add bdt tutorial

FNALLPC · Jan 9, 2024 · 734d30b · 734d30b
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diff --git a/README.md b/README.md
@@ -25,8 +25,8 @@ ______________________________________________________________________
 ## Introduction
 
 This is a set of tutorials for the LPC Data Analysis School (DAS) Machine Learning Short Exercise. 
-They are intended to show you how to build machine learning models in python, using `Keras`, `TensorFlow`, and `PyTorch`, and use them in your `ROOT`-based analyses. 
-We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using dense and convolutional neural networks.
+They are intended to show you how to build machine learning models in python, using `xgboost`, `Keras`, `TensorFlow`, and `PyTorch`, and use them in your `ROOT`-based analyses. 
+We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using BDTs, and dense and convolutional neural networks.
 We will also explore more advanced models such as graph neural networks (GNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) on simple datasets.
 
 ## Setup

diff --git a/machine-learning-das/_toc.yml b/machine-learning-das/_toc.yml
@@ -14,12 +14,13 @@ parts:
     maxdepth: 2
     chapters:
     - file: notebooks/1-datasets-uproot
-    - file: notebooks/2-dense
+    - file: notebooks/2-boosted-decision-trees
+    - file: notebooks/3-dense
       sections:
-        - file: notebooks/2.1-dense-keras
-        - file: notebooks/2.2-dense-pytorch
-        - file: notebooks/2.3-dense-bayesian-optimization
-    - file: notebooks/3-conv2d
-    - file: notebooks/4-gnn-cora
-    - file: notebooks/5-vae-mnist
-    - file: notebooks/6-gan-mnist
+        - file: notebooks/3.1-dense-keras
+        - file: notebooks/3.2-dense-pytorch
+        - file: notebooks/3.3-dense-bayesian-optimization
+    - file: notebooks/4-conv2d
+    - file: notebooks/5-gnn-cora
+    - file: notebooks/6-vae-mnist
+    - file: notebooks/7-gan-mnist
diff --git a/machine-learning-das/condaenv.thhr9qc_.requirements.txt b/machine-learning-das/condaenv.thhr9qc_.requirements.txt
@@ -0,0 +1 @@
+-r requirements.txt
diff --git a/machine-learning-das/notebooks/1-datasets-uproot.ipynb b/machine-learning-das/notebooks/1-datasets-uproot.ipynb
diff --git a/machine-learning-das/notebooks/2-boosted-decision-tree.ipynb b/machine-learning-das/notebooks/2-boosted-decision-tree.ipynb
diff --git a/machine-learning-das/notebooks/2-dense.md → machine-learning-das/notebooks/3-dense.md b/machine-learning-das/notebooks/2-dense.md → machine-learning-das/notebooks/3-dense.md
diff --git a/...rning-das/notebooks/2.1-dense-keras.ipynb → ...rning-das/notebooks/3.1-dense-keras.ipynb b/...rning-das/notebooks/2.1-dense-keras.ipynb → ...rning-das/notebooks/3.1-dense-keras.ipynb
diff --git a/...ing-das/notebooks/2.2-dense-pytorch.ipynb → ...ing-das/notebooks/3.2-dense-pytorch.ipynb b/...ing-das/notebooks/2.2-dense-pytorch.ipynb → ...ing-das/notebooks/3.2-dense-pytorch.ipynb
diff --git a/...oks/2.3-dense-bayesian-optimization.ipynb → ...oks/3.3-dense-bayesian-optimization.ipynb b/...oks/2.3-dense-bayesian-optimization.ipynb → ...oks/3.3-dense-bayesian-optimization.ipynb
diff --git a/...ine-learning-das/notebooks/3-conv2d.ipynb → ...ine-learning-das/notebooks/4-conv2d.ipynb b/...ine-learning-das/notebooks/3-conv2d.ipynb → ...ine-learning-das/notebooks/4-conv2d.ipynb
diff --git a/...e-learning-das/notebooks/4-gnn-cora.ipynb → ...e-learning-das/notebooks/5-gnn-cora.ipynb b/...e-learning-das/notebooks/4-gnn-cora.ipynb → ...e-learning-das/notebooks/5-gnn-cora.ipynb
diff --git a/...-learning-das/notebooks/5-vae-mnist.ipynb → ...-learning-das/notebooks/6-vae-mnist.ipynb b/...-learning-das/notebooks/5-vae-mnist.ipynb → ...-learning-das/notebooks/6-vae-mnist.ipynb
diff --git a/...-learning-das/notebooks/6-gan-mnist.ipynb → ...-learning-das/notebooks/7-gan-mnist.ipynb b/...-learning-das/notebooks/6-gan-mnist.ipynb → ...-learning-das/notebooks/7-gan-mnist.ipynb
diff --git a/machine-learning-das/setup-libraries.ipynb b/machine-learning-das/setup-libraries.ipynb
@@ -19,25 +19,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sourcing the conda and mamba setup scripts...\n",
-      "Activating the kernel \"machine-learning-hats\"...\n",
-      "Installed kernelspec machine-learning-hats in /home/cms.rkansal/.local/share/jupyter/kernels/machine-learning-hats\n",
-      "Installed \"machine-learning-hats\"!\n"
+      "Executing transaction: ...working... done\n"
      ]
     }
    ],
    "source": [
     "%%bash\n",
     "#!/bin/bash\n",
     "\n",
-    "KERNEL_NAME=\"machine-learning-hats\"\n",
+    "KERNEL_NAME=\"machine-learning-das\"\n",
     "\n",
     "# Download mambaforge install script\n",
     "if [[ ! -f $HOME/mambaforge.sh ]]; then\n",
@@ -81,11 +78,16 @@
     "If successful, you should see something similar to the following:\n",
     "\n",
     "```\n",
-    "Installed \"machine-learning-hats\"!\n",
+    "Installed \"machine-learning-das\"!\n",
     "```\n",
     "\n",
     "The new kernel you just made will then show up in the various Jupyter notebooks after refreshing the page."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -104,7 +106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,

diff --git a/requirements.txt b/requirements.txt
@@ -10,4 +10,5 @@ pandas
 torch
 ipykernel
 tqdm
-jupyter
+jupyter
+xgboost
-Original file line number
+Diff line change
@@ Expand Up / @@ -10,4 +10,5 @@ pandas @@
     torch
     ipykernel
     tqdm
-    jupyter
+    jupyter
+    xgboost