RL_fib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors: Giorgi Butbaia (Giorgi.Butbaia@unh.edu) & Elli Heyes (elli.heyes@city.ac.uk)\n",
    "Last edited: 11/06/2024\n",
    "\n",
    "In this notebook we construct a reinforcement learning algorithm to generate reflexive subpolytopes polytopes which define fibrations of CYs.\n",
    "We use a deep Q-learning algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-11 11:58:29.055187: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-11 11:58:29.148888: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-06-11 11:58:30.057841: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-11 11:58:36.604919: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import subprocess\n",
    "try:\n",
    "    import keras \n",
    "    import tensorflow as tf\n",
    "    tfk = tf.keras\n",
    "except:\n",
    "    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'tensorflow'])\n",
    "    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'keras'])\n",
    "    import keras\n",
    "    import tensorflow as tf\n",
    "    tfk = tf.keras\n",
    "try:\n",
    "    import mplhep as hep\n",
    "except:\n",
    "    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'mplhep'])\n",
    "    import mplhep as hep \n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from agent import Agent\n",
    "from tqdm import trange\n",
    "from copy import copy, deepcopy\n",
    "from collections import deque\n",
    "from cytools import Polytope, fetch_polytopes\n",
    "from utils import random_SP_state, SP_act, SP_fitness, reduce_polytope, intersect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider case for looking $d$-fibration structures of a polytope $P$ with vertices $(v_0,\\dots, v_{N-1})$. Then, state $s$ is:\n",
    "$$\n",
    "s = (n_0, \\dots, n_{d-1}) \\in \\mathbb{Z}_N^d =: \\mathcal{S}\n",
    "$$\n",
    "s.t. $n_j\\neq n_k$ for $j\\neq k$. A state defines a set of vectors $\\{v_{n_j}\\}_j$, whose intersection with $P$ defines the subpolytope.\n",
    "\n",
    "An action on the state space $\\mathcal{S}$ is a group action:\n",
    "$$\n",
    "\\mathbb{Z}_N^d \\times \\mathcal{S} \\rightarrow \\mathcal{S}\n",
    "$$\n",
    "induced by the group product. In particular, we have:\n",
    "$$\n",
    "(m_0,\\dots, m_{d-1})\\cdot (n_0,\\dots, n_{d-1}) = (m_0 + n_0,\\dots, m_{d-1} + n_{d-1})\n",
    "$$\n",
    "Note that $\\mathbb{Z}_N^d$ is cyclic and generated by elements:\n",
    "$$\n",
    "(1, 0, \\dots, 0)\\\\\n",
    "(0, 1,\\dots, 0)\\\\\n",
    "\\dots\\\\\n",
    "(0, 0, \\dots, 1)\n",
    "$$\n",
    "we shall use these as possible actions.\n",
    "\n",
    "> *Note*: There really is no reason for this to work, as the ordering of the vertices is completely arbitrary. Q: is there a \"natural\" way to order the vertices, in order to have a notion of \"neighboring\" subspaces?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FibrationEnvironment(object):\n",
    "    def __init__(self, polytope, fibration_dim):\n",
    "        self._poly = polytope\n",
    "        self._points = polytope.points()[1:]\n",
    "        self._fib_dim = fibration_dim\n",
    "        self._num_actions = fibration_dim\n",
    "\n",
    "    def random_state(self):\n",
    "        \"\"\" \n",
    "        Output: random state describing a subpolytope   \n",
    "        \"\"\"\n",
    "        return random_SP_state(self._poly, self._fib_dim)\n",
    "\n",
    "    @staticmethod\n",
    "    def reduce_polytope(vertices):\n",
    "        \"\"\" \n",
    "        Input: vertex matrix\n",
    "        Output: reduced vertex matrix\n",
    "        \"\"\"\n",
    "        return reduce_polytope(vertices)\n",
    "\n",
    "    def intersect(self, state):\n",
    "        \"\"\" \n",
    "        Input: list of point indices\n",
    "        Output: ...\n",
    "        \"\"\"\n",
    "        return intersect(self._points, state, self._fib_dim)\n",
    "\n",
    "    def fitness(self, state):\n",
    "        \"\"\"\n",
    "        Input: state describing subpolytope state\n",
    "        Output: the fitness value between 0 and 1 for the state \n",
    "        \"\"\"\n",
    "        return SP_fitness(self._points, state, self._fib_dim)\n",
    "\n",
    "    def act(self, state, action):\n",
    "        \"\"\"\n",
    "        Input: state-action pair\n",
    "        Output: new state obtained by acting on the old state with the action \n",
    "        \"\"\"\n",
    "        return SP_act(state, action, self._points.shape[0])\n",
    "\n",
    "    @property\n",
    "    def num_actions(self):\n",
    "        return self._fib_dim\n",
    "\n",
    "    @property\n",
    "    def get_actions(self):\n",
    "        return [i for i in range(self._fib_dim)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class QNet(tfk.Model):\n",
    "    def __init__(self, input_shape, output_shape):\n",
    "        super().__init__()\n",
    "        self._dense1 = tfk.layers.Dense(64, activation='relu', input_shape = input_shape)\n",
    "        self._dense2 = tfk.layers.Dense(128, activation='relu')\n",
    "        self._dense3 = tfk.layers.Dense(output_shape, activation='linear')\n",
    "        self.output_shape = [output_shape]\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self._dense1(x)\n",
    "        x = self._dense2(x)\n",
    "        x = self._dense3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cytools/cytools-venv/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# define polytope\n",
    "p = Polytope([\n",
    "    [-2, 0, -1, 2, 5],\n",
    "    [0, -1, -1, 1, 0],\n",
    "    [0, 0, 1, 2,  -1],\n",
    "    [1, 0, 0, -3, -3],\n",
    "    [1, 0, 0, -1, -3],\n",
    "    [2, 1, 1, -5, -2]])\n",
    "\n",
    "# initialise fibration environment\n",
    "F_env = FibrationEnvironment(p, 3) # look for 3-dimensionl subpolytopes\n",
    "\n",
    "# get the model dimensions\n",
    "state = F_env.random_state()\n",
    "state_shape = state.shape[0]\n",
    "action_shape = F_env.num_actions\n",
    "\n",
    "# define the RL agent\n",
    "buffer_size = 2**11\n",
    "\n",
    "model = QNet(input_shape=state_shape, output_shape=action_shape)\n",
    "model.compile(\n",
    "    loss = tfk.losses.MeanSquaredError(),\n",
    "    optimizer = tfk.optimizers.Adam(learning_rate =1e-3),\n",
    "    metrics = [tfk.metrics.MeanAbsoluteError()])\n",
    "\n",
    "target_model  = QNet(input_shape=state_shape, output_shape=action_shape)\n",
    "target_model.compile(\n",
    "    loss = tfk.losses.MeanSquaredError(),\n",
    "    optimizer = tfk.optimizers.Adam(learning_rate =1e-3),\n",
    "    metrics = [tfk.metrics.MeanAbsoluteError()])\n",
    "target_model.set_weights(model.get_weights())\n",
    "\n",
    "agent = Agent(model=model, target_model=target_model, buffer_size=buffer_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: 0.028587926177741214: 100%|███████████████████████████████████████████████████████████████| 1024/1024 [07:04<00:00,  2.41it/s]\n"
     ]
    }
   ],
   "source": [
    "# set parameters\n",
    "batch_size = 128\n",
    "MAX_ITER = 32\n",
    "MIN_MEMORY_SIZE = 1024\n",
    "\n",
    "# train RL model \n",
    "loss_vals = []\n",
    "steps_to_update_target_model = 0\n",
    "with trange(1024) as t_epochs:\n",
    "    for epoch in t_epochs:\n",
    "        # define initial random state\n",
    "        state = F_env.random_state()\n",
    "        fitness = F_env.fitness(state)\n",
    "        \n",
    "        # continue until terminated\n",
    "        terminated = False\n",
    "        total_loss = 0.0\n",
    "        count = 0\n",
    "        for _ in range(MAX_ITER):\n",
    "            steps_to_update_target_model += 1\n",
    "            \n",
    "            # get action and obtain new state \n",
    "            action = agent.act(state)\n",
    "            new_state = F_env.act(state, action)\n",
    "            new_fitness = F_env.fitness(new_state)\n",
    "            \n",
    "            # compute reward\n",
    "            if new_fitness == 1:\n",
    "                reward = 10\n",
    "                terminated = True\n",
    "            else:\n",
    "                reward = new_fitness - fitness\n",
    "            \n",
    "            # update history\n",
    "            agent.store(state, action, reward, new_state, terminated)\n",
    "\n",
    "            if terminated or (steps_to_update_target_model % 4 == 0):\n",
    "                if agent.current_memory_size > MIN_MEMORY_SIZE:\n",
    "                    history = agent.replay(batch_size)\n",
    "                    total_loss += history.history['loss'][-1]\n",
    "                    count += 1\n",
    "\n",
    "            # update state\n",
    "            state = deepcopy(new_state)\n",
    "            fitness = copy(new_fitness)\n",
    "\n",
    "            if terminated:\n",
    "                if steps_to_update_target_model >= 100:\n",
    "                    agent.update_target_model()\n",
    "                    steps_to_update_target_model = 0\n",
    "\n",
    "            if terminated:\n",
    "                break\n",
    "                \n",
    "        t_epochs.set_description(f\"Loss: {total_loss / (count+1e-10)}\")\n",
    "        loss_vals.append(total_loss / (count+1e-10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss curve\n",
    "hep.style.use(\"CMS\")\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": False, # LaTeX not available in cytools env\n",
    "    'font.size': 30\n",
    "})\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(loss_vals, 'b-', label=\"Loss\", linewidth=2)\n",
    "x0 = min(x for x in range(len(loss_vals)) if loss_vals[x]!=0)\n",
    "ax.set_xlim([x0, len(loss_vals)]) # start when ~MIN_MEMORY_SIZE is reached\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(\"Iteration\")\n",
    "ax.set_ylabel(\"Loss\")\n",
    "ax.grid(True, 'both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., -1.,  0.],\n",
       "       [ 2.,  1., -2.],\n",
       "       [-1.,  0.,  2.],\n",
       "       [ 1., -0., -3.],\n",
       "       [ 1., -0., -1.],\n",
       "       [ 1., -0., -2.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use trained RL model to find subpolytope\n",
    "state = F_env.random_state()\n",
    "done = False\n",
    "while not done:\n",
    "    action = agent.act(state)\n",
    "    state = F_env.act(state, action)\n",
    "    fitness = F_env.fitness(state)\n",
    "    if fitness == 1:\n",
    "        done = True\n",
    "F_env.reduce_polytope(F_env.intersect(state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}