anisotropic_readout-learning_anisotropic_larger-pool.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow reload of objects\n",
    "%load_ext autoreload\n",
    "%autoreload\n",
    "from pelenet.utils import Utils\n",
    "from pelenet.experiments.readoutanisotropic import AnisotropicReadoutExperiment\n",
    "\n",
    "# Official modules\n",
    "import numpy as np\n",
    "import nxsdk.api.n2a as nx\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import os\n",
    "from time import time\n",
    "from tqdm import tqdm\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilizes the experiment and utils\n",
    "p = {\n",
    "    'partitioningClusterSize': 3 #4 #5 #6\n",
    "} \n",
    "exp = AnisotropicReadoutExperiment(parameters = p)\n",
    "utils = Utils.instance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Available cores exceeded, change setup parameters or rearange network.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-5213ad458b50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Build network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Get spectral radius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetSpectralRadius\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialWeights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pelenet/pelenet/experiments/readoutanisotropic.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0;31m# Connect reservoir to output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectOutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0;31m# Add patch input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pelenet/pelenet/network/connect.py\u001b[0m in \u001b[0;36mconnectOutput\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputLayerChunks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnxNet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreateCompartmentGroup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprototype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutCompProto\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# Remove used core from core list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremoveCoreFromList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0;31m# Connect excitatory neurons to output layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/pelenet/pelenet/network/connect.py\u001b[0m in \u001b[0;36mremoveCoreFromList\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Available cores exceeded, change setup parameters or rearange network'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Available cores exceeded, change setup parameters or rearange network.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;31m# Update number of used cores and chips\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Available cores exceeded, change setup parameters or rearange network."
     ]
    }
   ],
   "source": [
    "# Build network\n",
    "exp.build()\n",
    "\n",
    "# Get spectral radius\n",
    "utils.getSpectralRadius(exp.net.initialWeights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run network\n",
    "exp.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reservoir\n",
    "\"\"\"\n",
    "INFO:DRV:      Executing...................Done 393.19s\n",
    "INFO:DRV:      Processing timeseries.......Done 2.09s\n",
    "INFO:DRV:  Executor: 6125 timesteps........Done 410.24s\n",
    "\"\"\"\n",
    "timePerTrial = 393.19/25\n",
    "realTimeFactor = timePerTrial/2\n",
    "print(timePerTrial)\n",
    "print(realTimeFactor)\n",
    "# Pool\n",
    "\"\"\"\n",
    "INFO:DRV:      Executing...................Done 37.24s\n",
    "INFO:DRV:      Processing timeseries.......Done 0.08s\n",
    "INFO:DRV:  Executor: 6125 timesteps........Done 52.68s\n",
    "\"\"\"\n",
    "timePerTrial = 37.24/25\n",
    "realTimeFactor = timePerTrial/2\n",
    "print(timePerTrial)\n",
    "print(realTimeFactor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.net.plot.landscape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.net.plot.plotDir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.net.plot.reservoirSpikeTrain(fr=0, to=750, colorEx=exp.p.pltColor3, figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.net.plot.reservoirRates(fr=0, to=750, ylim=(0.0,0.3), colorEx=exp.p.pltColor3, figsize=(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "exp.net.plot.outputSpikeTrain(fr=0, to=750, figsize=(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.net.plot.outputRates(fr=0, to=750, ylim=(0,0.6), figsize=(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.save('data/comparison/spikes_ex-anisotropic.npy', exp.net.condenseSpikeProbes(exp.net.exSpikeTrains))\n",
    "np.save('data/comparison/spikes_out-anisotropic_800.npy', exp.net.condenseSpikeProbes(exp.net.outSpikeTrains))"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}