160313-fuse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FN = '160313-fuse'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FN0 = '160306-patient'\n",
    "FN1 = '160306-train.std'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/udi/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import stats\n",
    "import json\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 200, 440)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import Dataset, params, Nt, Nv, Ns, temp_dir, out_dir, awscp\n",
    "Na = Nt+Nv+Ns\n",
    "ystd = np.array([ 43.24805568,  59.27725076]) # fix a bug\n",
    "Nt, Nv, Ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if out_dir.startswith('s3://'):\n",
    "    lscmd = 'aws s3 ls ' + out_dir+'/'\n",
    "else:\n",
    "    lscmd = 'ls -1 ' + out_dir+'/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aws s3 cp s3://udikaggle/dsb.2/160306-patient.pkl /tmp/160306-patient.pkl\n",
      "160306-patient.pkl bf4296c9f41fdc77679391bda051460333035a817d9144786bfaaf62\n"
     ]
    }
   ],
   "source": [
    "awscp(FN0+'.pkl', verbose=True)\n",
    "patient = pd.read_pickle(os.path.join(temp_dir,FN0+'.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ntraining = len(patient[['Systole','Diastole']].dropna())\n",
    "assert Ntraining == Nt or Ntraining == Nt+Nv\n",
    "Ntraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700, 2), array([ 3.93,  9.35]), array([  71.13735714,  164.12514286]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = patient[['Systole','Diastole']].values.astype(float)\n",
    "Y = Y[:Ntraining]\n",
    "Y.shape,Y.min(axis=0),Y.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def yfix(yp):\n",
    "    mask = np.where(yp[:,1] < yp[:,0])\n",
    "    yp[mask,:] = yp[mask,::-1]\n",
    "    yp = np.clip(yp,3,600)\n",
    "    ypm = np.sqrt(yp[:,0]*yp[:,1])\n",
    "    yp[:,0] = np.clip(yp[:,0],ypm*np.sqrt(0.1),ypm)\n",
    "    yp[:,1] = np.clip(yp[:,1],ypm,ypm/np.sqrt(0.1))\n",
    "    return yp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# each item in FNX has the following structure\n",
    "# (<file name>,-1) = load file as is. If file contains \"*\" then look for latest iteration\n",
    "# (<file name>,0) = look for last iteration\n",
    "# (<file name>,<seed>) = add seed to file name and look for last iteration \n",
    "# (<file name>,(<seed>,(None,<split>)) = add seed and split and look for latest iteration\n",
    "# (<file name>,(<seed>,(<iteration>,<split>)) = add seed itetation and split and load\n",
    "# <file name> can be a tupple in which case the first element is the file name to read and the second is to label it\n",
    "\n",
    "FNX = []\n",
    "for seed in range(1000,1020):\n",
    "    for isplit in range(3):\n",
    "        FNX.append((FN1,(seed,(None,isplit)),False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cPickle as pickle\n",
    "try:\n",
    "    with open(os.path.join(temp_dir,FN+'.files.pkl'),'rb') as fp:\n",
    "        files = pickle.load(fp)\n",
    "    print \"there are %d files already downloaded in %s\"%(len(files), temp_dir)\n",
    "except:\n",
    "    files = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ffilter(res, pattern, itersplit):\n",
    "    res = [r.split()[-1] for r in res]\n",
    "    res = [f for f in res if f.find(pattern) >= 0]\n",
    "    # filter wanted iteration and split\n",
    "    if itersplit is not None and itersplit[0] is not None:\n",
    "        res = [r for r in res if r.endswith('.iter-%d.%d.pkl'%itersplit)]\n",
    "        res = sorted(res,key=lambda x: -int(x.split('.')[-3].split('-')[1]))\n",
    "    if itersplit is not None and itersplit[0] is None:\n",
    "        res = [r for r in res if r.endswith('.%d.pkl'%itersplit[1])]\n",
    "        res = res[-1:]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S3 160306-train.std.seed1000.yp.iter-531.0.pkl\n",
      "S3 160306-train.std.seed1000.yp.iter-350.1.pkl\n",
      "S3 160306-train.std.seed1000.yp.iter-400.2.pkl\n",
      "S3 160306-train.std.seed1001.yp.iter-494.0.pkl\n",
      "S3 160306-train.std.seed1001.yp.iter-651.1.pkl\n",
      "S3 160306-train.std.seed1001.yp.iter-234.2.pkl\n",
      "S3 160306-train.std.seed1002.yp.iter-269.0.pkl\n",
      "S3 160306-train.std.seed1002.yp.iter-352.1.pkl\n",
      "S3 160306-train.std.seed1002.yp.iter-427.2.pkl\n",
      "S3 160306-train.std.seed1003.yp.iter-381.0.pkl\n",
      "S3 160306-train.std.seed1003.yp.iter-391.1.pkl\n",
      "S3 160306-train.std.seed1003.yp.iter-368.2.pkl\n",
      "S3 160306-train.std.seed1004.yp.iter-539.0.pkl\n",
      "S3 160306-train.std.seed1004.yp.iter-456.1.pkl\n",
      "S3 160306-train.std.seed1004.yp.iter-455.2.pkl\n",
      "S3 160306-train.std.seed1005.yp.iter-433.0.pkl\n",
      "S3 160306-train.std.seed1005.yp.iter-239.1.pkl\n",
      "S3 160306-train.std.seed1005.yp.iter-558.2.pkl\n",
      "S3 160306-train.std.seed1006.yp.iter-609.0.pkl\n",
      "S3 160306-train.std.seed1006.yp.iter-571.1.pkl\n",
      "S3 160306-train.std.seed1006.yp.iter-453.2.pkl\n",
      "S3 160306-train.std.seed1007.yp.iter-287.0.pkl\n",
      "S3 160306-train.std.seed1007.yp.iter-389.1.pkl\n",
      "S3 160306-train.std.seed1007.yp.iter-647.2.pkl\n",
      "S3 160306-train.std.seed1008.yp.iter-492.0.pkl\n",
      "S3 160306-train.std.seed1008.yp.iter-431.1.pkl\n",
      "S3 160306-train.std.seed1008.yp.iter-335.2.pkl\n",
      "S3 160306-train.std.seed1009.yp.iter-676.0.pkl\n",
      "S3 160306-train.std.seed1009.yp.iter-379.1.pkl\n",
      "S3 160306-train.std.seed1009.yp.iter-576.2.pkl\n",
      "S3 160306-train.std.seed1010.yp.iter-501.0.pkl\n",
      "S3 160306-train.std.seed1010.yp.iter-399.1.pkl\n",
      "S3 160306-train.std.seed1010.yp.iter-616.2.pkl\n",
      "S3 160306-train.std.seed1011.yp.iter-435.0.pkl\n",
      "S3 160306-train.std.seed1011.yp.iter-442.1.pkl\n",
      "S3 160306-train.std.seed1011.yp.iter-614.2.pkl\n",
      "S3 160306-train.std.seed1012.yp.iter-516.0.pkl\n",
      "S3 160306-train.std.seed1012.yp.iter-427.1.pkl\n",
      "S3 160306-train.std.seed1012.yp.iter-578.2.pkl\n",
      "S3 160306-train.std.seed1013.yp.iter-295.0.pkl\n",
      "S3 160306-train.std.seed1013.yp.iter-530.1.pkl\n",
      "S3 160306-train.std.seed1013.yp.iter-412.2.pkl\n",
      "S3 160306-train.std.seed1014.yp.iter-476.0.pkl\n",
      "S3 160306-train.std.seed1014.yp.iter-524.1.pkl\n",
      "S3 160306-train.std.seed1014.yp.iter-397.2.pkl\n",
      "S3 160306-train.std.seed1015.yp.iter-246.0.pkl\n",
      "S3 160306-train.std.seed1015.yp.iter-531.1.pkl\n",
      "S3 160306-train.std.seed1015.yp.iter-461.2.pkl\n",
      "S3 160306-train.std.seed1016.yp.iter-471.0.pkl\n",
      "S3 160306-train.std.seed1016.yp.iter-657.1.pkl\n",
      "S3 160306-train.std.seed1016.yp.iter-271.2.pkl\n",
      "S3 160306-train.std.seed1017.yp.iter-586.0.pkl\n",
      "S3 160306-train.std.seed1017.yp.iter-227.1.pkl\n",
      "S3 160306-train.std.seed1017.yp.iter-468.2.pkl\n",
      "S3 160306-train.std.seed1018.yp.iter-585.0.pkl\n",
      "S3 160306-train.std.seed1018.yp.iter-653.1.pkl\n",
      "S3 160306-train.std.seed1018.yp.iter-137.2.pkl\n",
      "S3 160306-train.std.seed1019.yp.iter-289.0.pkl\n",
      "S3 160306-train.std.seed1019.yp.iter-279.1.pkl\n",
      "S3 160306-train.std.seed1019.yp.iter-445.2.pkl\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "# files =[]\n",
    "df = {}\n",
    "df_count = {}\n",
    "df_var = {}\n",
    "df_var_count = {}\n",
    "itests = {}\n",
    "for fnseed in FNX:\n",
    "    if len(fnseed) == 2:\n",
    "        fn, seed = fnseed\n",
    "        bug_fix = True\n",
    "    else:\n",
    "        fn, seed, bug_fix = fnseed\n",
    "        \n",
    "    if isinstance(seed,int):\n",
    "        itersplit = None\n",
    "    else:\n",
    "        seed, itersplit = seed\n",
    "        \n",
    "    fn1 = fn\n",
    "    if isinstance(fn, tuple):\n",
    "        fn, fn1 = fn\n",
    "        \n",
    "    if seed >= 0:\n",
    "        didls = False\n",
    "        if seed:\n",
    "            pattern = '%s.seed%d.yp.iter-'%(fn,seed)\n",
    "        else:\n",
    "            pattern = '%s.yp.iter-'%(fn)\n",
    "        # default pattern in case pattern is not found\n",
    "        pattern1 = '%s.yp.iter-'%(fn)\n",
    "        # look for pattern in files we already know about\n",
    "        res = ffilter(files, pattern, itersplit)\n",
    "        if not res:\n",
    "            res = ffilter(files, pattern1, itersplit)\n",
    "            if res:\n",
    "                print \"Default\",fn\n",
    "        # if not found, look for pattern in S3\n",
    "        if not res:\n",
    "            didls = True\n",
    "            fres = !{lscmd}\n",
    "            res = ffilter(fres, pattern, itersplit)\n",
    "            if not res:\n",
    "                res = ffilter(fres, pattern1, itersplit)\n",
    "                if res:\n",
    "                    print \"Default S3\",fn\n",
    "        if not res:\n",
    "            print 'cant find',fnseed\n",
    "#             !{lscmd} | grep {pattern}\n",
    "            continue\n",
    "        fname = res[0]\n",
    "        if didls:\n",
    "            print \"S3\",fname\n",
    "        awscp(fname)\n",
    "        with open(os.path.join(temp_dir, fname),'rb') as fp:\n",
    "            yp_corrected, Y_itest, itest, yp_test = pickle.load(fp)\n",
    "        itest = np.concatenate((itest,np.arange(Ntraining,Na)))\n",
    "        yp = np.vstack((yp_corrected, yp_test))\n",
    "    else:\n",
    "        # load file directly\n",
    "        res = !{lscmd} | grep {fn}\n",
    "        res = sorted(res,key=lambda x: -int(x.split('.')[-3].split('-')[1]))\n",
    "        if not res:\n",
    "            print 'cant find',fnseed\n",
    "            break\n",
    "        fname = res[0]\n",
    "        awscp(fname)\n",
    "        if fname.endswith('.npy'):\n",
    "            itest = np.arange(Na)\n",
    "            yp = np.load(os.path.join(temp_dir, fname))\n",
    "        else:\n",
    "            assert fname.endswith('.pkl')\n",
    "            with open(os.path.join(temp_dir, fname),'rb') as fp:\n",
    "                yp_corrected, Y_itest, itest, yp_test = pickle.load(fp)\n",
    "            itest = np.concatenate((itest,np.arange(Ntraining,Na)))\n",
    "            yp = np.vstack((yp_corrected, yp_test))\n",
    "    # make sure predictions make sense\n",
    "    ypprb = yfix(yp[:,:2])\n",
    "#     fld = '%s.%d'%(fn1,seed)\n",
    "    fld = fname\n",
    "\n",
    "    if fld not in df:\n",
    "        df[fld] = np.zeros((Na,2))\n",
    "        df_count[fld] = np.zeros(Na)\n",
    "        if yp.shape[1] == 4:\n",
    "            df_var[fld] = np.zeros((Na,2))\n",
    "            df_var_count[fld] = np.zeros(Na)\n",
    "\n",
    "    itests[fname] = itest\n",
    "    df_count[fld][itest] += 1\n",
    "    df[fld][itest,:] += ypprb\n",
    "    if yp.shape[1] == 4:\n",
    "        df_var_count[fld][itest] += 1\n",
    "        if bug_fix:\n",
    "            # /ystd is a BUG FIX in 160221-train-std-clip\n",
    "            df_var[fld][itest,:] += yp[:,2:]*yp[:,2:]/ystd\n",
    "        else:\n",
    "            df_var[fld][itest,:] += yp[:,2:]*yp[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames = df.keys()\n",
    "len(fnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save files stored in temp_dir to save time on re-run\n",
    "with open(os.path.join(temp_dir,FN+'.files.pkl'),'wb') as fp:\n",
    "    pickle.dump(fnames, fp, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df0 = pd.DataFrame(dict((k,v[:,0]/df_count[k]) for k,v in df.items()),index=range(Na))\n",
    "df1 = pd.DataFrame(dict((k,v[:,1]/df_count[k]) for k,v in df.items()),index=range(Na))\n",
    "dfx = pd.concat((df0,df1),keys=('sys','dia')).swaplevel(0, 1, axis=0).sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df0 = pd.DataFrame(dict((k,v[:,0]/df_count[k]) for k,v in df_var.items()),index=range(Na))\n",
    "df1 = pd.DataFrame(dict((k,v[:,1]/df_count[k]) for k,v in df_var.items()),index=range(Na))\n",
    "dfx_var = pd.concat((df0,df1),keys=('sys','dia')).swaplevel(0, 1, axis=0).sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfy = pd.concat((pd.DataFrame({'y':Y[:,0]}),pd.DataFrame({'y':Y[:,1]})),keys=('sys','dia')).swaplevel(0, 1, axis=0).sort_index(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.0689327476\n",
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      "37 19.1124581599 160306-train.std.seed1005.yp.iter-433.0.pkl\n",
      "28 19.0895686093 160306-train.std.seed1011.yp.iter-614.2.pkl\n",
      "32 19.0738945174 160306-train.std.seed1003.yp.iter-391.1.pkl\n",
      "35 19.0554740223 160306-train.std.seed1005.yp.iter-558.2.pkl\n",
      "4 19.0365804982 160306-train.std.seed1019.yp.iter-445.2.pkl\n",
      "36 19.0196762202 160306-train.std.seed1013.yp.iter-412.2.pkl\n",
      "1 19.0019326647 160306-train.std.seed1016.yp.iter-657.1.pkl\n",
      "13 18.985396641 160306-train.std.seed1011.yp.iter-442.1.pkl\n",
      "27 18.9486530925 160306-train.std.seed1009.yp.iter-676.0.pkl\n",
      "2 18.920146309 160306-train.std.seed1002.yp.iter-269.0.pkl\n",
      "45 18.8898498508 160306-train.std.seed1015.yp.iter-246.0.pkl\n",
      "15 18.8582217994 160306-train.std.seed1006.yp.iter-453.2.pkl\n",
      "51 18.8350987942 160306-train.std.seed1004.yp.iter-456.1.pkl\n",
      "17 18.7993541805 160306-train.std.seed1012.yp.iter-578.2.pkl\n",
      "42 18.7832256863 160306-train.std.seed1003.yp.iter-381.0.pkl\n",
      "56 18.7671309587 160306-train.std.seed1015.yp.iter-531.1.pkl\n"
     ]
    }
   ],
   "source": [
    "def score(cols):\n",
    "    return np.sqrt(np.mean((dfx[cols].mean(axis=1).values[:Ntraining*2] - dfy.values[:,0])**2))\n",
    "\n",
    "remove= []\n",
    "print score(fnames)             \n",
    "for k in fnames:\n",
    "    vals = [(score([c for j, c in enumerate(fnames) if j != i and j not in remove]), i)\n",
    "            for i, _ in enumerate(fnames)]\n",
    "    vals.sort(key=lambda x: x[0])\n",
    "    val,j = vals[0]\n",
    "    if val!=val:\n",
    "        break\n",
    "    if j in remove:\n",
    "        break\n",
    "    remove.append(j)\n",
    "    print j,val, fnames[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['160306-train.std.seed1013.yp.iter-530.1.pkl',\n",
       " '160306-train.std.seed1008.yp.iter-492.0.pkl',\n",
       " '160306-train.std.seed1018.yp.iter-653.1.pkl',\n",
       " '160306-train.std.seed1000.yp.iter-531.0.pkl',\n",
       " '160306-train.std.seed1010.yp.iter-501.0.pkl',\n",
       " '160306-train.std.seed1014.yp.iter-476.0.pkl',\n",
       " '160306-train.std.seed1007.yp.iter-287.0.pkl',\n",
       " '160306-train.std.seed1014.yp.iter-524.1.pkl',\n",
       " '160306-train.std.seed1012.yp.iter-427.1.pkl',\n",
       " '160306-train.std.seed1010.yp.iter-616.2.pkl',\n",
       " '160306-train.std.seed1016.yp.iter-471.0.pkl',\n",
       " '160306-train.std.seed1006.yp.iter-609.0.pkl',\n",
       " '160306-train.std.seed1019.yp.iter-289.0.pkl',\n",
       " '160306-train.std.seed1007.yp.iter-647.2.pkl']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames_mean = [f for i, f in enumerate(fnames) if i not in remove]\n",
    "fnames_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_mean = dfx[fnames_mean].mean(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_mean = y_mean.reshape((-1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 60)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames_var = dfx_var.columns\n",
    "len(fnames_var),len(fnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.0689327476\n",
      "54 6.48509832794 160306-train.std.seed1018.yp.iter-137.2.pkl\n",
      "30 6.47781125446 160306-train.std.seed1010.yp.iter-399.1.pkl\n",
      "51 6.47050172758 160306-train.std.seed1017.yp.iter-227.1.pkl\n",
      "0 6.46395362075 160306-train.std.seed1000.yp.iter-350.1.pkl\n",
      "3 6.45725791005 160306-train.std.seed1001.yp.iter-234.2.pkl\n",
      "15 6.45136642217 160306-train.std.seed1005.yp.iter-239.1.pkl\n",
      "22 6.44694985533 160306-train.std.seed1007.yp.iter-389.1.pkl\n",
      "42 6.44262750716 160306-train.std.seed1014.yp.iter-397.2.pkl\n",
      "6 6.43849577309 160306-train.std.seed1002.yp.iter-269.0.pkl\n",
      "28 6.43433097799 160306-train.std.seed1009.yp.iter-576.2.pkl\n",
      "49 6.43031782481 160306-train.std.seed1016.yp.iter-471.0.pkl\n",
      "11 6.42649520971 160306-train.std.seed1003.yp.iter-391.1.pkl\n",
      "34 6.42259242195 160306-train.std.seed1011.yp.iter-442.1.pkl\n",
      "46 6.41879932717 160306-train.std.seed1015.yp.iter-461.2.pkl\n",
      "36 6.4146564032 160306-train.std.seed1012.yp.iter-427.1.pkl\n",
      "12 6.41041351011 160306-train.std.seed1004.yp.iter-455.2.pkl\n",
      "24 6.40614805435 160306-train.std.seed1008.yp.iter-335.2.pkl\n",
      "18 6.40172764342 160306-train.std.seed1006.yp.iter-453.2.pkl\n",
      "40 6.39722946059 160306-train.std.seed1013.yp.iter-412.2.pkl\n",
      "52 6.39273314409 160306-train.std.seed1017.yp.iter-468.2.pkl\n",
      "57 6.38788020635 160306-train.std.seed1019.yp.iter-279.1.pkl\n",
      "26 6.38084317362 160306-train.std.seed1008.yp.iter-492.0.pkl\n",
      "7 6.37049568536 160306-train.std.seed1002.yp.iter-352.1.pkl\n",
      "13 6.36610046545 160306-train.std.seed1004.yp.iter-456.1.pkl\n",
      "27 6.36232988663 160306-train.std.seed1009.yp.iter-379.1.pkl\n",
      "10 6.35834238333 160306-train.std.seed1003.yp.iter-381.0.pkl\n",
      "33 6.35422406058 160306-train.std.seed1011.yp.iter-435.0.pkl\n",
      "4 6.35000598467 160306-train.std.seed1001.yp.iter-494.0.pkl\n",
      "8 6.34588499109 160306-train.std.seed1002.yp.iter-427.2.pkl\n",
      "48 6.34193478466 160306-train.std.seed1016.yp.iter-271.2.pkl\n",
      "16 6.33821158733 160306-train.std.seed1005.yp.iter-433.0.pkl\n",
      "45 6.33371406401 160306-train.std.seed1015.yp.iter-246.0.pkl\n",
      "59 6.32934931008 160306-train.std.seed1019.yp.iter-445.2.pkl\n",
      "58 6.32543766908 160306-train.std.seed1019.yp.iter-289.0.pkl\n",
      "9 6.3211545778 160306-train.std.seed1003.yp.iter-368.2.pkl\n",
      "41 6.3163054869 160306-train.std.seed1013.yp.iter-530.1.pkl\n",
      "43 6.31218577769 160306-train.std.seed1014.yp.iter-476.0.pkl\n",
      "1 6.30793927332 160306-train.std.seed1000.yp.iter-400.2.pkl\n",
      "35 6.30311621187 160306-train.std.seed1011.yp.iter-614.2.pkl\n",
      "29 6.29910733861 160306-train.std.seed1009.yp.iter-676.0.pkl\n",
      "17 6.29669316729 160306-train.std.seed1005.yp.iter-558.2.pkl\n",
      "53 6.29559906268 160306-train.std.seed1017.yp.iter-586.0.pkl\n",
      "37 6.29457719357 160306-train.std.seed1012.yp.iter-516.0.pkl\n",
      "20 6.29431843252 160306-train.std.seed1006.yp.iter-609.0.pkl\n"
     ]
    }
   ],
   "source": [
    "def loss(cols):\n",
    "    err2 = np.square(dfx[cols].mean(axis=1).values[:Ntraining*2] - dfy.values[:,0]).reshape((-1,2))\n",
    "    var = dfx_var[cols].mean(axis=1).values[:Ntraining*2].reshape((-1,2))\n",
    "    loss = np.log(var) + err2/var\n",
    "    return loss.mean()\n",
    "\n",
    "remove= []\n",
    "print score(fnames_var)             \n",
    "for k in fnames_var:\n",
    "    vals = [(loss([c for j, c in enumerate(fnames_var) if j != i and j not in remove]), i)\n",
    "            for i, _ in enumerate(fnames_var)]\n",
    "    vals.sort(key=lambda x: x[0])\n",
    "    val,j = vals[0]\n",
    "    if val!=val:\n",
    "        break\n",
    "    if j in remove:\n",
    "        break\n",
    "    remove.append(j)\n",
    "    print j,val, fnames_var[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['160306-train.std.seed1000.yp.iter-531.0.pkl',\n",
       " '160306-train.std.seed1001.yp.iter-651.1.pkl',\n",
       " '160306-train.std.seed1004.yp.iter-539.0.pkl',\n",
       " '160306-train.std.seed1006.yp.iter-571.1.pkl',\n",
       " '160306-train.std.seed1007.yp.iter-287.0.pkl',\n",
       " '160306-train.std.seed1007.yp.iter-647.2.pkl',\n",
       " '160306-train.std.seed1008.yp.iter-431.1.pkl',\n",
       " '160306-train.std.seed1010.yp.iter-501.0.pkl',\n",
       " '160306-train.std.seed1010.yp.iter-616.2.pkl',\n",
       " '160306-train.std.seed1012.yp.iter-578.2.pkl',\n",
       " '160306-train.std.seed1013.yp.iter-295.0.pkl',\n",
       " '160306-train.std.seed1014.yp.iter-524.1.pkl',\n",
       " '160306-train.std.seed1015.yp.iter-531.1.pkl',\n",
       " '160306-train.std.seed1016.yp.iter-657.1.pkl',\n",
       " '160306-train.std.seed1018.yp.iter-585.0.pkl',\n",
       " '160306-train.std.seed1018.yp.iter-653.1.pkl']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames_var = [f for i, f in enumerate(fnames_var) if i not in remove]\n",
    "fnames_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_std = np.sqrt(dfx_var[fnames_var].mean(axis=1).values.reshape((-1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 108.3  246.7] [  99.20565865  241.93897913]\n",
      "[ 488.6  568.6] [ 355.2909373   490.02593913]\n",
      "[  4.6  12.9] [  4.76894473  12.84499409]\n",
      "[  88.4  157.6] [  85.39982702  152.98340747]\n",
      "[ 200.3  277.9] [ 233.27389699  347.04982996]\n"
     ]
    }
   ],
   "source": [
    "for p in [0,268,414,415,483]:\n",
    "    print Y[p],y_mean[p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 26  59  63  81 164 221 233 268 279 306 320 430 594 598 642]\n",
      "[ 59 211 213 221 240 266 302 306 320 430 594 598 642]\n"
     ]
    },
    {
     "data": {
      "image/png": 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uA1wUkQ7gYeDNYsdibBwWFkLYssUxMFRGsiykOreLEgU3167lXxO9d3paSCbn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\npaqeaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11005a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_outliers(Y,y_mean,y_std,mask=None):\n",
    "    plt.scatter(Y[:],y_mean[:Ntraining],alpha=0.1,color='b');\n",
    "    plt.errorbar(Y[:],y_mean[:Ntraining], yerr=y_std[:Ntraining], fmt='.',alpha=0.1,color='b')\n",
    "    if mask is None:\n",
    "        mask = np.where(np.abs(Y[:]-y_mean[:Ntraining])/(Y[:]+100) > 0.2)[0]\n",
    "        print mask\n",
    "#     plt.scatter(Y[mask],y_mean[mask],alpha=0.3,color='r')\n",
    "    plt.errorbar(Y[mask],y_mean[mask], yerr=y_std[mask], fmt='.',alpha=0.3,color='r')\n",
    "        \n",
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "plot_outliers(Y[:,0],y_mean[:,0],y_std[:,0])\n",
    "plt.subplot(122)\n",
    "plot_outliers(Y[:,1],y_mean[:,1],y_std[:,0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2pftU5F4R/ZiLnC9yNoidL3I2iJ0vcrY8GrmicxT4urtfCvwe8FUzezewGHjc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\njk50RERERESkcv4/MCyO25MJ6U0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11005aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "plt.hist(y_mean[:,0])\n",
    "plt.hist(y_mean[:,1],alpha=0.3);\n",
    "plt.subplot(122)\n",
    "plt.hist(y_std[:,0]/y_mean[:,0])\n",
    "plt.hist(y_std[:,1]/y_mean[:,1],alpha=0.3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kp2tkW8WmdDdhRPn03ipWCRL5QZGIiIiIiEgqRX5hnFTO1X7s1RrG5vd4rF9b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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11005a890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "for m in range(2):\n",
    "    plt.subplot(1,2,1+m)\n",
    "    plt.hist(Y[:Ntraining,m],bins=50,histtype='step',normed=True,label='true')\n",
    "    plt.hist(y_mean[:Ntraining,m],histtype='step',normed=True,label='train')\n",
    "    plt.hist(y_mean[Ntraining:,m],histtype='step',normed=True,label='test');\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OOHITR27iyA2K4alOeTpGiDWbPMUq+Yu3FKlpfgAAAAAgSalpfjytpS4U6zjj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\ns1Fb36vSlsefUuMPb0lsX51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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11005a510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "for m in range(2):\n",
    "    plt.subplot(1,2,1+m)\n",
    "    plt.hist(y_std[:Ntraining,m],histtype='step',normed=True,label='train')\n",
    "    plt.hist(y_std[Ntraining:,m],histtype='step',normed=True,label='test');\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RL+GdrXlpvfRt/zGj+G4nlsw1Zmayf+VKXvv7FubMuaDFa/ItNwv1eiOxFC2S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\nSZKkZet/8Y3NS3tAYGIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8c8dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.scatter(y_mean[:Ntraining,0],y_mean[:Ntraining,1]);\n",
    "plt.scatter(y_mean[Ntraining:,0],y_mean[Ntraining:,1],color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CUE6oO4qtO4qtG4qrO4qtO4qtO4qtO4pt/NJMjoiIiIiIJBTV5IiIiIiISNxQTY6IiIiI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\n/Yf8FyVlaeSkfktKRfSvJcnW8lnuiXQ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UW2v/UDeQ7GmtvaXdOtlBGGPygb3Ak9ba79fd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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132b6ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.scatter(y_mean[:Ntraining,0],y_std[:Ntraining,0]/y_mean[:Ntraining,0]);\n",
    "plt.scatter(y_mean[Ntraining:,0],y_std[Ntraining:,0]/y_mean[Ntraining:,0],color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bWju2ZvtWwFprZ3vtcwuQZK39Y83234C3rbWveB9LNTkiIiIiItKYcNXkfAb0Ncb0MsYk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\nYCpKDgAAMJVP7dqFYmZwYhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132ab950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.scatter(y_mean[:Ntraining,1],y_std[:Ntraining,1]/y_mean[:Ntraining,1]);\n",
    "plt.scatter(y_mean[Ntraining:,1],y_std[Ntraining:,1]/y_mean[Ntraining:,1],color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7.054057296638141, -0.10475041352964322, 0.78303011077196083)\n",
      "(5.2574032279106149, -0.0092558152107782531, 0.71269271221966024)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XbTRzbyK11Ljn2vVO+zlFiTKFwX2mLFNMxpNu/J9R6kc19ybayA/uZR7UUaxL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\nFuT/A8BCcfJe8bTXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b6e7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "def myplt(err,fit=scipy.stats.t):\n",
    "#     err -= err.mean()\n",
    "    param = fit.fit(err) # distribution fitting\n",
    "\n",
    "    # now, param[0] and param[1] are the mean and \n",
    "    # the standard deviation of the fitted distribution\n",
    "    x = np.linspace(-15,15,100)\n",
    "    # fitted distribution\n",
    "    pdf_fitted = fit.pdf(x,df=param[0],loc=param[1],scale=param[2])\n",
    "    plt.plot(x,pdf_fitted,'r-')\n",
    "    plt.hist(err,normed=1,alpha=.3,bins=40)\n",
    "    return param\n",
    "betagamma=[]\n",
    "for m in range(2):\n",
    "    plt.subplot(121+m)\n",
    "    res = myplt(((Y[:,m]-y_mean[:Ntraining,m])/y_std[:Ntraining,m]))\n",
    "    print res\n",
    "    betagamma.append(res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x114041050>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PRw9opk51M3NmQ4fyaF2dipujWl+cXLXKy9Ch3Vt8KPJYfvtbB3v3pnX5uYQQ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\nHCUPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11005b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "m=0\n",
    "plt.scatter(np.clip(np.abs(Y[:,m]-y_mean[:Ntraining,m]),0,400),y_std[:Ntraining,m]);\n",
    "plt.subplot(122)\n",
    "m=1\n",
    "plt.scatter(np.clip(np.abs(Y[:,m]-y_mean[:Ntraining,m]),0,400),y_std[:Ntraining,m]);\n",
    "\n",
    "# plt.scatter(y_mean[Ntraining:,1],y_std[Ntraining:,1]/y_mean[Ntraining:,1],color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([], dtype=int64), array([], dtype=int64)),\n",
       " (array([], dtype=int64), array([], dtype=int64)))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(y_std < 0),np.where(y_mean < 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "times = ['meanmeanTriggerTime_sax','stdmeanTriggerTime_sax']\n",
    "age = patient.PatientAge_sax.values\n",
    "logage = np.log(age)\n",
    "sex = patient.PatientSex_sax.values\n",
    "Xaux = np.array([sex,np.log(age)]+[patient[t].values for t in times]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1140, 4)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xaux.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "from sklearn import linear_model\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "class Ridge(BaseEstimator):\n",
    "    def __init__(self, alpha=72, nrm=True, naux=2):\n",
    "        self.alpha = alpha\n",
    "        self.nrm = nrm\n",
    "        self.naux = naux\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        self.lrs = []\n",
    "        for measure in range(2):\n",
    "            lr = linear_model.Ridge(alpha = self.alpha)\n",
    "            if self.nrm:\n",
    "                clf = Pipeline([('nrm',StandardScaler()),('lr',lr)])\n",
    "            else:\n",
    "                clf = lr\n",
    "            clf.fit(X[:,:self.naux],y[:,measure])\n",
    "            self.lrs.append(clf)\n",
    "\n",
    "    def predict(self,X):\n",
    "        lrs_predict = []\n",
    "        for measure in range(2):\n",
    "            lr_predict = self.lrs[measure].predict(X[:,:self.naux])\n",
    "            lrs_predict.append(lr_predict)\n",
    "        lrs_predict = np.array(lrs_predict).T\n",
    "        return lrs_predict\n",
    "    \n",
    "    def score(self, X, y):\n",
    "        return -np.sqrt(np.mean(np.square(y - self.predict(X)),axis=0)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'alpha': 31.622776601683793, 'naux': 4}, 43.863959729193517)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV, RandomizedSearchCV\n",
    "clfg = GridSearchCV(Ridge(),param_grid={'alpha':np.logspace(1,1.5,8),'naux':[1,2,3,4]},n_jobs=-1,cv=10) #,verbose=1000)\n",
    "clfg.fit(Xaux[:Ntraining,:],Y)\n",
    "clfg.best_params_,-clfg.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Yaux = np.clip(clfg.predict(Xaux),1,600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x112ce4710>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J6bg4hYKC0P47vt4469dbGTHCQkmJhvbtvTz/fGWY/nzoISd2u8BiUUhI8PKT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\nTwMfERERERHxvf8PuVXaijr2s94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11405c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(Y[:,0],Yaux[:Ntraining,0])\n",
    "plt.subplot(122)\n",
    "plt.scatter(Y[:,1],Yaux[:Ntraining,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TKTUeWKKU+rtS6jBwNXAfgNb6LeAZ4C3gRWBKpBnmwDr1VCwS6ZiWWK0rkeJNpFhbS8YQ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\n9pEjR9r1/c+cUeT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hWbSokgEDfCxeXMlXvuLj0Ucr+dvfHOTkGI2GxYudnDxppMHNmZPG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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11408dc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "for m in range(2):\n",
    "    plt.subplot(121+m)\n",
    "    plt.scatter(Yaux[:Ntraining,m],(Y[:,m]-y_mean[:Ntraining,m]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 2)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.hstack((y_mean[:Ntraining,:1],Yaux[:Ntraining,:1]))\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "mymodel = sm.OLS(Y[:,0],x,'y',['ym','aux']).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.861</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.861</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4320.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 13 Mar 2016</td> <th>  Prob (F-statistic):</th> <td>3.37e-301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:26:55</td>     <th>  Log-Likelihood:    </th> <td> -2920.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   700</td>      <th>  AIC:               </th> <td>   5844.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   698</td>      <th>  BIC:               </th> <td>   5853.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    1.0552</td> <td>    0.017</td> <td>   60.418</td> <td> 0.000</td> <td>    1.021     1.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>   -0.0528</td> <td>    0.019</td> <td>   -2.738</td> <td> 0.006</td> <td>   -0.091    -0.015</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>449.287</td> <th>  Durbin-Watson:     </th> <td>   2.019</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>11019.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.444</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>21.813</td>  <th>  Cond. No.          </th> <td>    4.52</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.861\n",
       "Model:                            OLS   Adj. R-squared:                  0.861\n",
       "Method:                 Least Squares   F-statistic:                     4320.\n",
       "Date:                Sun, 13 Mar 2016   Prob (F-statistic):          3.37e-301\n",
       "Time:                        11:26:55   Log-Likelihood:                -2920.1\n",
       "No. Observations:                 700   AIC:                             5844.\n",
       "Df Residuals:                     698   BIC:                             5853.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "x1             1.0552      0.017     60.418      0.000         1.021     1.090\n",
       "x2            -0.0528      0.019     -2.738      0.006        -0.091    -0.015\n",
       "==============================================================================\n",
       "Omnibus:                      449.287   Durbin-Watson:                   2.019\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11019.450\n",
       "Skew:                           2.444   Prob(JB):                         0.00\n",
       "Kurtosis:                      21.813   Cond. No.                         4.52\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pack data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1140, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yaux.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1140, 6), 6)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.hstack((y_mean, y_std, Yaux))\n",
    "NF = X.shape[1] # stat of auxiliary data\n",
    "X.shape,NF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((700, 6), (700, 2))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain = X[:Ntraining]\n",
    "Ytrain = Y[:Ntraining]\n",
    "Xtrain.shape, Ytrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def line_score(x,v):\n",
    "    scr = (x - ((np.arange(600) - v) >= 0))\n",
    "    return (scr*scr).mean()\n",
    "\n",
    "import itertools\n",
    "def score(y_true, y_pred):\n",
    "    scores = []\n",
    "    for yt, yp in itertools.izip(y_true, y_pred):\n",
    "        scores.append(line_score(yp[0], yt[0]))\n",
    "        scores.append(line_score(yp[1], yt[1]))\n",
    "    return np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.grid_search import GridSearchCV, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Student(BaseEstimator):\n",
    "    M = 600\n",
    "    domain = np.arange(M)\n",
    "    \n",
    "    def __init__(self, beta=1., gamma=1., delta=7):\n",
    "        self.beta = beta\n",
    "        self.gamma = gamma\n",
    "        self.delta=delta\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        return self\n",
    "    \n",
    "    def predict_llk(self,X):\n",
    "        N = len(X)\n",
    "        domain = np.tile(self.domain,N).reshape((N,self.M))\n",
    "        y = np.zeros((N,2,self.M))\n",
    "        stdscale = [self.beta, self.gamma]\n",
    "        for i in range(N):\n",
    "            for m in range(2):\n",
    "                y[i,m,:] = scipy.stats.t.logpdf((self.domain - X[i,m])/(stdscale[m]*X[i,m+2]), # /np.sqrt(X[i,4])\n",
    "                                    df=self.delta)\n",
    "        y = y - y.max(axis=2,keepdims=True)\n",
    "        return y\n",
    "    \n",
    "    def predict(self,X):\n",
    "        y = self.predict_llk(X)\n",
    "        y = np.exp(y)\n",
    "        y /= y.sum(axis=2,keepdims=True)\n",
    "        y = y.cumsum(axis=2)\n",
    "        return y\n",
    "    \n",
    "    def score(self, X, y):\n",
    "        y_pred = self.predict(X)\n",
    "        return 1.-score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014616891149281996"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.-Student().score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# clfg = GridSearchCV(Student(),param_grid={'beta':[0.83684210526315783], # np.linspace(0.7,0.9,20),\n",
    "#                                           'gamma':[0.77368421052631575], #np.linspace(0.7,0.9,20),\n",
    "#                                           'delta':np.linspace(10,100,20)},n_jobs=-1,cv=10)\n",
    "# clfg.fit(Xtrain,Ytrain)\n",
    "# clfg.best_params_,1.-clfg.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Normal(BaseEstimator):\n",
    "    M = 600\n",
    "    domain = np.arange(M)\n",
    "    \n",
    "    def __init__(self, beta=0.8368421, gamma=0.773684210, aux=-0.086):\n",
    "        self.beta = beta\n",
    "        self.gamma = gamma\n",
    "        self.aux = aux\n",
    "        \n",
    "    def fit(self,X,y):\n",
    "        return self\n",
    "    \n",
    "    def predict_llk(self,X):\n",
    "        N = len(X)\n",
    "        domain = np.tile(self.domain,N).reshape((N,self.M))\n",
    "        y = np.zeros((N,2,self.M))\n",
    "        stdscale = [self.beta, self.gamma]\n",
    "        for m in range(2):\n",
    "            std = stdscale[m]*X[:,m+2,None] + self.aux*np.sqrt(X[:,m+4,None])# *np.sqrt(X[:,4,None])\n",
    "            y[:,m,:] = -(domain - X[:,m,None])**2/(2.*std**2)\n",
    "        y = y - y.max(axis=2,keepdims=True)\n",
    "        return y\n",
    "    \n",
    "    def predict(self,X):\n",
    "        y = self.predict_llk(X)\n",
    "        y = np.exp(y)\n",
    "        y /= y.sum(axis=2,keepdims=True)\n",
    "        y = y.cumsum(axis=2)\n",
    "        return y\n",
    "    \n",
    "    def score(self, X, y):\n",
    "        y_pred = self.predict(X)\n",
    "        return 1.-score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0143392565007745"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.-Normal(aux=0).score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def objective(args):\n",
    "    clf = Normal(**args)\n",
    "    return 1.-clf.score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'aux': 0.0679741902035558,\n",
       "  'beta': 0.8269425778409031,\n",
       "  'gamma': 0.7484521570792948},\n",
       " 0.014335392984603068)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hyperopt\n",
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "best = fmin(objective,\n",
    "    space={\n",
    "        'beta':hp.loguniform('beta', -1, 0),\n",
    "        'gamma':hp.loguniform('gamma', -1, 0),\n",
    "        'aux':hp.normal('aux', 0, 0.1),\n",
    "        },\n",
    "    algo=tpe.suggest,\n",
    "    max_evals=1000)\n",
    "best, objective(best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal(aux=0.0679741902036, beta=0.826942577841, gamma=0.748452157079)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = Normal(**best)\n",
    "clf.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xtest = X[Ntraining:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub_columns = ['P%d'%p for p in range(600)]\n",
    "sub_index = ['%d_%s'%(i,m) for i in range(Ntraining+1,Na+1) for m in ['Diastole', 'Systole']]\n",
    "sub = pd.DataFrame(np.zeros((2*(Na-Ntraining), 600)), index=sub_index, columns=sub_columns)\n",
    "sub.index.name = 'Id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub[sub.columns] = y_pred[:,::-1,:].reshape((-1,600))\n",
    "fname = FN+'.csv.gz'\n",
    "sub.to_csv(os.path.join(temp_dir, fname),compression='gzip')\n",
    "awscp(fname, upload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!cp {os.path.join(temp_dir, fname)} /Users/udi/Desktop/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Private LB 0.015064"
   ]
  }
 ],
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