tutorial-WGAN_ALP-LHCb_RICH.ipynb

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      "source": [
        "# Parameterizing the LHCb RICH system using the pidgan `WGAN_ALP` algorithm\n",
        "\n",
        "**Author:** [mbarbetti](https://github.com/mbarbetti)\n",
        "\n",
        "**Date created:** 16/10/2023\n",
        "\n",
        "**Last modified:** 09/11/2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzrL35_aMf4X"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_ALP-LHCb_RICH.ipynb)\n",
        "[![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_ALP-LHCb_RICH.ipynb)\n",
        "[![Open in GitHub](https://img.shields.io/badge/Open_in_GitHub-blue?style=flat&logo=github&logoColor=white&labelColor=555)](https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-WGAN_ALP-LHCb_RICH.ipynb)\n",
        "\n",
        "**Description:** This tutorial demonstrates how to parameterize the high-level response of the LHCb RICH system using a Generative Adversarial Network (GAN) [[1](https://arxiv.org/abs/1406.2661)]. The code is written using the [pidgan](https://github.com/mbarbetti/pidgan) package that relies on TensorFlow and Keras as backends."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvTVEAa7i5xJ"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opu773xWi5xK"
      },
      "source": [
        "### What is LHCb?\n",
        "\n",
        "The [**LHCb experiment**](https://lhcb-outreach.web.cern.ch) has been originally designed to study rare decays of particles containing $b$ and $c$ quarks produced at the Large Hadron Collider (LHC). The LHCb detector, shown in the background of the following photo, is a single-arm forward spectrometer covering the pseudorapidity range of $2 < \\eta < 5$. The detector includes:\n",
        "\n",
        "- **Tracking system** - used for high-precision measurements of the momentum of charged particles and the position of the primary vertices\n",
        "- **Particle Identification (PID) system** - used to distiguish different species of traversing particles (i.e., muons, pions, kaons, protons)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5If4oPxki5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/lhcb.jpeg\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTEAA_GpgXXM"
      },
      "source": [
        "#### The RICH detectors\n",
        "\n",
        "The LHCb PID system counts two ring-imaging Cherenkov ([RICH](https://lhcb-outreach.web.cern.ch/detector/rich-detectors/)) detectors whose response allows to separate different types of charged hadrons (e.g., pions, kaons, protons) using the [Cherenkov radiation](https://en.wikipedia.org/wiki/Cherenkov_radiation) of the traversing particles. The size of the circles produced by the Cherenkov radiation (shown on the left) within the RICH detector is related to the mass of the emitting particle. Reconstructing such circles allows to distinguish particles accordingly to their species (shown on the right)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvTtCmaBhqQ_"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-response.png\" width=\"300\"/>\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-performance.png\" width=\"400\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPnfIVMoi5xK"
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      "source": [
        "### What are GANs?\n",
        "\n",
        "Generative Adversarial Networks [[1][1]] are a powerful class of _generative models_ based on the simultaneous training of two neural networks:\n",
        "\n",
        "*  **Discriminator network** ($D$) - trained by a classification task to separate the generator output from the reference dataset\n",
        "* **Generator network** ($G$) - trained by a simulation task to reproduce the reference dataset trying to fake the discriminator\n",
        "\n",
        "The goal is that $D$ optimally discriminates on the origin of the two samples, and simultaneously the training procedure for $G$ is to maximize the _probability_ of $D$ making a mistake. This framework corresponds to a **minimax two-player game** [[1][1]].\n",
        "\n",
        "Traditional GAN systems suffer from many issues, particularly during the training phase:\n",
        "\n",
        "* the generator may _collapse_ producing only a single sample or a small family of very similar samples (**mode collapse**)\n",
        "* the two players may _oscillate_ during training rather than converging to the [**Nash equilibrium**](https://en.wikipedia.org/wiki/Nash_equilibrium)\n",
        "* if _imbalance_ between the two players occurs, then the system is incapable of learning at all\n",
        "\n",
        "All these drawbacks result from the [**vanishing gradient problem**](https://en.wikipedia.org/wiki/Vanishing_gradient_problem), namely the lack of information for the update of the $G$ parameters. This is due to the saturation of the $D$ that is so good in distinguishing the origin of the two samples that no errors remain to the $G$ to improve the generated space [[2][2]]. To fix the problem, we need to change the loss function originally proposed in Ref. [[1][1]], moving to a _distance measurement_ robust against the vanishing gradient problem. This new loss function is the **Wasserstein distance** that allows to define the so-called **WGAN algorithm** [[3][3]]. In particular, penalizing the direct violation of the 1-Lipschitz constraint of the discriminator network, the literature refers to it as **WGAN-ALP algorithm** [[4][4]].\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1907.05681"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_Hs8F8pi5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/gan-scheme.png\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmUghh5Ii5xK"
      },
      "source": [
        "#### Mathematical details\n",
        "\n",
        "The generator $G(z)$, fed by elements $z$ sampled according to a known distribution $p_z$ (typically gaussian), maps the **latent space** $\\mathcal{Z}$ to the reference dataset $\\mathcal{X}$, inducing a distribution $p_\\rm{gen}$ trained to match with the target distribution $p_\\rm{ref}$. The discriminator $D(x)$ outputs a single scalar, readable as the **score** that $x$ comes from the reference dataset rather than $G$. Hence, the optimization problem corresponds to train $D$ to maximize the distance between the _reference score_ and the _generator score_, and simultaneously training $G$ to minimize the same quantity.\n",
        "\n",
        "To avoid _vanishing gradient issues_, the optimization problem for the two networks is based on the **Wasserstein distance** [[5][5]]:\n",
        "\n",
        "<center>$W \\left ( p_{\\rm{ref}}, p_{\\rm{gen}} \\right ) = \\displaystyle{\\sup_{\\| f \\|_L \\le 1}} \\left ( \\, \\mathbb{E}_{x \\sim p_{\\rm{ref}}} [f(x)] - \\mathbb{E}_{x \\sim p_{\\rm{gen}}} [f(x)] \\, \\right )$</center>\n",
        "\n",
        "where the supremum is over all the 1-Lipschitz functions $f : \\mathcal{X} \\to \\mathbb{R}$. Note that if we replace $\\| f \\|_L \\le 1$ with $\\| f \\|_L \\le K$ (consider K-Lipschitz for some constant $K$), then we end up with $K \\cdot W \\left ( p_{\\rm{ref}}, p_{\\rm{gen}} \\right )$ that ensures robustness to the optimization problem described above.\n",
        "\n",
        "Defining the **loss function** $\\mathcal{L}_{\\rm{WGAN}}$ as follows\n",
        "\n",
        "<center>$\\mathcal{L}_{\\rm{WGAN}} (\\theta_d, \\theta_g) = \\mathbb{E}_{x \\sim p_\\rm{ref}} \\left[ D_{\\theta_d}(x) \\right] - \\mathbb{E}_{z \\sim p_\\rm{z}} \\left[ D_{\\theta_d}(G_{\\theta_g}(z)) \\right]$</center>\n",
        "\n",
        "the _minimax game_ of the **WGAN-ALP algorithm** can be written in this form:\n",
        "\n",
        "<center>$\\begin{cases}\n",
        "\\displaystyle{\\min_G} \\, \\mathcal{L}_{\\rm{WGAN}} (\\theta_d, \\theta_g)\\\\\n",
        "\\displaystyle{\\max_D} \\, \\mathcal{L}_{\\rm{WGAN}} (\\theta_d, \\theta_g) - \\lambda \\, \\mathbb{E}_{x \\sim p_{\\rm{ref, gen}}} \\left ( \\frac{\\left | D_{\\theta_d}(x) - D_{\\theta_d}(x + r_{adv}) \\right |}{\\| r_{adv} \\|_2} - 1 \\right )^2\n",
        "\\end{cases}$</center>\n",
        "\n",
        "where the _lipschitzianity_ of $D$ is **explicitly** ensured constraining to 1 the Lipschitz constant computed along the **virtual adversarial perturbation** $r_{adv}$, namely the direction that approximates the maximum deviation from the target discriminator behaviour. As described in Ref. [[4][4]], this is achieved using a regularization term called **adversarial Lipschitz penalty** (ALP).\n",
        "\n",
        "[4]: https://arxiv.org/abs/1907.05681\n",
        "[5]: https://link.springer.com/book/10.1007/978-3-540-71050-9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "#### Using input conditions\n",
        "\n",
        "Feeding the generator with additional information besides the latent space sample allows to **conditionate** its output. In particular, it's sufficient to concatenate the conditional features $x$ to the random noise $z$ passed as input to $G$ to make it able to take into account this information [[6][6]]: $y_{\\rm{gen}}(x) = G(x, z)$. Obviously, to preserve the capability to learn through the minimax game, also the discriminator must be fed by the additional features $x$, simply concatenating them to either elements of the reference sample or of the generated space [[6][6]]: $D(x, y)$ with $y \\in \\{y_{\\rm{ref}}, y_{\\rm{gen}}\\}$.\n",
        "\n",
        "[6]: https://arxiv.org/abs/1411.1784"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvpIdCnSi5xL"
      },
      "source": [
        "## Tutorial\n",
        "\n",
        "The aim of this tutorial is to build a parameterization of the LHCb RICH detectors. Since we expect that the high-level response of the RICH system only depends on the kinematics of the traversing particles and the detector occupancy, the former can be parameterized training a **WGAN-ALP model** properly conditioned [[6][6], [7][7]]. The pidgan package provides several GAN [`algorithms`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/algorithms) implementing some of the most used tricks presented in the literature to stablize their training [[3][3], [4][4], [8][8]]. The pidgan [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players), namely the generator and discriminator networks, are designed to be trained taking conditions as input. In this notebook we will see how to build a model for the RICH system using the pidgan [`WGAN_ALP`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/WGAN_ALP.py) algorithm.\n",
        "\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1907.05681\n",
        "[6]: https://arxiv.org/abs/1411.1784\n",
        "[7]: https://arxiv.org/abs/1905.11825\n",
        "[8]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ohrh3dQi5xL"
      },
      "source": [
        "### Install\n",
        "\n",
        "The first step is to install the [pidgan](https://github.com/mbarbetti/pidgan) package and some other ones that are typically needed in machine learning applications to High Energy Physics (i.e., uproot, scipy, scikit-learn, matplotlib)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r8glZROOi5xL"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install pidgan[hep]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kmb7g7HZi5xL"
      },
      "source": [
        "Now, let's verify the correct installation of pidgan printing its version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CWg67WFhi5xL",
        "outputId": "87f988fa-74f9-415b-fcf0-63ed3f4da1d1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.0.6'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pidgan\n",
        "\n",
        "pidgan.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8lt6bjDi5xM"
      },
      "source": [
        "In case you're running this notebook on a machine equipped with a GPU, let's also verify the correct installation of TensorFlow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGoxRYbGi5xM",
        "outputId": "91eabb78-642f-400e-981c-f8260c188f33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.config.list_physical_devices(\"GPU\")  # outputs a non-empty list in case of GPU equipped"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uslhx_PjvOwk"
      },
      "source": [
        "Finally, we just have to import all the modules that we will use in the following code cells:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twM1ba1PvgVl"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from datetime import datetime\n",
        "from tensorflow import keras\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import QuantileTransformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz9PhSN8wP2h"
      },
      "source": [
        "### Data loading\n",
        "\n",
        "The data needed for this tutorial is provided through a CSV file downloadable from the [pidgan-notebooks](https://github.com/mbarbetti/pidgan-notebooks) repository.\n",
        "\n",
        "**Disclaimer:** The data results from a toy model inspired to the LHCb RICH system combining its high-level response when traversed by a set of long-lived charged particles, namely muons, pions, kaons and protons.\n",
        "\n",
        "The CSV file contains 250000 instances, 4 input features and 4 output features. Let's import the data as a pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "QfnPXl161n2f",
        "outputId": "dea5f852-642e-49a2-b2d0-87733dccac07"
      },
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d7a6d6bc-d47f-4519-bc7e-bc5b8ab28506 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d7a6d6bc-d47f-4519-bc7e-bc5b8ab28506');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0cad6157-46d7-409d-a1a7-35b8e04214ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0cad6157-46d7-409d-a1a7-35b8e04214ad')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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-0cad6157-46d7-409d-a1a7-35b8e04214ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge  \\\n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000   \n",
              "mean    19112.579988       3.362231     180.468471       0.021152   \n",
              "std     22610.408265       0.770202      91.626085       0.999778   \n",
              "min      3000.090800       1.800331       9.000999      -1.000000   \n",
              "25%      5970.525000       2.749856     116.000000      -1.000000   \n",
              "50%     10983.800000       3.340136     164.000000       1.000000   \n",
              "75%     22392.768000       3.949773     228.000000       1.000000   \n",
              "max    199837.420000       5.434650     739.993000       1.000000   \n",
              "\n",
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp  \n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000  \n",
              "mean       -8.959810      -1.853031      -3.614234      -3.626358  \n",
              "std        16.090133      11.512878      28.799922      28.006328  \n",
              "min      -140.804340    -110.494200    -149.289830    -149.591670  \n",
              "25%       -12.259203      -3.784543     -16.768035     -18.402633  \n",
              "50%        -3.499332      -0.634627       1.748902       1.705883  \n",
              "75%        -0.179516       0.651420      14.155344      15.179315  \n",
              "max        58.021923     106.868935     120.245310     110.364006  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url = \"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/data/pidgan-toy-data.csv\"\n",
        "data = pd.read_csv(url)\n",
        "\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhaU0lWcZ-6L"
      },
      "source": [
        "We expect that the response of the RICH detectors only depends on the **kinematics** of the traversing particles and the **detector occupancy**. Hence, to build the aimed parameterization, we will conditionate the generator training using the following information:\n",
        "* `data.P` - momentum in GeV/$c$ of the traversing parrticles\n",
        "* `data.ETA` - pseudorapidity of the traversing particles\n",
        "* `data.nTracks` - total number of reconstructed tracks describing the detector occupancy\n",
        "* `data.trackcharge` - charge of the traversing particles (boolean value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "j99DTVou5KdZ",
        "outputId": "9e77692d-e56f-4ec2-e4a7-4cb06d9eadf8"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 2400x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(24, 5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.xlabel(\"Momentum [GeV/$c$]\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"P\"]/1e3, bins=np.linspace(0.0, 200.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.xlabel(\"Pseudorapidity\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"ETA\"], bins=np.linspace(1.5, 5.5, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.xlabel(\"$\\mathtt{nTracks}$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"nTracks\"], bins=np.linspace(0.0, 750.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV9PdrDtb5gK"
      },
      "source": [
        "The high-level response of the RICH detectors describes how good the reconstructed circles match with a specific particle hypothesis in terms of **differential log-likelihood** (DLL). We aim to build a model able to reproduce the following output features:\n",
        "* `data.RichDLLe` - RICH DLL between the electron and pion tracks\n",
        "* `data.RichDLLmu` - RICH DLL between the muon and pion tracks\n",
        "* `data.RichDLLk` - RICH DLL between the kaon and pion tracks\n",
        "* `data.RichDLLp` - RICH DLL between the proton and pion tracks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "id": "2r8zWzbI7A2V",
        "outputId": "83373a7f-d077-4b1e-9606-bd09b8037bbb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLe\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLmu\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLk\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLp\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDfCdUkIi5xM"
      },
      "source": [
        "### Data preprocessing\n",
        "\n",
        "As usual in a machine learning application, the data sample is split into **training** and **test set**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xinGo4AJ9OFu",
        "outputId": "6794a9fb-8500-4595-9bac-d505c105882a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train shape: (150000, 4) \t x_test shape: (100000, 4)\n",
            "y_train shape: (150000, 4) \t y_test shape: (100000, 4)\n"
          ]
        }
      ],
      "source": [
        "data = shuffle(data)\n",
        "\n",
        "x_vars = [\"P\", \"ETA\", \"nTracks\", \"trackcharge\"]\n",
        "x = data[x_vars].values\n",
        "\n",
        "y_vars = [\"RichDLLe\", \"RichDLLmu\", \"RichDLLk\", \"RichDLLp\"]\n",
        "y = data[y_vars].values\n",
        "\n",
        "x_train, x_test = x[:150000], x[150000:]\n",
        "y_train, y_test = y[:150000], y[150000:]\n",
        "\n",
        "print(f\"x_train shape: {x_train.shape} \\t x_test shape: {x_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape} \\t y_test shape: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcGuX9oZ_HHN"
      },
      "source": [
        "To normalize the different ranges and orders of magnitude of input $x$ and output $y$ features highlighted by the previous histograms, we will use the scikit-learn [`QuantileTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html) using a Gaussian as output distribution.\n",
        "\n",
        "Since one of the $x$ features is boolean (`data.trackcharge`), the latter should be kept unchanged. Let's preprocess the rest of the conditional information and verify the results with the [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) `describe()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BElR-YUR_0df",
        "outputId": "03ba15c6-7e49-4640-bd10-56319bdade3e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b9f60903-e17a-40f5-aa85-a188e0eb833c\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>ETA</th>\n",
              "      <th>nTracks</th>\n",
              "      <th>trackcharge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.001591</td>\n",
              "      <td>-0.003995</td>\n",
              "      <td>0.014512</td>\n",
              "      <td>0.021800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.003139</td>\n",
              "      <td>0.996564</td>\n",
              "      <td>0.999468</td>\n",
              "      <td>0.999766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.674191</td>\n",
              "      <td>-0.667053</td>\n",
              "      <td>-0.659602</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.013157</td>\n",
              "      <td>0.005710</td>\n",
              "      <td>0.017565</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.677697</td>\n",
              "      <td>0.674754</td>\n",
              "      <td>0.686350</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9f60903-e17a-40f5-aa85-a188e0eb833c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b9f60903-e17a-40f5-aa85-a188e0eb833c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b9f60903-e17a-40f5-aa85-a188e0eb833c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1e598d9a-3dfa-4b9b-b3d4-273d19c5452b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e598d9a-3dfa-4b9b-b3d4-273d19c5452b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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-1e598d9a-3dfa-4b9b-b3d4-273d19c5452b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean        0.001591      -0.003995       0.014512       0.021800\n",
              "std         1.003139       0.996564       0.999468       0.999766\n",
              "min        -5.199338      -5.199338      -5.199338      -1.000000\n",
              "25%        -0.674191      -0.667053      -0.659602      -1.000000\n",
              "50%         0.013157       0.005710       0.017565       1.000000\n",
              "75%         0.677697       0.674754       0.686350       1.000000\n",
              "max         5.199338       5.199338       5.199338       1.000000"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "x_train_prep = x_scaler.fit_transform(x_train[:, :3])\n",
        "x_train_prep = np.c_[x_train_prep, x_train[:, 3]]\n",
        "\n",
        "pd.DataFrame(x_train_prep, columns=x_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY6OOsx6BhQP"
      },
      "source": [
        "Let's preprocess also the $y$ features checking what obtained:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "VvfBZUjvB4oT",
        "outputId": "912f84b9-b9ae-4f8d-9920-4d3bf302fb7c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ac49a463-7a0d-4b8c-9c4a-f015de9e8386\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RichDLLe</th>\n",
              "      <th>RichDLLmu</th>\n",
              "      <th>RichDLLk</th>\n",
              "      <th>RichDLLp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.007263</td>\n",
              "      <td>0.009329</td>\n",
              "      <td>-0.004098</td>\n",
              "      <td>0.022173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000119</td>\n",
              "      <td>1.000812</td>\n",
              "      <td>1.007984</td>\n",
              "      <td>0.994585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.690721</td>\n",
              "      <td>-0.660578</td>\n",
              "      <td>-0.691046</td>\n",
              "      <td>-0.644801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.009342</td>\n",
              "      <td>0.010999</td>\n",
              "      <td>-0.005763</td>\n",
              "      <td>0.016166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.672660</td>\n",
              "      <td>0.679805</td>\n",
              "      <td>0.680470</td>\n",
              "      <td>0.684228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac49a463-7a0d-4b8c-9c4a-f015de9e8386')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ac49a463-7a0d-4b8c-9c4a-f015de9e8386 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ac49a463-7a0d-4b8c-9c4a-f015de9e8386');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6c16d852-2979-4cdf-8f6f-4214a119a0fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c16d852-2979-4cdf-8f6f-4214a119a0fc')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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-6c16d852-2979-4cdf-8f6f-4214a119a0fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean       -0.007263       0.009329      -0.004098       0.022173\n",
              "std         1.000119       1.000812       1.007984       0.994585\n",
              "min        -5.199338      -5.199338      -5.199338      -5.199338\n",
              "25%        -0.690721      -0.660578      -0.691046      -0.644801\n",
              "50%        -0.009342       0.010999      -0.005763       0.016166\n",
              "75%         0.672660       0.679805       0.680470       0.684228\n",
              "max         5.199338       5.199338       5.199338       5.199338"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "y_train_prep = y_scaler.fit_transform(y_train)\n",
        "\n",
        "pd.DataFrame(y_train_prep, columns=y_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVNTdWLPi5xM"
      },
      "source": [
        "### Model definition\n",
        "\n",
        "We are finally ready to define the **WGAN-ALP model** to be used to parameterize the RICH response. GAN algorithms rely on the competition (namely the minimax game) between the generator and discriminator networks. The latters are provided by the [pidgan](https://github.com/mbarbetti/pidgan) package through the [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players) module that implements the two networks customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvx8Dkbz5o9Q"
      },
      "source": [
        "#### The generator\n",
        "\n",
        "The **generator player** can be implemented with the high-level [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Before to initialize a `Generator` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the generator output space.\n",
        "* **`latent_dim`** (_int_) - Dimensionality of the latent space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=None) - Activation function to use in the generator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the generator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the generator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0th7xpVKCGN0"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.generators import Generator\n",
        "\n",
        "generator = Generator(\n",
        "    output_dim=y.shape[-1],\n",
        "    latent_dim=64,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=None,\n",
        "    name=\"generator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luH3v1XVEBlc",
        "outputId": "1d6054c3-97b0-43e2-e269-067cd009d4df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"generator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               8832      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75396 (294.52 KB)\n",
            "Trainable params: 75396 (294.52 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = generator(keras.Input(shape=x.shape[1:]))\n",
        "generator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MkE6GA14-hz"
      },
      "source": [
        "#### The discriminator\n",
        "\n",
        "The **discriminator player** can be implemented with the high-level [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Again, before to initialize a `Discriminator` instance, let's have a look to its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the discriminator output space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=\"sigmoid\") - Activation function to use in the dicriminator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the discriminator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the discriminator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ufj8Xi4fC_Ot"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.discriminators import Discriminator\n",
        "\n",
        "discriminator = Discriminator(\n",
        "    output_dim=1,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=None,\n",
        "    name=\"discriminator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfqi9DHvETlY",
        "outputId": "979eb879-52ce-448a-fbb9-4530abca5005"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"discriminator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               1152      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67329 (263.00 KB)\n",
            "Trainable params: 67329 (263.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = discriminator((keras.Input(shape=x.shape[1:]), keras.Input(shape=y.shape[1:])))\n",
        "discriminator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpQQWkPS5htN"
      },
      "source": [
        "#### The WGAN-ALP algorithm\n",
        "\n",
        "The **WGAN-ALP algorithm** [[3][3], [4][4]] can be implemented with the high-level [`WGAN_ALP`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/WGAN_ALP.py) class that defines the training procedure of the two players customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class and, in particular, its `fit()` method. Once again, before to initialize a `WGAN_ALP` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`generator`** (_generator_like_) - The generator network. It must be passed as a pidgan [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) instance.\n",
        "* **`discriminator`** (_discriminator_like_) - The discriminator network. It must be passed as a pidgan [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) instance.\n",
        "* **`lipschitz_penalty`** (_float_, default=1.0) - Importance of the regularization term ALP that explicitly constrains the discriminator to induce a 1-Lipschitz function as proposed in Ref. [[4][4]].\n",
        "* **`lipschitz_penalty_strategy`** (_str_, default=\"one-sided\") - If \"two-sided\" is passed, the Lipschitz constant computed along the virtual adversarial direction is forced to be equal to 1. When \"one-sided\" is preferred, only Lipschitz constant values greater than 1 are penalized.\n",
        "* **`feature_matching_penalty`** (_float_, default=0.0) - Importance of the regularization term added to the generator training to force the hidden state of the discriminator to match between reference and generated examples. Its design follows from what proposed in Ref. [[8][8]].\n",
        "* **`referee`** (_classifier_like_ or _None_, default=None) - When passed it enables the training of a third independent player: the referee network. It can be used to assess the quality of the generator output during the training. If passed, it must be a pidgan [`Classifier`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/classifiers/Classifier.py) instance.\n",
        "* **`name`** (_str_ or _None_, default=\"WGAN_ALP\") - The name of the WGAN-ALP model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the WGAN-ALP model computations.\n",
        "\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1907.05681\n",
        "[8]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgW6y0Ru1vPp"
      },
      "source": [
        "To not further complicate this tutorial, we avoid to use the referee network in this case, even if its initialization is straightforward:\n",
        "\n",
        "```python\n",
        "from pidgan.players.classifiers import Classifier\n",
        "\n",
        "referee = Classifier(\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    name=\"referee\"\n",
        ")\n",
        "```\n",
        "\n",
        "It's time to initialize a [`WGAN_ALP`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/WGAN_ALP.py) instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBEOKmeEDWC4"
      },
      "outputs": [],
      "source": [
        "from pidgan.algorithms import WGAN_ALP\n",
        "\n",
        "gan = WGAN_ALP(\n",
        "    generator=generator,\n",
        "    discriminator=discriminator,\n",
        "    lipschitz_penalty=1.0,\n",
        "    lipschitz_penalty_strategy=\"one-sided\",\n",
        "    feature_matching_penalty=0.0,\n",
        "    referee=None,\n",
        "    name=\"WGAN_ALP\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrE8bs952KB"
      },
      "source": [
        "#### Model compilation\n",
        "\n",
        "To finalize the [`WGAN_ALP`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/WGAN_ALP.py) model preparation before to run the training procedure, we need to execute the `compile()` method. Differently from the TensorFlow `compile()` method, the [pidgan](https://github.com/mbarbetti/pidgan) one doesn't require to pass the loss function since it is encoded in the customized `gan.fit()` method. The arguments of the `gan.compile()` method follows:\n",
        "\n",
        "* **`metrics`** (_list_ or _None_, default=None) - If not None, list of metrics to be evaluated by the WGAN-ALP model during training and testing. Each of this can be a string (name of a built-in pidgan function) or a [`pidgan.metrics`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/metrics) instance.\n",
        "* **`generator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the generator network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`discriminator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the discriminator network. (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`generator_upds_per_batch`** (_int_, default=1) - Number of the generator weights updates per batch of data.\n",
        "* **`discriminator_upds_per_batch`** (_int_, default=1) - Number of the discriminator weights updates per batch of data.\n",
        "* **`virtual_adv_direction_upds`** (_int_, default=1) - Number of iterations to approximate the virtual adversarial direction per discriminator update as proposed in Ref. [[4][4]].\n",
        "* **`referee_optimizer`** (_optimizer_like_ or _None_, default=None) - If not None, string (name of optimizer) or optimizer instance for the referee network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`referee_upds_per_batch`** (_int_ or _None_, default=None) - If not None, number of the referee weights updates per batch of data.\n",
        "\n",
        "[4]: https://arxiv.org/abs/1907.05681"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kHL20AxZFNOq"
      },
      "outputs": [],
      "source": [
        "gan.compile(\n",
        "    metrics=[\"wass_dist\"],\n",
        "    generator_optimizer=keras.optimizers.RMSprop(learning_rate=0.002),\n",
        "    discriminator_optimizer=keras.optimizers.RMSprop(learning_rate=0.001),\n",
        "    generator_upds_per_batch=1,\n",
        "    discriminator_upds_per_batch=1,\n",
        "    virtual_adv_direction_upds=1,\n",
        "    referee_optimizer=None,\n",
        "    referee_upds_per_batch=None,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0htLzsSri5xM"
      },
      "source": [
        "### Training procedure\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides several learning rate scheduling strategies through the [`callbacks.schedulers`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/callbacks/schedulers) module. The pidgan schedulers are designed as [custom TensorFlow callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks) that also allow to trace the learning rate value of each passed optimizer during the training. In this tutorial, we will use an exponential decay schedule both for the generator and discriminator networks:\n",
        "\n",
        "<center>$\\eta(t) = \\eta_0 \\cdot e^{- \\alpha \\cdot t / \\tau}$</center>\n",
        "\n",
        "where $\\eta(t)$ indicates the learning rate value at a specific training step $t$, $\\eta_0$ the initial learning rate, $\\alpha$ the decay rate and $\\tau$ the decay step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0-PqvTDGd1J"
      },
      "outputs": [],
      "source": [
        "from pidgan.callbacks.schedulers import LearnRateExpDecay\n",
        "\n",
        "schedules = list()\n",
        "\n",
        "g_lr_sched = LearnRateExpDecay(\n",
        "    gan.generator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=50000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"g_lr\",\n",
        ")\n",
        "schedules.append(g_lr_sched)\n",
        "\n",
        "d_lr_sched = LearnRateExpDecay(\n",
        "    gan.discriminator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=75000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"d_lr\",\n",
        ")\n",
        "schedules.append(d_lr_sched)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hDNGvV_FI0S"
      },
      "source": [
        "Calling `gan.fit()` allows to perform the **minimax two-player game**, training the generator to reproduce the reference space $y$ on the basis of the conditions $x$ thanks to the discriminator feedbacks. The arguments taken by the pidgan `fit()` method are the same ones traditionally passed to a TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) instance. The schedulers are passed through the `callbacks` argument and, since their verbosity has been enabled (`verbose` and `key` passed), the learning rates of both the generator and discriminator are printed during the training procedure.\n",
        "\n",
        "**Note:** Training the [`WGAN_ALP`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/WGAN_ALP.py) model on Colab using the hyperparameters chosen for this tutorial (i.e., batch-size, epochs, updates per batch) takes about 25 minutes with no-GPU equipped or less than 15 minutes enabling the T4-powered runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1mGZ3_FHMNg",
        "outputId": "74d7b496-9d1a-4cf4-c0bc-64a0cea9f552"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training procedure completed in 0:11:29.430349\n"
          ]
        }
      ],
      "source": [
        "start = datetime.now()\n",
        "\n",
        "train = gan.fit(\n",
        "    x=x_train_prep,\n",
        "    y=y_train_prep,\n",
        "    batch_size=256,\n",
        "    epochs=100,\n",
        "    validation_split=0.3,\n",
        "    callbacks=schedules,\n",
        "    verbose=1,\n",
        ")\n",
        "\n",
        "stop = datetime.now()\n",
        "\n",
        "print(f\"Training procedure completed in {stop - start}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GomKgX8QLeQM"
      },
      "source": [
        "The following code cells report the **learning** and **metric curves** for the training just finished."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z97AGJog5-jN"
      },
      "source": [
        "#### Learning curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "-jdGN4aFIhdn",
        "outputId": "7dbc15bc-8f86-4d0f-eec6-2b94d7f81616"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Loss\", fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Learning rate scheduling\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Learning rate\", fontsize=12)\n",
        "plt.plot(train.history[\"g_lr\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_lr\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "2id4-AK5J-WW",
        "outputId": "cd5b3076-d9f0-466a-ae8f-9999aed9c32f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Generator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_g_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Discriminator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_d_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5wv91XH6BDC"
      },
      "source": [
        "#### Metric curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "P8rgOZgpKjPj",
        "outputId": "ea6c64c4-831c-4fb9-de66-221c28848f29"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5), dpi=100)\n",
        "\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Wasserstein distance\", fontsize=12)\n",
        "plt.plot(train.history[\"wass_dist\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_wass_dist\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hdn_BRai5xM"
      },
      "source": [
        "### Validation plots\n",
        "\n",
        "Once trained the WGAN-ALP system, the **generator model** can be used to parameterize the high-level response of the LHCb RICH detectors, namely the RICH differential log-likelihoods for different particle hypotheses. Let's have a look at the achieved performance on the **test set**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HVid9HKfLzPW"
      },
      "outputs": [],
      "source": [
        "x_test_prep = x_scaler.transform(x_test[:, :3])  # input pre-processing\n",
        "x_test_prep = np.c_[x_test_prep, x_test[:, -1]]\n",
        "\n",
        "out_test = gan.generate(x_test_prep, seed=None)\n",
        "out_test_post = y_scaler.inverse_transform(out_test)  # output post-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfMNEUEs6EHn"
      },
      "source": [
        "#### RICH differential log-likelihood $(e - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "XM985aPPNPR3",
        "outputId": "f3bab3bc-3987-4d73-d175-74fce337fa60"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-100.0, 50.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 0], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 0], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"WGAN-ALP model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df-bcHAW6lGH"
      },
      "source": [
        "#### RICH differential log-likelihood $(\\mu - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "KeRJbgTMPl6g",
        "outputId": "f0057852-02eb-43dc-cf17-2f5017fc969a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-75.0, 75.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 1], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 1], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"WGAN-ALP model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36gOX6UI6nsL"
      },
      "source": [
        "#### RICH differential log-likelihood $(K - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "rMh4nOK3PyEI",
        "outputId": "5b0a8945-9b39-41a5-b309-6dff3225d127"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 2], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 2], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"WGAN-ALP model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EW3FlfL6qJX"
      },
      "source": [
        "#### RICH differential log-likelihood $(p - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "z3WQRODrQE7H",
        "outputId": "154d01ad-5053-4398-b48a-caf8c9116084"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 3], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 3], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"WGAN-ALP model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOYaaPWMnJXD"
      },
      "source": [
        "### Model export\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides the **generator model** with the `export_model()` to export it as a plain [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to ease the **deployment** of the RICH parameterization through tools like [scikinC](https://github.com/landerlini/scikinC) or [keras2c](https://github.com/f0uriest/keras2c) [[9][9], [10][10]].\n",
        "\n",
        "[9]: https://pos.sissa.it/409/034\n",
        "[10]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxbF1xSaPlhf",
        "outputId": "0c59c385-092d-4963-ee96-8ecf516016fd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ],
      "source": [
        "keras.models.save_model(\n",
        "    gan.generator.export_model,  # TF Sequential model\n",
        "    filepath=\"saved_generator\",\n",
        "    save_format=\"tf\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUflfVY7bm5z"
      },
      "source": [
        "Exporting the generator as a [TensorFlow SavedModel format](https://www.tensorflow.org/guide/saved_model) file allows to use **scikinC** to transpile the [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to a C file as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxsMQAkhU2_S"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!scikinC saved_generator > generator.C"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZ9f1NZAafov",
        "outputId": "4059c982-90be-4eee-b14c-4f34605cc18c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.8M Oct 18 14:35 generator.C\n"
          ]
        }
      ],
      "source": [
        "!ls -lrth generator.C"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKqBzg-Fi5xM"
      },
      "source": [
        "## References\n",
        "\n",
        "1. I.J. Goodfellow _et al._, \"Generative Adversarial Networks\", [arXiv:1406.2661][1]\n",
        "2. M. Arjovsky, L. Bottou, \"Towards Principled Methods for Training Generative Adversarial Networks\", [arXiv:1701.04862][2]\n",
        "3. M. Arjovsky, S. Chintala, L. Bottou, \"Wasserstein GAN\", [arXiv:1701.07875][3]\n",
        "4. D. Terjék, \"Adversarial Lipschitz Regularization\", [arXiv:1907.05681][4]\n",
        "5. C. Villani, \"[Optimal Transport: Old and New][5]\", Grundlehren der mathematischen Wissenschaften, Springer, Berlin, 2009\n",
        "6. M. Mirza, S. Osindero, \"Conditional Generative Adversarial Nets\", [arXiv:1411.1784][6]\n",
        "7. A. Maevskiy _et al._, \"Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks\", [arXiv:1905.11825][7]\n",
        "8. T. Salimans _et al._, \"Improved Techniques for Training GANs\", [arXiv:1606.03498][8]\n",
        "9. L. Anderlini, M. Barbetti, \"scikinC: a tool for deploying machine learning as binaries\", [PoS **CompTools2021** (2022) 034][9]\n",
        "10. R. Conlin _et al._, \"Keras2c: A library for converting Keras neural networks to real-time compatible C\", [Eng Appl Artif Intell **100** (2021) 104182][10]\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1907.05681\n",
        "[5]: https://link.springer.com/book/10.1007/978-3-540-71050-9\n",
        "[6]: https://arxiv.org/abs/1411.1784\n",
        "[7]: https://arxiv.org/abs/1905.11825\n",
        "[8]: https://arxiv.org/abs/1606.03498\n",
        "[9]: https://pos.sissa.it/409/034\n",
        "[10]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f26wCo4i5xN"
      },
      "source": [
        "## Credits\n",
        "This tutorial is based on the notebooks provided by the [mbarbetti/tf-gen-models](https://github.com/mbarbetti/tf-gen-models) repository."
      ]
    }
  ],
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