fit_pgm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#%load_ext autoreload\n",
    "#%autoreload 2\n",
    "from pgmpy.models import BayesianModel\n",
    "from pgmpy.inference import VariableElimination\n",
    "from pgmpy.readwrite import XMLBIFReader\n",
    "from pgmpy.factors.discrete import TabularCPD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import gzip\n",
    "import re\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (1,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "review_full = pd.read_csv('data/review.tsv.gz',sep='\\t')\n",
    "tip_full = pd.read_csv('data/tip.tsv.gz',sep='\\t')\n",
    "business_full = pd.read_csv('data/business.tsv.gz',sep='\\t')\n",
    "checkin_full = pd.read_csv('data/checkin.tsv.gz',sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#rs = review_full.drop(['cool','date','funny','type','useful','user_id'],1)\n",
    "#rs = rs[['business_id','text','stars']]\n",
    "#rs.to_csv('out.dat',header=False,sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2577313, 10) (797099, 10) (48485, 16) (602833, 6) (46417, 3)\n",
      "(797099, 10) (45352, 16) (599707, 6) (43848, 3)\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    user_valid = review_full.groupby('user_id').count()\n",
    "    review_sub = review_full[review_full.user_id.isin(list(user_valid[user_valid.business_id > 20].index))]\n",
    "\n",
    "    print review_full.shape,review_sub.shape,business_full.shape,tip_full.shape,checkin_full.shape\n",
    "    review = review_sub#.sample(frac=0.01, replace=False)\n",
    "    business = business_full[business_full.business_id.isin(list(review.business_id.unique()))]\n",
    "    tip = tip_full[tip_full.business_id.isin(list(review.business_id.unique()))]\n",
    "    checkin = checkin_full[checkin_full.business_id.isin(list(review.business_id.unique()))]\n",
    "    print review.shape,business.shape,tip.shape,checkin.shape\n",
    "else:\n",
    "    review = review_full\n",
    "    tip = tip_full\n",
    "    business = business_full\n",
    "    checkin = checkin_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "categories = defaultdict(int)\n",
    "for row,cnt in zip(business.categories,business.review_count):\n",
    "    for ct in row.strip('[]').split(','):\n",
    "        categories[ct.strip()] += cnt\n",
    "cats = sorted([(v,k) for k,v in categories.iteritems()])[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "good_cats = []\n",
    "\n",
    "if False:\n",
    "    small_bus = business.loc[:,['business_id','categories']]\n",
    "    for cnt, cat in cats:\n",
    "        small_bus[cat] = small_bus.categories.str.contains(re.escape(cat))\n",
    "    small_bus = small_bus.loc[:,[k for k in small_bus.columns if k != 'categories']]\n",
    "    users = review.loc[:,['user_id','business_id','stars']]\n",
    "    all_reviews = users.merge(small_bus,on='business_id')\n",
    "    log_or = all_reviews.groupby('user_id').sum()\n",
    "    if True:\n",
    "        with open(\"filtered_100.txt\") as fp:\n",
    "            for line in fp:\n",
    "                cnt, label = line.split(',')\n",
    "                good_cats.append(label.strip())\n",
    "\n",
    "    cand_cats = [cat for cnt,cat in cats if cat not in good_cats]\n",
    "    while len(cand_cats) > 0:\n",
    "        cnts = []\n",
    "        if len(good_cats) > 0:\n",
    "            good_filter = log_or[good_cats[0]] > 0\n",
    "            for i in xrange(1,len(good_cats)):\n",
    "                good_filter = good_filter & (log_or[good_cats[1]] > 0)\n",
    "        for cat in cand_cats:\n",
    "            if len(good_cats) > 0:\n",
    "                cnts.append(((log_or[(log_or[cat] >0) & good_filter].shape[0],cat)))\n",
    "            else:\n",
    "                cnts.append((log_or[log_or[cat] > 0].shape[0],cat))\n",
    "\n",
    "        cnts = sorted(cnts)[::-1]\n",
    "        if cnts[0][0] > 500:\n",
    "            good_cats.append(cnts[0][1])\n",
    "            cand_cats.remove(cnts[0][1])\n",
    "            print cnts[0]\n",
    "        else:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n"
     ]
    }
   ],
   "source": [
    "if len(good_cats) > 0:\n",
    "    valid_cats = good_cats[:]\n",
    "    print len(valid_cats)\n",
    "elif True:\n",
    "    with open(\"filtered_100.txt\") as fp:\n",
    "        for line in fp:\n",
    "            cnt, label = line.split(',')\n",
    "            good_cats.append(label.strip())\n",
    "    valid_cats = good_cats[:50]\n",
    "    print len(valid_cats)\n",
    "else:\n",
    "    print len(cats)\n",
    "    valid_cats = [k for v,k in cats[:35]]\n",
    "    print valid_cats\n",
    "\n",
    "small_bus = business.loc[:,['business_id','categories']]\n",
    "small_bus['biz_review'] = business.stars\n",
    "for cat in valid_cats:\n",
    "    small_bus[cat] = small_bus.categories.str.contains(re.escape(cat))\n",
    "small_bus = small_bus.loc[:,[k for k in small_bus.columns if k != 'categories']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "users = review.loc[:,['user_id','business_id','stars']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#users['liked'] = users.stars > 3.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "all_reviews = users.merge(small_bus,on='business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(520, 50)\n",
      "0.654676258993\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((520, 50), (0, 50), (797099, 54))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#liking\n",
    "\n",
    "grps = all_reviews.groupby(['business_id']).count() > 200\n",
    "valid_biz = grps['stars'][grps['stars'] == True].index\n",
    "\n",
    "#sub_reviews = all_reviews[all_reviews.business_id.isin(valid_biz)]\n",
    "sub_reviews = all_reviews \n",
    "\n",
    "grps = sub_reviews.groupby(['user_id']).count() > 20\n",
    "valid_users = grps['stars'][grps['stars'] == True].index\n",
    "sub_reviews = sub_reviews[sub_reviews.user_id.isin(valid_users)]\n",
    "\n",
    "new_reviews = pd.DataFrame()\n",
    "new_groups = {\n",
    "    'Asian': ['Asian Fusion','Chinese','Japanese','Sushi Bars','Thai','Vietnamese','Noodles','Korean'],\n",
    "    'Morning': ['Breakfast & Brunch','Coffee & Tea','Cafes','Diners'],\n",
    "    'Night': ['Beer','Wine Bars'],\n",
    "    'Fast': ['Burgers','Pizza','Fast Food','Delis','Chicken Wings','Hot Dogs']\n",
    "}\n",
    "for cat in valid_cats:\n",
    "    #new_reviews[cat]= all_reviews[all_reviews[cat]].groupby('user_id').mean()['stars'] > 3\n",
    "    #new_reviews[cat]= all_reviews[all_reviews[cat]].groupby('user_id').mean()['stars'] > all_reviews[all_reviews[cat]].groupby('user_id').mean()['biz_review']\n",
    "    #new_reviews[cat]= all_reviews[all_reviews[cat]].groupby(['user_id','business_id']).mean()['stars'] > all_reviews[all_reviews[cat]].groupby(['user_id','business_id']).mean()['biz_review']\n",
    "    new_reviews[cat]= sub_reviews[sub_reviews[cat]].groupby('user_id').mean()['stars'] > sub_reviews[sub_reviews[cat]].groupby('user_id').mean()['biz_review']\n",
    "data_sample = new_reviews\n",
    "\n",
    "if False:\n",
    "    for cat in new_groups:\n",
    "        oc = new_groups[cat]\n",
    "        g1 = sub_reviews[sub_reviews[oc[0]]].groupby('user_id').sum()\n",
    "        for i in xrange(1,len(oc)):\n",
    "            g1 += sub_reviews[sub_reviews[oc[i]]].groupby('user_id').sum()\n",
    "        filered = g1#sub_reviews[sub_reviews[]].groupby('user_id').mean()\n",
    "        data_sample[cat]= filered['stars'] > filered['biz_review']\n",
    "data_sample.columns = [x.translate(None,' ()&/-') for x in data_sample.columns]\n",
    "#visting\n",
    "#from sklearn.linear_model import Lasso\n",
    "#ds = data_sample.copy().transpose()\n",
    "#for did, data in data_sample.transpose().iteritems():\n",
    "#    clf = Lasso(0.001,fit_intercept=False)\n",
    "#    clf.fit()\n",
    "\n",
    "if False:\n",
    "    import random\n",
    "    MODEL_FILE = 'bayesMAN.bif.xml'\n",
    "    reader = XMLBIFReader(MODEL_FILE)\n",
    "    model = reader.get_model()\n",
    "    belief_propagation = VariableElimination(model)\n",
    "    invalid_cols = [x for x in data_sample.columns if x not in model.nodes()]\n",
    "    try:\n",
    "        data_sample = data_sample.drop(invalid_cols,1)\n",
    "        data_sample = data_sample.drop(new_groups.keys(),1)\n",
    "    except:\n",
    "        pass\n",
    "    print data_sample.shape\n",
    "    correct = 0\n",
    "    preds = 0\n",
    "    for user,elem in data_sample.transpose().iteritems():\n",
    "        vld = pd.DataFrame(elem.dropna() > 0).transpose()\n",
    "        train = vld.sample(frac=0.8,axis=1)\n",
    "        #print train.columns,vld.columns\n",
    "        test = vld.drop(train.columns.tolist(),1)\n",
    "        train_d = {k:int(v[0]) for k,v in train.iteritems()}\n",
    "        test_d  = {k:int(v[0]) for k,v in test.iteritems()}\n",
    "        for node in test_d:\n",
    "            res = belief_propagation.query([node],evidence=train_d)[node]\n",
    "            correct += int((res.values[1] > 0.5) == bool(test_d[node]))\n",
    "            #correct += int((False) == bool(test_d[node]))\n",
    "            preds += 1\n",
    "    print float(correct)/float(preds)\n",
    "#    print did, \n",
    "#data_sample = all_reviews.loc[:,[k for k in small_bus.columns if k not in ['user_id','business_id','stars']]]\n",
    "data_sample.shape, data_sample.dropna().shape,all_reviews.shape # (1500, 21) or (28,51) or (500,35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#tmp_sample = data_sample#.dropna()\n",
    "#int_cols = tmp_sample.fillna(0).astype(int)-1*((1.0 - tmp_sample.fillna(1).astype(int))) #astype(np.int)\n",
    "\n",
    "#from pylab import *\n",
    "#hist(np.array(int_cols.corr()).ravel())\n",
    "#show()\n",
    "#np.sum(int_cols.corr() > 0.25)\n",
    "if False:\n",
    "    data_sample.fillna(0)\n",
    "    from sklearn.manifold import TSNE\n",
    "    model = TSNE(n_components=2, init='random',metric='euclidean')\n",
    "    pts = model.fit_transform(np.array(data_sample.fillna(0)).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "    plt.style.use('ggplot')\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.scatter(pts[:,0],pts[:,1])\n",
    "    for i, txt in enumerate(data_sample.columns):\n",
    "        plt.gca().annotate(txt, (pts[i,0],pts[i,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from pgmpy.estimators import BdeuScore, K2Score, BicScore\n",
    "from pgmpy.models import BayesianModel\n",
    "from pgmpy.estimators import HillClimbSearch\n",
    "from pgmpy.estimators import ConstraintBasedEstimator\n",
    "from pgmpy.estimators import MaximumLikelihoodEstimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#hc = HillClimbSearch(data_sample, scoring_method=BicScore(data_sample))\n",
    "#best_model = hc.estimate()\n",
    "#print(best_model.edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "est = ConstraintBasedEstimator(data_sample,complete_samples_only=False)\n",
    "data_sample.to_csv('bayes.csv')\n",
    "data_sample.dropna().to_csv('bayes_small.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "if True:\n",
    "    edges= [ ('BreakfastBrunch','CoffeeTea'),\n",
    "             ('BreakfastBrunch','Cafes'),\n",
    "             ('BreakfastBrunch','Diners'),\n",
    "             ('Cafes','Bakeries'),\n",
    "             ('Bakeries','Desserts'),\n",
    "             ('Desserts','IceCreamFrozenYogurt'),\n",
    "             ('AsianFusion','Chinese'),\n",
    "             ('AsianFusion','SushiBars'),\n",
    "             ('AsianFusion','Japanese'),\n",
    "             ('SushiBars','Japanese'),\n",
    "             ('AsianFusion','Thai'),\n",
    "             ('AsianFusion','Vietnamese'),\n",
    "             ('Japanese','Noodles'),\n",
    "             ('AsianFusion','Korean'),\n",
    "             ('Beer','WineBars'),\n",
    "             ('FastFood','Burgers'),\n",
    "             ('FastFood','Pizza'),\n",
    "             ('FastFood','ChickenWings'),\n",
    "             ('FastFood','Delis'),\n",
    "             ('FastFood','HotDogs'),\n",
    "             ('Diners','FastFood'),\n",
    "             ('SushiBars','FastFood'),\n",
    "             ('Delis','Sandwiches'),\n",
    "             ('Sandwiches','Soup'),\n",
    "             ('FastFood','Sandwiches'),\n",
    "             ('Burgers','Sandwiches'),\n",
    "             ('Burgers','Steakhouses'),\n",
    "             ('AmericanTraditional','Steakhouses'),\n",
    "             ('AmericanTraditional','FastFood'),\n",
    "             ('AmericanTraditional','AmericanNew'),\n",
    "             ('Steakhouses','Barbeque'),\n",
    "             ('Southern','Barbeque'),\n",
    "             ('Seafood','AmericanTraditional'),\n",
    "             ('Southern','ComfortFood'),\n",
    "             ('Steakhouses','Italian'),\n",
    "             ('Italian','French'),\n",
    "             ('Mexican','TexMex'),\n",
    "             ('Mexican','LatinAmerican'),\n",
    "             ('LatinAmerican','TapasBars'),\n",
    "             ('TapasBars','TapasSmallPlates'),\n",
    "             ('Vegetarian','Salad'),\n",
    "             ('Vegetarian','Vegan'),\n",
    "             ('JuiceBarsSmoothies','Vegetarian'),\n",
    "             ('SpecialtyFood','EthnicFood'),\n",
    "             ('SpecialtyFood','Mediterranean'),\n",
    "             ('SpecialtyFood','MiddleEastern'),\n",
    "             ('Mediterranean','Greek'),\n",
    "             ('EthnicFood','Indian'),\n",
    "             ('Salad','Buffets'),\n",
    "             ('Indian','Buffets'),\n",
    "             ('Vegetarian','Indian')]\n",
    "    for e in edges:\n",
    "        for i in xrange(2):\n",
    "            if e[i] not in data_sample.columns:\n",
    "                print e[i]\n",
    "    nodes = valid_cats\n",
    "\n",
    "else:\n",
    "    clf = est.estimate(significance_level=0.01)\n",
    "    print len(clf.edges())\n",
    "    print clf.edges()\n",
    "    edges = clf.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "model = BayesianModel(edges)\n",
    "model.fit(data_sample,complete_samples_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+----------------------------+---------------------------+\n",
      "| AmericanTraditional | AmericanTraditional(False) | AmericanTraditional(True) |\n",
      "+---------------------+----------------------------+---------------------------+\n",
      "| AmericanNew(False)  | 0.703996726231             | 0.32601299103             |\n",
      "+---------------------+----------------------------+---------------------------+\n",
      "| AmericanNew(True)   | 0.296003273769             | 0.67398700897             |\n",
      "+---------------------+----------------------------+---------------------------+\n",
      "+----------------------------+----------------+----------------+\n",
      "| Seafood                    | Seafood(False) | Seafood(True)  |\n",
      "+----------------------------+----------------+----------------+\n",
      "| AmericanTraditional(False) | 0.670975684826 | 0.383589059373 |\n",
      "+----------------------------+----------------+----------------+\n",
      "| AmericanTraditional(True)  | 0.329024315174 | 0.616410940627 |\n",
      "+----------------------------+----------------+----------------+\n",
      "+--------------------+----------+\n",
      "| AsianFusion(False) | 0.547891 |\n",
      "+--------------------+----------+\n",
      "| AsianFusion(True)  | 0.452109 |\n",
      "+--------------------+----------+\n",
      "+-----------------+----------------+----------------+\n",
      "| Cafes           | Cafes(False)   | Cafes(True)    |\n",
      "+-----------------+----------------+----------------+\n",
      "| Bakeries(False) | 0.683052749719 | 0.332965517241 |\n",
      "+-----------------+----------------+----------------+\n",
      "| Bakeries(True)  | 0.316947250281 | 0.667034482759 |\n",
      "+-----------------+----------------+----------------+\n",
      "+-----------------+--------------------+-------------------+--------------------+-------------------+\n",
      "| Southern        | Southern(False)    | Southern(False)   | Southern(True)     | Southern(True)    |\n",
      "+-----------------+--------------------+-------------------+--------------------+-------------------+\n",
      "| Steakhouses     | Steakhouses(False) | Steakhouses(True) | Steakhouses(False) | Steakhouses(True) |\n",
      "+-----------------+--------------------+-------------------+--------------------+-------------------+\n",
      "| Barbeque(False) | 0.774805867127     | 0.567328918322    | 0.526315789474     | 0.312703583062    |\n",
      "+-----------------+--------------------+-------------------+--------------------+-------------------+\n",
      "| Barbeque(True)  | 0.225194132873     | 0.432671081678    | 0.473684210526     | 0.687296416938    |\n",
      "+-----------------+--------------------+-------------------+--------------------+-------------------+\n",
      "+-------------+----------+\n",
      "| Beer(False) | 0.554916 |\n",
      "+-------------+----------+\n",
      "| Beer(True)  | 0.445084 |\n",
      "+-------------+----------+\n",
      "+------------------------+----------+\n",
      "| BreakfastBrunch(False) | 0.538412 |\n",
      "+------------------------+----------+\n",
      "| BreakfastBrunch(True)  | 0.461588 |\n",
      "+------------------------+----------+\n",
      "+----------------+----------------+----------------+----------------+----------------+\n",
      "| Indian         | Indian(False)  | Indian(False)  | Indian(True)   | Indian(True)   |\n",
      "+----------------+----------------+----------------+----------------+----------------+\n",
      "| Salad          | Salad(False)   | Salad(True)    | Salad(False)   | Salad(True)    |\n",
      "+----------------+----------------+----------------+----------------+----------------+\n",
      "| Buffets(False) | 0.730523627075 | 0.633257403189 | 0.418604651163 | 0.323268206039 |\n",
      "+----------------+----------------+----------------+----------------+----------------+\n",
      "| Buffets(True)  | 0.269476372925 | 0.366742596811 | 0.581395348837 | 0.676731793961 |\n",
      "+----------------+----------------+----------------+----------------+----------------+\n",
      "+----------------+-----------------+----------------+\n",
      "| FastFood       | FastFood(False) | FastFood(True) |\n",
      "+----------------+-----------------+----------------+\n",
      "| Burgers(False) | 0.682352941176  | 0.379219729129 |\n",
      "+----------------+-----------------+----------------+\n",
      "| Burgers(True)  | 0.317647058824  | 0.620780270871 |\n",
      "+----------------+-----------------+----------------+\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| BreakfastBrunch | BreakfastBrunch(False) | BreakfastBrunch(True) |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| Cafes(False)    | 0.698136840457         | 0.380943672275        |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| Cafes(True)     | 0.301863159543         | 0.619056327725        |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "+---------------------+-----------------+----------------+\n",
      "| FastFood            | FastFood(False) | FastFood(True) |\n",
      "+---------------------+-----------------+----------------+\n",
      "| ChickenWings(False) | 0.637479806139  | 0.399638336347 |\n",
      "+---------------------+-----------------+----------------+\n",
      "| ChickenWings(True)  | 0.362520193861  | 0.600361663653 |\n",
      "+---------------------+-----------------+----------------+\n",
      "+----------------+--------------------+-------------------+\n",
      "| AsianFusion    | AsianFusion(False) | AsianFusion(True) |\n",
      "+----------------+--------------------+-------------------+\n",
      "| Chinese(False) | 0.682340463143     | 0.3164887824      |\n",
      "+----------------+--------------------+-------------------+\n",
      "| Chinese(True)  | 0.317659536857     | 0.6835112176      |\n",
      "+----------------+--------------------+-------------------+\n",
      "+------------------+------------------------+-----------------------+\n",
      "| BreakfastBrunch  | BreakfastBrunch(False) | BreakfastBrunch(True) |\n",
      "+------------------+------------------------+-----------------------+\n",
      "| CoffeeTea(False) | 0.693570687867         | 0.354154154154        |\n",
      "+------------------+------------------------+-----------------------+\n",
      "| CoffeeTea(True)  | 0.306429312133         | 0.645845845846        |\n",
      "+------------------+------------------------+-----------------------+\n",
      "+--------------------+-----------------+----------------+\n",
      "| Southern           | Southern(False) | Southern(True) |\n",
      "+--------------------+-----------------+----------------+\n",
      "| ComfortFood(False) | 0.696774193548  | 0.481389578164 |\n",
      "+--------------------+-----------------+----------------+\n",
      "| ComfortFood(True)  | 0.303225806452  | 0.518610421836 |\n",
      "+--------------------+-----------------+----------------+\n",
      "+--------------+-----------------+----------------+\n",
      "| FastFood     | FastFood(False) | FastFood(True) |\n",
      "+--------------+-----------------+----------------+\n",
      "| Delis(False) | 0.634031904911  | 0.421036677781 |\n",
      "+--------------+-----------------+----------------+\n",
      "| Delis(True)  | 0.365968095089  | 0.578963322219 |\n",
      "+--------------+-----------------+----------------+\n",
      "+-----------------+-----------------+----------------+\n",
      "| Bakeries        | Bakeries(False) | Bakeries(True) |\n",
      "+-----------------+-----------------+----------------+\n",
      "| Desserts(False) | 0.666760802033  | 0.406568877551 |\n",
      "+-----------------+-----------------+----------------+\n",
      "| Desserts(True)  | 0.333239197967  | 0.593431122449 |\n",
      "+-----------------+-----------------+----------------+\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| BreakfastBrunch | BreakfastBrunch(False) | BreakfastBrunch(True) |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| Diners(False)   | 0.699877501021         | 0.382153249273        |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "| Diners(True)    | 0.300122498979         | 0.617846750727        |\n",
      "+-----------------+------------------------+-----------------------+\n",
      "+-------------------+----------------------+---------------------+\n",
      "| SpecialtyFood     | SpecialtyFood(False) | SpecialtyFood(True) |\n",
      "+-------------------+----------------------+---------------------+\n",
      "| EthnicFood(False) | 0.89753801593        | 0.145273200347      |\n",
      "+-------------------+----------------------+---------------------+\n",
      "| EthnicFood(True)  | 0.10246198407        | 0.854726799653      |\n",
      "+-------------------+----------------------+---------------------+\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "| AmericanTraditional | AmericanTraditional(False) | AmericanTraditional(False) | AmericanTraditional(False) | AmericanTraditional(False) | AmericanTraditional(True) | AmericanTraditional(True) | AmericanTraditional(True) | AmericanTraditional(True) |\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "| Diners              | Diners(False)              | Diners(False)              | Diners(True)               | Diners(True)               | Diners(False)             | Diners(False)             | Diners(True)              | Diners(True)              |\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "| SushiBars           | SushiBars(False)           | SushiBars(True)            | SushiBars(False)           | SushiBars(True)            | SushiBars(False)          | SushiBars(True)           | SushiBars(False)          | SushiBars(True)           |\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "| FastFood(False)     | 0.648531951641             | 0.560855263158             | 0.595041322314             | 0.471751412429             | 0.507865168539            | 0.388679245283            | 0.40221402214             | 0.322429906542            |\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "| FastFood(True)      | 0.351468048359             | 0.439144736842             | 0.404958677686             | 0.528248587571             | 0.492134831461            | 0.611320754717            | 0.59778597786             | 0.677570093458            |\n",
      "+---------------------+----------------------------+----------------------------+----------------------------+----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+\n",
      "+---------------+----------------+----------------+\n",
      "| Italian       | Italian(False) | Italian(True)  |\n",
      "+---------------+----------------+----------------+\n",
      "| French(False) | 0.607114201658 | 0.445083014049 |\n",
      "+---------------+----------------+----------------+\n",
      "| French(True)  | 0.392885798342 | 0.554916985951 |\n",
      "+---------------+----------------+----------------+\n",
      "+---------------+----------------------+---------------------+\n",
      "| Mediterranean | Mediterranean(False) | Mediterranean(True) |\n",
      "+---------------+----------------------+---------------------+\n",
      "| Greek(False)  | 0.832878953108       | 0.224416517056      |\n",
      "+---------------+----------------------+---------------------+\n",
      "| Greek(True)   | 0.167121046892       | 0.775583482944      |\n",
      "+---------------+----------------------+---------------------+\n",
      "+----------------+-----------------+----------------+\n",
      "| FastFood       | FastFood(False) | FastFood(True) |\n",
      "+----------------+-----------------+----------------+\n",
      "| HotDogs(False) | 0.674975562072  | 0.456          |\n",
      "+----------------+-----------------+----------------+\n",
      "| HotDogs(True)  | 0.325024437928  | 0.544          |\n",
      "+----------------+-----------------+----------------+\n",
      "+-----------------------------+-----------------+----------------+\n",
      "| Desserts                    | Desserts(False) | Desserts(True) |\n",
      "+-----------------------------+-----------------+----------------+\n",
      "| IceCreamFrozenYogurt(False) | 0.685352622061  | 0.378691983122 |\n",
      "+-----------------------------+-----------------+----------------+\n",
      "| IceCreamFrozenYogurt(True)  | 0.314647377939  | 0.621308016878 |\n",
      "+-----------------------------+-----------------+----------------+\n",
      "+---------------+-------------------+-------------------+-------------------+------------------+\n",
      "| EthnicFood    | EthnicFood(False) | EthnicFood(False) | EthnicFood(True)  | EthnicFood(True) |\n",
      "+---------------+-------------------+-------------------+-------------------+------------------+\n",
      "| Vegetarian    | Vegetarian(False) | Vegetarian(True)  | Vegetarian(False) | Vegetarian(True) |\n",
      "+---------------+-------------------+-------------------+-------------------+------------------+\n",
      "| Indian(False) | 0.65274151436     | 0.480093676815    | 0.525531914894    | 0.3125           |\n",
      "+---------------+-------------------+-------------------+-------------------+------------------+\n",
      "| Indian(True)  | 0.34725848564     | 0.519906323185    | 0.474468085106    | 0.6875           |\n",
      "+---------------+-------------------+-------------------+-------------------+------------------+\n",
      "+----------------+--------------------+-------------------+\n",
      "| Steakhouses    | Steakhouses(False) | Steakhouses(True) |\n",
      "+----------------+--------------------+-------------------+\n",
      "| Italian(False) | 0.618645657475     | 0.412116381028    |\n",
      "+----------------+--------------------+-------------------+\n",
      "| Italian(True)  | 0.381354342525     | 0.587883618972    |\n",
      "+----------------+--------------------+-------------------+\n",
      "+-----------------+--------------------+--------------------+-------------------+-------------------+\n",
      "| AsianFusion     | AsianFusion(False) | AsianFusion(False) | AsianFusion(True) | AsianFusion(True) |\n",
      "+-----------------+--------------------+--------------------+-------------------+-------------------+\n",
      "| SushiBars       | SushiBars(False)   | SushiBars(True)    | SushiBars(False)  | SushiBars(True)   |\n",
      "+-----------------+--------------------+--------------------+-------------------+-------------------+\n",
      "| Japanese(False) | 0.848093841642     | 0.313784764208     | 0.719275549806    | 0.153049482163    |\n",
      "+-----------------+--------------------+--------------------+-------------------+-------------------+\n",
      "| Japanese(True)  | 0.151906158358     | 0.686215235792     | 0.280724450194    | 0.846950517837    |\n",
      "+-----------------+--------------------+--------------------+-------------------+-------------------+\n",
      "+---------------------------+----------+\n",
      "| JuiceBarsSmoothies(False) | 0.561903 |\n",
      "+---------------------------+----------+\n",
      "| JuiceBarsSmoothies(True)  | 0.438097 |\n",
      "+---------------------------+----------+\n",
      "+---------------+--------------------+-------------------+\n",
      "| AsianFusion   | AsianFusion(False) | AsianFusion(True) |\n",
      "+---------------+--------------------+-------------------+\n",
      "| Korean(False) | 0.690043832185     | 0.378331900258    |\n",
      "+---------------+--------------------+-------------------+\n",
      "| Korean(True)  | 0.309956167815     | 0.621668099742    |\n",
      "+---------------+--------------------+-------------------+\n",
      "+----------------------+----------------+----------------+\n",
      "| Mexican              | Mexican(False) | Mexican(True)  |\n",
      "+----------------------+----------------+----------------+\n",
      "| LatinAmerican(False) | 0.70910464677  | 0.441891294741 |\n",
      "+----------------------+----------------+----------------+\n",
      "| LatinAmerican(True)  | 0.29089535323  | 0.558108705259 |\n",
      "+----------------------+----------------+----------------+\n",
      "+----------------------+----------------------+---------------------+\n",
      "| SpecialtyFood        | SpecialtyFood(False) | SpecialtyFood(True) |\n",
      "+----------------------+----------------------+---------------------+\n",
      "| Mediterranean(False) | 0.649475524476       | 0.471408148678      |\n",
      "+----------------------+----------------------+---------------------+\n",
      "| Mediterranean(True)  | 0.350524475524       | 0.528591851322      |\n",
      "+----------------------+----------------------+---------------------+\n",
      "+----------------+----------+\n",
      "| Mexican(False) | 0.530923 |\n",
      "+----------------+----------+\n",
      "| Mexican(True)  | 0.469077 |\n",
      "+----------------+----------+\n",
      "+----------------------+----------------------+---------------------+\n",
      "| SpecialtyFood        | SpecialtyFood(False) | SpecialtyFood(True) |\n",
      "+----------------------+----------------------+---------------------+\n",
      "| MiddleEastern(False) | 0.663005272408       | 0.459517426273      |\n",
      "+----------------------+----------------------+---------------------+\n",
      "| MiddleEastern(True)  | 0.336994727592       | 0.540482573727      |\n",
      "+----------------------+----------------------+---------------------+\n",
      "+----------------+-----------------+----------------+\n",
      "| Japanese       | Japanese(False) | Japanese(True) |\n",
      "+----------------+-----------------+----------------+\n",
      "| Noodles(False) | 0.719983207389  | 0.411185914034 |\n",
      "+----------------+-----------------+----------------+\n",
      "| Noodles(True)  | 0.280016792611  | 0.588814085966 |\n",
      "+----------------+-----------------+----------------+\n",
      "+--------------+-----------------+----------------+\n",
      "| FastFood     | FastFood(False) | FastFood(True) |\n",
      "+--------------+-----------------+----------------+\n",
      "| Pizza(False) | 0.611088295688  | 0.416752418193 |\n",
      "+--------------+-----------------+----------------+\n",
      "| Pizza(True)  | 0.388911704312  | 0.583247581807 |\n",
      "+--------------+-----------------+----------------+\n",
      "+--------------+-------------------+------------------+\n",
      "| Vegetarian   | Vegetarian(False) | Vegetarian(True) |\n",
      "+--------------+-------------------+------------------+\n",
      "| Salad(False) | 0.648530762167    | 0.412574288395   |\n",
      "+--------------+-------------------+------------------+\n",
      "| Salad(True)  | 0.351469237833    | 0.587425711605   |\n",
      "+--------------+-------------------+------------------+\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "| Burgers           | Burgers(False)  | Burgers(False) | Burgers(False)  | Burgers(False) | Burgers(True)   | Burgers(True)  | Burgers(True)   | Burgers(True)  |\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "| Delis             | Delis(False)    | Delis(False)   | Delis(True)     | Delis(True)    | Delis(False)    | Delis(False)   | Delis(True)     | Delis(True)    |\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "| FastFood          | FastFood(False) | FastFood(True) | FastFood(False) | FastFood(True) | FastFood(False) | FastFood(True) | FastFood(False) | FastFood(True) |\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "| Sandwiches(False) | 0.826024442847  | 0.715502555366 | 0.573394495413  | 0.389179755672 | 0.627490039841  | 0.506172839506 | 0.325635103926  | 0.187397708674 |\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "| Sandwiches(True)  | 0.173975557153  | 0.284497444634 | 0.426605504587  | 0.610820244328 | 0.372509960159  | 0.493827160494 | 0.674364896074  | 0.812602291326 |\n",
      "+-------------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+-----------------+----------------+\n",
      "+----------------+----------+\n",
      "| Seafood(False) | 0.520462 |\n",
      "+----------------+----------+\n",
      "| Seafood(True)  | 0.479538 |\n",
      "+----------------+----------+\n",
      "+-------------+-------------------+------------------+\n",
      "| Sandwiches  | Sandwiches(False) | Sandwiches(True) |\n",
      "+-------------+-------------------+------------------+\n",
      "| Soup(False) | 0.689098660171    | 0.374405850091   |\n",
      "+-------------+-------------------+------------------+\n",
      "| Soup(True)  | 0.310901339829    | 0.625594149909   |\n",
      "+-------------+-------------------+------------------+\n",
      "+-----------------+----------+\n",
      "| Southern(False) | 0.562833 |\n",
      "+-----------------+----------+\n",
      "| Southern(True)  | 0.437167 |\n",
      "+-----------------+----------+\n",
      "+----------------------+----------+\n",
      "| SpecialtyFood(False) | 0.545073 |\n",
      "+----------------------+----------+\n",
      "| SpecialtyFood(True)  | 0.454927 |\n",
      "+----------------------+----------+\n",
      "+---------------------+----------------------------+----------------------------+---------------------------+---------------------------+\n",
      "| AmericanTraditional | AmericanTraditional(False) | AmericanTraditional(False) | AmericanTraditional(True) | AmericanTraditional(True) |\n",
      "+---------------------+----------------------------+----------------------------+---------------------------+---------------------------+\n",
      "| Burgers             | Burgers(False)             | Burgers(True)              | Burgers(False)            | Burgers(True)             |\n",
      "+---------------------+----------------------------+----------------------------+---------------------------+---------------------------+\n",
      "| Steakhouses(False)  | 0.69336505419              | 0.581686429513             | 0.424747174301            | 0.300511508951            |\n",
      "+---------------------+----------------------------+----------------------------+---------------------------+---------------------------+\n",
      "| Steakhouses(True)   | 0.30663494581              | 0.418313570487             | 0.575252825699            | 0.699488491049            |\n",
      "+---------------------+----------------------------+----------------------------+---------------------------+---------------------------+\n",
      "+------------------+--------------------+-------------------+\n",
      "| AsianFusion      | AsianFusion(False) | AsianFusion(True) |\n",
      "+------------------+--------------------+-------------------+\n",
      "| SushiBars(False) | 0.670860685631     | 0.365913120567    |\n",
      "+------------------+--------------------+-------------------+\n",
      "| SushiBars(True)  | 0.329139314369     | 0.634086879433    |\n",
      "+------------------+--------------------+-------------------+\n",
      "+------------------+----------------------+---------------------+\n",
      "| LatinAmerican    | LatinAmerican(False) | LatinAmerican(True) |\n",
      "+------------------+----------------------+---------------------+\n",
      "| TapasBars(False) | 0.610743801653       | 0.456117021277      |\n",
      "+------------------+----------------------+---------------------+\n",
      "| TapasBars(True)  | 0.389256198347       | 0.543882978723      |\n",
      "+------------------+----------------------+---------------------+\n",
      "+-------------------------+------------------+-----------------+\n",
      "| TapasBars               | TapasBars(False) | TapasBars(True) |\n",
      "+-------------------------+------------------+-----------------+\n",
      "| TapasSmallPlates(False) | 0.782918149466   | 0.32380952381   |\n",
      "+-------------------------+------------------+-----------------+\n",
      "| TapasSmallPlates(True)  | 0.217081850534   | 0.67619047619   |\n",
      "+-------------------------+------------------+-----------------+\n",
      "+---------------+----------------+----------------+\n",
      "| Mexican       | Mexican(False) | Mexican(True)  |\n",
      "+---------------+----------------+----------------+\n",
      "| TexMex(False) | 0.719704952582 | 0.288546255507 |\n",
      "+---------------+----------------+----------------+\n",
      "| TexMex(True)  | 0.280295047418 | 0.711453744493 |\n",
      "+---------------+----------------+----------------+\n",
      "+-------------+--------------------+-------------------+\n",
      "| AsianFusion | AsianFusion(False) | AsianFusion(True) |\n",
      "+-------------+--------------------+-------------------+\n",
      "| Thai(False) | 0.677266867211     | 0.418082191781    |\n",
      "+-------------+--------------------+-------------------+\n",
      "| Thai(True)  | 0.322733132789     | 0.581917808219    |\n",
      "+-------------+--------------------+-------------------+\n",
      "+--------------+-------------------+------------------+\n",
      "| Vegetarian   | Vegetarian(False) | Vegetarian(True) |\n",
      "+--------------+-------------------+------------------+\n",
      "| Vegan(False) | 0.796666666667    | 0.327881619938   |\n",
      "+--------------+-------------------+------------------+\n",
      "| Vegan(True)  | 0.203333333333    | 0.672118380062   |\n",
      "+--------------+-------------------+------------------+\n",
      "+--------------------+---------------------------+--------------------------+\n",
      "| JuiceBarsSmoothies | JuiceBarsSmoothies(False) | JuiceBarsSmoothies(True) |\n",
      "+--------------------+---------------------------+--------------------------+\n",
      "| Vegetarian(False)  | 0.696313364055            | 0.401704199635           |\n",
      "+--------------------+---------------------------+--------------------------+\n",
      "| Vegetarian(True)   | 0.303686635945            | 0.598295800365           |\n",
      "+--------------------+---------------------------+--------------------------+\n",
      "+-------------------+--------------------+-------------------+\n",
      "| AsianFusion       | AsianFusion(False) | AsianFusion(True) |\n",
      "+-------------------+--------------------+-------------------+\n",
      "| Vietnamese(False) | 0.65756893641      | 0.427041004802    |\n",
      "+-------------------+--------------------+-------------------+\n",
      "| Vietnamese(True)  | 0.34243106359      | 0.572958995198    |\n",
      "+-------------------+--------------------+-------------------+\n",
      "+-----------------+----------------+----------------+\n",
      "| Beer            | Beer(False)    | Beer(True)     |\n",
      "+-----------------+----------------+----------------+\n",
      "| WineBars(False) | 0.644649257552 | 0.363548698168 |\n",
      "+-----------------+----------------+----------------+\n",
      "| WineBars(True)  | 0.355350742448 | 0.636451301832 |\n",
      "+-----------------+----------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "for cpd in model.cpds:\n",
    "    print cpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import pickle\n",
    "with open('bayesMAN.pkl','wb') as fp:\n",
    "    pickle.dump(model,fp)\n",
    "from pgmpy.readwrite import *\n",
    "writer = BIFWriter(model)\n",
    "writer.write_bif('bayesMAN.bif')\n",
    "\n",
    "writer = UAIWriter(model)\n",
    "writer.write_uai('bayesMAN.uai')\n",
    "\n",
    "writer = XMLBIFWriter(model)\n",
    "writer.write_xmlbif('bayesMAN.bif.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-173-470cf16980fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mHillClimbSearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_sample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring_method\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mBicScore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_sample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbest_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_indegree\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0medges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pgmpy-0.1.3-py2.7.egg/pgmpy/estimators/HillClimbSearch.pyc\u001b[0m in \u001b[0;36mestimate\u001b[1;34m(self, start, tabu_length, max_indegree)\u001b[0m\n\u001b[0;32m    152\u001b[0m             \u001b[0mbest_operation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 154\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0moperation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscore_delta\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legal_operations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtabu_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_indegree\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    155\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mscore_delta\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mbest_score_delta\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m                     \u001b[0mbest_operation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moperation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pgmpy-0.1.3-py2.7.egg/pgmpy/estimators/HillClimbSearch.pyc\u001b[0m in \u001b[0;36m_legal_operations\u001b[1;34m(self, model, tabu_list, max_indegree)\u001b[0m\n\u001b[0;32m     64\u001b[0m                     \u001b[0mnew_parents\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mold_parents\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mmax_indegree\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_parents\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mmax_indegree\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m                         \u001b[0mscore_delta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlocal_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_parents\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlocal_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mold_parents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m                         \u001b[1;32myield\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moperation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscore_delta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pgmpy-0.1.3-py2.7.egg/pgmpy/estimators/BicScore.pyc\u001b[0m in \u001b[0;36mlocal_score\u001b[1;34m(self, variable, parents)\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mvar_states\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate_names\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvariable\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[0mvar_cardinality\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_states\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m         \u001b[0mstate_counts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m         \u001b[0msample_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[0mnum_parents_states\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_counts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pgmpy-0.1.3-py2.7.egg/pgmpy/estimators/base.pyc\u001b[0m in \u001b[0;36mstate_counts\u001b[1;34m(self, variable, parents, complete_samples_only)\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0mcomplete_samples_only\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomplete_samples_only\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m         \u001b[1;31m# ignores either any row containing NaN, or only those where the variable or its parents is NaN\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcomplete_samples_only\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvariable\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mparents\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mparents\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mdropna\u001b[1;34m(self, axis, how, thresh, subset, inplace)\u001b[0m\n\u001b[0;32m   3105\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'must specify how or thresh'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3107\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3109\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy, **kwargs)\u001b[0m\n\u001b[0;32m   1667\u001b[0m         new_data = self._data.take(indices,\n\u001b[0;32m   1668\u001b[0m                                    \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1669\u001b[1;33m                                    convert=True, verify=True)\n\u001b[0m\u001b[0;32m   1670\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1671\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m   3960\u001b[0m         \u001b[0mnew_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3961\u001b[0m         return self.reindex_indexer(new_axis=new_labels, indexer=indexer,\n\u001b[1;32m-> 3962\u001b[1;33m                                     axis=axis, allow_dups=True)\n\u001b[0m\u001b[0;32m   3963\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3964\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m   3846\u001b[0m             new_blocks = [blk.take_nd(indexer, axis=axis, fill_tuple=(\n\u001b[0;32m   3847\u001b[0m                 fill_value if fill_value is not None else blk.fill_value,))\n\u001b[1;32m-> 3848\u001b[1;33m                 for blk in self.blocks]\n\u001b[0m\u001b[0;32m   3849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3850\u001b[0m         \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_tuple)\u001b[0m\n\u001b[0;32m   1021\u001b[0m             \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfill_tuple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1022\u001b[0m             new_values = algos.take_nd(values, indexer, axis=axis,\n\u001b[1;32m-> 1023\u001b[1;33m                                        allow_fill=True, fill_value=fill_value)\n\u001b[0m\u001b[0;32m   1024\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_mgr_locs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/algorithms.pyc\u001b[0m in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, out, fill_value, mask_info, allow_fill)\u001b[0m\n\u001b[0;32m   1030\u001b[0m             \u001b[1;31m# check for promotion based on types only (do this first because\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m             \u001b[1;31m# it's faster than computing a mask)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1032\u001b[1;33m             \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_maybe_promote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1033\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1034\u001b[0m                 \u001b[1;31m# check if promotion is actually required based on indexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/types/cast.pyc\u001b[0m in \u001b[0;36m_maybe_promote\u001b[1;34m(dtype, fill_value)\u001b[0m\n\u001b[0;32m    300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m     \u001b[1;31m# in case we have a string that looked like a number\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m         \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_datetimetz\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/types/common.pyc\u001b[0m in \u001b[0;36mis_categorical_dtype\u001b[1;34m(arr_or_dtype)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mCategoricalDtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/types/dtypes.pyc\u001b[0m in \u001b[0;36mis_dtype\u001b[1;34m(cls, dtype)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[0mwe\u001b[0m \u001b[0mcan\u001b[0m \u001b[0mmatch\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvia\u001b[0m \u001b[0mstring\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \"\"\"\n\u001b[1;32m---> 79\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'dtype'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "est = HillClimbSearch(data_sample.dropna(), scoring_method=BicScore(data_sample.dropna()))\n",
    "best_model = est.estimate(max_indegree=2).edges()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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