{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "import shelve\n",
    "\n",
    "import pickle\n",
    "\n",
    "import pandas\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nltk\n",
    "\n",
    "import codecs\n",
    "\n",
    "import gensim\n",
    "\n",
    "import scipy\n",
    "\n",
    "from scipy import sparse\n",
    "\n",
    "import scipy.sparse\n",
    "\n",
    "import scipy.io\n",
    "\n",
    "import sklearn\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "import sklearn.metrics\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation,AutoEncoder\n",
    "from keras.optimizers import SGD,Adam\n",
    "from keras.layers import containers\n",
    "from mlp import *\n",
    "import mlp\n",
    "import sys\n",
    "import utils\n",
    "from sklearn.preprocessing import LabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corps=shelve.open(\"models/DECODA_AE_TANH_1060_TFIDF.shelve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lb=LabelBinarizer()\n",
    "y_train=lb.fit_transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"TRAIN\"]])\n",
    "y_dev=lb.transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"DEV\"]])\n",
    "y_test=lb.transform([utils.select(ligneid) for ligneid in corps[\"LABEL\"][\"TEST\"]])\n",
    "keys = corps.keys()\n",
    "if \"LABEL\" in keys:\n",
    "    keys.remove(\"LABEL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keys=[\"TRS_SPARSE\",\"ASR_SPARSE\",\"ASR_H2_TRANFORMED_OUT\",\"ASR_H1_TRANFORMED_OUT\",\"TRS_AE_OUT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out_db=shelve.open(\"scores/RAW_TRS_TRAIN.shelve\",writeback=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Save 286\n",
      "Save 289\n",
      "Save 301\n",
      "Save 313\n",
      "Save 317\n",
      "Save 322\n",
      "Save 326\n",
      "Save 327\n",
      "Save 334\n",
      "Save 345\n",
      "Save 350\n",
      "Save 353\n",
      "Save 359\n",
      "Save 362\n",
      "Save 364\n",
      "Save 365\n",
      "Save 369\n",
      "Save 373\n",
      "Save 376\n",
      "Save 382\n",
      "Save 384\n",
      "Save 388\n",
      "Save 401\n",
      "Save 404\n",
      "Save 409\n",
      "Save 415\n",
      "Save 418\n",
      "Save 420\n",
      "Save 421\n",
      "Save 432\n",
      "Save 436\n",
      "Save 450\n",
      "Save 454\n",
      "Save 455\n",
      "Save 459\n",
      "Save 467\n",
      "Save 480\n",
      "Save 484\n",
      "Save 496\n"
     ]
    }
   ],
   "source": [
    "nb_epochs=500\n",
    "for key in keys:\n",
    "    print key\n",
    "    try:\n",
    "        x_train=corps[\"TRS_SPARSE\"][\"TRAIN\"].todense()\n",
    "        x_dev=corps[key][\"DEV\"].todense()\n",
    "        x_test=corps[key][\"TEST\"].todense()\n",
    "    except :\n",
    "        x_train=corps[\"TRS_SPARSE\"][\"TRAIN\"].todense()\n",
    "        x_dev=corps[key][\"DEV\"]\n",
    "        x_test=corps[key][\"TEST\"]\n",
    "\n",
    "    out_db[key]=mlp.train_mlp(x_train,y_train,x_dev,y_dev,x_test,y_test,[256,128,256],dropouts=[0.5,0,0],sgd=Adam(lr=0.0001),epochs=nb_epochs,batch_size=8,save_pred=True,keep_histo=True,fit_verbose=0)\n",
    "out_db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRS_SPARSE\n",
      "Save 0\n",
      "Save 4\n",
      "Save 5\n",
      "Save 6\n",
      "Save 7\n",
      "Save 9\n",
      "Save 10\n",
      "Save 12\n",
      "Save 16\n",
      "Save 17\n",
      "Save 18\n",
      "Save 19\n",
      "Save 22\n",
      "Save 24\n",
      "Save 28\n",
      "Save 29\n",
      "Save 33\n",
      "Save 34\n",
      "Save 35\n",
      "Save 36\n",
      "Save 38\n",
      "Save 39\n",
      "Save 42\n",
      "Save 44\n",
      "Save 46\n",
      "Save 48\n",
      "Save 50\n",
      "Save 57\n",
      "Save 60\n",
      "Save 64\n",
      "Save 65\n",
      "Save 74\n",
      "Save 76\n",
      "Save 78\n",
      "Save 83\n",
      "Save 88\n",
      "Save 92\n",
      "Save 102\n",
      "Save 104\n",
      "Save 106\n",
      "Save 108\n",
      "Save 115\n",
      "Save 119\n",
      "Save 121\n",
      "Save 123\n",
      "Save 128\n",
      "Save 133\n",
      "Save 134\n",
      "Save 136\n",
      "Save 141\n",
      "Save 146\n",
      "Save 153\n",
      "Save 162\n",
      "Save 165\n",
      "Save 169\n",
      "Save 171\n",
      "Save 173\n",
      "Save 175\n",
      "Save 177\n",
      "Save 182\n",
      "Save 189\n",
      "Save 193\n",
      "Save 197\n",
      "Save 202\n",
      "Save 205\n",
      "Save 211\n",
      "Save 215\n",
      "Save 216\n",
      "Save 233\n",
      "Save 238\n",
      "Save 251\n",
      "Save 254\n",
      "Save 262\n",
      "Save 266\n",
      "Save 269\n",
      "Save 274\n",
      "Save 277\n",
      "Save 280\n",
      "Save 282\n",
      "Save 286\n",
      "Save 290\n",
      "Save 291\n",
      "Save 294\n",
      "Save 298\n",
      "Save 301\n",
      "Save 303\n",
      "Save 310\n",
      "Save 314\n",
      "Save 319\n",
      "Save 323\n",
      "Save 334\n",
      "Save 339\n",
      "Save 359\n",
      "Save 363\n",
      "Save 365\n",
      "Save 375\n",
      "Save 380\n",
      "Save 382\n",
      "Save 388\n",
      "Save 390\n",
      "Save 393\n",
      "Save 396\n",
      "Save 406\n",
      "Save 412\n",
      "Save 422\n",
      "Save 433\n",
      "Save 438\n",
      "Save 452\n",
      "Save 459\n",
      "Save 464\n",
      "Save 475\n",
      "Save 485\n",
      "Save 493\n",
      "Save 494\n",
      "ASR_SPARSE\n",
      "Save 0\n",
      "Save 3\n",
      "Save 4\n",
      "Save 5\n",
      "Save 6\n",
      "Save 7\n",
      "Save 8\n",
      "Save 10\n",
      "Save 16\n",
      "Save 17\n",
      "Save 19\n",
      "Save 26\n",
      "Save 28\n",
      "Save 31\n",
      "Save 32\n",
      "Save 34\n",
      "Save 35\n",
      "Save 39\n",
      "Save 41\n",
      "Save 43\n",
      "Save 46\n",
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      "Save 51\n",
      "Save 52\n",
      "Save 53\n",
      "Save 55\n",
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      "Save 60\n",
      "Save 61\n",
      "Save 63\n",
      "Save 66\n",
      "Save 68\n",
      "Save 71\n",
      "Save 73\n",
      "Save 75\n",
      "Save 77\n",
      "Save 79\n",
      "Save 83\n",
      "Save 85\n",
      "Save 90\n",
      "Save 93\n",
      "Save 94\n",
      "Save 96\n",
      "Save 99\n",
      "Save 101\n",
      "Save 102\n",
      "Save 104\n",
      "Save 106\n",
      "Save 109\n",
      "Save 112\n",
      "Save 113\n",
      "Save 117\n",
      "Save 123\n",
      "Save 126\n",
      "Save 127\n",
      "Save 129\n",
      "Save 130\n",
      "Save 133\n",
      "Save 137\n",
      "Save 142\n",
      "Save 145\n",
      "Save 146\n",
      "Save 147\n",
      "Save 148\n",
      "Save 149\n",
      "Save 151\n",
      "Save 152\n",
      "Save 155\n",
      "Save 157\n",
      "Save 160\n",
      "Save 164\n",
      "Save 167\n",
      "Save 173\n",
      "Save 176\n",
      "Save 177\n",
      "Save 184\n",
      "Save 189\n",
      "Save 193\n",
      "Save 195\n",
      "Save 196\n",
      "Save 204\n",
      "Save 209\n",
      "Save 212\n",
      "Save 215\n",
      "Save 218\n",
      "Save 219\n",
      "Save 221\n",
      "Save 223\n",
      "Save 226\n",
      "Save 229\n",
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      "Save 237\n",
      "Save 239\n",
      "Save 242\n",
      "Save 244\n",
      "Save 246\n",
      "Save 249\n",
      "Save 250\n",
      "Save 255\n",
      "Save 258\n",
      "Save 259\n",
      "Save 261\n",
      "Save 263\n",
      "Save 267\n",
      "Save 271\n",
      "Save 274\n",
      "Save 276\n",
      "Save 277\n",
      "Save 279\n",
      "Save 283\n",
      "Save 284\n",
      "Save 286\n",
      "Save 288\n",
      "Save 289\n",
      "Save 298\n",
      "Save 301\n",
      "Save 304\n",
      "Save 310\n",
      "Save 314\n",
      "Save 318\n",
      "Save 321\n",
      "Save 331\n",
      "Save 338\n",
      "Save 340\n",
      "Save 342\n",
      "Save 347\n",
      "Save 351\n",
      "Save 352\n",
      "Save 353\n",
      "Save 357\n",
      "Save 367\n",
      "Save 379\n",
      "Save 384\n",
      "Save 388\n",
      "Save 390\n",
      "Save 392\n",
      "Save 394\n",
      "Save 398\n",
      "Save 399\n",
      "Save 401\n",
      "Save 405\n",
      "Save 409\n",
      "Save 411\n",
      "Save 414\n",
      "Save 415\n",
      "Save 419\n",
      "Save 427\n",
      "Save 431\n",
      "Save 436\n",
      "Save 438\n",
      "Save 440\n",
      "Save 445\n",
      "Save 454\n",
      "Save 456\n",
      "Save 466\n",
      "Save 469\n",
      "Save 481\n",
      "Save 484\n",
      "Save 492\n",
      "Save 494\n",
      "Save 498\n",
      "ASR_H2_TRANFORMED_OUT\n",
      "Save 0\n",
      "Save 4\n",
      "Save 5\n",
      "Save 6\n",
      "Save 7\n",
      "Save 8\n",
      "Save 9\n",
      "Save 11\n",
      "Save 12\n",
      "Save 13\n",
      "Save 15\n",
      "Save 18\n",
      "Save 20\n",
      "Save 24\n",
      "Save 27\n",
      "Save 29\n",
      "Save 33\n",
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      "Save 36\n",
      "Save 39\n",
      "Save 42\n",
      "Save 44\n",
      "Save 46\n",
      "Save 47\n",
      "Save 48\n",
      "Save 50\n",
      "Save 55\n",
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      "Save 59\n",
      "Save 61\n",
      "Save 63\n",
      "Save 64\n",
      "Save 65\n",
      "Save 66\n",
      "Save 70\n",
      "Save 71\n",
      "Save 74\n",
      "Save 75\n",
      "Save 77\n",
      "Save 79\n",
      "Save 81\n",
      "Save 86\n",
      "Save 90\n",
      "Save 93\n",
      "Save 96\n",
      "Save 98\n",
      "Save 100\n",
      "Save 103\n",
      "Save 107\n",
      "Save 108\n",
      "Save 109\n",
      "Save 110\n",
      "Save 112\n",
      "Save 114\n",
      "Save 117\n",
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      "Save 119\n",
      "Save 122\n",
      "Save 123\n",
      "Save 125\n",
      "Save 127\n",
      "Save 129\n",
      "Save 131\n",
      "Save 133\n",
      "Save 135\n",
      "Save 138\n",
      "Save 139\n",
      "Save 142\n",
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      "Save 149\n",
      "Save 151\n",
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      "Save 161\n",
      "Save 162\n",
      "Save 164\n",
      "Save 166\n",
      "Save 168\n",
      "Save 172\n",
      "Save 174\n",
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      "Save 181\n",
      "Save 182\n",
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      "Save 192\n",
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      "Save 194\n",
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      "Save 218\n",
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      "Save 232\n",
      "Save 233\n",
      "Save 235\n",
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      "Save 241\n",
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      "Save 244\n",
      "Save 245\n",
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      "Save 256\n",
      "Save 261\n",
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      "Save 263\n",
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      "Save 267\n",
      "Save 269\n",
      "Save 277\n",
      "Save 280\n",
      "Save 282\n",
      "Save 287\n",
      "Save 290\n",
      "Save 294\n",
      "Save 295\n",
      "Save 296\n",
      "Save 298\n",
      "Save 303\n",
      "Save 306\n",
      "Save 308\n",
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      "Save 312\n",
      "Save 319\n",
      "Save 323\n",
      "Save 329\n",
      "Save 332\n",
      "Save 335\n",
      "Save 345\n",
      "Save 348\n",
      "Save 349\n",
      "Save 353\n",
      "Save 355\n",
      "Save 363\n",
      "Save 364\n",
      "Save 365\n",
      "Save 371\n",
      "Save 372\n",
      "Save 373\n",
      "Save 374\n",
      "Save 375\n",
      "Save 376\n",
      "Save 378\n",
      "Save 381\n",
      "Save 385\n",
      "Save 386\n",
      "Save 388\n",
      "Save 391\n",
      "Save 396\n",
      "Save 400\n",
      "Save 405\n",
      "Save 409\n",
      "Save 411\n",
      "Save 418\n",
      "Save 421\n",
      "Save 427\n",
      "Save 428\n",
      "Save 431\n",
      "Save 432\n",
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      "Save 437\n",
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      "Save 443\n",
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      "Save 453\n",
      "Save 457\n",
      "Save 460\n",
      "Save 461\n",
      "Save 465\n",
      "Save 466\n",
      "Save 477\n",
      "Save 484\n",
      "Save 486\n",
      "Save 489\n",
      "Save 496\n",
      "Save 498\n",
      "ASR_H1_TRANFORMED_OUT\n",
      "Save 0\n",
      "Save 4\n",
      "Save 5\n",
      "Save 6\n",
      "Save 7\n",
      "Save 9\n",
      "Save 11\n",
      "Save 14\n",
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      "Save 48\n",
      "Save 50\n",
      "Save 51\n",
      "Save 54\n",
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      "Save 65\n",
      "Save 66\n",
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      "Save 73\n",
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      "Save 86\n",
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      "Save 93\n",
      "Save 94\n",
      "Save 95\n",
      "Save 99\n",
      "Save 101\n",
      "Save 102\n",
      "Save 103\n",
      "Save 106\n",
      "Save 107\n",
      "Save 109\n",
      "Save 111\n",
      "Save 113\n",
      "Save 115\n",
      "Save 117\n",
      "Save 118\n",
      "Save 120\n",
      "Save 126\n",
      "Save 127\n",
      "Save 129\n",
      "Save 133\n",
      "Save 134\n",
      "Save 135\n",
      "Save 139\n",
      "Save 140\n",
      "Save 144\n",
      "Save 148\n",
      "Save 150\n",
      "Save 153\n",
      "Save 155\n",
      "Save 156\n",
      "Save 157\n",
      "Save 159\n",
      "Save 160\n",
      "Save 162\n",
      "Save 164\n",
      "Save 168\n",
      "Save 169\n",
      "Save 171\n",
      "Save 175\n",
      "Save 177\n",
      "Save 180\n",
      "Save 182\n",
      "Save 183\n",
      "Save 186\n",
      "Save 189\n",
      "Save 191\n",
      "Save 195\n",
      "Save 198\n",
      "Save 200\n",
      "Save 201\n",
      "Save 202\n",
      "Save 206\n",
      "Save 208\n",
      "Save 209\n",
      "Save 213\n",
      "Save 214\n",
      "Save 215\n",
      "Save 217\n",
      "Save 219\n",
      "Save 220\n",
      "Save 234\n",
      "Save 236\n",
      "Save 237\n",
      "Save 239\n",
      "Save 241\n",
      "Save 244\n",
      "Save 249\n",
      "Save 250\n",
      "Save 251\n",
      "Save 254\n",
      "Save 256\n",
      "Save 257\n",
      "Save 261\n",
      "Save 263\n",
      "Save 264\n",
      "Save 265\n",
      "Save 269\n",
      "Save 271\n",
      "Save 273\n",
      "Save 276\n",
      "Save 277\n",
      "Save 279\n",
      "Save 280\n",
      "Save 287\n",
      "Save 288\n",
      "Save 290\n",
      "Save 291\n",
      "Save 294\n",
      "Save 296\n",
      "Save 301\n",
      "Save 307\n",
      "Save 309\n",
      "Save 310\n",
      "Save 315\n",
      "Save 318\n",
      "Save 322\n",
      "Save 325\n",
      "Save 328\n",
      "Save 330\n",
      "Save 331\n",
      "Save 332\n",
      "Save 334\n",
      "Save 339\n",
      "Save 341\n",
      "Save 342\n",
      "Save 343\n",
      "Save 352\n",
      "Save 357\n",
      "Save 361\n",
      "Save 364\n",
      "Save 369\n",
      "Save 371\n",
      "Save 374\n",
      "Save 381\n",
      "Save 384\n",
      "Save 385\n",
      "Save 389\n",
      "Save 392\n",
      "Save 393\n",
      "Save 397\n",
      "Save 399\n",
      "Save 401\n",
      "Save 403\n",
      "Save 407\n",
      "Save 409\n",
      "Save 410\n",
      "Save 411\n",
      "Save 413\n",
      "Save 416\n",
      "Save 421\n",
      "Save 425\n",
      "Save 427\n",
      "Save 435\n",
      "Save 438\n",
      "Save 440\n",
      "Save 441\n",
      "Save 446\n",
      "Save 447\n",
      "Save 449\n",
      "Save 450\n",
      "Save 451\n",
      "Save 452\n",
      "Save 454\n",
      "Save 458\n",
      "Save 460\n",
      "Save 461\n",
      "Save 463\n",
      "Save 470\n",
      "Save 475\n",
      "Save 480\n",
      "Save 481\n",
      "Save 482\n",
      "Save 483\n",
      "Save 488\n",
      "Save 494\n",
      "Save 497\n",
      "Save 499\n",
      "TRS_AE_OUT\n",
      "Save 0\n",
      "Save 3\n",
      "Save 4\n",
      "Save 5\n",
      "Save 6\n",
      "Save 7\n",
      "Save 8\n",
      "Save 10\n",
      "Save 12\n",
      "Save 15\n",
      "Save 17\n",
      "Save 18\n",
      "Save 20\n",
      "Save 23\n",
      "Save 28\n",
      "Save 29\n",
      "Save 30\n",
      "Save 33\n",
      "Save 34\n",
      "Save 37\n",
      "Save 40\n",
      "Save 42\n",
      "Save 46\n",
      "Save 47\n",
      "Save 49\n",
      "Save 54\n",
      "Save 55\n",
      "Save 57\n",
      "Save 58\n",
      "Save 60\n",
      "Save 63\n",
      "Save 67\n",
      "Save 69\n",
      "Save 71\n",
      "Save 74\n",
      "Save 76\n",
      "Save 79\n",
      "Save 84\n",
      "Save 86\n",
      "Save 88\n",
      "Save 94\n",
      "Save 96\n",
      "Save 104\n",
      "Save 105\n",
      "Save 107\n",
      "Save 108\n",
      "Save 112\n",
      "Save 116\n",
      "Save 118\n",
      "Save 121\n",
      "Save 127\n",
      "Save 130\n",
      "Save 131\n",
      "Save 132\n",
      "Save 135\n",
      "Save 136\n",
      "Save 141\n",
      "Save 144\n",
      "Save 145\n",
      "Save 147\n",
      "Save 148\n",
      "Save 149\n",
      "Save 152\n",
      "Save 154\n",
      "Save 156\n",
      "Save 157\n",
      "Save 159\n",
      "Save 163\n",
      "Save 165\n",
      "Save 167\n",
      "Save 172\n",
      "Save 176\n",
      "Save 178\n",
      "Save 180\n",
      "Save 190\n",
      "Save 192\n",
      "Save 197\n",
      "Save 198\n",
      "Save 201\n",
      "Save 203\n",
      "Save 210\n",
      "Save 215\n",
      "Save 218\n",
      "Save 220\n",
      "Save 225\n",
      "Save 229\n",
      "Save 239\n",
      "Save 241\n",
      "Save 243\n",
      "Save 246\n",
      "Save 257\n",
      "Save 259\n",
      "Save 266\n",
      "Save 270\n",
      "Save 272\n",
      "Save 276\n",
      "Save 279\n",
      "Save 281\n",
      "Save 286\n",
      "Save 288\n",
      "Save 289\n",
      "Save 292\n",
      "Save 294\n",
      "Save 299\n",
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      "Save 310\n",
      "Save 311\n",
      "Save 312\n",
      "Save 316\n",
      "Save 319\n",
      "Save 328\n",
      "Save 336\n",
      "Save 337\n",
      "Save 340\n",
      "Save 350\n",
      "Save 353\n",
      "Save 354\n",
      "Save 358\n",
      "Save 360\n",
      "Save 367\n",
      "Save 368\n",
      "Save 374\n",
      "Save 378\n",
      "Save 384\n",
      "Save 385\n",
      "Save 388\n",
      "Save 391\n",
      "Save 393\n",
      "Save 401\n",
      "Save 405\n",
      "Save 412\n",
      "Save 418\n",
      "Save 419\n",
      "Save 421\n",
      "Save 428\n",
      "Save 429\n",
      "Save 431\n",
      "Save 433\n",
      "Save 436\n",
      "Save 439\n",
      "Save 440\n",
      "Save 443\n",
      "Save 455\n",
      "Save 456\n",
      "Save 461\n",
      "Save 466\n",
      "Save 467\n",
      "Save 471\n",
      "Save 475\n",
      "Save 476\n",
      "Save 478\n",
      "Save 482\n",
      "Save 487\n",
      "Save 494\n",
      "Save 495\n",
      "Save 496\n"
     ]
    }
   ],
   "source": [
    "out_db=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\",writeback=True)\n",
    "nb_epochs=500\n",
    "for key in keys:\n",
    "    print key\n",
    "    try:\n",
    "        x_train=corps[\"ASR_SPARSE\"][\"TRAIN\"].todense()\n",
    "        x_dev=corps[key][\"DEV\"].todense()\n",
    "        x_test=corps[key][\"TEST\"].todense()\n",
    "    except :\n",
    "        x_train=corps[\"ASR_SPARSE\"][\"TRAIN\"].todense()\n",
    "        x_dev=corps[key][\"DEV\"]\n",
    "        x_test=corps[key][\"TEST\"]\n",
    "\n",
    "    out_db[key]=mlp.train_mlp(x_train,y_train,x_dev,y_dev,x_test,y_test,[256,128,256],dropouts=[0.5,0,0],sgd=Adam(lr=0.0001),epochs=nb_epochs,batch_size=8,save_pred=True,keep_histo=True,fit_verbose=0)\n",
    "out_db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ASR_H1_TRANFORMED_OUT', 'ASR_H2_TRANFORMED_OUT', 'TRS_AE_OUT', 'TRS_SPARSE', 'ASR_SPARSE']\n"
     ]
    }
   ],
   "source": [
    "out_db=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")\n",
    "print out_db.keys()\n",
    "out_db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASR_H1_TRANFORMED_OUT 0.697\n",
      "ASR_H2_TRANFORMED_OUT 0.682\n",
      "TRS_AE_OUT 0.838\n",
      "TRS_SPARSE 0.841\n",
      "ASR_SPARSE 0.78\n"
     ]
    }
   ],
   "source": [
    "data=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")\n",
    "scores={}\n",
    "#del scores_ordoned\n",
    "for key,table in data.iteritems():\n",
    "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)\n",
    "    print key,scores[key]\n",
    "    pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()\n",
    "data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASR_H1_TRANFORMED_OUT 0.688\n",
      "ASR_H2_TRANFORMED_OUT 0.654\n",
      "TRS_AE_OUT 0.832\n",
      "TRS_SPARSE 0.832\n",
      "ASR_SPARSE 0.734\n"
     ]
    }
   ],
   "source": [
    "data=shelve.open(\"scores/RAW_TRS_TRAIN.shelve\")\n",
    "scores={}\n",
    "#del scores_ordoned\n",
    "for key,table in data.iteritems():\n",
    "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)\n",
    "    print key,scores[key]\n",
    "    pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()\n",
    "data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASR_H1_TRANFORMED_OUT 0.697\n",
      "ASR_H2_TRANFORMED_OUT 0.682\n",
      "TRS_AE_OUT 0.838\n",
      "TRS_SPARSE 0.841\n",
      "ASR_SPARSE 0.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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blp7dOXPInLRtSzH6yEhqiXXpQtvOnevdiXsT8ZEjqVI+doyun1RKrdR773Ud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\niUspXavI2Sq+VVQnWBOHoKPcE3/yBO+Z9R6SiST7ju4jUZm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc258328890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G4jxPtnohj3RwNS5WXERWmWvod+edpLbLyqgjX7iQriOTud5poFPc4+LonURF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\nRJqYgriISBNTEBcRaWL/D0/4iN+zdJOWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc26840aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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O82XZsWV58MJBRkc7GFo1lj4dB7HEyBDKoFL0HupDr/csLP1WM+J/oWzRbSdHbxxNiRA2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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc251f4ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qCPg1AF4eXhj33Dh4eniiQekGyOudN03d+gT3QWRsJK7dvpa4b6w+thqnrp/C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc251dff390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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41oGrgytOPTiFt+u8nWte3xz9Bl6PvbBnyB4M3D4Qpx+ehlKthIpU6FW/F/YP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\nuUqAGdlr2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc252bb85d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=shelve.open(\"scores/RAW_ASR_TRAIN.shelve\")\n",
    "scores={}\n",
    "#del scores_ordoned\n",
    "for key,table in data.iteritems():\n",
    "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)\n",
    "    print key,scores[key]\n",
    "    pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()\n",
    "data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}