2022-11-29_SPADE_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e607479",
   "metadata": {},
   "source": [
    "#  Spike PAtterns DEtection using SPADE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c0163f5-b685-412d-b5c6-8ff1670e9998",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import quantities as pq\n",
    "import neo\n",
    "import elephant\n",
    "import viziphant\n",
    "np.random.seed(4542)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488bfdb",
   "metadata": {},
   "source": [
    "## computing accuracy\n",
    "\n",
    "the function developed in the scan + a shift in time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "717a2fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_stop = 10*pq.s\n",
    "slope = 1.\n",
    "A_sync = 0.05\n",
    "shift = 0.0\n",
    "A = [0]+[1.-A_sync]+[0]*8+[A_sync]\n",
    "spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
    "                             rate=5*pq.Hz, A=A, shift=shift*pq.ms, t_stop=10*pq.s)\n",
    "                             \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "Add a shift:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1746b8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.010336536702265134,\n",
       " 2.306153249870878,\n",
       " 2.3244673050892177,\n",
       " 3.0204975456785346,\n",
       " 3.351244642640878,\n",
       " 4.075165225675521,\n",
       " 4.838414228441322,\n",
       " 5.744525279457682,\n",
       " 6.028529977645755,\n",
       " 6.543172175511355,\n",
       " 8.164216241659327}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ground_truth_spike_synchronous(spiketrains):\n",
    "    # Function to find the real position of the pattern\n",
    "    # Works only for synchronus activity, when ALL neurons \n",
    "    # are activated at the same time\n",
    "    # INPUT : spike trains without the added noise\n",
    "    dict_spiketrains = {}\n",
    "    # remove the quantity (s) of the spiketrains array\n",
    "    for neurons in range(len(spiketrains)):\n",
    "        a = [float(x) for x in spiketrains[neurons]]\n",
    "        dict_spiketrains[str(neurons)] = a\n",
    "\n",
    "    list_keys=list(dict_spiketrains.keys())\n",
    "    common_spikes = set(dict_spiketrains.get(list_keys[0])).intersection(dict_spiketrains.get(list_keys[1]))\n",
    "    for key in list_keys[1:-1]:\n",
    "        num_key=int(key)\n",
    "        common_spikes = set(common_spikes).intersection(dict_spiketrains.get(str(num_key+1)))\n",
    "\n",
    "    return common_spikes\n",
    "    \n",
    "ground_truth_spike_synchronous(spiketrains)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e834692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.eventplot([spiketrains[i].magnitude for i in range(len(spiketrains))], linelengths=0.75, color='black')\n",
    "plt.xlabel('Time (ms)', fontsize=16)\n",
    "plt.ylabel('Neuron')\n",
    "plt.title(\"Raster plot\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf629ff",
   "metadata": {},
   "source": [
    "All in one function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79922ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "A_sync = 0.02\n",
    "shift = 0.\n",
    "\n",
    "def generate_and_test(A_sync=A_sync, shift=shift):\n",
    "    A = [0]+[1.-A_sync]+[0]*8+[A_sync]\n",
    "    spiketrains = elephant.spike_train_generation.compound_poisson_process(\n",
    "                             rate=5*pq.Hz, A=A, shift=shift*pq.ms, t_stop=10*pq.s)\n",
    "    common_spikes =  ground_truth_spike_synchronous(spiketrains)\n",
    "\n",
    "    for i in range(90):\n",
    "        spiketrains.append(elephant.spike_train_generation.homogeneous_poisson_process(\n",
    "            rate=5*pq.Hz, t_stop=10*pq.s))\n",
    "    patterns = elephant.spade.spade(\n",
    "                                spiketrains=spiketrains, binsize=1*pq.ms, winlen=1, min_spikes=3,\n",
    "                                n_surr=100,dither=5*pq.ms,\n",
    "                                psr_param=[0,0,0],\n",
    "                                output_format='patterns')['patterns']\n",
    "    \n",
    "    idx_detect = pattern[0].get('windows_ids')\n",
    "    common_spikes = np.array(list(common_spikes))*1000\n",
    "    idx_real = common_spikes.astype(int)\n",
    "\n",
    "    tp = len(list(set(idx_detect).intersection(real_idx)))\n",
    "    fp = len(idx_detect) - tp\n",
    "    fn = len(real_idx) - tp\n",
    "\n",
    "    return tp, fp, fn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488bfdb",
   "metadata": {},
   "source": [
    "\n",
    "* changing the synchrony probability to check when SPADE will begin to fail:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bc9a171",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurentperrinet/opt/anaconda3/envs/brainhack/lib/python3.9/site-packages/elephant/spade.py:679: UserWarning: Optimized C implementation of FCA (fim.so/fim.pyd) not found in elephant/spade_src folder, or not compatible with this Python version. You are using the pure Python implementation of fast fca.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for data mining: 11.420428037643433\n",
      "Time for pvalue spectrum computation: 1239.357334136963\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'pattern' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tp, fp, fn \u001b[39m=\u001b[39m generate_and_test()\n\u001b[1;32m      2\u001b[0m tp, fp, fn\n",
      "Cell \u001b[0;32mIn [5], line 18\u001b[0m, in \u001b[0;36mgenerate_and_test\u001b[0;34m(A_sync, shift)\u001b[0m\n\u001b[1;32m     11\u001b[0m     spiketrains\u001b[39m.\u001b[39mappend(elephant\u001b[39m.\u001b[39mspike_train_generation\u001b[39m.\u001b[39mhomogeneous_poisson_process(\n\u001b[1;32m     12\u001b[0m         rate\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39mHz, t_stop\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39ms))\n\u001b[1;32m     13\u001b[0m patterns \u001b[39m=\u001b[39m elephant\u001b[39m.\u001b[39mspade\u001b[39m.\u001b[39mspade(\n\u001b[1;32m     14\u001b[0m                             spiketrains\u001b[39m=\u001b[39mspiketrains, binsize\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39mms, winlen\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, min_spikes\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m,\n\u001b[1;32m     15\u001b[0m                             n_surr\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m,dither\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m\u001b[39m*\u001b[39mpq\u001b[39m.\u001b[39mms,\n\u001b[1;32m     16\u001b[0m                             psr_param\u001b[39m=\u001b[39m[\u001b[39m0\u001b[39m,\u001b[39m0\u001b[39m,\u001b[39m0\u001b[39m],\n\u001b[1;32m     17\u001b[0m                             output_format\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mpatterns\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mpatterns\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m---> 18\u001b[0m idx_detect \u001b[39m=\u001b[39m pattern[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mwindows_ids\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     19\u001b[0m common_spikes \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(\u001b[39mlist\u001b[39m(common_spikes))\u001b[39m*\u001b[39m\u001b[39m1000\u001b[39m\n\u001b[1;32m     20\u001b[0m idx_real \u001b[39m=\u001b[39m common_spikes\u001b[39m.\u001b[39mastype(\u001b[39mint\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'pattern' is not defined"
     ]
    }
   ],
   "source": [
    "tp, fp, fn = generate_and_test()\n",
    "tp, fp, fn"
   ]
  }
 ],
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