Training_HLF_Classifier/XGBoost_with_Pandas_Parquet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traininig of the High Level Feature classifier using XGBoost on GPU\n",
    "\n",
    "**XGBoost** This notebook trains a particle classifier using High Level Features. It uses XGBoost. Pandas is used to read the data and pass it to XGBoost.  \n",
    "\n",
    "Credits: this notebook is part of the work: \n",
    "- [Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics Comput Softw Big Sci 4, 8 (2020)](https://rdcu.be/b4Wk9)  \n",
    "- Code and data at:https://github.com/cerndb/SparkDLTrigger\n",
    "- The model is a classifier implemented as a DNN\n",
    "  - Model input: 14 \"high level features\", described in [ Topology classification with deep learning to improve real-time event selection at the LHC](https://link.springer.com/epdf/10.1007/s41781-019-0028-1?author_access_token=eTrqfrCuFIP2vF4nDLnFfPe4RwlQNchNByi7wbcMAY7NPT1w8XxcX1ECT83E92HWx9dJzh9T9_y5Vfi9oc80ZXe7hp7PAj21GjdEF2hlNWXYAkFiNn--k5gFtNRj6avm0UukUt9M9hAH_j4UR7eR-g%3D%3D)\n",
    "  - Model output: 3 classes, \"W + jet\", \"QCD\", \"$t\\bar{t}$\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load train and test datasets via Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the datasets from \n",
    "# https://github.com/cerndb/SparkDLTrigger/tree/master/Data\n",
    "#\n",
    "# For CERN users, data is already available on EOS\n",
    "PATH = \"/eos/project/s/sparkdltrigger/public/\"\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "testPDF = pd.read_parquet(path= PATH + 'testUndersampled_HLF_features.parquet', \n",
    "                          columns=['HLF_input', 'encoded_label'])\n",
    "\n",
    "trainPDF = pd.read_parquet(path= PATH + 'trainUndersampled_HLF_features.parquet', \n",
    "                           columns=['HLF_input', 'encoded_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are HLF_input        856090\n",
      "encoded_label    856090\n",
      "dtype: int64 events in the test dataset\n",
      "There are HLF_input        3426083\n",
      "encoded_label    3426083\n",
      "dtype: int64 events in the train dataset\n"
     ]
    }
   ],
   "source": [
    "# Check the number of events in the train and test datasets\n",
    "\n",
    "num_test = testPDF.count()\n",
    "num_train = trainPDF.count()\n",
    "\n",
    "print('There are {} events in the test dataset'.format(num_test))\n",
    "print('There are {} events in the train dataset'.format(num_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>HLF_input</th>\n",
       "      <th>encoded_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.015150733133517018, 0.003511028294205839, 0...</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0, 0.003881822832783805, 0.7166341448458555...</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.009639073600865505, 0.0010022659022912096, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.016354407625436572, 0.002108937905084598, 0...</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.01925979125354152, 0.004603697276827594, 0....</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856085</th>\n",
       "      <td>[0.020383967386165446, 0.0022348975484913444, ...</td>\n",
       "      <td>[0.0, 1.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856086</th>\n",
       "      <td>[0.02475209699743233, 0.00867502196073073, 0.3...</td>\n",
       "      <td>[0.0, 1.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856087</th>\n",
       "      <td>[0.03498179428310887, 0.02506331737284528, 0.9...</td>\n",
       "      <td>[0.0, 1.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856088</th>\n",
       "      <td>[0.03735147362869153, 0.003645269183639405, 0....</td>\n",
       "      <td>[0.0, 1.0, 0.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856089</th>\n",
       "      <td>[0.04907273976147946, 0.003058462073646085, 0....</td>\n",
       "      <td>[0.0, 1.0, 0.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>856090 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                HLF_input    encoded_label\n",
       "0       [0.015150733133517018, 0.003511028294205839, 0...  [1.0, 0.0, 0.0]\n",
       "1       [0.0, 0.003881822832783805, 0.7166341448458555...  [1.0, 0.0, 0.0]\n",
       "2       [0.009639073600865505, 0.0010022659022912096, ...  [1.0, 0.0, 0.0]\n",
       "3       [0.016354407625436572, 0.002108937905084598, 0...  [1.0, 0.0, 0.0]\n",
       "4       [0.01925979125354152, 0.004603697276827594, 0....  [1.0, 0.0, 0.0]\n",
       "...                                                   ...              ...\n",
       "856085  [0.020383967386165446, 0.0022348975484913444, ...  [0.0, 1.0, 0.0]\n",
       "856086  [0.02475209699743233, 0.00867502196073073, 0.3...  [0.0, 1.0, 0.0]\n",
       "856087  [0.03498179428310887, 0.02506331737284528, 0.9...  [0.0, 1.0, 0.0]\n",
       "856088  [0.03735147362869153, 0.003645269183639405, 0....  [0.0, 1.0, 0.0]\n",
       "856089  [0.04907273976147946, 0.003058462073646085, 0....  [0.0, 1.0, 0.0]\n",
       "\n",
       "[856090 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the schema and a data sample of the test dataset\n",
    "testPDF\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert training and test datasets from Pandas DataFrames to Numpy arrays\n",
    "\n",
    "Now we will collect and convert the Pandas DataFrame into numpy arrays in order to be able to feed them to TensorFlow/Keras.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.stack(trainPDF[\"HLF_input\"])\n",
    "y = np.stack(trainPDF[\"encoded_label\"])\n",
    "\n",
    "X_test = np.stack(testPDF[\"HLF_input\"])\n",
    "y_test = np.stack(testPDF[\"encoded_label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.3'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgboost.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model instance\n",
    "# Use XGBoost on GPU resources\n",
    "#bst = XGBClassifier(tree_method='gpu_hist', n_estimators=3, max_depth=2, learning_rate=1, objective='multi:softprob')\n",
    "\n",
    "bst =  XGBClassifier(tree_method = \"hist\", device = \"cuda\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.1 s, sys: 4.69 s, total: 17.8 s\n",
      "Wall time: 11.7 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=&#x27;cuda&#x27;, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=&#x27;cuda&#x27;, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device='cuda', early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model on the training dataset\n",
    "%time bst.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Classifier - Performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cvmfs/sft-nightlies.cern.ch/lcg/views/dev3cuda/Tue/x86_64-el9-gcc11-opt/lib/python3.9/site-packages/xgboost/core.py:160: UserWarning: [15:14:56] WARNING: /build/jenkins/workspace/lcg_nightly_pipeline/build/pyexternals/xgboost-2.0.3/src/xgboost/2.0.3/src/common/error_msg.cc:58: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.\n",
      "Potential solutions:\n",
      "- Use a data structure that matches the device ordinal in the booster.\n",
      "- Set the device for booster before call to inplace_predict.\n",
      "\n",
      "This warning will only be shown once.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "y_pred =  preds = bst.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the HLF classifier: 0.9172\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Accuracy of the HLF classifier: {:.4f}'.format(\n",
    "    accuracy_score(np.argmax(y_test, axis=1),np.argmax(y_pred, axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "labels_name = ['qcd', 'tt', 'wjets']\n",
    "labels = [0,1,2]\n",
    "\n",
    "cm = confusion_matrix(np.argmax(y_pred, axis=1), np.argmax(y_test, axis=1), labels=labels)\n",
    "\n",
    "## Normalize CM\n",
    "cm = cm / cm.astype(np.float64).sum(axis=1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax = sns.heatmap(cm, annot=True, fmt='g')\n",
    "ax.xaxis.set_ticklabels(labels_name)\n",
    "ax.yaxis.set_ticklabels(labels_name)\n",
    "plt.xlabel('True labels')\n",
    "plt.ylabel('Predicted labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC and AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "for i in range(3):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_pred[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.944949717170571, 1: 0.9418716018584723, 2: 0.9302782133005137}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary containign ROC-AUC for the three classes \n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Plot roc curve \n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(fpr[0], tpr[0], lw=2, \\\n",
    "         label='HLF classifier (AUC) = %0.4f' % roc_auc[0])\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Background Contamination (FPR)')\n",
    "plt.ylabel('Signal Efficiency (TPR)')\n",
    "plt.title('$tt$ selector')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.12"
  },
  "sparkconnect": {
   "bundled_options": [],
   "list_of_options": [
    {
     "name": "spark.dynamicAllocation.enabled",
     "value": "false"
    },
    {
     "name": "spark.executor.memory",
     "value": "14G"
    },
    {
     "name": "spark.executor.cores",
     "value": "6"
    },
    {
     "name": "spark.executor.instances",
     "value": "6"
    },
    {
     "name": "spark.driver.memory",
     "value": "10G"
    },
    {
     "name": "spark.driver.maxResultSize",
     "value": "10G"
    }
   ]
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}