tutorial-CramerGAN-LHCb_RICH.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcVGQpi5i5xG"
      },
      "source": [
        "# Parameterizing the LHCb RICH system using the pidgan `CramerGAN` algorithm\n",
        "\n",
        "**Author:** [mbarbetti](https://github.com/mbarbetti)\n",
        "\n",
        "**Date created:** 16/10/2023\n",
        "\n",
        "**Last modified:** 09/11/2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzrL35_aMf4X"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-CramerGAN-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-CramerGAN-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-CramerGAN-LHCb_RICH.ipynb)\n",
        "\n",
        "**Description:** This tutorial demonstrates how to parameterize the high-level response of the LHCb RICH system using a Generative Adversarial Network (GAN) [[1](https://arxiv.org/abs/1406.2661)]. The code is written using the [pidgan](https://github.com/mbarbetti/pidgan) package that relies on TensorFlow and Keras as backends."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvTVEAa7i5xJ"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opu773xWi5xK"
      },
      "source": [
        "### What is LHCb?\n",
        "\n",
        "The [**LHCb experiment**](https://lhcb-outreach.web.cern.ch) has been originally designed to study rare decays of particles containing $b$ and $c$ quarks produced at the Large Hadron Collider (LHC). The LHCb detector, shown in the background of the following photo, is a single-arm forward spectrometer covering the pseudorapidity range of $2 < \\eta < 5$. The detector includes:\n",
        "\n",
        "- **Tracking system** - used for high-precision measurements of the momentum of charged particles and the position of the primary vertices\n",
        "- **Particle Identification (PID) system** - used to distiguish different species of traversing particles (i.e., muons, pions, kaons, protons)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5If4oPxki5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/lhcb.jpeg\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTEAA_GpgXXM"
      },
      "source": [
        "#### The RICH detectors\n",
        "\n",
        "The LHCb PID system counts two ring-imaging Cherenkov ([RICH](https://lhcb-outreach.web.cern.ch/detector/rich-detectors/)) detectors whose response allows to separate different types of charged hadrons (e.g., pions, kaons, protons) using the [Cherenkov radiation](https://en.wikipedia.org/wiki/Cherenkov_radiation) of the traversing particles. The size of the circles produced by the Cherenkov radiation (shown on the left) within the RICH detector is related to the mass of the emitting particle. Reconstructing such circles allows to distinguish particles accordingly to their species (shown on the right)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvTtCmaBhqQ_"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-response.png\" width=\"300\"/>\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-performance.png\" width=\"400\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPnfIVMoi5xK"
      },
      "source": [
        "### What are GANs?\n",
        "\n",
        "Generative Adversarial Networks [[1][1]] are a powerful class of _generative models_ based on the simultaneous training of two neural networks:\n",
        "\n",
        "*  **Discriminator network** ($D$) - trained by a classification task to separate the generator output from the reference dataset\n",
        "* **Generator network** ($G$) - trained by a simulation task to reproduce the reference dataset trying to fake the discriminator\n",
        "\n",
        "The goal is that $D$ optimally discriminates on the origin of the two samples, and simultaneously the training procedure for $G$ is to maximize the _probability_ of $D$ making a mistake. This framework corresponds to a **minimax two-player game** [[1][1]].\n",
        "\n",
        "Traditional GAN systems suffer from many issues, particularly during the training phase:\n",
        "\n",
        "* the generator may _collapse_ producing only a single sample or a small family of very similar samples (**mode collapse**)\n",
        "* the two players may _oscillate_ during training rather than converging to the [**Nash equilibrium**](https://en.wikipedia.org/wiki/Nash_equilibrium)\n",
        "* if _imbalance_ between the two players occurs, then the system is incapable of learning at all\n",
        "\n",
        "All these drawbacks result from the [**vanishing gradient problem**](https://en.wikipedia.org/wiki/Vanishing_gradient_problem), namely the lack of information for the update of the $G$ parameters. This is due to the saturation of the $D$ that is so good in distinguishing the origin of the two samples that no errors remain to the $G$ to improve the generated space [[2][2]]. To fix the problem, we need to change the loss function originally proposed in Ref. [[1][1]], moving to a _distance measurement_ robust against the vanishing gradient problem. The literature soon proposed to use the **Wasserstein distance** to overcome this issue [[3][3]]. However, Ref. [[4][4]] highlighted that estimating the Wasserstein distance from poor populated samples (e.g., a batch of data) yields to **biased gradients** and may lead to a wrong solution for the optimization problem. Hence, to overcome both these drawbacks, the authors poposed the adoption of the **Cramér distance**, defining the so-called **CramerGAN algorithm** [[4][4]].\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1705.10743"
      ]
    },
    {
      "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 $p_\\rm{ref}$. On the other hand, the discriminator $D(x)$, fed by $x$ elements taken from the reference dataset or the generator outputs, maps the input to the **critic space** $\\mathcal{C}$, a multidimensional space built to separate the input elements according to their origin. Upon this discriminative space, we can use a distance notion robust against the _vanishing gradient issues_ and the _biased sample gradients problem_, like the **Energy distance** [[5][5]], the natural extension of the Cramér distance to the multivariate case. Hence, the optimization problem corresponds to train $D$ to maximize the distance between the _reference critic vectors_ and the _generator critic vectors_, and simultaneously training $G$ to minimize the same quantity.\n",
        "\n",
        "Let $p_\\rm{ref}$ and $p_\\rm{gen}$ be probability distributions over $\\mathbb{R}^d$ and let $x$, $x^\\prime$ and $y$, $y^\\prime$ be independent random variables distributed according to $p_\\rm{ref}$ and $p_\\rm{gen}$ respectively (it means that $y$ and $y^\\prime$ results from $G$). Then, the **Energy distance** can be approximated as follows [[4][4]]:\n",
        "\n",
        "<center>$\\mathcal{E} (p_{\\rm{ref}}, p_{\\rm{gen}}) = \\mathbb{E}_{x \\sim p_{\\rm{ref}}} [f_{\\rm{critic}}(x)] - \\mathbb{E}_{y \\sim p_{\\rm{gen}}} [f_{\\rm{critic}}(y)]$</center>\n",
        "\n",
        "where $f_{\\rm{critic}}$ is named **critic function** and can be written as\n",
        "\n",
        "<center>$f_{\\rm{critic}} (a) = \\mathbb{E}_{y^\\prime \\sim p_\\rm{gen}} \\left [ \\, \\| D(a) - D(y^\\prime) \\|_2 \\, \\right ] - \\mathbb{E}_{x^\\prime \\sim p_\\rm{ref}} \\left [ \\, \\| D(a) - D(x^\\prime) \\|_2 \\, \\right ]$</center>\n",
        "\n",
        "where the discriminator $D$ should be trained to ensure that $f_{\\rm{critic}}$ belong to $\\mathcal{F}_2 = \\left \\{ f : f \\mbox{ is absolutely continuous, } \\left \\| \\nabla_a f(a) \\right \\|_2 \\le 1 \\right \\}$.\n",
        "\n",
        "Defining the **loss function** $\\mathcal{L}_{\\rm{CramerGAN}}$ as follows\n",
        "\n",
        "<center>$\\mathcal{L}_{\\rm{CramerGAN}} \\left ( \\theta_d, \\theta_g \\right ) = \\mathbb{E}_{x \\sim p_{\\rm{ref}}} \\left [ f^*_{\\rm{critic}} \\left ( x; \\theta_d, \\theta_g \\right ) \\right ] - \\mathbb{E}_{z \\sim p_{z}} \\left [ f^*_{\\rm{critic}} \\left ( G_{\\theta_g}(z); \\theta_d, \\theta_g \\right ) \\right ]$</center>\n",
        "\n",
        "where $f^*_{\\rm{critic}}$ is obtained simplifying the _critic function_ defined above as\n",
        "\n",
        "<center>$f^*_{\\rm{critic}} \\left ( a; \\theta_d, \\theta_g \\right ) = \\mathbb{E}_{z^\\prime \\sim p_z} \\left [ \\, \\left \\| D_{\\theta_d} (a) - D_{\\theta_d} \\left ( G_{\\theta_g}(z^\\prime) \\right ) \\right \\|_2 \\, \\right ] - \\left \\| D_{\\rm{\\theta_d}}(a) \\right \\|_2$</center>\n",
        "\n",
        "the _minimax game_ of the **CramerGAN algorithm** can be written in this form:\n",
        "\n",
        "<center>$\\begin{cases}\n",
        "\\displaystyle{\\min_G} \\, \\mathcal{L}_{\\rm{CramerGAN}} \\left ( \\theta_d, \\theta_g \\right )\\\\\n",
        "\\displaystyle{\\max_D} \\, \\mathcal{L}_{\\rm{CramerGAN}} \\left ( \\theta_d, \\theta_g \\right ) - \\lambda \\, \\mathbb{E}_{\\hat{x} \\sim p_{\\hat{x}}} \\left [ \\left ( \\left \\| \\nabla_{\\hat{x}} f^*_{\\rm{critic}} (\\hat{x};\\theta_d, \\theta_g) \\right \\|_2 - 1 \\right )^2 \\right ]\n",
        "\\end{cases}$</center>\n",
        "\n",
        "where, to ensure that $f^*_{\\rm{critic}} \\in \\mathcal{F}_2$, a regularization term called **gradient penalty** (GP) and taken from the WGAN-GP algorithm [[6][6]] is used, as suggested by the Ref. [[4][4]].\n",
        "\n",
        "[4]: https://arxiv.org/abs/1705.10743\n",
        "[5]: https://www.sciencedirect.com/science/article/pii/S0378375813000633\n",
        "[6]: https://arxiv.org/abs/1704.00028"
      ]
    },
    {
      "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 [[7][7]]: $y_{\\rm{gen}}(x) = G(x, z)$. 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 [[7][7]]: $D(x, y)$ with $y \\in \\{y_{\\rm{ref}}, y_{\\rm{gen}}\\}$.\n",
        "\n",
        "[7]: 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 **CramerGAN model** properly conditioned [[7][7], [8][8]]. The pidgan package provides several GAN [`algorithms`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/algorithms) implementing some of the most used tricks presented in the literature to stablize their training [[3][3], [4][4], [6][6], [9][9]]. 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 [`CramerGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/CramerGAN.py) algorithm.\n",
        "\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1705.10743\n",
        "[6]: https://arxiv.org/abs/1704.00028\n",
        "[7]: https://arxiv.org/abs/1411.1784\n",
        "[8]: https://arxiv.org/abs/1905.11825\n",
        "[9]: 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": "269651ef-5ec3-46e1-f381-cfb5ba4f743a"
      },
      "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": "e2069421-dbd5-4260-ac3e-ad41ab76802d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.config.list_physical_devices(\"GPU\")  # outputs a non-empty list in case of GPU equipped"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uslhx_PjvOwk"
      },
      "source": [
        "Finally, we just have to import all the modules that we will use in the following code cells:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twM1ba1PvgVl"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from datetime import datetime\n",
        "from tensorflow import keras\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import QuantileTransformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz9PhSN8wP2h"
      },
      "source": [
        "### Data loading\n",
        "\n",
        "The data needed for this tutorial is provided through a CSV file downloadable from the [pidgan-notebooks](https://github.com/mbarbetti/pidgan-notebooks) repository.\n",
        "\n",
        "**Disclaimer:** The data results from a toy model inspired to the LHCb RICH system combining its high-level response when traversed by a set of long-lived charged particles, namely muons, pions, kaons and protons.\n",
        "\n",
        "The CSV file contains 250000 instances, 4 input features and 4 output features. Let's import the data as a pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QfnPXl161n2f",
        "outputId": "1b699c5d-8d9b-4910-b778-3eeb460f7102"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f341ad07-1d5f-436e-b1f7-38c2ad6fa31f\" 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-f341ad07-1d5f-436e-b1f7-38c2ad6fa31f')\"\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-f341ad07-1d5f-436e-b1f7-38c2ad6fa31f 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-f341ad07-1d5f-436e-b1f7-38c2ad6fa31f');\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-900e730f-45de-4161-a9dc-c8de3dc19fed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-900e730f-45de-4161-a9dc-c8de3dc19fed')\"\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-900e730f-45de-4161-a9dc-c8de3dc19fed 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": "d849a9d9-7572-41d4-886e-6c2248c8f534"
      },
      "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": "958bb065-6226-4785-b95b-a0147cbf7c96"
      },
      "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": "967bf5a0-2073-4feb-ec95-2a101db1582d"
      },
      "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": "5c6b403a-fb1e-475e-fac4-c2677f4964f1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ece65f66-df06-4be9-89ca-14f6bdab8ba1\" 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>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.013508</td>\n",
              "      <td>-0.001909</td>\n",
              "      <td>0.001884</td>\n",
              "      <td>0.022093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.001442</td>\n",
              "      <td>0.997180</td>\n",
              "      <td>1.001872</td>\n",
              "      <td>0.999759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.655109</td>\n",
              "      <td>-0.672691</td>\n",
              "      <td>-0.681594</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.010430</td>\n",
              "      <td>-0.002148</td>\n",
              "      <td>-0.011291</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.685530</td>\n",
              "      <td>0.676685</td>\n",
              "      <td>0.686350</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-ece65f66-df06-4be9-89ca-14f6bdab8ba1')\"\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-ece65f66-df06-4be9-89ca-14f6bdab8ba1 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-ece65f66-df06-4be9-89ca-14f6bdab8ba1');\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-68224002-fd70-4854-bf7c-03462b46365f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68224002-fd70-4854-bf7c-03462b46365f')\"\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-68224002-fd70-4854-bf7c-03462b46365f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean        0.013508      -0.001909       0.001884       0.022093\n",
              "std         1.001442       0.997180       1.001872       0.999759\n",
              "min        -5.199338      -5.199338      -5.199338      -1.000000\n",
              "25%        -0.655109      -0.672691      -0.681594      -1.000000\n",
              "50%         0.010430      -0.002148      -0.011291       1.000000\n",
              "75%         0.685530       0.676685       0.686350       1.000000\n",
              "max         5.199338       5.199338       5.199338       1.000000"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "x_train_prep = x_scaler.fit_transform(x_train[:, :3])\n",
        "x_train_prep = np.c_[x_train_prep, x_train[:, 3]]\n",
        "\n",
        "pd.DataFrame(x_train_prep, columns=x_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY6OOsx6BhQP"
      },
      "source": [
        "Let's preprocess also the $y$ features checking what obtained:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "VvfBZUjvB4oT",
        "outputId": "79bbecf4-0df9-4094-db85-c8f0ba717abf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e6523a6d-8027-4e5d-aa56-4e90813a7a76\" 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>0.008195</td>\n",
              "      <td>0.010103</td>\n",
              "      <td>0.015486</td>\n",
              "      <td>-0.001222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.994545</td>\n",
              "      <td>1.011177</td>\n",
              "      <td>1.017552</td>\n",
              "      <td>0.990601</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>-0.667885</td>\n",
              "      <td>-0.654602</td>\n",
              "      <td>-0.669828</td>\n",
              "      <td>-0.678471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.004381</td>\n",
              "      <td>0.010049</td>\n",
              "      <td>0.021249</td>\n",
              "      <td>-0.004441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.678521</td>\n",
              "      <td>0.690566</td>\n",
              "      <td>0.687516</td>\n",
              "      <td>0.678237</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-e6523a6d-8027-4e5d-aa56-4e90813a7a76')\"\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-e6523a6d-8027-4e5d-aa56-4e90813a7a76 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-e6523a6d-8027-4e5d-aa56-4e90813a7a76');\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-ea6d8073-3d07-49e1-ba20-6fcefe3d1e3b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea6d8073-3d07-49e1-ba20-6fcefe3d1e3b')\"\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-ea6d8073-3d07-49e1-ba20-6fcefe3d1e3b 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        0.008195       0.010103       0.015486      -0.001222\n",
              "std         0.994545       1.011177       1.017552       0.990601\n",
              "min        -5.199338      -5.199338      -5.199338      -5.199338\n",
              "25%        -0.667885      -0.654602      -0.669828      -0.678471\n",
              "50%         0.004381       0.010049       0.021249      -0.004441\n",
              "75%         0.678521       0.690566       0.687516       0.678237\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 **CramerGAN 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": "658d7736-daf3-4626-95d6-e5b6fdcb4bf4"
      },
      "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=256,  # > 1 for CramerGAN system\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=None,\n",
        "    name=\"discriminator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfqi9DHvETlY",
        "outputId": "16a801f2-8ab4-4bc7-bfa2-ee07ad4e592b"
      },
      "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, 256)               33024     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 100224 (391.50 KB)\n",
            "Trainable params: 100224 (391.50 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 CramerGAN algorithm\n",
        "\n",
        "The **CramerGAN algorithm** [[4][4]] can be implemented with the high-level [`CramerGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/CramerGAN.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 `CramerGAN` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`generator`** (_generator_like_) - The generator network. It must be passed as a pidgan [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) instance.\n",
        "* **`discriminator`** (_discriminator_like_) - The discriminator network. It must be passed as a pidgan [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) instance.\n",
        "* **`lipschitz_penalty`** (_float_, default=1.0) - Importance of the regularization term GP that ensures the $f^*_{\\rm{critic}} \\in \\mathcal{F}_2$ condition as described in Ref. [[4][4], [6][6]].\n",
        "* **`lipschitz_penalty_strategy`** (_str_, default=\"two-sided\") - If \"two-sided\" is passed, the norm of the critic gradient is forced to be equal to 1. When \"one-sided\" is preferred, only gradient norm values greater than 1 are penalized.\n",
        "* **`feature_matching_penalty`** (_float_, default=0.0) - Importance of the regularization term added to the generator training to force the hidden state of the discriminator to match between reference and generated examples. Its design follows from what proposed in Ref. [[9][9]].\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=\"CramerGAN\") - The name of the CramerGAN model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the CramerGAN model computations.\n",
        "\n",
        "[4]: https://arxiv.org/abs/1705.10743\n",
        "[6]: https://arxiv.org/abs/1704.00028\n",
        "[9]: 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 [`CramerGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/CramerGAN.py) instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBEOKmeEDWC4"
      },
      "outputs": [],
      "source": [
        "from pidgan.algorithms import CramerGAN\n",
        "\n",
        "gan = CramerGAN(\n",
        "    generator=generator,\n",
        "    discriminator=discriminator,\n",
        "    lipschitz_penalty=1.0,\n",
        "    lipschitz_penalty_strategy=\"two-sided\",\n",
        "    feature_matching_penalty=0.0,\n",
        "    referee=None,\n",
        "    name=\"CramerGAN\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrE8bs952KB"
      },
      "source": [
        "#### Model compilation\n",
        "\n",
        "To finalize the [`CramerGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/CramerGAN.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 CramerGAN 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=[\"wass_dist\"],\n",
        "    generator_optimizer=keras.optimizers.RMSprop(learning_rate=0.002),\n",
        "    discriminator_optimizer=keras.optimizers.RMSprop(learning_rate=0.001),\n",
        "    generator_upds_per_batch=1,\n",
        "    discriminator_upds_per_batch=1,\n",
        "    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 [`CramerGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/CramerGAN.py) model on Colab using the hyperparameters chosen for this tutorial (i.e., batch-size, epochs, updates per batch) takes less than 25 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": "0847b254-c56f-4909-e012-6d222e5a4a71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training procedure completed in 0:12:31.007354\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": "9d060eda-4d7b-4b0f-8ad8-e92b7914d6b6"
      },
      "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": "4da8a1b8-2897-4429-a1c9-4b99fa4a37cf"
      },
      "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=\"upper 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": "ae44bec9-d025-4df6-a9fd-9d0220c7f3ba"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5), dpi=100)\n",
        "\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Wasserstein distance\", fontsize=12)\n",
        "plt.plot(train.history[\"wass_dist\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_wass_dist\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hdn_BRai5xM"
      },
      "source": [
        "### Validation plots\n",
        "\n",
        "Once trained the CramerGAN 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": "5d7d777b-0487-452f-be4f-c7ac06f6951e"
      },
      "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=\"CramerGAN 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": "94ea6ec7-ac23-481a-fecd-2b6171e9af2b"
      },
      "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=\"CramerGAN 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": 479
        },
        "id": "rMh4nOK3PyEI",
        "outputId": "3109628b-403f-41c4-8454-ad8c01003c9c"
      },
      "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=\"CramerGAN 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": "2c83582f-00a0-4f13-eddd-e21194977c87"
      },
      "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=\"CramerGAN 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) [[10][10], [11][11]].\n",
        "\n",
        "[10]: https://pos.sissa.it/409/034\n",
        "[11]: 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": "0e47fd49-1e18-4dd7-8ba2-db7147cc0ad8"
      },
      "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": "fbd759f1-7ce6-449c-81e4-c48a56f4e664"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.8M Oct 18 14:17 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](https://arxiv.org/abs/1406.2661)\n",
        "2. M. Arjovsky, L. Bottou, \"Towards Principled Methods for Training Generative Adversarial Networks\", [arXiv:1701.04862](https://arxiv.org/abs/1701.04862)\n",
        "3. M. Arjovsky, S. Chintala, L. Bottou, \"Wasserstein GAN\", [arXiv:1701.07875](https://arxiv.org/abs/1701.07875)\n",
        "4. M. G. Bellemare _et al._, \"The Cramer Distance as a Solution to Biased Wasserstein Gradients\", [arXiv:1705.10743](https://arxiv.org/abs/1705.10743)\n",
        "5. G. J. Székely and M. L. Rizzo, \"Energy statistics: A class of statistics based on distances\", [Journal of Statistical Planning and Inference **143** (2013) 1249-1272](https://www.sciencedirect.com/science/article/pii/S0378375813000633)\n",
        "6. I. Gulrajani _et al._, \"Improved Training of Wasserstein GANs\", [arXiv:1704.00028](https://arxiv.org/abs/1704.00028)\n",
        "7. M. Mirza, S. Osindero, \"Conditional Generative Adversarial Nets\", [arXiv:1411.1784](https://arxiv.org/abs/1411.1784)\n",
        "8. A. Maevskiy _et al._, \"Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks\", [arXiv:1905.11825](https://arxiv.org/abs/1905.11825)\n",
        "9. T. Salimans _et al._, \"Improved Techniques for Training GANs\", [arXiv:1606.03498](https://arxiv.org/abs/1606.03498)\n",
        "10. L. Anderlini, M. Barbetti, \"scikinC: a tool for deploying machine learning as binaries\", [PoS **CompTools2021** (2022) 034](https://pos.sissa.it/409/034)\n",
        "11. R. Conlin _et al._, \"Keras2c: A library for converting Keras neural networks to real-time compatible C\", [Eng Appl Artif Intell **100** (2021) 104182](https://doi.org/10.1016/j.engappai.2021.104182)\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1701.04862\n",
        "[3]: https://arxiv.org/abs/1701.07875\n",
        "[4]: https://arxiv.org/abs/1705.10743\n",
        "[5]: https://www.sciencedirect.com/science/article/pii/S0378375813000633\n",
        "[6]: https://arxiv.org/abs/1704.00028\n",
        "[7]: https://arxiv.org/abs/1411.1784\n",
        "[8]: https://arxiv.org/abs/1905.11825\n",
        "[9]: https://arxiv.org/abs/1606.03498\n",
        "[10]: https://pos.sissa.it/409/034\n",
        "[11]: 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
}