{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial\n", "\n", "## Learning unbinned observables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LHCfitNikhef/ML4EFT/blob/gh-pages/docs/sphinx/source/installation/training.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this tutorial is to demonstrate and to give a handle on how to obtain unbinned multivariate observables from machine learning. We do this by considering inclusive top quark pair production at the particle level i.e. including top quark\n", "decays in the dilepton channel: $p p \\rightarrow t \\bar{t}, t \\bar{t} \\rightarrow b \\bar{b} \\ell^{+} \\ell^{-} \\nu_{\\ell} \\bar{\\nu}_{\\ell}$, at the LHC 14 TeV as an example. We focus on the following two dimension-six operators that modifiy top-quark pair production:\n", "\n", "$$ \\begin{align}\\mathcal{O}_{tG} &= ig_s(\\bar{Q}\\tau^{\\mu\\nu}T_A t)\\tilde{\\varphi}G_{\\mu\\nu}^A + \\mathrm{h.c.}\\\\\n", "\\mathcal{O}_{tq}^{8} &= \\sum\\limits_{i=1,2}c_{qu}^{8(ii33)},\n", "\\end{align}$$\n", "where $$\\mathcal{O}_{qu}^{8(ijkl)} = (\\bar{q}_i\\gamma^\\mu T^A q_j)(\\bar{u}_k\\gamma_\\mu T^A u_l).$$\n", "\n", "The ML4EFT method generalises to an abritrary number of operators, but for the purpose of this tutorial we focus on the two operators defined above. We will parameterise the likelihood ratio (EFT over SM) using models that have either been trained on a pair of features, or on the full set of kinematic features available in the final state at the particle level. Since the actual training takes a bit of time, especially when working on a single core, we provide pretrained models that can be loaded and analysed directly. However, we do give instructions on how to train models yourself in the first part of this tutorial applied in a simplified setup. \n", "\n", "The likelihood ratio, denoted $r(x, c)$, takes the following form in the presence of $\\mathcal{O}_{tG}$ and $\\mathcal{O}_{tq}^{(8)}$:\n", "\n", "$$\\begin{align}r(\\boldsymbol{x}, c_{tG}, c_{tq}^{(8)}) =& 1 + c_{tG}\\cdot \\mathrm{NN}^{c_{tG}}(\\boldsymbol{x}) + c_{tq}^{(8)}\\cdot \\mathrm{NN}^{c_{tq}^{(8)}}(\\boldsymbol{x}) +\\\\\n", "&c_{tG}^2\\cdot \\mathrm{NN}^{c_{tG}\\cdot c_{tG}}(\\boldsymbol{x}) + c_{tq}^{(8)^2}\\cdot \\mathrm{NN}^{c_{tq}^{(8)}\\cdot c_{tq}^{(8)}}(\\boldsymbol{x}) + c_{tq}^{(8)}\\cdot c_{tG} \\cdot\\mathrm{NN}^{c_{tG}\\cdot c_{tq}^{(8)}}(\\boldsymbol{x})\\end{align}$$\n", "\n", "where $\\boldsymbol{x}$ denotes the kinematic feature vector, e.g $(p_T^{\\ell\\bar{\\ell}}, \\eta_\\ell)$, corresponding to the transverse momentum of the lepton-pair and the pseudorapidity of the lepton respectively.\n", "\n", "The first part of this tutorial shows how to load and study the distribution of the training data, while the second part is about training unbinned observables in the presence of $p_T^{\\ell\\bar{\\ell}}$ and $\\eta_\\ell$. We will finish by showing how this method generalises to the fully multivariate analysis that we trained on 18 features. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Installing ML4EFT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To install ML4EFT, if not done already, one should run the following command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install ml4eft " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When running this notebook on google colab, please make sure to execute the cell below to enable interactive plotting. Currently, the default version of matplotlib might lead to some errors on google colab for some users. In that case we advise to simply rerun all cells." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install ipympl \n", "\n", "from google.colab import output\n", "output.enable_custom_widget_manager()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading the training data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import wget\n", "import tarfile\n", "import os\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib import rc\n", "\n", "import ml4eft.core.classifier as classifier\n", "import ml4eft.analyse.analyse as analyse\n", "import ml4eft.plotting.features as features\n", "\n", "rc('text', usetex=False)\n", "rc('font', **{'family': 'DejaVu Sans', 'size': 22})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define two helper functions that serve to download and untar remote files:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def file_downloader(url, download_dir='./downloads'):\n", "\n", " if not os.path.exists(download_dir):\n", " os.mkdir(download_dir)\n", "\n", " file = wget.download(url, out=download_dir)\n", " return file\n", "\n", "def untar(path_to_tar, destination='./downloads'):\n", " \n", " with tarfile.open(path_to_tar) as f:\n", " f.extractall(destination)\n", "\n", "download_dir = './downloads'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For simplicity, we will focus on a single replica here, but the method can be easily applied to $n_{\\mathrm{rep}} > 1$ replicas in case one has a cluster available. Training data can be downloaded as follows and contains 100K events with 18 particle level features each:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "training_data_url = 'https://dl.dropboxusercontent.com/s/z16fz2ggbn244pl/training_data.tar.gz?dl=0'\n", "training_data = file_downloader(training_data_url);\n", "data_train_loc = training_data.split('.tar')[0]\n", "\n", "untar(training_data);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The events are stored as pandas dataframes that we will now load" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# load eft events\n", "coeff = [\"ctGRe\", \"ctj8\"]\n", "events_eft = []\n", "for c in coeff:\n", " path_to_events = os.path.join(data_train_loc, 'tt_{}_{}/events_0.pkl.gz'.format(c, c))\n", " events, xsec = analyse.Analyse.load_events(path_to_events)\n", " events_eft.append(events)\n", "\n", "# load sm events\n", "events_sm, xsec_sm = analyse.Analyse.load_events(os.path.join(download_dir, 'training_data/tt_sm/events_0.pkl.gz'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us plot the distribution of the training we have just loaded. We will plot the SM and the SM complemented with quadratic corrections induced by $\\mathcal{O}_{tG}$ and $\\mathcal{O}_{tq}^{(8)}$. The cell directly below is just for plotting purposes to denote the axes and legend labels." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "feature_dict = {'pt_l1': r'$p_T^{\\ell}\\;[\\mathrm{GeV}]$',\n", " 'pt_l2': r'$p_T^{\\bar{\\ell}}\\;[\\mathrm{GeV}]$',\n", " 'pt_l_leading': r'$p_T^{\\ell}\\;(\\mathrm{leading})\\;[\\mathrm{GeV}]$',\n", " 'pt_l_trailing': r'$p_T^{\\ell}\\;(\\mathrm{trailing})\\;[\\mathrm{GeV}]$',\n", " 'eta_l1': r'$\\eta_\\ell$',\n", " 'eta_l2': r'$\\eta_{\\bar{\\ell}}$',\n", " 'eta_l_leading': r'$\\eta_\\ell\\;(\\mathrm{leading})$',\n", " 'eta_l_trailing': r'$\\eta_\\ell\\;(\\mathrm{trailing})$',\n", " 'pt_ll': r'$p_T^{\\ell\\bar{\\ell}}\\;[\\mathrm{GeV}]$',\n", " 'm_ll': r'$m_{\\ell\\bar{\\ell}}\\;[\\mathrm{GeV}]$',\n", " 'DeltaPhi_ll': r'$\\Delta\\phi(\\ell, \\bar{\\ell})$',\n", " 'DeltaEta_ll': r'$\\Delta\\eta(\\ell, \\bar{\\ell})$',\n", " 'pt_b_leading': r'$p_T^{b}\\;(\\mathrm{leading})\\;[\\mathrm{GeV}]$',\n", " 'pt_b_trailing': r'$p_T^{b}\\;(\\mathrm{trailing})\\;[\\mathrm{GeV}]$',\n", " 'eta_b_leading': r'$\\eta_{b}\\;(\\mathrm{leading})$',\n", " 'eta_b_trailing': r'$\\eta_{b}\\;(\\mathrm{trailing})$',\n", " 'pt_bb': r'$p_T^{b\\bar{b}}\\;[\\mathrm{GeV}]$',\n", " 'm_bb': r'$m_{b\\bar{b}}\\;[\\mathrm{GeV}]$'\n", " }\n", "\n", "legend_labels = [r'$c_{tG}=10$', r'$c_{qt}^{(8)}=10$', r'$\\mathrm{SM}$']\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = features.plot_features(events_sm, events_eft, feature_dict, legend_labels);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the distribution of the 18 features plotted above, we notice some clear departures away from the SM in nearly all kinematic features. We thus expect a full multivariate analysis to pay off compared to a simple binned analysis in one or two kinematics. The latter is also inherently bound by the curse of dimensionality: a binned analysis would require populating a 18-dim feature space through Monte Carlo, which is simply impossible since the number of simulated events required to populate a 18-dim feature space scales exponentially with the number of dimensions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training unbinned observables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having loaded the training data, we now move on to the actual training part of this introductory tutorial. To this end, we download a training runcard that contains information regarding the setup, such as the architecture, number of mini-batches, epochs and data points, etc..." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 0% [ ] 0 / 781\r", "100% [..............................................................] 781 / 781" ] } ], "source": [ "path_to_runcard = 'https://dl.dropboxusercontent.com/s/v4ulo6icveh63fw/run_card_tt_llvlvlbb.json?dl=0'\n", "runcard = file_downloader(path_to_runcard)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us open the training runcard to see the possible options that can be set" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'process_id': 'tt',\n", " 'epochs': 2000,\n", " 'lr': 0.0001,\n", " 'n_batches': 50,\n", " 'output_size': 1,\n", " 'hidden_sizes': [100, 100, 100],\n", " 'n_dat': 100000,\n", " 'event_data': './downloads/training_data/',\n", " 'features': ['pt_l1',\n", " 'pt_l2',\n", " 'pt_l_leading',\n", " 'pt_l_trailing',\n", " 'eta_l1',\n", " 'eta_l2',\n", " 'eta_l_leading',\n", " 'eta_l_trailing',\n", " 'pt_ll',\n", " 'm_ll',\n", " 'DeltaPhi_ll',\n", " 'DeltaEta_ll',\n", " 'pt_b_leading',\n", " 'pt_b_trailing',\n", " 'eta_b_leading',\n", " 'eta_b_trailing',\n", " 'pt_bb',\n", " 'm_bb'],\n", " 'loss_type': 'CE',\n", " 'scaler_type': 'robust',\n", " 'patience': 200,\n", " 'val_ratio': 0.2,\n", " 'c_train': {'cQd8': 10,\n", " 'cQj18': 10,\n", " 'cQj38': 10,\n", " 'cQu8': 10,\n", " 'ctd8': 10,\n", " 'ctGRe': -10,\n", " 'ctj8': 10,\n", " 'ctu8': 10}}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "\n", "with open(runcard) as json_runcard:\n", " json_runcard_loaded = json.load(json_runcard)\n", " \n", "json_runcard_loaded" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The features on which we would like to train can be specified after the ``features`` key. We will train on all available features and keep it as it is. Feel free to change the settings, for instance by following the syntax below" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "json_runcard_loaded['epochs'] = 200\n", "json_runcard_loaded['lr'] = 0.001\n", "json_runcard_loaded['n_batches'] = 50\n", "json_runcard_loaded['patience'] = 20 # needs to be bigger than number of epochs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case you have modified some settings, it is important to write them back to the original file:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "with open(runcard, 'w') as runcard_updated:\n", " json.dump(json_runcard_loaded, runcard_updated)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you are happy with the settings, the training can be launched as " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output_dir = './models'\n", "c_name = 'ctGRe'\n", "\n", "fitter = classifier.Fitter(json_path = runcard, \n", " mc_run = 0, \n", " c_name = c_name,\n", " output_dir = output_dir, \n", " print_log=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this runcard, ``mc_run`` specifies the replica number (must match with ``events_.pkl.gz``), and ``c_name`` denotes the NN that will be trained. Possible options here are for instance ``ctGRe_ctGRe``, ``ctj8_ctj8`` in case of a quadratic coefficient function, or ``ctGRe`` in case one wants to train the linear term in the likelihood ratio accomponying $c_{tG}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trained models can be loaded into an ``Analyse`` object to make them accesible for later use" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "path_to_models_root = os.path.join('./models', time.strftime(\"%Y/%m/%d\"))\n", "order = 'lin' # or quad when quadratic models have been trained\n", "\n", "models_paths_dict = analyse.Analyse.build_path_dict(root=path_to_models_root,\n", " order=order,\n", " prefix='model')\n", "\n", "analyser = analyse.Analyse(models_paths_dict, order, all=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The loss profile of the models we have just trained can be plotted via" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, _ = analyser.plot_loss_overview(c_name, order, xlim=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pretrained models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since properly training models takes a bit of time, we have provided a set of pretrained models that can be directly loaded and analysed. We consider the 2 feature case applied to $p_T^{\\ell\\bar{\\ell}}$ and $\\eta_\\ell$ first." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "models_2_feat_url = 'https://dl.dropboxusercontent.com/s/dy7ni8t4g8x68u0/tt_particle_ptll_etal.tar.gz?dl=0'\n", "pretrained_models_2_feat = file_downloader(models_2_feat_url);\n", "untar(pretrained_models_2_feat, destination='./downloads/models/tt_llvlvlbb_2_feat');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like before, we load the models into a ``Analyse`` object:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "path_to_models_2_feat_root = './downloads/models/tt_llvlvlbb_2_feat'\n", "order = 'quad'\n", "\n", "models_paths_dict_2_feat = analyse.Analyse.build_path_dict(root=path_to_models_2_feat_root,\n", " order=order,\n", " prefix='model')\n", "\n", "analyser_2_feat = analyse.Analyse(models_paths_dict_2_feat, order)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given some \"observed\" data (not used during training) drawn from the SM, we can evaluate the NNs on this data through the following syntax:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "observed_data_url = 'https://dl.dropboxusercontent.com/s/igjt97vfaxdn9y1/events_0.pkl.gz?dl=0'\n", "observed_data = file_downloader(observed_data_url, './downloads/observed_data');\n", "\n", "events_sm, _ = analyse.Analyse.load_events('./downloads/observed_data/events_0.pkl.gz')\n", "\n", "# evaluate pretrained NNs on observed data\n", "analyser_2_feat.evaluate_models(events_sm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of all NN replicas for all coefficients is stored inside a pandas dataframe that can be called like" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "models_evaluated_2_feat = analyser_2_feat.models_evaluated_df;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see for instance the output associated to the NN associated to $c_{tG}$ at the linear level, one can run" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "models_evaluated_2_feat['models']['lin', 'ctGRe'];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us finally plot the (median) $\\mathrm{NN}^{c_{tG}}$ as a 2-dimensional surface in the 2-dim feature space $(p_{T}^{\\ell, \\bar{\\ell}}, \\eta_\\ell)$:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# create the 2-dim grid in feature space\n", "eta_l_span = np.linspace(-2.5, 2.5, 100)\n", "pt_ll_span = np.linspace(0, 450, 50)\n", "eta_l_grid, pt_ll_grid = np.meshgrid(eta_l_span, pt_ll_span)\n", "grid = np.c_[eta_l_grid.ravel(), pt_ll_grid.ravel()]\n", "df = pd.DataFrame({'pt_ll': grid[:, 1], 'eta_l1': grid[:, 0]})\n", "\n", "# evaluate the NNs on this grid and \n", "analyser_2_feat.evaluate_models(df)\n", "models_evaluated = analyser_2_feat.models_evaluated_df['models']\n", "analyser_2_feat.models_evaluated_df['models'] = models_evaluated.apply(lambda row: np.median(row, axis=0))\n", "\n", "nn_ctG = analyser_2_feat.models_evaluated_df['models']['lin', 'ctGRe'].reshape(eta_l_grid.shape)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# make a 3D interactive plot\n", "%matplotlib ipympl\n", "fig = plt.figure(figsize=(10,10))\n", "ax = plt.axes(projection='3d')\n", "ax.set_xlabel(r'$\\eta_\\ell$')\n", "ax.set_ylabel(r'$p_T^{\\ell\\bar{\\ell}}\\;\\mathrm{[GeV]}$')\n", "ax.set_zlabel(r'$\\mathrm{NN}^{c_{tG}}$')\n", "ax.plot_surface(eta_l_grid, pt_ll_grid, nn_ctG, cmap='viridis', alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 18 features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fully multivariate models are also available, and can be loaded exactly like before" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# download the models\n", "models_18_feat_url = 'https://dl.dropboxusercontent.com/s/54uchr1w7pkjrqf/tt_particle_18_feat.tar.gz?dl=0'\n", "pretrained_models_18_feat = file_downloader(models_18_feat_url);\n", "untar(pretrained_models_18_feat, destination='./downloads/models/tt_llvlvlbb_18_feat');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load the models\n", "path_to_models_18_feat_root = './downloads/models/tt_llvlvlbb_18_feat'\n", "order = 'quad'\n", "\n", "models_paths_dict_18_feat = analyse.Analyse.build_path_dict(root=path_to_models_18_feat_root,\n", " order=order,\n", " prefix='model')\n", "\n", "analyser_18_feat = analyse.Analyse(models_paths_dict_18_feat, order)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# evaluate pretrained NNs on observed data\n", "analyser_18_feat.evaluate_models(events_sm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "analyser_18_feat.models_evaluated_df;" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "models_evaluated_18_feat = analyser_18_feat.models_evaluated_df['models']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "models_evaluated_18_feat['lin', 'ctGRe'];" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }