Synthetic_Data.ipynb

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        "# Generating Synthetic Data with AGENT Framework\n",
        "## Introduction\n",
        "This document illustrates the process of generating synthetic data using the Antigranular Enterprise (AGENT) framework. By leveraging AGENT, we aim to produce a dataset that mimics the statistical properties of the original diamonds dataset while ensuring the privacy of the underlying data through differential privacy techniques.\n",
        "\n",
        "## Setup and Configuration\n",
        "Before generating synthetic data, we import necessary libraries and configure the AGENT framework with the required settings, including the server URL and API key for authentication."
      ]
    },
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          "text": [
            "Your request is pending approval. Please visit the following URL to approve the request: http://agent-dev.antigranular.com\n",
            "\u001b[92mRequest approved.\u001b[0m\n",
            "Connected to Antigranular server session id: faba2199-9237-4c57-888b-c27fd2d7640c\n",
            "Cell magic '%%ag' registered successfully, use `%%ag` in a notebook cell to execute your python code on Antigranular private python server\n"
          ]
        }
      ],
      "source": [
        "!pip install antigranular_enterprise -U 1> /dev/null 2> /dev/null\n",
        "import antigranular_enterprise as ag\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# These details will be used going forward, you can have and toggle between multiple profiles\n",
        "ag.write_config(profile='default', yaml_config=\"\"\"\n",
        "AGENT_JUPYTER_URL: https://agent.antigranular.com\n",
        "AGENT_JUPYTER_PORT: 443\n",
        "AGENT_CONSOLE_URL: http://agent-api-dev.antigranular.com\n",
        "\"\"\")\n",
        "\n",
        "# Connect in via your API key\n",
        "ag.read_config(\"default\")\n",
        "session = ag.login(api_key=\"<api_key>\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWsGnslNWG64"
      },
      "source": [
        "## Loading and Preparing the Dataset\n",
        "We load the `diamonds` dataset from Seaborn and prepare it for synthesis by importing it into the AGENT session."
      ]
    },
    {
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      "outputs": [
        {
          "name": "stdout",
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          "text": [
            "   carat      cut color clarity  depth  table  price     x     y     z\n",
            "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
            "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
            "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
            "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
            "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n"
          ]
        }
      ],
      "source": [
        "# Load the dataset\n",
        "diamonds = sns.load_dataset('diamonds')\n",
        "\n",
        "# Display the first few rows of the dataframe\n",
        "print(diamonds.head())"
      ]
    },
    {
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          "text": [
            "dataframe cached to server, loading to kernel...\n",
            "DataFrame loaded successfully to the kernel\n",
            "\n"
          ]
        }
      ],
      "source": [
        "session.private_import(diamonds, \"df\")"
      ]
    },
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        "id": "X-u8SqFdGxw-"
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      "source": [
        "%%ag\n",
        "import op_pandas as opd\n",
        "\n",
        "pdf = opd.PrivateDataFrame(\n",
        "    df,\n",
        "    metadata = {\n",
        "        \"carat\": (0.0, 5.1),\n",
        "        \"depth\": (40, 80),\n",
        "        \"table\": (2, 100),\n",
        "        \"price\": (100, 20000),\n",
        "        \"x\": (0, 11),\n",
        "        \"y\": (0, 60),\n",
        "        \"z\": (0, 35),\n",
        "    },\n",
        "    categorical_metadata = {\n",
        "        \"cut\": ['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'],\n",
        "        \"color\": ['E', 'I', 'J', 'H', 'F', 'G', 'D'],\n",
        "        \"clarity\": ['SI2', 'SI1', 'VS1', 'VS2', 'VVS2', 'VVS1', 'I1', 'IF']\n",
        "    }\n",
        ")"
      ]
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    {
      "cell_type": "markdown",
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        "id": "pQwfDs8aWRMe"
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      "source": [
        "## Synthesizing Data with AGENT\n",
        "Using AGENT's op_snsynth module, we create a synthesizer object and generate synthetic data that resembles the original dataset in statistical properties but is generated in a way that protects the privacy of the underlying data."
      ]
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          "text": [
            "Fitting with 552960000000 dimensions\n",
            "\n",
            "Getting cliques\n",
            "\n",
            "Estimating marginals\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%%ag\n",
        "import op_snsynth\n",
        "import pandas as pd\n",
        "\n",
        "categorical_columns = list(pdf.categorical_metadata.keys())\n",
        "\n",
        "synth = op_snsynth.Synthesizer.create('mst', epsilon=1.0, verbose=True)\n",
        "sample = synth.fit_sample(\n",
        "  pdf,\n",
        "  categorical_columns = list(pdf.categorical_metadata.keys()),\n",
        "  continuous_columns = list(pdf.metadata.keys()),\n",
        "  preprocessor_eps=0.5,\n",
        "  nullable=True\n",
        ")"
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          "text": [
            "Setting up exported variable in local environment: samples\n"
          ]
        }
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      "source": [
        "%%ag\n",
        "export(sample, \"samples\")"
      ]
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    {
      "cell_type": "markdown",
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        "id": "LMxjH0anWjee"
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      "source": [
        "## Analyzing the Synthetic Data\n",
        "\n",
        "After generating the synthetic data, we compare its statistical properties to the original dataset. This comparison helps validate the utility of the synthetic data for analytical purposes."
      ]
    },
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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",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      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-83a4f179-fcf4-482c-84a2-6ef622314ef5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                         carat              depth              table  \\\n",
              "True | ε-DP                                                            \n",
              "count        53940.0 | 53937.0  53940.0 | 53909.0  53940.0 | 53934.0   \n",
              "mean                0.8 | 0.79       61.75 | 63.8      57.46 | 56.88   \n",
              "std                0.47 | 0.51        1.43 | 4.05        2.23 | 2.93   \n",
              "min                  0.2 | 0.2        43.0 | 36.8        43.0 | 36.8   \n",
              "25%                  0.4 | 0.6        61.0 | 65.6        56.0 | 56.0   \n",
              "50%                  0.7 | 0.6        61.8 | 65.6        57.0 | 56.0   \n",
              "75%                 1.04 | 1.0        62.5 | 65.6        59.0 | 56.0   \n",
              "max                 5.01 | 3.8       79.0 | 123.2       95.0 | 123.2   \n",
              "\n",
              "                         price                  x                  y  \\\n",
              "True | ε-DP                                                            \n",
              "count        53940.0 | 53915.0  53940.0 | 53937.0  53940.0 | 53929.0   \n",
              "mean          3932.8 | 4289.11         5.73 | 5.7         5.73 | 5.7   \n",
              "std          3989.44 | 3813.93        1.12 | 1.28        1.14 | 1.27   \n",
              "min             326.0 | 1881.6          0.0 | 2.7          0.0 | 2.7   \n",
              "25%             950.0 | 1881.6         4.71 | 4.1         4.72 | 4.1   \n",
              "50%            2401.0 | 1881.6          5.7 | 5.5         5.71 | 5.5   \n",
              "75%           5324.25 | 5132.8         6.54 | 6.9         6.54 | 6.9   \n",
              "max          18823.0 | 31142.4       10.74 | 15.3        58.9 | 15.3   \n",
              "\n",
              "                             z  \n",
              "True | ε-DP                     \n",
              "count        53940.0 | 53917.0  \n",
              "mean               3.54 | 3.59  \n",
              "std                 0.71 | 0.7  \n",
              "min                  0.0 | 2.3  \n",
              "25%                 2.91 | 2.9  \n",
              "50%                 3.53 | 3.5  \n",
              "75%                 4.04 | 4.1  \n",
              "max                 31.8 | 7.7  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "def compare_describe(desc1, desc2):\n",
        "  # Manually construct a new DataFrame\n",
        "  combined_stats = pd.DataFrame()\n",
        "  combined_stats.index.name = f'True | ε-DP'\n",
        "\n",
        "  for col in desc1.columns:\n",
        "      for index in desc1.index:\n",
        "          val1 = desc1.at[index, col]\n",
        "          val2 = desc2.at[index, col]\n",
        "          combined_stats.at[index, col] = f\"{val1} | {val2}\"\n",
        "\n",
        "  return combined_stats\n",
        "\n",
        "# Compare descriptive statistics\n",
        "compare_describe(diamonds.describe().round(2), samples.describe().round(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ptD0UZsre6JN",
        "outputId": "86407970-09de-4c98-e7c5-6c5210d43f5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "corr_matrix = samples.select_dtypes(\"float\").corr()\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm',\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "jKJUJu6Ce86J",
        "outputId": "e638f948-5b5a-4540-c57e-d314709f5576"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr_matrix = diamonds.select_dtypes(\"float\").corr()\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm',\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VsTyUjXtfZdS"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}