tutorial-GAN-LHCb_RICH.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcVGQpi5i5xG"
      },
      "source": [
        "# Parameterizing the LHCb RICH system using the pidgan `GAN` algorithm\n",
        "\n",
        "**Author:** [mbarbetti](https://github.com/mbarbetti)\n",
        "\n",
        "**Date created:** 12/10/2023\n",
        "\n",
        "**Last modified:** 07/11/2023"
      ]
    },
    {
      "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-GAN-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-GAN-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-GAN-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][1]]. The code is written using the [pidgan](https://github.com/mbarbetti/pidgan) package that relies on TensorFlow and Keras as backends.\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661"
      ]
    },
    {
      "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"
      },
      "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",
        "[1]: https://arxiv.org/abs/1406.2661"
      ]
    },
    {
      "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 **probability** that $x$ comes from the reference dataset rather than $G$. Hence, the optimization problem corresponds to train $D$ to maximize the probability of correct labelling, and simultaneously training $G$ to minimize $\\log(1 - D(G(z)))$.\n",
        "\n",
        "Defining the **loss function** $\\mathcal{L}_{\\rm{GAN}}$ as follows\n",
        "\n",
        "<center>$\\mathcal{L}_{\\rm{GAN}} (\\theta_d, \\theta_g) = \\mathbb{E}_{x \\sim p_\\rm{ref}} \\left[ \\log{D_{\\theta_d}(x)} \\right] + \\mathbb{E}_{z \\sim p_\\rm{z}} \\left[ \\log(1 - D_{\\theta_d}(G_{\\theta_g}(z))) \\right]$</center>\n",
        "\n",
        "the _minimax game_ of the **GAN algorithm** can be written in this form:\n",
        "\n",
        "<center>$\\displaystyle{\\min_G \\, \\max_D \\, \\mathcal{L}_{\\rm{GAN}} (\\theta_d, \\theta_g)}$</center>\n",
        "\n",
        "A unique solution exists, with $G$ recovering the reference distribution $p_\\rm{ref}$ and $D$ equal to 1/2 everywhere [[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. To fix the problem, one can add _continuous noise_ to both $D$ and $G$. This trick allows to learn thanks to a non-zero gradient [[2][2]].\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnwMxPJUi5xK"
      },
      "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 [[3][3]]: $y_{\\rm{gen}}(x) = G(x, z)$. 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 [[3][3]]: $D(x, y)$ with $y \\in \\{y_{\\rm{ref}}, y_{\\rm{gen}}\\}$.\n",
        "\n",
        "[3]: 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 **GAN model** properly conditioned [[3][3], [4][4]]. 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 [[1][1], [2][2], [5][5]]. 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 [`GAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/GAN.py) algorithm.\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1411.1784\n",
        "[4]: https://arxiv.org/abs/1905.11825\n",
        "[5]: 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": "c28628bc-7cc7-48b0-b508-649f7ae3913e"
      },
      "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": "d20d77fb-2485-4d63-bac1-b453f9434675"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "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,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QfnPXl161n2f",
        "outputId": "2b7ec967-452a-4ed1-c45e-a348c7a0937a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c6faa6f4-c74a-4388-ab9f-44384e1b97dc\" 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>P</th>\n",
              "      <th>ETA</th>\n",
              "      <th>nTracks</th>\n",
              "      <th>trackcharge</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>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19112.579988</td>\n",
              "      <td>3.362231</td>\n",
              "      <td>180.468471</td>\n",
              "      <td>0.021152</td>\n",
              "      <td>-8.959810</td>\n",
              "      <td>-1.853031</td>\n",
              "      <td>-3.614234</td>\n",
              "      <td>-3.626358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>22610.408265</td>\n",
              "      <td>0.770202</td>\n",
              "      <td>91.626085</td>\n",
              "      <td>0.999778</td>\n",
              "      <td>16.090133</td>\n",
              "      <td>11.512878</td>\n",
              "      <td>28.799922</td>\n",
              "      <td>28.006328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3000.090800</td>\n",
              "      <td>1.800331</td>\n",
              "      <td>9.000999</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-140.804340</td>\n",
              "      <td>-110.494200</td>\n",
              "      <td>-149.289830</td>\n",
              "      <td>-149.591670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5970.525000</td>\n",
              "      <td>2.749856</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-12.259203</td>\n",
              "      <td>-3.784543</td>\n",
              "      <td>-16.768035</td>\n",
              "      <td>-18.402633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10983.800000</td>\n",
              "      <td>3.340136</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-3.499332</td>\n",
              "      <td>-0.634627</td>\n",
              "      <td>1.748902</td>\n",
              "      <td>1.705883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>22392.768000</td>\n",
              "      <td>3.949773</td>\n",
              "      <td>228.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.179516</td>\n",
              "      <td>0.651420</td>\n",
              "      <td>14.155344</td>\n",
              "      <td>15.179315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>199837.420000</td>\n",
              "      <td>5.434650</td>\n",
              "      <td>739.993000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>58.021923</td>\n",
              "      <td>106.868935</td>\n",
              "      <td>120.245310</td>\n",
              "      <td>110.364006</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-c6faa6f4-c74a-4388-ab9f-44384e1b97dc')\"\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-c6faa6f4-c74a-4388-ab9f-44384e1b97dc 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-c6faa6f4-c74a-4388-ab9f-44384e1b97dc');\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-3860096b-7381-4a17-b590-932073f3a63c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3860096b-7381-4a17-b590-932073f3a63c')\"\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-3860096b-7381-4a17-b590-932073f3a63c 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": "2aa92fa1-3db8-4d58-f645-ed9266ec3ece"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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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": "7759a9c9-b618-4a49-f08a-1dd849c8af5a"
      },
      "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": "eca853b1-d26e-447e-a4f3-133358fc221c"
      },
      "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": "aaa66153-c5b4-4e18-a045-674354f567f9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d6b7b29b-22dd-40c0-a209-54d2fbcd9e1f\" 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>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>150000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.005390</td>\n",
              "      <td>-0.010063</td>\n",
              "      <td>0.006928</td>\n",
              "      <td>0.02160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.998662</td>\n",
              "      <td>1.003666</td>\n",
              "      <td>1.003947</td>\n",
              "      <td>0.99977</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>-1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.666396</td>\n",
              "      <td>-0.695321</td>\n",
              "      <td>-0.668987</td>\n",
              "      <td>-1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.011818</td>\n",
              "      <td>-0.015472</td>\n",
              "      <td>-0.001255</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.661154</td>\n",
              "      <td>0.672476</td>\n",
              "      <td>0.691121</td>\n",
              "      <td>1.00000</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>1.00000</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-d6b7b29b-22dd-40c0-a209-54d2fbcd9e1f')\"\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-d6b7b29b-22dd-40c0-a209-54d2fbcd9e1f 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-d6b7b29b-22dd-40c0-a209-54d2fbcd9e1f');\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-3bdde859-ce2a-4ec0-a0d4-c1f1961a592a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3bdde859-ce2a-4ec0-a0d4-c1f1961a592a')\"\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-3bdde859-ce2a-4ec0-a0d4-c1f1961a592a 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  150000.00000\n",
              "mean       -0.005390      -0.010063       0.006928       0.02160\n",
              "std         0.998662       1.003666       1.003947       0.99977\n",
              "min        -5.199338      -5.199338      -5.199338      -1.00000\n",
              "25%        -0.666396      -0.695321      -0.668987      -1.00000\n",
              "50%        -0.011818      -0.015472      -0.001255       1.00000\n",
              "75%         0.661154       0.672476       0.691121       1.00000\n",
              "max         5.199338       5.199338       5.199338       1.00000"
            ]
          },
          "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": "9823862c-e165-4034-968a-b2788d8d5f38"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e8d91987-1e59-43f5-bd5c-bae13f756296\" 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>0.008522</td>\n",
              "      <td>0.001869</td>\n",
              "      <td>-0.001110</td>\n",
              "      <td>0.020709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.987064</td>\n",
              "      <td>0.984693</td>\n",
              "      <td>1.004062</td>\n",
              "      <td>0.996860</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>-0.663192</td>\n",
              "      <td>-0.659289</td>\n",
              "      <td>-0.681356</td>\n",
              "      <td>-0.657550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.004086</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>0.003593</td>\n",
              "      <td>0.026465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.670605</td>\n",
              "      <td>0.667470</td>\n",
              "      <td>0.679687</td>\n",
              "      <td>0.700523</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-e8d91987-1e59-43f5-bd5c-bae13f756296')\"\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-e8d91987-1e59-43f5-bd5c-bae13f756296 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-e8d91987-1e59-43f5-bd5c-bae13f756296');\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-af3c37ea-e338-4b0c-b94e-4b3d29787b9f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-af3c37ea-e338-4b0c-b94e-4b3d29787b9f')\"\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-af3c37ea-e338-4b0c-b94e-4b3d29787b9f 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        0.008522       0.001869      -0.001110       0.020709\n",
              "std         0.987064       0.984693       1.004062       0.996860\n",
              "min        -5.199338      -5.199338      -5.199338      -5.199338\n",
              "25%        -0.663192      -0.659289      -0.681356      -0.657550\n",
              "50%         0.004086       0.003002       0.003593       0.026465\n",
              "75%         0.670605       0.667470       0.679687       0.700523\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 **GAN 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": "0759e9c9-7305-44ce-b6a6-2fe06de078b3"
      },
      "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=\"sigmoid\",\n",
        "    name=\"discriminator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfqi9DHvETlY",
        "outputId": "6a1af07a-8731-4651-c2dd-583a0125107a"
      },
      "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 GAN algorithm\n",
        "\n",
        "The **original GAN algorithm** [[1][1]] can be implemented with the high-level [`GAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/GAN.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 `GAN` 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",
        "* **`use_original_loss`** (_bool_, default=True) - If True, the generator is trained using the loss function $\\mathcal{L}_{\\rm{G}} = \\log{D(x)} + \\log{(1 - D(G(z)))}$. If False, the generator training is driven by $\\mathcal{L}_{\\rm{G}}^\\prime = \\log{D(x)} - \\log{D(G(z))}$ that, as suggested in Ref. [[1][1]], is more robust against the vanishing gradient problem.\n",
        "* **`injected_noise_stddev`** (_float_, default=0.0) - When different from zero, it represents the standard deviation of the gaussian noise added to the discriminator input to stabilize the training procedure following what described in Ref. [[2][2]].\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. [[5][5]].\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=\"GAN\") - The name of the GAN model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the GAN model computations.\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[5]: 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 [`GAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/GAN.py) instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBEOKmeEDWC4"
      },
      "outputs": [],
      "source": [
        "from pidgan.algorithms import GAN\n",
        "\n",
        "gan = GAN(\n",
        "    generator=generator,\n",
        "    discriminator=discriminator,\n",
        "    use_original_loss=True,\n",
        "    injected_noise_stddev=0.05,\n",
        "    feature_matching_penalty=0.0,\n",
        "    referee=None,\n",
        "    name=\"GAN\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrE8bs952KB"
      },
      "source": [
        "#### Model compilation\n",
        "\n",
        "To finalize the [`GAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/GAN.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 GAN 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",
        "* **`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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kHL20AxZFNOq"
      },
      "outputs": [],
      "source": [
        "gan.compile(\n",
        "    metrics=[\"accuracy\", \"bce\"],\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",
        "    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 [`GAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/GAN.py) model on Colab using the hyperparameters chosen for this tutorial (i.e., batch-size, epochs, updates per batch) takes less than 20 minutes with no-GPU equipped or about 10 minutes enabling the T4-powered runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1mGZ3_FHMNg",
        "outputId": "44b6acff-81f7-4b0e-9606-5fc312e495cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training procedure completed in 0:18:29.176792\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": "a1ab2252-4704-4235-9c43-973a74c535a6"
      },
      "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": "914ebca1-0bef-4fa8-c65d-8c91528dbe70"
      },
      "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": "a38837ef-9399-4cb7-ac46-fafee72c6e05"
      },
      "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(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Accuracy\", fontsize=12)\n",
        "plt.plot(train.history[\"accuracy\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_accuracy\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Binary cross-entropy\", fontsize=12)\n",
        "plt.plot(train.history[\"bce\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_bce\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hdn_BRai5xM"
      },
      "source": [
        "### Validation plots\n",
        "\n",
        "Once trained the GAN 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": "4ad6c95e-da9a-43da-9c0c-23bf97f104df"
      },
      "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(-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=\"GAN 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": "eb2056af-3401-488f-d775-46c0427a8aae"
      },
      "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=\"GAN 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": 474
        },
        "id": "rMh4nOK3PyEI",
        "outputId": "28a82269-298d-4bcb-d284-ce03850850a9"
      },
      "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=\"GAN 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": "911455bd-d996-4934-8787-e83ebb343f91"
      },
      "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=\"GAN 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) [[6][6], [7][7]].\n",
        "\n",
        "[6]: https://pos.sissa.it/409/034\n",
        "[7]: 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": "932baf9f-6d56-4d16-9c17-86336b2e6ac8"
      },
      "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": "3497e43a-7dae-4021-e6a1-ecff8d10c9ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.8M Oct 18 08:50 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. Mirza, S. Osindero, \"Conditional Generative Adversarial Nets\", [arXiv:1411.1784][3]\n",
        "4. A. Maevskiy _et al._, \"Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks\", [arXiv:1905.11825][4]\n",
        "5. T. Salimans _et al._, \"Improved Techniques for Training GANs\", [arXiv:1606.03498][5]\n",
        "6. L. Anderlini, M. Barbetti, \"scikinC: a tool for deploying machine learning as binaries\", [PoS **CompTools2021** (2022) 034][6]\n",
        "7. R. Conlin _et al._, \"Keras2c: A library for converting Keras neural networks to real-time compatible C\", [Eng Appl Artif Intell **100** (2021) 104182][7]\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1411.1784\n",
        "[4]: https://arxiv.org/abs/1905.11825\n",
        "[5]: https://arxiv.org/abs/1606.03498\n",
        "[6]: https://pos.sissa.it/409/034\n",
        "[7]: 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."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.16"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}