how_to_classic-limit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "p = 10\n",
    "k = 2\n",
    "d = 3\n",
    "gamma = 5e-2\n",
    "noise = 1e-3\n",
    "\n",
    "log_time = 4\n",
    "dt = 1e-3\n",
    "activation = 'erf'\n",
    "\n",
    "store_path = f'computation-database/classic-limit/{activation}/'\n",
    "ic_string = 'classic-limit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from committee_learning.initial_conditions import RandomNormalInitialConditions\n",
    "from committee_learning.simulation import Simulation\n",
    "from committee_learning.ode import ErfActivationODE, SquaredActivationODE\n",
    "from committee_learning.result import SimulationResult, FullODEResult\n",
    "\n",
    "ic = RandomNormalInitialConditions(p, k, d, seed = 3, orth_teacher=True)\n",
    "\n",
    "\n",
    "sim = Simulation(\n",
    "  d=d,\n",
    "  p=p,\n",
    "  k=k,\n",
    "  noise=noise,\n",
    "  Wt=ic.Wt,\n",
    "  gamma=gamma,\n",
    "  activation=activation,\n",
    "  W0=ic.W0\n",
    ")\n",
    "\n",
    "simr = SimulationResult(ic_string, id = 2)\n",
    "\n",
    "\n",
    "if activation == 'erf':\n",
    "  FullODE = ErfActivationODE\n",
    "elif activation == 'square':\n",
    "  FullODE = SquaredActivationODE\n",
    "\n",
    "ode = FullODE(ic.P,ic.Q,ic.M,dt, gamma_over_p = gamma/p, noise = noise)\n",
    "oder = FullODEResult(ic_string, id = 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "simr.from_file_or_run(sim,log_time+np.log10(d*p/gamma),path=store_path + 'sim/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "oder.from_file_or_run(ode,log_time,path=store_path+'ode/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from committee_learning.utilities import plot_style\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with plot_style():\n",
    "  fig, ax = plt.subplots()\n",
    "  # ax.set_title(f'$\\\\gamma\\\\to0$ ($\\\\gamma=\\\\num{{{gamma}}}$ for the simulation)')\n",
    "  ax.set_xlim(dt,10**(log_time))\n",
    "  ax.set_xscale('log')\n",
    "  ax.set_yscale('log')\n",
    "  ax.set_xlabel(r'$t$')\n",
    "  ax.set_ylabel(r'$\\mathcal{R}-\\frac{\\Delta}{2}$')\n",
    "\n",
    "  ax.plot(np.array(\n",
    "    simr.steps[::15])*(gamma/(p*d)),\n",
    "    np.array(simr.risks[::15]),# + noise,\n",
    "    ls='',\n",
    "    label=f'Simulation',\n",
    "    marker='o'\n",
    "  )\n",
    "  ax.plot(np.array(oder.times), np.array(oder.risks), label='ODE')\n",
    "  ax.legend()\n",
    "  # fig.savefig(f'figures/classic_limit-k{k}_p{p}_d{d}_gamma{gamma:.4f}_noise{noise}.pdf', bbox_inches='tight')\n",
    "  plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "from IPython import display\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "def norm_M(Q, P):\n",
    "  npQ = np.array(Q)\n",
    "  npP = np.array(P)\n",
    "  return np.sqrt(np.einsum('tj,r->tjr', np.diagonal(npQ, axis1=1, axis2=2), np.diagonal(npP, axis1=0, axis2=1)))\n",
    "\n",
    "Msim = np.array(simr.Ms)/norm_M(simr.Qs, simr.P)\n",
    "Mode = np.array(oder.Ms)/norm_M(oder.Qs, oder.P)\n",
    "\n",
    "with plot_style(style='classic'):\n",
    "  fig, ax = plt.subplots(figsize=(5,5))\n",
    "  ax.set_xlim(-1,1)\n",
    "  ax.set_ylim(-1,1)\n",
    "  ax.axvline(0.,0.,2., ls='-.', c='red')\n",
    "  ax.axhline(0.,0.,2., ls='-.', c='red')\n",
    "  ax.set_xlabel(r'$M_{j1}/\\sqrt{Q_{jj}P_{11}}$')\n",
    "  ax.set_ylabel(r'$M_{j2}/\\sqrt{Q_{jj}P_{22}}$')\n",
    "  ax.set_yticklabels([])\n",
    "  ax.set_xticklabels([])\n",
    "  ax.plot(1./np.sqrt(2), 1./np.sqrt(2), marker='o', c='yellow', ms = 15.)\n",
    "  ax.plot(1, 0, marker='s', c='red', ms = 15.)\n",
    "  ax.plot(0, 1, marker='s', c='red', ms = 15.)\n",
    "  risk_ax = inset_axes(ax, width='35%', height='32%', loc = 3)\n",
    "  risk_ax.set_xscale('log')\n",
    "  risk_ax.set_yscale('log')\n",
    "  risk_ax.set_xlabel('$t$', labelpad= -18.)\n",
    "  risk_ax.set_ylabel(r'$\\mathcal{R}-\\frac{\\Delta}{2}$', labelpad= -27.)\n",
    "  risk_ax.set_yticklabels([])\n",
    "  risk_ax.set_xticklabels([])\n",
    "  risk_ax.set_xlim(5e-2,10**4)\n",
    "  risk_ax.set_ylim(5e-7,.15)\n",
    "\n",
    "  sim_particles = [ax.plot([], [])[0] for j in range(p)]\n",
    "  ode_particles = [ax.plot([], [])[0] for j in range(p)]\n",
    "  risk_plot = risk_ax.plot([], [])[0]\n",
    "  timestamp = ax.text(-.9, .9, '', fontsize=15)\n",
    "\n",
    "  def init():\n",
    "    for sp in sim_particles:\n",
    "      sp.set_data([],[])\n",
    "    for op in ode_particles:\n",
    "      op.set_data([],[])\n",
    "    risk_plot.set_data([],[])\n",
    "    timestamp.set_text(f'$t={oder.times[0]}$')\n",
    "    return (*sim_particles, *ode_particles, risk_plot, timestamp)\n",
    "\n",
    "  def animate(frame_number):\n",
    "    for j,op in enumerate(ode_particles):\n",
    "      op.set_data(Mode[:frame_number,j,0],Mode[:frame_number,j,1])\n",
    "\n",
    "    risk_plot.set_data(oder.times[:frame_number], oder.risks[:frame_number])\n",
    "    timestamp.set_text(f'$t={oder.times[frame_number]:.2f}$')\n",
    "    return (*sim_particles, *ode_particles, risk_plot, timestamp)\n",
    "\n",
    "  anim = animation.FuncAnimation(fig, animate, init_func=init, frames=range(0,Mode.shape[0], 10), interval=150, blit=True)\n",
    "\n",
    "  # To save the animation as a gif\n",
    "  writer = animation.PillowWriter(fps=15, bitrate=1800)\n",
    "  anim.save('figures/scatter.gif', writer=writer)\n",
    "\n",
    "  video = anim.to_html5_video()\n",
    "  html = display.HTML(video)\n",
    "  display.display(html)\n"
   ]
  }
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