Compare shared responses-Hungarian algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy.optimize import linear_sum_assignment\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shared_resp_dir = '/home/sshankar/raiders/3mm/fastsrm/single_run/'\n",
    "os.chdir(shared_resp_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlate runs 1 and 11 since they have the same input\n",
    "r1 = np.load('shared_resp_run1_niter1000.npy')\n",
    "r11 = np.load('shared_resp_run11_niter1000.npy')\n",
    "\n",
    "# Same with runs 2 and 12\n",
    "r2 = np.load('shared_resp_run2_niter1000.npy')\n",
    "r12 = np.load('shared_resp_run12_niter1000.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "374 297 375 314\n"
     ]
    }
   ],
   "source": [
    "# Print the lengths of the different runs\n",
    "# Useful for correlation\n",
    "print(len(r1[0]), len(r2[0]), len(r11[0]), len(r12[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fee8ae93470>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair-wise correlate shared responses of runs 1 and 11\n",
    "r1_11_pairwise = np.zeros((20,20))\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        r1_11_pairwise[i,j] = np.abs(np.corrcoef(r1[i], r11[j,0:-1])[0,1])\n",
    "    \n",
    "plt.imshow(r1_11_pairwise, cmap=plt.cm.gist_heat, origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fee8ad754e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair-wise correlate shared responses of runs 2 and 12\n",
    "r2_12_pairwise = np.zeros((20,20))\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        r2_12_pairwise[i,j] = np.abs(np.corrcoef(r2[i], r12[j,0:-17])[0,1])\n",
    "    \n",
    "plt.imshow(r2_12_pairwise, cmap=plt.cm.gist_heat, origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fee8a665710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair-wise correlate shared responses of runs 1 and 2\n",
    "r1_2_pairwise = np.zeros((20,20))\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        r1_2_pairwise[i,j] = np.abs(np.corrcoef(r1[i,0:-77], r2[j])[0,1])\n",
    "    \n",
    "plt.imshow(r1_2_pairwise, cmap=plt.cm.gist_heat, origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fee8a5db940>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair-wise correlate shared responses of runs 11 and 12\n",
    "r11_12_pairwise = np.zeros((20,20))\n",
    "\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        r11_12_pairwise[i,j] = np.abs(np.corrcoef(r11[i,0:-61], r12[j])[0,1])\n",
    "    \n",
    "plt.imshow(r11_12_pairwise, cmap=plt.cm.gist_heat, origin='lower')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the Hungarian algorithm to match components:\n",
    "# Runs 1 and 11\n",
    "rind, cind_1_11 = linear_sum_assignment(1-r1_11_pairwise)\n",
    "\n",
    "# Runs 2 and 12\n",
    "rind, cind_2_12 = linear_sum_assignment(1-r2_12_pairwise)\n",
    "\n",
    "# Runs 1 and 2\n",
    "rind, cind_1_2 = linear_sum_assignment(1-r1_2_pairwise)\n",
    "\n",
    "# Runs 11 and 12\n",
    "rind, cind_11_12 = linear_sum_assignment(1-r11_12_pairwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run1\tRun11\tCorrCoef\n",
      "1 \t 4 \t 0.55\n",
      "2 \t 1 \t 0.63\n",
      "3 \t 2 \t 0.64\n",
      "4 \t 3 \t 0.75\n",
      "5 \t 5 \t 0.72\n",
      "6 \t 8 \t 0.53\n",
      "7 \t 7 \t 0.52\n",
      "8 \t 9 \t 0.32\n",
      "9 \t 20 \t 0.28\n",
      "10 \t 18 \t 0.27\n",
      "11 \t 6 \t 0.14\n",
      "12 \t 12 \t 0.29\n",
      "13 \t 17 \t 0.2\n",
      "14 \t 19 \t 0.22\n",
      "15 \t 14 \t 0.31\n",
      "16 \t 15 \t 0.28\n",
      "17 \t 16 \t 0.18\n",
      "18 \t 10 \t 0.17\n",
      "19 \t 13 \t 0.34\n",
      "20 \t 11 \t 0.19\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 11 for components of run 1\n",
    "print('Run1\\tRun11\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(i+1, '\\t', cind_1_11[i]+1, '\\t', np.round(r1_11_pairwise[i,cind_1_11[i]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run2\tRun12\tCorrCoef\n",
      "1 \t 1 \t 0.89\n",
      "2 \t 4 \t 0.43\n",
      "3 \t 2 \t 0.72\n",
      "4 \t 3 \t 0.74\n",
      "5 \t 18 \t 0.16\n",
      "6 \t 7 \t 0.22\n",
      "7 \t 13 \t 0.24\n",
      "8 \t 5 \t 0.36\n",
      "9 \t 17 \t 0.2\n",
      "10 \t 8 \t 0.3\n",
      "11 \t 9 \t 0.26\n",
      "12 \t 16 \t 0.15\n",
      "13 \t 6 \t 0.21\n",
      "14 \t 19 \t 0.22\n",
      "15 \t 14 \t 0.14\n",
      "16 \t 20 \t 0.15\n",
      "17 \t 15 \t 0.19\n",
      "18 \t 10 \t 0.11\n",
      "19 \t 11 \t 0.13\n",
      "20 \t 12 \t 0.16\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 12 for components of run 2\n",
    "print('Run2\\tRun12\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(i+1, '\\t', cind_2_12[i]+1, '\\t', np.round(r2_12_pairwise[i,cind_2_12[i]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run1\tRun2\tCorrcoef\n",
      "1 \t 1 \t 0.84\n",
      "2 \t 10 \t 0.14\n",
      "3 \t 3 \t 0.85\n",
      "4 \t 2 \t 0.28\n",
      "5 \t 4 \t 0.58\n",
      "6 \t 9 \t 0.27\n",
      "7 \t 8 \t 0.37\n",
      "8 \t 20 \t 0.25\n",
      "9 \t 18 \t 0.25\n",
      "10 \t 15 \t 0.19\n",
      "11 \t 17 \t 0.23\n",
      "12 \t 13 \t 0.15\n",
      "13 \t 14 \t 0.18\n",
      "14 \t 12 \t 0.21\n",
      "15 \t 11 \t 0.13\n",
      "16 \t 19 \t 0.14\n",
      "17 \t 7 \t 0.19\n",
      "18 \t 16 \t 0.24\n",
      "19 \t 5 \t 0.16\n",
      "20 \t 6 \t 0.23\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 2 for components of run 1\n",
    "print('Run1\\tRun2\\tCorrcoef')\n",
    "for i in range(20):\n",
    "    print(i+1, '\\t', cind_1_2[i]+1, '\\t', np.round(r1_2_pairwise[i,cind_1_2[i]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run11\tRun12\tCorrcoef\n",
      "1 \t 6 \t 0.16\n",
      "2 \t 4 \t 0.63\n",
      "3 \t 3 \t 0.82\n",
      "4 \t 5 \t 0.33\n",
      "5 \t 1 \t 0.51\n",
      "6 \t 7 \t 0.37\n",
      "7 \t 12 \t 0.24\n",
      "8 \t 2 \t 0.27\n",
      "9 \t 13 \t 0.3\n",
      "10 \t 9 \t 0.23\n",
      "11 \t 10 \t 0.18\n",
      "12 \t 20 \t 0.14\n",
      "13 \t 17 \t 0.22\n",
      "14 \t 11 \t 0.25\n",
      "15 \t 19 \t 0.15\n",
      "16 \t 8 \t 0.34\n",
      "17 \t 16 \t 0.15\n",
      "18 \t 14 \t 0.18\n",
      "19 \t 15 \t 0.23\n",
      "20 \t 18 \t 0.1\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 12 for components of run 11\n",
    "print('Run11\\tRun12\\tCorrcoef')\n",
    "for i in range(20):\n",
    "    print(i+1, '\\t', cind_11_12[i]+1, '\\t', np.round(r11_12_pairwise[i,cind_11_12[i]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the correlation coefficients in descending order and \n",
    "# extract corresponding array indices\n",
    "r1_11_sortidx = np.fliplr(np.argsort(r1_11_pairwise[[range(20)],[cind_1_11]]))[0]\n",
    "r2_12_sortidx = np.fliplr(np.argsort(r2_12_pairwise[[range(20)],[cind_2_12]]))[0]\n",
    "r1_2_sortidx = np.fliplr(np.argsort(r1_2_pairwise[[range(20)],[cind_1_2]]))[0]\n",
    "r11_12_sortidx = np.fliplr(np.argsort(r11_12_pairwise[[range(20)],[cind_11_12]]))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run1\tRun11\tCorrCoef\n",
      "4 \t 3 \t 0.75\n",
      "5 \t 5 \t 0.72\n",
      "3 \t 2 \t 0.64\n",
      "2 \t 1 \t 0.63\n",
      "1 \t 4 \t 0.55\n",
      "6 \t 8 \t 0.53\n",
      "7 \t 7 \t 0.52\n",
      "19 \t 13 \t 0.34\n",
      "8 \t 9 \t 0.32\n",
      "15 \t 14 \t 0.31\n",
      "12 \t 12 \t 0.29\n",
      "16 \t 15 \t 0.28\n",
      "9 \t 20 \t 0.28\n",
      "10 \t 18 \t 0.27\n",
      "14 \t 19 \t 0.22\n",
      "13 \t 17 \t 0.2\n",
      "20 \t 11 \t 0.19\n",
      "17 \t 16 \t 0.18\n",
      "18 \t 10 \t 0.17\n",
      "11 \t 6 \t 0.14\n"
     ]
    }
   ],
   "source": [
    "# Print sorted best matched components of run 11 for components of run 1\n",
    "print('Run1\\tRun11\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(r1_11_sortidx[i]+1, '\\t', cind_1_11[r1_11_sortidx[i]]+1, '\\t', \\\n",
    "          np.round(r1_11_pairwise[r1_11_sortidx[i],cind_1_11[r1_11_sortidx[i]]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run2\tRun12\tCorrCoef\n",
      "1 \t 1 \t 0.89\n",
      "4 \t 3 \t 0.74\n",
      "3 \t 2 \t 0.72\n",
      "2 \t 4 \t 0.43\n",
      "8 \t 5 \t 0.36\n",
      "10 \t 8 \t 0.3\n",
      "11 \t 9 \t 0.26\n",
      "7 \t 13 \t 0.24\n",
      "6 \t 7 \t 0.22\n",
      "14 \t 19 \t 0.22\n",
      "13 \t 6 \t 0.21\n",
      "9 \t 17 \t 0.2\n",
      "17 \t 15 \t 0.19\n",
      "20 \t 12 \t 0.16\n",
      "5 \t 18 \t 0.16\n",
      "12 \t 16 \t 0.15\n",
      "16 \t 20 \t 0.15\n",
      "15 \t 14 \t 0.14\n",
      "19 \t 11 \t 0.13\n",
      "18 \t 10 \t 0.11\n"
     ]
    }
   ],
   "source": [
    "# Print sorted best matched components of run 12 for components of run 2\n",
    "print('Run2\\tRun12\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(r2_12_sortidx[i]+1, '\\t', cind_2_12[r2_12_sortidx[i]]+1, '\\t', \\\n",
    "          np.round(r2_12_pairwise[r2_12_sortidx[i],cind_2_12[r2_12_sortidx[i]]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run1\tRun2\tCorrCoef\n",
      "3 \t 3 \t 0.85\n",
      "1 \t 1 \t 0.84\n",
      "5 \t 4 \t 0.58\n",
      "7 \t 8 \t 0.37\n",
      "4 \t 2 \t 0.28\n",
      "6 \t 9 \t 0.27\n",
      "8 \t 20 \t 0.25\n",
      "9 \t 18 \t 0.25\n",
      "18 \t 16 \t 0.24\n",
      "20 \t 6 \t 0.23\n",
      "11 \t 17 \t 0.23\n",
      "14 \t 12 \t 0.21\n",
      "17 \t 7 \t 0.19\n",
      "10 \t 15 \t 0.19\n",
      "13 \t 14 \t 0.18\n",
      "19 \t 5 \t 0.16\n",
      "12 \t 13 \t 0.15\n",
      "16 \t 19 \t 0.14\n",
      "2 \t 10 \t 0.14\n",
      "15 \t 11 \t 0.13\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 2 for components of run 1\n",
    "print('Run1\\tRun2\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(r1_2_sortidx[i]+1, '\\t', cind_1_2[r1_2_sortidx[i]]+1, '\\t', \\\n",
    "          np.round(r1_2_pairwise[r1_2_sortidx[i],cind_1_2[r1_2_sortidx[i]]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run11\tRun12\tCorrCoef\n",
      "3 \t 3 \t 0.82\n",
      "2 \t 4 \t 0.63\n",
      "5 \t 1 \t 0.51\n",
      "6 \t 7 \t 0.37\n",
      "16 \t 8 \t 0.34\n",
      "4 \t 5 \t 0.33\n",
      "9 \t 13 \t 0.3\n",
      "8 \t 2 \t 0.27\n",
      "14 \t 11 \t 0.25\n",
      "7 \t 12 \t 0.24\n",
      "19 \t 15 \t 0.23\n",
      "10 \t 9 \t 0.23\n",
      "13 \t 17 \t 0.22\n",
      "11 \t 10 \t 0.18\n",
      "18 \t 14 \t 0.18\n",
      "1 \t 6 \t 0.16\n",
      "17 \t 16 \t 0.15\n",
      "15 \t 19 \t 0.15\n",
      "12 \t 20 \t 0.14\n",
      "20 \t 18 \t 0.1\n"
     ]
    }
   ],
   "source": [
    "# Print best matched components of run 12 for components of run 11\n",
    "print('Run11\\tRun12\\tCorrCoef')\n",
    "for i in range(20):\n",
    "    print(r11_12_sortidx[i]+1, '\\t', cind_11_12[r11_12_sortidx[i]]+1, '\\t', \\\n",
    "          np.round(r11_12_pairwise[r11_12_sortidx[i],cind_11_12[r11_12_sortidx[i]]],decimals=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate singular values of correlation matrices\n",
    "u_1_11, s_1_11, vh_1_11 = np.linalg.svd(r1_11_pairwise)\n",
    "u_2_12, s_2_12, vh_2_12 = np.linalg.svd(r2_12_pairwise)\n",
    "u_1_2, s_1_2, vh_1_2 = np.linalg.svd(r1_2_pairwise)\n",
    "u_11_12, s_11_12, vh_11_12 = np.linalg.svd(r11_12_pairwise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the singular values\n",
    "plt.plot(s_1_11,'r.-')\n",
    "plt.plot(s_2_12,'g.-')\n",
    "plt.plot(s_1_2,'b.-')\n",
    "plt.plot(s_11_12,'k.-')\n",
    "plt.title('Red: runs 1 and 11\\nGreen: runs 2 and 12\\nBlue: runs 1 and 2\\nBlack: runs 11 and 12')\n",
    "plt.xlabel('Components')\n",
    "plt.ylabel('Singular values');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}