simulateSBM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from graspy.simulations import sbm\n",
    "from graspy.plot import heatmap, pairplot\n",
    "from graspy.embed import AdjacencySpectralEmbed, select_dimension\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.decomposition import FastICA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
    "from src.cluster import BinaryCluster\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "def savefig(\n",
    "    name,\n",
    "    fmt=\"png\",\n",
    "    dpi=300,\n",
    "    foldername=None,\n",
    "    subfoldername=\"outs\",\n",
    "    pathname=\"/Users/TingshanLiu/Desktop/2020 summer/TL_maggot\",\n",
    "    bbox_inches=\"tight\",\n",
    "    pad_inches=0.5,\n",
    "    save_on=True,\n",
    "    transparent=False,\n",
    "    **kws,\n",
    "):\n",
    "    if save_on:\n",
    "        path = _handle_dirs(pathname, foldername, subfoldername)\n",
    "        savename = path / str(name + \".\" + fmt)\n",
    "        plt.savefig(\n",
    "            savename,\n",
    "            format=fmt,\n",
    "            facecolor=\"white\",\n",
    "            transparent=transparent,\n",
    "            bbox_inches=bbox_inches,\n",
    "            pad_inches=pad_inches,\n",
    "            dpi=dpi,\n",
    "            **kws,\n",
    "        )\n",
    "        print(f\"Saved figure to {savename}\")\n",
    "\n",
    "def _handle_dirs(pathname, foldername, subfoldername):\n",
    "    path = Path(pathname)\n",
    "    if foldername is not None:\n",
    "        path = path / foldername\n",
    "        if not os.path.isdir(path):\n",
    "            os.mkdir(path)\n",
    "        if subfoldername is not None:\n",
    "            path = path / subfoldername\n",
    "            if not os.path.isdir(path):\n",
    "                os.mkdir(path)\n",
    "    return path\n",
    "\n",
    "def savecsv(\n",
    "    df,\n",
    "    name,\n",
    "    foldername=None,\n",
    "    subfoldername=\"csvs\",\n",
    "    pathname=\"/Users/TingshanLiu/Desktop/2020 summer/TL_maggot\",\n",
    "    save_on=True,\n",
    "):\n",
    "    if save_on:\n",
    "        path = _handle_dirs(pathname, foldername, subfoldername)\n",
    "        savename = path / str(name + \".csv\")\n",
    "        df.to_csv(savename)\n",
    "        print(f\"Saved DataFrame to {savename}\")\n",
    "\n",
    "\n",
    "\n",
    "FNAME = os.path.basename('/Users/TingshanLiu/Desktop/2020 summer/TL_maggot/maggot_repo/maggot_models')\n",
    "\n",
    "def stashfig(name, **kws):\n",
    "    savefig(name, foldername = FNAME, save_on = True, **kws)\n",
    "    \n",
    "def stashcsv(df, name, **kws):\n",
    "    savecsv(df, name, foldername=FNAME, **kws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def undirected_p(nclusters):\n",
    "    p = np.zeros([nclusters, nclusters])\n",
    "    for i in range(nclusters):\n",
    "        for j in range(i,nclusters):\n",
    "            if i == j:\n",
    "                p[i,j] = np.random.uniform(0.8, 1)\n",
    "            # first-off-diagonal alone\n",
    "            elif j - i == 1:\n",
    "                p[i,j] = np.random.uniform(0.2, 0.6)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "            # second-off-diagonal alone\n",
    "            elif j - i == 2:\n",
    "                p[i,j] = np.random.uniform(0.2, 0.6)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "            # third-off-diagonal alone\n",
    "            # elif j - i == 3:\n",
    "            #     p[i,j] = np.random.uniform(off1, off1 + 0.05)\n",
    "            #     p[j,i] = p[i,j]\n",
    "\n",
    "            # off-diagonal bulk\n",
    "            else: \n",
    "                p[i,j] = np.random.uniform(0, 0.2)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "    return p\n",
    "\n",
    "def direct_p(nclusters):\n",
    "    p = np.random.rand(nclusters,nclusters).round(decimals = 3)\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1st/2nd/3rd off-diag = mean of adjacent on-diags\n",
    "\n",
    "def undirected_p(nclusters, on1):\n",
    "    p = np.zeros([nclusters, nclusters])\n",
    "    for i in range(nclusters):\n",
    "        p[i,i] = np.random.uniform(on1, 1)\n",
    "    for i in range(nclusters):\n",
    "        for j in range(i,nclusters):\n",
    "            # first-off diagonal \n",
    "            if  j - i == 1:\n",
    "                #p[i,j] = (p[i,i] + p[j,j]) / 4\n",
    "                p[i,j] = np.random.uniform(0, 0.3)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "            # off-diagonal bulk\n",
    "            elif j - i > 1: \n",
    "                p[i,j] = np.random.uniform(0, 0.3)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1st off-diag = mean of adjacent on-diags & 2nd-diag = mean of adjacent 1st off-diags\n",
    "\n",
    "def undirected_p(nclusters, on1):\n",
    "    p = np.zeros([nclusters, nclusters])\n",
    "    for i in range(nclusters):\n",
    "        p[i,i] = np.random.uniform(on1, 1)\n",
    "    for i in range(nclusters):\n",
    "        for j in range(i,nclusters):\n",
    "            # first-off diagonal \n",
    "            if  j - i == 1:\n",
    "                p[i,j] = (p[i,i] + p[j,j]) / 2\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "    for i in range(nclusters):\n",
    "        for j in range(i,nclusters):\n",
    "            # 2nd-off diagonal \n",
    "            if  j - i == 2:\n",
    "                p[i,j] = (p[i,j-1] + p[i+1,j]) / 2\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "            # off-diagonal bulk\n",
    "            elif j - i > 2: \n",
    "                p[i,j] = np.random.uniform(0, 0.3)\n",
    "                p[j,i] = p[i,j]\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x13314c050>"
     },
     "metadata": {},
     "execution_count": 96
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 216x216 with 2 Axes>",
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     "metadata": {
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   ],
   "source": [
    "heatmap(undirected_p(len(n),0.3), figsize= (3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_score(labels, nclusters, size):\n",
    "    score = np.ones(nclusters)\n",
    "    for i in np.arange(1, len(score)+1):\n",
    "        labels_pred = labels[size * (i-1): size * i]\n",
    "        score[i-1] = max(Counter(labels_pred).most_common(), key = lambda item: item[1])[1]\n",
    "    return np.mean(score) / size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_true = []\n",
    "ncluster = 5\n",
    "size = 20\n",
    "\n",
    "for i in range(ncluster):\n",
    "    label_true = np.concatenate((label_true, np.ones(size)+i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ica_score(G, ite, d, k):\n",
    "    score = np.zeros([ite, 3])\n",
    "    for i in range(ite):\n",
    "        #p = undirected_p(len(n))\n",
    "        #G = sbm(n= n, p= p)\n",
    "        ica = FastICA(algorithm = \"parallel\", max_iter=1000, n_components= d)\n",
    "        embed = ica.fit_transform(G)\n",
    "        labels = KMeans(n_clusters= k).fit(embed).labels_\n",
    "        #score[i,0] = cluster_score(labels, len(n), n[0])\n",
    "        #score[i,1] = silhouette_score(embed, labels)\n",
    "        score[i,2] = adjusted_rand_score(label_true, labels)\n",
    "\n",
    "    return np.mean(score[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ase_score(G, ite, d, k):\n",
    "    score = np.zeros([ite, 3])\n",
    "    for i in range(ite):\n",
    "        #p = undirected_p(len(n))\n",
    "        #G = sbm(n= n, p= p)\n",
    "        #ase = AdjacencySpectralEmbed(n_components= d)\n",
    "        ase = AdjacencySpectralEmbed()\n",
    "        embed = ase.fit_transform(G)\n",
    "        labels = KMeans(n_clusters= k).fit(embed).labels_\n",
    "        #score[i,0] = cluster_score(labels, len(n), n[0])\n",
    "        #score[i,1] = silhouette_score(embed, labels)\n",
    "        score[i,2] = adjusted_rand_score(label_true, labels)\n",
    "\n",
    "    return np.mean(score[:, 2])"
   ]
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   },
   "outputs": [],
   "source": [
    "# fixed prob interval\n",
    "\n",
    "ncluster = 10\n",
    "k = ncluster\n",
    "n = np.repeat(size, ncluster)\n",
    "ite = 10\n",
    "#dims = np.arange(3, 11)\n",
    "probs = np.arange(0.3, 0.8, 0.05)\n",
    "#score_ase_mean = np.zeros(len(dims))\n",
    "#score_ica_mean = np.zeros(len(dims))\n",
    "#idx = 0\n",
    "\n",
    "score_ase = np.zeros([len(probs), 3]) #score_ase = np.zeros([len(probs), len(dims)])\n",
    "score_ica = np.zeros([len(probs), 3]) #score_ica = np.zeros([len(probs), len(dims)])\n",
    "\n",
    "# varying embedding dimensions\n",
    "# for d in dims:\n",
    "#     for i in range(len(probs)):\n",
    "#         score_ase[i, idx]  = ase_score(n, probs[i], ite, d, k)\n",
    "#         score_ica[i, idx]  = ica_score(n, probs[i], ite, d, k)\n",
    "    \n",
    "#     #score_ase_mean[idx] = np.mean(score_ase)\n",
    "#     #score_ica_mean[idx] = np.mean(score_ica)\n",
    "#     idx = idx + 1\n",
    "\n",
    "\n",
    "# fixed embedding dimensions: d = k for ICA, d is selected w/ select_dimension\n",
    "d = k\n",
    "for i in range(len(probs)):\n",
    "    score_ase[i, :]  = ase_score(n, probs[i], ite, d, k)\n",
    "    score_ica[i, :]  = ica_score(n, probs[i], ite, d, k)\n",
    "    "
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     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# various prob interval\n",
    "\n",
    "ncluster = 5\n",
    "k = ncluster\n",
    "n = np.repeat(size, ncluster)\n",
    "ite = 3000\n",
    "prob_on = np.zeros([ite, 1])\n",
    "prob_off = np.zeros([ite, 1])\n",
    "off_len = (ncluster - 1) + (ncluster - 2)\n",
    "#dims = np.arange(3, 11)\n",
    "\n",
    "score_ase = np.zeros([ite, 1]) #score_ase = np.zeros([len(probs), len(dims)])\n",
    "score_ica = np.zeros([ite, 1]) #score_ica = np.zeros([len(probs), len(dims)])\n",
    "\n",
    "\n",
    "d = k\n",
    "for i in range(ite):\n",
    "    p = undirected_p(len(n))\n",
    "    G = sbm(n= n, p= p)\n",
    "    prob_on[i] = np.mean(np.diag(p))\n",
    "    off_diags = np.zeros(off_len)\n",
    "    idx = 0\n",
    "    for j in np.array([1, 2]): # 1st- and 2nd-off-diagonal\n",
    "        for k in np.arange(j, ncluster):\n",
    "            off_diags[idx] = p[k, k-j]\n",
    "            idx = idx + 1\n",
    "    prob_off[i] = np.mean(off_diags)\n",
    "    score_ase[i]  = ase_score(G, 1, d, k)\n",
    "    score_ica[i]  = ica_score(G, 1, d, k)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = pd.DataFrame(np.concatenate((np.reshape(prob_on, (len(score_ica),1)), np.reshape(prob_off, (len(score_ica),1)), score_ica-score_ase), axis = 1))\n",
    "score.rename(columns={2: \"ARI\"}, inplace=True)\n",
    "score.rename(columns={0: \"prob_on\"}, inplace=True)\n",
    "score.rename(columns={1: \"prob_off\"}, inplace=True)\n",
    "score[\"prob_off\"] = score[\"prob_off\"].round(decimals=3)\n",
    "score[\"prob_on\"] = score[\"prob_on\"].round(decimals=3)\n",
    "prob_on_round = prob_on.round(decimals=3)\n",
    "prob_off_round = prob_off.round(decimals=3)\n",
    "score_diff = score_ica-score_ase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(min(prob_on_round), max(prob_on_round), 0.001).round(decimals = 3)\n",
    "b = np.arange(min(prob_off_round), max(prob_off_round), 0.001).round(decimals = 3)\n",
    "A,B = np.meshgrid(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_grid = np.zeros((A.shape[0], A.shape[1])) + 100\n",
    "\n",
    "for i in range(len(score_diff)):\n",
    "    idx1 = np.where(b == prob_off_round[i])\n",
    "    idx2 = np.where(a == prob_on_round[i])\n",
    "    score_grid[idx1, idx2] = score_diff[i]\n",
    "\n",
    "#sns.heatmap(score_grid, vmax = max(score_diff), vmin = min(score_diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "# sns.heatmap(np.transpose(score_ase), square = True, ax = ax1)\n",
    "# sns.heatmap(np.transpose(score_ica), square = True, ax = ax2)\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Saved DataFrame to /Users/TingshanLiu/Desktop/2020 summer/TL_maggot/maggot_models/csvs/ARI-ICA-ASE-OnDiag=[0`6,0`8].csv\n"
    }
   ],
   "source": [
    "#stashcsv(score_diff, \"ARI-ICA-ASE-OnDiag=[0`6,0`8]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale = 1)\n",
    "fig, ax = plt.subplots(1,1, figsize = (8,6))\n",
    "heat_map = sns.heatmap(np.transpose(score_ica - score_ase), square = True, xticklabels = probs.round(decimals = 2), yticklabels = dims)\n",
    "ax.set_xlabel(\"1st-off-diag prob interval\")\n",
    "ax.set_ylabel(\"ndimensions\")\n",
    "\n",
    "#stashfig(\"SBM20*10-ARI-ICAminusASE-OnDiag=[0`6,0`8]-1stOff=[0`3,0`8,0`05]-d=[3,10]-k=10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot lines: score versus prob interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = pd.DataFrame(np.concatenate((score_ase, score_ica)))\n",
    "name = [[\"ASE\"], [\"ICA\"]]\n",
    "name = np.tile(name , (1,len(probs)))\n",
    "name = np.reshape(name, (2 * len(probs),1))\n",
    "score[\"method\"] = name\n",
    "score[\"prob\"] = np.reshape(np.concatenate((probs, probs)), (2 * len(probs),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(constrained_layout = True, figsize = (12,6))\n",
    "gs = fig.add_gridspec(2, 3)\n",
    "ax1 = fig.add_subplot(gs[0,:-1])\n",
    "ax2 = fig.add_subplot(gs[1,:-1])\n",
    "ax3 = fig.add_subplot(gs[1,2])\n",
    "sns.lineplot(x = \"prob\", y = 0, hue = 'method', style= 'method', ax = ax1, data = score)\n",
    "sns.lineplot(x = \"prob\", y = 2, hue = 'method', style= 'method', ax = ax2, data = score)\n",
    "heatmap(undirected_p(len(n), 0.8), ax = ax3)\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"Percent of Perfect Cluster\")\n",
    "ax2.set_xlabel(\"On-diagonal Probability\")\n",
    "ax2.set_ylabel(\"Silhouette Score\")\n",
    "ax3.set_xlabel(\"On-diag prob = [0.8, 1] * \\n bulk off-diag prob = [0, 0.3] \\n  1st-off-diag prob \\n = mean of adjacent on-diag probs \\n 2nd-off-diag prob \\n = mean of adjacent 1st-off-diag probs \")\n",
    "\n",
    "#stashfig(\"SBM-ASE-ICA-OnDiag=[0`6,0`9,0`05]-1st=mean_adj_On-2nd=mean_1st-OffBulk[0,0`3]\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Saved figure to /Users/TingshanLiu/Desktop/2020 summer/TL_maggot/maggot_models/outs/SBM-ASEOptimal_d-ICAd=k-OnDiag=[0`8,1]-1stOff=[0`2,0`8,0`05]-OffBulk[0,0`2].png\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(constrained_layout = True, figsize = (9,3))\n",
    "gs = fig.add_gridspec(1, 3)\n",
    "ax1 = fig.add_subplot(gs[0,:-1])\n",
    "ax3 = fig.add_subplot(gs[0,2])\n",
    "sns.lineplot(x = \"prob\", y = 0, hue = 'method', style= 'method', ax = ax1, data = score)\n",
    "heatmap(undirected_p(len(n), 0.6), ax = ax3)\n",
    "ax1.set_ylabel(\"ARI\")\n",
    "ax1.set_xlabel(\"First-off-diagonal Probability\")\n",
    "ax3.set_xlabel(\"On-diag prob = [0.8, 1] \\n bulk off-diag prob = [0, 0.2] \\n  1st-off-diag prob = [0.6, 0.65] * \")\n",
    "\n",
    "stashfig(\"SBM-ASEOptimal_d-ICAd=k-OnDiag=[0`8,1]-1stOff=[0`2,0`8,0`05]-OffBulk[0,0`2]\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
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