wenqiang.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 1]\n"
     ]
    }
   ],
   "source": [
    "print lables_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(genotype_data, lables_arr, test_size=0.1,\n",
    "                                                    random_state=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier as rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rf_params = {'n_estimators': 3000, 'criterion': 'entropy', 'max_depth': 3, 'random_state': 1234}\n",
    "clf = rf(**rf_params)\n",
    "clf.fit(X_train, y_train)\n",
    "train_acc = clf.score(X_train, y_train)\n",
    "test_acc = clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.54\n"
     ]
    }
   ],
   "source": [
    "print train_acc, test_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于一些准则进行特征选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feature_num = np.size(genotype_data, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 互信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2652: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n",
      "  VisibleDeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0a5ffa9810>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mutual_info_score\n",
    "MI = np.zeros(feature_num)\n",
    "\n",
    "for i in range(feature_num):\n",
    "    MI[i] = mutual_info_score(genotype_data[:, i], lables_arr)\n",
    "    \n",
    "plt.figure(3)\n",
    "plt.plot(MI)\n",
    "plt.xlabel(\"feature_index\")\n",
    "plt.ylabel(\"MI\")\n",
    "\n",
    "mi_rank = np.argsort(np.abs(MI))[-1::-1]\n",
    "plt.figure(4)\n",
    "plt.plot(MI[mi_rank])\n",
    "plt.ylabel(\"MI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['clf', 'f']\n",
      "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0a6546e110>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure(3)\n",
    "plot(MI)\n",
    "xlabel(\"feature_index\")\n",
    "ylabel(\"MI\")\n",
    "\n",
    "mi_rank = np.argsort(np.abs(MI))[-1::-1]\n",
    "figure(4)\n",
    "plot(MI[mi_rank])\n",
    "ylabel(\"MI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0a65329150>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_params['n_estimators'] = 300\n",
    "clf = rf(**rf_params)\n",
    "clf.fit(X_train, y_train)\n",
    "train_acc = clf.score(X_train, y_train)\n",
    "test_acc = clf.score(X_test, y_test)\n",
    "\n",
    "max_feature_num = 200\n",
    "rf_mi_train_acc_list = []\n",
    "rf_mi_test_acc_list = []\n",
    "for cur_feature_num in xrange(1, max_feature_num+1):\n",
    "    feature_ind = mi_rank[0:cur_feature_num]\n",
    "    cur_X_train = X_train[:, feature_ind]\n",
    "    cur_X_test = X_test[:, feature_ind]\n",
    "\n",
    "    clf = rf(**rf_params)\n",
    "    clf.fit(cur_X_train, y_train)\n",
    "    mi_train_acc = clf.score(cur_X_train, y_train)\n",
    "    rf_mi_train_acc_list.append(mi_train_acc)\n",
    "    mi_test_acc = clf.score(cur_X_test, y_test)\n",
    "    rf_mi_test_acc_list.append(mi_test_acc)\n",
    "    \n",
    "figure(5)\n",
    "plot(xrange(1, max_feature_num+1), rf_mi_train_acc_list,'r*--')\n",
    "plot(xrange(1, max_feature_num+1), rf_mi_test_acc_list, 'gs--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.931111111111 0.81\n"
     ]
    }
   ],
   "source": [
    "print max(rf_mi_train_acc_list), max(rf_mi_test_acc_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GDBT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gdbt_params = {'n_estimators': 100, 'max_depth': 3, 'subsample': 0.9, \n",
    "          'learning_rate': 0.1, 'min_samples_leaf': 1, 'random_state': 1234}\n",
    "\n",
    "gdbt_params['n_estimators'] = 200\n",
    "clf = GradientBoostingClassifier(**gdbt_params)\n",
    "clf.fit(X_train, y_train)\n",
    "train_acc = clf.score(X_train, y_train)\n",
    "test_acc = clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.5\n"
     ]
    }
   ],
   "source": [
    "print train_acc, test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  -7.26047785e-04  -2.08224541e-03  -3.04035160e-03  -6.47195957e-04\n",
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      "  -5.79656912e-04  -2.65259397e-03  -2.69471092e-03  -3.67646773e-03\n",
      "  -5.36915596e-04   8.31840455e-05  -1.90592099e-03  -4.80428040e-04\n",
      "  -1.68155276e-03  -2.38080572e-03  -3.62103772e-03  -2.34214333e-04\n",
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      "  -4.47367849e-04  -1.54596174e-03  -1.42911634e-03   1.47689070e-05\n",
      "  -6.75000925e-04  -2.67149365e-03  -5.10288289e-04  -2.70814742e-03\n",
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      "  -1.09472626e-03   4.15660149e-04  -6.57451954e-04   2.19323802e-04\n",
      "  -1.66733493e-03  -3.65087850e-04  -2.00412817e-03  -8.98399020e-04\n",
      "  -4.85129993e-04  -1.30696823e-03  -8.74363161e-04   4.63686554e-04\n",
      "  -1.14269298e-03  -8.60911141e-04  -2.48086781e-03  -1.21421864e-03\n",
      "  -1.07147335e-03  -2.73898236e-04  -9.62835543e-04  -1.16767352e-03\n",
      "  -1.04535746e-03  -4.33815062e-04  -7.63658239e-04  -1.04639065e-03\n",
      "  -2.93186458e-04   1.33613643e-04  -1.06440405e-03  -4.85981957e-04\n",
      "  -9.66838311e-04  -1.80012873e-03  -1.22237808e-03  -4.05332232e-04\n",
      "  -1.02270775e-03  -1.50874278e-03  -9.11069418e-04   5.02350263e-04\n",
      "  -1.45760042e-03  -1.22011017e-03  -2.55046348e-03  -9.61168326e-04]\n"
     ]
    }
   ],
   "source": [
    "print clf.oob_improvement_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0a5e199fd0>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdbt_mi_train_acc_list = []\n",
    "gdbt_mi_test_acc_list = []\n",
    "gdbt_params['subsample'] = 1\n",
    "gdbt_params['n_estimators'] = 200\n",
    "for cur_feature_num in xrange(1, max_feature_num+1):\n",
    "    feature_ind = mi_rank[0:cur_feature_num]\n",
    "    cur_X_train = X_train[:, feature_ind]\n",
    "    cur_X_test = X_test[:, feature_ind]\n",
    "\n",
    "    clf = GradientBoostingClassifier(**gdbt_params)\n",
    "    clf.fit(cur_X_train, y_train)\n",
    "    mi_train_acc = clf.score(cur_X_train, y_train)\n",
    "    gdbt_mi_train_acc_list.append(mi_train_acc)\n",
    "    mi_test_acc = clf.score(cur_X_test, y_test)\n",
    "    gdbt_mi_test_acc_list.append(mi_test_acc)\n",
    "    \n",
    "figure(6)\n",
    "plot(xrange(1, max_feature_num+1), gdbt_mi_train_acc_list,'r*--')\n",
    "plot(xrange(1, max_feature_num+1), gdbt_mi_test_acc_list, 'gs--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.84\n"
     ]
    }
   ],
   "source": [
    "print max(gdbt_mi_train_acc_list), max(gdbt_mi_test_acc_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103\n"
     ]
    }
   ],
   "source": [
    "print np.argmax(gdbt_mi_test_acc_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结论\n",
    "互信息前82名的特征得到的测试集效果最好， 从这82个特征中前向进行剔除特征看效果是否会提升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cur_feature_num = 82\n",
    "feature_ind = mi_rank[0:cur_feature_num]\n",
    "cur_X_train = X_train[:, feature_ind]\n",
    "cur_X_test = X_test[:, feature_ind]\n",
    "clf = GradientBoostingClassifier(**gdbt_params)\n",
    "clf.fit(cur_X_train, y_train)\n",
    "mi_train_acc = clf.score(cur_X_train, y_train)\n",
    "mi_test_acc = clf.score(cur_X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.985555555556 0.84\n"
     ]
    }
   ],
   "source": [
    "print mi_train_acc, mi_test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.01899327  0.01758077  0.01133334  0.01559472  0.01832101  0.01341661\n",
      "  0.01369941  0.01241654  0.01658318  0.00969473  0.00954414  0.01378536\n",
      "  0.01083615  0.01056841  0.02391773  0.01301598  0.01408381  0.01925907\n",
      "  0.01468732  0.01350517  0.01610973  0.01467619  0.00691329  0.01249541\n",
      "  0.01047473  0.01456688  0.01501269  0.01275605  0.01884516  0.00619831\n",
      "  0.00936878  0.00959829  0.00700818  0.0145365   0.00828712  0.00786879\n",
      "  0.01498916  0.00855832  0.01540081  0.00721711  0.00743429  0.0098883\n",
      "  0.01003532  0.00431773  0.00856916  0.01593708  0.01903795  0.0076498\n",
      "  0.01205824  0.02253112  0.0121903   0.01033782  0.00989067  0.00779281\n",
      "  0.01445723  0.01041881  0.01314126  0.00726177  0.01495005  0.00776485\n",
      "  0.01632536  0.01587569  0.01659572  0.0114799   0.01010051  0.01110534\n",
      "  0.01462398  0.0052181   0.01854182  0.01261601  0.01244281  0.00633086\n",
      "  0.00938596  0.00810117  0.00937094  0.01380671  0.00316573  0.01130853\n",
      "  0.02267741  0.00380837  0.01146635  0.00826995]\n"
     ]
    }
   ],
   "source": [
    "print clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2937 1791 8379 7736   79 8588 9423 1540  961 3753 3587 1592 6793 4525 7841\n",
      "  756 5943 5936 6840 3771  477 4285 4931 1194 7961  800 8138 1234 3327 2178\n",
      " 5564 6377 1923 6076 2372 8434 3572 8354 5660 3306 9220 4659 1274 3340 4886\n",
      " 2201 8587 3009 1598 5224 6970 9277 1226 8389 4919 8383 3816 7741 9195 3397\n",
      " 5720 7381 5092 4222 6784 7994 9163 6097 1381  679 7414 3926 2258 6603 2982\n",
      " 7047  592 5368  609 8355 5168 8779]\n"
     ]
    }
   ],
   "source": [
    "print feature_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 遗传算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "gdbt_params['subsample'] = 1\n",
    "gdbt_params['n_estimators'] = 200\n",
    "cur_feature_num = 120\n",
    "feature_ind = mi_rank[0:cur_feature_num]\n",
    "\n",
    "popsize = 200 #种群的大小\n",
    "pop = [np.random.randint(0,2,cur_feature_num).tolist()  for i in range(popsize)]\n",
    "pc = 0.6 #两个个体交叉的概率\n",
    "pm = 0.01; #基因突变的概率\n",
    "results = [[]]\n",
    "bestindividual = []\n",
    "bestfit = 0\n",
    "fitvalue = []\n",
    "tempop = [[]]\n",
    "\n",
    "def b2d(b): #将二进制转化为十进制 x∈[0,10]\n",
    "\tt = 0\n",
    "\tfor j in range(len(b)):\n",
    "\t\tt += b[j] * (math.pow(2, j))\n",
    "\tt = t * 10 / 1023\n",
    "\treturn t\n",
    "\n",
    "def best(pop, fitvalue): #找出适应函数值中最大值，和对应的个体\n",
    "\tpx = len(pop)\n",
    "\tbestindividual = []\n",
    "\tbestfit = fitvalue[0]\n",
    "\tfor i in range(1,px):\n",
    "\t\tif(fitvalue[i] > bestfit):\n",
    "\t\t\tbestfit = fitvalue[i]\n",
    "\t\t\tbestindividual = pop[i]\n",
    "\treturn [bestindividual, bestfit]\n",
    "\n",
    "def calfitvalue(objvalue):#转化为适应值，目标函数值越大越好，负值淘汰。\n",
    "    fitvalue = []\n",
    "    temp = 0.0\n",
    "    Cmin = 0;\n",
    "    for i in range(len(objvalue)):\n",
    "        if(objvalue[i] + Cmin > 0):\n",
    "            temp = Cmin + objvalue[i]\n",
    "        else:\n",
    "            temp = 0.0\n",
    "        fitvalue.append(temp)\n",
    "    return fitvalue\n",
    "\n",
    "import math\n",
    "\n",
    "def decodechrom(pop): #将种群的二进制基因转化为十进制（0,1023）\n",
    "    temp = [];\n",
    "    for i in range(len(pop)):\n",
    "        t = 0;\n",
    "        for j in range(10):\n",
    "            t += pop[i][j] * (math.pow(2, j))\n",
    "        temp.append(t)\n",
    "    return temp\n",
    "\n",
    "def calobjvalue(pop): #计算目标函数值\n",
    "    temp1 = [];\n",
    "    objvalue = [];\n",
    "    for cur_pop in pop:\n",
    "        \n",
    "        if np.size(np.where(np.array(cur_pop)==1)[0])<1:\n",
    "            objvalue.append(0)\n",
    "            continue\n",
    "        \n",
    " #       print np.size(np.where(cur_pop==1)[0])\n",
    "        cur_feature_indices = feature_ind[np.where(np.array(cur_pop)==1)[0]]\n",
    "        cur_X_train = X_train[:, cur_feature_indices]\n",
    "        cur_X_test = X_test[:, cur_feature_indices]\n",
    "        clf = GradientBoostingClassifier(**gdbt_params)\n",
    "        clf.fit(cur_X_train, y_train)\n",
    " #       mi_train_acc = clf.score(cur_X_train, y_train)\n",
    "        mi_test_acc = clf.score(cur_X_test, y_test)\n",
    "        objvalue.append(mi_test_acc)\n",
    "    return objvalue #目标函数值objvalue[m] 与个体基因 pop[m] 对应 \n",
    "\n",
    "import random\n",
    "\n",
    "def crossover(pop, pc): #个体间交叉，实现基因交换\n",
    "    poplen = len(pop)\n",
    "    for i in range(poplen - 1):\n",
    "        if(random.random() < pc):\n",
    "            cpoint = random.randint(0,len(pop[0]))\n",
    "            temp1 = []\n",
    "            temp2 = []\n",
    "            temp1.extend(pop[i][0 : cpoint])\n",
    "            temp1.extend(pop[i+1][cpoint : len(pop[i])])\n",
    "            temp2.extend(pop[i+1][0 : cpoint])\n",
    "            temp2.extend(pop[i][cpoint : len(pop[i])])\n",
    "            pop[i] = temp1\n",
    "            pop[i+1] = temp2\n",
    "\n",
    "import random\n",
    "\n",
    "def mutation(pop, pm): #基因突变\n",
    "    px = len(pop)\n",
    "    py = len(pop[0])\n",
    "    \n",
    "    for i in range(px):\n",
    "        if(random.random() < pm):\n",
    "            mpoint = random.randint(0,py-1)\n",
    "            if(pop[i][mpoint] == 1):\n",
    "                pop[i][mpoint] = 0\n",
    "            else:\n",
    "                pop[i][mpoint] = 1\n",
    "                \n",
    "import random\n",
    "\n",
    "def sum(fitvalue):\n",
    "    total = 0\n",
    "    for i in range(len(fitvalue)):\n",
    "        total += fitvalue[i]\n",
    "    return total\n",
    "\n",
    "def cumsum(fitvalue):\n",
    "    for i in range(len(fitvalue)):\n",
    "        t = 0;\n",
    "        j = 0;\n",
    "        while(j <= i):\n",
    "            t += fitvalue[j]\n",
    "            j = j + 1\n",
    "        fitvalue[i] = t;\n",
    "\n",
    "def selection(pop, fitvalue): #自然选择（轮盘赌算法）\n",
    "\tnewfitvalue = []\n",
    "\ttotalfit = sum(fitvalue)\n",
    "\tif totalfit==0.0:\n",
    "\t\tpop = [np.random.randint(0,2,cur_feature_num).tolist()  for i in range(popsize)]\n",
    "        return\n",
    "\n",
    "\tfor i in range(len(fitvalue)):\n",
    "\t\tnewfitvalue.append(fitvalue[i] / totalfit)\n",
    "\tcumsum(newfitvalue)\n",
    "\tms = [];\n",
    "\tpoplen = len(pop)\n",
    "\tfor i in range(poplen):\n",
    "\t\tms.append(random.random()) #random float list ms\n",
    "\tms.sort()\n",
    "\tfitin = 0\n",
    "\tnewin = 0\n",
    "\tnewpop = pop\n",
    "\twhile newin < poplen:\n",
    "\t\tif(ms[newin] < newfitvalue[fitin]):\n",
    "\t\t\tnewpop[newin] = pop[fitin]\n",
    "\t\t\tnewin = newin + 1\n",
    "\t\telse:\n",
    "\t\t\tfitin = fitin + 1\n",
    "\tpop = newpop\n",
    "\n",
    "for i in range(100): #繁殖100代\n",
    "    print i\n",
    "    objvalue = calobjvalue(pop) #计算目标函数值\n",
    "    fitvalue = calfitvalue(objvalue); #计算个体的适应值\n",
    "    [bestindividual, bestfit] = best(pop, fitvalue) #选出最好的个体和最好的函数值\n",
    "    results.append([bestfit,bestindividual]) #每次繁殖，将最好的结果记录下来\n",
    "    selection(pop, fitvalue) #自然选择，淘汰掉一部分适应性低的个体\n",
    "    crossover(pop, pc) #交叉繁殖\n",
    "    mutation(pop, pc) #基因突变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[], [0.84999999999999998, [0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0]], [0.84999999999999998, [0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0]], [0.81000000000000005, [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0]], [0.79000000000000004, [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1]], [0.81000000000000005, [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1]], [0.81000000000000005, [0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 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1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0]], [0.81999999999999995, [1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0]], [0.79000000000000004, [0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1]], [0.81999999999999995, [0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1]], [0.83999999999999997, [0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1]], [0.81000000000000005, [1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0]], [0.85999999999999999, [0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0]], [0.81000000000000005, [1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0]], [0.81000000000000005, [1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0]], [0.81000000000000005, [1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0]], [0.79000000000000004, [0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0]], [0.81000000000000005, [0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0]]]\n"
     ]
    }
   ],
   "source": [
    "print results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72\n"
     ]
    }
   ],
   "source": [
    "print sum(results[-6][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.86\n",
      "[1791 8379   79 8588 1540  961 3753 3587 4525 5936 7961 8138 1234 6377 1923\n",
      " 3572 3306 3340 4886 5224 6970 9277 1226 3816 3397 5720 7381 6784 9163 6097\n",
      " 1381 7414 3926 2982  592 5368 8355 1974 5904 2091 4254 6759 7622 8796 3384\n",
      " 7302 7672 4049 8995 5157 7935 5273 7365 3077 5906]\n",
      "55\n"
     ]
    }
   ],
   "source": [
    "max_acc = 0\n",
    "for cur_result in results:\n",
    "    if len(cur_result)==0:\n",
    "        continue\n",
    "    cur_acc = cur_result[0]\n",
    "    if cur_acc>max_acc:\n",
    "        max_acc = cur_acc\n",
    "        feature_flag = cur_result[1]\n",
    "        \n",
    "best_feature = feature_ind[np.where(np.array(feature_flag)==1)[0]]\n",
    "print max_acc\n",
    "print best_feature\n",
    "print len(best_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.01009204  0.0084789   0.00790148  0.00781446  0.00730066  0.00720742\n",
      "  0.00708501  0.00678139  0.00647837  0.00645415  0.00597278  0.0057852\n",
      "  0.0056969   0.0055131   0.00549771  0.00536705  0.00527317  0.00517987\n",
      "  0.00515105  0.0049785   0.00495251  0.00493022  0.00490166  0.00485899\n",
      "  0.00480929  0.00480081  0.00477613  0.00475673  0.00473247  0.00473203\n",
      "  0.00471263  0.0046856   0.00468038  0.00465749  0.00463503  0.00462565\n",
      "  0.00458339  0.00451413  0.00449469  0.00447955  0.00447709  0.00445471\n",
      "  0.00443459  0.00443322  0.00441209  0.00440781  0.0043974   0.00439115\n",
      "  0.0043658   0.00436335  0.00432519  0.00432203  0.0043216   0.00431365\n",
      "  0.00430654]\n"
     ]
    }
   ],
   "source": [
    "print MI[best_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import KFold\n",
    "from multiprocessing import Pool\n",
    "\n",
    "genotype = pd.read_csv(\"/home/flame/Desktop/solutions/data/genotype.csv\")\n",
    "genotype_data = genotype.values\n",
    "\n",
    "lables_list = []\n",
    "with open(\"/home/flame/Desktop/solutions/data/phenotype.txt\", mode = \"r\") as f:\n",
    "    for line in f.readlines():\n",
    "        lables_list.append(int(line.strip()))\n",
    "        \n",
    "lables_arr = np.array(lables_list)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "调整树的深度，实验性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gdbt_params['subsample'] = 1\n",
    "gdbt_params['n_estimators'] = 200\n",
    "text_tree_depth_result = {}\n",
    "for i in xrange(3,9):\n",
    "    gdbt_params['max_depth'] = i\n",
    "    cur_X_train = X_train[:, best_feature]\n",
    "    cur_X_test = X_test[:, best_feature]\n",
    "    clf = GradientBoostingClassifier(**gdbt_params)\n",
    "    clf.fit(cur_X_train, y_train)\n",
    "    mi_train_acc = clf.score(cur_X_train, y_train)\n",
    "    mi_test_acc = clf.score(cur_X_test, y_test)\n",
    "    text_tree_depth_result[i] = (mi_train_acc, mi_test_acc)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3: (0.96111111111111114, 0.85999999999999999), 4: (0.99888888888888894, 0.85999999999999999), 5: (1.0, 0.81999999999999995), 6: (1.0, 0.83999999999999997), 7: (1.0, 0.77000000000000002), 8: (1.0, 0.75)}\n"
     ]
    }
   ],
   "source": [
    "print text_tree_depth_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进一步统计每个位点各个取值下得病的百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def disease_statistic(cur_feature_ind):\n",
    "    cur_feature = genotype_data[:,cur_feature_ind]\n",
    "    disease_dict = {}\n",
    "    for i in xrange(1,4):\n",
    "        ind_arr1 = np.where(cur_feature==i)[0]\n",
    "        positive_num = len(np.where(lables_arr[ind_arr1]==1)[0])\n",
    "        negtive_num = len(ind_arr1)-positive_num\n",
    "        disease_dict[i] = [positive_num, negtive_num]\n",
    "    return disease_dict\n",
    " \n",
    "def disease_statistic_plot(disease_dict):\n",
    "    men1 = []\n",
    "    men2 = []\n",
    "    for i in xrange(1,4):\n",
    "        cur_item = disease_dict[i]\n",
    "        men2.append(cur_item[0])\n",
    "        men1.append(sum(cur_item))\n",
    "        \n",
    "    men1 = tuple(men1)\n",
    "    men2 = tuple(men2)\n",
    "    \n",
    "    ind = np.arange(3) + .15\n",
    "    width = 0.40\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(ind, men1, width, color='g') \n",
    "    rects2 = ax.bar(ind, men2, width, color='r')\n",
    "    \n",
    "    ax.set_xticks(ind+width-0.2)\n",
    "    ax.set_xticklabels(('1', '2', '3'))\n",
    "    plt.show()\n",
    "disease_dict = disease_statistic(1)\n",
    "disease_statistic_plot(disease_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 找出与疾病最相关的一个或者几个基因"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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8218, 8219, 8220, 8221, 8222, 8223, 8224], u'270': [8492, 8493, 8494, 8495, 8496, 8497, 8498, 8499, 8500], u'260': [8136, 8137, 8138, 8139, 8140, 8141, 8142, 8143, 8144, 8145, 8146, 8147, 8148, 8149, 8150, 8151, 8152, 8153, 8154, 8155, 8156, 8157, 8158, 8159, 8160, 8161, 8162, 8163, 8164, 8165, 8166, 8167, 8168, 8169, 8170, 8171, 8172], u'276': [8689, 8690, 8691, 8692, 8693, 8694, 8695, 8696, 8697, 8698, 8699, 8700, 8701, 8702, 8703, 8704, 8705, 8706, 8707, 8708, 8709, 8710, 8711, 8712, 8713, 8714], u'267': [8397, 8398, 8399, 8400], u'266': [8394, 8395, 8396]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "f = open(\"/home/flame/Desktop/solutions/data/gene_encoding.json\",mode=\"r\")\n",
    "gene_encoding = json.load(f)\n",
    "f.close()\n",
    "print gene_encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'best_feature' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-9cc13a3f7e40>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtarget_gene\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcur_feature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbest_feature\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcur_gene\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_snp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgene_encoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcur_feature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcur_snp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtarget_gene\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_gene\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'best_feature' is not defined"
     ]
    }
   ],
   "source": [
    "target_gene = {}\n",
    "for cur_feature in best_feature:\n",
    "    for cur_gene, cur_snp in gene_encoding.iteritems():\n",
    "        if cur_feature in cur_snp:\n",
    "            if target_gene.has_key(cur_gene):\n",
    "                target_gene[cur_gene].append(cur_feature)\n",
    "            else:\n",
    "                target_gene[cur_gene] = [cur_feature]\n",
    "\n",
    "sorted(target_gene.iteritems(), key=lambda d:d[0])\n",
    "print target_gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gdbt_params['subsample'] = 1\n",
    "gdbt_params['n_estimators'] = 200\n"
   ]
  }
 ],
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