plot_conll_f12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the entropy of  the trained P_lambda and pi_theta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import argparse, os\n",
    "\n",
    "import seaborn as sns\n",
    "from prettytable import PrettyTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "logdir = '/tmp-network/user/tparshak'\n",
    "\n",
    "# 0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,5:tstamp,\n",
    "# 6:lambd,7:mfeat_pl,8:plambda_time,9:motif,10:train_reg,11:feat,12:n,13:ds_size,14:job\n",
    "with sqlite3.connect(os.path.join(logdir,'r_plambda_distill_pitheta.db')) as conn:\n",
    "    cur = conn.cursor()\n",
    "    cur.execute('SELECT * FROM results')\n",
    "    d_pl = cur.fetchall()\n",
    "#for tpl in d_pl:\n",
    "#    print(tpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_motif(n, sz, motif, ds_type, nmotif):\n",
    "    ds = \"\"\n",
    "    # input: <bos> binary string <eos>\n",
    "    # 3 {0,1}^n 2\n",
    "    if nmotifs == 1:\n",
    "        data_file = os.path.join(logdir, 'data/pfsa_%d_%s'%(n, motif),\"%s.txt\"%ds_type)\n",
    "    else:\n",
    "        data_file = os.path.join(logdir, 'data/pfsa_%d_%s'%(n-1, motif),\"%s.txt\"%ds_type)\n",
    "    \n",
    "    with open(data_file, \"r\") as file:\n",
    "        for line in file:\n",
    "            #assert motif in line\n",
    "            ds += line.strip()\n",
    "            #print(line.strip())\n",
    "            if len(ds)>=sz*n:\n",
    "                break\n",
    "    original = ''.join(c+' ' for c in ds[:sz*n]).strip()\n",
    "    original = np.fromstring(original, dtype=int, sep=' ')\n",
    "    original = original.reshape((original.shape[0]//n, n)).transpose()\n",
    "    \n",
    "    for i in range(original.shape[1]):\n",
    "        res = ''.join([str(original[j,i]) for j in range(original.shape[0])])\n",
    "        #assert flag\n",
    "    \n",
    "    dataset = (np.ones((n+2, original.shape[1]))).astype(int)\n",
    "    dataset[1:-1] = original\n",
    "    dataset[0] = dataset[0]*3\n",
    "    dataset[-1] = dataset[-1]*2\n",
    "    assert dataset.shape[1] >= sz\n",
    "    ds = dataset\n",
    "    return ds\n",
    "\n",
    "def oracle_features(s, motifs, feat, nmotifs, n):\n",
    "    # s: seq_len x 1\n",
    "    # output: [ nfeat ]\n",
    "    # (motif, supermotif, submotif__2, 1st bit==0, 10101_len_m, 1001001_le_m_2, 00110011_len_m__2)\n",
    "    out = []\n",
    "    idx = min(1, len(s)-1)\n",
    "    nfeat = sum([sum([int(e!='0') for e in el]) for el in feat])\n",
    "    \n",
    "    i = 0\n",
    "    for j in range(len(feat)):\n",
    "        if feat[j] == '1':\n",
    "            if j < len(feat)-4:\n",
    "                if nmotifs == 1:\n",
    "                    if j==0:\n",
    "                        # motif\n",
    "                        out += [1 - int(motifs[i] in s)]\n",
    "                    elif j==1:\n",
    "                        # supermotif\n",
    "                        motif_j = motifs[i] + '0'*1\n",
    "                        out += [1 - int(motif_j in s)]\n",
    "                    elif j==2:\n",
    "                        # submotif\n",
    "                        motif_j = motifs[i][:len(motifs[i])//2]\n",
    "                        out += [1 - int(motif_j in s)]\n",
    "                elif nmotifs == 2:\n",
    "                    if j in [0,2]:\n",
    "                        # motif\n",
    "                        out += [1 - int(motifs[max(0, j-1)] in s)]\n",
    "                    elif j in [1,3]:\n",
    "                        # submotif\n",
    "                        motif_j = motifs[max(0, j-2)][:len(motifs[max(0, j-2)])//2]\n",
    "                        out += [1 - int(motif_j in s)]\n",
    "            else:\n",
    "                if j == len(feat)-4:\n",
    "                        # first bit\n",
    "                        out += [1 - int(s[idx]=='1')]\n",
    "                # distractor\n",
    "                elif j == len(feat)-3:\n",
    "                    pref = '10101'\n",
    "                    motif_j = (pref*n)[:len(motifs[i])]\n",
    "                    out += [1 - int(motif_j in s)]\n",
    "                elif j == len(feat)-2:\n",
    "                    pref = '1001001'\n",
    "                    motif_j = (pref*n)[:len(motifs[i])+2]\n",
    "                    out += [1 - int(motif_j in s)]\n",
    "                elif j == len(feat)-1:\n",
    "                    pref = '00110011'\n",
    "                    motif_j = (pref*n)[:len(motifs[i])//2]\n",
    "                    out += [1 - int(motif_j in s)]\n",
    "        elif feat[j] == 'e':\n",
    "            out += [get_edit_frc(s, motifs[i])]\n",
    "        elif feat[j] == 's':\n",
    "            out += [get_longestsubstr_frc(s, motifs[i])]\n",
    "        elif feat[j] == 'm':\n",
    "            out += [get_longestsubstr_frc(s, motifs[i]) + get_edit_frc(s, motifs[i])]\n",
    "    return out\n",
    "\n",
    "\n",
    "def get_longestsubstr_frc(s, motif):\n",
    "    \n",
    "    n, m = len(s)+1, len(motif)+1\n",
    "    #assert m<=n\n",
    "    e = np.zeros((m,n))\n",
    "    max_lss = 0\n",
    "    for j in range(1,m):\n",
    "        for i in range(1,n):\n",
    "            e_ij = []\n",
    "            if s[i-1]!=motif[j-1]:\n",
    "                e[j,i]=0\n",
    "            else: \n",
    "                e[j,i] = 1 + e[j-1,i-1]\n",
    "            max_lss = max(max_lss, e[j,i])\n",
    "    return 1-max_lss/len(motif)\n",
    "\n",
    "def get_edit_frc(s, motif):\n",
    "    def edit_distance(subs, motif):\n",
    "        n, m = len(subs)+1, len(motif)+1\n",
    "        #assert m<=n\n",
    "        e = np.zeros((m,n))\n",
    "        e[0,0] = 0\n",
    "        for j in range(1,m):\n",
    "            e[j,0]= j\n",
    "        \n",
    "        for j in range(1,m):\n",
    "            for i in range(1,n):\n",
    "                e_ij = []\n",
    "                if j>0:\n",
    "                    e_ij += [e[j-1,i]+1]\n",
    "                if i>0:\n",
    "                    e_ij += [e[j,i-1]+1]\n",
    "                if j>0 and i>0:\n",
    "                    e_ij += [e[j-1, i-1]+ int(subs[i-1]!=motif[j-1])]\n",
    "                if e_ij:\n",
    "                    e[j,i] = min(e_ij)\n",
    "        return 1.0*min(e[-1,:])\n",
    "    ed_dist =  edit_distance(s, motif)\n",
    "    edit_frac = ed_dist/len(motif)\n",
    "    assert edit_frac<=1\n",
    "    return edit_frac\n",
    "\n",
    "    \n",
    "def get_features(var, motifs, feat, nmotifs, n):\n",
    "    # returns the results of identifying oracle features in the input binary sequence\n",
    "    # 0 = feature exists\n",
    "    # var:      [ seq_len x batch ]\n",
    "    # output:   [batch x nfeat]\n",
    "    def var_to_str(a):\n",
    "        b = []\n",
    "        for i in range(a.shape[1]):\n",
    "            b += [''.join([str(el) for el in a[:,i]])]\n",
    "        return b\n",
    "    m = motifs[0].split('.')[1]\n",
    "    x = var_to_str(var)\n",
    "    out = []\n",
    "    for b in x:\n",
    "        out += [oracle_features(b, [m], feat, nmotifs, n)]\n",
    "    train_feat = np.array(out).mean(0)\n",
    "    print(train_feat.shape, feat, train_feat[0])\n",
    "    return train_feat\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_feats = np.load('true_feats.npy').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "motifs = ['m.10001011111000', 'mam.100010100011', 'mam.1000101111100011', \n",
    "          'mam.10001011111000', 'm.10001010001',\n",
    "         'm.01011101101', 'mam.01011101101', 'm.001001100111',\n",
    "         'mam.001001100111', 'm.1011100111001', 'mam.1011100111001', 'm.0111010000011101']\n",
    "feats = [ '1001111','1011111','0111111', 's001111', 'm001111']\n",
    "#feats = [ 's001111']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.save('true_feats.npy', true_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r_freqs[timestamp+'_'+m+'_'+tr] = (freq, avg_len)\n",
    "r_freqs = np.load(os.path.join(logdir,'exp_gams/r_freqs.npy')).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{30: {'m.10001010001', 'm.10001011111000', 'm.001001100111', 'mam.100010100011', 'mam.1011100111001', 'mam.1000101111100011', 'mam.001001100111', 'm.01011101101', 'mam.10001011111000', 'mam.01011101101', 'm.1011100111001'}, 31: {'m1m2.100010111110001', 'm1m2.100010111110'}, 100: {'m.0111010000011101'}}\n"
     ]
    }
   ],
   "source": [
    "# 0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,5:tstamp,\n",
    "# 6:lambd,7:mfeat_pl,8:plambda_time,9:motif,10:train_reg,11:feat,12:n,13:ds_size,14:job\n",
    "\n",
    "data_pl = list(zip(*d_pl))\n",
    "ds_range = [500, 1000, 5000, 10000, 15000, 20000, 30000, 50000, 100000]\n",
    "train_r = ['rs', 'snis_mix', 'snis_r', 'rs_c', 'snis_r_c']\n",
    "#feats = ['1011111', '0111111']\n",
    "seq_len = [30, 31, 100]\n",
    "motifs = ['m.10001011111000', 'mam.100010100011', 'mam.1000101111100011', \n",
    "          'mam.10001011111000', 'm.10001010001',\n",
    "         'm.01011101101', 'mam.01011101101', 'm.001001100111',\n",
    "         'mam.001001100111', 'm.1011100111001', 'mam.1011100111001', 'm.0111010000011101']\n",
    "feats = [ '1001111','1011111','0111111', 's001111', 'm001111']\n",
    " \n",
    "all_motifs = {n_i:set() for n_i in seq_len}\n",
    "for i in range(len(data_pl[0])):\n",
    "    n_i = data_pl[12][i]\n",
    "    m = data_pl[9][i]\n",
    "    m = '.'.join(m.split('.')[:2])\n",
    "    all_motifs[n_i].add(m)\n",
    "    \n",
    "print(all_motifs)\n",
    "dict_pl = {n_i:{m:{f:{i:{tr:[] for tr in train_r} for i in ds_range} for f in feats} for m in all_motifs[n_i]}for n_i in seq_len}\n",
    "for i in range(len(d_pl)):\n",
    "    n_i = data_pl[12][i]\n",
    "    # n -> motif -> feat -> ds_size -> train_reg -> \n",
    "    #[0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,5:tstamp,6:lambd,7:mfeat_pl,8:plambda_time,\n",
    "    #                                 9:job,10:l1_diff,11:r_feat]\n",
    "    m = data_pl[9][i]\n",
    "    m = '.'.join(m.split('.')[:2])\n",
    "    ds = data_pl[13][i]\n",
    "    f  = data_pl[11][i]\n",
    "    if (not m in motifs) or (not f in feats): continue\n",
    "    tr = data_pl[10][i]\n",
    "    timestamp = '_'.join(data_pl[5][i].split('_')[:2])\n",
    "    \n",
    "    mfeat = [float(el.strip()) for el in data_pl[7][i][1:-1].split(',')]\n",
    "    tfeat = true_feats[m+'.'+f]\n",
    "    l1_diff_all = np.abs(tfeat- np.array(mfeat))\n",
    "    \n",
    "    l1_diff = l1_diff_all.sum()\n",
    "    \n",
    "    if timestamp+'_'+m+'_'+tr in r_freqs:\n",
    "        r_freq = r_freqs[timestamp+'_'+m+'_'+tr][0]\n",
    "    elif len(data_pl[5][i].split('_'))>4:\n",
    "        r_freq = float(data_pl[5][i].split('_')[4])\n",
    "    else:\n",
    "        r_freq='-'\n",
    "     \n",
    "    dict_pl[n_i][m][f][ds][tr] += [list(d_pl[i][:3])+ [l1_diff]+list(d_pl[i][4:9])+[d_pl[i][14], l1_diff_all, r_freq]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ent_motifs = {30:{'mam.100010100011':16.282530254126048/31, 'mam.1000101111100011':13.57540128031525/31,\n",
    "                    'm.10001010001':16.15303451776991/31,'m.10001011111000':13.923144487457433/31,\n",
    "                 'mam.10001011111000':14.935250784153713/31, 'm.01011101101':16.1633538708637/31, \n",
    "                 'm.001001100111':15.420728378322668/31,'m.1011100111001':14.6736907/31, 'mam.01011101101':16.950563779/31,\n",
    "                 'mam.001001100111':16.2827152768/31, 'mam.1011100111001':15.61062622/31, 'm.1000101000101':14.66329972621143/31}, \n",
    "                  100:{'m.0111010000011101':62.665668876452344/101}}\n",
    "\n",
    "z_motifs = {30:{'mam.100010100011':0.0046360, 'mam.1000101111100011':0.00022888,\n",
    "                    'm.10001010001':0.00964437,'m.10001011111000':0.0010371580,\n",
    "                 'mam.10001011111000':0.001037158, 'm.01011101101':0.0097444, \n",
    "                 'm.001001100111':0.004637, 'm.1011100111001':0.002196863, 'mam.01011101101':0.00974440,\n",
    "                 'mam.001001100111':0.004637, 'mam.1011100111001':0.002196863, 'm.1000101000101':0.0021741539239883423}, \n",
    "            100:{'m.0111010000011101':0.0012952530732785747}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6204521670935875"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ent_motifs[100]['m.0111010000011101']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paper plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms_feats = [['m.001001100111','0111111'], ['m.1011100111001','1011111'],\n",
    "            ['m.10001011111000','1001111'], ['mam.10001011111000','1001111']]\n",
    "           #['mam.1000101111100011', '1001111']]\n",
    "fig=plt.figure(figsize=(10*len(ms_feats)//2, 20), dpi= 100, facecolor='w', edgecolor='k')\n",
    "\n",
    "n = 30\n",
    "train_r = ['rs', 'snis_r']\n",
    "ds_range = [500, 1000, 5000, 10000, 20000]\n",
    "\n",
    "def get_freq(s):\n",
    "    return float(s.split('_')[2])\n",
    "\n",
    "i=0\n",
    "for m, feat in ms_feats:\n",
    "        #plt.subplot(1, len(ms_feats), i+1)\n",
    "        ax1 = fig.add_subplot(2, len(ms_feats)//2, i+1)\n",
    "        \n",
    "        # n -> motif -> feat -> ds_size -> train_reg -> \n",
    "        #[0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,5:tstamp,6:lambd,7:mfeat_pl,8:plambda_time,\n",
    "        #                                 10:job,10:l1_diff]\n",
    "            \n",
    "        ce_hybr = {t:np.array([dict_pl[n][m][feat][ds][t][0][1] for ds in ds_range])\n",
    "                             for t in train_r}\n",
    "        ce_r = [np.array([dict_pl[n][m][feat][ds][t][0][0] for t in train_r]) \n",
    "                   for ds in ds_range] \n",
    "\n",
    "        df1 = {'x': ds_range, \n",
    "                            'rs':  ce_hybr['rs'], \n",
    "                            #snis_mix': ce_hybr[1], \n",
    "                            'snis_r': ce_hybr['snis_r'],\n",
    "                            'ce_r': ce_r,\n",
    "                            'theor': np.array([ent_motifs[n][m]]*len(ds_range))}\n",
    "\n",
    "        #plt.errorbar(df1['x'], np.array(df1['ce_r']).mean(1), yerr=np.array(df1['ce_r']).std(1), \n",
    "        #                 fmt='o', capthick=2, ecolor='g',color='g', capsize=5,label='r')\n",
    "        ax1.plot(df1['x'], np.array(df1['ce_r']).mean(1),  \n",
    "                         marker='D', color='g', label='r')\n",
    "        ax1.plot( df1['x'], df1['theor'], marker='', color='red', linewidth=1,label='true')\n",
    "        ax1.plot( df1['x'], df1['rs'], marker='x', color='slateblue', linewidth=1,label='rs')\n",
    "        #plt.plot( df1['x'], df1['snis_mix'], marker='', color='olive', linewidth=1,label='snis_mix')\n",
    "        ax1.plot( df1['x'], df1['snis_r'], marker='x', color='darkorange', linewidth=1,label='snis_r')\n",
    "        ax1.set_xlabel('Dataset size')\n",
    "        ax1.set_ylabel('cross_entropy')\n",
    "        ax1.set_title('Cross-entropy (frequency) vs |D| for %s ft=%s Z=%.6f'%(m, feat,z_motifs[n][m]))\n",
    "        ax1.legend()\n",
    "        ax1.set_ylim(0.43, 0.78)\n",
    "        \n",
    "        ax2 = ax1.twinx()\n",
    "        \n",
    "        freq_hybr = {t:np.array([get_freq(dict_pl[n][m][feat][ds][t][0][5]) for ds in ds_range])\n",
    "                             for t in train_r}\n",
    "        \n",
    "        freq_r = [np.array([dict_pl[n][m][feat][ds][t][0][11] for t in train_r]) \n",
    "                   for ds in ds_range] \n",
    "        \n",
    "        if 'mam' in m:\n",
    "            theor = np.array([0.9]*len(ds_range))\n",
    "        else:\n",
    "            theor = np.array([1.0]*len(ds_range))\n",
    "\n",
    "        df1 = {'x': ds_range, \n",
    "                            'rs':  freq_hybr['rs'], \n",
    "                            #'snis_mix': freq_hybr[1], \n",
    "                            'snis_r': freq_hybr['snis_r'],\n",
    "                            'theor': theor,\n",
    "                            'r_freq':freq_r}\n",
    "\n",
    "        ax2.plot( df1['x'], df1['rs'], marker='x', color='slateblue', linewidth=1,label='rs',  linestyle=':')\n",
    "        #plt.plot( df1['x'], df1['snis_mix'], marker='', color='olive', linewidth=1,label='snis_mix')\n",
    "        ax2.plot( df1['x'], df1['snis_r'], marker='x', color='darkorange', linewidth=1,label='snis_r',  linestyle=':')\n",
    "        ax2.plot( df1['x'], df1['theor'], marker='', color='red', linewidth=1,label='true',  linestyle=':')\n",
    "        ax2.plot(df1['x'], np.array(df1['r_freq']).mean(1), marker='D', color='g', label='r',  linestyle=':')\n",
    "\n",
    "        #ax2.xlabel('Dataset size')\n",
    "        ax2.set_ylabel('motif frequency')\n",
    "        #ax2.title('Motif freq vs |D| for %s ft=%s Z=%.6f'%(m, feat,z_motifs[n][m]))\n",
    "        ax2.legend()\n",
    "        ax2.set_ylim(-0.05, 1.05)\n",
    "\n",
    "        i += 1\n",
    "        \n",
    "fig.savefig(\"ce_freq.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('rs500', [1.24, 0.07])\n",
      "('rs1000', [1.24, 0.07])\n",
      "('rs5000', [1.18, 0.08])\n",
      "('rs10000', [1.08, 0.1])\n",
      "('rs20000', [0.99, 0.01])\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('rs500', [1.23, 0.03])\n",
      "('rs1000', [1.21, 0.03])\n",
      "('rs5000', [1.16, 0.05])\n",
      "('rs10000', [1.02, 0.03])\n",
      "('rs20000', [0.99, 0.0])\n"
     ]
    }
   ],
   "source": [
    "# n -> motif -> feat -> ds_size -> train_reg -> \n",
    "#[0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,\n",
    "# 5:tstamp,6:lambd,7:mfeat_pl,8:plambda_time,\n",
    "# 10:job,10:l1_diff,11:r_feat]\n",
    "\n",
    "n = 30\n",
    "train_r = ['rs']\n",
    "ds_range = [500, 1000, 5000, 10000, 20000]\n",
    "\n",
    "ms = ['m.10001010001','m.01011101101','m.001001100111','m.1011100111001','m.10001011111000']\n",
    "mams = ['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', \n",
    "      'mam.100010100011', 'mam.10001011111000']\n",
    "feats = [ '1001111','1011111','0111111']\n",
    "\n",
    "ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    ce_ratios[t+str(ds)] += [dict_pl[n][m][feat][ds][t][0][0]/dict_pl[n][m][feat][ds][t][0][1]]\n",
    "                    \n",
    "                    #ent_motifs[n][m]\n",
    "            #print(np.array(ce_ratios[t+str(ds)]).shape)\n",
    "            ce_ratios[t+str(ds)] = [np.round(np.array(ce_ratios[t+str(ds)]).mean(), decimals=2), \n",
    "                                    np.round(np.array(ce_ratios[t+str(ds)]).std(), decimals=2)]\n",
    "    print(motifs)\n",
    "    for el in ce_ratios.items():\n",
    "        print(el)\n",
    "    ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('rs500', [1.19, 0.07])\n",
      "('rs1000', [1.16, 0.07])\n",
      "('rs5000', [1.09, 0.05])\n",
      "('rs10000', [1.04, 0.02])\n",
      "('rs20000', [1.02, 0.01])\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('rs500', [1.16, 0.03])\n",
      "('rs1000', [1.14, 0.03])\n",
      "('rs5000', [1.08, 0.04])\n",
      "('rs10000', [1.04, 0.03])\n",
      "('rs20000', [1.02, 0.0])\n"
     ]
    }
   ],
   "source": [
    "# ce(pi_theta)/true_ce\n",
    "n = 30\n",
    "train_r = ['rs']\n",
    "ds_range = [500, 1000, 5000, 10000, 20000]\n",
    "\n",
    "ms = ['m.10001010001','m.01011101101','m.001001100111','m.1011100111001','m.10001011111000']\n",
    "mams = ['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', \n",
    "      'mam.100010100011', 'mam.10001011111000']\n",
    "feats = [ '1001111','1011111','0111111']\n",
    "\n",
    "ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    ce_ratios[t+str(ds)] += [dict_pl[n][m][feat][ds][t][0][1]/ent_motifs[n][m]]\n",
    "                    \n",
    "                    #ent_motifs[n][m]\n",
    "            #print(np.array(ce_ratios[t+str(ds)]).shape)\n",
    "            ce_ratios[t+str(ds)] = [np.round(np.array(ce_ratios[t+str(ds)]).mean(), decimals=2), \n",
    "                                    np.round(np.array(ce_ratios[t+str(ds)]).std(), decimals=2)]           \n",
    "\n",
    "    print(motifs)\n",
    "    for el in ce_ratios.items():\n",
    "        print(el)\n",
    "    ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[81.73913043478261, 70.0, 30.000000000000004, 76.8, 75.38461538461539, 23.870967741935484, 242.5, 653.3333333333333, 130.0, 265.7142857142857, 140.0, 77.77777777777779, 118.74999999999999, 72.5925925925926, 40.0]\n",
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('rs500', [32.0, 392.0])\n",
      "('rs1000', [23.87, 653.33])\n",
      "('rs5000', [3.59, 206.67])\n",
      "('rs10000', [0.89, 196.0])\n",
      "('rs20000', [0.81, 1.76])\n",
      "[26.285714285714285, 233.33333333333331, 193.33333333333331, 106.66666666666667, 132.5]\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('rs500', [59.26, 433.33])\n",
      "('rs1000', [26.29, 233.33])\n",
      "('rs5000', [7.32, 130.0])\n",
      "('rs10000', [1.0, 4.97])\n",
      "('rs20000', [0.85, 1.04])\n"
     ]
    }
   ],
   "source": [
    "# freq\n",
    "n = 30\n",
    "train_r = ['rs']\n",
    "ds_range = [500, 1000, 5000, 10000, 20000]\n",
    "\n",
    "ms = ['m.10001010001','m.01011101101','m.001001100111','m.1011100111001','m.10001011111000']\n",
    "mams = ['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', \n",
    "      'mam.100010100011', 'mam.10001011111000']\n",
    "feats = [ '1001111','1011111','0111111']\n",
    "\n",
    "ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    tmp = dict_pl[n][m][feat][ds][t][0][5].split('_')\n",
    "                    freq, l = '-', '-'\n",
    "                    if len(tmp)>2:\n",
    "                        freq = np.round(float(tmp[2]), decimals=2)\n",
    "                    else:\n",
    "                        continue\n",
    "                    ce_ratios[t+str(ds)] += [freq/dict_pl[n][m][feat][ds][t][0][11]]\n",
    "                    \n",
    "                    #ent_motifs[n][m]\n",
    "            if t+str(ds) == 'rs1000':\n",
    "                print(ce_ratios[t+str(ds)])\n",
    "            #print(np.array(ce_ratios[t+str(ds)]).shape)\n",
    "            ce_ratios[t+str(ds)] = [np.round(np.array(ce_ratios[t+str(ds)]).min(), decimals=2), \n",
    "                                    np.round(np.array(ce_ratios[t+str(ds)]).max(), decimals=2)] \n",
    "    print(motifs)\n",
    "    for el in ce_ratios.items():\n",
    "        print(el)\n",
    "    ce_ratios = {t+str(ds):[] for t in train_r for ds in ds_range}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('500', 1.2372880046027155)\n",
      "('1000', 1.2339075005277678)\n",
      "('5000', 1.1874898231056645)\n",
      "('10000', 1.0741186900252901)\n",
      "('20000', 1.003246871249985)\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('500', 1.2244510612797461)\n",
      "('1000', 1.2129129337055664)\n",
      "('5000', 1.1723577135237122)\n",
      "('10000', 1.059039804415695)\n",
      "('20000', 0.9913039074800155)\n"
     ]
    }
   ],
   "source": [
    "ce_ratios = {str(ds):[] for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    ce_ratios[str(ds)] += [dict_pl[n][m][feat][ds][t][0][0]/dict_pl[n][m][feat][ds][t][0][1]]\n",
    "\n",
    "        #print(np.array(ce_ratios[str(ds)]).shape)\n",
    "        ce_ratios[str(ds)] = np.array(ce_ratios[str(ds)]).mean()\n",
    "    print(motifs)\n",
    "    for el in ce_ratios.items():\n",
    "        print(el)\n",
    "    ce_ratios = {str(ds):[] for ds in ds_range}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('500', 1.1999260024655198)\n",
      "('1000', 1.1617556120777535)\n",
      "('5000', 1.1050445899697452)\n",
      "('10000', 1.056494744331952)\n",
      "('20000', 1.0279834594978015)\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('500', 1.1467860568495225)\n",
      "('1000', 1.1292696347879159)\n",
      "('5000', 1.0806733747992552)\n",
      "('10000', 1.0444561771362788)\n",
      "('20000', 1.0161587295630616)\n"
     ]
    }
   ],
   "source": [
    "# ce(pi_theta)/true_ce\n",
    "ce_ratios = {str(ds):[] for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    ce_ratios[str(ds)] += [dict_pl[n][m][feat][ds][t][0][1]/ent_motifs[n][m]]\n",
    "        #print(np.array(ce_ratios[str(ds)]).shape)\n",
    "        ce_ratios[str(ds)] = np.array(ce_ratios[str(ds)]).mean()\n",
    "    print(motifs)\n",
    "    for el in ce_ratios.items():\n",
    "        print(el)\n",
    "    ce_ratios = {str(ds):[] for ds in ds_range}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['m.10001010001', 'm.01011101101', 'm.001001100111', 'm.1011100111001', 'm.10001011111000']\n",
      "('500', 161.2977725433608)\n",
      "('1000', 128.95254656239442)\n",
      "('5000', 51.79281845725731)\n",
      "('10000', 16.219589796004094)\n",
      "('20000', 2.8402650225257293)\n",
      "['mam.01011101101', 'mam.001001100111', 'mam.1011100111001', 'mam.100010100011', 'mam.10001011111000']\n",
      "('500', 221.15002405002406)\n",
      "('1000', 111.79477189477188)\n",
      "('5000', 49.51536392615351)\n",
      "('10000', 17.12763724124569)\n",
      "('20000', 0.9790789831804197)\n"
     ]
    }
   ],
   "source": [
    "# n -> motif -> feat -> ds_size -> train_reg -> \n",
    "#[0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,\n",
    "# 5:tstamp,6:lambd,7:mfeat_pl,8:plambda_time,\n",
    "# 10:job,10:l1_diff,11:r_feat]\n",
    "freq_hybr = {t:np.array([get_freq(dict_pl[n][m][feat][ds][t][0][5]) for ds in ds_range])\n",
    "                             for t in train_r}\n",
    "        \n",
    "freq_r = [np.array([dict_pl[n][m][feat][ds][t][0][11] for t in train_r]) \n",
    "                   for ds in ds_range]\n",
    "\n",
    "freq_ratios = {str(ds):[] for ds in ds_range}\n",
    "for motifs, fts in [(ms, feats), (mams, ['1001111'])]:\n",
    "    for ds in ds_range:\n",
    "        for t in train_r:\n",
    "            for m in motifs:\n",
    "                for feat in fts:\n",
    "                    tmp = dict_pl[n][m][feat][ds][t][0][5].split('_')\n",
    "                    freq, l = '-', '-'\n",
    "                    if len(tmp)>2:\n",
    "                        freq = np.round(float(tmp[2]), decimals=2)\n",
    "                    else:\n",
    "                        continue\n",
    "                    freq_ratios[str(ds)] += [freq/dict_pl[n][m][feat][ds][t][0][11]]\n",
    "        #print(np.array(freq_ratios[str(ds)]).shape)\n",
    "        freq_ratios[str(ds)] = np.array(freq_ratios[str(ds)]).mean()\n",
    "    print(motifs)\n",
    "    for el in freq_ratios.items():\n",
    "        print(el)\n",
    "    freq_ratios = {str(ds):[] for ds in ds_range}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5646450519561768, 0.4689084589481354, 0.4662000834941864, 0.04167740456000019, 0.44913369314378815, '05m27d_144653104726_0.9595_30.7_0.045_27.8', '[-10.090909, -0.15051788, -0.062074546, 0.0, -0.14176218]', '[0.00054545456, 0.4351818, 0.9996818, 0.9999545, 0.91349995]', 0.4023709797196918, 974270, array([5.4545456e-04, 3.9618200e-02, 1.1820000e-04, 4.5500000e-05,\n",
      "       1.3500500e-03]), 0.045]]\n",
      "0.44913369314378815\n"
     ]
    }
   ],
   "source": [
    "n = 30; m = 'm.10001011111000'; ds = 5000; f = '1001111'; tr = 'rs'\n",
    "print(dict_pl[n][m][f][ds][tr])\n",
    "print(ent_motifs[n][m])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "\n",
    "class RNNModel(nn.Module):\n",
    "    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):\n",
    "        super(RNNModel, self).__init__()\n",
    "        self.drop = nn.Dropout(dropout)\n",
    "        \n",
    "        self.encoder = nn.Embedding(ntoken, ninp)\n",
    "        one_hot_vecs = np.array([[1,0,0], [0,1,0], [0,0,1], [0,0,0], [0,0,0]])\n",
    "        self.encoder.weight.data.copy_(torch.from_numpy(one_hot_vecs))\n",
    "        self.freeze_layer(self.encoder)\n",
    "        \n",
    "        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout) \n",
    "        # <bos> is not in the output vocabulary       \n",
    "        self.decoder = nn.Linear(nhid, ninp)\n",
    "\n",
    "        self.init_weights()\n",
    "        \n",
    "        self.nhid = nhid\n",
    "        self.nlayers = nlayers\n",
    "    \n",
    "    def freeze_layer(self, layer):\n",
    "        for param in layer.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "    def init_weights(self):\n",
    "        initrange = 0.1\n",
    "        self.decoder.bias.data.fill_(0)\n",
    "        self.decoder.weight.data.uniform_(-initrange, initrange)\n",
    "\n",
    "    def forward(self, input, hidden, len_inp, mask):\n",
    "        emb = self.drop(self.encoder(input))\n",
    "        output, hidden = self.rnn(emb, hidden)\n",
    "        output = self.drop(output) # [seq_len ,batch, nhid]\n",
    "        # [seq_len*batch, ntok]\n",
    "        decoded = self.decoder(output.view(output.size(0)*output.size(1), output.size(2)))\n",
    "        decoded = torch.mul(decoded.view(output.size(0), output.size(1), decoded.size(1)), mask)\n",
    "        return decoded, hidden\n",
    "\n",
    "    def init_hidden(self, bsz):\n",
    "        weight = next(self.parameters()).data\n",
    "        return (Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()),\n",
    "                    Variable(weight.new(self.nlayers, bsz, self.nhid).zero_()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamp = tmpsmt = '_'.join(dict_pl[n][m][f][ds][tr][0][5].split('_')[:2])\n",
    "log_dir = '/home/tparshak/jupyter_code/exp_gams/runs/chpt_%s'%(timestamp)\n",
    "model_r = torch.load(os.path.join(log_dir,'chpt_%s_r.pt'%(timestamp)))\n",
    "model_r.cuda()\n",
    "\n",
    "class Args:       # =1= short motif, =2= long motif\n",
    "    n=30\n",
    "    train='rs'    # =rs= rejection sampling\n",
    "    feat = ['1001111']\n",
    "    motif = 4\n",
    "\n",
    "\n",
    "args=Args()\n",
    "start_symbol = torch.tensor([[3]*10000]).cuda()\n",
    "PAD = 4\n",
    "ninp = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_lm_vary(model, batch_size_i, max_len=None):\n",
    "    # output: [ seq_len x batch ]\n",
    "    \n",
    "    # [ 1 x batch ]\n",
    "    # <pad> idx = 4\n",
    "    if not max_len:\n",
    "        max_len=args.n*2+1\n",
    "    out = [(torch.ones(1)*3).cuda().long()]*batch_size_i # contains sequences of variable lenght\n",
    "    symb = start_symbol[:,:batch_size_i]\n",
    "    hidden = model.init_hidden(batch_size_i)\n",
    "    len_inp = torch.ones((batch_size_i), dtype=torch.int64)\n",
    "    mask = torch.ones((1, batch_size_i, 1)).cuda()\n",
    "    all_logits = torch.ones((1, batch_size_i, ninp)).cuda()\n",
    "    \n",
    "    for i in range(max_len):\n",
    "        # [1 x batch x ntok]\n",
    "        logits, hidden = model(symb, hidden, len_inp, mask)[:2]\n",
    "        probs = softm(logits)\n",
    "        cat_dist = torch.distributions.Categorical(probs=probs)\n",
    "        # [ 1 x batch ]\n",
    "        symb = cat_dist.sample()\n",
    "        flag = False\n",
    "        for b in range(batch_size_i):\n",
    "            if i==0 or (i>0 and out[b][-1] != 2): \n",
    "                out[b] = torch.cat((out[b], symb[:1,b]), dim=0)\n",
    "                flag = True\n",
    "        all_logits = torch.cat((all_logits, logits), dim=0)\n",
    "        if not flag:\n",
    "            break\n",
    "        \n",
    "    out = torch.nn.utils.rnn.pad_sequence(out, batch_first=False, padding_value=PAD)   \n",
    "    # <bos> 010010101 <eos>\n",
    "    return out, all_logits[1:out.size(0),:,:]\n",
    "\n",
    "softm = nn.Softmax(dim=2)\n",
    "\n",
    "def sample_data_inp_targ_vary(model, batch_size_i, max_len=None):\n",
    "    # padded variable lengths sequences\n",
    "    # [ seq_len x batch ]\n",
    "    if not max_len:\n",
    "        max_len = args.n*2+1\n",
    "    x, log_pi = sample_lm_vary(model, batch_size_i, max_len)\n",
    "    len_inp = (x!= PAD).sum(0)\n",
    "    len_inp, perm_idx = len_inp.sort(0, descending=True)\n",
    "    len_inp = len_inp - 1\n",
    "    x = x[:, perm_idx]\n",
    "    inp = x[:-1,:]\n",
    "\n",
    "    # [(n+1) x batch]\n",
    "    targets = x[1:,:] \n",
    "    mask_tar = (targets != PAD).unsqueeze(2).float().cuda()   \n",
    "    len_tar = (targets != PAD).sum(0)\n",
    "    \n",
    "    return x, log_pi, inp, len_inp, targets, mask_tar\n",
    "\n",
    "def cat_variable_length(a, b):\n",
    "    seq_len = max(a.size()[0], b.size()[0])\n",
    "    if a.size()[0] < seq_len:\n",
    "        a = torch.cat((a, torch.ones((seq_len-a.size()[0], a.size(1))).long().cuda()*PAD), dim=0)\n",
    "    if b.size()[0] < seq_len:\n",
    "        b = torch.cat((b, torch.ones((seq_len-b.size()[0], b.size(1))).long().cuda()*PAD), dim=0)\n",
    "\n",
    "    return torch.cat((a,b), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cyclic_distill_r(model, feat, ds_size):\n",
    "    # q(x)=r(x), Q(x)>=P_lambda(x) for any x \n",
    "    # sample from LM: x ~ q(x)\n",
    "    # accept with probability ro = P_lambda(x)/Q(x)\n",
    "\n",
    "    nfeat = sum([sum([int(e!='0') for e in el]) for el in feat])\n",
    "    samples = torch.zeros((args.n+2,0)).long().cuda()\n",
    "    batch_size_i = 1024\n",
    "    \n",
    "    count = 0\n",
    "\n",
    "    while count < ds_size:            \n",
    "        #x, inp, len_inp, targets, mask_tar = sample_data_inp_targ(model, batch_size_i)\n",
    "        x, log_pi, inp, len_inp, target, mask_tar = sample_data_inp_targ_vary(model, batch_size_i)\n",
    "\n",
    "\n",
    "        samples = cat_variable_length(samples, x)\n",
    "        count += x.size(1)\n",
    "        print(np.round(len_inp.float().mean().data.cpu().numpy(),decimals=1))\n",
    "                   \n",
    "    # samples [ seq_len x ds_size ]\n",
    "    return samples[:,:ds_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.3\n",
      "28.1\n",
      "27.7\n",
      "27.9\n",
      "28.0\n",
      "28.0\n",
      "28.2\n",
      "27.9\n",
      "27.5\n",
      "28.4\n",
      "27.9\n",
      "27.6\n",
      "27.8\n",
      "28.1\n",
      "28.1\n",
      "27.7\n",
      "27.9\n",
      "27.6\n",
      "27.8\n",
      "27.8\n"
     ]
    }
   ],
   "source": [
    "train_data = cyclic_distill_r(model_r, feat, 20000)\n",
    "train_data = train_data.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 20000)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,) 1001111 0.952\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.952  , 0.53125, 0.9998 , 1.     , 0.9109 ])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_features(train_data, [m], feat, 1, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.load(os.path.join(log_dir,'train_n%d_f%s_m%d.npy'%(n, feat, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,) 1001111 0.0006\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([6.0000e-04, 4.3745e-01, 9.9965e-01, 9.9995e-01, 9.1350e-01])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_features(D, [m], feat, 1, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5646450519561768, 0.4689084589481354, 0.4662000834941864, 0.04167740456000019, 0.44913369314378815, '05m27d_144653104726_0.9595_30.7_0.045_27.8', '[-10.090909, -0.15051788, -0.062074546, 0.0, -0.14176218]', '[0.00054545456, 0.4351818, 0.9996818, 0.9999545, 0.91349995]', 0.4023709797196918, 974270, array([5.4545456e-04, 3.9618200e-02, 1.1820000e-04, 4.5500000e-05,\n",
      "       1.3500500e-03]), 0.045]\n",
      "0.44913369314378815\n"
     ]
    }
   ],
   "source": [
    "# n -> motif -> feat -> ds_size -> train_reg -> \n",
    "#[0:test_ce_r,1:test_ce_pi,2:test_ce_pl,3:train_l1_pl,4:theor_ent,\n",
    "# 5:tstamp,6:lambd,7:mfeat_pl,8:plambda_time,\n",
    "# 10:job,10:l1_diff,11:r_feat]\n",
    "\n",
    "print(dict_pl[n][m][f][ds][tr][0])\n",
    "print(ent_motifs[n][m])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_from_rnn(model, n, motifs):\n",
    "    batch_size = 2000\n",
    "    x, log_pi, inp, len_inp, action, mask_tar = sample_data_inp_targ_vary(model, \n",
    "                                                    batch_size, max_len=500)\n",
    "    avg_len = np.round(len_inp.float().mean().data.cpu().numpy(),decimals=1)\n",
    "    print('avg len ', avg_len)\n",
    "    x = x.data.cpu().numpy()\n",
    "    count = 0     \n",
    "    for i in range(x.shape[1]):\n",
    "            res = ''.join([str(x[j,i]) for j in range(x.shape[0])])\n",
    "            curr_count = 0\n",
    "            for motif in motifs:\n",
    "                if motif in res:\n",
    "                    curr_count += 1\n",
    "            count += min(1, curr_count)\n",
    "\n",
    "    print('%d motifs in total %d' % (count, x.shape[1]))\n",
    "    motif_freq = (1.0*count)/x.shape[1]\n",
    "    return motif_freq, avg_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pi = torch.load(os.path.join(log_dir,'chpt_%s_pi.pt'%(timestamp)))\n",
    "model_pi.cuda()\n",
    "sample_from_rnn(model_pi, n, m.split('.')[1:1+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg len  27.4\n",
      "91 motifs in total 2000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0455, 27.4)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_from_rnn(model_r, n, m.split('.')[1:1+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.9\n",
      "30.7\n",
      "30.5\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.5\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.7\n",
      "30.6\n",
      "30.6\n",
      "30.8\n",
      "30.7\n"
     ]
    }
   ],
   "source": [
    "train_data_pi = cyclic_distill_r(model_pi, feat, 20000)\n",
    "train_data_pi = train_data_pi.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3010011011000011111000011100010244\n",
      "3000001010111110001100101111100244\n",
      "3011111000010111110001110001011244\n",
      "3001101111100001000001111110000244\n",
      "3101000001010100010010111110000244\n",
      "3101111011000011111100010001011244\n",
      "3110101000001111110001100110000244\n",
      "3110001110000100011111100010001244\n",
      "3010100100101011110001101111010244\n",
      "3000111111000010000111111000111244\n",
      "3110011110101001011111100010011244\n",
      "3000100001000111111000111001001244\n",
      "3101100111111100101010010010011244\n",
      "3000010110100011111110001011100244\n",
      "3000001000000001011110001000111244\n",
      "3010001001000101111000110111000244\n",
      "3001001110111000011111000101101244\n",
      "3110101100011111100011010010111244\n",
      "3010101111000011101101001001010244\n",
      "3010000011111100010101011010001244\n"
     ]
    }
   ],
   "source": [
    "for i in range(2200, 2220):\n",
    "    print(''.join([str(el) for el in train_data[:,i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300010001011111000100110000110024444\n",
      "301110100101010100010111110000024444\n",
      "301000000101000101111100000010024444\n",
      "311010010100100010111110000111024444\n",
      "311110010110001011111000100010024444\n",
      "311101010001011111000011111110024444\n",
      "310001011111000000100100001100124444\n",
      "300011100010011110001011111000024444\n",
      "300010001011111000011111001001124444\n",
      "311110110011000101111100010010024444\n",
      "301101001000101111100010010101124444\n",
      "301100001000000101111100001010124444\n",
      "300001111100010111110000001101124444\n",
      "310101111111001100010111110000024444\n",
      "311011000111100010111110000010024444\n",
      "301011001110110001011111000000124444\n",
      "300000100100010111110000000111024444\n",
      "311010101011111000101111100000124444\n",
      "310100100010011110001011111000024444\n",
      "300010001011111000010011000110024444\n"
     ]
    }
   ],
   "source": [
    "for i in range(2200,2220):\n",
    "    print(''.join([str(el) for el in train_data_pi[:,i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_motif(n, sz, motif, ds_type):\n",
    "    ds = \"\"\n",
    "    # input: <bos> binary string <eos>\n",
    "    # 3 {0,1}^n 2\n",
    "    data_file = os.path.join(logdir, 'data/pfsa_%d_%s'%(n, motif),\"%s.txt\"%ds_type)\n",
    "\n",
    "    with open(data_file, \"r\") as file:\n",
    "        for line in file:\n",
    "            #assert motif in line\n",
    "            ds += line.strip()\n",
    "            #print(line.strip())\n",
    "            if len(ds)>=sz*n:\n",
    "                break\n",
    "    original = ''.join(c+' ' for c in ds[:sz*n]).strip()\n",
    "    original = np.fromstring(original, dtype=int, sep=' ')\n",
    "    original = original.reshape((original.shape[0]//n, n)).transpose()\n",
    "    \n",
    "    for i in range(original.shape[1]):\n",
    "        res = ''.join([str(original[j,i]) for j in range(original.shape[0])])\n",
    "        #assert flag\n",
    "    \n",
    "    dataset = (np.ones((n+2, original.shape[1]))).astype(int)\n",
    "    dataset[1:-1] = original\n",
    "    dataset[0] = dataset[0]*3\n",
    "    dataset[-1] = dataset[-1]*2\n",
    "    print(dataset.shape, batch_size)\n",
    "    assert dataset.shape[1] >= sz\n",
    "    ds = dataset[:, :batch_size*int(1.0*dataset.shape[1]/batch_size)]\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 20000) 1000\n",
      "(32, 2000) 1000\n",
      "(32, 5000) 1000\n"
     ]
    }
   ],
   "source": [
    "if args.motif == 4:\n",
    "            all_motifs = {30:'10001011111000', 50:'100110001111111'}\n",
    "train_size = 20000\n",
    "test_size = 5000\n",
    "valid_size = 2000\n",
    "batch_size = 1000\n",
    "train_data_D = load_data_motif(n, train_size, all_motifs[n], 'train')\n",
    "valid_data_V = load_data_motif(n, valid_size, all_motifs[n], 'valid')\n",
    "test_data_T  = load_data_motif(n, test_size, all_motifs[n], 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31101100001000101111100001111112\n",
      "31011000101111100010000010010012\n",
      "31111011011000101111100001100012\n",
      "31111110001011111000111000000112\n",
      "31110110001011111000100010001102\n",
      "30010001001000101111100001100012\n",
      "30111100010001011111000001011002\n",
      "30000101100101100010111110000002\n",
      "30000011101011111000101111100002\n",
      "30000001100101110001011111000002\n",
      "30100010111110000010011111011112\n",
      "30010101100010111110001010000112\n",
      "31000000100100110001011111000002\n",
      "31101100010100010111110001001102\n",
      "30110001101010001011111000010012\n",
      "31101111000101111100010110110102\n",
      "30000000000100110100010111110002\n",
      "31010000110111110100010111110002\n",
      "30100010100010111110001001100002\n",
      "30001101110010001011111000101112\n"
     ]
    }
   ],
   "source": [
    "for i in range(200,220):\n",
    "    print(''.join([str(el) for el in train_data_D[:,i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,) 1001111 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.47374074, 0.99985185, 1.        , 0.91403704])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_data = np.concatenate((train_data_D, valid_data_V, test_data_T), axis=1)\n",
    "get_features(true_data, [m], feat, 1, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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