rainfall_logistic_neurals_features_B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "mingw_path = 'C:\\Program Files\\mingw-w64\\x86_64-6.3.0-posix-seh-rt_v5-rev2\\mingw64\\bin'\n",
    "os.environ['PATH'] = mingw_path + ';' + os.environ['PATH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import sklearn\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "train_y = []\n",
    "count = 0\n",
    "with open('CIKM2017_train/train.txt') as fhdl:\n",
    "    for line in fhdl:\n",
    "        count += 1\n",
    "        if count % 50 == 0:\n",
    "            print (float(count) / 10000,end=' '),\n",
    "        linenum,label,datas = line.strip().split(',')\n",
    "        label = float(label)\n",
    "        datas = np.asarray(datas.split(\" \"),dtype=np.int)\n",
    "        datas[datas < 0] = 0\n",
    "        datas = np.asarray(datas,dtype=np.uint8)\n",
    "        train_x.append(datas)\n",
    "        train_y.append(label)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "import pickle\n",
    "with open('datauint8.pkl','wb') as whdl:\n",
    "    pickle.dump((train_x,train_y),whdl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "del train_y\n",
    "del train_x\n",
    "del test_x\n",
    "del test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "train_x,train_y = pickle.load(open('datauint8.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),len(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "train_x,test_x,train_y,test_y = train_test_split(train_x,train_y,test_size=0.2,random_state=1120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# do not use this now, will cause future shuffle slow\n",
    "#train_x = np.asarray(train_x,dtype=np.uint8)\n",
    "#test_x = np.asarray(test_x,dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-2b81e72ba91a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "len(train_x),len(train_y),test_x.shape,len(test_y)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "from sklearn.utils import shuffle\n",
    "train_x,train_y = shuffle(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "#pickle.dump((train_x,train_y),open('data.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "960000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.getsizeof(train_x[0]) * 10000"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "sample = np.asarray([-2,2],dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "sample[sample < 0] = 0\n",
    "sample"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# process negative value\n",
    "for each_ele in train_x:\n",
    "    each_ele[each_ele < 0] = 0\n",
    "test_x[test_x < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, (2000, 612060))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_x),test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_y = [1 if i >= 20 else 0 for i in train_y  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_y = [1 if i >= 20 else 0 for i in test_y  ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression to split data as >= 20 and < 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-25-6f3461f3388a>:32: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "WARNING:tensorflow:From <ipython-input-25-6f3461f3388a>:36: initialize_local_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.local_variables_initializer` instead.\n",
      "shuffleing ...\n",
      "itertion 0 693.146 (0.0, 0.5)\n",
      "itertion 40 656.117 (0.5, 0.55959266)\n",
      "shuffleing ...\n",
      "itertion 80 586.144 (0.55959266, 0.61323798)\n",
      "itertion 120 551.246 (0.61323798, 0.63766861)\n",
      "shuffleing ...\n",
      "itertion 160 576.606 (0.63766861, 0.64245129)\n",
      "itertion 200 638.061 (0.64245129, 0.64118272)\n",
      "shuffleing ...\n",
      "itertion 240 544.944 (0.64118272, 0.65306818)\n",
      "itertion 280 563.815 (0.65306818, 0.65521824)\n",
      "shuffleing ...\n",
      "itertion 320 598.007 (0.65521824, 0.65608406)\n",
      "itertion 360 465.461 (0.65608406, 0.66346192)\n",
      "shuffleing ...\n",
      "itertion 400 494.872 (0.66346192, 0.66928351)\n",
      "itertion 440 553.856 (0.66928351, 0.6676082)\n",
      "shuffleing ...\n",
      "itertion 480 481.567 (0.6676082, 0.67257905)\n",
      "itertion 520 466.14 (0.67257905, 0.67638451)\n",
      "shuffleing ...\n",
      "itertion 560 509.345 (0.67638451, 0.67667329)\n",
      "itertion 600 439.857 (0.67667329, 0.68019283)\n",
      "shuffleing ...\n",
      "itertion 640 430.099 (0.68019283, 0.68330431)\n",
      "itertion 680 421.916 (0.68330431, 0.6856904)\n",
      "shuffleing ...\n",
      "itertion 720 581.082 (0.6856904, 0.68307024)\n",
      "itertion 760 548.723 (0.68307024, 0.68021923)\n",
      "shuffleing ...\n",
      "itertion 800 432.335 (0.68021923, 0.68180412)\n",
      "itertion 840 408.318 (0.68180412, 0.68422234)\n",
      "shuffleing ...\n",
      "itertion 880 547.378 (0.68422234, 0.68338865)\n",
      "itertion 920 407.851 (0.68338865, 0.68552136)\n"
     ]
    }
   ],
   "source": [
    "# TODO use 100% data ,not 80% now\n",
    "# iter 3000 test auc 0.71 still improving\n",
    "\n",
    "weights_dic = {}\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "feed_size = 100\n",
    "n_input = 101 * 101 * 60\n",
    "X = tf.placeholder(tf.uint8,[None,n_input])\n",
    "W = tf.Variable(tf.zeros([n_input,1]),tf.float32)\n",
    "b = tf.Variable([0.0],tf.float32)\n",
    "y = tf.sigmoid(tf.add(tf.matmul(tf.to_float(X),W),b))\n",
    "y_ = tf.placeholder(tf.float32)\n",
    "resm = tf.reduce_sum(tf.pow(tf.transpose(y) - y_,2))\n",
    "\n",
    "val_train_size = 1000\n",
    "\n",
    "\n",
    "debug_var = tf.log(tf.transpose(y))\n",
    "\n",
    "max_ent =  - tf.reduce_sum(\n",
    "    tf.reduce_sum(tf.multiply(y_,tf.log(tf.transpose(y))))\n",
    "    + tf.reduce_sum(tf.multiply((1 - y_),tf.log(1 - tf.transpose(y))))\n",
    ") \n",
    "#accuracy = \n",
    "#auc = tf.contrib.metrics.streaming_auc(, b)\n",
    "#auc = tf.contrib.metrics.streaming_auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "auc = tf.metrics.auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "\n",
    "\n",
    "train_step = tf.train.AdamOptimizer(0.0000003).minimize(max_ent)\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "sess.run(tf.initialize_local_variables())\n",
    "\n",
    "for i in range(4000):\n",
    "    indexbegin = feed_size * i % len(train_y)\n",
    "    #indexbegin = 0\n",
    "    if i % (len(train_y) / (feed_size)) == 0:\n",
    "        print(\"shuffleing ...\")\n",
    "        train_x,train_y = shuffle(train_x,train_y)\n",
    "    batch_xs,batch_ys = np.asarray(train_x[indexbegin:indexbegin + feed_size],dtype=np.uint8),train_y[indexbegin:indexbegin + feed_size]\n",
    "    if i % (len(train_y) / (feed_size * 2)) == 0:\n",
    "        print('itertion %s' % (i),end=' ')\n",
    "        curr_loss = sess.run(max_ent,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        print (curr_loss,end=\" \")\n",
    "        sysauc = sess.run(auc,feed_dict={X:test_x,y_:test_y})\n",
    "        print (sysauc)\n",
    "        #dv = sess.run(debug_var,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        #print (dv)\n",
    "        #print (\"train loss: %s\" % (curr_loss),end=' ')\n",
    "        #curr_loss = sess.run(real_resm,feed_dict={X:test_x,y_:test_y})\n",
    "        #print (\"test loss: %s\" % (curr_loss),end=\" \")\n",
    "        #print(\" check weights_dic[%s]\" % (i),end=\" \")\n",
    "        #yy = sess.run(y,feed_dict={X:test_x})\n",
    "        #print (yy[:2,0],end=\" \")\n",
    "        #debug_val = sess.run(debug_var,feed_dict={X:test_x,y_:test_y})\n",
    "        weights_dic[i] = (sess.run(W),sess.run(b))\n",
    "        #print(\"b: %s\" % (weights_dic[i][1]))\n",
    "    sess.run(train_step,feed_dict={X:batch_xs,y_:batch_ys})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tensorflow's linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.contrib.learn.LogisticRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression with L2 regulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-32-85dceb24fa29>:33: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "WARNING:tensorflow:From <ipython-input-32-85dceb24fa29>:37: initialize_local_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.local_variables_initializer` instead.\n",
      "shuffleing ...\n",
      "itertion 0 693.146 (0.0, 0.5)\n",
      "[[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " ..., \n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "itertion 40 677.817 (0.5, 0.59133518)\n",
      "[[  3.70351472e-07]\n",
      " [  4.14065170e-07]\n",
      " [  2.94666080e-07]\n",
      " ..., \n",
      " [ -4.44581161e-07]\n",
      " [ -4.26739888e-07]\n",
      " [ -5.02523903e-07]]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-85dceb24fa29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mweights_dic\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mW\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[1;31m#print(\"b: %s\" % (weights_dic[i][1]))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m     \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_xs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mbatch_ys\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\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[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# TODO use 100% data ,not 80% now\n",
    "# iter 3000 test auc 0.71 still improving\n",
    "\n",
    "weights_dic = {}\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "feed_size = 100\n",
    "n_input = 101 * 101 * 60\n",
    "X = tf.placeholder(tf.uint8,[None,n_input])\n",
    "W = tf.Variable(tf.zeros([n_input,1]),tf.float32)\n",
    "b = tf.Variable([0.0],tf.float32)\n",
    "y = tf.sigmoid(tf.add(tf.matmul(tf.to_float(X),W),b))\n",
    "y_ = tf.placeholder(tf.float32)\n",
    "C = 1.0\n",
    "resm = tf.reduce_sum(tf.pow(tf.transpose(y) - y_,2))\n",
    "\n",
    "val_train_size = 1000\n",
    "\n",
    "\n",
    "debug_var = tf.log(tf.transpose(y))\n",
    "\n",
    "max_ent =  - C * tf.reduce_sum(\n",
    "    tf.reduce_sum(tf.multiply(y_,tf.log(tf.transpose(y))))\n",
    "    + tf.reduce_sum(tf.multiply((1 - y_),tf.log(1 - tf.transpose(y))))\n",
    ") + tf.reduce_sum(tf.square(W)) / 2\n",
    "#accuracy = \n",
    "#auc = tf.contrib.metrics.streaming_auc(, b)\n",
    "#auc = tf.contrib.metrics.streaming_auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "auc = tf.metrics.auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "\n",
    "\n",
    "train_step = tf.train.AdamOptimizer(0.0000003).minimize(max_ent)\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "sess.run(tf.initialize_local_variables())\n",
    "\n",
    "for i in range(4000):\n",
    "    indexbegin = feed_size * i % len(train_y)\n",
    "    #indexbegin = 0\n",
    "    if i % (len(train_y) / (feed_size)) == 0:\n",
    "        print(\"shuffleing ...\")\n",
    "        train_x,train_y = shuffle(train_x,train_y)\n",
    "    batch_xs,batch_ys = np.asarray(train_x[indexbegin:indexbegin + feed_size],dtype=np.uint8),train_y[indexbegin:indexbegin + feed_size]\n",
    "    if i % (len(train_y) / (feed_size * 2)) == 0:\n",
    "        print('itertion %s' % (i),end=' ')\n",
    "        curr_loss = sess.run(max_ent,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        print (curr_loss,end=\" \")\n",
    "        sysauc = sess.run(auc,feed_dict={X:test_x,y_:test_y})\n",
    "        print (sysauc)\n",
    "        pw = sess.run(W)\n",
    "        print pw\n",
    "        #dv = sess.run(debug_var,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        #print (dv)\n",
    "        #print (\"train loss: %s\" % (curr_loss),end=' ')\n",
    "        #curr_loss = sess.run(real_resm,feed_dict={X:test_x,y_:test_y})\n",
    "        #print (\"test loss: %s\" % (curr_loss),end=\" \")\n",
    "        #print(\" check weights_dic[%s]\" % (i),end=\" \")\n",
    "        #yy = sess.run(y,feed_dict={X:test_x})\n",
    "        #print (yy[:2,0],end=\" \")\n",
    "        #debug_val = sess.run(debug_var,feed_dict={X:test_x,y_:test_y})\n",
    "        weights_dic[i] = (sess.run(W),sess.run(b))\n",
    "        #print(\"b: %s\" % (weights_dic[i][1]))\n",
    "    sess.run(train_step,feed_dict={X:batch_xs,y_:batch_ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-54-16d00063dc42>:40: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-54-16d00063dc42>:40: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-54-16d00063dc42>:44: initialize_local_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.local_variables_initializer` instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-54-16d00063dc42>:44: initialize_local_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.local_variables_initializer` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shuffleing ...\n",
      "itertion 0 693.147 (0.0, 0.5)\n",
      "(array([[ 0.5136044 ,  0.50802714,  0.49657726, ...,  0.52290815,\n",
      "         0.52888525,  0.47885343],\n",
      "       [ 0.57239354,  0.50408453,  0.54045767, ...,  0.52862555,\n",
      "         0.49819279,  0.48902223],\n",
      "       [ 0.54862732,  0.51271766,  0.50438553, ...,  0.53175998,\n",
      "         0.50536907,  0.49551305],\n",
      "       ..., \n",
      "       [ 0.55407554,  0.51917845,  0.49668944, ...,  0.5076161 ,\n",
      "         0.52474838,  0.47016791],\n",
      "       [ 0.5072298 ,  0.48702788,  0.50005656, ...,  0.52026439,\n",
      "         0.53776526,  0.48651916],\n",
      "       [ 0.51337868,  0.48788637,  0.51672179, ...,  0.50389409,\n",
      "         0.49963972,  0.48899737]], dtype=float32), array([[ 0.49999905],\n",
      "       [ 0.49999899],\n",
      "       [ 0.49999905],\n",
      "       ..., \n",
      "       [ 0.49999905],\n",
      "       [ 0.49999905],\n",
      "       [ 0.49999905]], dtype=float32))\n",
      "itertion 40 693.138 (0.5, 0.5)\n",
      "(array([[  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          1.66973774e-19,   1.00000000e+00,   1.00000000e+00],\n",
      "       [  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          0.00000000e+00,   1.00000000e+00,   1.00000000e+00],\n",
      "       [  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          6.23242365e-30,   1.00000000e+00,   1.00000000e+00],\n",
      "       ..., \n",
      "       [  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          2.16492537e-20,   1.00000000e+00,   1.00000000e+00],\n",
      "       [  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          3.74074804e-24,   1.00000000e+00,   1.00000000e+00],\n",
      "       [  1.00000000e+00,   1.00000000e+00,   1.00000000e+00, ...,\n",
      "          7.64403207e-09,   1.00000000e+00,   1.00000000e+00]], dtype=float32), array([[ 0.49997163],\n",
      "       [ 0.49997163],\n",
      "       [ 0.49997163],\n",
      "       ..., \n",
      "       [ 0.49997163],\n",
      "       [ 0.49997163],\n",
      "       [ 0.49997163]], dtype=float32))\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-16d00063dc42>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"shuffleing ...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     \u001b[0mbatch_xs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_ys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexbegin\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mindexbegin\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfeed_size\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexbegin\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mindexbegin\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfeed_size\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfeed_size\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m2\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[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'itertion %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \"\"\"\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# TODO use 100% data ,not 80% now\n",
    "# iter 3000 test auc 0.71 still improving\n",
    "\n",
    "weights_dic = {}\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "feed_size = 100\n",
    "n_input = 101 * 101 * 60\n",
    "X = tf.placeholder(tf.uint8,[None,n_input])\n",
    "W1 = tf.Variable(tf.random_normal([n_input,10],stddev=1.0/n_input),tf.float32)\n",
    "W2 = tf.Variable(tf.random_normal([10,1],stddev=1.0/n_input),tf.float32)\n",
    "b1 = tf.Variable(tf.zeros([10]),tf.float32)\n",
    "b2 = tf.Variable([0.0],tf.float32)\n",
    "y1 = tf.sigmoid(\n",
    "    tf.add(tf.matmul(tf.to_float(X),W1),b1)\n",
    ")\n",
    "y = tf.sigmoid(\n",
    "    tf.add(tf.matmul(tf.to_float(y1),W2),b2)\n",
    ")\n",
    "y_ = tf.placeholder(tf.float32)\n",
    "C = 1.0\n",
    "resm = tf.reduce_sum(tf.pow(tf.transpose(y) - y_,2))\n",
    "\n",
    "val_train_size = 1000\n",
    "\n",
    "\n",
    "debug_var = y1,y\n",
    "max_ent =  - C * tf.reduce_sum(\n",
    "    tf.reduce_sum(tf.multiply(y_,tf.log(tf.transpose(y))))\n",
    "    + tf.reduce_sum(tf.multiply((1 - y_),tf.log(1 - tf.transpose(y))))\n",
    ") \n",
    "#accuracy = \n",
    "#auc = tf.contrib.metrics.streaming_auc(, b)\n",
    "#auc = tf.contrib.metrics.streaming_auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "auc = tf.metrics.auc(tf.reshape(y_,[-1]), tf.reshape(y,[-1]))\n",
    "\n",
    "\n",
    "train_step = tf.train.AdamOptimizer(0.0000003).minimize(max_ent)\n",
    "#train_step = tf.train.GradientDescentOptimizer(0.00003).minimize(max_ent)\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "sess.run(tf.initialize_local_variables())\n",
    "\n",
    "for i in range(4000):\n",
    "    indexbegin = feed_size * i % len(train_y)\n",
    "    #indexbegin = 0\n",
    "    if i % (len(train_y) / (feed_size)) == 0:\n",
    "        print(\"shuffleing ...\")\n",
    "        train_x,train_y = shuffle(train_x,train_y)\n",
    "    batch_xs,batch_ys = np.asarray(train_x[indexbegin:indexbegin + feed_size],dtype=np.uint8),train_y[indexbegin:indexbegin + feed_size]\n",
    "    if i % (len(train_y) / (feed_size * 2)) == 0:\n",
    "        print('itertion %s' % (i),end=' ')\n",
    "        curr_loss = sess.run(max_ent,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        print (curr_loss,end=\" \")\n",
    "        sysauc = sess.run(auc,feed_dict={X:test_x,y_:test_y})\n",
    "        print (sysauc)\n",
    "        #dv = sess.run(debug_var,feed_dict={X:train_x[:val_train_size],y_:train_y[:val_train_size]})\n",
    "        #print (dv)\n",
    "        #print (\"train loss: %s\" % (curr_loss),end=' ')\n",
    "        #curr_loss = sess.run(real_resm,feed_dict={X:test_x,y_:test_y})\n",
    "        #print (\"test loss: %s\" % (curr_loss),end=\" \")\n",
    "        #print(\" check weights_dic[%s]\" % (i),end=\" \")\n",
    "        #yy = sess.run(y,feed_dict={X:test_x})\n",
    "        #print (yy[:2,0],end=\" \")\n",
    "        debug_val = sess.run(debug_var,feed_dict={X:test_x,y_:test_y})\n",
    "        print(debug_val)\n",
    "        weights_dic[i] = (sess.run(W1),sess.run(W2),sess.run(b1),sess.run(b2))\n",
    "        #print(\"b: %s\" % (weights_dic[i][1]))\n",
    "    sess.run(train_step,feed_dict={X:batch_xs,y_:batch_ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "onehoty = np.zeros([len(train_y),2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for num,i in enumerate(train_y):\n",
    "    onehoty[num][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.],\n",
       "       [ 0.,  1.],\n",
       "       [ 0.,  1.],\n",
       "       ..., \n",
       "       [ 1.,  0.],\n",
       "       [ 0.,  1.],\n",
       "       [ 1.,  0.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehoty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1],\n",
       "       ..., \n",
       "       [0],\n",
       "       [1],\n",
       "       [0]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(train_y).reshape([len(train_y),1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x = np.asarray(train_x,dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_keep_checkpoint_max': 5, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001C67BA78EB8>, '_is_chief': True, '_keep_checkpoint_every_n_hours': 10000, '_task_type': None, '_tf_random_seed': None, '_save_checkpoints_secs': 600, '_task_id': 0, '_environment': 'local', '_num_ps_replicas': 0, '_save_summary_steps': 100, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_evaluation_master': '', '_master': '', '_save_checkpoints_steps': None}\n"
     ]
    }
   ],
   "source": [
    "n_input = 101 * 101 * 60\n",
    "feature_columns = [tf.contrib.layers.real_valued_column(\"x\",dimension=n_input)]\n",
    "classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,hidden_units=[10],n_classes=2,model_dir=\"model_dir\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_fn = tf.contrib.learn.io.numpy_input_fn({\"x\":train_x},np.asarray(train_y).reshape([len(train_y)]),batch_size=100,num_epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
      "Instructions for updating:\n",
      "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 2745 into model_dir\\model.ckpt.\n",
      "INFO:tensorflow:step = 2745, loss = 0.673018\n",
      "INFO:tensorflow:Loss for final step: 0.673018.\n",
      "WARNING:tensorflow:From <ipython-input-22-1d21ff89e9a1>:3: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-22-1d21ff89e9a1>:3: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  equality = a == b\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Features are incompatible with given information. Given features: Tensor(\"input:0\", shape=(?, 612060), dtype=uint8), required signatures: {'x': TensorSignature(dtype=tf.uint8, shape=TensorShape([Dimension(None), Dimension(612060)]), is_sparse=False)}.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-1d21ff89e9a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\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[0m     \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mauc_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_y\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[1;34m\"auc\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mauc_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    278\u001b[0m             \u001b[0m_call_location\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecorator_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_qualified_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             func.__module__, arg_name, date, instructions)\n\u001b[0;32m--> 280\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m     new_func.__doc__ = _add_deprecated_arg_notice_to_docstring(\n\u001b[1;32m    282\u001b[0m         func.__doc__, date, instructions)\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, input_fn, feed_fn, batch_size, steps, metrics, name, checkpoint_path, hooks, log_progress)\u001b[0m\n\u001b[1;32m    499\u001b[0m     \u001b[0m_verify_input_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mSKCompat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, x, y, batch_size, steps, metrics)\u001b[0m\n\u001b[1;32m   1366\u001b[0m         \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m         \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1368\u001b[0;31m         name='score')\n\u001b[0m\u001b[1;32m   1369\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0meval_results\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m       \u001b[0meval_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'global_step'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mglobal_step\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36m_evaluate_model\u001b[0;34m(self, input_fn, steps, feed_fn, metrics, name, checkpoint_path, hooks, log_progress)\u001b[0m\n\u001b[1;32m    799\u001b[0m       \u001b[0mglobal_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontrib_framework\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_global_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    800\u001b[0m       \u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 801\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    802\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    803\u001b[0m       \u001b[1;31m# The default return type of _get_eval_ops is ModelFnOps. But there are\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\estimator.py\u001b[0m in \u001b[0;36m_check_inputs\u001b[0;34m(self, features, labels)\u001b[0m\n\u001b[1;32m    729\u001b[0m         raise ValueError('Features are incompatible with given information. '\n\u001b[1;32m    730\u001b[0m                          \u001b[1;34m'Given features: %s, required signatures: %s.'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 731\u001b[0;31m                          (str(features), str(self._features_info)))\n\u001b[0m\u001b[1;32m    732\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_features_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor_signature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_signatures\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Features are incompatible with given information. Given features: Tensor(\"input:0\", shape=(?, 612060), dtype=uint8), required signatures: {'x': TensorSignature(dtype=tf.uint8, shape=TensorShape([Dimension(None), Dimension(612060)]), is_sparse=False)}."
     ]
    }
   ],
   "source": [
    "for i in range(100):\n",
    "    classifier.fit(input_fn=input_fn, steps=1)\n",
    "    auc_score = classifier.evaluate(x=test_x,y=np.asarray(test_y).reshape([len(test_y)]))[\"auc\"]\n",
    "    print (\"auc: %s\" % (auc_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# keras logistic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# keras simple dnn model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.initializers import Zeros\n",
    "\n",
    "model = Sequential()\n",
    "#model.add(Dense(32,input_shape=(n_input,)))\n",
    "#model.add(Activation('relu'))\n",
    "#model.add(Dense(2,))\n",
    "#model.add(Activation('softmax'))\n",
    "model.add(Dense(1,activation=Activation('sigmoid'),input_shape=(n_input,),kernel_initializer=Zeros(),bias_initializer=Zeros()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "from keras import losses\n",
    "model.compile(optimizer=optimizers.SGD(0.000000001),\n",
    "              loss=losses.binary_crossentropy,\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1000/1000 [==============================] - 3s - loss: 0.6062 - acc: 0.6680     \n",
      "Epoch 2/3\n",
      "1000/1000 [==============================] - 3s - loss: 0.6046 - acc: 0.6730     \n",
      "Epoch 3/3\n",
      "1000/1000 [==============================] - 3s - loss: 0.6024 - acc: 0.6830     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c8dd592588>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x[:1000,:],train_y[:1000],epochs=3,batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ -4.28051505e-09],\n",
       "        [  1.29339801e-08],\n",
       "        [ -2.76458012e-09],\n",
       "        ..., \n",
       "        [ -8.41064391e-08],\n",
       "        [ -9.81695223e-08],\n",
       "        [ -1.15521466e-07]], dtype=float32),\n",
       " array([  2.37839926e-09], dtype=float32)]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denselayer = model.get_layer(\"Dense\",1)\n",
    "denselayer.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 6s     \n",
      "0.652727233207\n",
      "[[ 0.35454312]\n",
      " [ 0.40631643]\n",
      " [ 0.383625  ]\n",
      " ..., \n",
      " [ 0.50460935]\n",
      " [ 0.25425366]\n",
      " [ 0.40119845]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
    "y_pred = model.predict_proba(test_x)\n",
    "print (roc_auc_score(test_y, y_pred[:,0]))\n",
    "print (y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# keras neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# keras simple dnn model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.initializers import Zeros\n",
    "from keras.initializers import RandomNormal\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32,input_shape=(n_input,)))\n",
    "model.add(Activation('sigmoid'))\n",
    "#model.add(Dense(2,))\n",
    "#model.add(Activation('softmax'))\n",
    "model.add(Dense(1,activation=Activation('sigmoid')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "from keras import losses\n",
    "model.compile(optimizer=optimizers.Adam(0.000001),\n",
    "              loss=losses.binary_crossentropy,\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/100\n",
      "8000/8000 [==============================] - 113s - loss: 0.5805 - acc: 0.6975 - val_loss: 0.6249 - val_acc: 0.6445\n",
      "Epoch 2/100\n",
      "4100/8000 [==============>...............] - ETA: 51s - loss: 0.5816 - acc: 0.6956"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-216-77c1c18fe5bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[1;31m#model.fit(train_x,train_y,epochs=3,batch_size=100)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    851\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1484\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1485\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1488\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1139\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1142\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2101\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2103\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2104\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\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[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\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[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(train_x,train_y,validation_data=(test_x,test_y),epochs=100,batch_size=100)\n",
    "#model.fit(train_x,train_y,epochs=3,batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[  2.61578988e-03,   4.31450790e-05,  -1.07274123e-03, ...,\n",
      "         -2.41255411e-03,  -1.12752779e-03,  -1.77984429e-03],\n",
      "       [  1.24636665e-03,  -1.08006503e-03,   4.19366552e-04, ...,\n",
      "         -2.32193107e-03,   2.47841864e-03,   3.00782296e-04],\n",
      "       [ -2.76038633e-03,   6.22449967e-04,  -1.95119798e-03, ...,\n",
      "          6.67266315e-04,   1.28507393e-03,   1.50949706e-03],\n",
      "       ..., \n",
      "       [  2.19647284e-03,   1.25725835e-03,  -2.87730154e-03, ...,\n",
      "          6.32804236e-04,   3.03505966e-03,  -1.75152894e-03],\n",
      "       [ -1.34491804e-03,   1.47229829e-03,   1.52007164e-03, ...,\n",
      "         -1.52969663e-03,  -3.16242658e-04,  -1.12127780e-03],\n",
      "       [ -2.53355969e-03,  -9.65016254e-04,   9.57014254e-06, ...,\n",
      "         -1.95050577e-03,   7.20960030e-04,   8.64711474e-04]], dtype=float32), array([ -3.29610120e-05,   6.87132269e-05,   2.66756379e-05,\n",
      "        -2.58741729e-05,   2.27163218e-06,  -2.94978163e-05,\n",
      "         2.24443393e-05,   1.25335109e-05,  -5.00098213e-05,\n",
      "         4.56337475e-05,  -1.14182149e-04,  -7.93335312e-06,\n",
      "         3.53937423e-07,   4.09962595e-05,   3.96027208e-05,\n",
      "        -4.04390048e-05,  -7.34597415e-06,  -4.27251725e-05,\n",
      "         7.19176751e-05,   4.24689961e-06,   1.81992000e-05,\n",
      "        -2.95541759e-05,  -1.25542661e-04,   6.39335776e-05,\n",
      "        -1.80547868e-05,  -2.45432147e-05,   1.25945944e-05,\n",
      "        -6.14160745e-05,  -5.95465608e-05,  -3.08738504e-06,\n",
      "         2.60112483e-05,   1.19700717e-05], dtype=float32)]\n",
      "2000/2000 [==============================] - 13s    \n",
      "0.69634961248\n",
      "[[ 0.28831807]\n",
      " [ 0.43403462]\n",
      " [ 0.3611832 ]\n",
      " ..., \n",
      " [ 0.63964134]\n",
      " [ 0.28831807]\n",
      " [ 0.34126031]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C9085FC6A0>]], dtype=object)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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esr49dQGR+HiPetkXeclZBjUQFbNmjZdfNi9NXUBEXrKuTl1AJD7eo172RV5y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\nktv/Dynzjqspnha9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8f608b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "denselayer = model.get_layer(\"Dense\",1)\n",
    "print (denselayer.get_weights())\n",
    "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
    "y_pred = model.predict_proba(test_x)\n",
    "print (roc_auc_score(test_y, y_pred[:,0]))\n",
    "print (y_pred)\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.DataFrame( y_pred[:,0]).hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.3232036074894618, 0.21713557494363298, 0.25232820311734144, 0.13812371336143514]\n",
      "[array([ 63.94422115]), array([ 74.31771395]), array([ 72.36486619]), array([ 70.52671307])]\n",
      "[array([ 51.87548648]), array([ 51.00001758]), array([ 50.16668615]), array([ 39.07233152])]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c8732f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gjr5Oq89Oaku2Eo33ZFnMaitiK/Bu9YefpQOvuqLiWSQlZjKbTTuRBVhOBN9Z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hALPmMT2BrVQJr8veXYVaFZHnKwzLIWg+p8n/0W8zg+LfGDk2pt1TC0o9a//L/b6Odk242MM55e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rK1heXka320Wv1zvz9Y6OjqbqpDrHTpfCaDabaXMcUygsQ2F5eTk9juUuz0GVZTBnGQJZ6Xp5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\nWLWSPUdfCqoPjvcdIjCKWD9MwCNTJIIpfsbn+TJnvTYO/c0T83h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Q7fj4uJMP/Wjf+JYD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mbpnIldIcesxJJuA8RHfLySrwNWVlTYUNhMbUSs2+zscSv2qySU+vHryM9eTcZ23S7ybwaD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\nqYMYGG7MRIXveyHniMR0v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JvX6H/FLFOeRVDPjwQrc5YZq/O6A2IRdIkghP+tzO7K2WjP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\nr/+6fu3Xfq1gKP3gBz/Qb/3Wb03so+eALlVf/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zyw4aeONBoNtdvtgh3mEXZvLOvj9nlo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\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hssiapJNIqogPLAYnN2ca3Yw8M3I2GAxifZMlRVy2AQNOKaOSLiMFl4UbZCTsvARy3Qzv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\nESlvaX0zh8OhM6rpMOHnLIqiirVNUeG23F47FIxGI3S7Xbx58yb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TwY9//GNneFuqEZBk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\nNLwJIYQQQgghhJAh8v/llJ3jg/ydDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c8732ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.9\n"
     ]
    }
   ],
   "source": [
    "train_x[0].reshape([15,4,101,101])[0][0]\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "#index = 100\n",
    "index += 1\n",
    "\n",
    "print (\n",
    "    [float(sum(train_x[index].reshape([15,4,101,101])[14][i].reshape(-1) == 0)) / (101*101) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.average(train_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.std(train_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "plt.figure(figsize=(10,10))\n",
    "f,axarr = plt.subplots(3,4,figsize=(10,10))\n",
    "for j in range(3):\n",
    "    for i in range(4):\n",
    "        #plt.subplot(340 + j * 4 + i + 1)\n",
    "        axarr[j][i].imshow(train_x[index].reshape([15,4,101,101])[14-5*j][i],cmap='gray')\n",
    "f.tight_layout()\n",
    "plt.show()\n",
    "print (train_y[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try to do some feature engineer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25.484462307616901,\n",
       " 25.484462307616901,\n",
       " 25.484462307616901,\n",
       " 25.484462307616901]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[np.average(train_x[0].reshape([15,4,101,101])[0][0].reshape(-1)) for i in range(4)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.484462307616901"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gap = 50\n",
    "np.average(train_x[0].reshape([15,4,101,101])[0][0][50-gap:50+gap +  1,50-gap:50+gap + 1].reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14, 9, 4]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(14,0,-5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "class RainFallFeatures():\n",
    "    def __init__(self):\n",
    "        self.gaps = [1,5,10,20,50]\n",
    "        self.heightvalues = [0,1,2,3]\n",
    "        self.timatange = list(range(14,0,-5))\n",
    "        \n",
    "    def fit_and_extrace(self,tx):\n",
    "        featurepandas = []\n",
    "        for funs in (i  for i in dir(self) if i[:3] == 'fe_'):\n",
    "             featurepandas.append(pd.DataFrame(getattr(self, funs)(tx,funs)))\n",
    "        return pd.concat(featurepandas,axis=1)\n",
    "    \n",
    "    def fe_avg(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1))\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_std(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.std(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1))\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_avg_without_zero(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    def getnonzero(inputarr):\n",
    "                        return inputarr[inputarr.nonzero()]\n",
    "                    features = [\n",
    "                        np.average(\n",
    "                            getnonzero(\n",
    "                                item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1)\n",
    "                            )\n",
    "                        )\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_std_without_zero(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    def getnonzero(inputarr):\n",
    "                        return inputarr[inputarr.nonzero()]\n",
    "                    features = [\n",
    "                        np.std(\n",
    "                            getnonzero(\n",
    "                                item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1)\n",
    "                            )\n",
    "                        )\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_zero_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) == 0)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_zero_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) == 0)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_gt50_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) >= 50)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_gt100_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) >= 100)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_gt150_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) >= 100)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_gt200_ratio(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.average(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) >= 200)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    \n",
    "    def fe_max(self,tx,feature_prefix):\n",
    "        feature_dic = {}\n",
    "        for gap in self.gaps:\n",
    "            for heights in self.heightvalues:\n",
    "                for timetag in self.timatange:\n",
    "                    featurename = \"%s_%s_%s_%s\" % (feature_prefix,gap,heights,timetag)\n",
    "                    print(\"extracing %s\" % (featurename),end=\" \")\n",
    "                    features = [\n",
    "                        np.max(item.reshape([15,4,101,101])[timetag][heights][50 - gap:51 + gap,50 - gap:51 + gap].reshape(-1) == 0)\n",
    "                        for item in tx\n",
    "                    ]\n",
    "                    feature_dic[featurename] = features\n",
    "        print()\n",
    "        return feature_dic\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000016C8C6CF748>]], dtype=object)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8HY9rdwV3/6KkL0qSmVkbq37f3R9p9/0QZ+PGjWWXUDlkHo/M45F5OqLOaTBJd5rZA2Z2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\nlMEGXh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c86d0fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#pd.DataFrame(np.asarray(train_x[:1000]).reshape(-1)).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_1_0_14 extracing fe_avg_1_0_9 extracing fe_avg_1_0_4 extracing fe_avg_1_1_14 extracing fe_avg_1_1_9 extracing fe_avg_1_1_4 extracing fe_avg_1_2_14 extracing fe_avg_1_2_9 extracing fe_avg_1_2_4 extracing fe_avg_1_3_14 extracing fe_avg_1_3_9 extracing fe_avg_1_3_4 extracing fe_avg_5_0_14 extracing fe_avg_5_0_9 extracing fe_avg_5_0_4 extracing fe_avg_5_1_14 extracing fe_avg_5_1_9 extracing fe_avg_5_1_4 extracing fe_avg_5_2_14 extracing fe_avg_5_2_9 extracing fe_avg_5_2_4 extracing fe_avg_5_3_14 extracing fe_avg_5_3_9 extracing fe_avg_5_3_4 extracing fe_avg_10_0_14 extracing fe_avg_10_0_9 extracing fe_avg_10_0_4 extracing fe_avg_10_1_14 extracing fe_avg_10_1_9 extracing fe_avg_10_1_4 extracing fe_avg_10_2_14 extracing fe_avg_10_2_9 extracing fe_avg_10_2_4 extracing fe_avg_10_3_14 extracing fe_avg_10_3_9 extracing fe_avg_10_3_4 extracing fe_avg_20_0_14 extracing fe_avg_20_0_9 extracing fe_avg_20_0_4 extracing fe_avg_20_1_14 extracing fe_avg_20_1_9 extracing fe_avg_20_1_4 extracing fe_avg_20_2_14 extracing fe_avg_20_2_9 extracing fe_avg_20_2_4 extracing fe_avg_20_3_14 extracing fe_avg_20_3_9 extracing fe_avg_20_3_4 extracing fe_avg_50_0_14 extracing fe_avg_50_0_9 extracing fe_avg_50_0_4 extracing fe_avg_50_1_14 extracing fe_avg_50_1_9 extracing fe_avg_50_1_4 extracing fe_avg_50_2_14 extracing fe_avg_50_2_9 extracing fe_avg_50_2_4 extracing fe_avg_50_3_14 extracing fe_avg_50_3_9 extracing fe_avg_50_3_4 \n",
      "extracing fe_avg_without_zero_1_0_14 extracing fe_avg_without_zero_1_0_9 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_without_zero_1_0_4 extracing fe_avg_without_zero_1_1_14 extracing fe_avg_without_zero_1_1_9 extracing fe_avg_without_zero_1_1_4 extracing fe_avg_without_zero_1_2_14 extracing fe_avg_without_zero_1_2_9 extracing fe_avg_without_zero_1_2_4 extracing fe_avg_without_zero_1_3_14 extracing fe_avg_without_zero_1_3_9 extracing fe_avg_without_zero_1_3_4 extracing fe_avg_without_zero_5_0_14 extracing fe_avg_without_zero_5_0_9 extracing fe_avg_without_zero_5_0_4 extracing fe_avg_without_zero_5_1_14 extracing fe_avg_without_zero_5_1_9 extracing fe_avg_without_zero_5_1_4 extracing fe_avg_without_zero_5_2_14 extracing fe_avg_without_zero_5_2_9 extracing fe_avg_without_zero_5_2_4 extracing fe_avg_without_zero_5_3_14 extracing fe_avg_without_zero_5_3_9 extracing fe_avg_without_zero_5_3_4 extracing fe_avg_without_zero_10_0_14 extracing fe_avg_without_zero_10_0_9 extracing fe_avg_without_zero_10_0_4 extracing fe_avg_without_zero_10_1_14 extracing fe_avg_without_zero_10_1_9 extracing fe_avg_without_zero_10_1_4 extracing fe_avg_without_zero_10_2_14 extracing fe_avg_without_zero_10_2_9 extracing fe_avg_without_zero_10_2_4 extracing fe_avg_without_zero_10_3_14 extracing fe_avg_without_zero_10_3_9 extracing fe_avg_without_zero_10_3_4 extracing fe_avg_without_zero_20_0_14 extracing fe_avg_without_zero_20_0_9 extracing fe_avg_without_zero_20_0_4 extracing fe_avg_without_zero_20_1_14 extracing fe_avg_without_zero_20_1_9 extracing fe_avg_without_zero_20_1_4 extracing fe_avg_without_zero_20_2_14 extracing fe_avg_without_zero_20_2_9 extracing fe_avg_without_zero_20_2_4 extracing fe_avg_without_zero_20_3_14 extracing fe_avg_without_zero_20_3_9 extracing fe_avg_without_zero_20_3_4 extracing fe_avg_without_zero_50_0_14 extracing fe_avg_without_zero_50_0_9 extracing fe_avg_without_zero_50_0_4 extracing fe_avg_without_zero_50_1_14 extracing fe_avg_without_zero_50_1_9 extracing fe_avg_without_zero_50_1_4 extracing fe_avg_without_zero_50_2_14 extracing fe_avg_without_zero_50_2_9 extracing fe_avg_without_zero_50_2_4 extracing fe_avg_without_zero_50_3_14 extracing fe_avg_without_zero_50_3_9 extracing fe_avg_without_zero_50_3_4 \n",
      "extracing fe_gt100_ratio_1_0_14 extracing fe_gt100_ratio_1_0_9 extracing fe_gt100_ratio_1_0_4 extracing fe_gt100_ratio_1_1_14 extracing fe_gt100_ratio_1_1_9 extracing fe_gt100_ratio_1_1_4 extracing fe_gt100_ratio_1_2_14 extracing fe_gt100_ratio_1_2_9 extracing fe_gt100_ratio_1_2_4 extracing fe_gt100_ratio_1_3_14 extracing fe_gt100_ratio_1_3_9 extracing fe_gt100_ratio_1_3_4 extracing fe_gt100_ratio_5_0_14 extracing fe_gt100_ratio_5_0_9 extracing fe_gt100_ratio_5_0_4 extracing fe_gt100_ratio_5_1_14 extracing fe_gt100_ratio_5_1_9 extracing fe_gt100_ratio_5_1_4 extracing fe_gt100_ratio_5_2_14 extracing fe_gt100_ratio_5_2_9 extracing fe_gt100_ratio_5_2_4 extracing fe_gt100_ratio_5_3_14 extracing fe_gt100_ratio_5_3_9 extracing fe_gt100_ratio_5_3_4 extracing fe_gt100_ratio_10_0_14 extracing fe_gt100_ratio_10_0_9 extracing fe_gt100_ratio_10_0_4 extracing fe_gt100_ratio_10_1_14 extracing fe_gt100_ratio_10_1_9 extracing fe_gt100_ratio_10_1_4 extracing fe_gt100_ratio_10_2_14 extracing fe_gt100_ratio_10_2_9 extracing fe_gt100_ratio_10_2_4 extracing fe_gt100_ratio_10_3_14 extracing fe_gt100_ratio_10_3_9 extracing fe_gt100_ratio_10_3_4 extracing fe_gt100_ratio_20_0_14 extracing fe_gt100_ratio_20_0_9 extracing fe_gt100_ratio_20_0_4 extracing fe_gt100_ratio_20_1_14 extracing fe_gt100_ratio_20_1_9 extracing fe_gt100_ratio_20_1_4 extracing fe_gt100_ratio_20_2_14 extracing fe_gt100_ratio_20_2_9 extracing fe_gt100_ratio_20_2_4 extracing fe_gt100_ratio_20_3_14 extracing fe_gt100_ratio_20_3_9 extracing fe_gt100_ratio_20_3_4 extracing fe_gt100_ratio_50_0_14 extracing fe_gt100_ratio_50_0_9 extracing fe_gt100_ratio_50_0_4 extracing fe_gt100_ratio_50_1_14 extracing fe_gt100_ratio_50_1_9 extracing fe_gt100_ratio_50_1_4 extracing fe_gt100_ratio_50_2_14 extracing fe_gt100_ratio_50_2_9 extracing fe_gt100_ratio_50_2_4 extracing fe_gt100_ratio_50_3_14 extracing fe_gt100_ratio_50_3_9 extracing fe_gt100_ratio_50_3_4 \n",
      "extracing fe_gt150_ratio_1_0_14 extracing fe_gt150_ratio_1_0_9 extracing fe_gt150_ratio_1_0_4 extracing fe_gt150_ratio_1_1_14 extracing fe_gt150_ratio_1_1_9 extracing fe_gt150_ratio_1_1_4 extracing fe_gt150_ratio_1_2_14 extracing fe_gt150_ratio_1_2_9 extracing fe_gt150_ratio_1_2_4 extracing fe_gt150_ratio_1_3_14 extracing fe_gt150_ratio_1_3_9 extracing fe_gt150_ratio_1_3_4 extracing fe_gt150_ratio_5_0_14 extracing fe_gt150_ratio_5_0_9 extracing fe_gt150_ratio_5_0_4 extracing fe_gt150_ratio_5_1_14 extracing fe_gt150_ratio_5_1_9 extracing fe_gt150_ratio_5_1_4 extracing fe_gt150_ratio_5_2_14 extracing fe_gt150_ratio_5_2_9 extracing fe_gt150_ratio_5_2_4 extracing fe_gt150_ratio_5_3_14 extracing fe_gt150_ratio_5_3_9 extracing fe_gt150_ratio_5_3_4 extracing fe_gt150_ratio_10_0_14 extracing fe_gt150_ratio_10_0_9 extracing fe_gt150_ratio_10_0_4 extracing fe_gt150_ratio_10_1_14 extracing fe_gt150_ratio_10_1_9 extracing fe_gt150_ratio_10_1_4 extracing fe_gt150_ratio_10_2_14 extracing fe_gt150_ratio_10_2_9 extracing fe_gt150_ratio_10_2_4 extracing fe_gt150_ratio_10_3_14 extracing fe_gt150_ratio_10_3_9 extracing fe_gt150_ratio_10_3_4 extracing fe_gt150_ratio_20_0_14 extracing fe_gt150_ratio_20_0_9 extracing fe_gt150_ratio_20_0_4 extracing fe_gt150_ratio_20_1_14 extracing fe_gt150_ratio_20_1_9 extracing fe_gt150_ratio_20_1_4 extracing fe_gt150_ratio_20_2_14 extracing fe_gt150_ratio_20_2_9 extracing fe_gt150_ratio_20_2_4 extracing fe_gt150_ratio_20_3_14 extracing fe_gt150_ratio_20_3_9 extracing fe_gt150_ratio_20_3_4 extracing fe_gt150_ratio_50_0_14 extracing fe_gt150_ratio_50_0_9 extracing fe_gt150_ratio_50_0_4 extracing fe_gt150_ratio_50_1_14 extracing fe_gt150_ratio_50_1_9 extracing fe_gt150_ratio_50_1_4 extracing fe_gt150_ratio_50_2_14 extracing fe_gt150_ratio_50_2_9 extracing fe_gt150_ratio_50_2_4 extracing fe_gt150_ratio_50_3_14 extracing fe_gt150_ratio_50_3_9 extracing fe_gt150_ratio_50_3_4 \n",
      "extracing fe_gt200_ratio_1_0_14 extracing fe_gt200_ratio_1_0_9 extracing fe_gt200_ratio_1_0_4 extracing fe_gt200_ratio_1_1_14 extracing fe_gt200_ratio_1_1_9 extracing fe_gt200_ratio_1_1_4 extracing fe_gt200_ratio_1_2_14 extracing fe_gt200_ratio_1_2_9 extracing fe_gt200_ratio_1_2_4 extracing fe_gt200_ratio_1_3_14 extracing fe_gt200_ratio_1_3_9 extracing fe_gt200_ratio_1_3_4 extracing fe_gt200_ratio_5_0_14 extracing fe_gt200_ratio_5_0_9 extracing fe_gt200_ratio_5_0_4 extracing fe_gt200_ratio_5_1_14 extracing fe_gt200_ratio_5_1_9 extracing fe_gt200_ratio_5_1_4 extracing fe_gt200_ratio_5_2_14 extracing fe_gt200_ratio_5_2_9 extracing fe_gt200_ratio_5_2_4 extracing fe_gt200_ratio_5_3_14 extracing fe_gt200_ratio_5_3_9 extracing fe_gt200_ratio_5_3_4 extracing fe_gt200_ratio_10_0_14 extracing fe_gt200_ratio_10_0_9 extracing fe_gt200_ratio_10_0_4 extracing fe_gt200_ratio_10_1_14 extracing fe_gt200_ratio_10_1_9 extracing fe_gt200_ratio_10_1_4 extracing fe_gt200_ratio_10_2_14 extracing fe_gt200_ratio_10_2_9 extracing fe_gt200_ratio_10_2_4 extracing fe_gt200_ratio_10_3_14 extracing fe_gt200_ratio_10_3_9 extracing fe_gt200_ratio_10_3_4 extracing fe_gt200_ratio_20_0_14 extracing fe_gt200_ratio_20_0_9 extracing fe_gt200_ratio_20_0_4 extracing fe_gt200_ratio_20_1_14 extracing fe_gt200_ratio_20_1_9 extracing fe_gt200_ratio_20_1_4 extracing fe_gt200_ratio_20_2_14 extracing fe_gt200_ratio_20_2_9 extracing fe_gt200_ratio_20_2_4 extracing fe_gt200_ratio_20_3_14 extracing fe_gt200_ratio_20_3_9 extracing fe_gt200_ratio_20_3_4 extracing fe_gt200_ratio_50_0_14 extracing fe_gt200_ratio_50_0_9 extracing fe_gt200_ratio_50_0_4 extracing fe_gt200_ratio_50_1_14 extracing fe_gt200_ratio_50_1_9 extracing fe_gt200_ratio_50_1_4 extracing fe_gt200_ratio_50_2_14 extracing fe_gt200_ratio_50_2_9 extracing fe_gt200_ratio_50_2_4 extracing fe_gt200_ratio_50_3_14 extracing fe_gt200_ratio_50_3_9 extracing fe_gt200_ratio_50_3_4 \n",
      "extracing fe_gt50_ratio_1_0_14 extracing fe_gt50_ratio_1_0_9 extracing fe_gt50_ratio_1_0_4 extracing fe_gt50_ratio_1_1_14 extracing fe_gt50_ratio_1_1_9 extracing fe_gt50_ratio_1_1_4 extracing fe_gt50_ratio_1_2_14 extracing fe_gt50_ratio_1_2_9 extracing fe_gt50_ratio_1_2_4 extracing fe_gt50_ratio_1_3_14 extracing fe_gt50_ratio_1_3_9 extracing fe_gt50_ratio_1_3_4 extracing fe_gt50_ratio_5_0_14 extracing fe_gt50_ratio_5_0_9 extracing fe_gt50_ratio_5_0_4 extracing fe_gt50_ratio_5_1_14 extracing fe_gt50_ratio_5_1_9 extracing fe_gt50_ratio_5_1_4 extracing fe_gt50_ratio_5_2_14 extracing fe_gt50_ratio_5_2_9 extracing fe_gt50_ratio_5_2_4 extracing fe_gt50_ratio_5_3_14 extracing fe_gt50_ratio_5_3_9 extracing fe_gt50_ratio_5_3_4 extracing fe_gt50_ratio_10_0_14 extracing fe_gt50_ratio_10_0_9 extracing fe_gt50_ratio_10_0_4 extracing fe_gt50_ratio_10_1_14 extracing fe_gt50_ratio_10_1_9 extracing fe_gt50_ratio_10_1_4 extracing fe_gt50_ratio_10_2_14 extracing fe_gt50_ratio_10_2_9 extracing fe_gt50_ratio_10_2_4 extracing fe_gt50_ratio_10_3_14 extracing fe_gt50_ratio_10_3_9 extracing fe_gt50_ratio_10_3_4 extracing fe_gt50_ratio_20_0_14 extracing fe_gt50_ratio_20_0_9 extracing fe_gt50_ratio_20_0_4 extracing fe_gt50_ratio_20_1_14 extracing fe_gt50_ratio_20_1_9 extracing fe_gt50_ratio_20_1_4 extracing fe_gt50_ratio_20_2_14 extracing fe_gt50_ratio_20_2_9 extracing fe_gt50_ratio_20_2_4 extracing fe_gt50_ratio_20_3_14 extracing fe_gt50_ratio_20_3_9 extracing fe_gt50_ratio_20_3_4 extracing fe_gt50_ratio_50_0_14 extracing fe_gt50_ratio_50_0_9 extracing fe_gt50_ratio_50_0_4 extracing fe_gt50_ratio_50_1_14 extracing fe_gt50_ratio_50_1_9 extracing fe_gt50_ratio_50_1_4 extracing fe_gt50_ratio_50_2_14 extracing fe_gt50_ratio_50_2_9 extracing fe_gt50_ratio_50_2_4 extracing fe_gt50_ratio_50_3_14 extracing fe_gt50_ratio_50_3_9 extracing fe_gt50_ratio_50_3_4 \n",
      "extracing fe_max_1_0_14 extracing fe_max_1_0_9 extracing fe_max_1_0_4 extracing fe_max_1_1_14 extracing fe_max_1_1_9 extracing fe_max_1_1_4 extracing fe_max_1_2_14 extracing fe_max_1_2_9 extracing fe_max_1_2_4 extracing fe_max_1_3_14 extracing fe_max_1_3_9 extracing fe_max_1_3_4 extracing fe_max_5_0_14 extracing fe_max_5_0_9 extracing fe_max_5_0_4 extracing fe_max_5_1_14 extracing fe_max_5_1_9 extracing fe_max_5_1_4 extracing fe_max_5_2_14 extracing fe_max_5_2_9 extracing fe_max_5_2_4 extracing fe_max_5_3_14 extracing fe_max_5_3_9 extracing fe_max_5_3_4 extracing fe_max_10_0_14 extracing fe_max_10_0_9 extracing fe_max_10_0_4 extracing fe_max_10_1_14 extracing fe_max_10_1_9 extracing fe_max_10_1_4 extracing fe_max_10_2_14 extracing fe_max_10_2_9 extracing fe_max_10_2_4 extracing fe_max_10_3_14 extracing fe_max_10_3_9 extracing fe_max_10_3_4 extracing fe_max_20_0_14 extracing fe_max_20_0_9 extracing fe_max_20_0_4 extracing fe_max_20_1_14 extracing fe_max_20_1_9 extracing fe_max_20_1_4 extracing fe_max_20_2_14 extracing fe_max_20_2_9 extracing fe_max_20_2_4 extracing fe_max_20_3_14 extracing fe_max_20_3_9 extracing fe_max_20_3_4 extracing fe_max_50_0_14 extracing fe_max_50_0_9 extracing fe_max_50_0_4 extracing fe_max_50_1_14 extracing fe_max_50_1_9 extracing fe_max_50_1_4 extracing fe_max_50_2_14 extracing fe_max_50_2_9 extracing fe_max_50_2_4 extracing fe_max_50_3_14 extracing fe_max_50_3_9 extracing fe_max_50_3_4 \n",
      "extracing fe_std_1_0_14 extracing fe_std_1_0_9 extracing fe_std_1_0_4 extracing fe_std_1_1_14 extracing fe_std_1_1_9 extracing fe_std_1_1_4 extracing fe_std_1_2_14 extracing fe_std_1_2_9 extracing fe_std_1_2_4 extracing fe_std_1_3_14 extracing fe_std_1_3_9 extracing fe_std_1_3_4 extracing fe_std_5_0_14 extracing fe_std_5_0_9 extracing fe_std_5_0_4 extracing fe_std_5_1_14 extracing fe_std_5_1_9 extracing fe_std_5_1_4 extracing fe_std_5_2_14 extracing fe_std_5_2_9 extracing fe_std_5_2_4 extracing fe_std_5_3_14 extracing fe_std_5_3_9 extracing fe_std_5_3_4 extracing fe_std_10_0_14 extracing fe_std_10_0_9 extracing fe_std_10_0_4 extracing fe_std_10_1_14 extracing fe_std_10_1_9 extracing fe_std_10_1_4 extracing fe_std_10_2_14 extracing fe_std_10_2_9 extracing fe_std_10_2_4 extracing fe_std_10_3_14 extracing fe_std_10_3_9 extracing fe_std_10_3_4 extracing fe_std_20_0_14 extracing fe_std_20_0_9 extracing fe_std_20_0_4 extracing fe_std_20_1_14 extracing fe_std_20_1_9 extracing fe_std_20_1_4 extracing fe_std_20_2_14 extracing fe_std_20_2_9 extracing fe_std_20_2_4 extracing fe_std_20_3_14 extracing fe_std_20_3_9 extracing fe_std_20_3_4 extracing fe_std_50_0_14 extracing fe_std_50_0_9 extracing fe_std_50_0_4 extracing fe_std_50_1_14 extracing fe_std_50_1_9 extracing fe_std_50_1_4 extracing fe_std_50_2_14 extracing fe_std_50_2_9 extracing fe_std_50_2_4 extracing fe_std_50_3_14 extracing fe_std_50_3_9 extracing fe_std_50_3_4 \n",
      "extracing fe_std_without_zero_1_0_14 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_std_without_zero_1_0_9 extracing fe_std_without_zero_1_0_4 extracing fe_std_without_zero_1_1_14 extracing fe_std_without_zero_1_1_9 extracing fe_std_without_zero_1_1_4 extracing fe_std_without_zero_1_2_14 extracing fe_std_without_zero_1_2_9 extracing fe_std_without_zero_1_2_4 extracing fe_std_without_zero_1_3_14 extracing fe_std_without_zero_1_3_9 extracing fe_std_without_zero_1_3_4 extracing fe_std_without_zero_5_0_14 extracing fe_std_without_zero_5_0_9 extracing fe_std_without_zero_5_0_4 extracing fe_std_without_zero_5_1_14 extracing fe_std_without_zero_5_1_9 extracing fe_std_without_zero_5_1_4 extracing fe_std_without_zero_5_2_14 extracing fe_std_without_zero_5_2_9 extracing fe_std_without_zero_5_2_4 extracing fe_std_without_zero_5_3_14 extracing fe_std_without_zero_5_3_9 extracing fe_std_without_zero_5_3_4 extracing fe_std_without_zero_10_0_14 extracing fe_std_without_zero_10_0_9 extracing fe_std_without_zero_10_0_4 extracing fe_std_without_zero_10_1_14 extracing fe_std_without_zero_10_1_9 extracing fe_std_without_zero_10_1_4 extracing fe_std_without_zero_10_2_14 extracing fe_std_without_zero_10_2_9 extracing fe_std_without_zero_10_2_4 extracing fe_std_without_zero_10_3_14 extracing fe_std_without_zero_10_3_9 extracing fe_std_without_zero_10_3_4 extracing fe_std_without_zero_20_0_14 extracing fe_std_without_zero_20_0_9 extracing fe_std_without_zero_20_0_4 extracing fe_std_without_zero_20_1_14 extracing fe_std_without_zero_20_1_9 extracing fe_std_without_zero_20_1_4 extracing fe_std_without_zero_20_2_14 extracing fe_std_without_zero_20_2_9 extracing fe_std_without_zero_20_2_4 extracing fe_std_without_zero_20_3_14 extracing fe_std_without_zero_20_3_9 extracing fe_std_without_zero_20_3_4 extracing fe_std_without_zero_50_0_14 extracing fe_std_without_zero_50_0_9 extracing fe_std_without_zero_50_0_4 extracing fe_std_without_zero_50_1_14 extracing fe_std_without_zero_50_1_9 extracing fe_std_without_zero_50_1_4 extracing fe_std_without_zero_50_2_14 extracing fe_std_without_zero_50_2_9 extracing fe_std_without_zero_50_2_4 extracing fe_std_without_zero_50_3_14 extracing fe_std_without_zero_50_3_9 extracing fe_std_without_zero_50_3_4 \n",
      "extracing fe_zero_ratio_1_0_14 extracing fe_zero_ratio_1_0_9 extracing fe_zero_ratio_1_0_4 extracing fe_zero_ratio_1_1_14 extracing fe_zero_ratio_1_1_9 extracing fe_zero_ratio_1_1_4 extracing fe_zero_ratio_1_2_14 extracing fe_zero_ratio_1_2_9 extracing fe_zero_ratio_1_2_4 extracing fe_zero_ratio_1_3_14 extracing fe_zero_ratio_1_3_9 extracing fe_zero_ratio_1_3_4 extracing fe_zero_ratio_5_0_14 extracing fe_zero_ratio_5_0_9 extracing fe_zero_ratio_5_0_4 extracing fe_zero_ratio_5_1_14 extracing fe_zero_ratio_5_1_9 extracing fe_zero_ratio_5_1_4 extracing fe_zero_ratio_5_2_14 extracing fe_zero_ratio_5_2_9 extracing fe_zero_ratio_5_2_4 extracing fe_zero_ratio_5_3_14 extracing fe_zero_ratio_5_3_9 extracing fe_zero_ratio_5_3_4 extracing fe_zero_ratio_10_0_14 extracing fe_zero_ratio_10_0_9 extracing fe_zero_ratio_10_0_4 extracing fe_zero_ratio_10_1_14 extracing fe_zero_ratio_10_1_9 extracing fe_zero_ratio_10_1_4 extracing fe_zero_ratio_10_2_14 extracing fe_zero_ratio_10_2_9 extracing fe_zero_ratio_10_2_4 extracing fe_zero_ratio_10_3_14 extracing fe_zero_ratio_10_3_9 extracing fe_zero_ratio_10_3_4 extracing fe_zero_ratio_20_0_14 extracing fe_zero_ratio_20_0_9 extracing fe_zero_ratio_20_0_4 extracing fe_zero_ratio_20_1_14 extracing fe_zero_ratio_20_1_9 extracing fe_zero_ratio_20_1_4 extracing fe_zero_ratio_20_2_14 extracing fe_zero_ratio_20_2_9 extracing fe_zero_ratio_20_2_4 extracing fe_zero_ratio_20_3_14 extracing fe_zero_ratio_20_3_9 extracing fe_zero_ratio_20_3_4 extracing fe_zero_ratio_50_0_14 extracing fe_zero_ratio_50_0_9 extracing fe_zero_ratio_50_0_4 extracing fe_zero_ratio_50_1_14 extracing fe_zero_ratio_50_1_9 extracing fe_zero_ratio_50_1_4 extracing fe_zero_ratio_50_2_14 extracing fe_zero_ratio_50_2_9 extracing fe_zero_ratio_50_2_4 extracing fe_zero_ratio_50_3_14 extracing fe_zero_ratio_50_3_9 extracing fe_zero_ratio_50_3_4 \n"
     ]
    }
   ],
   "source": [
    "rf = RainFallFeatures()\n",
    "result = rf.fit_and_extrace(train_x)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "float(sum(train_x[0].reshape([15,4,101,101])[4][1].reshape(-1) == 0)) / (101*101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = pd.DataFrame(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 600)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fe_avg_10_0_14</th>\n",
       "      <th>fe_avg_10_0_4</th>\n",
       "      <th>fe_avg_10_0_9</th>\n",
       "      <th>fe_avg_10_1_14</th>\n",
       "      <th>fe_avg_10_1_4</th>\n",
       "      <th>fe_avg_10_1_9</th>\n",
       "      <th>fe_avg_10_2_14</th>\n",
       "      <th>fe_avg_10_2_4</th>\n",
       "      <th>fe_avg_10_2_9</th>\n",
       "      <th>fe_avg_10_3_14</th>\n",
       "      <th>...</th>\n",
       "      <th>fe_zero_ratio_5_0_9</th>\n",
       "      <th>fe_zero_ratio_5_1_14</th>\n",
       "      <th>fe_zero_ratio_5_1_4</th>\n",
       "      <th>fe_zero_ratio_5_1_9</th>\n",
       "      <th>fe_zero_ratio_5_2_14</th>\n",
       "      <th>fe_zero_ratio_5_2_4</th>\n",
       "      <th>fe_zero_ratio_5_2_9</th>\n",
       "      <th>fe_zero_ratio_5_3_14</th>\n",
       "      <th>fe_zero_ratio_5_3_4</th>\n",
       "      <th>fe_zero_ratio_5_3_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95.816327</td>\n",
       "      <td>138.884354</td>\n",
       "      <td>98.115646</td>\n",
       "      <td>105.510204</td>\n",
       "      <td>138.129252</td>\n",
       "      <td>125.312925</td>\n",
       "      <td>101.680272</td>\n",
       "      <td>125.952381</td>\n",
       "      <td>105.414966</td>\n",
       "      <td>84.945578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.244898</td>\n",
       "      <td>126.272109</td>\n",
       "      <td>134.272109</td>\n",
       "      <td>115.897959</td>\n",
       "      <td>136.040816</td>\n",
       "      <td>142.931973</td>\n",
       "      <td>107.448980</td>\n",
       "      <td>126.244898</td>\n",
       "      <td>136.088435</td>\n",
       "      <td>90.734694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143.292517</td>\n",
       "      <td>24.725624</td>\n",
       "      <td>123.698413</td>\n",
       "      <td>143.945578</td>\n",
       "      <td>9.612245</td>\n",
       "      <td>120.353741</td>\n",
       "      <td>135.346939</td>\n",
       "      <td>16.947846</td>\n",
       "      <td>118.312925</td>\n",
       "      <td>117.816327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.867769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016529</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105.380952</td>\n",
       "      <td>2.238095</td>\n",
       "      <td>0.920635</td>\n",
       "      <td>128.102041</td>\n",
       "      <td>0.702948</td>\n",
       "      <td>14.158730</td>\n",
       "      <td>119.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.310658</td>\n",
       "      <td>95.312925</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.561983</td>\n",
       "      <td>0.041322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.117914</td>\n",
       "      <td>111.861678</td>\n",
       "      <td>122.614512</td>\n",
       "      <td>109.809524</td>\n",
       "      <td>126.768707</td>\n",
       "      <td>144.272109</td>\n",
       "      <td>99.748299</td>\n",
       "      <td>120.931973</td>\n",
       "      <td>138.428571</td>\n",
       "      <td>87.408163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.514739</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>7.181406</td>\n",
       "      <td>55.795918</td>\n",
       "      <td>2.891156</td>\n",
       "      <td>4.258503</td>\n",
       "      <td>52.138322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136054</td>\n",
       "      <td>76.503401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.776860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.958678</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43.936508</td>\n",
       "      <td>21.170068</td>\n",
       "      <td>51.739229</td>\n",
       "      <td>118.503401</td>\n",
       "      <td>49.739229</td>\n",
       "      <td>138.625850</td>\n",
       "      <td>105.469388</td>\n",
       "      <td>78.054422</td>\n",
       "      <td>137.149660</td>\n",
       "      <td>79.217687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.669421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.154195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.569161</td>\n",
       "      <td>142.761905</td>\n",
       "      <td>14.167800</td>\n",
       "      <td>83.269841</td>\n",
       "      <td>130.693878</td>\n",
       "      <td>59.514739</td>\n",
       "      <td>95.054422</td>\n",
       "      <td>101.360544</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.826446</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95.027211</td>\n",
       "      <td>95.038549</td>\n",
       "      <td>139.639456</td>\n",
       "      <td>126.448980</td>\n",
       "      <td>102.020408</td>\n",
       "      <td>151.911565</td>\n",
       "      <td>117.360544</td>\n",
       "      <td>89.462585</td>\n",
       "      <td>143.414966</td>\n",
       "      <td>100.258503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>56.741497</td>\n",
       "      <td>1.049887</td>\n",
       "      <td>22.546485</td>\n",
       "      <td>130.755102</td>\n",
       "      <td>25.287982</td>\n",
       "      <td>73.759637</td>\n",
       "      <td>123.102041</td>\n",
       "      <td>12.015873</td>\n",
       "      <td>66.049887</td>\n",
       "      <td>114.986395</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.338843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.628099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>95.061224</td>\n",
       "      <td>134.013605</td>\n",
       "      <td>97.544218</td>\n",
       "      <td>107.850340</td>\n",
       "      <td>134.591837</td>\n",
       "      <td>116.979592</td>\n",
       "      <td>104.857143</td>\n",
       "      <td>120.925170</td>\n",
       "      <td>105.442177</td>\n",
       "      <td>86.836735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>123.292517</td>\n",
       "      <td>28.208617</td>\n",
       "      <td>133.782313</td>\n",
       "      <td>132.931973</td>\n",
       "      <td>29.331066</td>\n",
       "      <td>151.646259</td>\n",
       "      <td>116.585034</td>\n",
       "      <td>38.478458</td>\n",
       "      <td>146.877551</td>\n",
       "      <td>110.272109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.471074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>114.074830</td>\n",
       "      <td>130.698413</td>\n",
       "      <td>127.571429</td>\n",
       "      <td>136.006803</td>\n",
       "      <td>138.938776</td>\n",
       "      <td>142.292517</td>\n",
       "      <td>128.741497</td>\n",
       "      <td>135.204082</td>\n",
       "      <td>134.925170</td>\n",
       "      <td>110.659864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>106.251701</td>\n",
       "      <td>113.777778</td>\n",
       "      <td>116.551020</td>\n",
       "      <td>114.013605</td>\n",
       "      <td>124.251701</td>\n",
       "      <td>154.251701</td>\n",
       "      <td>106.272109</td>\n",
       "      <td>121.755102</td>\n",
       "      <td>115.047619</td>\n",
       "      <td>96.204082</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>104.328798</td>\n",
       "      <td>123.607710</td>\n",
       "      <td>112.319728</td>\n",
       "      <td>112.278912</td>\n",
       "      <td>131.795918</td>\n",
       "      <td>152.979592</td>\n",
       "      <td>103.775510</td>\n",
       "      <td>129.836735</td>\n",
       "      <td>111.462585</td>\n",
       "      <td>94.761905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>52.342404</td>\n",
       "      <td>44.564626</td>\n",
       "      <td>65.115646</td>\n",
       "      <td>107.802721</td>\n",
       "      <td>84.546485</td>\n",
       "      <td>138.149660</td>\n",
       "      <td>96.081633</td>\n",
       "      <td>99.455782</td>\n",
       "      <td>129.408163</td>\n",
       "      <td>72.877551</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>96.108844</td>\n",
       "      <td>137.748299</td>\n",
       "      <td>110.816327</td>\n",
       "      <td>117.462585</td>\n",
       "      <td>151.231293</td>\n",
       "      <td>139.727891</td>\n",
       "      <td>109.027211</td>\n",
       "      <td>135.265306</td>\n",
       "      <td>128.482993</td>\n",
       "      <td>93.510204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>144.108844</td>\n",
       "      <td>18.346939</td>\n",
       "      <td>105.832200</td>\n",
       "      <td>146.163265</td>\n",
       "      <td>43.011338</td>\n",
       "      <td>116.578231</td>\n",
       "      <td>134.142857</td>\n",
       "      <td>52.433107</td>\n",
       "      <td>108.714286</td>\n",
       "      <td>123.469388</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280992</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>107.761905</td>\n",
       "      <td>91.281179</td>\n",
       "      <td>138.931973</td>\n",
       "      <td>137.401361</td>\n",
       "      <td>99.231293</td>\n",
       "      <td>150.149660</td>\n",
       "      <td>127.877551</td>\n",
       "      <td>81.442177</td>\n",
       "      <td>138.639456</td>\n",
       "      <td>110.836735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>63.269841</td>\n",
       "      <td>15.421769</td>\n",
       "      <td>6.777778</td>\n",
       "      <td>104.904762</td>\n",
       "      <td>20.893424</td>\n",
       "      <td>4.129252</td>\n",
       "      <td>108.648526</td>\n",
       "      <td>6.374150</td>\n",
       "      <td>0.274376</td>\n",
       "      <td>95.346939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.289256</td>\n",
       "      <td>0.859504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.652893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.570248</td>\n",
       "      <td>0.661157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.877551</td>\n",
       "      <td>6.242630</td>\n",
       "      <td>11.825397</td>\n",
       "      <td>10.489796</td>\n",
       "      <td>4.260771</td>\n",
       "      <td>11.990930</td>\n",
       "      <td>40.839002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>0.016529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.179138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.108844</td>\n",
       "      <td>18.564626</td>\n",
       "      <td>8.129252</td>\n",
       "      <td>40.129252</td>\n",
       "      <td>14.909297</td>\n",
       "      <td>2.770975</td>\n",
       "      <td>71.734694</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041322</td>\n",
       "      <td>0.578512</td>\n",
       "      <td>0.859504</td>\n",
       "      <td>0.280992</td>\n",
       "      <td>0.752066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.102041</td>\n",
       "      <td>14.555556</td>\n",
       "      <td>29.941043</td>\n",
       "      <td>6.269841</td>\n",
       "      <td>12.671202</td>\n",
       "      <td>26.705215</td>\n",
       "      <td>23.952381</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.206612</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.214876</td>\n",
       "      <td>0.528926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.702479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.863946</td>\n",
       "      <td>34.718821</td>\n",
       "      <td>62.920635</td>\n",
       "      <td>29.380952</td>\n",
       "      <td>32.292517</td>\n",
       "      <td>55.049887</td>\n",
       "      <td>9.430839</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462810</td>\n",
       "      <td>0.264463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.462810</td>\n",
       "      <td>0.264463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.438017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>90.362812</td>\n",
       "      <td>131.124717</td>\n",
       "      <td>115.560091</td>\n",
       "      <td>100.666667</td>\n",
       "      <td>152.979592</td>\n",
       "      <td>119.102041</td>\n",
       "      <td>99.170068</td>\n",
       "      <td>147.006803</td>\n",
       "      <td>110.551020</td>\n",
       "      <td>79.768707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>91.120181</td>\n",
       "      <td>30.582766</td>\n",
       "      <td>101.351474</td>\n",
       "      <td>143.265306</td>\n",
       "      <td>64.727891</td>\n",
       "      <td>132.006803</td>\n",
       "      <td>135.340136</td>\n",
       "      <td>58.589569</td>\n",
       "      <td>121.639456</td>\n",
       "      <td>118.870748</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>78.752834</td>\n",
       "      <td>26.746032</td>\n",
       "      <td>90.882086</td>\n",
       "      <td>143.102041</td>\n",
       "      <td>64.079365</td>\n",
       "      <td>130.285714</td>\n",
       "      <td>136.258503</td>\n",
       "      <td>57.342404</td>\n",
       "      <td>121.585034</td>\n",
       "      <td>119.632653</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>61.142857</td>\n",
       "      <td>22.399093</td>\n",
       "      <td>1.113379</td>\n",
       "      <td>100.605442</td>\n",
       "      <td>16.285714</td>\n",
       "      <td>9.208617</td>\n",
       "      <td>89.918367</td>\n",
       "      <td>3.603175</td>\n",
       "      <td>13.578231</td>\n",
       "      <td>95.068027</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.322314</td>\n",
       "      <td>0.809917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.942149</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.462810</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>58.068027</td>\n",
       "      <td>10.603175</td>\n",
       "      <td>12.956916</td>\n",
       "      <td>130.442177</td>\n",
       "      <td>3.718821</td>\n",
       "      <td>77.517007</td>\n",
       "      <td>122.836735</td>\n",
       "      <td>6.215420</td>\n",
       "      <td>97.165533</td>\n",
       "      <td>94.843537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.661157</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.809917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066116</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>112.419501</td>\n",
       "      <td>0.848073</td>\n",
       "      <td>106.081633</td>\n",
       "      <td>142.374150</td>\n",
       "      <td>16.149660</td>\n",
       "      <td>133.510204</td>\n",
       "      <td>131.802721</td>\n",
       "      <td>22.190476</td>\n",
       "      <td>124.326531</td>\n",
       "      <td>112.299320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.884298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>1.394558</td>\n",
       "      <td>0.784580</td>\n",
       "      <td>2.269841</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.698413</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.605442</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.338843</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>5.537415</td>\n",
       "      <td>4.356009</td>\n",
       "      <td>4.768707</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.004535</td>\n",
       "      <td>2.501134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.401361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.760331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966942</td>\n",
       "      <td>0.950413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>79.285714</td>\n",
       "      <td>90.462585</td>\n",
       "      <td>73.843537</td>\n",
       "      <td>74.068027</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>71.530612</td>\n",
       "      <td>62.823129</td>\n",
       "      <td>82.482993</td>\n",
       "      <td>68.836735</td>\n",
       "      <td>47.147392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>64.362812</td>\n",
       "      <td>47.428571</td>\n",
       "      <td>28.306122</td>\n",
       "      <td>105.074830</td>\n",
       "      <td>72.292517</td>\n",
       "      <td>85.619048</td>\n",
       "      <td>86.965986</td>\n",
       "      <td>55.358277</td>\n",
       "      <td>83.217687</td>\n",
       "      <td>64.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>55.920635</td>\n",
       "      <td>40.142857</td>\n",
       "      <td>25.020408</td>\n",
       "      <td>107.204082</td>\n",
       "      <td>76.197279</td>\n",
       "      <td>93.952381</td>\n",
       "      <td>94.238095</td>\n",
       "      <td>59.102041</td>\n",
       "      <td>88.714286</td>\n",
       "      <td>76.226757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.652893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>17.655329</td>\n",
       "      <td>10.832200</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>111.721088</td>\n",
       "      <td>99.986395</td>\n",
       "      <td>68.972789</td>\n",
       "      <td>103.217687</td>\n",
       "      <td>95.911565</td>\n",
       "      <td>70.136054</td>\n",
       "      <td>78.498866</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>24.528345</td>\n",
       "      <td>31.433107</td>\n",
       "      <td>38.834467</td>\n",
       "      <td>57.979592</td>\n",
       "      <td>69.503401</td>\n",
       "      <td>85.190476</td>\n",
       "      <td>52.428571</td>\n",
       "      <td>69.319728</td>\n",
       "      <td>81.319728</td>\n",
       "      <td>32.519274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.115702</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>75.696145</td>\n",
       "      <td>60.052154</td>\n",
       "      <td>108.074830</td>\n",
       "      <td>88.857143</td>\n",
       "      <td>63.151927</td>\n",
       "      <td>121.591837</td>\n",
       "      <td>72.448980</td>\n",
       "      <td>51.326531</td>\n",
       "      <td>106.197279</td>\n",
       "      <td>52.496599</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082645</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.775510</td>\n",
       "      <td>115.176871</td>\n",
       "      <td>112.986395</td>\n",
       "      <td>92.299320</td>\n",
       "      <td>112.755102</td>\n",
       "      <td>107.401361</td>\n",
       "      <td>73.727891</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>73.879819</td>\n",
       "      <td>63.900227</td>\n",
       "      <td>65.716553</td>\n",
       "      <td>76.306122</td>\n",
       "      <td>70.605442</td>\n",
       "      <td>62.136054</td>\n",
       "      <td>70.204082</td>\n",
       "      <td>68.285714</td>\n",
       "      <td>60.108844</td>\n",
       "      <td>53.517007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>0.696145</td>\n",
       "      <td>0.702948</td>\n",
       "      <td>0.804989</td>\n",
       "      <td>74.374150</td>\n",
       "      <td>69.401361</td>\n",
       "      <td>79.673469</td>\n",
       "      <td>66.598639</td>\n",
       "      <td>66.659864</td>\n",
       "      <td>79.319728</td>\n",
       "      <td>38.854875</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>113.931973</td>\n",
       "      <td>105.489796</td>\n",
       "      <td>120.190476</td>\n",
       "      <td>109.918367</td>\n",
       "      <td>106.442177</td>\n",
       "      <td>119.156463</td>\n",
       "      <td>81.918367</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>47.664399</td>\n",
       "      <td>63.530612</td>\n",
       "      <td>71.142857</td>\n",
       "      <td>60.734694</td>\n",
       "      <td>74.598639</td>\n",
       "      <td>73.108844</td>\n",
       "      <td>59.782313</td>\n",
       "      <td>64.809524</td>\n",
       "      <td>57.115646</td>\n",
       "      <td>43.736961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049587</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>57.405896</td>\n",
       "      <td>38.995465</td>\n",
       "      <td>2.968254</td>\n",
       "      <td>121.938776</td>\n",
       "      <td>104.523810</td>\n",
       "      <td>54.514739</td>\n",
       "      <td>104.176871</td>\n",
       "      <td>91.224490</td>\n",
       "      <td>63.639456</td>\n",
       "      <td>77.802721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054422</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>106.591837</td>\n",
       "      <td>116.176871</td>\n",
       "      <td>89.278912</td>\n",
       "      <td>101.884354</td>\n",
       "      <td>115.238095</td>\n",
       "      <td>78.414966</td>\n",
       "      <td>72.265306</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9.916100</td>\n",
       "      <td>12.317460</td>\n",
       "      <td>15.052154</td>\n",
       "      <td>63.047619</td>\n",
       "      <td>64.292517</td>\n",
       "      <td>84.380952</td>\n",
       "      <td>54.836735</td>\n",
       "      <td>65.782313</td>\n",
       "      <td>82.469388</td>\n",
       "      <td>33.124717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.371901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>0.131519</td>\n",
       "      <td>4.496599</td>\n",
       "      <td>0.600907</td>\n",
       "      <td>49.616780</td>\n",
       "      <td>94.183673</td>\n",
       "      <td>60.027211</td>\n",
       "      <td>46.383220</td>\n",
       "      <td>93.884354</td>\n",
       "      <td>48.546485</td>\n",
       "      <td>37.294785</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>0.198347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>42.428571</td>\n",
       "      <td>26.891156</td>\n",
       "      <td>0.594104</td>\n",
       "      <td>120.176871</td>\n",
       "      <td>109.414966</td>\n",
       "      <td>46.630385</td>\n",
       "      <td>104.843537</td>\n",
       "      <td>103.789116</td>\n",
       "      <td>50.591837</td>\n",
       "      <td>77.380952</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049587</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>68.321995</td>\n",
       "      <td>52.444444</td>\n",
       "      <td>91.553288</td>\n",
       "      <td>93.088435</td>\n",
       "      <td>71.530612</td>\n",
       "      <td>117.074830</td>\n",
       "      <td>77.482993</td>\n",
       "      <td>53.823129</td>\n",
       "      <td>104.340136</td>\n",
       "      <td>58.251701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>74.580499</td>\n",
       "      <td>60.317460</td>\n",
       "      <td>104.294785</td>\n",
       "      <td>91.374150</td>\n",
       "      <td>70.870748</td>\n",
       "      <td>119.639456</td>\n",
       "      <td>75.482993</td>\n",
       "      <td>54.453515</td>\n",
       "      <td>106.108844</td>\n",
       "      <td>55.931973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.918367</td>\n",
       "      <td>80.006803</td>\n",
       "      <td>80.360544</td>\n",
       "      <td>68.523810</td>\n",
       "      <td>65.965986</td>\n",
       "      <td>84.142857</td>\n",
       "      <td>54.265306</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>64.780045</td>\n",
       "      <td>72.020408</td>\n",
       "      <td>78.947846</td>\n",
       "      <td>101.693878</td>\n",
       "      <td>121.081633</td>\n",
       "      <td>118.952381</td>\n",
       "      <td>96.387755</td>\n",
       "      <td>120.625850</td>\n",
       "      <td>114.707483</td>\n",
       "      <td>78.503401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>118.448980</td>\n",
       "      <td>110.353741</td>\n",
       "      <td>115.598639</td>\n",
       "      <td>111.687075</td>\n",
       "      <td>116.401361</td>\n",
       "      <td>109.149660</td>\n",
       "      <td>81.027211</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>69.843537</td>\n",
       "      <td>68.263039</td>\n",
       "      <td>44.272109</td>\n",
       "      <td>60.598639</td>\n",
       "      <td>57.623583</td>\n",
       "      <td>44.195011</td>\n",
       "      <td>49.376417</td>\n",
       "      <td>46.761905</td>\n",
       "      <td>34.739229</td>\n",
       "      <td>42.836735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.132231</td>\n",
       "      <td>0.074380</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.892562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>102.836735</td>\n",
       "      <td>117.054422</td>\n",
       "      <td>108.809524</td>\n",
       "      <td>96.680272</td>\n",
       "      <td>114.462585</td>\n",
       "      <td>102.625850</td>\n",
       "      <td>74.020408</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>64.401361</td>\n",
       "      <td>105.238095</td>\n",
       "      <td>80.972789</td>\n",
       "      <td>55.646259</td>\n",
       "      <td>104.931973</td>\n",
       "      <td>73.068027</td>\n",
       "      <td>37.555556</td>\n",
       "      <td>97.210884</td>\n",
       "      <td>50.523810</td>\n",
       "      <td>23.213152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.471074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>65.482993</td>\n",
       "      <td>76.183673</td>\n",
       "      <td>76.360544</td>\n",
       "      <td>65.829932</td>\n",
       "      <td>77.414966</td>\n",
       "      <td>75.761905</td>\n",
       "      <td>58.993197</td>\n",
       "      <td>66.530612</td>\n",
       "      <td>66.285714</td>\n",
       "      <td>33.040816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.206612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>70.748299</td>\n",
       "      <td>55.562358</td>\n",
       "      <td>92.941043</td>\n",
       "      <td>92.333333</td>\n",
       "      <td>83.578231</td>\n",
       "      <td>111.244898</td>\n",
       "      <td>76.843537</td>\n",
       "      <td>72.027211</td>\n",
       "      <td>102.054422</td>\n",
       "      <td>56.621315</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fe_avg_10_0_14  fe_avg_10_0_4  fe_avg_10_0_9  fe_avg_10_1_14  \\\n",
       "0          95.816327     138.884354      98.115646      105.510204   \n",
       "1          91.244898     126.272109     134.272109      115.897959   \n",
       "2         143.292517      24.725624     123.698413      143.945578   \n",
       "3         105.380952       2.238095       0.920635      128.102041   \n",
       "4          94.117914     111.861678     122.614512      109.809524   \n",
       "5          10.514739       0.897959       7.181406       55.795918   \n",
       "6          43.936508      21.170068      51.739229      118.503401   \n",
       "7           5.154195       0.000000       2.569161      142.761905   \n",
       "8          95.027211      95.038549     139.639456      126.448980   \n",
       "9          56.741497       1.049887      22.546485      130.755102   \n",
       "10         95.061224     134.013605      97.544218      107.850340   \n",
       "11        123.292517      28.208617     133.782313      132.931973   \n",
       "12        114.074830     130.698413     127.571429      136.006803   \n",
       "13        106.251701     113.777778     116.551020      114.013605   \n",
       "14        104.328798     123.607710     112.319728      112.278912   \n",
       "15         52.342404      44.564626      65.115646      107.802721   \n",
       "16         96.108844     137.748299     110.816327      117.462585   \n",
       "17        144.108844      18.346939     105.832200      146.163265   \n",
       "18        107.761905      91.281179     138.931973      137.401361   \n",
       "19         63.269841      15.421769       6.777778      104.904762   \n",
       "20          0.000000       0.000000       0.000000        8.877551   \n",
       "21          0.179138       0.000000       0.000000       48.108844   \n",
       "22          0.000000       0.000000       0.000000        6.102041   \n",
       "23          0.000000       0.000000       0.000000       30.863946   \n",
       "24         90.362812     131.124717     115.560091      100.666667   \n",
       "25         91.120181      30.582766     101.351474      143.265306   \n",
       "26         78.752834      26.746032      90.882086      143.102041   \n",
       "27         61.142857      22.399093       1.113379      100.605442   \n",
       "28         58.068027      10.603175      12.956916      130.442177   \n",
       "29        112.419501       0.848073     106.081633      142.374150   \n",
       "...              ...            ...            ...             ...   \n",
       "9970        1.394558       0.784580       2.269841        0.000000   \n",
       "9971        0.000000      13.698413      12.000000        0.000000   \n",
       "9972        0.000000       0.000000       0.000000        0.000000   \n",
       "9973        5.537415       4.356009       4.768707        0.000000   \n",
       "9974       79.285714      90.462585      73.843537       74.068027   \n",
       "9975       64.362812      47.428571      28.306122      105.074830   \n",
       "9976       55.920635      40.142857      25.020408      107.204082   \n",
       "9977       17.655329      10.832200       0.115646      111.721088   \n",
       "9978       24.528345      31.433107      38.834467       57.979592   \n",
       "9979       75.696145      60.052154     108.074830       88.857143   \n",
       "9980        0.000000       0.000000       0.000000       98.775510   \n",
       "9981       73.879819      63.900227      65.716553       76.306122   \n",
       "9982        0.696145       0.702948       0.804989       74.374150   \n",
       "9983        0.000000       0.000000       0.000000      113.931973   \n",
       "9984       47.664399      63.530612      71.142857       60.734694   \n",
       "9985       57.405896      38.995465       2.968254      121.938776   \n",
       "9986        0.000000       0.054422       0.530612      106.591837   \n",
       "9987        9.916100      12.317460      15.052154       63.047619   \n",
       "9988        0.131519       4.496599       0.600907       49.616780   \n",
       "9989       42.428571      26.891156       0.594104      120.176871   \n",
       "9990       68.321995      52.444444      91.553288       93.088435   \n",
       "9991       74.580499      60.317460     104.294785       91.374150   \n",
       "9992        0.000000       0.000000       0.000000       78.918367   \n",
       "9993       64.780045      72.020408      78.947846      101.693878   \n",
       "9994        0.000000       0.000000       0.000000      118.448980   \n",
       "9995       69.843537      68.263039      44.272109       60.598639   \n",
       "9996        0.000000       0.000000       0.000000      102.836735   \n",
       "9997       64.401361     105.238095      80.972789       55.646259   \n",
       "9998       65.482993      76.183673      76.360544       65.829932   \n",
       "9999       70.748299      55.562358      92.941043       92.333333   \n",
       "\n",
       "      fe_avg_10_1_4  fe_avg_10_1_9  fe_avg_10_2_14  fe_avg_10_2_4  \\\n",
       "0        138.129252     125.312925      101.680272     125.952381   \n",
       "1        136.040816     142.931973      107.448980     126.244898   \n",
       "2          9.612245     120.353741      135.346939      16.947846   \n",
       "3          0.702948      14.158730      119.666667       0.000000   \n",
       "4        126.768707     144.272109       99.748299     120.931973   \n",
       "5          2.891156       4.258503       52.138322       0.000000   \n",
       "6         49.739229     138.625850      105.469388      78.054422   \n",
       "7         14.167800      83.269841      130.693878      59.514739   \n",
       "8        102.020408     151.911565      117.360544      89.462585   \n",
       "9         25.287982      73.759637      123.102041      12.015873   \n",
       "10       134.591837     116.979592      104.857143     120.925170   \n",
       "11        29.331066     151.646259      116.585034      38.478458   \n",
       "12       138.938776     142.292517      128.741497     135.204082   \n",
       "13       124.251701     154.251701      106.272109     121.755102   \n",
       "14       131.795918     152.979592      103.775510     129.836735   \n",
       "15        84.546485     138.149660       96.081633      99.455782   \n",
       "16       151.231293     139.727891      109.027211     135.265306   \n",
       "17        43.011338     116.578231      134.142857      52.433107   \n",
       "18        99.231293     150.149660      127.877551      81.442177   \n",
       "19        20.893424       4.129252      108.648526       6.374150   \n",
       "20         6.242630      11.825397       10.489796       4.260771   \n",
       "21        18.564626       8.129252       40.129252      14.909297   \n",
       "22        14.555556      29.941043        6.269841      12.671202   \n",
       "23        34.718821      62.920635       29.380952      32.292517   \n",
       "24       152.979592     119.102041       99.170068     147.006803   \n",
       "25        64.727891     132.006803      135.340136      58.589569   \n",
       "26        64.079365     130.285714      136.258503      57.342404   \n",
       "27        16.285714       9.208617       89.918367       3.603175   \n",
       "28         3.718821      77.517007      122.836735       6.215420   \n",
       "29        16.149660     133.510204      131.802721      22.190476   \n",
       "...             ...            ...             ...            ...   \n",
       "9970       0.000000       0.000000        0.000000       0.000000   \n",
       "9971       0.000000       0.605442        0.000000       0.000000   \n",
       "9972       0.095238       0.000000        0.000000       0.000000   \n",
       "9973       3.004535       2.501134        0.000000       0.401361   \n",
       "9974      87.000000      71.530612       62.823129      82.482993   \n",
       "9975      72.292517      85.619048       86.965986      55.358277   \n",
       "9976      76.197279      93.952381       94.238095      59.102041   \n",
       "9977      99.986395      68.972789      103.217687      95.911565   \n",
       "9978      69.503401      85.190476       52.428571      69.319728   \n",
       "9979      63.151927     121.591837       72.448980      51.326531   \n",
       "9980     115.176871     112.986395       92.299320     112.755102   \n",
       "9981      70.605442      62.136054       70.204082      68.285714   \n",
       "9982      69.401361      79.673469       66.598639      66.659864   \n",
       "9983     105.489796     120.190476      109.918367     106.442177   \n",
       "9984      74.598639      73.108844       59.782313      64.809524   \n",
       "9985     104.523810      54.514739      104.176871      91.224490   \n",
       "9986     116.176871      89.278912      101.884354     115.238095   \n",
       "9987      64.292517      84.380952       54.836735      65.782313   \n",
       "9988      94.183673      60.027211       46.383220      93.884354   \n",
       "9989     109.414966      46.630385      104.843537     103.789116   \n",
       "9990      71.530612     117.074830       77.482993      53.823129   \n",
       "9991      70.870748     119.639456       75.482993      54.453515   \n",
       "9992      80.006803      80.360544       68.523810      65.965986   \n",
       "9993     121.081633     118.952381       96.387755     120.625850   \n",
       "9994     110.353741     115.598639      111.687075     116.401361   \n",
       "9995      57.623583      44.195011       49.376417      46.761905   \n",
       "9996     117.054422     108.809524       96.680272     114.462585   \n",
       "9997     104.931973      73.068027       37.555556      97.210884   \n",
       "9998      77.414966      75.761905       58.993197      66.530612   \n",
       "9999      83.578231     111.244898       76.843537      72.027211   \n",
       "\n",
       "      fe_avg_10_2_9  fe_avg_10_3_14         ...           fe_zero_ratio_5_0_9  \\\n",
       "0        105.414966       84.945578         ...                      0.000000   \n",
       "1        136.088435       90.734694         ...                      0.000000   \n",
       "2        118.312925      117.816327         ...                      0.000000   \n",
       "3         20.310658       95.312925         ...                      1.000000   \n",
       "4        138.428571       87.408163         ...                      0.008264   \n",
       "5          0.136054       76.503401         ...                      0.900826   \n",
       "6        137.149660       79.217687         ...                      0.669421   \n",
       "7         95.054422      101.360544         ...                      0.900826   \n",
       "8        143.414966      100.258503         ...                      0.000000   \n",
       "9         66.049887      114.986395         ...                      0.776860   \n",
       "10       105.442177       86.836735         ...                      0.000000   \n",
       "11       146.877551      110.272109         ...                      0.000000   \n",
       "12       134.925170      110.659864         ...                      0.000000   \n",
       "13       115.047619       96.204082         ...                      0.066116   \n",
       "14       111.462585       94.761905         ...                      0.008264   \n",
       "15       129.408163       72.877551         ...                      0.487603   \n",
       "16       128.482993       93.510204         ...                      0.074380   \n",
       "17       108.714286      123.469388         ...                      0.000000   \n",
       "18       138.639456      110.836735         ...                      0.000000   \n",
       "19         0.274376       95.346939         ...                      0.793388   \n",
       "20        11.990930       40.839002         ...                      1.000000   \n",
       "21         2.770975       71.734694         ...                      1.000000   \n",
       "22        26.705215       23.952381         ...                      1.000000   \n",
       "23        55.049887        9.430839         ...                      1.000000   \n",
       "24       110.551020       79.768707         ...                      0.000000   \n",
       "25       121.639456      118.870748         ...                      0.206612   \n",
       "26       121.585034      119.632653         ...                      0.355372   \n",
       "27        13.578231       95.068027         ...                      1.000000   \n",
       "28        97.165533       94.843537         ...                      0.776860   \n",
       "29       124.326531      112.299320         ...                      0.000000   \n",
       "...             ...             ...         ...                           ...   \n",
       "9970       0.000000        0.000000         ...                      1.000000   \n",
       "9971       0.469388        0.000000         ...                      0.338843   \n",
       "9972       0.000000        0.000000         ...                      1.000000   \n",
       "9973       0.000000        0.000000         ...                      0.760331   \n",
       "9974      68.836735       47.147392         ...                      0.000000   \n",
       "9975      83.217687       64.428571         ...                      0.198347   \n",
       "9976      88.714286       76.226757         ...                      0.652893   \n",
       "9977      70.136054       78.498866         ...                      1.000000   \n",
       "9978      81.319728       32.519274         ...                      0.479339   \n",
       "9979     106.197279       52.496599         ...                      0.082645   \n",
       "9980     107.401361       73.727891         ...                      1.000000   \n",
       "9981      60.108844       53.517007         ...                      0.000000   \n",
       "9982      79.319728       38.854875         ...                      1.000000   \n",
       "9983     119.156463       81.918367         ...                      1.000000   \n",
       "9984      57.115646       43.736961         ...                      0.000000   \n",
       "9985      63.639456       77.802721         ...                      0.925620   \n",
       "9986      78.414966       72.265306         ...                      1.000000   \n",
       "9987      82.469388       33.124717         ...                      0.983471   \n",
       "9988      48.546485       37.294785         ...                      1.000000   \n",
       "9989      50.591837       77.380952         ...                      1.000000   \n",
       "9990     104.340136       58.251701         ...                      0.008264   \n",
       "9991     106.108844       55.931973         ...                      0.033058   \n",
       "9992      84.142857       54.265306         ...                      1.000000   \n",
       "9993     114.707483       78.503401         ...                      0.190083   \n",
       "9994     109.149660       81.027211         ...                      1.000000   \n",
       "9995      34.739229       42.836735         ...                      0.000000   \n",
       "9996     102.625850       74.020408         ...                      1.000000   \n",
       "9997      50.523810       23.213152         ...                      0.000000   \n",
       "9998      66.285714       33.040816         ...                      0.000000   \n",
       "9999     102.054422       56.621315         ...                      0.008264   \n",
       "\n",
       "      fe_zero_ratio_5_1_14  fe_zero_ratio_5_1_4  fe_zero_ratio_5_1_9  \\\n",
       "0                 0.000000             0.000000             0.000000   \n",
       "1                 0.000000             0.000000             0.000000   \n",
       "2                 0.000000             0.867769             0.000000   \n",
       "3                 0.000000             1.000000             0.867769   \n",
       "4                 0.000000             0.000000             0.000000   \n",
       "5                 0.000000             0.966942             0.776860   \n",
       "6                 0.000000             0.000000             0.000000   \n",
       "7                 0.000000             0.826446             0.024793   \n",
       "8                 0.000000             0.000000             0.000000   \n",
       "9                 0.000000             0.338843             0.000000   \n",
       "10                0.000000             0.000000             0.000000   \n",
       "11                0.000000             0.471074             0.000000   \n",
       "12                0.000000             0.000000             0.000000   \n",
       "13                0.000000             0.000000             0.000000   \n",
       "14                0.000000             0.000000             0.000000   \n",
       "15                0.000000             0.000000             0.000000   \n",
       "16                0.000000             0.000000             0.000000   \n",
       "17                0.000000             0.280992             0.000000   \n",
       "18                0.000000             0.000000             0.000000   \n",
       "19                0.000000             0.289256             0.859504   \n",
       "20                0.966942             0.991736             0.834711   \n",
       "21                0.041322             0.578512             0.859504   \n",
       "22                1.000000             0.727273             0.206612   \n",
       "23                0.462810             0.264463             0.000000   \n",
       "24                0.000000             0.000000             0.000000   \n",
       "25                0.000000             0.000000             0.000000   \n",
       "26                0.000000             0.000000             0.000000   \n",
       "27                0.000000             0.322314             0.809917   \n",
       "28                0.000000             0.661157             0.008264   \n",
       "29                0.000000             0.884298             0.000000   \n",
       "...                    ...                  ...                  ...   \n",
       "9970              1.000000             1.000000             1.000000   \n",
       "9971              1.000000             1.000000             0.900826   \n",
       "9972              1.000000             1.000000             1.000000   \n",
       "9973              1.000000             0.966942             0.950413   \n",
       "9974              0.000000             0.000000             0.000000   \n",
       "9975              0.000000             0.000000             0.000000   \n",
       "9976              0.000000             0.000000             0.000000   \n",
       "9977              0.000000             0.000000             0.000000   \n",
       "9978              0.000000             0.000000             0.000000   \n",
       "9979              0.000000             0.000000             0.000000   \n",
       "9980              0.000000             0.000000             0.000000   \n",
       "9981              0.000000             0.000000             0.000000   \n",
       "9982              0.000000             0.000000             0.000000   \n",
       "9983              0.000000             0.000000             0.000000   \n",
       "9984              0.000000             0.000000             0.000000   \n",
       "9985              0.000000             0.000000             0.000000   \n",
       "9986              0.000000             0.000000             0.000000   \n",
       "9987              0.000000             0.000000             0.000000   \n",
       "9988              0.000000             0.000000             0.000000   \n",
       "9989              0.000000             0.000000             0.049587   \n",
       "9990              0.000000             0.000000             0.000000   \n",
       "9991              0.000000             0.000000             0.000000   \n",
       "9992              0.000000             0.000000             0.000000   \n",
       "9993              0.000000             0.000000             0.000000   \n",
       "9994              0.000000             0.000000             0.000000   \n",
       "9995              0.000000             0.000000             0.033058   \n",
       "9996              0.000000             0.000000             0.000000   \n",
       "9997              0.000000             0.000000             0.000000   \n",
       "9998              0.000000             0.000000             0.000000   \n",
       "9999              0.000000             0.000000             0.000000   \n",
       "\n",
       "      fe_zero_ratio_5_2_14  fe_zero_ratio_5_2_4  fe_zero_ratio_5_2_9  \\\n",
       "0                 0.000000             0.000000             0.000000   \n",
       "1                 0.000000             0.000000             0.000000   \n",
       "2                 0.000000             0.743802             0.000000   \n",
       "3                 0.000000             1.000000             0.752066   \n",
       "4                 0.000000             0.000000             0.000000   \n",
       "5                 0.000000             1.000000             1.000000   \n",
       "6                 0.000000             0.000000             0.000000   \n",
       "7                 0.000000             0.000000             0.000000   \n",
       "8                 0.000000             0.000000             0.000000   \n",
       "9                 0.000000             0.628099             0.000000   \n",
       "10                0.000000             0.000000             0.000000   \n",
       "11                0.000000             0.000000             0.000000   \n",
       "12                0.000000             0.000000             0.000000   \n",
       "13                0.000000             0.000000             0.000000   \n",
       "14                0.000000             0.000000             0.000000   \n",
       "15                0.000000             0.000000             0.000000   \n",
       "16                0.000000             0.000000             0.000000   \n",
       "17                0.000000             0.016529             0.000000   \n",
       "18                0.000000             0.000000             0.000000   \n",
       "19                0.000000             0.652893             1.000000   \n",
       "20                0.991736             0.991736             0.834711   \n",
       "21                0.280992             0.752066             1.000000   \n",
       "22                1.000000             0.727273             0.214876   \n",
       "23                0.462810             0.264463             0.000000   \n",
       "24                0.000000             0.000000             0.000000   \n",
       "25                0.000000             0.000000             0.000000   \n",
       "26                0.000000             0.000000             0.000000   \n",
       "27                0.000000             0.942149             0.793388   \n",
       "28                0.000000             0.809917             0.000000   \n",
       "29                0.000000             0.760331             0.000000   \n",
       "...                    ...                  ...                  ...   \n",
       "9970              1.000000             1.000000             1.000000   \n",
       "9971              1.000000             1.000000             0.925620   \n",
       "9972              1.000000             1.000000             1.000000   \n",
       "9973              1.000000             1.000000             1.000000   \n",
       "9974              0.000000             0.000000             0.000000   \n",
       "9975              0.000000             0.000000             0.000000   \n",
       "9976              0.000000             0.000000             0.000000   \n",
       "9977              0.000000             0.000000             0.000000   \n",
       "9978              0.000000             0.000000             0.000000   \n",
       "9979              0.000000             0.000000             0.000000   \n",
       "9980              0.000000             0.000000             0.000000   \n",
       "9981              0.000000             0.000000             0.000000   \n",
       "9982              0.000000             0.000000             0.000000   \n",
       "9983              0.000000             0.000000             0.000000   \n",
       "9984              0.000000             0.000000             0.000000   \n",
       "9985              0.000000             0.000000             0.000000   \n",
       "9986              0.000000             0.000000             0.000000   \n",
       "9987              0.000000             0.000000             0.000000   \n",
       "9988              0.231405             0.000000             0.024793   \n",
       "9989              0.000000             0.000000             0.000000   \n",
       "9990              0.000000             0.000000             0.000000   \n",
       "9991              0.000000             0.000000             0.000000   \n",
       "9992              0.000000             0.000000             0.000000   \n",
       "9993              0.000000             0.000000             0.000000   \n",
       "9994              0.000000             0.000000             0.000000   \n",
       "9995              0.000000             0.000000             0.132231   \n",
       "9996              0.000000             0.000000             0.000000   \n",
       "9997              0.000000             0.000000             0.000000   \n",
       "9998              0.000000             0.000000             0.000000   \n",
       "9999              0.000000             0.000000             0.000000   \n",
       "\n",
       "      fe_zero_ratio_5_3_14  fe_zero_ratio_5_3_4  fe_zero_ratio_5_3_9  \n",
       "0                 0.000000             0.000000             0.000000  \n",
       "1                 0.000000             0.000000             0.000000  \n",
       "2                 0.000000             0.016529             0.000000  \n",
       "3                 0.000000             0.561983             0.041322  \n",
       "4                 0.000000             0.000000             0.000000  \n",
       "5                 0.000000             0.958678             0.000000  \n",
       "6                 0.000000             0.000000             0.000000  \n",
       "7                 0.000000             0.000000             0.000000  \n",
       "8                 0.000000             0.000000             0.000000  \n",
       "9                 0.000000             0.000000             0.000000  \n",
       "10                0.000000             0.000000             0.000000  \n",
       "11                0.000000             0.000000             0.000000  \n",
       "12                0.000000             0.000000             0.000000  \n",
       "13                0.000000             0.000000             0.000000  \n",
       "14                0.000000             0.000000             0.000000  \n",
       "15                0.000000             0.000000             0.000000  \n",
       "16                0.000000             0.000000             0.000000  \n",
       "17                0.000000             0.000000             0.000000  \n",
       "18                0.000000             0.000000             0.000000  \n",
       "19                0.000000             0.570248             0.661157  \n",
       "20                0.016529             1.000000             0.140496  \n",
       "21                0.000000             1.000000             0.190083  \n",
       "22                0.528926             1.000000             0.702479  \n",
       "23                1.000000             1.000000             0.438017  \n",
       "24                0.000000             0.000000             0.000000  \n",
       "25                0.000000             0.000000             0.000000  \n",
       "26                0.000000             0.000000             0.000000  \n",
       "27                0.000000             0.462810             0.000000  \n",
       "28                0.000000             0.066116             0.000000  \n",
       "29                0.000000             0.000000             0.000000  \n",
       "...                    ...                  ...                  ...  \n",
       "9970              1.000000             1.000000             1.000000  \n",
       "9971              1.000000             1.000000             1.000000  \n",
       "9972              1.000000             1.000000             1.000000  \n",
       "9973              1.000000             1.000000             1.000000  \n",
       "9974              0.041322             0.000000             0.000000  \n",
       "9975              0.008264             0.000000             0.000000  \n",
       "9976              0.033058             0.000000             0.000000  \n",
       "9977              0.000000             0.000000             0.000000  \n",
       "9978              0.115702             0.000000             0.000000  \n",
       "9979              0.000000             0.000000             0.000000  \n",
       "9980              0.000000             0.000000             0.000000  \n",
       "9981              0.000000             0.000000             0.008264  \n",
       "9982              0.000000             0.000000             0.000000  \n",
       "9983              0.000000             0.000000             0.000000  \n",
       "9984              0.049587             0.000000             0.000000  \n",
       "9985              0.000000             0.000000             0.000000  \n",
       "9986              0.000000             0.000000             0.000000  \n",
       "9987              0.371901             0.000000             0.000000  \n",
       "9988              0.198347             0.000000             0.000000  \n",
       "9989              0.000000             0.000000             0.000000  \n",
       "9990              0.000000             0.000000             0.000000  \n",
       "9991              0.000000             0.000000             0.000000  \n",
       "9992              0.000000             0.000000             0.000000  \n",
       "9993              0.008264             0.000000             0.000000  \n",
       "9994              0.000000             0.000000             0.000000  \n",
       "9995              0.074380             0.181818             0.892562  \n",
       "9996              0.000000             0.000000             0.000000  \n",
       "9997              0.471074             0.000000             0.000000  \n",
       "9998              0.206612             0.000000             0.000000  \n",
       "9999              0.000000             0.000000             0.000000  \n",
       "\n",
       "[10000 rows x 600 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_labels = [1 if i >= 20 else 0 for i in train_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 2000)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_y),len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n",
       "              max_depth=11, max_features=None, max_leaf_nodes=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(max_depth=11)\n",
    "clf.fit(result.values,train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrainset-auc:0.909649\ttestset-auc:0.726234\n",
      "Multiple eval metrics have been passed: 'testset-auc' will be used for early stopping.\n",
      "\n",
      "Will train until testset-auc hasn't improved in 50 rounds.\n",
      "[20]\ttrainset-auc:0.997748\ttestset-auc:0.860039\n",
      "[40]\ttrainset-auc:0.999806\ttestset-auc:0.872011\n",
      "[60]\ttrainset-auc:0.999982\ttestset-auc:0.876981\n",
      "[80]\ttrainset-auc:0.999999\ttestset-auc:0.88144\n",
      "[100]\ttrainset-auc:1\ttestset-auc:0.885054\n",
      "[120]\ttrainset-auc:1\ttestset-auc:0.886289\n",
      "[140]\ttrainset-auc:1\ttestset-auc:0.888034\n",
      "[160]\ttrainset-auc:1\ttestset-auc:0.889595\n",
      "[180]\ttrainset-auc:1\ttestset-auc:0.890478\n",
      "[200]\ttrainset-auc:1\ttestset-auc:0.890949\n",
      "[220]\ttrainset-auc:1\ttestset-auc:0.891512\n",
      "[240]\ttrainset-auc:1\ttestset-auc:0.891756\n",
      "[260]\ttrainset-auc:1\ttestset-auc:0.891918\n",
      "[280]\ttrainset-auc:1\ttestset-auc:0.892258\n",
      "[300]\ttrainset-auc:1\ttestset-auc:0.891766\n",
      "[320]\ttrainset-auc:1\ttestset-auc:0.892133\n",
      "[340]\ttrainset-auc:1\ttestset-auc:0.891976\n",
      "[360]\ttrainset-auc:1\ttestset-auc:0.892408\n",
      "[380]\ttrainset-auc:1\ttestset-auc:0.892638\n",
      "[400]\ttrainset-auc:1\ttestset-auc:0.892741\n",
      "[420]\ttrainset-auc:1\ttestset-auc:0.893072\n",
      "[440]\ttrainset-auc:1\ttestset-auc:0.893558\n",
      "[460]\ttrainset-auc:1\ttestset-auc:0.893846\n",
      "[480]\ttrainset-auc:1\ttestset-auc:0.893797\n",
      "[500]\ttrainset-auc:1\ttestset-auc:0.893813\n",
      "[520]\ttrainset-auc:1\ttestset-auc:0.893775\n",
      "[540]\ttrainset-auc:1\ttestset-auc:0.8937\n",
      "Stopping. Best iteration:\n",
      "[490]\ttrainset-auc:1\ttestset-auc:0.894054\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    'max_depth':11, \n",
    "    'eta':0.06, \n",
    "    'silent':1, \n",
    "    'objective':'binary:logistic',\n",
    "    'eval_metric':'auc',\n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.45,\n",
    "    #'min_child_weight':50,\n",
    "    'seed':1123,\n",
    "    'nthread':-1,\n",
    "    \n",
    "}\n",
    "num_round = 4000\n",
    "dtrain = xgb.DMatrix(result.values,train_labels)\n",
    "dtest = xgb.DMatrix(test_data,test_label)\n",
    "#bst = xgb.train(param,dtrain,\n",
    "#                evals_result={'testset':dtest},\n",
    "#                num_boost_round=num_round,\n",
    "#                verbose_eval=10,\n",
    "#                )\n",
    "#bst = xgb.XGBClassifier()\n",
    "bst = xgb.train(param,dtrain,\n",
    "                evals=[(dtrain,'trainset'),(dtest,'testset')],\n",
    "                num_boost_round=num_round,\n",
    "                verbose_eval=20,early_stopping_rounds=50\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_1_0_14 extracing fe_avg_1_0_9 extracing fe_avg_1_0_4 extracing fe_avg_1_1_14 extracing fe_avg_1_1_9 extracing fe_avg_1_1_4 extracing fe_avg_1_2_14 extracing fe_avg_1_2_9 extracing fe_avg_1_2_4 extracing fe_avg_1_3_14 extracing fe_avg_1_3_9 extracing fe_avg_1_3_4 extracing fe_avg_5_0_14 extracing fe_avg_5_0_9 extracing fe_avg_5_0_4 extracing fe_avg_5_1_14 extracing fe_avg_5_1_9 extracing fe_avg_5_1_4 extracing fe_avg_5_2_14 extracing fe_avg_5_2_9 extracing fe_avg_5_2_4 extracing fe_avg_5_3_14 extracing fe_avg_5_3_9 extracing fe_avg_5_3_4 extracing fe_avg_10_0_14 extracing fe_avg_10_0_9 extracing fe_avg_10_0_4 extracing fe_avg_10_1_14 extracing fe_avg_10_1_9 extracing fe_avg_10_1_4 extracing fe_avg_10_2_14 extracing fe_avg_10_2_9 extracing fe_avg_10_2_4 extracing fe_avg_10_3_14 extracing fe_avg_10_3_9 extracing fe_avg_10_3_4 extracing fe_avg_20_0_14 extracing fe_avg_20_0_9 extracing fe_avg_20_0_4 extracing fe_avg_20_1_14 extracing fe_avg_20_1_9 extracing fe_avg_20_1_4 extracing fe_avg_20_2_14 extracing fe_avg_20_2_9 extracing fe_avg_20_2_4 extracing fe_avg_20_3_14 extracing fe_avg_20_3_9 extracing fe_avg_20_3_4 extracing fe_avg_50_0_14 extracing fe_avg_50_0_9 extracing fe_avg_50_0_4 extracing fe_avg_50_1_14 extracing fe_avg_50_1_9 extracing fe_avg_50_1_4 extracing fe_avg_50_2_14 extracing fe_avg_50_2_9 extracing fe_avg_50_2_4 extracing fe_avg_50_3_14 extracing fe_avg_50_3_9 extracing fe_avg_50_3_4 \n",
      "extracing fe_avg_without_zero_1_0_14 extracing fe_avg_without_zero_1_0_9 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_without_zero_1_0_4 extracing fe_avg_without_zero_1_1_14 extracing fe_avg_without_zero_1_1_9 extracing fe_avg_without_zero_1_1_4 extracing fe_avg_without_zero_1_2_14 extracing fe_avg_without_zero_1_2_9 extracing fe_avg_without_zero_1_2_4 extracing fe_avg_without_zero_1_3_14 extracing fe_avg_without_zero_1_3_9 extracing fe_avg_without_zero_1_3_4 extracing fe_avg_without_zero_5_0_14 extracing fe_avg_without_zero_5_0_9 extracing fe_avg_without_zero_5_0_4 extracing fe_avg_without_zero_5_1_14 extracing fe_avg_without_zero_5_1_9 extracing fe_avg_without_zero_5_1_4 extracing fe_avg_without_zero_5_2_14 extracing fe_avg_without_zero_5_2_9 extracing fe_avg_without_zero_5_2_4 extracing fe_avg_without_zero_5_3_14 extracing fe_avg_without_zero_5_3_9 extracing fe_avg_without_zero_5_3_4 extracing fe_avg_without_zero_10_0_14 extracing fe_avg_without_zero_10_0_9 extracing fe_avg_without_zero_10_0_4 extracing fe_avg_without_zero_10_1_14 extracing fe_avg_without_zero_10_1_9 extracing fe_avg_without_zero_10_1_4 extracing fe_avg_without_zero_10_2_14 extracing fe_avg_without_zero_10_2_9 extracing fe_avg_without_zero_10_2_4 extracing fe_avg_without_zero_10_3_14 extracing fe_avg_without_zero_10_3_9 extracing fe_avg_without_zero_10_3_4 extracing fe_avg_without_zero_20_0_14 extracing fe_avg_without_zero_20_0_9 extracing fe_avg_without_zero_20_0_4 extracing fe_avg_without_zero_20_1_14 extracing fe_avg_without_zero_20_1_9 extracing fe_avg_without_zero_20_1_4 extracing fe_avg_without_zero_20_2_14 extracing fe_avg_without_zero_20_2_9 extracing fe_avg_without_zero_20_2_4 extracing fe_avg_without_zero_20_3_14 extracing fe_avg_without_zero_20_3_9 extracing fe_avg_without_zero_20_3_4 extracing fe_avg_without_zero_50_0_14 extracing fe_avg_without_zero_50_0_9 extracing fe_avg_without_zero_50_0_4 extracing fe_avg_without_zero_50_1_14 extracing fe_avg_without_zero_50_1_9 extracing fe_avg_without_zero_50_1_4 extracing fe_avg_without_zero_50_2_14 extracing fe_avg_without_zero_50_2_9 extracing fe_avg_without_zero_50_2_4 extracing fe_avg_without_zero_50_3_14 extracing fe_avg_without_zero_50_3_9 extracing fe_avg_without_zero_50_3_4 \n",
      "extracing fe_gt100_ratio_1_0_14 extracing fe_gt100_ratio_1_0_9 extracing fe_gt100_ratio_1_0_4 extracing fe_gt100_ratio_1_1_14 extracing fe_gt100_ratio_1_1_9 extracing fe_gt100_ratio_1_1_4 extracing fe_gt100_ratio_1_2_14 extracing fe_gt100_ratio_1_2_9 extracing fe_gt100_ratio_1_2_4 extracing fe_gt100_ratio_1_3_14 extracing fe_gt100_ratio_1_3_9 extracing fe_gt100_ratio_1_3_4 extracing fe_gt100_ratio_5_0_14 extracing fe_gt100_ratio_5_0_9 extracing fe_gt100_ratio_5_0_4 extracing fe_gt100_ratio_5_1_14 extracing fe_gt100_ratio_5_1_9 extracing fe_gt100_ratio_5_1_4 extracing fe_gt100_ratio_5_2_14 extracing fe_gt100_ratio_5_2_9 extracing fe_gt100_ratio_5_2_4 extracing fe_gt100_ratio_5_3_14 extracing fe_gt100_ratio_5_3_9 extracing fe_gt100_ratio_5_3_4 extracing fe_gt100_ratio_10_0_14 extracing fe_gt100_ratio_10_0_9 extracing fe_gt100_ratio_10_0_4 extracing fe_gt100_ratio_10_1_14 extracing fe_gt100_ratio_10_1_9 extracing fe_gt100_ratio_10_1_4 extracing fe_gt100_ratio_10_2_14 extracing fe_gt100_ratio_10_2_9 extracing fe_gt100_ratio_10_2_4 extracing fe_gt100_ratio_10_3_14 extracing fe_gt100_ratio_10_3_9 extracing fe_gt100_ratio_10_3_4 extracing fe_gt100_ratio_20_0_14 extracing fe_gt100_ratio_20_0_9 extracing fe_gt100_ratio_20_0_4 extracing fe_gt100_ratio_20_1_14 extracing fe_gt100_ratio_20_1_9 extracing fe_gt100_ratio_20_1_4 extracing fe_gt100_ratio_20_2_14 extracing fe_gt100_ratio_20_2_9 extracing fe_gt100_ratio_20_2_4 extracing fe_gt100_ratio_20_3_14 extracing fe_gt100_ratio_20_3_9 extracing fe_gt100_ratio_20_3_4 extracing fe_gt100_ratio_50_0_14 extracing fe_gt100_ratio_50_0_9 extracing fe_gt100_ratio_50_0_4 extracing fe_gt100_ratio_50_1_14 extracing fe_gt100_ratio_50_1_9 extracing fe_gt100_ratio_50_1_4 extracing fe_gt100_ratio_50_2_14 extracing fe_gt100_ratio_50_2_9 extracing fe_gt100_ratio_50_2_4 extracing fe_gt100_ratio_50_3_14 extracing fe_gt100_ratio_50_3_9 extracing fe_gt100_ratio_50_3_4 \n",
      "extracing fe_gt150_ratio_1_0_14 extracing fe_gt150_ratio_1_0_9 extracing fe_gt150_ratio_1_0_4 extracing fe_gt150_ratio_1_1_14 extracing fe_gt150_ratio_1_1_9 extracing fe_gt150_ratio_1_1_4 extracing fe_gt150_ratio_1_2_14 extracing fe_gt150_ratio_1_2_9 extracing fe_gt150_ratio_1_2_4 extracing fe_gt150_ratio_1_3_14 extracing fe_gt150_ratio_1_3_9 extracing fe_gt150_ratio_1_3_4 extracing fe_gt150_ratio_5_0_14 extracing fe_gt150_ratio_5_0_9 extracing fe_gt150_ratio_5_0_4 extracing fe_gt150_ratio_5_1_14 extracing fe_gt150_ratio_5_1_9 extracing fe_gt150_ratio_5_1_4 extracing fe_gt150_ratio_5_2_14 extracing fe_gt150_ratio_5_2_9 extracing fe_gt150_ratio_5_2_4 extracing fe_gt150_ratio_5_3_14 extracing fe_gt150_ratio_5_3_9 extracing fe_gt150_ratio_5_3_4 extracing fe_gt150_ratio_10_0_14 extracing fe_gt150_ratio_10_0_9 extracing fe_gt150_ratio_10_0_4 extracing fe_gt150_ratio_10_1_14 extracing fe_gt150_ratio_10_1_9 extracing fe_gt150_ratio_10_1_4 extracing fe_gt150_ratio_10_2_14 extracing fe_gt150_ratio_10_2_9 extracing fe_gt150_ratio_10_2_4 extracing fe_gt150_ratio_10_3_14 extracing fe_gt150_ratio_10_3_9 extracing fe_gt150_ratio_10_3_4 extracing fe_gt150_ratio_20_0_14 extracing fe_gt150_ratio_20_0_9 extracing fe_gt150_ratio_20_0_4 extracing fe_gt150_ratio_20_1_14 extracing fe_gt150_ratio_20_1_9 extracing fe_gt150_ratio_20_1_4 extracing fe_gt150_ratio_20_2_14 extracing fe_gt150_ratio_20_2_9 extracing fe_gt150_ratio_20_2_4 extracing fe_gt150_ratio_20_3_14 extracing fe_gt150_ratio_20_3_9 extracing fe_gt150_ratio_20_3_4 extracing fe_gt150_ratio_50_0_14 extracing fe_gt150_ratio_50_0_9 extracing fe_gt150_ratio_50_0_4 extracing fe_gt150_ratio_50_1_14 extracing fe_gt150_ratio_50_1_9 extracing fe_gt150_ratio_50_1_4 extracing fe_gt150_ratio_50_2_14 extracing fe_gt150_ratio_50_2_9 extracing fe_gt150_ratio_50_2_4 extracing fe_gt150_ratio_50_3_14 extracing fe_gt150_ratio_50_3_9 extracing fe_gt150_ratio_50_3_4 \n",
      "extracing fe_gt200_ratio_1_0_14 extracing fe_gt200_ratio_1_0_9 extracing fe_gt200_ratio_1_0_4 extracing fe_gt200_ratio_1_1_14 extracing fe_gt200_ratio_1_1_9 extracing fe_gt200_ratio_1_1_4 extracing fe_gt200_ratio_1_2_14 extracing fe_gt200_ratio_1_2_9 extracing fe_gt200_ratio_1_2_4 extracing fe_gt200_ratio_1_3_14 extracing fe_gt200_ratio_1_3_9 extracing fe_gt200_ratio_1_3_4 extracing fe_gt200_ratio_5_0_14 extracing fe_gt200_ratio_5_0_9 extracing fe_gt200_ratio_5_0_4 extracing fe_gt200_ratio_5_1_14 extracing fe_gt200_ratio_5_1_9 extracing fe_gt200_ratio_5_1_4 extracing fe_gt200_ratio_5_2_14 extracing fe_gt200_ratio_5_2_9 extracing fe_gt200_ratio_5_2_4 extracing fe_gt200_ratio_5_3_14 extracing fe_gt200_ratio_5_3_9 extracing fe_gt200_ratio_5_3_4 extracing fe_gt200_ratio_10_0_14 extracing fe_gt200_ratio_10_0_9 extracing fe_gt200_ratio_10_0_4 extracing fe_gt200_ratio_10_1_14 extracing fe_gt200_ratio_10_1_9 extracing fe_gt200_ratio_10_1_4 extracing fe_gt200_ratio_10_2_14 extracing fe_gt200_ratio_10_2_9 extracing fe_gt200_ratio_10_2_4 extracing fe_gt200_ratio_10_3_14 extracing fe_gt200_ratio_10_3_9 extracing fe_gt200_ratio_10_3_4 extracing fe_gt200_ratio_20_0_14 extracing fe_gt200_ratio_20_0_9 extracing fe_gt200_ratio_20_0_4 extracing fe_gt200_ratio_20_1_14 extracing fe_gt200_ratio_20_1_9 extracing fe_gt200_ratio_20_1_4 extracing fe_gt200_ratio_20_2_14 extracing fe_gt200_ratio_20_2_9 extracing fe_gt200_ratio_20_2_4 extracing fe_gt200_ratio_20_3_14 extracing fe_gt200_ratio_20_3_9 extracing fe_gt200_ratio_20_3_4 extracing fe_gt200_ratio_50_0_14 extracing fe_gt200_ratio_50_0_9 extracing fe_gt200_ratio_50_0_4 extracing fe_gt200_ratio_50_1_14 extracing fe_gt200_ratio_50_1_9 extracing fe_gt200_ratio_50_1_4 extracing fe_gt200_ratio_50_2_14 extracing fe_gt200_ratio_50_2_9 extracing fe_gt200_ratio_50_2_4 extracing fe_gt200_ratio_50_3_14 extracing fe_gt200_ratio_50_3_9 extracing fe_gt200_ratio_50_3_4 \n",
      "extracing fe_gt50_ratio_1_0_14 extracing fe_gt50_ratio_1_0_9 extracing fe_gt50_ratio_1_0_4 extracing fe_gt50_ratio_1_1_14 extracing fe_gt50_ratio_1_1_9 extracing fe_gt50_ratio_1_1_4 extracing fe_gt50_ratio_1_2_14 extracing fe_gt50_ratio_1_2_9 extracing fe_gt50_ratio_1_2_4 extracing fe_gt50_ratio_1_3_14 extracing fe_gt50_ratio_1_3_9 extracing fe_gt50_ratio_1_3_4 extracing fe_gt50_ratio_5_0_14 extracing fe_gt50_ratio_5_0_9 extracing fe_gt50_ratio_5_0_4 extracing fe_gt50_ratio_5_1_14 extracing fe_gt50_ratio_5_1_9 extracing fe_gt50_ratio_5_1_4 extracing fe_gt50_ratio_5_2_14 extracing fe_gt50_ratio_5_2_9 extracing fe_gt50_ratio_5_2_4 extracing fe_gt50_ratio_5_3_14 extracing fe_gt50_ratio_5_3_9 extracing fe_gt50_ratio_5_3_4 extracing fe_gt50_ratio_10_0_14 extracing fe_gt50_ratio_10_0_9 extracing fe_gt50_ratio_10_0_4 extracing fe_gt50_ratio_10_1_14 extracing fe_gt50_ratio_10_1_9 extracing fe_gt50_ratio_10_1_4 extracing fe_gt50_ratio_10_2_14 extracing fe_gt50_ratio_10_2_9 extracing fe_gt50_ratio_10_2_4 extracing fe_gt50_ratio_10_3_14 extracing fe_gt50_ratio_10_3_9 extracing fe_gt50_ratio_10_3_4 extracing fe_gt50_ratio_20_0_14 extracing fe_gt50_ratio_20_0_9 extracing fe_gt50_ratio_20_0_4 extracing fe_gt50_ratio_20_1_14 extracing fe_gt50_ratio_20_1_9 extracing fe_gt50_ratio_20_1_4 extracing fe_gt50_ratio_20_2_14 extracing fe_gt50_ratio_20_2_9 extracing fe_gt50_ratio_20_2_4 extracing fe_gt50_ratio_20_3_14 extracing fe_gt50_ratio_20_3_9 extracing fe_gt50_ratio_20_3_4 extracing fe_gt50_ratio_50_0_14 extracing fe_gt50_ratio_50_0_9 extracing fe_gt50_ratio_50_0_4 extracing fe_gt50_ratio_50_1_14 extracing fe_gt50_ratio_50_1_9 extracing fe_gt50_ratio_50_1_4 extracing fe_gt50_ratio_50_2_14 extracing fe_gt50_ratio_50_2_9 extracing fe_gt50_ratio_50_2_4 extracing fe_gt50_ratio_50_3_14 extracing fe_gt50_ratio_50_3_9 extracing fe_gt50_ratio_50_3_4 \n",
      "extracing fe_max_1_0_14 extracing fe_max_1_0_9 extracing fe_max_1_0_4 extracing fe_max_1_1_14 extracing fe_max_1_1_9 extracing fe_max_1_1_4 extracing fe_max_1_2_14 extracing fe_max_1_2_9 extracing fe_max_1_2_4 extracing fe_max_1_3_14 extracing fe_max_1_3_9 extracing fe_max_1_3_4 extracing fe_max_5_0_14 extracing fe_max_5_0_9 extracing fe_max_5_0_4 extracing fe_max_5_1_14 extracing fe_max_5_1_9 extracing fe_max_5_1_4 extracing fe_max_5_2_14 extracing fe_max_5_2_9 extracing fe_max_5_2_4 extracing fe_max_5_3_14 extracing fe_max_5_3_9 extracing fe_max_5_3_4 extracing fe_max_10_0_14 extracing fe_max_10_0_9 extracing fe_max_10_0_4 extracing fe_max_10_1_14 extracing fe_max_10_1_9 extracing fe_max_10_1_4 extracing fe_max_10_2_14 extracing fe_max_10_2_9 extracing fe_max_10_2_4 extracing fe_max_10_3_14 extracing fe_max_10_3_9 extracing fe_max_10_3_4 extracing fe_max_20_0_14 extracing fe_max_20_0_9 extracing fe_max_20_0_4 extracing fe_max_20_1_14 extracing fe_max_20_1_9 extracing fe_max_20_1_4 extracing fe_max_20_2_14 extracing fe_max_20_2_9 extracing fe_max_20_2_4 extracing fe_max_20_3_14 extracing fe_max_20_3_9 extracing fe_max_20_3_4 extracing fe_max_50_0_14 extracing fe_max_50_0_9 extracing fe_max_50_0_4 extracing fe_max_50_1_14 extracing fe_max_50_1_9 extracing fe_max_50_1_4 extracing fe_max_50_2_14 extracing fe_max_50_2_9 extracing fe_max_50_2_4 extracing fe_max_50_3_14 extracing fe_max_50_3_9 extracing fe_max_50_3_4 \n",
      "extracing fe_std_1_0_14 extracing fe_std_1_0_9 extracing fe_std_1_0_4 extracing fe_std_1_1_14 extracing fe_std_1_1_9 extracing fe_std_1_1_4 extracing fe_std_1_2_14 extracing fe_std_1_2_9 extracing fe_std_1_2_4 extracing fe_std_1_3_14 extracing fe_std_1_3_9 extracing fe_std_1_3_4 extracing fe_std_5_0_14 extracing fe_std_5_0_9 extracing fe_std_5_0_4 extracing fe_std_5_1_14 extracing fe_std_5_1_9 extracing fe_std_5_1_4 extracing fe_std_5_2_14 extracing fe_std_5_2_9 extracing fe_std_5_2_4 extracing fe_std_5_3_14 extracing fe_std_5_3_9 extracing fe_std_5_3_4 extracing fe_std_10_0_14 extracing fe_std_10_0_9 extracing fe_std_10_0_4 extracing fe_std_10_1_14 extracing fe_std_10_1_9 extracing fe_std_10_1_4 extracing fe_std_10_2_14 extracing fe_std_10_2_9 extracing fe_std_10_2_4 extracing fe_std_10_3_14 extracing fe_std_10_3_9 extracing fe_std_10_3_4 extracing fe_std_20_0_14 extracing fe_std_20_0_9 extracing fe_std_20_0_4 extracing fe_std_20_1_14 extracing fe_std_20_1_9 extracing fe_std_20_1_4 extracing fe_std_20_2_14 extracing fe_std_20_2_9 extracing fe_std_20_2_4 extracing fe_std_20_3_14 extracing fe_std_20_3_9 extracing fe_std_20_3_4 extracing fe_std_50_0_14 extracing fe_std_50_0_9 extracing fe_std_50_0_4 extracing fe_std_50_1_14 extracing fe_std_50_1_9 extracing fe_std_50_1_4 extracing fe_std_50_2_14 extracing fe_std_50_2_9 extracing fe_std_50_2_4 extracing fe_std_50_3_14 extracing fe_std_50_3_9 extracing fe_std_50_3_4 \n",
      "extracing fe_std_without_zero_1_0_14 extracing fe_std_without_zero_1_0_9 extracing fe_std_without_zero_1_0_4 extracing fe_std_without_zero_1_1_14 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_std_without_zero_1_1_9 extracing fe_std_without_zero_1_1_4 extracing fe_std_without_zero_1_2_14 extracing fe_std_without_zero_1_2_9 extracing fe_std_without_zero_1_2_4 extracing fe_std_without_zero_1_3_14 extracing fe_std_without_zero_1_3_9 extracing fe_std_without_zero_1_3_4 extracing fe_std_without_zero_5_0_14 extracing fe_std_without_zero_5_0_9 extracing fe_std_without_zero_5_0_4 extracing fe_std_without_zero_5_1_14 extracing fe_std_without_zero_5_1_9 extracing fe_std_without_zero_5_1_4 extracing fe_std_without_zero_5_2_14 extracing fe_std_without_zero_5_2_9 extracing fe_std_without_zero_5_2_4 extracing fe_std_without_zero_5_3_14 extracing fe_std_without_zero_5_3_9 extracing fe_std_without_zero_5_3_4 extracing fe_std_without_zero_10_0_14 extracing fe_std_without_zero_10_0_9 extracing fe_std_without_zero_10_0_4 extracing fe_std_without_zero_10_1_14 extracing fe_std_without_zero_10_1_9 extracing fe_std_without_zero_10_1_4 extracing fe_std_without_zero_10_2_14 extracing fe_std_without_zero_10_2_9 extracing fe_std_without_zero_10_2_4 extracing fe_std_without_zero_10_3_14 extracing fe_std_without_zero_10_3_9 extracing fe_std_without_zero_10_3_4 extracing fe_std_without_zero_20_0_14 extracing fe_std_without_zero_20_0_9 extracing fe_std_without_zero_20_0_4 extracing fe_std_without_zero_20_1_14 extracing fe_std_without_zero_20_1_9 extracing fe_std_without_zero_20_1_4 extracing fe_std_without_zero_20_2_14 extracing fe_std_without_zero_20_2_9 extracing fe_std_without_zero_20_2_4 extracing fe_std_without_zero_20_3_14 extracing fe_std_without_zero_20_3_9 extracing fe_std_without_zero_20_3_4 extracing fe_std_without_zero_50_0_14 extracing fe_std_without_zero_50_0_9 extracing fe_std_without_zero_50_0_4 extracing fe_std_without_zero_50_1_14 extracing fe_std_without_zero_50_1_9 extracing fe_std_without_zero_50_1_4 extracing fe_std_without_zero_50_2_14 extracing fe_std_without_zero_50_2_9 extracing fe_std_without_zero_50_2_4 extracing fe_std_without_zero_50_3_14 extracing fe_std_without_zero_50_3_9 extracing fe_std_without_zero_50_3_4 \n",
      "extracing fe_zero_ratio_1_0_14 extracing fe_zero_ratio_1_0_9 extracing fe_zero_ratio_1_0_4 extracing fe_zero_ratio_1_1_14 extracing fe_zero_ratio_1_1_9 extracing fe_zero_ratio_1_1_4 extracing fe_zero_ratio_1_2_14 extracing fe_zero_ratio_1_2_9 extracing fe_zero_ratio_1_2_4 extracing fe_zero_ratio_1_3_14 extracing fe_zero_ratio_1_3_9 extracing fe_zero_ratio_1_3_4 extracing fe_zero_ratio_5_0_14 extracing fe_zero_ratio_5_0_9 extracing fe_zero_ratio_5_0_4 extracing fe_zero_ratio_5_1_14 extracing fe_zero_ratio_5_1_9 extracing fe_zero_ratio_5_1_4 extracing fe_zero_ratio_5_2_14 extracing fe_zero_ratio_5_2_9 extracing fe_zero_ratio_5_2_4 extracing fe_zero_ratio_5_3_14 extracing fe_zero_ratio_5_3_9 extracing fe_zero_ratio_5_3_4 extracing fe_zero_ratio_10_0_14 extracing fe_zero_ratio_10_0_9 extracing fe_zero_ratio_10_0_4 extracing fe_zero_ratio_10_1_14 extracing fe_zero_ratio_10_1_9 extracing fe_zero_ratio_10_1_4 extracing fe_zero_ratio_10_2_14 extracing fe_zero_ratio_10_2_9 extracing fe_zero_ratio_10_2_4 extracing fe_zero_ratio_10_3_14 extracing fe_zero_ratio_10_3_9 extracing fe_zero_ratio_10_3_4 extracing fe_zero_ratio_20_0_14 extracing fe_zero_ratio_20_0_9 extracing fe_zero_ratio_20_0_4 extracing fe_zero_ratio_20_1_14 extracing fe_zero_ratio_20_1_9 extracing fe_zero_ratio_20_1_4 extracing fe_zero_ratio_20_2_14 extracing fe_zero_ratio_20_2_9 extracing fe_zero_ratio_20_2_4 extracing fe_zero_ratio_20_3_14 extracing fe_zero_ratio_20_3_9 extracing fe_zero_ratio_20_3_4 extracing fe_zero_ratio_50_0_14 extracing fe_zero_ratio_50_0_9 extracing fe_zero_ratio_50_0_4 extracing fe_zero_ratio_50_1_14 extracing fe_zero_ratio_50_1_9 extracing fe_zero_ratio_50_1_4 extracing fe_zero_ratio_50_2_14 extracing fe_zero_ratio_50_2_9 extracing fe_zero_ratio_50_2_4 extracing fe_zero_ratio_50_3_14 extracing fe_zero_ratio_50_3_9 extracing fe_zero_ratio_50_3_4 \n"
     ]
    }
   ],
   "source": [
    "test_data = rf.fit_and_extrace(test_x).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predy = clf.predict_proba(test_data)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xgb_predy = bst.predict(xgb.DMatrix(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_label = [1 if i >= 20 else 0 for i in test_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.88543529743\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
    "auc = roc_auc_score(test_label,predy)\n",
    "print (auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.893216746158\n"
     ]
    }
   ],
   "source": [
    "# xgb auc\n",
    "from sklearn.metrics import roc_curve, auc,roc_auc_score\n",
    "auc = roc_auc_score(test_label,xgb_predy)\n",
    "print (auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yy = clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8225\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(test_label,yy))\n",
    "print(accuracy_score(train_labels,clf.predict(result.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xgbpredict = bst.predict(xgb.DMatrix(test_data))\n",
    "xgbpredict = [1 if i > 0.5 else 0 for i in xgbpredict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8225\n",
      "0.829\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(test_label,yy))\n",
    "print(accuracy_score(test_label,xgbpredict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-122-91b326fed043>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mGradientBoostingRegressor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mreg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGradientBoostingRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, monitor)\u001b[0m\n\u001b[1;32m    968\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    969\u001b[0m         \u001b[1;31m# Check input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 970\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'csc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'coo'\u001b[0m\u001b[1;33m]\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[0m\n\u001b[0m\u001b[1;32m    971\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    972\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    508\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    509\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    511\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    396\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    397\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     52\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     53\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 54\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "reg = GradientBoostingRegressor(max_depth=11)\n",
    "reg.fit(result.values,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrainset-rmse:20.7761\ttestset-rmse:21.5201\n",
      "Multiple eval metrics have been passed: 'testset-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until testset-rmse hasn't improved in 50 rounds.\n",
      "[20]\ttrainset-rmse:9.38605\ttestset-rmse:13.5594\n",
      "[40]\ttrainset-rmse:5.36719\ttestset-rmse:12.0473\n",
      "[60]\ttrainset-rmse:3.64257\ttestset-rmse:11.7041\n",
      "[80]\ttrainset-rmse:2.66995\ttestset-rmse:11.5634\n",
      "[100]\ttrainset-rmse:2.10267\ttestset-rmse:11.4802\n",
      "[120]\ttrainset-rmse:1.70883\ttestset-rmse:11.442\n",
      "[140]\ttrainset-rmse:1.3837\ttestset-rmse:11.4147\n",
      "[160]\ttrainset-rmse:1.08292\ttestset-rmse:11.3838\n",
      "[180]\ttrainset-rmse:0.858483\ttestset-rmse:11.3651\n",
      "[200]\ttrainset-rmse:0.672654\ttestset-rmse:11.3554\n",
      "[220]\ttrainset-rmse:0.546797\ttestset-rmse:11.3481\n",
      "[240]\ttrainset-rmse:0.43974\ttestset-rmse:11.3426\n",
      "[260]\ttrainset-rmse:0.343865\ttestset-rmse:11.3376\n",
      "[280]\ttrainset-rmse:0.275571\ttestset-rmse:11.3358\n",
      "[300]\ttrainset-rmse:0.220118\ttestset-rmse:11.333\n",
      "[320]\ttrainset-rmse:0.178055\ttestset-rmse:11.332\n",
      "[340]\ttrainset-rmse:0.143562\ttestset-rmse:11.3308\n",
      "[360]\ttrainset-rmse:0.11438\ttestset-rmse:11.3292\n",
      "[380]\ttrainset-rmse:0.093818\ttestset-rmse:11.3286\n",
      "[400]\ttrainset-rmse:0.077222\ttestset-rmse:11.3283\n",
      "[420]\ttrainset-rmse:0.062462\ttestset-rmse:11.3275\n",
      "[440]\ttrainset-rmse:0.051615\ttestset-rmse:11.3269\n",
      "[460]\ttrainset-rmse:0.042861\ttestset-rmse:11.3266\n",
      "[480]\ttrainset-rmse:0.035565\ttestset-rmse:11.3264\n",
      "[500]\ttrainset-rmse:0.029174\ttestset-rmse:11.326\n",
      "[520]\ttrainset-rmse:0.023691\ttestset-rmse:11.326\n",
      "[540]\ttrainset-rmse:0.020468\ttestset-rmse:11.326\n",
      "[560]\ttrainset-rmse:0.016756\ttestset-rmse:11.3259\n",
      "[580]\ttrainset-rmse:0.014378\ttestset-rmse:11.3258\n",
      "[600]\ttrainset-rmse:0.011751\ttestset-rmse:11.3257\n",
      "[620]\ttrainset-rmse:0.009899\ttestset-rmse:11.3257\n",
      "[640]\ttrainset-rmse:0.008199\ttestset-rmse:11.3257\n",
      "[660]\ttrainset-rmse:0.006953\ttestset-rmse:11.3257\n",
      "[680]\ttrainset-rmse:0.005844\ttestset-rmse:11.3256\n",
      "[700]\ttrainset-rmse:0.004885\ttestset-rmse:11.3256\n",
      "[720]\ttrainset-rmse:0.004131\ttestset-rmse:11.3256\n",
      "[740]\ttrainset-rmse:0.003497\ttestset-rmse:11.3256\n",
      "[760]\ttrainset-rmse:0.002866\ttestset-rmse:11.3256\n",
      "[780]\ttrainset-rmse:0.002421\ttestset-rmse:11.3256\n",
      "[800]\ttrainset-rmse:0.002052\ttestset-rmse:11.3255\n",
      "[820]\ttrainset-rmse:0.001755\ttestset-rmse:11.3255\n",
      "[840]\ttrainset-rmse:0.001497\ttestset-rmse:11.3255\n",
      "[860]\ttrainset-rmse:0.001253\ttestset-rmse:11.3255\n",
      "[880]\ttrainset-rmse:0.001057\ttestset-rmse:11.3255\n",
      "[900]\ttrainset-rmse:0.000883\ttestset-rmse:11.3255\n",
      "[920]\ttrainset-rmse:0.000738\ttestset-rmse:11.3255\n",
      "[940]\ttrainset-rmse:0.000645\ttestset-rmse:11.3255\n",
      "[960]\ttrainset-rmse:0.00056\ttestset-rmse:11.3255\n",
      "[980]\ttrainset-rmse:0.000508\ttestset-rmse:11.3255\n",
      "Stopping. Best iteration:\n",
      "[941]\ttrainset-rmse:0.000639\ttestset-rmse:11.3255\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    'max_depth':11, \n",
    "    'eta':0.06, \n",
    "    'silent':1, \n",
    "    'objective':'reg:linear',\n",
    "    'eval_metric':'rmse',\n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.45,\n",
    "    #'min_child_weight':50,\n",
    "    'seed':1123,\n",
    "    'nthread':-1,\n",
    "    \n",
    "}\n",
    "num_round = 4000\n",
    "dtrain = xgb.DMatrix(result.values,train_y)\n",
    "dtest = xgb.DMatrix(test_data,test_y)\n",
    "#bst = xgb.train(param,dtrain,\n",
    "#                evals_result={'testset':dtest},\n",
    "#                num_boost_round=num_round,\n",
    "#                verbose_eval=10,\n",
    "#                )\n",
    "#bst = xgb.XGBClassifier()\n",
    "bst = xgb.train(param,dtrain,\n",
    "                evals=[(dtrain,'trainset'),(dtest,'testset')],\n",
    "                num_boost_round=num_round,\n",
    "                verbose_eval=20,early_stopping_rounds=50\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrainset-rmse:20.8981\n",
      "Will train until trainset-rmse hasn't improved in 50 rounds.\n",
      "[20]\ttrainset-rmse:9.67028\n",
      "[40]\ttrainset-rmse:5.68382\n",
      "[60]\ttrainset-rmse:3.87606\n",
      "[80]\ttrainset-rmse:3.07732\n",
      "[100]\ttrainset-rmse:2.43053\n",
      "[120]\ttrainset-rmse:1.9814\n",
      "[140]\ttrainset-rmse:1.62417\n",
      "[160]\ttrainset-rmse:1.33983\n",
      "[180]\ttrainset-rmse:1.09867\n",
      "[200]\ttrainset-rmse:0.919959\n",
      "[220]\ttrainset-rmse:0.772268\n",
      "[240]\ttrainset-rmse:0.618871\n",
      "[260]\ttrainset-rmse:0.507119\n",
      "[280]\ttrainset-rmse:0.424567\n",
      "[300]\ttrainset-rmse:0.350699\n",
      "[320]\ttrainset-rmse:0.291127\n",
      "[340]\ttrainset-rmse:0.238366\n",
      "[360]\ttrainset-rmse:0.199141\n",
      "[380]\ttrainset-rmse:0.168448\n",
      "[400]\ttrainset-rmse:0.141749\n",
      "[420]\ttrainset-rmse:0.120765\n",
      "[440]\ttrainset-rmse:0.102921\n",
      "[460]\ttrainset-rmse:0.08513\n",
      "[480]\ttrainset-rmse:0.070936\n",
      "[500]\ttrainset-rmse:0.061431\n",
      "[520]\ttrainset-rmse:0.052551\n",
      "[540]\ttrainset-rmse:0.044004\n",
      "[560]\ttrainset-rmse:0.03795\n",
      "[580]\ttrainset-rmse:0.032952\n",
      "[600]\ttrainset-rmse:0.028094\n",
      "[620]\ttrainset-rmse:0.02366\n",
      "[640]\ttrainset-rmse:0.020599\n",
      "[660]\ttrainset-rmse:0.017736\n",
      "[680]\ttrainset-rmse:0.014986\n",
      "[700]\ttrainset-rmse:0.012753\n",
      "[720]\ttrainset-rmse:0.010849\n",
      "[740]\ttrainset-rmse:0.009259\n",
      "[760]\ttrainset-rmse:0.007834\n",
      "[780]\ttrainset-rmse:0.006937\n",
      "[800]\ttrainset-rmse:0.005855\n",
      "[820]\ttrainset-rmse:0.00503\n",
      "[840]\ttrainset-rmse:0.004425\n",
      "[860]\ttrainset-rmse:0.003818\n",
      "[880]\ttrainset-rmse:0.003248\n",
      "[900]\ttrainset-rmse:0.002859\n",
      "[920]\ttrainset-rmse:0.002524\n",
      "[940]\ttrainset-rmse:0.002163\n",
      "[960]\ttrainset-rmse:0.001852\n",
      "[980]\ttrainset-rmse:0.001587\n",
      "[1000]\ttrainset-rmse:0.001383\n",
      "[1020]\ttrainset-rmse:0.001187\n",
      "[1040]\ttrainset-rmse:0.00102\n",
      "[1060]\ttrainset-rmse:0.000892\n",
      "[1079]\ttrainset-rmse:0.000788\n"
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    'max_depth':11, \n",
    "    'eta':0.06, \n",
    "    'silent':1, \n",
    "    'objective':'reg:linear',\n",
    "    'eval_metric':'rmse',\n",
    "    'subsample':0.85,\n",
    "    'colsample_bytree':0.45,\n",
    "    #'min_child_weight':50,\n",
    "    'seed':1123,\n",
    "    'nthread':-1,\n",
    "    \n",
    "}\n",
    "num_round = 1080\n",
    "dtrain = xgb.DMatrix(result.values,train_y)\n",
    "#bst = xgb.train(param,dtrain,\n",
    "#                evals_result={'testset':dtest},\n",
    "#                num_boost_round=num_round,\n",
    "#                verbose_eval=10,\n",
    "#                )\n",
    "#bst = xgb.XGBClassifier()\n",
    "bst = xgb.train(param,dtrain,\n",
    "                evals=[(dtrain,'trainset')],\n",
    "                num_boost_round=num_round,\n",
    "                verbose_eval=20,early_stopping_rounds=50\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000021A1D7034A8>]], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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045xNNm3axNDQUN3cMlBi5iGCRqdcVOKx9U9T2wtsrZs3AXyswTpGiI7M1LsMGAV6a+Yd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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a1d717cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "#plt.subplot(121)\n",
    "#pd.DataFrame(reg.predict(test_data)).hist(bins=50)\n",
    "#plt.subplot(122)\n",
    "pd.DataFrame(bst.predict(xgb.DMatrix(test_data))).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000021A1A487C88>]], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PsiFJkqqybEiSpKosG+q0ffv2NT1CK5hDYRaZORRmUZ9lQ502MjLS9AitYA6FWWTmUJhF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\nSZJU1f8PJb+ouzPNm/4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a1a48b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(reg.predict(result.values)).hist(bins=50)\n",
    "pd.DataFrame(bst.predict(xgb.DMatrix(result.values))).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rsem\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11.858509993785722"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regyy = reg.predict(test_data)\n",
    "print(\"rsem\")\n",
    "np.sqrt(np.mean(np.square(np.array(regyy) - np.array(test_y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1 0.105 0.11 0.115 0.12 0.125 0.13 0.135 0.14 0.145 0.15 0.155 0.16 0.165 0.17 0.175 0.18 0.185 0.19 0.195 0.2 "
     ]
    }
   ],
   "source": [
    "predict_x = []\n",
    "count = 0\n",
    "with open('CIKM2017_testA/testA.txt') as fhdl:\n",
    "    for line in fhdl:\n",
    "        count += 1\n",
    "        if count % 50 == 0:\n",
    "            print (float(count) / 10000,end=' '),\n",
    "        linenum,label,datas = line.strip().split(',')\n",
    "        label = float(label)\n",
    "        datas = np.asarray(datas.split(\" \"),dtype=np.int)\n",
    "        datas[datas < 0] = 0\n",
    "        datas = np.asarray(datas,dtype=np.uint8)\n",
    "        predict_x.append(datas)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "with open('onlinedata.plk','wb') as whdl:\n",
    "    pickle.dump(predict_x,whdl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <bound method Booster.__del__ of <xgboost.core.Booster object at 0x0000021A2441E828>>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Program Files\\Anaconda3\\lib\\site-packages\\xgboost-0.6-py3.5.egg\\xgboost\\core.py\", line 669, in __del__\n",
      "    _LIB.XGBoosterFree(self.handle)\n",
      "AttributeError: 'Booster' object has no attribute 'handle'\n"
     ]
    }
   ],
   "source": [
    "with open('onlinedata.plk','rb') as fhdl:\n",
    "    predict_x = pickle.load(fhdl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([131, 131, 131, ..., 137, 131, 131], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to make sure online data is the right version(contains values larger than 128)\n",
    "predict_x[0][predict_x[0] > 128]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_1_0_14 extracing fe_avg_1_0_9 extracing fe_avg_1_0_4 extracing fe_avg_1_1_14 extracing fe_avg_1_1_9 extracing fe_avg_1_1_4 extracing fe_avg_1_2_14 extracing fe_avg_1_2_9 extracing fe_avg_1_2_4 extracing fe_avg_1_3_14 extracing fe_avg_1_3_9 extracing fe_avg_1_3_4 extracing fe_avg_5_0_14 extracing fe_avg_5_0_9 extracing fe_avg_5_0_4 extracing fe_avg_5_1_14 extracing fe_avg_5_1_9 extracing fe_avg_5_1_4 extracing fe_avg_5_2_14 extracing fe_avg_5_2_9 extracing fe_avg_5_2_4 extracing fe_avg_5_3_14 extracing fe_avg_5_3_9 extracing fe_avg_5_3_4 extracing fe_avg_10_0_14 extracing fe_avg_10_0_9 extracing fe_avg_10_0_4 extracing fe_avg_10_1_14 extracing fe_avg_10_1_9 extracing fe_avg_10_1_4 extracing fe_avg_10_2_14 extracing fe_avg_10_2_9 extracing fe_avg_10_2_4 extracing fe_avg_10_3_14 extracing fe_avg_10_3_9 extracing fe_avg_10_3_4 extracing fe_avg_20_0_14 extracing fe_avg_20_0_9 extracing fe_avg_20_0_4 extracing fe_avg_20_1_14 extracing fe_avg_20_1_9 extracing fe_avg_20_1_4 extracing fe_avg_20_2_14 extracing fe_avg_20_2_9 extracing fe_avg_20_2_4 extracing fe_avg_20_3_14 extracing fe_avg_20_3_9 extracing fe_avg_20_3_4 extracing fe_avg_50_0_14 extracing fe_avg_50_0_9 extracing fe_avg_50_0_4 extracing fe_avg_50_1_14 extracing fe_avg_50_1_9 extracing fe_avg_50_1_4 extracing fe_avg_50_2_14 extracing fe_avg_50_2_9 extracing fe_avg_50_2_4 extracing fe_avg_50_3_14 extracing fe_avg_50_3_9 extracing fe_avg_50_3_4 \n",
      "extracing fe_avg_without_zero_1_0_14 extracing fe_avg_without_zero_1_0_9 extracing fe_avg_without_zero_1_0_4 extracing fe_avg_without_zero_1_1_14 extracing fe_avg_without_zero_1_1_9 extracing fe_avg_without_zero_1_1_4 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_avg_without_zero_1_2_14 extracing fe_avg_without_zero_1_2_9 extracing fe_avg_without_zero_1_2_4 extracing fe_avg_without_zero_1_3_14 extracing fe_avg_without_zero_1_3_9 extracing fe_avg_without_zero_1_3_4 extracing fe_avg_without_zero_5_0_14 extracing fe_avg_without_zero_5_0_9 extracing fe_avg_without_zero_5_0_4 extracing fe_avg_without_zero_5_1_14 extracing fe_avg_without_zero_5_1_9 extracing fe_avg_without_zero_5_1_4 extracing fe_avg_without_zero_5_2_14 extracing fe_avg_without_zero_5_2_9 extracing fe_avg_without_zero_5_2_4 extracing fe_avg_without_zero_5_3_14 extracing fe_avg_without_zero_5_3_9 extracing fe_avg_without_zero_5_3_4 extracing fe_avg_without_zero_10_0_14 extracing fe_avg_without_zero_10_0_9 extracing fe_avg_without_zero_10_0_4 extracing fe_avg_without_zero_10_1_14 extracing fe_avg_without_zero_10_1_9 extracing fe_avg_without_zero_10_1_4 extracing fe_avg_without_zero_10_2_14 extracing fe_avg_without_zero_10_2_9 extracing fe_avg_without_zero_10_2_4 extracing fe_avg_without_zero_10_3_14 extracing fe_avg_without_zero_10_3_9 extracing fe_avg_without_zero_10_3_4 extracing fe_avg_without_zero_20_0_14 extracing fe_avg_without_zero_20_0_9 extracing fe_avg_without_zero_20_0_4 extracing fe_avg_without_zero_20_1_14 extracing fe_avg_without_zero_20_1_9 extracing fe_avg_without_zero_20_1_4 extracing fe_avg_without_zero_20_2_14 extracing fe_avg_without_zero_20_2_9 extracing fe_avg_without_zero_20_2_4 extracing fe_avg_without_zero_20_3_14 extracing fe_avg_without_zero_20_3_9 extracing fe_avg_without_zero_20_3_4 extracing fe_avg_without_zero_50_0_14 extracing fe_avg_without_zero_50_0_9 extracing fe_avg_without_zero_50_0_4 extracing fe_avg_without_zero_50_1_14 extracing fe_avg_without_zero_50_1_9 extracing fe_avg_without_zero_50_1_4 extracing fe_avg_without_zero_50_2_14 extracing fe_avg_without_zero_50_2_9 extracing fe_avg_without_zero_50_2_4 extracing fe_avg_without_zero_50_3_14 extracing fe_avg_without_zero_50_3_9 extracing fe_avg_without_zero_50_3_4 \n",
      "extracing fe_gt100_ratio_1_0_14 extracing fe_gt100_ratio_1_0_9 extracing fe_gt100_ratio_1_0_4 extracing fe_gt100_ratio_1_1_14 extracing fe_gt100_ratio_1_1_9 extracing fe_gt100_ratio_1_1_4 extracing fe_gt100_ratio_1_2_14 extracing fe_gt100_ratio_1_2_9 extracing fe_gt100_ratio_1_2_4 extracing fe_gt100_ratio_1_3_14 extracing fe_gt100_ratio_1_3_9 extracing fe_gt100_ratio_1_3_4 extracing fe_gt100_ratio_5_0_14 extracing fe_gt100_ratio_5_0_9 extracing fe_gt100_ratio_5_0_4 extracing fe_gt100_ratio_5_1_14 extracing fe_gt100_ratio_5_1_9 extracing fe_gt100_ratio_5_1_4 extracing fe_gt100_ratio_5_2_14 extracing fe_gt100_ratio_5_2_9 extracing fe_gt100_ratio_5_2_4 extracing fe_gt100_ratio_5_3_14 extracing fe_gt100_ratio_5_3_9 extracing fe_gt100_ratio_5_3_4 extracing fe_gt100_ratio_10_0_14 extracing fe_gt100_ratio_10_0_9 extracing fe_gt100_ratio_10_0_4 extracing fe_gt100_ratio_10_1_14 extracing fe_gt100_ratio_10_1_9 extracing fe_gt100_ratio_10_1_4 extracing fe_gt100_ratio_10_2_14 extracing fe_gt100_ratio_10_2_9 extracing fe_gt100_ratio_10_2_4 extracing fe_gt100_ratio_10_3_14 extracing fe_gt100_ratio_10_3_9 extracing fe_gt100_ratio_10_3_4 extracing fe_gt100_ratio_20_0_14 extracing fe_gt100_ratio_20_0_9 extracing fe_gt100_ratio_20_0_4 extracing fe_gt100_ratio_20_1_14 extracing fe_gt100_ratio_20_1_9 extracing fe_gt100_ratio_20_1_4 extracing fe_gt100_ratio_20_2_14 extracing fe_gt100_ratio_20_2_9 extracing fe_gt100_ratio_20_2_4 extracing fe_gt100_ratio_20_3_14 extracing fe_gt100_ratio_20_3_9 extracing fe_gt100_ratio_20_3_4 extracing fe_gt100_ratio_50_0_14 extracing fe_gt100_ratio_50_0_9 extracing fe_gt100_ratio_50_0_4 extracing fe_gt100_ratio_50_1_14 extracing fe_gt100_ratio_50_1_9 extracing fe_gt100_ratio_50_1_4 extracing fe_gt100_ratio_50_2_14 extracing fe_gt100_ratio_50_2_9 extracing fe_gt100_ratio_50_2_4 extracing fe_gt100_ratio_50_3_14 extracing fe_gt100_ratio_50_3_9 extracing fe_gt100_ratio_50_3_4 \n",
      "extracing fe_gt150_ratio_1_0_14 extracing fe_gt150_ratio_1_0_9 extracing fe_gt150_ratio_1_0_4 extracing fe_gt150_ratio_1_1_14 extracing fe_gt150_ratio_1_1_9 extracing fe_gt150_ratio_1_1_4 extracing fe_gt150_ratio_1_2_14 extracing fe_gt150_ratio_1_2_9 extracing fe_gt150_ratio_1_2_4 extracing fe_gt150_ratio_1_3_14 extracing fe_gt150_ratio_1_3_9 extracing fe_gt150_ratio_1_3_4 extracing fe_gt150_ratio_5_0_14 extracing fe_gt150_ratio_5_0_9 extracing fe_gt150_ratio_5_0_4 extracing fe_gt150_ratio_5_1_14 extracing fe_gt150_ratio_5_1_9 extracing fe_gt150_ratio_5_1_4 extracing fe_gt150_ratio_5_2_14 extracing fe_gt150_ratio_5_2_9 extracing fe_gt150_ratio_5_2_4 extracing fe_gt150_ratio_5_3_14 extracing fe_gt150_ratio_5_3_9 extracing fe_gt150_ratio_5_3_4 extracing fe_gt150_ratio_10_0_14 extracing fe_gt150_ratio_10_0_9 extracing fe_gt150_ratio_10_0_4 extracing fe_gt150_ratio_10_1_14 extracing fe_gt150_ratio_10_1_9 extracing fe_gt150_ratio_10_1_4 extracing fe_gt150_ratio_10_2_14 extracing fe_gt150_ratio_10_2_9 extracing fe_gt150_ratio_10_2_4 extracing fe_gt150_ratio_10_3_14 extracing fe_gt150_ratio_10_3_9 extracing fe_gt150_ratio_10_3_4 extracing fe_gt150_ratio_20_0_14 extracing fe_gt150_ratio_20_0_9 extracing fe_gt150_ratio_20_0_4 extracing fe_gt150_ratio_20_1_14 extracing fe_gt150_ratio_20_1_9 extracing fe_gt150_ratio_20_1_4 extracing fe_gt150_ratio_20_2_14 extracing fe_gt150_ratio_20_2_9 extracing fe_gt150_ratio_20_2_4 extracing fe_gt150_ratio_20_3_14 extracing fe_gt150_ratio_20_3_9 extracing fe_gt150_ratio_20_3_4 extracing fe_gt150_ratio_50_0_14 extracing fe_gt150_ratio_50_0_9 extracing fe_gt150_ratio_50_0_4 extracing fe_gt150_ratio_50_1_14 extracing fe_gt150_ratio_50_1_9 extracing fe_gt150_ratio_50_1_4 extracing fe_gt150_ratio_50_2_14 extracing fe_gt150_ratio_50_2_9 extracing fe_gt150_ratio_50_2_4 extracing fe_gt150_ratio_50_3_14 extracing fe_gt150_ratio_50_3_9 extracing fe_gt150_ratio_50_3_4 \n",
      "extracing fe_gt200_ratio_1_0_14 extracing fe_gt200_ratio_1_0_9 extracing fe_gt200_ratio_1_0_4 extracing fe_gt200_ratio_1_1_14 extracing fe_gt200_ratio_1_1_9 extracing fe_gt200_ratio_1_1_4 extracing fe_gt200_ratio_1_2_14 extracing fe_gt200_ratio_1_2_9 extracing fe_gt200_ratio_1_2_4 extracing fe_gt200_ratio_1_3_14 extracing fe_gt200_ratio_1_3_9 extracing fe_gt200_ratio_1_3_4 extracing fe_gt200_ratio_5_0_14 extracing fe_gt200_ratio_5_0_9 extracing fe_gt200_ratio_5_0_4 extracing fe_gt200_ratio_5_1_14 extracing fe_gt200_ratio_5_1_9 extracing fe_gt200_ratio_5_1_4 extracing fe_gt200_ratio_5_2_14 extracing fe_gt200_ratio_5_2_9 extracing fe_gt200_ratio_5_2_4 extracing fe_gt200_ratio_5_3_14 extracing fe_gt200_ratio_5_3_9 extracing fe_gt200_ratio_5_3_4 extracing fe_gt200_ratio_10_0_14 extracing fe_gt200_ratio_10_0_9 extracing fe_gt200_ratio_10_0_4 extracing fe_gt200_ratio_10_1_14 extracing fe_gt200_ratio_10_1_9 extracing fe_gt200_ratio_10_1_4 extracing fe_gt200_ratio_10_2_14 extracing fe_gt200_ratio_10_2_9 extracing fe_gt200_ratio_10_2_4 extracing fe_gt200_ratio_10_3_14 extracing fe_gt200_ratio_10_3_9 extracing fe_gt200_ratio_10_3_4 extracing fe_gt200_ratio_20_0_14 extracing fe_gt200_ratio_20_0_9 extracing fe_gt200_ratio_20_0_4 extracing fe_gt200_ratio_20_1_14 extracing fe_gt200_ratio_20_1_9 extracing fe_gt200_ratio_20_1_4 extracing fe_gt200_ratio_20_2_14 extracing fe_gt200_ratio_20_2_9 extracing fe_gt200_ratio_20_2_4 extracing fe_gt200_ratio_20_3_14 extracing fe_gt200_ratio_20_3_9 extracing fe_gt200_ratio_20_3_4 extracing fe_gt200_ratio_50_0_14 extracing fe_gt200_ratio_50_0_9 extracing fe_gt200_ratio_50_0_4 extracing fe_gt200_ratio_50_1_14 extracing fe_gt200_ratio_50_1_9 extracing fe_gt200_ratio_50_1_4 extracing fe_gt200_ratio_50_2_14 extracing fe_gt200_ratio_50_2_9 extracing fe_gt200_ratio_50_2_4 extracing fe_gt200_ratio_50_3_14 extracing fe_gt200_ratio_50_3_9 extracing fe_gt200_ratio_50_3_4 \n",
      "extracing fe_gt50_ratio_1_0_14 extracing fe_gt50_ratio_1_0_9 extracing fe_gt50_ratio_1_0_4 extracing fe_gt50_ratio_1_1_14 extracing fe_gt50_ratio_1_1_9 extracing fe_gt50_ratio_1_1_4 extracing fe_gt50_ratio_1_2_14 extracing fe_gt50_ratio_1_2_9 extracing fe_gt50_ratio_1_2_4 extracing fe_gt50_ratio_1_3_14 extracing fe_gt50_ratio_1_3_9 extracing fe_gt50_ratio_1_3_4 extracing fe_gt50_ratio_5_0_14 extracing fe_gt50_ratio_5_0_9 extracing fe_gt50_ratio_5_0_4 extracing fe_gt50_ratio_5_1_14 extracing fe_gt50_ratio_5_1_9 extracing fe_gt50_ratio_5_1_4 extracing fe_gt50_ratio_5_2_14 extracing fe_gt50_ratio_5_2_9 extracing fe_gt50_ratio_5_2_4 extracing fe_gt50_ratio_5_3_14 extracing fe_gt50_ratio_5_3_9 extracing fe_gt50_ratio_5_3_4 extracing fe_gt50_ratio_10_0_14 extracing fe_gt50_ratio_10_0_9 extracing fe_gt50_ratio_10_0_4 extracing fe_gt50_ratio_10_1_14 extracing fe_gt50_ratio_10_1_9 extracing fe_gt50_ratio_10_1_4 extracing fe_gt50_ratio_10_2_14 extracing fe_gt50_ratio_10_2_9 extracing fe_gt50_ratio_10_2_4 extracing fe_gt50_ratio_10_3_14 extracing fe_gt50_ratio_10_3_9 extracing fe_gt50_ratio_10_3_4 extracing fe_gt50_ratio_20_0_14 extracing fe_gt50_ratio_20_0_9 extracing fe_gt50_ratio_20_0_4 extracing fe_gt50_ratio_20_1_14 extracing fe_gt50_ratio_20_1_9 extracing fe_gt50_ratio_20_1_4 extracing fe_gt50_ratio_20_2_14 extracing fe_gt50_ratio_20_2_9 extracing fe_gt50_ratio_20_2_4 extracing fe_gt50_ratio_20_3_14 extracing fe_gt50_ratio_20_3_9 extracing fe_gt50_ratio_20_3_4 extracing fe_gt50_ratio_50_0_14 extracing fe_gt50_ratio_50_0_9 extracing fe_gt50_ratio_50_0_4 extracing fe_gt50_ratio_50_1_14 extracing fe_gt50_ratio_50_1_9 extracing fe_gt50_ratio_50_1_4 extracing fe_gt50_ratio_50_2_14 extracing fe_gt50_ratio_50_2_9 extracing fe_gt50_ratio_50_2_4 extracing fe_gt50_ratio_50_3_14 extracing fe_gt50_ratio_50_3_9 extracing fe_gt50_ratio_50_3_4 \n",
      "extracing fe_max_1_0_14 extracing fe_max_1_0_9 extracing fe_max_1_0_4 extracing fe_max_1_1_14 extracing fe_max_1_1_9 extracing fe_max_1_1_4 extracing fe_max_1_2_14 extracing fe_max_1_2_9 extracing fe_max_1_2_4 extracing fe_max_1_3_14 extracing fe_max_1_3_9 extracing fe_max_1_3_4 extracing fe_max_5_0_14 extracing fe_max_5_0_9 extracing fe_max_5_0_4 extracing fe_max_5_1_14 extracing fe_max_5_1_9 extracing fe_max_5_1_4 extracing fe_max_5_2_14 extracing fe_max_5_2_9 extracing fe_max_5_2_4 extracing fe_max_5_3_14 extracing fe_max_5_3_9 extracing fe_max_5_3_4 extracing fe_max_10_0_14 extracing fe_max_10_0_9 extracing fe_max_10_0_4 extracing fe_max_10_1_14 extracing fe_max_10_1_9 extracing fe_max_10_1_4 extracing fe_max_10_2_14 extracing fe_max_10_2_9 extracing fe_max_10_2_4 extracing fe_max_10_3_14 extracing fe_max_10_3_9 extracing fe_max_10_3_4 extracing fe_max_20_0_14 extracing fe_max_20_0_9 extracing fe_max_20_0_4 extracing fe_max_20_1_14 extracing fe_max_20_1_9 extracing fe_max_20_1_4 extracing fe_max_20_2_14 extracing fe_max_20_2_9 extracing fe_max_20_2_4 extracing fe_max_20_3_14 extracing fe_max_20_3_9 extracing fe_max_20_3_4 extracing fe_max_50_0_14 extracing fe_max_50_0_9 extracing fe_max_50_0_4 extracing fe_max_50_1_14 extracing fe_max_50_1_9 extracing fe_max_50_1_4 extracing fe_max_50_2_14 extracing fe_max_50_2_9 extracing fe_max_50_2_4 extracing fe_max_50_3_14 extracing fe_max_50_3_9 extracing fe_max_50_3_4 \n",
      "extracing fe_std_1_0_14 extracing fe_std_1_0_9 extracing fe_std_1_0_4 extracing fe_std_1_1_14 extracing fe_std_1_1_9 extracing fe_std_1_1_4 extracing fe_std_1_2_14 extracing fe_std_1_2_9 extracing fe_std_1_2_4 extracing fe_std_1_3_14 extracing fe_std_1_3_9 extracing fe_std_1_3_4 extracing fe_std_5_0_14 extracing fe_std_5_0_9 extracing fe_std_5_0_4 extracing fe_std_5_1_14 extracing fe_std_5_1_9 extracing fe_std_5_1_4 extracing fe_std_5_2_14 extracing fe_std_5_2_9 extracing fe_std_5_2_4 extracing fe_std_5_3_14 extracing fe_std_5_3_9 extracing fe_std_5_3_4 extracing fe_std_10_0_14 extracing fe_std_10_0_9 extracing fe_std_10_0_4 extracing fe_std_10_1_14 extracing fe_std_10_1_9 extracing fe_std_10_1_4 extracing fe_std_10_2_14 extracing fe_std_10_2_9 extracing fe_std_10_2_4 extracing fe_std_10_3_14 extracing fe_std_10_3_9 extracing fe_std_10_3_4 extracing fe_std_20_0_14 extracing fe_std_20_0_9 extracing fe_std_20_0_4 extracing fe_std_20_1_14 extracing fe_std_20_1_9 extracing fe_std_20_1_4 extracing fe_std_20_2_14 extracing fe_std_20_2_9 extracing fe_std_20_2_4 extracing fe_std_20_3_14 extracing fe_std_20_3_9 extracing fe_std_20_3_4 extracing fe_std_50_0_14 extracing fe_std_50_0_9 extracing fe_std_50_0_4 extracing fe_std_50_1_14 extracing fe_std_50_1_9 extracing fe_std_50_1_4 extracing fe_std_50_2_14 extracing fe_std_50_2_9 extracing fe_std_50_2_4 extracing fe_std_50_3_14 extracing fe_std_50_3_9 extracing fe_std_50_3_4 \n",
      "extracing fe_std_without_zero_1_0_14 extracing fe_std_without_zero_1_0_9 extracing fe_std_without_zero_1_0_4 extracing fe_std_without_zero_1_1_14 extracing fe_std_without_zero_1_1_9 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "extracing fe_std_without_zero_1_1_4 extracing fe_std_without_zero_1_2_14 extracing fe_std_without_zero_1_2_9 extracing fe_std_without_zero_1_2_4 extracing fe_std_without_zero_1_3_14 extracing fe_std_without_zero_1_3_9 extracing fe_std_without_zero_1_3_4 extracing fe_std_without_zero_5_0_14 extracing fe_std_without_zero_5_0_9 extracing fe_std_without_zero_5_0_4 extracing fe_std_without_zero_5_1_14 extracing fe_std_without_zero_5_1_9 extracing fe_std_without_zero_5_1_4 extracing fe_std_without_zero_5_2_14 extracing fe_std_without_zero_5_2_9 extracing fe_std_without_zero_5_2_4 extracing fe_std_without_zero_5_3_14 extracing fe_std_without_zero_5_3_9 extracing fe_std_without_zero_5_3_4 extracing fe_std_without_zero_10_0_14 extracing fe_std_without_zero_10_0_9 extracing fe_std_without_zero_10_0_4 extracing fe_std_without_zero_10_1_14 extracing fe_std_without_zero_10_1_9 extracing fe_std_without_zero_10_1_4 extracing fe_std_without_zero_10_2_14 extracing fe_std_without_zero_10_2_9 extracing fe_std_without_zero_10_2_4 extracing fe_std_without_zero_10_3_14 extracing fe_std_without_zero_10_3_9 extracing fe_std_without_zero_10_3_4 extracing fe_std_without_zero_20_0_14 extracing fe_std_without_zero_20_0_9 extracing fe_std_without_zero_20_0_4 extracing fe_std_without_zero_20_1_14 extracing fe_std_without_zero_20_1_9 extracing fe_std_without_zero_20_1_4 extracing fe_std_without_zero_20_2_14 extracing fe_std_without_zero_20_2_9 extracing fe_std_without_zero_20_2_4 extracing fe_std_without_zero_20_3_14 extracing fe_std_without_zero_20_3_9 extracing fe_std_without_zero_20_3_4 extracing fe_std_without_zero_50_0_14 extracing fe_std_without_zero_50_0_9 extracing fe_std_without_zero_50_0_4 extracing fe_std_without_zero_50_1_14 extracing fe_std_without_zero_50_1_9 extracing fe_std_without_zero_50_1_4 extracing fe_std_without_zero_50_2_14 extracing fe_std_without_zero_50_2_9 extracing fe_std_without_zero_50_2_4 extracing fe_std_without_zero_50_3_14 extracing fe_std_without_zero_50_3_9 extracing fe_std_without_zero_50_3_4 \n",
      "extracing fe_zero_ratio_1_0_14 extracing fe_zero_ratio_1_0_9 extracing fe_zero_ratio_1_0_4 extracing fe_zero_ratio_1_1_14 extracing fe_zero_ratio_1_1_9 extracing fe_zero_ratio_1_1_4 extracing fe_zero_ratio_1_2_14 extracing fe_zero_ratio_1_2_9 extracing fe_zero_ratio_1_2_4 extracing fe_zero_ratio_1_3_14 extracing fe_zero_ratio_1_3_9 extracing fe_zero_ratio_1_3_4 extracing fe_zero_ratio_5_0_14 extracing fe_zero_ratio_5_0_9 extracing fe_zero_ratio_5_0_4 extracing fe_zero_ratio_5_1_14 extracing fe_zero_ratio_5_1_9 extracing fe_zero_ratio_5_1_4 extracing fe_zero_ratio_5_2_14 extracing fe_zero_ratio_5_2_9 extracing fe_zero_ratio_5_2_4 extracing fe_zero_ratio_5_3_14 extracing fe_zero_ratio_5_3_9 extracing fe_zero_ratio_5_3_4 extracing fe_zero_ratio_10_0_14 extracing fe_zero_ratio_10_0_9 extracing fe_zero_ratio_10_0_4 extracing fe_zero_ratio_10_1_14 extracing fe_zero_ratio_10_1_9 extracing fe_zero_ratio_10_1_4 extracing fe_zero_ratio_10_2_14 extracing fe_zero_ratio_10_2_9 extracing fe_zero_ratio_10_2_4 extracing fe_zero_ratio_10_3_14 extracing fe_zero_ratio_10_3_9 extracing fe_zero_ratio_10_3_4 extracing fe_zero_ratio_20_0_14 extracing fe_zero_ratio_20_0_9 extracing fe_zero_ratio_20_0_4 extracing fe_zero_ratio_20_1_14 extracing fe_zero_ratio_20_1_9 extracing fe_zero_ratio_20_1_4 extracing fe_zero_ratio_20_2_14 extracing fe_zero_ratio_20_2_9 extracing fe_zero_ratio_20_2_4 extracing fe_zero_ratio_20_3_14 extracing fe_zero_ratio_20_3_9 extracing fe_zero_ratio_20_3_4 extracing fe_zero_ratio_50_0_14 extracing fe_zero_ratio_50_0_9 extracing fe_zero_ratio_50_0_4 extracing fe_zero_ratio_50_1_14 extracing fe_zero_ratio_50_1_9 extracing fe_zero_ratio_50_1_4 extracing fe_zero_ratio_50_2_14 extracing fe_zero_ratio_50_2_9 extracing fe_zero_ratio_50_2_4 extracing fe_zero_ratio_50_3_14 extracing fe_zero_ratio_50_3_9 extracing fe_zero_ratio_50_3_4 \n"
     ]
    }
   ],
   "source": [
    "predictdata = rf.fit_and_extrace(predict_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictvals = bst.predict(xgb.DMatrix(predictdata.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001EC07324588>]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M4793JdaPEx9hmpcEmGizfiTxehDYBnSWI/aBxOtmjjuBVQwNDbF+/fq2y7OedRm//uu/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ec1e8f3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "pd.DataFrame(predictvals).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.163013"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(predictvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.345297149742326"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(reg.predict(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.472075"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('answers/4_20_xgboost_deep11_alldata.csv','w') as whdl:\n",
    "    for val in predictvals:\n",
    "        whdl.write(\"%s\\n\" % (val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(np.array(predictvals) < 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# try to find out what's going on in online dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8940299970591119, 0.039996078815802374, 0.025193608469757867, 0.0]\n",
      "[array([ 6.32104696]), array([ 87.08371728]), array([ 88.08175669]), array([ 81.15341633])]\n",
      "[array([ 21.60333777]), array([ 30.57163218]), array([ 26.30296332]), array([ 12.83622377])]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c92c9cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bncblpNZnbgO2FXEKdlN9EYHgwcvy0zHA3utIJvAMUm/54LnvZb9xYBBsdso2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\ni36/Xwj+fr9fME1ahhsGNCaYYpJlWbH7swqhXq9XHNWU53mRW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qOHBEedf5OUBZ6WW0ifs2xgEsepppEHCQ3a+DqropnoH/a1QsHstysFxxEZI43YVl1OfXe2gd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\ntRlJc3l52RjETEtpMAR9NBoVZWXElVxWRFm2RIpy3ttIjrzxeByz2SzOzs7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4GtsyusV0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v2x/+v961m+IynszJ8RLd53OcacvSWUJh2E2dzCfq80uMESI1JoeNP41pyT7cTsyMZOjV3z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\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P2e/3yx3WWfYow7goiqFjxtlJz55oczuiBnTOa8nJVdfycMLWtWVVUS/Cc6IyTpmvk1tm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vHZYqn6+td//dfxxS9+cY411vRsTSvnZ7pGmcf79kDO9LgMAGJWhQ4q78ex7swy5zKd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\n061QuVk73s8YrVYLGxsb+aSuU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e2h060yRGWfBysomzz44XNXr09omrk70VrV2te6c85H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\nMPaoAcAXhxD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c947a5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "predict_x[0].reshape([15,4,101,101])[0][0]\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "#index = 100\n",
    "index += 1\n",
    "\n",
    "print (\n",
    "    [float(sum(predict_x[index].reshape([15,4,101,101])[14][i].reshape(-1) == 0)) / (101*101) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.average(predict_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "print (\n",
    "    [np.std(predict_x[index].reshape([15,4,101,101])[14][i]).reshape(-1) for i in range(4)]\n",
    ")\n",
    "plt.figure(figsize=(10,10))\n",
    "f,axarr = plt.subplots(3,4,figsize=(10,10))\n",
    "for j in range(3):\n",
    "    for i in range(4):\n",
    "        #plt.subplot(340 + j * 4 + i + 1)\n",
    "        axarr[j][i].imshow(predict_x[index].reshape([15,4,101,101])[14-5*j][i],cmap='gray')\n",
    "f.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}