Enc_BreastCancer_detection.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ParthN9i4/ML-using-TenSEAL/blob/main/Enc_BreastCancer_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZOWuiAxxpb6",
        "outputId": "6c693187-ba32-4c46-cefd-62bbf67334d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tenseal\n",
            "  Downloading tenseal-0.3.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tenseal\n",
            "Successfully installed tenseal-0.3.14\n"
          ]
        }
      ],
      "source": [
        "!pip install tenseal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wd-_E5HMGLQT",
        "outputId": "7ef8b1b6-47bb-4f08-9613-2dccb7167cfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<tenseal.enc_context.Context object at 0x783a22921900>\n"
          ]
        }
      ],
      "source": [
        "import tenseal as ts\n",
        "\n",
        "context = ts.context(ts.SCHEME_TYPE.CKKS, poly_modulus_degree = 4096, plain_modulus = 1032193)\n",
        "print(context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bkf8RHBRlTml"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import tenseal as ts\n",
        "import pandas as pd\n",
        "import random\n",
        "from time import time\n",
        "\n",
        "# those are optional and are not necessary for training\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCHo7XTjmrVe",
        "outputId": "598d2425-de6a-470d-af68-0031ea101737"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution:\n",
            "0    444\n",
            "1    239\n",
            "Name: Class, dtype: int64\n",
            "\n",
            "Minority class: 1\n",
            "Majority class: 0\n"
          ]
        }
      ],
      "source": [
        "#Set random seeds to initialize the model weight of the model\n",
        "torch.random.manual_seed(73)\n",
        "random.seed(73)\n",
        "\n",
        "def split_train_test(x, y, test_ratio=0.3, random_state=0):\n",
        "    # And test set\n",
        "    idxs = [i for i in range(len(x))]\n",
        "    random.seed(random_state)  # Set the random seed\n",
        "    random.shuffle(idxs)\n",
        "    # Delimiter between test and train data\n",
        "    delim = int(len(x) * test_ratio)\n",
        "    test_idxs, train_idxs = idxs[:delim], idxs[delim:]\n",
        "    #convert y to a tensor\n",
        "    return x[train_idxs], y[train_idxs], x[test_idxs], y[test_idxs]\n",
        "\n",
        "# Assuming 'Class' is the column representing the target variable\n",
        "# Replace 'Class' with the actual name of your target variable if different\n",
        "target_column = 'Class'\n",
        "\n",
        "# Load your DataFrame 'data'\n",
        "data = pd.read_csv(\"breast_cancer.csv\")\n",
        "\n",
        "# Display class distribution\n",
        "class_distribution = data[target_column].value_counts()\n",
        "\n",
        "# Find minority and majority classes\n",
        "minority_class = class_distribution.idxmin()\n",
        "majority_class = class_distribution.idxmax()\n",
        "\n",
        "# Print class distribution and identified minority/majority classes\n",
        "print(\"Class distribution:\")\n",
        "print(class_distribution)\n",
        "\n",
        "print(\"\\nMinority class:\", minority_class)\n",
        "print(\"Majority class:\", majority_class)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Assuming 'Class' is the column representing the target variable\n",
        "# Replace 'Class' with the actual name of your target variable if different\n",
        "target_column = 'Class'\n",
        "\n",
        "# Load your DataFrame 'data'\n",
        "data = pd.read_csv(\"breast_cancer.csv\")  # Replace with your dataset path\n",
        "\n",
        "# Display class distribution before oversampling\n",
        "print(\"Class distribution before oversampling:\")\n",
        "print(data[target_column].value_counts())\n",
        "\n",
        "# Separate features (X) and target variable (y)\n",
        "X = data.drop(target_column, axis=1)\n",
        "y = data[target_column]\n",
        "\n",
        "# Initialize SMOTE with desired sampling strategy\n",
        "# In this case, 'minority' indicates that we want to oversample the minority class\n",
        "smote = SMOTE(sampling_strategy='minority', random_state=42)\n",
        "\n",
        "# Apply SMOTE to the dataset\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "# Concatenate the resampled data to create the oversampled DataFrame\n",
        "oversampled_data = pd.concat([pd.DataFrame(X_resampled, columns=X.columns), pd.Series(y_resampled, name=target_column)], axis=1)\n",
        "\n",
        "# Display class distribution after oversampling\n",
        "print(\"\\nClass distribution after oversampling:\")\n",
        "print(oversampled_data[target_column].value_counts())\n",
        "\n",
        "# Triple the size by repeating the process\n",
        "oversampled_data_tripled = oversampled_data.copy()\n",
        "for _ in range(2):\n",
        "    X_resampled, y_resampled = smote.fit_resample(oversampled_data.drop(target_column, axis=1), oversampled_data[target_column])\n",
        "    oversampled_data = pd.concat([pd.DataFrame(X_resampled, columns=X.columns), pd.Series(y_resampled, name=target_column)], axis=1)\n",
        "    oversampled_data_tripled = pd.concat([oversampled_data_tripled, oversampled_data], ignore_index=True)\n",
        "\n",
        "# Display class distribution after tripling the size\n",
        "print(\"\\nClass distribution after tripling the size:\")\n",
        "print(oversampled_data_tripled[target_column].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UeT-yXJa_By6",
        "outputId": "542866e4-e960-4912-828d-e09145a0552d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution before oversampling:\n",
            "0    444\n",
            "1    239\n",
            "Name: Class, dtype: int64\n",
            "\n",
            "Class distribution after oversampling:\n",
            "0    444\n",
            "1    444\n",
            "Name: Class, dtype: int64\n",
            "\n",
            "Class distribution after tripling the size:\n",
            "0    1332\n",
            "1    1332\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#oversampled_data_tripled['Class'].value_counts()\n",
        "df = oversampled_data_tripled\n",
        "print(df['Class'].value_counts())\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "22xL1Qy-u4kI",
        "outputId": "e1cde7e3-b89e-44fb-fa3f-bfe0737953b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1332\n",
            "1    1332\n",
            "Name: Class, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
              "0                5                        1                         1   \n",
              "1                5                        4                         4   \n",
              "2                3                        1                         1   \n",
              "3                6                        8                         8   \n",
              "4                4                        1                         1   \n",
              "\n",
              "   Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
              "0                  1                            2            1   \n",
              "1                  5                            7           10   \n",
              "2                  1                            2            2   \n",
              "3                  1                            3            4   \n",
              "4                  3                            2            1   \n",
              "\n",
              "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
              "0                3                1        1      0  \n",
              "1                3                2        1      0  \n",
              "2                3                1        1      0  \n",
              "3                3                7        1      0  \n",
              "4                3                1        1      0  "
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bdc171cb-9966-410a-b78b-759d05803b7b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2664,\n  \"fields\": [\n    {\n      \"column\": \"Clump Thickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Uniformity of Cell Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Uniformity of Cell Shape\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7,\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marginal Adhesion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Single Epithelial Cell Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          10,\n          7,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bare Nuclei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          10,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bland Chromatin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          9,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Normal Nucleoli\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mitoses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def breast_cancer_data():\n",
        "  global df\n",
        "\n",
        "  print(df.head())\n",
        "  print(df['Class'].value_counts())\n",
        "  df['Class'].replace(2,0,inplace=True)\n",
        "  df['Class'].replace(4,1,inplace=True)\n",
        "  grouped = df.groupby('Class')\n",
        "  df = grouped.apply(lambda x: x.sample(grouped.size().min(), random_state = 73).reset_index(drop = True))\n",
        "  print(df.head())\n",
        "  #extract labels\n",
        "  y = torch.tensor(df[\"Class\"].values).float().unsqueeze(1)\n",
        "  df = df.drop(\"Class\", 'columns')\n",
        "  #standardize data\n",
        "  df = (df - df.mean()) / df.std()\n",
        "  x = torch.tensor(df.values).float()\n",
        "  print(df.head())\n",
        "  return split_train_test(x, y)\n",
        "\n",
        "x_train, y_train, x_test, y_test = breast_cancer_data()\n",
        "\n",
        "\n",
        "featuresTrain = x_train\n",
        "targetsTrain = y_train\n",
        "featuresTest = x_test\n",
        "targetsTest = y_test\n",
        "\n",
        "print(\"############# Data summary #############\")\n",
        "print(f\"x_train has shape: {x_train.shape}\")\n",
        "print(f\"y_train has shape: {y_train.shape}\")\n",
        "print(f\"x_test has shape: {x_test.shape}\")\n",
        "print(f\"y_test has shape: {y_test.shape}\")\n",
        "print(\"#######################################\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4W7ju5q-j9f",
        "outputId": "1e6bbd18-18d7-431f-819c-309c3e061307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
            "0                5                        1                         1   \n",
            "1                5                        4                         4   \n",
            "2                3                        1                         1   \n",
            "3                6                        8                         8   \n",
            "4                4                        1                         1   \n",
            "\n",
            "   Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
            "0                  1                            2            1   \n",
            "1                  5                            7           10   \n",
            "2                  1                            2            2   \n",
            "3                  1                            3            4   \n",
            "4                  3                            2            1   \n",
            "\n",
            "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
            "0                3                1        1      0  \n",
            "1                3                2        1      0  \n",
            "2                3                1        1      0  \n",
            "3                3                7        1      0  \n",
            "4                3                1        1      0  \n",
            "0    1332\n",
            "1    1332\n",
            "Name: Class, dtype: int64\n",
            "         Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
            "Class                                                                         \n",
            "0     0                4                        1                         3   \n",
            "      1                4                        3                         2   \n",
            "      2                2                        1                         1   \n",
            "      3                5                        1                         1   \n",
            "      4                1                        1                         1   \n",
            "\n",
            "         Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
            "Class                                                                  \n",
            "0     0                  3                            2            1   \n",
            "      1                  1                            3            1   \n",
            "      2                  1                            1            1   \n",
            "      3                  1                            2            1   \n",
            "      4                  1                            2            1   \n",
            "\n",
            "         Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
            "Class                                                      \n",
            "0     0                1                1        1      0  \n",
            "      1                2                1        1      0  \n",
            "      2                1                1        1      0  \n",
            "      3                2                2        1      0  \n",
            "      4                3                1        1      0  \n",
            "         Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
            "Class                                                                         \n",
            "0     0        -0.346072                -0.888849                 -0.294781   \n",
            "      1        -0.346072                -0.266620                 -0.613619   \n",
            "      2        -1.038997                -0.888849                 -0.932456   \n",
            "      3         0.000390                -0.888849                 -0.932456   \n",
            "      4        -1.385459                -0.888849                 -0.932456   \n",
            "\n",
            "         Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
            "Class                                                                  \n",
            "0     0          -0.115373                    -0.697908     -0.89014   \n",
            "      1          -0.772111                    -0.269613     -0.89014   \n",
            "      2          -0.772111                    -1.126202     -0.89014   \n",
            "      3          -0.772111                    -0.697908     -0.89014   \n",
            "      4          -0.772111                    -0.697908     -0.89014   \n",
            "\n",
            "         Bland Chromatin  Normal Nucleoli  Mitoses  \n",
            "Class                                               \n",
            "0     0        -1.159160        -0.763807 -0.39282  \n",
            "      1        -0.765684        -0.763807 -0.39282  \n",
            "      2        -1.159160        -0.763807 -0.39282  \n",
            "      3        -0.765684        -0.463824 -0.39282  \n",
            "      4        -0.372207        -0.763807 -0.39282  \n",
            "############# Data summary #############\n",
            "x_train has shape: torch.Size([1865, 9])\n",
            "y_train has shape: torch.Size([1865, 1])\n",
            "x_test has shape: torch.Size([799, 9])\n",
            "y_test has shape: torch.Size([799, 1])\n",
            "#######################################\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-b163b7f24087>:13: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
            "  df = df.drop(\"Class\", 'columns')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPP-m4zUYjfZ",
        "outputId": "44083689-6cda-45fe-ce1d-6cc579eb027b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 89.11%\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Define a simple logistic regression model\n",
        "class LogisticRegression(nn.Module):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super(LogisticRegression, self).__init__()\n",
        "        self.lr = nn.Linear(input_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = torch.sigmoid(self.lr(x))\n",
        "        return out\n",
        "\n",
        "# Initialize model, optimizer, and loss function\n",
        "input_size = featuresTrain.size(1)\n",
        "output_size = 1  # Assuming binary classification\n",
        "model = LogisticRegression(input_size, output_size)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "criterion = nn.BCELoss()\n",
        "\n",
        "# Batch size and DataLoader\n",
        "batch_size = 500\n",
        "train_dataset = TensorDataset(featuresTrain, targetsTrain)\n",
        "test_dataset = TensorDataset(featuresTest, targetsTest)\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "# Training function with batching\n",
        "def train(model, optimizer, criterion, train_loader, num_epochs=10):\n",
        "    for epoch in range(num_epochs):\n",
        "        for i, (features, labels) in enumerate(train_loader):\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Forward pass\n",
        "            outputs = model(features)\n",
        "            loss = criterion(outputs, labels)\n",
        "\n",
        "            # Backward pass and optimization\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Print loss occasionally\n",
        "            if (i+1) % 10 == 0:\n",
        "                print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')\n",
        "\n",
        "# Testing function with accuracy calculation\n",
        "def test(model, test_loader):\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for features, labels in test_loader:\n",
        "            outputs = model(features)\n",
        "            predicted = (outputs >= 0.5).float()  # Assuming binary classification\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    accuracy = 100 * correct / total\n",
        "    print(f'Test Accuracy: {accuracy:.2f}%')\n",
        "\n",
        "# Train the model with batching\n",
        "train(model, optimizer, criterion, train_loader)\n",
        "\n",
        "# Test the model and calculate accuracy\n",
        "test(model, test_loader)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIxvS1GOfjT8",
        "outputId": "af902653-164c-4096-8056-2fc2b275622e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encryption of the test-set took 5997.629364000005 ms\n",
            "Loss at epoch 1: 0.5985201001167297\n",
            "Loss at epoch 2: 0.18451660871505737\n",
            "Loss at epoch 3: 0.15817533433437347\n",
            "Accuracy on plain test_set: 0.96620774269104\n"
          ]
        }
      ],
      "source": [
        "class LR(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, n_features):\n",
        "        super(LR, self).__init__()\n",
        "        self.lr = torch.nn.Linear(n_features, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = torch.sigmoid(self.lr(x))\n",
        "        return out\n",
        "\n",
        "n_features = x_train.shape[1]\n",
        "model = LR(n_features)\n",
        "# use gradient descent with a learning_rate=1\n",
        "optim = torch.optim.SGD(model.parameters(), lr=1)\n",
        "# use Binary Cross Entropy Loss\n",
        "criterion = torch.nn.BCELoss()\n",
        "\n",
        "# parameters\n",
        "poly_mod_degree = 4096\n",
        "coeff_mod_bit_sizes = [40, 20, 40]\n",
        "# create TenSEALContext\n",
        "ctx_eval = ts.context(ts.SCHEME_TYPE.CKKS, poly_mod_degree, -1, coeff_mod_bit_sizes)\n",
        "# scale of ciphertext to use\n",
        "ctx_eval.global_scale = 2 ** 20\n",
        "# this key is needed for doing dot-product operations\n",
        "ctx_eval.generate_galois_keys()\n",
        "\n",
        "# define the number of epochs for both plain and encrypted training\n",
        "EPOCHS = 3\n",
        "\n",
        "from time import perf_counter\n",
        "\n",
        "t_start = perf_counter()\n",
        "enc_x_test = [ts.ckks_vector(ctx_eval, x.tolist()) for x in x_test]\n",
        "t_end = perf_counter()\n",
        "\n",
        "# Calculate the elapsed time in milliseconds\n",
        "elapsed_time_milliseconds = (t_end - t_start) * 1000\n",
        "\n",
        "print(f\"Encryption of the test-set took {elapsed_time_milliseconds} ms\")\n",
        "\n",
        "def train(model, optim, criterion, x, y, epochs=EPOCHS):\n",
        "    for e in range(1, epochs + 1):\n",
        "        optim.zero_grad()\n",
        "        out = model(x)\n",
        "        loss = criterion(out, y)\n",
        "        loss.backward()\n",
        "        optim.step()\n",
        "        print(f\"Loss at epoch {e}: {loss.data}\")\n",
        "    return model\n",
        "\n",
        "model = train(model, optim, criterion, x_train, y_train)\n",
        "def accuracy(model, x, y):\n",
        "    out = model(x)\n",
        "    correct = torch.abs(y - out) < 0.5\n",
        "    return correct.float().mean()\n",
        "\n",
        "plain_accuracy = accuracy(model, x_test, y_test)\n",
        "print(f\"Accuracy on plain test_set: {plain_accuracy}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class EncryptedLR:\n",
        "\n",
        "    def __init__(self, torch_lr):\n",
        "        self.weight = torch_lr.lr.weight.data.tolist()[0]\n",
        "        self.bias = torch_lr.lr.bias.data.tolist()\n",
        "        # we accumulate gradients and counts the number of iterations\n",
        "        self._delta_w = 0\n",
        "        self._delta_b = 0\n",
        "        self._count = 0\n",
        "\n",
        "    def forward(self, enc_x):\n",
        "        enc_out = enc_x.dot(self.weight) + self.bias\n",
        "        enc_out = EncryptedLR.sigmoid(enc_out)\n",
        "        return enc_out\n",
        "\n",
        "    def backward(self, enc_x, enc_out, enc_y):\n",
        "        out_minus_y = (enc_out - enc_y)\n",
        "        self._delta_w += enc_x * out_minus_y\n",
        "        self._delta_b += out_minus_y\n",
        "        self._count += 1\n",
        "\n",
        "    def update_parameters(self):\n",
        "        if self._count == 0:\n",
        "            raise RuntimeError(\"You should at least run one forward iteration\")\n",
        "        # update weights\n",
        "        # We use a small regularization term to keep the output\n",
        "        # of the linear layer in the range of the sigmoid approximation\n",
        "        self.weight -= self._delta_w * (1 / self._count) + self.weight * 0.05\n",
        "        self.bias -= self._delta_b * (1 / self._count)\n",
        "        # reset gradient accumulators and iterations count\n",
        "        self._delta_w = 0\n",
        "        self._delta_b = 0\n",
        "        self._count = 0\n",
        "\n",
        "    @staticmethod\n",
        "    def sigmoid(enc_x):\n",
        "        return enc_x.polyval([0.5, 0.197, 0, -0.004])\n",
        "\n",
        "    def plain_accuracy(self, x_test, y_test):\n",
        "        # evaluate accuracy of the model on\n",
        "        # the plain (x_test, y_test) dataset\n",
        "        w = torch.tensor(self.weight)\n",
        "        b = torch.tensor(self.bias)\n",
        "        out = torch.sigmoid(x_test.matmul(w) + b).reshape(-1, 1)\n",
        "        correct = torch.abs(y_test - out) < 0.5\n",
        "        return correct.float().mean()\n",
        "\n",
        "    def encrypt(self, context):\n",
        "        self.weight = ts.ckks_vector(context, self.weight)\n",
        "        self.bias = ts.ckks_vector(context, self.bias)\n",
        "\n",
        "    def decrypt(self):\n",
        "        self.weight = self.weight.decrypt()\n",
        "        self.bias = self.bias.decrypt()\n",
        "\n",
        "    def __call__(self, *args, **kwargs):\n",
        "        return self.forward(*args, **kwargs)\n",
        "\n",
        "# parameters\n",
        "poly_mod_degree = 8192\n",
        "coeff_mod_bit_sizes = [40, 21, 21, 21, 21, 21, 21, 40]\n",
        "# create TenSEALContext\n",
        "ctx_training = ts.context(ts.SCHEME_TYPE.CKKS, poly_mod_degree, -1, coeff_mod_bit_sizes)\n",
        "ctx_training.global_scale = 2 ** 21\n",
        "ctx_training.generate_galois_keys()\n",
        "\n",
        "t_start = time()\n",
        "enc_x_train = [ts.ckks_vector(ctx_training, x.tolist()) for x in x_train]\n",
        "enc_y_train = [ts.ckks_vector(ctx_training, y.tolist()) for y in y_train]\n",
        "t_end = time()\n",
        "print(f\"Encryption of the training_set took {int(t_end - t_start)} seconds\")\n",
        "\n",
        "normal_dist = lambda x, mean, var: np.exp(- np.square(x - mean) / (2 * var)) / np.sqrt(2 * np.pi * var)\n",
        "\n",
        "def plot_normal_dist(mean, var, rmin=-10, rmax=10):\n",
        "    x = np.arange(rmin, rmax, 0.01)\n",
        "    y = normal_dist(x, mean, var)\n",
        "    fig = plt.plot(x, y)\n",
        "\n",
        "# plain distribution\n",
        "lr = LR(n_features)\n",
        "data = lr.lr(x_test)\n",
        "mean, var = map(float, [data.mean(), data.std() ** 2])\n",
        "plot_normal_dist(mean, var)\n",
        "print(\"Distribution on plain data:\")\n",
        "plt.show()\n",
        "\n",
        "# encrypted distribution\n",
        "def encrypted_out_distribution(eelr, enc_x_test):\n",
        "    w = eelr.weight\n",
        "    b = eelr.bias\n",
        "    data = []\n",
        "    for enc_x in enc_x_test:\n",
        "        enc_out = enc_x.dot(w) + b\n",
        "        data.append(enc_out.decrypt())\n",
        "    data = torch.tensor(data)\n",
        "    mean, var = map(float, [data.mean(), data.std() ** 2])\n",
        "    plot_normal_dist(mean, var)\n",
        "    print(\"Distribution on encrypted data:\")\n",
        "    plt.show()\n",
        "\n",
        "eelr = EncryptedLR(lr)\n",
        "eelr.encrypt(ctx_training)\n",
        "encrypted_out_distribution(eelr, enc_x_train)\n",
        "\n",
        "eelr = EncryptedLR(LR(n_features))\n",
        "accuracy = eelr.plain_accuracy(x_test, y_test)\n",
        "print(f\"Accuracy at epoch #0 is {accuracy}\")\n",
        "\n",
        "times = []\n",
        "for epoch in range(3):\n",
        "    eelr.encrypt(ctx_training)\n",
        "\n",
        "    # if you want to keep an eye on the distribution to make sure\n",
        "    # the function approxiamation is still working fine\n",
        "    # WARNING: this operation is time consuming\n",
        "    # encrypted_out_distribution(eelr, enc_x_train)\n",
        "\n",
        "    t_start = time()\n",
        "    for enc_x, enc_y in zip(enc_x_train, enc_y_train):\n",
        "        enc_out = eelr.forward(enc_x)\n",
        "        eelr.backward(enc_x, enc_out, enc_y)\n",
        "    eelr.update_parameters()\n",
        "    t_end = time()\n",
        "    times.append(t_end - t_start)\n",
        "\n",
        "    eelr.decrypt()\n",
        "    accuracy = eelr.plain_accuracy(x_test, y_test)\n",
        "    print(f\"Accuracy at epoch #{epoch + 1} is {accuracy}\")\n",
        "\n",
        "print(f\"\\nAverage time per epoch: {int(sum(times) / len(times))} seconds\")\n",
        "print(f\"Final accuracy is {accuracy}\")\n",
        "\n",
        "diff_accuracy = plain_accuracy - accuracy\n",
        "print(f\"Difference between plain and encrypted accuracies: {diff_accuracy}\")\n",
        "if diff_accuracy < 0:\n",
        "    print(\"Oh! We got a better accuracy when training on encrypted data! The noise was on our side...\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZENPn8AIhMyu",
        "outputId": "22e86bc2-be60-4318-b6c2-0a6a88e377d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-10-9ff75078d462>\", line 68, in <cell line: 68>\n",
            "    enc_x_train = [ts.ckks_vector(ctx_training, x.tolist()) for x in x_train]\n",
            "  File \"<ipython-input-10-9ff75078d462>\", line 68, in <listcomp>\n",
            "    enc_x_train = [ts.ckks_vector(ctx_training, x.tolist()) for x in x_train]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tenseal/__init__.py\", line 102, in ckks_vector\n",
            "    return CKKSVector(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tenseal/tensors/ckksvector.py\", line 42, in __init__\n",
            "    self.data = ts._ts_cpp.CKKSVector(context.data, vector)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
            "    module = getmodule(object, filename)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 878, in getmodule\n",
            "    os.path.realpath(f)] = module.__name__\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 397, in realpath\n",
            "    return abspath(path)\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 380, in abspath\n",
            "    if not isabs(path):\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 63, in isabs\n",
            "    sep = _get_sep(s)\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 42, in _get_sep\n",
            "    if isinstance(path, bytes):\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-10-9ff75078d462>\", line 68, in <cell line: 68>\n",
            "    enc_x_train = [ts.ckks_vector(ctx_training, x.tolist()) for x in x_train]\n",
            "  File \"<ipython-input-10-9ff75078d462>\", line 68, in <listcomp>\n",
            "    enc_x_train = [ts.ckks_vector(ctx_training, x.tolist()) for x in x_train]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tenseal/__init__.py\", line 102, in ckks_vector\n",
            "    return CKKSVector(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tenseal/tensors/ckksvector.py\", line 42, in __init__\n",
            "    self.data = ts._ts_cpp.CKKSVector(context.data, vector)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n",
            "    if (await self.run_code(code, result,  async_=asy)):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n",
            "    self.showtraceback(running_compiled_code=True)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2101, in showtraceback\n",
            "    stb = self.InteractiveTB.structured_traceback(etype,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1367, in structured_traceback\n",
            "    return FormattedTB.structured_traceback(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1267, in structured_traceback\n",
            "    return VerboseTB.structured_traceback(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1124, in structured_traceback\n",
            "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n",
            "    last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n",
            "    return len(records), 0\n",
            "TypeError: object of type 'NoneType' has no len()\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1624, in getframeinfo\n",
            "    lines, lnum = findsource(frame)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 170, in findsource\n",
            "    file = getsourcefile(object) or getfile(object)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
            "    module = getmodule(object, filename)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 869, in getmodule\n",
            "    if ismodule(module) and hasattr(module, '__file__'):\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 189, in ismodule\n",
            "    return isinstance(object, types.ModuleType)\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "object of type 'NoneType' has no len()",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-9ff75078d462>\u001b[0m in \u001b[0;36m<cell line: 68>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0mt_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0menc_x_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mckks_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx_training\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0menc_y_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mckks_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx_training\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-10-9ff75078d462>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0mt_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0menc_x_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mckks_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx_training\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0menc_y_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mckks_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx_training\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenseal/__init__.py\u001b[0m in \u001b[0;36mckks_vector\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0;34m\"\"\"Constructor function for tenseal.CKKSVector\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mCKKSVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tenseal/tensors/ckksvector.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, context, vector, scale, data)\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mscale\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ts_cpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCKKSVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2099\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3472\u001b[0m                         \u001b[0masy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3473\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0masync_\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3474\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m   2102\u001b[0m                                             value, tb, tb_offset=tb_offset)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1268\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[1;32m   1125\u001b[0m                                                                tb_offset)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2099\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'TypeError' object has no attribute '_render_traceback_'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\u001b[0m in \u001b[0;36m_pseudo_sync_runner\u001b[0;34m(coro)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \"\"\"\n\u001b[1;32m     77\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_async\u001b[0;34m(self, raw_cell, store_history, silent, shell_futures, transformed_cell, preprocessing_exc_tuple, cell_id)\u001b[0m\n\u001b[1;32m   3255\u001b[0m                     \u001b[0minteractivity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'async'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3257\u001b[0;31m                 has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n\u001b[0m\u001b[1;32m   3258\u001b[0m                        interactivity=interactivity, compiler=compiler, result=result)\n\u001b[1;32m   3259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_ast_nodes\u001b[0;34m(self, nodelist, cell_name, interactivity, compiler, result)\u001b[0m\n\u001b[1;32m   3490\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3491\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_before_exec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3492\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3493\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2099\u001b[0m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m   2102\u001b[0m                                             value, tb, tb_offset=tb_offset)\n\u001b[1;32m   2103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1265\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1268\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1140\u001b[0m         \u001b[0mchained_exc_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1142\u001b[0;31m             formatted_exceptions += self.format_exception_as_a_whole(etype, evalue, etb, lines_of_context,\n\u001b[0m\u001b[1;32m   1143\u001b[0m                                                                      chained_exceptions_tb_offset)\n\u001b[1;32m   1144\u001b[0m             \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parts_of_chained_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import psutil"
      ],
      "metadata": {
        "id": "GDcVtvMKi3KK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import resource\n",
        "from time import perf_counter\n",
        "import matplotlib.pyplot as plt\n",
        "import psutil\n",
        "\n",
        "# Function to print resource usage\n",
        "def print_resource_usage():\n",
        "    print(\"Resource usage:\")\n",
        "    print(resource.getrusage(resource.RUSAGE_SELF))\n",
        "    print(\"CPU usage:\", psutil.cpu_percent(), \"%\")\n",
        "    print(\"Memory usage:\", psutil.virtual_memory().percent, \"%\")\n",
        "    print(\"\")\n",
        "\n",
        "# Initialize lists to store resource usage data\n",
        "cpu_peak_usage = []\n",
        "cpu_average_usage = []\n",
        "memory_peak_usage = []\n",
        "memory_average_usage = []\n",
        "epoch_numbers = []\n",
        "\n",
        "# Get resource usage before encryption\n",
        "print_resource_usage()\n",
        "\n",
        "# parameters\n",
        "poly_mod_degree = 4096\n",
        "coeff_mod_bit_sizes = [40, 20, 40]\n",
        "\n",
        "# create TenSEALContext\n",
        "ctx_eval = ts.context(ts.SCHEME_TYPE.CKKS, poly_mod_degree, -1, coeff_mod_bit_sizes)\n",
        "\n",
        "# scale of ciphertext to use\n",
        "ctx_eval.global_scale = 2 ** 20\n",
        "\n",
        "# this key is needed for doing dot-product operations\n",
        "ctx_eval.generate_galois_keys()\n",
        "\n",
        "# define the number of epochs for both plain and encrypted training\n",
        "EPOCHS = 3\n",
        "\n",
        "# Encrypt the test set\n",
        "for epoch in range(EPOCHS):\n",
        "    t_start = perf_counter()\n",
        "    enc_x_test = [ts.ckks_vector(ctx_eval, x.tolist()) for x in x_test]\n",
        "    t_end = perf_counter()\n",
        "\n",
        "    # Calculate elapsed time\n",
        "    elapsed_time_milliseconds = (t_end - t_start) * 1000\n",
        "\n",
        "    # Append resource usage data\n",
        "    cpu_peak_usage.append(psutil.cpu_percent())\n",
        "    cpu_average_usage.append(psutil.cpu_percent(interval=1))\n",
        "    memory_peak_usage.append(psutil.virtual_memory().percent)\n",
        "    memory_average_usage.append(psutil.virtual_memory().percent)\n",
        "    epoch_numbers.append(epoch)\n",
        "\n",
        "    # Print resource usage after each epoch\n",
        "    print(f\"After epoch {epoch + 1}:\")\n",
        "    print_resource_usage()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_epIQu-jInN",
        "outputId": "92547578-1bf6-44a2-df9a-c3f8a32b9440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=1435.470612, ru_stime=10.716048, ru_maxrss=5269224, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1253094, ru_majflt=2709, ru_nswap=0, ru_inblock=574600, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=495346, ru_nivcsw=288934)\n",
            "CPU usage: 11.5 %\n",
            "Memory usage: 48.6 %\n",
            "\n",
            "After epoch 1:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=1438.956519, ru_stime=10.773732, ru_maxrss=5289820, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1258298, ru_majflt=2709, ru_nswap=0, ru_inblock=574600, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=496109, ru_nivcsw=291080)\n",
            "CPU usage: 100.0 %\n",
            "Memory usage: 48.9 %\n",
            "\n",
            "After epoch 2:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=1441.958918, ru_stime=10.810596, ru_maxrss=5289820, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1258299, ru_majflt=2709, ru_nswap=0, ru_inblock=574600, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=496644, ru_nivcsw=292153)\n",
            "CPU usage: 100.0 %\n",
            "Memory usage: 49.1 %\n",
            "\n",
            "After epoch 3:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=1444.322676, ru_stime=10.83555, ru_maxrss=5289820, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1258299, ru_majflt=2709, ru_nswap=0, ru_inblock=574600, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=497317, ru_nivcsw=292555)\n",
            "CPU usage: 0.0 %\n",
            "Memory usage: 49.1 %\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "class EncryptedLR:\n",
        "\n",
        "    def __init__(self, torch_lr):\n",
        "        self.weight = torch_lr.lr.weight.data.tolist()[0]\n",
        "        self.bias = torch_lr.lr.bias.data.tolist()\n",
        "        # we accumulate gradients and counts the number of iterations\n",
        "        self._delta_w = 0\n",
        "        self._delta_b = 0\n",
        "        self._count = 0\n",
        "\n",
        "    def forward(self, enc_x):\n",
        "        enc_out = enc_x.dot(self.weight) + self.bias\n",
        "        enc_out = EncryptedLR.sigmoid(enc_out)\n",
        "        return enc_out\n",
        "\n",
        "    def backward(self, enc_x, enc_out, enc_y):\n",
        "        out_minus_y = (enc_out - enc_y)\n",
        "        self._delta_w += enc_x * out_minus_y\n",
        "        self._delta_b += out_minus_y\n",
        "        self._count += 1\n",
        "\n",
        "    def update_parameters(self):\n",
        "        if self._count == 0:\n",
        "            raise RuntimeError(\"You should at least run one forward iteration\")\n",
        "        # update weights\n",
        "        # We use a small regularization term to keep the output\n",
        "        # of the linear layer in the range of the sigmoid approximation\n",
        "        self.weight -= self._delta_w * (1 / self._count) + self.weight * 0.05\n",
        "        self.bias -= self._delta_b * (1 / self._count)\n",
        "        # reset gradient accumulators and iterations count\n",
        "        self._delta_w = 0\n",
        "        self._delta_b = 0\n",
        "        self._count = 0\n",
        "\n",
        "    @staticmethod\n",
        "    def sigmoid(enc_x):\n",
        "        return enc_x.polyval([0.5, 0.197, 0, -0.004])\n",
        "\n",
        "    def plain_accuracy(self, x_test, y_test):\n",
        "        # evaluate accuracy of the model on\n",
        "        # the plain (x_test, y_test) dataset\n",
        "        w = torch.tensor(self.weight)\n",
        "        b = torch.tensor(self.bias)\n",
        "        out = torch.sigmoid(x_test.matmul(w) + b).reshape(-1, 1)\n",
        "        correct = torch.abs(y_test - out) < 0.5\n",
        "        return correct.float().mean()\n",
        "\n",
        "    def encrypt(self, context):\n",
        "        self.weight = ts.ckks_vector(context, self.weight)\n",
        "        self.bias = ts.ckks_vector(context, self.bias)\n",
        "\n",
        "    def decrypt(self):\n",
        "        self.weight = self.weight.decrypt()\n",
        "        self.bias = self.bias.decrypt()\n",
        "\n",
        "    def __call__(self, *args, **kwargs):\n",
        "        return self.forward(*args, **kwargs)\n",
        "\n",
        "\n",
        "\n",
        "# parameters\n",
        "poly_mod_degree = 8192\n",
        "coeff_mod_bit_sizes = [40, 21, 21, 21, 21, 21, 21, 40]\n",
        "# create TenSEALContext\n",
        "ctx_training = ts.context(ts.SCHEME_TYPE.CKKS, poly_mod_degree, -1, coeff_mod_bit_sizes)\n",
        "ctx_training.global_scale = 2 ** 21\n",
        "ctx_training.generate_galois_keys()\n",
        "\n",
        "# Initialize lists to store resource usage data for each epoch/batch\n",
        "cpu_usages = []\n",
        "memory_usages = []\n",
        "enc_x_train = []\n",
        "enc_y_train = []\n",
        "# Define the batch size\n",
        "batch_size = 500\n",
        "\n",
        "# Calculate the total number of batches\n",
        "num_batches = math.ceil(len(x_train) / batch_size)\n",
        "\n",
        "# Encryption of the training set with batching\n",
        "for i in range(num_batches):\n",
        "    t_start = perf_counter()  # Record start time for each batch\n",
        "\n",
        "    start_idx = i * batch_size\n",
        "    end_idx = min((i + 1) * batch_size, len(x_train))  # Adjust end index for the last batch\n",
        "\n",
        "    # Extract a batch of data\n",
        "    batch_x = x_train[start_idx:end_idx]\n",
        "    batch_y = y_train[start_idx:end_idx]\n",
        "\n",
        "    # Encrypt the batch\n",
        "    enc_x_batch = [ts.ckks_vector(ctx_training, x.tolist()) for x in batch_x]\n",
        "    enc_y_batch = [ts.ckks_vector(ctx_training, y.tolist()) for y in batch_y]\n",
        "\n",
        "    # Append the batch to the encrypted training set\n",
        "    enc_x_train.extend(enc_x_batch)\n",
        "    enc_y_train.extend(enc_y_batch)\n",
        "\n",
        "    t_end = perf_counter()  # Record end time for each batch\n",
        "\n",
        "    # Append CPU and memory usage data for each batch\n",
        "    cpu_usages.append(psutil.cpu_percent())\n",
        "    memory_usages.append(psutil.virtual_memory().percent)\n",
        "\n",
        "    print(f\"Batch {i+1}:\")\n",
        "    print_resource_usage()\n",
        "    print(f\"Encryption of batch {i+1} took {int(t_end - t_start)} seconds\\n\")\n",
        "\n",
        "normal_dist = lambda x, mean, var: np.exp(- np.square(x - mean) / (2 * var)) / np.sqrt(2 * np.pi * var)\n",
        "\n",
        "def plot_normal_dist(mean, var, rmin=-10, rmax=10):\n",
        "    x = np.arange(rmin, rmax, 0.01)\n",
        "    y = normal_dist(x, mean, var)\n",
        "    fig = plt.plot(x, y)\n",
        "\n",
        "# plain distribution\n",
        "lr = LR(n_features)\n",
        "data = lr.lr(x_test)\n",
        "mean, var = map(float, [data.mean(), data.std() ** 2])\n",
        "plot_normal_dist(mean, var)\n",
        "print(\"Distribution on plain data:\")\n",
        "plt.show()\n",
        "\n",
        "# encrypted distribution\n",
        "def encrypted_out_distribution(eelr, enc_x_test):\n",
        "    w = eelr.weight\n",
        "    b = eelr.bias\n",
        "    data = []\n",
        "    for enc_x in enc_x_test:\n",
        "        enc_out = enc_x.dot(w) + b\n",
        "        data.append(enc_out.decrypt())\n",
        "    data = torch.tensor(data)\n",
        "    mean, var = map(float, [data.mean(), data.std() ** 2])\n",
        "    plot_normal_dist(mean, var)\n",
        "    print(\"Distribution on encrypted data:\")\n",
        "    plt.show()\n",
        "\n",
        "eelr = EncryptedLR(lr)\n",
        "eelr.encrypt(ctx_training)\n",
        "encrypted_out_distribution(eelr, enc_x_train)\n",
        "\n",
        "eelr = EncryptedLR(LR(n_features))\n",
        "accuracy = eelr.plain_accuracy(x_test, y_test)\n",
        "print(f\"Accuracy at epoch #0 is {accuracy}\")\n",
        "\n",
        "times = []\n",
        "print(\"before testing: \")\n",
        "print_resource_usage()\n",
        "for epoch in range(EPOCHS):\n",
        "    eelr.encrypt(ctx_training)\n",
        "\n",
        "\n",
        "    t_start = perf_counter()\n",
        "    for enc_x, enc_y in zip(enc_x_train, enc_y_train):\n",
        "        enc_out = eelr.forward(enc_x)\n",
        "        eelr.backward(enc_x, enc_out, enc_y)\n",
        "    eelr.update_parameters()\n",
        "    t_end = perf_counter()\n",
        "    times.append(t_end - t_start)\n",
        "\n",
        "    eelr.decrypt()\n",
        "    accuracy = eelr.plain_accuracy(x_test, y_test)\n",
        "\n",
        "    print(f\"after testing at epoch #{epoch + 1}: \")\n",
        "    print_resource_usage()\n",
        "    print(f\"Accuracy at epoch #{epoch + 1} is {accuracy}\")\n",
        "print(\"Last print: \")\n",
        "print_resource_usage()\n",
        "print(f\"\\nAverage time per epoch: {int(sum(times) / len(times))} seconds\")\n",
        "print(f\"Total execution time: {times} (seconds)\")\n",
        "print(f\"Final accuracy is {accuracy}\")\n",
        "\n",
        "diff_accuracy = plain_accuracy - accuracy\n",
        "print(f\"Difference between plain and encrypted accuracies: {diff_accuracy}\")\n",
        "if diff_accuracy < 0:\n",
        "    print(\"Oh! We got a better accuracy when training on encrypted data! The noise was on our side...\")\n"
      ],
      "metadata": {
        "id": "hUn6ncyYeyZQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "89ff15cb-8328-4449-a833-c99c167959f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch 1:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2363.495565, ru_stime=15.188427, ru_maxrss=5301444, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261127, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=810482, ru_nivcsw=471163)\n",
            "CPU usage: 0.0 %\n",
            "Memory usage: 47.4 %\n",
            "\n",
            "Encryption of batch 1 took 26 seconds\n",
            "\n",
            "Batch 2:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2376.067194, ru_stime=15.252332, ru_maxrss=5301444, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261127, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=812441, ru_nivcsw=473244)\n",
            "CPU usage: 0.0 %\n",
            "Memory usage: 47.6 %\n",
            "\n",
            "Encryption of batch 2 took 12 seconds\n",
            "\n",
            "Batch 3:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2389.243349, ru_stime=15.312294, ru_maxrss=5301444, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261127, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=814480, ru_nivcsw=475467)\n",
            "CPU usage: 0.0 %\n",
            "Memory usage: 47.5 %\n",
            "\n",
            "Encryption of batch 3 took 13 seconds\n",
            "\n",
            "Batch 4:\n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2397.669932, ru_stime=15.362235, ru_maxrss=5301444, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261128, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=816065, ru_nivcsw=476971)\n",
            "CPU usage: 0.0 %\n",
            "Memory usage: 47.2 %\n",
            "\n",
            "Encryption of batch 4 took 8 seconds\n",
            "\n",
            "Distribution on plain data:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution on encrypted data:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy at epoch #0 is 0.7609511613845825\n",
            "before testing: \n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2521.070319, ru_stime=15.867457, ru_maxrss=5304348, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261888, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=846428, ru_nivcsw=499538)\n",
            "CPU usage: 66.4 %\n",
            "Memory usage: 46.7 %\n",
            "\n",
            "after testing at epoch #1: \n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=2763.939652, ru_stime=16.598747, ru_maxrss=5304612, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261967, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=904480, ru_nivcsw=544366)\n",
            "CPU usage: 65.3 %\n",
            "Memory usage: 46.6 %\n",
            "\n",
            "Accuracy at epoch #1 is 0.9624530673027039\n",
            "after testing at epoch #2: \n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=3005.71915, ru_stime=17.31707, ru_maxrss=5304612, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261970, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=961882, ru_nivcsw=586185)\n",
            "CPU usage: 65.3 %\n",
            "Memory usage: 46.7 %\n",
            "\n",
            "Accuracy at epoch #2 is 0.9561952352523804\n",
            "after testing at epoch #3: \n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=3247.586555, ru_stime=18.073087, ru_maxrss=5304876, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261985, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=1018481, ru_nivcsw=627887)\n",
            "CPU usage: 65.7 %\n",
            "Memory usage: 46.7 %\n",
            "\n",
            "Accuracy at epoch #3 is 0.9536921381950378\n",
            "Last print: \n",
            "Resource usage:\n",
            "resource.struct_rusage(ru_utime=3247.59229, ru_stime=18.073091, ru_maxrss=5304876, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=1261985, ru_majflt=2709, ru_nswap=0, ru_inblock=574728, ru_oublock=24, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=1018536, ru_nivcsw=627887)\n",
            "CPU usage: 50.0 %\n",
            "Memory usage: 46.7 %\n",
            "\n",
            "\n",
            "Average time per epoch: 244 seconds\n",
            "Total execution time: [245.3827026250001, 244.37315202100035, 244.81433902900062] (seconds)\n",
            "Final accuracy is 0.9536921381950378\n",
            "Difference between plain and encrypted accuracies: 0.012515604496002197\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sVb_0vEGeJEW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x7rnYHdI1zpV"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "10J5qKIMkGdx3oBXhKe6mBTXdEWOq4rvc",
      "authorship_tag": "ABX9TyMvrY7gx3JATJJOQZoeyT4S",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}