              "                \n",
              "                    &plusmn; 0.0001\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_81\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 93.63%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0054\n",
              "                \n",
              "                    &plusmn; 0.0004\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_2\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 95.97%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0028\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_76\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 96.04%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0027\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Aggregate_feature_70\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 96.51%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0023\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_43\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 96.97%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0019\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_14\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.01%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0018\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_47\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.08%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0018\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Aggregate_feature_67\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.19%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0017\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_5\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.23%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0016\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_41\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.37%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0015\n",
              "                \n",
              "                    &plusmn; 0.0004\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_49\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.45%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0015\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_10\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.57%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0014\n",
              "                \n",
              "                    &plusmn; 0.0005\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_48\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.58%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0014\n",
              "                \n",
              "                    &plusmn; 0.0002\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_52\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.59%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0013\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_60\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.62%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0013\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_89\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.76%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0012\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Aggregate_feature_61\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "        <tr style=\"background-color: hsl(120, 100.00%, 97.83%); border: none;\">\n",
              "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "                0.0012\n",
              "                \n",
              "                    &plusmn; 0.0003\n",
              "                \n",
              "            </td>\n",
              "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "                Local_feature_3\n",
              "            </td>\n",
              "        </tr>\n",
              "    \n",
              "    \n",
              "        \n",
              "            <tr style=\"background-color: hsl(120, 100.00%, 97.83%); border: none;\">\n",
              "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
              "                    <i>&hellip; 162 more &hellip;</i>\n",
              "                </td>\n",
              "            </tr>\n",
              "        \n",
              "    \n",
              "    </tbody>\n",
              "</table>\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "\n"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop column feature importance:"
      ],
      "metadata": {
        "id": "7BsujnFC5HNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = cRF\n",
        "X_train = X_train\n",
        "y_train = y_train\n",
        "random_state = 42\n",
        "\n",
        "# clone the model to have the exact same specification as the one initially trained\n",
        "model_clone = clone(model)\n",
        "# set random_state for comparability\n",
        "model_clone.random_state = random_state\n",
        "# training and scoring the benchmark model\n",
        "model_clone.fit(X_train.values, y_train.values)\n",
        "benchmark_score = model_clone.score(X_train.values, y_train.values)\n",
        "# list for storing feature importances\n",
        "importances = []\n",
        "\n",
        "# iterating over all columns and storing feature importance (difference between benchmark and new model)\n",
        "for col in X_train.columns:\n",
        "    model_clone = clone(model)\n",
        "    model_clone.random_state = random_state\n",
        "    model_clone.fit(X_train.drop(col, axis = 1).values, y_train.values)\n",
        "    drop_col_score = model_clone.score(X_train.drop(col, axis = 1).values, y_train.values)\n",
        "    importances.append(benchmark_score - drop_col_score)\n",
        "\n",
        "importances_df = pd.DataFrame (X_train.columns, importances)\n",
        "get_drop_importance = importances_df"
      ],
      "metadata": {
        "id": "s96_VBV_5ZTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_drop_importance.reset_index(inplace=True)\n",
        "get_drop_importance = get_drop_importance.rename(columns = {'index':'Imp', 0:'Feature'})\n",
        "get_drop_importance = get_drop_importance.sort_values('Imp', ascending=False)\n",
        "get_drop_importance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Djp_w0ZsKERY",
        "outputId": "be29487d-bbe4-4953-e3a4-ef0d1042eda9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Imp           Feature\n",
              "86   0.000034  Local_feature_87\n",
              "43   0.000034  Local_feature_44\n",
              "28   0.000034  Local_feature_29\n",
              "85   0.000034  Local_feature_86\n",
              "33   0.000034  Local_feature_34\n",
              "..        ...               ...\n",
              "73  -0.000034  Local_feature_74\n",
              "71  -0.000034  Local_feature_72\n",
              "70  -0.000034  Local_feature_71\n",
              "69  -0.000034  Local_feature_70\n",
              "181 -0.000034     out_BTC_total\n",
              "\n",
              "[182 rows x 2 columns]"
            ],
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              "\n",
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Imp</th>\n",
              "      <th>Feature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>0.000034</td>\n",
              "      <td>Local_feature_87</td>\n",
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              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.000034</td>\n",
              "      <td>Local_feature_44</td>\n",
              "    </tr>\n",
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              "      <th>28</th>\n",
              "      <td>0.000034</td>\n",
              "      <td>Local_feature_29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>0.000034</td>\n",
              "      <td>Local_feature_86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.000034</td>\n",
              "      <td>Local_feature_34</td>\n",
              "    </tr>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>-0.000034</td>\n",
              "      <td>Local_feature_74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>-0.000034</td>\n",
              "      <td>Local_feature_72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>-0.000034</td>\n",
              "      <td>Local_feature_71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>-0.000034</td>\n",
              "      <td>Local_feature_70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>-0.000034</td>\n",
              "      <td>out_BTC_total</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>182 rows × 2 columns</p>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hsezkLy5ao4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RF Trees Analysis\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "This section explores the voting of all 50 RF trees for transactions in the testing set."
      ],
      "metadata": {
        "id": "Z6N7Zznvapf0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis on RF tree voting can show the effect of increased classification accuracy after selecting important features. Refer to the paper to see the before and after effects of selecting features on the RF tree voting (Figure 12a)."
      ],
      "metadata": {
        "id": "E1yCuBkUcRSn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the features making up each of the 50 RF trees (there are many trees as we don't set a limit of number of features per tree, but for easier analysis the number of features per tree can be limited):"
      ],
      "metadata": {
        "id": "mlR-cfjZdiNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n=0\n",
        "while n < 50:\n",
        "    classes = ['0','1']\n",
        "    fig = plt.figure(figsize=(50, 50))\n",
        "    plot_tree(cRF.estimators_[n], class_names=classes, filled=True, impurity=True, rounded=True)\n",
        "    n+=1"
      ],
      "metadata": {
        "id": "bKyTU8c9-HFF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Look at individual RF tree votes for a transaction. Change the \"sample_id\" below for the index of the transaction in the testing set:"
      ],
      "metadata": {
        "id": "pMAtcpeCgr4c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "estimator = cRF\n",
        "\n",
        "n_nodes_ = [t.tree_.node_count for t in estimator.estimators_]\n",
        "children_left_ = [t.tree_.children_left for t in estimator.estimators_]\n",
        "children_right_ = [t.tree_.children_right for t in estimator.estimators_]\n",
        "feature_ = [t.tree_.feature for t in estimator.estimators_]\n",
        "threshold_ = [t.tree_.threshold for t in estimator.estimators_]\n",
        "\n",
        "\n",
        "def explore_tree(estimator, n_nodes, children_left,children_right, feature,threshold,\n",
        "                suffix='', print_tree= False, sample_id=0, feature_names=None):\n",
        "\n",
        "    if not feature_names:\n",
        "        feature_names = feature\n",
        "\n",
        "    # The tree structure can be traversed to compute various properties such\n",
        "    # as the depth of each node and whether or not it is a leaf.\n",
        "    node_depth = np.zeros(shape=n_nodes, dtype=np.int64)\n",
        "    is_leaves = np.zeros(shape=n_nodes, dtype=bool)\n",
        "\n",
        "    stack = [(0, -1)]  # seed is the root node id and its parent depth\n",
        "    while len(stack) > 0:\n",
        "        node_id, parent_depth = stack.pop()\n",
        "        node_depth[node_id] = parent_depth + 1\n",
        "\n",
        "        # If we have a test node\n",
        "        if (children_left[node_id] != children_right[node_id]):\n",
        "            stack.append((children_left[node_id], parent_depth + 1))\n",
        "            stack.append((children_right[node_id], parent_depth + 1))\n",
        "        else:\n",
        "            is_leaves[node_id] = True\n",
        "\n",
        "    # First let's retrieve the decision path of each sample. The decision_path\n",
        "    # method allows to retrieve the node indicator functions. A non zero element of\n",
        "    # indicator matrix at the position (i, j) indicates that the sample i goes\n",
        "    # through the node j.\n",
        "\n",
        "    node_indicator = estimator.decision_path(X_test.values)\n",
        "\n",
        "    # Similarly, we can also have the leaves ids reached by each sample.\n",
        "\n",
        "    leave_id = estimator.apply(X_test.values)\n",
        "\n",
        "    # Now, it's possible to get the tests that were used to predict a sample or\n",
        "    # a group of samples. First, let's make it for the sample.\n",
        "\n",
        "    node_index = node_indicator.indices[node_indicator.indptr[sample_id]:\n",
        "                                        node_indicator.indptr[sample_id + 1]]\n",
        "\n",
        "    for node_id in node_index:\n",
        "        tabulation = \"\"\n",
        "\n",
        "        if (X_test.values[sample_id, feature[node_id]] <= threshold[node_id]):\n",
        "            threshold_sign = \"<=\"\n",
        "        else:\n",
        "            threshold_sign = \">\"\n",
        "\n",
        "    print(\"%sPrediction for sample %d: %s\"%(tabulation,\n",
        "                                          sample_id,\n",
        "                                          estimator.predict(X_test.values)[sample_id]))\n",
        "\n",
        "    # For a group of samples, we have the following common node.\n",
        "    sample_ids = [sample_id, 1]\n",
        "    common_nodes = (node_indicator.toarray()[sample_ids].sum(axis=0) ==\n",
        "                    len(sample_ids))\n",
        "\n",
        "    common_node_id = np.arange(n_nodes)[common_nodes]\n",
        "\n",
        "    for sample_id_ in sample_ids:\n",
        "        print(\"Prediction for sample %d: %s\"%(sample_id_,\n",
        "                                          estimator.predict(X_test.values)[sample_id_]))\n",
        "        \n",
        "for i,e in enumerate(estimator.estimators_):\n",
        "\n",
        "    print(\"Tree %d\\n\"%i)\n",
        "    explore_tree(estimator.estimators_[i],n_nodes_[i],children_left_[i],\n",
        "                 children_right_[i], feature_[i],threshold_[i],\n",
        "                suffix=i, sample_id=11983, feature_names=list(df_features.columns)[2:])\n",
        "    print('\\n'*2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVTWTj9tutEO",
        "outputId": "31f06503-2fda-43d9-a35f-685079ba2f06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tree 0\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 1\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 2\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 3\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 4\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 5\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 6\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 7\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 8\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 9\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 10\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 11\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 12\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 13\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 14\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 15\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 16\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 17\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 18\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 19\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 20\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 21\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 22\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 23\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 24\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 25\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 26\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 27\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 28\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 29\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 30\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 31\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 32\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 33\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 34\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 35\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 36\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 37\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 38\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 39\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 40\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 41\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 42\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 43\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 44\n",
            "\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 11983: 1.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 45\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 46\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 47\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 48\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n",
            "Tree 49\n",
            "\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 11983: 0.0\n",
            "Prediction for sample 1: 0.0\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l0HuhbLX4UAI"
      },
      "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"
      }
    }
  ]
}