one_class_svm_mode_nu0_05l.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nazma98/NLP-based-Email-Content-Digital-Forensic-using-ML-model-and-Explainable-AI/blob/main/one_class_svm_mode_nu0_05l.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vDbWLJRumW5p"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler  # Optional\n",
        "from sklearn.svm import OneClassSVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "normal_documents"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Assuming \"normal_documents\" ids DataFrame with topic features\n",
        "normal_documents = pd.read_csv('/content/drive/MyDrive/topic_features_00.csv')\n",
        "normal_documents\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "R8w28ParH1od",
        "outputId": "0004ed08-0606-4632-f146-3f39045f0cd6"
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      "outputs": [
        {
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          },
          "metadata": {},
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      ],
      "source": [
        "normal_features = normal_documents.drop(\"doc_id\", axis=1)  # removing the document id\n",
        "\n",
        "normal_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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            "text/plain": [
              "OneClassSVM(nu=0.05)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Scale features (optional)\n",
        "scaler = StandardScaler()\n",
        "scaled_features = scaler.fit_transform(normal_features)  # Or use normal_features directly\n",
        "\n",
        "# Train one-class SVM model\n",
        "model = OneClassSVM(nu=0.05)  # Adjust nu based on the proportion of anomalies you expect\n",
        "model.fit(scaled_features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYMAIPajUpTB"
      },
      "source": [
        "after topic features of test data is calculated"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_documents = pd.read_csv('/content/drive/MyDrive/test_topic_features_final_labeled.csv')\n",
        "new_documents"
      ],
      "metadata": {
        "id": "W0XHwWt2pyAB",
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          "height": 424
        },
        "outputId": "a9448a49-dc70-4394-8f2d-d09c11ae97f6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0  doc_id  topic 0  topic 1  topic 2  topic 3  topic 4  \\\n",
              "0                0       0        1        0        1        2        0   \n",
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              "\n",
              "        topic 5  topic 6  topic 7  topic 8  topic 9  topic 10  topic 11  \\\n",
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              "\n",
              "        topic 12  topic 13  topic 14 labeled  \n",
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              "2             36         1        13   False  \n",
              "3              0         1         0   False  \n",
              "4              0         0         0   False  \n",
              "...          ...       ...       ...     ...  \n",
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              "103324         0         0         3   False  \n",
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              "\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103325</th>\n",
              "      <td>103325</td>\n",
              "      <td>103479</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>15</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103326</th>\n",
              "      <td>103326</td>\n",
              "      <td>103480</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103327 rows × 18 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_documents')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2fcc0471-5c19-46f8-b926-43c39de1e7af button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_documents');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_documents"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "je5b-n8EUaId",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "bec722ce-2323-48d5-e03d-afa899c71f10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>103322</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103323</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103324</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103325</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103326</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103327 rows × 15 columns</p>\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_features"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#  \"new_documents\" is  DataFrame with topic features for new documents\n",
        "new_features = new_documents.drop(columns=[\"doc_id\",\"labeled\", \"Unnamed: 0\"] )\n",
        "\n",
        "new_features\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale new features\n",
        "new_scaled_features = scaler.transform(new_features)\n",
        "\n",
        "# Predict labels for new documents (1 for inliers, -1 for outliers)\n",
        "new_document_labels = model.predict(new_scaled_features)\n",
        "\n",
        "# Convert predicted labels to Pandas Series\n",
        "new_document_labels = pd.Series(new_document_labels)\n",
        "\n",
        "# Identify documents classified as outliers (label -1)\n",
        "outlier_indices = new_document_labels[new_document_labels == -1].index\n",
        "\n",
        "outlier_documents = new_documents.iloc[outlier_indices]  # Assuming row indices for documents\n",
        "\n",
        "print(\"Potential anomaly documents:\")\n",
        "print(outlier_documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpJiC2tOgWOR",
        "outputId": "0b8d62be-f381-4318-8ece-c1f7a8c4bfe4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Potential anomaly documents:\n",
            "        Unnamed: 0  doc_id  topic 0  topic 1  topic 2  topic 3  topic 4  \\\n",
            "42              42      42        6        4        0        3       37   \n",
            "78              78      79        0       23        0        0        0   \n",
            "85              85      86      166        9       48      405      120   \n",
            "94              94      95       44       20        1       24       94   \n",
            "101            101     102        7        2        1       28        3   \n",
            "...            ...     ...      ...      ...      ...      ...      ...   \n",
            "103219      103219  103373        1       17        1       40        7   \n",
            "103226      103226  103380        5        3        0       37       20   \n",
            "103241      103241  103395        7        1        0        8       20   \n",
            "103284      103284  103438       11        1        2        0        1   \n",
            "103302      103302  103456       70        1       15       96       63   \n",
            "\n",
            "        topic 5  topic 6  topic 7  topic 8  topic 9  topic 10  topic 11  \\\n",
            "42            9        6       27       10        0         3         5   \n",
            "78            0        0        0        0        0         0         6   \n",
            "85          130      938        1       74      369       460        12   \n",
            "94           39       44        3       62        6        39         5   \n",
            "101          48       10        0        1       10        12         4   \n",
            "...         ...      ...      ...      ...      ...       ...       ...   \n",
            "103219        4       16        1        2       32        14       105   \n",
            "103226        9       43        1        0       34        36         3   \n",
            "103241       64        5        1        1        2        18         2   \n",
            "103284        0       46        0        1        1         0         2   \n",
            "103302       83      340        2       30       58       110         6   \n",
            "\n",
            "        topic 12  topic 13  topic 14 labeled  \n",
            "42             2        21        15   False  \n",
            "78             0         1         0   False  \n",
            "85            67       389       185   False  \n",
            "94             1       161       129   False  \n",
            "101           23         3        16   False  \n",
            "...          ...       ...       ...     ...  \n",
            "103219        21         1         5   False  \n",
            "103226         9        35        31   False  \n",
            "103241         5         4        42   False  \n",
            "103284         1        46        46   False  \n",
            "103302        20       423       108     NaN  \n",
            "\n",
            "[5115 rows x 18 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "true_labels = new_documents[\"labeled\"]  # Extract true labels\n",
        "\n",
        "\n",
        "# Encode true labels\n",
        "le = LabelEncoder()\n",
        "encoded_labels = le.fit_transform(true_labels)\n",
        "\n",
        "\n",
        "predicted_labels = new_document_labels\n",
        "\n"
      ],
      "metadata": {
        "id": "OzaTF30kh5wV"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate evaluation metrics\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "accuracy = accuracy_score(encoded_labels, predicted_labels)\n",
        "precision = precision_score(encoded_labels, predicted_labels, average='macro')\n",
        "recall = recall_score(encoded_labels, predicted_labels, average='macro')\n",
        "f1 = f1_score(encoded_labels, predicted_labels, average='macro')\n",
        "\n",
        "# Print evaluation results\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWpHyU7eiYyz",
        "outputId": "3b0b050c-a451-48da-9053-54e398558079"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.003542152583545443\n",
            "Precision: 0.0009316580458599763\n",
            "Recall: 0.23890339425587467\n",
            "F1 Score: 0.0018560778944165526\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    }
  ],
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
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    },
    "language_info": {
      "name": "python"
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