stt2_gnn_train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## GNN Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob, os, sys, yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "import seaborn as sns\n",
    "import trackml.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch_geometric.data import Data\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import draw_event\n",
    "from src import compose_event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set EXATRKX_DATA env variable\n",
    "os.environ['EXATRKX_DATA'] = os.path.abspath(os.curdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a device\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load processing config file (trusted source)\n",
    "config_file = os.path.join(os.curdir, 'LightningModules/GNN/train_quickstart_GNN.yaml')\n",
    "with open(config_file) as f:\n",
    "    try:\n",
    "        config = yaml.load(f, Loader=yaml.FullLoader) # equiv: yaml.full_load(f)\n",
    "    except yaml.YAMLError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Input Data_\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from LightningModules.Processing.utils.event_utils import graph_intersection\n",
    "from LightningModules.Processing.utils.draw_utils import draw_proc_event, cylindrical_to_cartesian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to feature_store, use os.path.expandvars for ${HOME}\n",
    "FEATURE_DATA = os.path.expandvars(config['input_dir'])\n",
    "print(\"FEATURE_DATA: {}\".format(os.path.basename(FEATURE_DATA)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nqqzb3EO5g4A"
   },
   "outputs": [],
   "source": [
    "# examine an event, give integer value to event_id\n",
    "event_id = 1\n",
    "feature_data = torch.load(os.path.join(FEATURE_DATA, str(event_id)), map_location=device)\n",
    "print(\"Length of Data: {}\".format(len(feature_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data.x[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data.y_pid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(feature_data.y_pid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HfvKUCvjUlQA"
   },
   "source": [
    "## _GNN Stage_\n",
    "\n",
    "- For GNN stage, I will run the `InteractionGNN` model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from LightningModules.GNN.Models.interaction_gnn import InteractionGNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# see params used in this stage\n",
    "pp = pprint.PrettyPrinter(indent=2)\n",
    "pp.pprint(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change some params here\n",
    "config['datatype_names'] = [['train', 'val', 'test']]\n",
    "config['datatype_split'] = [[800, 100, 100]]\n",
    "config['input_dir']  = os.path.join(os.environ['EXATRKX_DATA'],'run/feature_store')\n",
    "config['output_dir'] = os.path.join(os.environ['EXATRKX_DATA'],'run/gnn_processed/quickstart_example')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# see params used in this stage\n",
    "pp = pprint.PrettyPrinter(indent=2)\n",
    "pp.pprint(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- _EDA_ :: Let's test `load_event()`, it gave error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_data(events, pt_background_cut, pt_signal_cut, noise):\n",
    "    \"\"\"Select data after applying pt cuts OR return without applying it if pt's set to zero.\"\"\"\n",
    "    \n",
    "    # Handle event in batched form\n",
    "    if type(events) is not list:\n",
    "        events = [events]\n",
    "\n",
    "    # NOTE: Cutting background by pT BY DEFINITION removes noise\n",
    "    if (pt_background_cut > 0) | (pt_signal_cut > 0):\n",
    "        for event in events:\n",
    "\n",
    "            edge_mask = (event.pt[event.edge_index] > pt_background_cut).all(0)\n",
    "            event.edge_index = event.edge_index[:, edge_mask]\n",
    "            event.y = event.y[edge_mask]\n",
    "\n",
    "            if \"weights\" in event.__dict__.keys():\n",
    "                if event.weights.shape[0] == edge_mask.shape[0]:\n",
    "                    event.weights = event.weights[edge_mask]\n",
    "\n",
    "            if (pt_signal_cut > pt_background_cut) and (\n",
    "                \"signal_true_edges\" in event.__dict__.keys()\n",
    "            ):\n",
    "                signal_mask = (event.pt[event.signal_true_edges] > pt_signal_cut).all(0)\n",
    "                event.signal_true_edges = event.signal_true_edges[:, signal_mask]\n",
    "\n",
    "    return events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset(input_dir, num, pt_background_cut, pt_signal_cut, noise):\n",
    "    \"\"\"Load data and apply pt cuts.\"\"\"\n",
    "    if input_dir is not None:\n",
    "        all_events = os.listdir(input_dir)\n",
    "        all_events = sorted([os.path.join(input_dir, event) for event in all_events])\n",
    "        loaded_events = [\n",
    "            torch.load(event, map_location=torch.device(\"cpu\"))\n",
    "            for event in all_events[:num]\n",
    "        ]\n",
    "        loaded_events = select_data(\n",
    "            loaded_events, pt_background_cut, pt_signal_cut, noise\n",
    "        )\n",
    "        return loaded_events\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "    return included_edges, included_edges_mask # FIXME::ADAK: This will never execute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_events = os.listdir(config['input_dir'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the InteractionGNN\n",
    "model = InteractionGNN(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summarize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset as accessed in model\n",
    "model.setup(stage=\"fit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset = model.trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_data = trainset[0]\n",
    "r, phi, ir = example_data.x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = r * np.cos(phi * np.pi), r * np.sin(phi * np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(x, y, s=2)\n",
    "plt.title(\"Azimuthal View of Detector\", fontsize=24), plt.xlabel(\n",
    "    \"x\", fontsize=18\n",
    "), plt.ylabel(\"y\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = example_data.edge_index\n",
    "pid = example_data.pid\n",
    "true_edges = pid[e[0]] == pid[e[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "# plt.plot(x[e[:, ~true_edges]], y[e[:, ~true_edges]], c=\"r\")\n",
    "plt.plot(x[e[:, true_edges]], y[e[:, true_edges]], c=\"k\")\n",
    "plt.scatter(x, y, s=5)\n",
    "plt.title(\"Azimuthal View of Detector\", fontsize=24), plt.xlabel(\n",
    "    \"x\", fontsize=18\n",
    "), plt.ylabel(\"y\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(x[e[:, (~true_edges)][:, 0:-1:5]], y[e[:, (~true_edges)][:, 0:-1:5]], c=\"r\")\n",
    "plt.scatter(x, y, s=5)\n",
    "plt.title(\"Azimuthal View of Detector\", fontsize=24), plt.xlabel(\n",
    "    \"x\", fontsize=18\n",
    "), plt.ylabel(\"y\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_lightning import Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(max_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.fit(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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