weight_schema-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "plt.rcParams['figure.figsize'] = 16, 12\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "import seaborn as sns\n",
    "sns.set_style(\"dark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read data\n",
    "data = {}\n",
    "is_first_line = True\n",
    "with open('./../data/raw/train.csv', 'r') as f: \n",
    "    for line in f:\n",
    "        if is_first_line:\n",
    "            is_first_line = False\n",
    "            continue\n",
    "        parts = line.strip().split(',')\n",
    "        data[int(parts[0])] = pd.DataFrame(np.array(map(int, parts[1].strip().split(' '))), columns=['nday'])\n",
    "        \n",
    "#add week day column \n",
    "def nday_to_weekday(v):\n",
    "    v = np.remainder(v, 7)\n",
    "    v[v == 0] = 7\n",
    "    return v\n",
    "\n",
    "for key in data:\n",
    "    data[key]['weekday'] = nday_to_weekday(data[key]['nday'])\n",
    "    data[key]['gap'] = \\\n",
    "        data[key]['nday'].values - np.hstack((data[key]['nday'].values[0], data[key]['nday'].values[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25c4ef810>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88KLdcQAAAHCLKLIA5tW5K8O60hdT8+pqVZWV2B1nSmNNiRqqfXrzdK+6w3G74wAAAOAW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\nIQsAAEApQhYAAIBShCwAAAClPABrBE8+SWfjLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a859b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visits frequency\n",
    "mat_visits = np.zeros((len(data), 1100))\n",
    "for k in data.keys():\n",
    "    mat_visits[k - 1, data[k]['nday'].values] = 1\n",
    "\n",
    "visit_freq = mat_visits.sum(axis=1)/1099.0\n",
    "\n",
    "sns.distplot(mat_visits.sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x267c54590>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7FknEUCOgDQEpAADAANngZpaQIQ2NsT2kbsru6vfp8wU2EZACAAAMkMqnuCaJHZijPb8i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\n7ZIhXX3vX5B1OIpyOfk27iJ5j88NrY38yv/yf8u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5D0jtepG9M6S6qWSXlRZdvXGr34FG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TA5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25c4a2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# typical gap dynamics between visits\n",
    "plt.plot(data[128000]['gap'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8MhI921LLM7Nty+iN6mR8tG+NRnRnFndW+zsLlj9Vo485lZ+2sPt0O+aRrUBb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\nRHTchUw2KxtaMMqmx71Bib3wL9ur1nB8J28yzYzdwbrMgqKnzdePsYJ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zjBljn/Tci6ty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otHdF7W6fZ1k4lkKrleCWhMgIUOSKz4C+KL4vYW9u/oX+UzeV9KcZ6wZyXOb03sBOsWIOweOaQT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\nzHTIduVK94SmcVXyOjB2IqK8n+yA7Lqsy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Tn4oYYU3WE2yFBjNpOTrnmYsMYdyOsuOuuU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4oT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\nmo1TEDORAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x239411e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# density of visits: each line is users visits (sampled each 250th user)\n",
    "plt.imshow(mat_visits[np.arange(0, mat_visits.shape[0], 250), :], interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# some useful functions\n",
    "\n",
    "# save submit in correct format (with space)\n",
    "def save_submit(name, ids, pred):\n",
    "    with open(name, 'w') as f:\n",
    "        f.write('id,nextvisit\\n')\n",
    "        for t in zip(ids, pred):\n",
    "            f.write('%i, %i\\n' % t)\n",
    "            \n",
    "# vector of visits to week X day matrix, can skip empty weeks\n",
    "def visits_to_weekmatrix(v, skip_empty_weeks=True, d_max=1099):\n",
    "    u = np.zeros(d_max)\n",
    "    u[v - 1] = 1\n",
    "    if not skip_empty_weeks: \n",
    "        return u.reshape((d_max/7, 7))\n",
    "    m = u.reshape((d_max/7, 7))\n",
    "    return m[m.sum(axis=1) > 0, :]\n",
    "\n",
    "# distribution of visits ovew weeks\n",
    "def weekvisit_distr(m):\n",
    "    d = m.sum(axis=0)\n",
    "    return d/d.sum()\n",
    "\n",
    "# distribution of first visited day of weeks using recalculation\n",
    "def firstweekvisit_distr(m):\n",
    "    d = weekvisit_distr(m)\n",
    "    return d*np.hstack((1, np.cumprod((1 - d))[:-1]))\n",
    "\n",
    "\n",
    "# vector of visits to week X day matrix, where each line contains one at the day when first visit was done\n",
    "def visits_to_firstvisitweekmatrix(v, skip_empty_weeks=True):\n",
    "    m = visits_to_weekmatrix(v, skip_empty_weeks)\n",
    "    mi = np.array([t for t in \n",
    "                   map(lambda i: (i, np.where(m[i, :] > 0)[0][0] if m[i, :].sum() > 0 else -1), \n",
    "                       range(m.shape[0])) \n",
    "                   if t[1] > -1])\n",
    "    f = np.zeros(m.shape)\n",
    "    f[mi[:, 0], mi[:, 1]] = 1\n",
    "    return f\n",
    "\n",
    "def visitsmatrix_to_firstvisitweekmatrix(m):\n",
    "    i = np.apply_along_axis(lambda v: np.where(v > 0)[0][0] if v.sum() > 0 else 0, 1, m)\n",
    "    f = np.zeros(m.shape)\n",
    "    f[np.where(i > 0), i[i > 0]] = 1\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# calculate visit matrices for each data row\n",
    "vmats = {}\n",
    "for key in data:\n",
    "    vmats[key] = {\n",
    "        'visits_skiped': visits_to_weekmatrix(data[key]['nday'].values),\n",
    "        'firstvisits_skiped': visits_to_firstvisitweekmatrix(data[key]['nday'].values),\n",
    "        'visits': visits_to_weekmatrix(data[key]['nday'].values, skip_empty_weeks=False),\n",
    "        'firstvisits': visits_to_firstvisitweekmatrix(data[key]['nday'].values, skip_empty_weeks=False)        \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TSZLJycls2LChZIgArMCzzz6bgwcP5uDBg/mDP/iD/If/8B9y4403ep2HBfyu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267cd4a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of week visits of five users \n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(vmats[keys[i]]['visits_skiped'], interpolation='nearest')\n",
    "\n",
    "# ax = plt.subplot(gs[0, 0])\n",
    "# ax.imshow(vmats[1]['firstvisits'], interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rjQeA5rB8EQAAoCCVMqChFnPlfyGVjcU+JgDQfWb7TDHXZ4iSny9UygAAAAoS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1622e9d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of first week visits of five users \n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(vmats[keys[i]]['firstvisits_skiped'], interpolation='nearest')\n",
    "\n",
    "# ax = plt.subplot(gs[0, 0])\n",
    "# ax.imshow(vmats[1]['firstvisits'], interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nY2xsrNKLhGGScUon45RMvildqRnvefvWb/zGb8SXv/zliIj4/ve/H//7v/8bv/mbvxlH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\nAABAKqUEAABIpZQAAACp/g/b8uO67fwoPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1628fab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of week visits of five users \n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(vmats[keys[i]]['visits'], interpolation='nearest')\n",
    "\n",
    "# ax = plt.subplot(gs[0, 0])\n",
    "# ax.imshow(vmats[1]['firstvisits'], interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CAACkUkoAAIBUQ9kXACWq4wAZAPPnhSWUpI7ZtVICAACkUkoAAIBUSgkAAJCq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267ca3710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of first week visits of five users \n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(vmats[keys[i]]['firstvisits'], interpolation='nearest')\n",
    "\n",
    "# ax = plt.subplot(gs[0, 0])\n",
    "# ax.imshow(vmats[1]['firstvisits'], interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for validation will be used FIRST day of LAST week with visit, because 1099/7 is 157, \n",
    "# so we are to predict first visited day at 158th week\n",
    "vmats_valid = {}\n",
    "y_valid = {}\n",
    "for key in data.keys():\n",
    "    v = data[key]['nday'].values\n",
    "    m = visits_to_weekmatrix(v, skip_empty_weeks=False)\n",
    "    week_visits = m.sum(axis=1)\n",
    "    last_visited_week = np.where(week_visits > 0)[0][-1]\n",
    "    t = np.where(m[last_visited_week, :] > 0)[0][0] + 1\n",
    "    u = np.where(m[:last_visited_week, :].flatten() > 0)[0] + 1\n",
    "\n",
    "    vmats_valid[key] = {\n",
    "        'visits_skiped': visits_to_weekmatrix(u),\n",
    "        'firstvisits_skiped': visits_to_firstvisitweekmatrix(u),\n",
    "        'visits': visits_to_weekmatrix(u, skip_empty_weeks=False),\n",
    "        'firstvisits': visits_to_firstvisitweekmatrix(u, skip_empty_weeks=False)        \n",
    "    }\n",
    "    \n",
    "    y_valid[key] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EAAAglVICAACkquz2rYkcGgcA1IktYND6z4GVEgAAIJVSAgAApFJKAACAVLWZKZkq+zkB\nqss9mpLIM0ydlRIAACCVUgIAAKRSSgAAgFRKCQAAkKo2g+4OTwSoLvdoAKbDSgkAAJBKKQEAAFIp\nJQAAQKrazJRMlYOMyCB34OcAoCSdnh20UgIAAKRSSgAAgFRKCQAAkKr4mRIAgG4wR0VJup1nKyUA\nAEAqpQQAAEillAAAAKlSZkrsuQQA8ExEfTinBAAAKJpSAgAApFJKAACAVEoJAACQqi/7AoDpMSRJ\nFXR6ABJK5eeEunB4IgAAUDSlBAAASKWUAAAAqVJmSqayJ81+ZdgzPwcAnec5BPZtOj8nVkoAAIBU\nSgkAAJBKKQEAAFI5pwSASXM+DjNNOzJuLgWem5USAAAglVICAACkUkoAAIBUPc1ms5l9EQAAVWJu\nCrrLSgkAAJBKKQEAAFIpJQAAQCqlBAAASFWJQXeHCQEAQL2084UQVkoAAIBUSgkAAJBKKQEAAFJV\nYqakXcymAFSHw+cAaPXfAislAABAKqUEAABIpZQAAACpUmZK7DMGAErneQdaZ6UEAABIpZQAAACp\nlBIAACCVUgIAAKTqy/hDDXoBAEBZpnOQuZUSAAAglVICAACkUkoAAIBUKYcnTsV09qgBAADd1+oh\nolZKAACAVEoJAACQSikBAABS1WamBIDqMvcHu2t1Lz1gpQQAAEimlAAAAKmUEgAAIFVf9gW0yn5l\ngOpyPwZgT5xTAgAA1IJSAgAApFJKAACAVEoJAACQqhKHJxpiBwCA6ur087qVEgAAINWkXwncaDTi\n4osvjocffjhmzZoVn/vc56K3tzcGBwdj1qxZsXDhwli9enX09PR04nqh42Sc0sk4JZNvSldqxnvX\nrFmzZjL/wx133BH33ntv3HDDDfHSl740rr/++ti8eXOcd9558dGPfjRuu+22+M9//hOvetWrWv6e\nTzzxxG6fe/GLXzyZy4K26UTGoUpknJLJN6XLyninn9cnvX3rec97XgwNDUWz2YyhoaGYPXt2/OpX\nv4olS5ZERMTSpUtjy5YtbbtA6DYZp3TtyPj27dt3+QVV4R5O6bIyvmjRot1+tdOkt28tXrw4RkdH\nY9myZfGPf/wjrrvuuvjpT386/vW5c+fG0NBQWy8SuknGKZ2MUzL5pnSlZnzSpeSGG26IxYsXxyc+\n8Yn485//HGeccUbs3Llz/OsjIyMxMDDQ1ouEbpJxSifjlEy+KV2pGZ/09q2nn3465s2bFxERAwMD\nsXPnznjNa14TW7dujYiIzZs3xxFHHNHeq4QuknFKJ+OUTL4pXakZn/Q5Jf/85z/jwgsvjCeffDJ2\n7twZZ555Zrz2ta+NSy65JMbGxmLBggVx2WWX1W7iH54l45ROximZfFO6bmV84rxgp88QrMThiQAA\nQHV0u5Q4PBEAAEillAAAAKls3wIgTbe3BwBQTVZKAACAVEoJAACQSikBAABSKSUAAEAqg+4AAB3g\nRQ6Urp0Zt1ICAACkUkoAAIBUSgkAAJCqqJmSifvaIuzfpCwyTp3YTw9QTVW8P1spAQAAUiklAABA\nKqUEAABIlTJTUsV9bFBFflYonYwDlGM6s69WSgAAgFRKCQAAkEopAQAAUiklAABAqsoenmj4kZI4\n9JDSuEcD0E5WSgAAgFRKCQAAkEopAQAAUlV2pqRd7HsGqAazVbA7zymUrtWMWykBAABSKSUAAEAq\npQQAAEhV/EwJAABQbVZKAACAVEoJAACQSikBAABS9WVfQKu83x6gPM5ooKpkE7rLSgkAAJBKKQEA\nAFIpJQAAQCqlBAAASOXwRAAAIJWVEgAAIJVSAgAApFJKAACAVCmHJzqQCAAAeJaVEgAAIJVSAgAA\npFJKAACAVM4pASCNGUNmOj8DlGRiniNaz7SVEgAAIJVSAgAApFJKAACAVLWZKZnOHjUAAOAZVZxl\nslICAACkUkoAAIBUSgkAAJBKKQEAAFLVZtB9qqo4yANA69zHqSvZpXTtzLiVEgAAIJVSAgAApFJK\nAACAVH3ZF9Bp9m8C1Jv7OED5rJQAAACplBIAACCVUgIAAKQq/pwSADpv4rvqI8yCANA6KyUAAEAq\npQQAAEillAAAAKmUEgAAIJVBdwCACSa+vMGLG5jpOv1CEyslAABAKqUEAABIpZQAAACpip8psScU\nAACqzUoJAACQSikBAABSKSUAAECq2syUdPrdyAAAQA4rJQAAQCqlBAAASLXPUrJt27ZYuXJlRET8\n/ve/jxUrVsRpp50Wa9asiWd3ft18881x0kknxcknnxy33357Ry8Y2k3GKZ2MUzL5pnQzJeN9e/vi\n9ddfH7fcckvMmzcvIiLWrl0bq1atiiVLlsTq1avj1ltvjcMPPzw2bNgQmzZtih07dsSKFSviyCOP\njP3226+tF2p+hE6oUsbNTdEJncq4M6Cogirdw1vhPs9k1S3je9Lqvxd7XSmZP39+rFu3bryF3X//\n/bFkyZKIiFi6dGls2bIl7r333li8eHHMnj07+vv7Y/78+Xv8oYMqknFKJ+OUTL4p3UzK+F5LyfHH\nHx+9vb3jH//vi7rmzZsXQ0NDMTw8HAcccMAunx8eHu7ApUL7yTilk3FKJt+UbiZlfFKD7rNm/fe3\nDw8Px8DAQPT398fIyMj450dGRmJgYKB9VwhdJOOUTsYpmXxTupIzPqlScuihh8bWrVsjImLz5s1x\nxBFHxGGHHRY/+9nPYnR0NIaGhuKhhx6KhQsXduRiodNknNLJOCWTb0pXcsb3Ouj+rJ6enoiIGBwc\njEsuuSTGxsZiwYIFsWzZsujp6YkzzjgjTj311Gg0GrFq1arKDNZAq6qQccOOdFK7M96pvBoEZiqq\ncA/fEy+EoF2qmvGJpnMPr82J7gCUTymhJEoJM8107uEOTwQAAFIpJQAAQKqWZkrazXImQL116j7u\n3wNKIs/QOislAABAKqUEAABIpZQAAACpavNKYK+JpM7kFwDguVkpAQAAUiklAABAKqUEAABIVZuZ\nkj1x3glAWcxfAcxMVkoAAIBUSgkAAJBKKQEAAFIpJQAAQKpaD7q3wjA8QA73X4CZZTovK7FSAgAA\npFJKAACAVEoJAACQqi/7AmAmcCAcM9FUMu5nBaC+pnO/tlICAACkUkoAAIBUSgkAAJCqNueU2GcM\nUB5nmTDT+RmAZ1gpAQAAUiklAABAKqUEAABIpZQAAACpKjHobogdAADqpZ0varBSAgAApFJKAACA\nVEoJAACQKmWmxEFBAOyJGUOAmclKCQAAkEopAQAAUiklAABAKueUAAC0gZlZmDorJQAAQCqlBAAA\nSKWUAAAAqfqyLwCA+rF3Hnbn5wCmzkoJAACQSikBAABSKSUAAEAqpQQAAEhVicMTW+GARUpjUBgA\n/xZQZ+3Mr5USAAAglVICAACkUkoAAIBUtZkpAaC6zP3B7syLQOuslAAAAKmUEgAAIJVSAgAApCpq\npsSeZkon48xE9uUDlM9KCQAAkEopAQAAUiklAABAKqUEAABIlTLobmgRoN7cx2HyvKwEnpuVEgAA\nIJVSAgAApFJKAACAVJU9PNF+ZQAAqIZOz0RZKQEAAFIpJQAAQCqlBAAASFXZmZJ2MZsCAHSDZw5m\nuunMnVgpAQAAUiklAABAKqUEAABI1Zfxh3Zzz6X9nAA57K9nppFxmDorJQAAQCqlBAAASKWUAAAA\nqZQSAAAgVWUPTzQgCQAAM4OVEgAAINU+S8m2bdti5cqVERHxwAMPxGmnnRYrV66Ms88+O5544omI\niLj55pvjpJNOipNPPjluv/32jl4wtJuMUzoZp2TyTelmTMabe/GVr3yluXz58ubJJ5/cbDabzdNP\nP735wAMPNJvNZnPjxo3NtWvXNh9//PHm8uXLm6Ojo82hoaHm8uXLmzt27Njbt23Jgw8+uMsv6ITM\njEM3yDglk29KN5MyvteVkvnz58e6deui+f/HTr74xS/Gq1/96oiI2LlzZ8yZMyfuueeeWLx4ccye\nPTv6+/tj/vz5u82D7Mv27dt3+7Vo0aJdfkEndCvjkEXGKZl8U7qZlPG9lpLjjz8+ent7xz8+6KCD\nIiLi7rvvjptuuik++MEPxvDwcBxwwAHjv2fevHkxPDzcocuF9pJxSifjlEy+Kd1MynjfZP+H733v\ne3HdddfFV77ylTjwwAOjv78/RkZGxr8+MjISAwMDbb1I6CYZp3QyTsnkm9KVmvFJvX3r29/+dtx0\n002xYcOGeMUrXhEREYcddlj87Gc/i9HR0RgaGoqHHnooFi5c2JGLhU6TcUon45RMvildyRlvaaWk\np6cnGo1GXHHFFfGyl70sPvKRj0RExJve9Kb4yEc+EmeccUaceuqp0Wg0YtWqVbHffvtN6iJamRnZ\n0944sya0S6czDtlknJJ1I9+eQ8g0E+7hlT08cSI3AwAgi+cQ6CyHJwIAAKmUEgAAIFVttm+1wtIq\nQL1MvG+7ZwPMTFZKAACAVEoJAACQSikBAABSKSUAAECqogbdAag3LyyhJF7kQEk6fX+2UgIAAKRS\nSgAAgFRKCQAAkCplpsQeS4B6cx8HoJ3/FlgpAQAAUiklAABAKqUEAABIVfw5JfY9AwDAf1Xx+dhK\nCQAAkEopAQAAUiklAABAKqUEAABIVfygO9TFxKGziGoMngHQOVUcOIY96fRzipUSAAAglVICAACk\nUkoAAIBUlZgpsZcegOdizz11IaswdVZKAACAVEoJAACQSikBAABSVWKmZKrs3QTI4f4L++bnhKqq\nYjatlAAAAKmUEgAAIJVSAgAApEqZKaniPjYAps55UwBMh5USAAAglVICAACkUkoAAIBUSgkAAJCq\n1ocnApDDC0sAylGFl5VYKQEAAFIpJQAAQCqlBAAASOXwRKg5P08A9VCFfftQVVZKAACAVEoJAACQ\nSikBAABS1eacEvswKZ2MA9SbGT+YOislAABAKqUEAABIpZQAAACpajNTAkB1mYkCqI8qzj9ZKQEA\nAFIpJQAAQCqlBAAASKWUAAAAqSox6G5AEgAA6m06z/RWSgAAgFRKCQAAkEopAQAAUlVipgRKU8VD\niQDoLv8WQOuslAAAAKmUEgAAIJVSAgAApDJTAl3gLB4A9sTcCTzDSgkAAJBKKQEAAFIpJQAAQCql\nBAAASGXQHYBp8zIHAKbzb4GVEgAAIJVSAgAApFJKAACAVLWeKXHgEED53OsB2quK91UrJQAAQCql\nBAAASKWUAAAAqWozU+Id+ABAt1Rxzz1UnXNKAACA2tpnKdm2bVusXLlyl8995zvfiVNOOWX845tv\nvjlOOumkOPnkk+P2229v+0VCJ8k4pZNxSibflG6mZLxvb1+8/vrr45Zbbol58+aNf+7++++Pb37z\nm+MfP/7447Fhw4bYtGlT7NixI1asWBFHHnlk7Lfffp27amgTGad0Mk7J5JvSzaSM73WlZP78+bFu\n3bp4duzkySefjGuuuSYuuuii8c/dc889sXjx4pg9e3b09/fH/Pnz97ifbLoWLVq026/t27fv8gsm\nq0oZh07IzLh7NJ3WyXxPfOboFD8n7E3dnlOm87y+11Jy/PHHR29vb0RENBqNuPjii2NwcDDmzp07\n/nuGh4fjgAMOGP943rx5MTw8PN2/E3SFjFM6Gadk8k3pZlLG97p963/dd9998Yc//CHWrFkTo6Oj\n8Zvf/CbWrl0bb3rTm2JkZGT8942MjMTAwEBHLhY6ScYpnYxTMvmmdKVnvOVScthhh8V3v/vdiIh4\n5JFHYtWqVXHhhRfG448/Htdcc02Mjo7Gjh074qGHHoqFCxd27IKhU2Sc0sk4JZNvSld6xlsqJT09\nPbt83Gw2xz930EEHxRlnnBGnnnpqNBqNWLVqVe0Ga0DGKZ2MUzL5pnQzIeO1OTwRgPI4oA6gvtp5\nD3d4IgAAkEopAQAAUiklAABAqqJmSvZ0IIv9yQAAMD2dfs62UgIAAKRSSgAAgFRKCQAAkKqomRIA\nyuMsE0om39SZc0oAAIBiKCUAAEAqpQQAAEhV2ZkSeywBqss9GoCJpnOWiZUSAAAglVICAACkUkoA\nAIBUSgkAAJAqZdDdgCQAUBrPN9RFFbNqpQQAAEillAAAAKmUEgAAIFVfxh86lX1r0zmMBYDOco8G\nmYfpsFJcH4P8AAAGqUlEQVQCAACkUkoAAIBUSgkAAJAq5ZySVlTx/ckAAO3keQeeYaUEAABIpZQA\nAACplBIAACCVUgIAAKRKGXQ31AV+DgDqxAGhMDWtPu9YKQEAAFIpJQAAQCqlBAAASOXwRAAAIJWV\nEgAAIJVSAgAApFJKAACAVH3ZFwAzQU9Pz26fq+g4FwBdZIYWnmGlBAAASKWUAAAAqZQSAAAgVWXP\nKQEAyGLWA7rLSgkAAJBKKQEAAFIpJQAAQCqlBAAASGXQHQAASGWlBAAASKWUAAAAqZQSAAAgVV/2\nBTwXhxYBAEB9THx+j2j9Gd5KCQAAkEopAQAAUiklAABAqsrOlAAw80xnPzJUjflYaJ2VEgAAIJVS\nAgAApFJKAACAVD3NZrOZfRFTZa8mQL25j1MX5p2gs6yUAAAAqZQSAAAglVICAACkUkoAAIBUtR50\nn6inp2e3zxX01wMonvs4M83EzMs7dTade7iVEgAAIJVSAgAApFJKAACAVEXNlAAAAPVjpQQAAEil\nlAAAAKmUEgAAIJVSAgAApFJKAACAVEoJAACQqi/jD200GrFmzZr49a9/HbNnz47LL788Dj744Ni2\nbVt84QtfiA0bNsTY2FhcdNFF8eijj8bo6Gh8+MMfjltvvTUefvjh6OnpiUsvvTQWLlwYTzzxRJx4\n4onxta99LVatWhX9/f0REfHKV74yrrjiili/fn3cdtttMTY2Fqeffnq8973vzfgrM8PsK+Nf/epX\nd8n3ueeeG8ccc0x8+tOfnlTG58+fL9+kmGzGW7mHH3LIIfHe975Xxkn3XPmOCBmnCFV8Fk8pJT/6\n0Y9ibGwsNm7cGNu2bYsrr7wyXv/618ctt9wS8+bNi4iI73znO/HCF74wrr766njqqafiHe94R7zt\nbW+Lb3zjG7F169a45ppr4tprr43PfOYzsf/++8fo6GhERGzYsGH8z7nrrrviF7/4RWzcuDH+9a9/\nxQ033JDx12UG2lfGJ+b7hBNOiEajEbNmzWo543fddVfceOON8k2KyWZ8X/fwiIgdO3ZEhIyTb0/5\n/vKXvxzXX3+9jFOEKj6Lp2zfuvvuu+Poo4+OiIjDDz887rvvvpg/f36sW7cunj3LcdmyZXH++edH\nxDNtrr+/Pz772c9GRMQjjzwSz3/+8+Oqq66KFStWxEEHHRS//e1v4+mnn46zzz47zjzzzNi2bVvc\neeedsWjRojjvvPPinHPOiWOPPTbjr8sMtK+MT8x3b29vHHfccZPK+KZNm+SbNJPN+L7u4RERDz74\noIxTCXvKd0TIOMWo4rN4ykrJ8PDw+NJORIw/kD366KPjn5s7d+747/3Yxz4Wn/jEJ6K3tzcGBwfj\nhz/8YXzgAx+IgYGBOOqoo2L9+vUxZ86cOPvss+P9739/PPzww/GhD30o3vjGN8af//znWL9+ffzx\nj3+Mc889N77//e93/e/LzLOvjO8p38/+vlYzfuKJJ8brX/96+SbFVDK+t3w3m83Yf//9ZZxK2FO+\nG41GHH/88fGnP/0pImSceqvis3jKSkl/f3+MjIyMf/zstpWJHnvssTjzzDPjhBNOiHe9610REXHl\nlVfGD37wg/j6178eP/nJT2LlypXx4IMPxpe//OU48sgjIyLi//7v/+IFL3hBzJkzJ4466qjo6+uL\nQw45JObMmRN///vfu/OXZEZrJeN7yndE6xmfO3duHH744fJNiqlm/LnyPTg4GAcccEC8+93vjggZ\nJ1cnnlNknCqp4rN4SilZvHhxbN68OSIifvnLX8aiRYt2+z1/+9vf4qyzzooLLrggTjzxxPjWt74V\n69evj4iI5z3vefGSl7wkbrzxxtiwYUO8+tWvjre+9a3j+9T+8pe/xPDwcBx11FFxxx13jH/u6aef\njgMPPLBLf0tmsn1lfGK+I2LSGe/t7Y1f/OIX4x/LN9002YzvK9+f//zn48c//nFceeWVESHj5OrE\nc4qMUyVVfBZP2b719re/Pe6888445ZRTIiJi7dq141/r6emJiIjrrrsuhoaG4ktf+lJ86Utfikaj\nES960Yvi9NNPj507d8bFF18c++233/j/t2zZsli/fn2cdtpp49/zda97Xfz85z+P973vfdFoNGL1\n6tXj3x86aV8Zn5jviIh169bF6tWrW874tddeGz/84Q/lmxSTzfi+7uEREe973/viwgsvlHHS7S3f\nETJO/VXxWbyn+ew0CwAAQAKHJwIAAKmUEgAAIJVSAgAApFJKAACAVEoJAACQSikBAABSKSUAAEAq\npQQAAEj1/wCV0Wxsje+J3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16312a290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examples of validation data, black dot is the our goal\n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    m = vmats[keys[i - 1]]['firstvisits'] + \\\n",
    "            3*(vmats[keys[i - 1]]['firstvisits'] - vmats_valid[keys[i - 1]]['firstvisits'])\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(m, interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# results of the first level models\n",
    "first_lvl = {}\n",
    "valid_lb = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def search_train_predict(a_grid, f_predict):\n",
    "    best_a = None\n",
    "    best_score = -1\n",
    "    for a in a_grid:\n",
    "        wins = 0\n",
    "        for key in vmats_valid.keys():\n",
    "            y = f_predict(key, vmats_valid, a)\n",
    "            wins += 1 if (y.argmax() + 1) == y_valid[key] else 0\n",
    "        score = wins/float(len(vmats_valid))\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_a = a\n",
    "        print a, score\n",
    "    \n",
    "    print '---'\n",
    "    print best_a, best_score\n",
    "\n",
    "    res = dict(map(lambda key: (key, \n",
    "                                (f_predict(key, vmats_valid, best_a), \n",
    "                                 f_predict(key, vmats, best_a))), \n",
    "                   vmats_valid.keys()))\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zHAAaV19jvbEdoPislAAAAKmEEgAAIJVQAgAApGqIc0rOO++8XtcuvfTShJ4A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1635ace90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let each week have its own weight\n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    m = vmats[keys[i - 1]]['visits']\n",
    "    w = (np.linspace(1, m.shape[0], m.shape[0])/m.shape[0])**1.4\n",
    "    w = w/w.sum()\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow((m*w.reshape((w.shape[0], 1))), interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "first part of first layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.22506\n",
      "0.4 0.226813333333\n",
      "0.6 0.22827\n",
      "0.8 0.229286666667\n",
      "1.0 0.229446666667\n",
      "1.2 0.2297\n",
      "1.4 0.229883333333\n",
      "1.6 0.230056666667\n",
      "1.8 0.22996\n",
      "2.0 0.22999\n",
      "2.2 0.22999\n",
      "2.4 0.229753333333\n",
      "2.6 0.229316666667\n",
      "2.8 0.22927\n",
      "---\n",
      "1.6 0.230056666667\n"
     ]
    }
   ],
   "source": [
    "# visits week weighted model = vwwm\n",
    "name = 'vwwm'\n",
    "def vwwm(key, d, a):\n",
    "    mat_valid = d[key]['visits']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = np.dot(w, mat_valid)\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, vwwm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.230056666667, 0.233)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.237506666667\n",
      "0.4 0.238793333333\n",
      "0.6 0.239796666667\n",
      "0.8 0.240423333333\n",
      "1.0 0.240556666667\n",
      "1.2 0.240786666667\n",
      "1.4 0.240666666667\n",
      "1.6 0.24081\n",
      "1.8 0.240606666667\n",
      "2.0 0.240443333333\n",
      "2.2 0.23985\n",
      "2.4 0.23936\n",
      "2.6 0.23887\n",
      "2.8 0.238366666667\n",
      "---\n",
      "1.6 0.24081\n"
     ]
    }
   ],
   "source": [
    "# first visits week weighted model = fvwwm\n",
    "name = 'fvwwm'\n",
    "def fvwwm(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = np.dot(w, mat_valid)\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvwwm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.24081, 0.24573)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.224996666667\n",
      "0.4 0.22657\n",
      "0.6 0.228193333333\n",
      "0.8 0.22897\n",
      "1.0 0.229456666667\n",
      "1.2 0.22955\n",
      "1.4 0.229623333333\n",
      "1.6 0.229863333333\n",
      "1.8 0.229823333333\n",
      "2.0 0.229746666667\n",
      "2.2 0.229666666667\n",
      "2.4 0.22955\n",
      "2.6 0.229296666667\n",
      "2.8 0.229073333333\n",
      "---\n",
      "1.6 0.229863333333\n"
     ]
    }
   ],
   "source": [
    "# visits week with skipped weighted model = vwwswm\n",
    "name = 'vwwswm'\n",
    "def vwwswm(key, d, a):\n",
    "    mat_valid = d[key]['visits_skiped']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = np.dot(w, mat_valid)\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, vwwswm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.229863333333, 0.23227)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.23733\n",
      "0.4 0.238676666667\n",
      "0.6 0.23954\n",
      "0.8 0.240356666667\n",
      "1.0 0.240433333333\n",
      "1.2 0.240656666667\n",
      "1.4 0.240813333333\n",
      "1.6 0.24068\n",
      "1.8 0.240786666667\n",
      "2.0 0.240406666667\n",
      "2.2 0.23987\n",
      "2.4 0.23943\n",
      "2.6 0.238916666667\n",
      "2.8 0.23841\n",
      "---\n",
      "1.4 0.240813333333\n"
     ]
    }
   ],
   "source": [
    "# first visits week with skipped weighted model = fvwwswm\n",
    "name = 'fvwwswm'\n",
    "def fvwwswm(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits_skiped']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = np.dot(w, mat_valid)\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvwwswm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.240813333333, 0.24617)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "second part of first layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.236683333333\n",
      "0.4 0.238636666667\n",
      "0.6 0.240013333333\n",
      "0.8 0.240783333333\n",
      "1.0 0.24109\n",
      "1.2 0.242\n",
      "1.4 0.242256666667\n",
      "1.6 0.242103333333\n",
      "1.8 0.241733333333\n",
      "2.0 0.24142\n",
      "2.2 0.241243333333\n",
      "2.4 0.240786666667\n",
      "2.6 0.240236666667\n",
      "2.8 0.24003\n",
      "---\n",
      "1.4 0.242256666667\n"
     ]
    }
   ],
   "source": [
    "# visits week weighted model recalculation = vwwmr\n",
    "name = 'vwwmr'\n",
    "def vwwmr(key, d, a):\n",
    "    mat_valid = d[key]['visits']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = firstweekvisit_distr(mat_valid*w.reshape((w.shape[0], 1)))\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, vwwmr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.242256666667, 0.24490)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.22423\n",
      "0.4 0.22622\n",
      "0.6 0.227086666667\n",
      "0.8 0.228003333333\n",
      "1.0 0.22871\n",
      "1.2 0.229133333333\n",
      "1.4 0.22878\n",
      "1.6 0.22903\n",
      "1.8 0.228906666667\n",
      "2.0 0.228826666667\n",
      "2.2 0.22869\n",
      "2.4 0.228283333333\n",
      "2.6 0.228063333333\n",
      "2.8 0.227803333333\n",
      "---\n",
      "1.2 0.229133333333\n"
     ]
    }
   ],
   "source": [
    "# first visits week weighted model recalculation = fvwwmr\n",
    "name = 'fvwwmr'\n",
    "def fvwwmr(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = firstweekvisit_distr(mat_valid*w.reshape((w.shape[0], 1)))\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvwwmr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.229133333333, 0.23067)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.23682\n",
      "0.4 0.238626666667\n",
      "0.6 0.24009\n",
      "0.8 0.24079\n",
      "1.0 0.241143333333\n",
      "1.2 0.241763333333\n",
      "1.4 0.242036666667\n",
      "1.6 0.2419\n",
      "1.8 0.24183\n",
      "2.0 0.24144\n",
      "2.2 0.241206666667\n",
      "2.4 0.240676666667\n",
      "2.6 0.24032\n",
      "2.8 0.239856666667\n",
      "---\n",
      "1.4 0.242036666667\n"
     ]
    }
   ],
   "source": [
    "# visits week with skipped weighted model recalculation = vwwswmr\n",
    "name = 'vwwswmr'\n",
    "def vwwswmr(key, d, a):\n",
    "    mat_valid = d[key]['visits_skiped']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = firstweekvisit_distr(mat_valid*w.reshape((w.shape[0], 1)))\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, vwwswmr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.242036666667, 0.24537)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.22423\n",
      "0.4 0.226156666667\n",
      "0.6 0.227046666667\n",
      "0.8 0.227776666667\n",
      "1.0 0.228463333333\n",
      "1.2 0.22893\n",
      "1.4 0.2292\n",
      "1.6 0.229223333333\n",
      "1.8 0.22925\n",
      "2.0 0.229086666667\n",
      "2.2 0.228753333333\n",
      "2.4 0.2283\n",
      "2.6 0.22822\n",
      "2.8 0.227653333333\n",
      "---\n",
      "1.8 0.22925\n"
     ]
    }
   ],
   "source": [
    "# first visits week with skipped weighted model recalculation = fvwwswmr\n",
    "name = 'fvwwswmr'\n",
    "def fvwwswmr(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits_skiped']\n",
    "    w = (np.linspace(1, mat_valid.shape[0], mat_valid.shape[0])/mat_valid.shape[0])**a\n",
    "    w = w/w.sum()\n",
    "    p = firstweekvisit_distr(mat_valid*w.reshape((w.shape[0], 1)))\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvwwswmr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "save_submit('./../submits/%s.csv' % name, \n",
    "            first_lvl[name].keys(), \n",
    "            [(first_lvl[name][k][1].argmax() + 1) for k in first_lvl[name].keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "valid_lb[name] = (0.22925, 0.23103)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1632359d0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OmDpTnD3bzAhbAAAA4GMYazVf8FSp+nJcV5E2eoa2FhhNvLU44XjmcrlubXt/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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x162ed6150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check validation\n",
    "# for today, I have used all submit attempt, so further I will rely only on validation score\n",
    "# because as we can see that schema is highly correlated with LB one\n",
    "sns.regplot(np.array(valid_lb.values())[:, 0], np.array(valid_lb.values())[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "third part of the first layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GKZ3GKZ3GKVmn+7ZTAgAApDKUAAAAqQwlAABAKkMJAACQKuVG91tuuaXpeP/99+/0EqCt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x163170190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let each day has its own weight\n",
    "\n",
    "fig = plt.figure() \n",
    "gs = gridspec.GridSpec(1, 5)\n",
    "keys = [1, 60000, 120000, 180000, 240000]\n",
    "for i in range(5):\n",
    "    mat_valid = vmats_valid[key]['visits']\n",
    "    w = (np.where(mat_valid.flatten() > 0)[0]/float(np.prod(mat_valid.shape)))\n",
    "    w = w/w.sum()\n",
    "    m = np.zeros(np.prod(mat_valid.shape))\n",
    "    m[np.where(mat_valid.flatten() > 0)[0]] = w\n",
    "    m = m.reshape(mat_valid.shape)\n",
    "    ax = plt.subplot(gs[0, i])\n",
    "    ax.imshow(m, interpolation='nearest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.2374\n",
      "0.4 0.23861\n",
      "0.6 0.239893333333\n",
      "0.8 0.240476666667\n",
      "1.0 0.24067\n",
      "1.2 0.24063\n",
      "1.4 0.24056\n",
      "1.6 0.24046\n",
      "1.8 0.24043\n",
      "2.0 0.24028\n",
      "2.2 0.239893333333\n",
      "2.4 0.239266666667\n",
      "2.6 0.238483333333\n",
      "2.8 0.23829\n",
      "---\n",
      "1.0 0.24067\n"
     ]
    }
   ],
   "source": [
    "# first visits day weighted model = fvdwm\n",
    "name = 'fvdwm'\n",
    "def fvdwm(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits']\n",
    "    w = (np.where(mat_valid.flatten() > 0)[0]/float(np.prod(mat_valid.shape)))**a\n",
    "    w = w/w.sum()\n",
    "    m = np.zeros(np.prod(mat_valid.shape))\n",
    "    m[np.where(mat_valid.flatten() > 0)[0]] = w\n",
    "    m = m.reshape(mat_valid.shape)\n",
    "    p = m.sum(axis=0)\n",
    "    p = p/p.sum()\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvdwm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.237313333333\n",
      "0.4 0.23849\n",
      "0.6 0.239443333333\n",
      "0.8 0.24034\n",
      "1.0 0.240623333333\n",
      "1.2 0.24031\n",
      "1.4 0.240413333333\n",
      "1.6 0.24064\n",
      "1.8 0.24036\n",
      "2.0 0.239926666667\n",
      "2.2 0.23939\n",
      "2.4 0.239196666667\n",
      "2.6 0.238646666667\n",
      "2.8 0.238113333333\n",
      "---\n",
      "1.6 0.24064\n"
     ]
    }
   ],
   "source": [
    "# first visits skipped day weighted model = fvsdwm\n",
    "name = 'fvsdwm'\n",
    "def fvsdwm(key, d, a):\n",
    "    mat_valid = d[key]['firstvisits_skiped']\n",
    "    w = (np.where(mat_valid.flatten() > 0)[0]/float(np.prod(mat_valid.shape)))**a\n",
    "    w = w/w.sum()\n",
    "    m = np.zeros(np.prod(mat_valid.shape))\n",
    "    m[np.where(mat_valid.flatten() > 0)[0]] = w\n",
    "    m = m.reshape(mat_valid.shape)\n",
    "    p = m.sum(axis=0)\n",
    "    p = p/p.sum()\n",
    "    return p\n",
    "\n",
    "a_grid = np.arange(0.2, 3, 0.2)\n",
    "\n",
    "first_lvl[name] = search_train_predict(a_grid, fvsdwm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# union of all predictions for new dataset for second layer\n",
    "\n",
    "X_train = []\n",
    "X_test = []\n",
    "ids = np.array(data.keys())\n",
    "y_train = np.array(map(lambda i: y_valid[i], ids))\n",
    "\n",
    "\n",
    "for i in ids:\n",
    "    v_train = []\n",
    "    v_test = []\n",
    "    for pred_type in first_lvl.keys():\n",
    "        v_train.append(first_lvl[pred_type][i][0])\n",
    "        v_test.append(first_lvl[pred_type][i][1])\n",
    "    X_train.append(np.hstack(tuple(v_train)))\n",
    "    X_test.append(np.hstack(tuple(v_test)))\n",
    "\n",
    "X_train = np.array(X_train)\n",
    "X_test = np.array(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save data\n",
    "\n",
    "with open('./first_lvl_bak.npz', 'wb') as f:\n",
    "    np.savez(f, X_train=X_train, y_train=y_train, X_test=X_test, ids=ids)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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