{
   "cells": [
    {
     "cell_type": "code",
     "execution_count": 1,
     "metadata": {
      "collapsed": true,
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "source": [
      "import pandas
  ",
      "import shelve
  ",
      "import matplotlib
  ",
      "import matplotlib.pyplot as plt
  ",
      "import numpy as np
  ",
      "import itertools
  ",
      "import glob
  ",
      "from sklearn.metrics import precision_recall_fscore_support
  ",
      "import utils
  ",
      "import numpy as np"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 2,
     "metadata": {
      "collapsed": true,
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "source": [
      "%matplotlib inline"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 5,
     "metadata": {
      "collapsed": true,
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "source": [
      "def show_network_TRANS(scores,zero=10,unite=120,tailleu=100,title=\"Transform NEwtork\"):
  ",
      "    plt.figure(figsize=(20,10))
  ",
      "    plt.axes()
  ",
      "    plt.title(title)
  ",
      "    #zero=10
  ",
      "    #unite=200
  ",
      "    #tailleu=100
  ",
      "    inside=0
  ",
      "    rectangle = plt.Rectangle((zero, zero), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_SPARSE\" in scores:
  ",
      "        plt.text(zero+inside,zero+inside,scores[\"ASR_SPARSE\"],color=\"white\")
  ",
      "        #plt.text(zero+inside+60,zero+inside,\"0.58\",color=\"red\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H1\" in scores:
  ",
      "        plt.text(zero+inside,zero+1*unite+inside,scores[\"ASR_AE_H1\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+2*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H2\" in scores:
  ",
      "        plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AE_H2\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+3*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_OUT\" in scores:
  ",
      "        plt.text(zero+inside,zero+3*unite+inside,scores[\"ASR_AE_OUT\"],color=\"white\")
  ",
      " 
  ",
      "    
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_SPARSE\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+inside,scores[\"TRS_SPARSE\"],color=\"black\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero+1*unite), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_AE_H1\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+1*unite+inside,scores[\"TRS_AE_H1\"],color=\"black\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero+2*unite), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_AE_H2\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+2*unite+inside,scores[\"TRS_AE_H2\"],color=\"black\")
  ",
      "    if \"ASR_H1_TRANFORMED_TRSH2\" in scores:
  ",
      "        plt.text(zero+3*unite+tailleu/2,zero+2*unite+inside,scores[\"ASR_H1_TRANFORMED_TRSH2\"],color=\"red\")   
  ",
      "    if \"ASR_H2_TRANFORMED_TRSH2\" in scores:
  ",
      "        plt.text(zero+3*unite-tailleu/2,zero+2*unite+inside,scores[\"ASR_H2_TRANFORMED_TRSH2\"],color=\"green\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero+3*unite), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_AE_OUT\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+3*unite+inside,scores[\"TRS_AE_OUT\"],color=\"black\")
  ",
      "    if \"ASR_H1_TRANFORMED_OUT\" in scores:
  ",
      "        plt.text(zero+3*unite+tailleu/2,zero+3*unite+inside,scores[\"ASR_H1_TRANFORMED_OUT\"],color=\"red\")
  ",
      "    if \"ASR_H2_TRANFORMED_OUT\" in scores:
  ",
      "        plt.text(zero+3*unite-tailleu/2,zero+3*unite+inside,scores[\"ASR_H2_TRANFORMED_OUT\"],color=\"green\")
  ",
      "
  ",
      "    rectangle = plt.Rectangle((zero+1*unite, zero+1*unite), tailleu, tailleu, fc='r')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_H1_TRANSFORMED_W1\" in scores:
  ",
      "        plt.text(zero+1*unite+inside,zero+1*unite+inside,scores[\"ASR_H1_TRANSFORMED_W1\"],color=\"black\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+1*unite, zero+2*unite), tailleu, tailleu, fc='green')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_H2_TRANSFORMED_W1\" in scores:
  ",
      "        plt.text(zero+1*unite+inside,zero+2*unite+inside,scores[\"ASR_H2_TRANSFORMED_W1\"],color=\"white\")
  ",
      " 
  ",
      "
  ",
      "    rectangle = plt.Rectangle((zero+2*unite, zero+1*unite), tailleu, tailleu, fc='r')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_H1_TRANSFORMED_W2\" in scores:
  ",
      "        plt.text(zero+2*unite+inside,zero+1*unite+inside,scores[\"ASR_H1_TRANSFORMED_W2\"],color=\"black\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+2*unite, zero+2*unite), tailleu, tailleu, fc='green')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_H2_TRANSFORMED_W2\" in scores:
  ",
      "        plt.text(zero+2*unite+inside,zero+2*unite+inside,scores[\"ASR_H2_TRANSFORMED_W2\"],color=\"white\")
  ",
      "
  ",
      "    plt.axis('scaled')
  ",
      "    plt.show()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 6,
     "metadata": {
      "collapsed": true,
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "source": [
      "
  ",
      "def show_network_RSPE(scores,zero=10,unite=120,tailleu=100,title=\"REAL SPE NEwtork\"):
  ",
      "    plt.figure(figsize=(20,10))
  ",
      "    plt.axes()
  ",
      "    plt.title(title)
  ",
      "    #zero=10
  ",
      "    #unite=200
  ",
      "    #tailleu=100
  ",
      "    inside=0
  ",
      "    rectangle = plt.Rectangle((zero, zero), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR\" in scores:
  ",
      "        plt.text(zero+inside,zero+inside,scores[\"ASR\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H1\" in scores:
  ",
      "        plt.text(zero+inside,zero+1*unite+inside,scores[\"ASR_AE_H1\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+2*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H2\" in scores:
  ",
      "        plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AE_H2\"],color=\"white\")
  ",
      "    if \"ASR_AEH2_SPARSE\" in scores :
  ",
      "         plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AEH2_SPARSE\"],color=\"white\")
  ",
      "    rectangle = plt.Rectangle((zero, zero+3*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_OUT\" in scores:
  ",
      "        plt.text(zero+inside,zero+3*unite+inside,scores[\"ASR_AE_OUT\"],color=\"white\")
  ",
      "    if \"ASR_AEOUT_SPARSE\" in scores :
  ",
      "         plt.text(zero+inside,zero+3*unite+inside,scores[\"ASR_AEOUT_SPARSE\"],color=\"white\")
  ",
      "    
  ",
      "   
  ",
      "    plt.axis('scaled')
  ",
      "    plt.show()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 7,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "#'', '', '', '', '', '', '', '', 'ASR_W1_TRANSFORMED', 'ASR_AE_H1']
  ",
      "
  ",
      "def show_network_UNFIXED(scores,zero=10,unite=120,tailleu=100,title=\"Transform NEwtork\"):
  ",
      "    plt.figure(figsize=(20,10))
  ",
      "    plt.axes()
  ",
      "    plt.title(title)
  ",
      "    #zero=10
  ",
      "    #unite=200
  ",
      "    #tailleu=100
  ",
      "    inside=0
  ",
      "    rectangle = plt.Rectangle((zero, zero), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_SPARSE\" in scores:
  ",
      "        plt.text(zero+inside,zero+inside,scores[\"ASR_SPARSE\"],color=\"white\")
  ",
      "        #plt.text(zero+inside+60,zero+inside,\"0.58\",color=\"red\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H1\" in scores:
  ",
      "        plt.text(zero+inside,zero+1*unite+inside,scores[\"ASR_AE_H1\"],color=\"white\")
  ",
      "    if \"ASR_H1_TRANSFORMED\" in scores:
  ",
      "        plt.text(zero+inside+tailleu,zero+1*unite+inside,scores[\"ASR_H1_TRANSFORMED\"],color=\"green\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+2*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_OUT\" in scores:
  ",
      "        plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AE_OUT\"],color=\"white\")
  ",
      "
  ",
      "    
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_SPARSE\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+inside,scores[\"TRS_SPARSE\"],color=\"black\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero+1*unite), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_AE_H1\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+1*unite+inside,scores[\"TRS_AE_H1\"],color=\"black\")
  ",
      "    if \"ASR_H2_TRANSFORMED\" in scores:
  ",
      "        plt.text(zero+3*unite+inside-tailleu,zero+1*unite+inside,scores[\"ASR_H2_TRANSFORMED\"],color=\"green\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+3*unite, zero+2*unite), tailleu, tailleu, fc='y')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"TRS_AE_OUT\" in scores:
  ",
      "        plt.text(zero+3*unite+inside,zero+2*unite+inside,scores[\"TRS_AE_OUT\"],color=\"black\")
  ",
      "    if \"ASR_TRANFORMED_OUT\" in scores:
  ",
      "        plt.text(zero+3*unite+inside-tailleu,zero+2*unite+inside,scores[\"ASR_TRANFORMED_OUT\"],color=\"green\")
  ",
      " 
  ",
      "
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero+1*unite, zero+1*unite), tailleu, tailleu, fc='green')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_W1_TRANSFORMED\" in scores:
  ",
      "        plt.text(zero+1*unite+inside,zero+1*unite+inside,scores[\"ASR_W1_TRANSFORMED\"],color=\"white\")
  ",
      "
  ",
      "
  ",
      "    plt.axis('scaled')
  ",
      "    plt.show()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 8,
     "metadata": {
      "collapsed": true,
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "source": [
      "#['ASR_H1_TRANFORMED_OUT', 'ASR_H2_TRANFORMED_OUT', 'TRS_AE_OUT', 'TRS_SPARSE', 'ASR_SPARSE']
  ",
      "def show_network_RAW(scores,zero=10,unite=120,tailleu=100,title=\"RAW NEwtork\"):
  ",
      "    plt.figure(figsize=(20,10))
  ",
      "    plt.axes()
  ",
      "    plt.title(title)
  ",
      "    #zero=10
  ",
      "    #unite=200
  ",
      "    #tailleu=100
  ",
      "    inside=0
  ",
      "    rectangle = plt.Rectangle((zero, zero), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"SPARSE\" in scores:
  ",
      "        plt.text(zero+inside,zero+inside,scores[\"ASR\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H1\" in scores:
  ",
      "        plt.text(zero+inside,zero+1*unite+inside,scores[\"ASR_AE_H1\"],color=\"white\")
  ",
      " 
  ",
      "    rectangle = plt.Rectangle((zero, zero+2*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_H2\" in scores:
  ",
      "        plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AE_H2\"],color=\"white\")
  ",
      "    if \"ASR_AEH2_SPARSE\" in scores :
  ",
      "         plt.text(zero+inside,zero+2*unite+inside,scores[\"ASR_AEH2_SPARSE\"],color=\"white\")
  ",
      "    rectangle = plt.Rectangle((zero, zero+3*unite), tailleu, tailleu, fc='b')
  ",
      "    plt.gca().add_patch(rectangle)
  ",
      "    if \"ASR_AE_OUT\" in scores:
  ",
      "        plt.text(zero+inside,zero+3*unite+inside,scores[\"ASR_AE_OUT\"],color=\"white\")
  ",
      "    if \"ASR_AEOUT_SPARSE\" in scores :
  ",
      "         plt.text(zero+inside,zero+3*unite+inside,scores[\"ASR_AEOUT_SPARSE\"],color=\"white\")
  ",
      "    
  ",
      "   
  ",
      "    plt.axis('scaled')
  ",
      "    plt.show()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 152,
     "metadata": {
      "collapsed": false,
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH_H50_DO.shelve
  "
       ]
      },
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79574b4650>"
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79605d8dd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/MINIAE_TANH_H100_DO50.shelve
  "
       ]
      },
      {
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795e212890>"
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f796a9ad5d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/MINIAE_TANH_H100_DOmlp.shelve
  "
       ]
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795eee7510>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH_TFIDF_H30_DO.shelve
  "
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  6OIAAAAASUVORK5CYII=
  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79601db210>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "scores_ordoned={}
  ",
      "for i in glob.glob(\"scores/*DO*.bak\"):
  ",
      "    #if \"MINIAE\" not in i :
  ",
      "     #   continue
  ",
      "    scores={}
  ",
      "    print i[:-4]
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "        if key not in scores_ordoned:
  ",
      "            scores_ordoned[key]=[scores[key]]
  ",
      "        else :
  ",
      "            scores_ordoned[key].append(scores[key])
  ",
      "            
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][0] ],[x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][1] ])).plot()
  ",
      "    data.close()
  ",
      "    show_network_TRANS(scores,title=i,unite=200)
  ",
      "    #except:
  ",
      "    #    print \"C4EST LA MERDE\",i"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 153,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
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        "text/plain": [
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795f579890>"
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        "text/plain": [
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       },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f796883cc50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "
  ",
      "for i in glob.glob(\"real_spe_scores/*DO*.bak\"):
  ",
      "    scores={}
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "    show_network_RSPE(scores,title=i)
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()
  ",
      "    data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 139,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.bak\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.dat\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.dir\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.bak\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.dat\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.dir\r
  "
       ]
      }
     ],
     "source": [
      "ls UNFIXED_TRANS_scores"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 154,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "UNFIXED_TRANS_scores/DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve
  ",
        "['TRS_AE_H1', 'TRS_AE_OUT', 'TRS_SPARSE', 'ASR_AE_OUT', 'ASR_H2_TRANSFORMED', 'ASR_SPARSE', 'ASR_TRANFORMED_OUT', 'ASR_H1_TRANSFORMED', 'ASR_W1_TRANSFORMED', 'ASR_AE_H1']
  "
       ]
      },
      {
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79518de350>"
        ]
       },
       "metadata": {},
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      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79650b2d50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "scores_ordoned={}
  ",
      "for i in glob.glob(\"UNFIXED_TRANS_scores/*DO*.bak\"):
  ",
      "    scores={}
  ",
      "    print i[:-4]
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    print data.keys()
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "        if key not in scores_ordoned:
  ",
      "            scores_ordoned[key]=[scores[key]]
  ",
      "        else :
  ",
      "            scores_ordoned[key].append(scores[key])
  ",
      "            
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_W1_TRANSFORMED\"][0] ],[x[0] for x in data[\"ASR_W1_TRANSFORMED\"][1] ])).plot()
  ",
      "    data.close()
  ",
      "    show_network_UNFIXED(scores,title=i,unite=200)
  ",
      "    #except:
  ",
      "    #    print \"C4EST LA MERDE\",i"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": []
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "collapsed": false
     },
     "source": [
      "# Ci dessous  Mes tests rien de super interessant"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "pred_train=data[\"TRS_AE_H2\"][2].pred_train
  ",
      "y_pred_train=np.argmax(pred_train,axis=1)
  ",
      "
  ",
      "pred_dev=data[\"TRS_AE_H2\"][2].pred_dev
  ",
      "y_pred_dev=np.argmax(pred_dev,axis=1)
  ",
      "
  ",
      "pred_test=data[\"TRS_AE_H2\"][2].pred_test
  ",
      "y_pred_test=np.argmax(pred_test,axis=1)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "[0,1,2]*3"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "corps=shelve.open(\"models/DECODA_AE_TANH_MINIBIN.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "y_train=corps[\"LABEL\"][\"TRAIN\"].apply(utils.select).values
  ",
      "y_dev=corps[\"LABEL\"][\"DEV\"].apply(utils.select).values
  ",
      "y_test=corps[\"LABEL\"][\"TEST\"].apply(utils.select).values"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "y_pred_train+1"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "y_train"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "precision_recall_fscore_support(y_train,y_pred_train+1,average=\"micro\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "precision_recall_fscore_support(y_dev,y_pred_dev+1,average=\"micro\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "precision_recall_fscore_support(y_test,y_pred_test+1,average=\"micro\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"scores/DECODA_AE_TANH_MINIBIN.shelve\")
  ",
      "#data.close()
  ",
      "data"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 71,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"./scores/DECODA_MINIAE_TANH.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 72,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f7966f3cb90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "show_network_TRANS(scores,title=i)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 18,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "data.keys()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 73,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968041f10>"
        ]
       },
       "execution_count": 73,
       "metadata": {},
       "output_type": "execute_result"
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f7965dfab10>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 53,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "histo=data[\"ASR_AE_H1\"][3]"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 69,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 61,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "corps=data=shelve.open(\"./models/DECODA_MINIAE_TANH.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 66,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/html": [
         "<div>
  ",
         "<table border=\"1\" class=\"dataframe\">
  ",
         "  <thead>
  ",
         "    <tr style=\"text-align: right;\">
  ",
         "      <th></th>
  ",
         "      <th>0</th>
  ",
         "      <th>1</th>
  ",
         "      <th>2</th>
  ",
         "      <th>3</th>
  ",
         "      <th>4</th>
  ",
         "      <th>5</th>
  ",
         "      <th>6</th>
  ",
         "      <th>7</th>
  ",
         "      <th>8</th>
  ",
         "      <th>9</th>
  ",
         "      <th>...</th>
  ",
         "      <th>40</th>
  ",
         "      <th>41</th>
  ",
         "      <th>42</th>
  ",
         "      <th>43</th>
  ",
         "      <th>44</th>
  ",
         "      <th>45</th>
  ",
         "      <th>46</th>
  ",
         "      <th>47</th>
  ",
         "      <th>48</th>
  ",
         "      <th>49</th>
  ",
         "    </tr>
  ",
         "  </thead>
  ",
         "  <tbody>
  ",
         "    <tr>
  ",
         "      <th>count</th>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>...</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "      <td>740.000000</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>mean</th>
  ",
         "      <td>0.000490</td>
  ",
         "      <td>0.003229</td>
  ",
         "      <td>0.000185</td>
  ",
         "      <td>-0.003558</td>
  ",
         "      <td>-0.000550</td>
  ",
         "      <td>0.004855</td>
  ",
         "      <td>-0.000353</td>
  ",
         "      <td>-0.004828</td>
  ",
         "      <td>0.000823</td>
  ",
         "      <td>-0.004653</td>
  ",
         "      <td>...</td>
  ",
         "      <td>-0.002893</td>
  ",
         "      <td>0.001393</td>
  ",
         "      <td>0.000034</td>
  ",
         "      <td>-0.000325</td>
  ",
         "      <td>-0.000867</td>
  ",
         "      <td>-0.001082</td>
  ",
         "      <td>-0.004496</td>
  ",
         "      <td>-0.005450</td>
  ",
         "      <td>0.006736</td>
  ",
         "      <td>0.001526</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>std</th>
  ",
         "      <td>0.078052</td>
  ",
         "      <td>0.076671</td>
  ",
         "      <td>0.080473</td>
  ",
         "      <td>0.080584</td>
  ",
         "      <td>0.080445</td>
  ",
         "      <td>0.083181</td>
  ",
         "      <td>0.080656</td>
  ",
         "      <td>0.080346</td>
  ",
         "      <td>0.078400</td>
  ",
         "      <td>0.084584</td>
  ",
         "      <td>...</td>
  ",
         "      <td>0.075171</td>
  ",
         "      <td>0.082434</td>
  ",
         "      <td>0.076875</td>
  ",
         "      <td>0.078159</td>
  ",
         "      <td>0.081996</td>
  ",
         "      <td>0.080196</td>
  ",
         "      <td>0.084964</td>
  ",
         "      <td>0.083766</td>
  ",
         "      <td>0.081879</td>
  ",
         "      <td>0.081213</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>min</th>
  ",
         "      <td>-0.222832</td>
  ",
         "      <td>-0.210684</td>
  ",
         "      <td>-0.203158</td>
  ",
         "      <td>-0.220618</td>
  ",
         "      <td>-0.251787</td>
  ",
         "      <td>-0.245800</td>
  ",
         "      <td>-0.319214</td>
  ",
         "      <td>-0.257791</td>
  ",
         "      <td>-0.256614</td>
  ",
         "      <td>-0.269018</td>
  ",
         "      <td>...</td>
  ",
         "      <td>-0.205919</td>
  ",
         "      <td>-0.272927</td>
  ",
         "      <td>-0.216589</td>
  ",
         "      <td>-0.232542</td>
  ",
         "      <td>-0.239660</td>
  ",
         "      <td>-0.215522</td>
  ",
         "      <td>-0.237121</td>
  ",
         "      <td>-0.217695</td>
  ",
         "      <td>-0.253268</td>
  ",
         "      <td>-0.216798</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>25%</th>
  ",
         "      <td>-0.049280</td>
  ",
         "      <td>-0.051630</td>
  ",
         "      <td>-0.055798</td>
  ",
         "      <td>-0.060583</td>
  ",
         "      <td>-0.053978</td>
  ",
         "      <td>-0.050834</td>
  ",
         "      <td>-0.055398</td>
  ",
         "      <td>-0.058382</td>
  ",
         "      <td>-0.045658</td>
  ",
         "      <td>-0.061431</td>
  ",
         "      <td>...</td>
  ",
         "      <td>-0.052172</td>
  ",
         "      <td>-0.051161</td>
  ",
         "      <td>-0.054269</td>
  ",
         "      <td>-0.053610</td>
  ",
         "      <td>-0.054938</td>
  ",
         "      <td>-0.056238</td>
  ",
         "      <td>-0.066248</td>
  ",
         "      <td>-0.068832</td>
  ",
         "      <td>-0.048655</td>
  ",
         "      <td>-0.054703</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>50%</th>
  ",
         "      <td>-0.003148</td>
  ",
         "      <td>-0.002347</td>
  ",
         "      <td>0.003714</td>
  ",
         "      <td>-0.000112</td>
  ",
         "      <td>-0.003216</td>
  ",
         "      <td>0.007316</td>
  ",
         "      <td>-0.000941</td>
  ",
         "      <td>-0.004883</td>
  ",
         "      <td>0.007292</td>
  ",
         "      <td>-0.004308</td>
  ",
         "      <td>...</td>
  ",
         "      <td>-0.004255</td>
  ",
         "      <td>0.000609</td>
  ",
         "      <td>-0.001102</td>
  ",
         "      <td>-0.001316</td>
  ",
         "      <td>-0.002811</td>
  ",
         "      <td>0.000356</td>
  ",
         "      <td>-0.000773</td>
  ",
         "      <td>-0.008352</td>
  ",
         "      <td>0.006316</td>
  ",
         "      <td>-0.005236</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>75%</th>
  ",
         "      <td>0.053316</td>
  ",
         "      <td>0.051167</td>
  ",
         "      <td>0.051243</td>
  ",
         "      <td>0.054151</td>
  ",
         "      <td>0.044473</td>
  ",
         "      <td>0.058679</td>
  ",
         "      <td>0.054497</td>
  ",
         "      <td>0.044757</td>
  ",
         "      <td>0.052775</td>
  ",
         "      <td>0.050786</td>
  ",
         "      <td>...</td>
  ",
         "      <td>0.044183</td>
  ",
         "      <td>0.056505</td>
  ",
         "      <td>0.054361</td>
  ",
         "      <td>0.052624</td>
  ",
         "      <td>0.049942</td>
  ",
         "      <td>0.053420</td>
  ",
         "      <td>0.056256</td>
  ",
         "      <td>0.049547</td>
  ",
         "      <td>0.060626</td>
  ",
         "      <td>0.050734</td>
  ",
         "    </tr>
  ",
         "    <tr>
  ",
         "      <th>max</th>
  ",
         "      <td>0.202271</td>
  ",
         "      <td>0.241509</td>
  ",
         "      <td>0.310983</td>
  ",
         "      <td>0.215986</td>
  ",
         "      <td>0.279413</td>
  ",
         "      <td>0.259838</td>
  ",
         "      <td>0.224492</td>
  ",
         "      <td>0.234719</td>
  ",
         "      <td>0.283880</td>
  ",
         "      <td>0.251888</td>
  ",
         "      <td>...</td>
  ",
         "      <td>0.229372</td>
  ",
         "      <td>0.252486</td>
  ",
         "      <td>0.237473</td>
  ",
         "      <td>0.236919</td>
  ",
         "      <td>0.300617</td>
  ",
         "      <td>0.227033</td>
  ",
         "      <td>0.250967</td>
  ",
         "      <td>0.276590</td>
  ",
         "      <td>0.261683</td>
  ",
         "      <td>0.316733</td>
  ",
         "    </tr>
  ",
         "  </tbody>
  ",
         "</table>
  ",
         "<p>8 rows × 50 columns</p>
  ",
         "</div>"
        ],
        "text/plain": [
         "               0           1           2           3           4           5   \\
  ",
         "count  740.000000  740.000000  740.000000  740.000000  740.000000  740.000000   
  ",
         "mean     0.000490    0.003229    0.000185   -0.003558   -0.000550    0.004855   
  ",
         "std      0.078052    0.076671    0.080473    0.080584    0.080445    0.083181   
  ",
         "min     -0.222832   -0.210684   -0.203158   -0.220618   -0.251787   -0.245800   
  ",
         "25%     -0.049280   -0.051630   -0.055798   -0.060583   -0.053978   -0.050834   
  ",
         "50%     -0.003148   -0.002347    0.003714   -0.000112   -0.003216    0.007316   
  ",
         "75%      0.053316    0.051167    0.051243    0.054151    0.044473    0.058679   
  ",
         "max      0.202271    0.241509    0.310983    0.215986    0.279413    0.259838   
  ",
         "
  ",
         "               6           7           8           9      ...              40  \\
  ",
         "count  740.000000  740.000000  740.000000  740.000000     ...      740.000000   
  ",
         "mean    -0.000353   -0.004828    0.000823   -0.004653     ...       -0.002893   
  ",
         "std      0.080656    0.080346    0.078400    0.084584     ...        0.075171   
  ",
         "min     -0.319214   -0.257791   -0.256614   -0.269018     ...       -0.205919   
  ",
         "25%     -0.055398   -0.058382   -0.045658   -0.061431     ...       -0.052172   
  ",
         "50%     -0.000941   -0.004883    0.007292   -0.004308     ...       -0.004255   
  ",
         "75%      0.054497    0.044757    0.052775    0.050786     ...        0.044183   
  ",
         "max      0.224492    0.234719    0.283880    0.251888     ...        0.229372   
  ",
         "
  ",
         "               41          42          43          44          45          46  \\
  ",
         "count  740.000000  740.000000  740.000000  740.000000  740.000000  740.000000   
  ",
         "mean     0.001393    0.000034   -0.000325   -0.000867   -0.001082   -0.004496   
  ",
         "std      0.082434    0.076875    0.078159    0.081996    0.080196    0.084964   
  ",
         "min     -0.272927   -0.216589   -0.232542   -0.239660   -0.215522   -0.237121   
  ",
         "25%     -0.051161   -0.054269   -0.053610   -0.054938   -0.056238   -0.066248   
  ",
         "50%      0.000609   -0.001102   -0.001316   -0.002811    0.000356   -0.000773   
  ",
         "75%      0.056505    0.054361    0.052624    0.049942    0.053420    0.056256   
  ",
         "max      0.252486    0.237473    0.236919    0.300617    0.227033    0.250967   
  ",
         "
  ",
         "               47          48          49  
  ",
         "count  740.000000  740.000000  740.000000  
  ",
         "mean    -0.005450    0.006736    0.001526  
  ",
         "std      0.083766    0.081879    0.081213  
  ",
         "min     -0.217695   -0.253268   -0.216798  
  ",
         "25%     -0.068832   -0.048655   -0.054703  
  ",
         "50%     -0.008352    0.006316   -0.005236  
  ",
         "75%      0.049547    0.060626    0.050734  
  ",
         "max      0.276590    0.261683    0.316733  
  ",
         "
  ",
         "[8 rows x 50 columns]"
        ]
       },
       "execution_count": 66,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "pandas.DataFrame(corps[\"ASR_AE_H1\"][\"TRAIN\"]).describe()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 83,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "corps.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 84,
     "metadata": {
      "collapsed": false,
      "scrolled": false
     },
     "outputs": [
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f7964811fd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"./scores/DECODA_MINIAE_TANH_TFIDF_H30.shelve\")
  ",
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "show_network_TRANS(scores)
  ",
      "pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 103,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795cb007d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"./scores/DECODA_MINIAE_TANH_TFIDF_H30_DO.shelve\")
  ",
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "show_network_TRANS(scores)
  ",
      "pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 79,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "array([ 0.1998101 ,  0.12073141,  0.10901488, ...,  0.25165449,
  ",
         "        0.07732746,  0.08457387])"
        ]
       },
       "execution_count": 79,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "shelve.open(\"./Sparse_mat_tfidf.shelve\")[\"ASR\"][\"TRAIN\"].data"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 101,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"./real_spe_scores/REAL_SPE_1060_TFIDF.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 102,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795be7b150>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "show_network_RSPE(scores)pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 96,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "['ASR', 'TRS_AE_OUT', 'ASR_AE_OUT', 'TRS', 'ASR_AE_H2', 'ASR_AE_H1']"
        ]
       },
       "execution_count": 96,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "data.keys()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 104,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79595c9410>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"./real_spe_scores/DECODA_MINIAE_REAL_SPE_H50.shelve\")
  ",
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "show_network_RSPE(scores,title=\"DECODA_MINIAE_REAL_SPE_H50\")
  ",
      "pandas.DataFrame(zip([x[0] for x in data[\"ASR_AE_H1\"][0] ],[x[0] for x in data[\"ASR_AE_H1\"][1] ])).plot()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 108,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"./scores/DECODA_MINIAE_TANH_TFIDF_H30_DO.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 109,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "['TRS_AE_H1',
  ",
         " 'TRS_AE_OUT',
  ",
         " 'TRS_SPARSE',
  ",
         " 'ASR_H1_TRANFORMED_TRSH1',
  ",
         " 'ASR_AE_OUT',
  ",
         " 'ASR_H2_TRANFORMED_OUT',
  ",
         " 'ASR_SPARSE',
  ",
         " 'ASR_H1_TRANSFORMED_W1',
  ",
         " 'ASR_AE_H1']"
        ]
       },
       "execution_count": 109,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "data.keys()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 111,
     "metadata": {
      "collapsed": false
     },
     "outputs": [],
     "source": [
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 141,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH_H50_DO.shelve
  "
       ]
      },
      {
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795883e9d0>"
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       "metadata": {},
       "output_type": "display_data"
      },
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        "scores/MINIAE_TANH_H100_DO50.shelve
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795a61c7d0>"
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       },
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       "output_type": "display_data"
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f797db69710>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH_TFIDF_H30_DO.shelve
  "
       ]
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79605a94d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "scores_ordoned={}
  ",
      "for i in glob.glob(\"scores/*DO*.bak\"):
  ",
      "    if \"MINIAE\" not in i :
  ",
      "        continue
  ",
      "    scores={}
  ",
      "    print i[:-4]
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "        if key not in scores_ordoned:
  ",
      "            scores_ordoned[key]=[scores[key]]
  ",
      "        else :
  ",
      "            scores_ordoned[key].append(scores[key])
  ",
      "            
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][0] ],[x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][1] ])).plot()
  ",
      "    data.close()
  ",
      "    show_network_TRANS(scores,title=i,unite=200)
  ",
      "    #except:
  ",
      "    #    print \"C4EST LA MERDE\",i"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": []
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": []
    },
    {
     "cell_type": "code",
     "execution_count": 149,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANFORMED_OUT 0.697
  ",
        "ASR_H2_TRANFORMED_OUT 0.682
  ",
        "TRS_AE_OUT 0.838
  ",
        "TRS_SPARSE 0.841
  ",
        "ASR_SPARSE 0.78
  "
       ]
      },
      {
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795924e810>"
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        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f797d2cd6d0>"
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        "text/plain": [
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f797c06cd90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"./scores/RAW_ASR_TRAIN.shelve\")
  ",
      "scores={}
  ",
      "#del scores_ordoned
  ",
      "for key,table in data.iteritems():
  ",
      "    scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "    print key,scores[key]
  ",
      "   # if key not in scores_ordoned:
  ",
      "   #     scores_ordoned[key]=[scores[key]]
  ",
      "   # else :
  ",
      "   #     scores_ordoned[key].append(scores[key])
  ",
      "#data.close()
  ",
      "#show_network_TRANS(scores)
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0] ],[x[0] for x in data[key][1] ])).plot()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 155,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/MINIAE_BIGBIN_TANH.shelve
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795e02b410>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_AE_TANH_TFIDF_BIGTRANS.shelve
  "
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f795d2667d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH_H50_DO.shelve
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f798bf31d90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/MINIAE_TANH_H100_DO50.shelve
  "
       ]
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f79631c5ad0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/MINIAE_TANH_H100_DOmlp.shelve
  "
       ]
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f796a9b25d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/DECODA_MINIAE_TANH.shelve
  "
       ]
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        "text/plain": [
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7f7957cf73d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "scores/RAW_TRS_TRAIN.shelve
  "
       ]
      },
      {
       "ename": "KeyError",
       "evalue": "'ASR_H1_TRANSFORMED_W1'",
       "output_type": "error",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-155-90df67f98f27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m
  \u001b[0;32m     13\u001b[0m             \u001b[0mscores_ordoned\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m---> 15\u001b[1;33m     \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ASR_H1_TRANSFORMED_W1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ASR_H1_TRANSFORMED_W1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m     17\u001b[0m     \u001b[0mshow_network_TRANS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/shelve.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    119\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    120\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 121\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    122\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    123\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/dumbdbm.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    119\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 120\u001b[1;33m         \u001b[0mpos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msiz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m     \u001b[1;31m# may raise KeyError\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_datfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    122\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;31mKeyError\u001b[0m: 'ASR_H1_TRANSFORMED_W1'"
       ]
      }
     ],
     "source": [
      "scores_ordoned={}
  ",
      "for i in glob.glob(\"scores/**.bak\"):
  ",
      "    #if \"MINIAE\" not in i :
  ",
      "     #   continue
  ",
      "    scores={}
  ",
      "    print i[:-4]
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "        if key not in scores_ordoned:
  ",
      "            scores_ordoned[key]=[scores[key]]
  ",
      "        else :
  ",
      "            scores_ordoned[key].append(scores[key])
  ",
      "            
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][0] ],[x[0] for x in data[\"ASR_H1_TRANSFORMED_W1\"][1] ])).plot()
  ",
      "    data.close()
  ",
      "    show_network_TRANS(scores,title=i,unite=200)
  ",
      "    #except:
  ",
      "    #    print \"C4EST LA MERDE\",i"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 12,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "UNFIXED_TRANS_scores/MINIAE_TANH_H100_MODELDO50_DOMLP.shelve
  ",
        "[]
  "
       ]
      },
      {
       "ename": "KeyError",
       "evalue": "'ASR_W1_TRANSFORMED'",
       "output_type": "error",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-12-4daed94a92f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m
  \u001b[0;32m     12\u001b[0m             \u001b[0mscores_ordoned\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m---> 14\u001b[1;33m     \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ASR_W1_TRANSFORMED\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ASR_W1_TRANSFORMED\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m     15\u001b[0m     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m     16\u001b[0m     \u001b[0mshow_network_UNFIXED\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0munite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/shelve.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    119\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    120\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 121\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    122\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    123\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/dumbdbm.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    119\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 120\u001b[1;33m         \u001b[0mpos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msiz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m     \u001b[1;31m# may raise KeyError\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    121\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_datfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    122\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;31mKeyError\u001b[0m: 'ASR_W1_TRANSFORMED'"
       ]
      }
     ],
     "source": [
      "scores_ordoned={}
  ",
      "for i in glob.glob(\"UNFIXED_TRANS_scores/MINIAE*.bak\"):
  ",
      "    scores={}
  ",
      "    print i[:-4]
  ",
      "    data=shelve.open(i[:-4])
  ",
      "    print data.keys()
  ",
      "    for key,table in data.iteritems():
  ",
      "        scores[key]=round(table[1][np.argmax([x[0] for x in table[0]])][0],3)
  ",
      "        if key not in scores_ordoned:
  ",
      "            scores_ordoned[key]=[scores[key]]
  ",
      "        else :
  ",
      "            scores_ordoned[key].append(scores[key])
  ",
      "            
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[\"ASR_W1_TRANSFORMED\"][0] ],[x[0] for x in data[\"ASR_W1_TRANSFORMED\"][1] ])).plot()
  ",
      "    data.close()
  ",
      "    show_network_UNFIXED(scores,title=i,unite=200)"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 11,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.bak\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.dat\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_DO.shelve.dir\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.bak\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.dat\r
  ",
        "DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve.dir\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30_DOMLP.shelve.bak\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30_DOMLP.shelve.dat\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30_DOMLP.shelve.dir\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30.shelve.bak\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30.shelve.dat\r
  ",
        "DECODA_MINIAE_TANH_TFIDF_H30.shelve.dir\r
  ",
        "MINIAE_TANH_H100_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H100_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H100_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H100_MODELDO50_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H100_MODELDO50_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H100_MODELDO50_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H100_MODELDO50.shelve.bak\r
  ",
        "MINIAE_TANH_H100_MODELDO50.shelve.dat\r
  ",
        "MINIAE_TANH_H100_MODELDO50.shelve.dir\r
  ",
        "MINIAE_TANH_H100.shelve.bak\r
  ",
        "MINIAE_TANH_H100.shelve.dat\r
  ",
        "MINIAE_TANH_H100.shelve.dir\r
  ",
        "MINIAE_TANH_H100_W3000_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H100_W3000_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H100_W3000_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO.shelve.bak\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO.shelve.dat\r
  ",
        "MINIAE_TANH_H100_W3000_MODELDO.shelve.dir\r
  ",
        "MINIAE_TANH_H100_W3000.shelve.bak\r
  ",
        "MINIAE_TANH_H100_W3000.shelve.dat\r
  ",
        "MINIAE_TANH_H100_W3000.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W3000_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W3000_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W3000_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W3000_MODELDO.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W3000.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W3000.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W3000.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO.shelve.bak\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO.shelve.dat\r
  ",
        "MINIAE_TANH_H30_W300_MODELSDO.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W3000_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W3000_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W3000_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W3000_MODELSDO.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W3000.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W3000.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W3000.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W300_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W300_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W300_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO_DOMLP.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO_DOMLP.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO_DOMLP.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W300_MODELSDO.shelve.dir\r
  ",
        "MINIAE_TANH_H50_W300.shelve.bak\r
  ",
        "MINIAE_TANH_H50_W300.shelve.dat\r
  ",
        "MINIAE_TANH_H50_W300.shelve.dir\r
  "
       ]
      }
     ],
     "source": [
      "ls UNFIXED_TRANS_scores/"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 46,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_H1 92
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde972e910>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_OUT 129
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1a7eacd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_SPARSE 46
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1c1307d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANFORMED_TRSH1 181
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1a7d49d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_OUT 104
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde34137d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H2_TRANFORMED_OUT 123
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1c12b350>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_SPARSE 199
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      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde2b077d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANSFORMED_W1 109
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fddecc31e50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_H1 116
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde31bf7d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"AUTO_scores/MINIAE_TANH_H50_W300_MODELSDO_DOMLP.shelve\")
  ",
      "for key,table in data.iteritems():
  ",
      "    print key, np.argmax([x[0] for x in data[key][0] ][2:])
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0][:200] ],[x[0] for x in data[key][1][:200]  ])).plot()
  ",
      "    plt.show()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 45,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_OUT 39
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3333f90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR 48
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fddec8ce8d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS 66
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fddec956150>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_OUT 127
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3fcf3d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_H0 120
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3b87d90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"AUTO_real_spe_scores/V2_REAL_SPE_TANH_DOMODELS_50_DOMLP.shelve\")
  ",
      "for key,table in data.iteritems():
  ",
      "    print key, np.argmax([x[0] for x in data[key][0] ])
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0][:200] ],[x[0] for x in data[key][1][:200]  ])).plot()
  ",
      "    plt.show()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 44,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_H1 110
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3f80a50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_OUT 29
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1c176bd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_SPARSE 54
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde1841e50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_OUT 75
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde2be6d10>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H2_TRANSFORMED 68
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde9051e50>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_SPARSE 95
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde1d98f10>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_TRANFORMED_OUT 64
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3908f90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANSFORMED 101
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde9262710>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_W1_TRANSFORMED 59
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde13f9ad0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_H1 132
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3b775d0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"UNFIXED_TRANS_scores/V2_UNFIXED_TANH_50_W300_DOMLP.shelve\")
  ",
      "for key,table in data.iteritems():
  ",
      "    print key, np.argmax([x[0] for x in data[key][0] ])
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0][:200] ],[x[0] for x in data[key][1][:200]  ])).plot()
  ",
      "    plt.show()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 37,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_H1 94
  "
       ]
      },
      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde138bb90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_AE_OUT 131
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde98c5150>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "TRS_SPARSE 48
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde146f450>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANFORMED_TRSH1 183
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde4b47910>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_OUT 106
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde1513150>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H2_TRANFORMED_OUT 125
  "
       ]
      },
      {
       "data": {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde2d2f290>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_SPARSE 201
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fde1c2aad90>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_H1_TRANSFORMED_W1 111
  "
       ]
      },
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde3f91bd0>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "ASR_AE_H1 118
  "
       ]
      },
      {
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  ",
        "text/plain": [
         "<matplotlib.figure.Figure at 0x7fdde2dc0110>"
        ]
       },
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "source": [
      "data=shelve.open(\"AUTO_scores/MINIAE_TANH_H50_W300_MODELSDO_DOMLP.shelve\")
  ",
      "for key,table in data.iteritems():
  ",
      "    print key, np.argmax([x[0] for x in data[key][0] ])
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0][:200] ],[x[0] for x in data[key][1][:200]  ])).plot()
  ",
      "    plt.show()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 42,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "text": [
        "['TRS_AE_H1', 'TRS_AE_OUT', 'TRS_SPARSE', 'LABEL', 'ASR_AE_OUT', 'ASR_H2_TRANFORMED_OUT', 'ASR_SPARSE', 'ASR_H1_TRANFORMED_TRSH1', 'ASR_H1_TRANSFORMED_W1', 'ASR_AE_H1']
  "
       ]
      }
     ],
     "source": [
      "data=shelve.open(\"./models/MINIAE_TANH_H50_W300.shelve\")
  ",
      "print data.keys()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 21,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "data=shelve.open(\"./UNFIXED_TRANS_scores/DECODA_AEUNFIXED_TANH_TFIDF_MODELS.shelve\")"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 22,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "data": {
        "text/plain": [
         "['TRS_AE_H1',
  ",
         " 'TRS_AE_OUT',
  ",
         " 'TRS_SPARSE',
  ",
         " 'ASR_AE_OUT',
  ",
         " 'ASR_H2_TRANSFORMED',
  ",
         " 'ASR_SPARSE',
  ",
         " 'ASR_TRANFORMED_OUT',
  ",
         " 'ASR_H1_TRANSFORMED',
  ",
         " 'ASR_W1_TRANSFORMED',
  ",
         " 'ASR_AE_H1']"
        ]
       },
       "execution_count": 22,
       "metadata": {},
       "output_type": "execute_result"
      }
     ],
     "source": [
      "data.keys()
  "
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 25,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "ename": "AssertionError",
       "evalue": "(AssertionError('The following error happened while compiling the node', MaxAndArgmax(y, TensorConstant{(1,) of 1}), '\
  '), <function _constructor_Function at 0x7fbb1b6e4140>, (<theano.compile.function_module.FunctionMaker object at 0x7fbab61abc10>, [<None>, <None>, <None>, <<CudaNdarray object at 0x7fbab99bc8b0>>, <<CudaNdarray object at 0x7fbab5abfaf0>>, <<CudaNdarray object at 0x7fbab5e048f0>>, <<CudaNdarray object at 0x7fbab5c050f0>>, <<CudaNdarray object at 0x7fbab5df9cb0>>, <<CudaNdarray object at 0x7fbab5d5ddf0>>, <<CudaNdarray object at 0x7fbab5c2b3f0>>, <<CudaNdarray object at 0x7fbab5c34db0>>], [None, None, None, <CudaNdarray object at 0x7fbab99bc8b0>, <CudaNdarray object at 0x7fbab5abfaf0>, <CudaNdarray object at 0x7fbab5e048f0>, <CudaNdarray object at 0x7fbab5c050f0>, <CudaNdarray object at 0x7fbab5df9cb0>, <CudaNdarray object at 0x7fbab5d5ddf0>, <CudaNdarray object at 0x7fbab5c2b3f0>, <CudaNdarray object at 0x7fbab5c34db0>]))",
       "output_type": "error",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-25-584a6fed03b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m
  \u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ASR_TRANFORMED_OUT\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/shelve.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    120\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    121\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 122\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    123\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    124\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m_constructor_Function\u001b[1;34m(maker, input_storage, inputs_data)\u001b[0m
  \u001b[0;32m    745\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munpickle_function\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    746\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 747\u001b[1;33m     \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrustme\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    748\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    749\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, input_storage, trustme)\u001b[0m
  \u001b[0;32m   1322\u001b[0m             \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1323\u001b[0m             _fn, _i, _o = self.linker.make_thunk(
  \u001b[1;32m-> 1324\u001b[1;33m                 input_storage=input_storage_lists)
  \u001b[0m\u001b[0;32m   1325\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1326\u001b[0m             \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlimit_orig\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/link.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, input_storage, output_storage)\u001b[0m
  \u001b[0;32m    517\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmake_thunk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_storage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    518\u001b[0m         return self.make_all(input_storage=input_storage,
  \u001b[1;32m--> 519\u001b[1;33m                              output_storage=output_storage)[:3]
  \u001b[0m\u001b[0;32m    520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    521\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmake_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/vm.pyc\u001b[0m in \u001b[0;36mmake_all\u001b[1;34m(self, profiler, input_storage, output_storage)\u001b[0m
  \u001b[0;32m    895\u001b[0m                                                  \u001b[0mstorage_map\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    896\u001b[0m                                                  \u001b[0mcompute_map\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 897\u001b[1;33m                                                  no_recycling))
  \u001b[0m\u001b[0;32m    898\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthunks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'lazy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    899\u001b[0m                     \u001b[1;31m# We don't want all ops maker to think about lazy Ops.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, node, storage_map, compute_map, no_recycling)\u001b[0m
  \u001b[0;32m    737\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Trying CLinker.make_thunk'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    738\u001b[0m                 outputs = cl.make_thunk(input_storage=node_input_storage,
  \u001b[1;32m--> 739\u001b[1;33m                                         output_storage=node_output_storage)
  \u001b[0m\u001b[0;32m    740\u001b[0m                 \u001b[0mfill_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_input_filters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_output_filters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    741\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, input_storage, output_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1071\u001b[0m         cthunk, in_storage, out_storage, error_storage = self.__compile__(
  \u001b[0;32m   1072\u001b[0m             \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1073\u001b[1;33m             keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1074\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1075\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_CThunk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcthunk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_storage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36m__compile__\u001b[1;34m(self, input_storage, output_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1013\u001b[0m                                     \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1014\u001b[0m                                     \u001b[0moutput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1015\u001b[1;33m                                     keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1016\u001b[0m         return (thunk,
  \u001b[0;32m   1017\u001b[0m                 [link.Container(input, storage) for input, storage in
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcthunk_factory\u001b[1;34m(self, error_storage, in_storage, out_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1440\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1441\u001b[0m             module = get_module_cache().module_from_key(
  \u001b[1;32m-> 1442\u001b[1;33m                 key=key, lnk=self, keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1444\u001b[0m         \u001b[0mvars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morphans\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cmodule.pyc\u001b[0m in \u001b[0;36mmodule_from_key\u001b[1;34m(self, key, lnk, keep_lock)\u001b[0m
  \u001b[0;32m   1049\u001b[0m         \u001b[0mlock_taken\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1050\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1051\u001b[1;33m         \u001b[0msrc_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlnk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_src_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;31m# Is the source code already in the cache?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1053\u001b[0m         \u001b[0mmodule_hash\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_module_hash\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mget_src_code\u001b[1;34m(self)\u001b[0m
  \u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1314\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_src_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1315\u001b[1;33m         \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dynamic_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1316\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mget_dynamic_module\u001b[1;34m(self)\u001b[0m
  \u001b[0;32m   1368\u001b[0m         \"\"\"
  \u001b[0;32m   1369\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_mod'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1370\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode_gen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1372\u001b[0m             \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDynamicModule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcode_gen\u001b[1;34m(self)\u001b[0m
  \u001b[0;32m    747\u001b[0m             \u001b[1;31m# emit c_code\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    748\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 749\u001b[1;33m                 \u001b[0mbehavior\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0misyms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mosyms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msub\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    750\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMethodNotDefined\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    751\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%s cannot produce C code\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/tensor/basic.pyc\u001b[0m in \u001b[0;36mc_code\u001b[1;34m(self, node, name, inp, out, sub)\u001b[0m
  \u001b[0;32m   1300\u001b[0m             \u001b[0maxis_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"axis = NPY_MAXDIMS;\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1301\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1302\u001b[1;33m             \u001b[1;32massert\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1303\u001b[0m             axis_code = \"\"\"
  \u001b[0;32m   1304\u001b[0m             \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype_\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0mPyArray_DATA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;31mAssertionError\u001b[0m: (AssertionError('The following error happened while compiling the node', MaxAndArgmax(y, TensorConstant{(1,) of 1}), '\
  '), <function _constructor_Function at 0x7fbb1b6e4140>, (<theano.compile.function_module.FunctionMaker object at 0x7fbab61abc10>, [<None>, <None>, <None>, <<CudaNdarray object at 0x7fbab99bc8b0>>, <<CudaNdarray object at 0x7fbab5abfaf0>>, <<CudaNdarray object at 0x7fbab5e048f0>>, <<CudaNdarray object at 0x7fbab5c050f0>>, <<CudaNdarray object at 0x7fbab5df9cb0>>, <<CudaNdarray object at 0x7fbab5d5ddf0>>, <<CudaNdarray object at 0x7fbab5c2b3f0>>, <<CudaNdarray object at 0x7fbab5c34db0>>], [None, None, None, <CudaNdarray object at 0x7fbab99bc8b0>, <CudaNdarray object at 0x7fbab5abfaf0>, <CudaNdarray object at 0x7fbab5e048f0>, <CudaNdarray object at 0x7fbab5c050f0>, <CudaNdarray object at 0x7fbab5df9cb0>, <CudaNdarray object at 0x7fbab5d5ddf0>, <CudaNdarray object at 0x7fbab5c2b3f0>, <CudaNdarray object at 0x7fbab5c34db0>]))"
       ]
      }
     ],
     "source": [
      "data[\"ASR_TRANFORMED_OUT\"].key()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": [
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": 8,
     "metadata": {
      "collapsed": false
     },
     "outputs": [
      {
       "name": "stderr",
       "output_type": "stream",
       "text": [
        "Using gpu device 1: GeForce GTX TITAN X
  "
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "(KeyboardInterrupt(), <function _constructor_Function at 0x7fbb1b6e4140>, (<theano.compile.function_module.FunctionMaker object at 0x7fbb2603f5d0>, [<None>, <None>, <None>, <<CudaNdarray object at 0x7fbabf90ec70>>, <<CudaNdarray object at 0x7fbabf90eab0>>, <<CudaNdarray object at 0x7fbabf91c0b0>>, <<CudaNdarray object at 0x7fbabf90ef30>>, <<CudaNdarray object at 0x7fbabf91c4b0>>, <<CudaNdarray object at 0x7fbabf91c370>>, <<CudaNdarray object at 0x7fbabf91c8b0>>, <<CudaNdarray object at 0x7fbabf91c770>>], [None, None, None, <CudaNdarray object at 0x7fbabf90ec70>, <CudaNdarray object at 0x7fbabf90eab0>, <CudaNdarray object at 0x7fbabf91c0b0>, <CudaNdarray object at 0x7fbabf90ef30>, <CudaNdarray object at 0x7fbabf91c4b0>, <CudaNdarray object at 0x7fbabf91c370>, <CudaNdarray object at 0x7fbabf91c8b0>, <CudaNdarray object at 0x7fbabf91c770>]))",
       "output_type": "error",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-8-04e41364c71e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m
  \u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtable\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m      3\u001b[0m     \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m      4\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/UserDict.pyc\u001b[0m in \u001b[0;36miteritems\u001b[1;34m(self)\u001b[0m
  \u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    110\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 111\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0miterkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    113\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/shelve.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m
  \u001b[0;32m    120\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    121\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 122\u001b[1;33m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    123\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriteback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    124\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m_constructor_Function\u001b[1;34m(maker, input_storage, inputs_data)\u001b[0m
  \u001b[0;32m    745\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munpickle_function\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    746\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 747\u001b[1;33m     \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrustme\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    748\u001b[0m     \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    749\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, input_storage, trustme)\u001b[0m
  \u001b[0;32m   1322\u001b[0m             \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1323\u001b[0m             _fn, _i, _o = self.linker.make_thunk(
  \u001b[1;32m-> 1324\u001b[1;33m                 input_storage=input_storage_lists)
  \u001b[0m\u001b[0;32m   1325\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1326\u001b[0m             \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlimit_orig\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/link.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, input_storage, output_storage)\u001b[0m
  \u001b[0;32m    517\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmake_thunk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_storage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    518\u001b[0m         return self.make_all(input_storage=input_storage,
  \u001b[1;32m--> 519\u001b[1;33m                              output_storage=output_storage)[:3]
  \u001b[0m\u001b[0;32m    520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    521\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmake_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/vm.pyc\u001b[0m in \u001b[0;36mmake_all\u001b[1;34m(self, profiler, input_storage, output_storage)\u001b[0m
  \u001b[0;32m    895\u001b[0m                                                  \u001b[0mstorage_map\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    896\u001b[0m                                                  \u001b[0mcompute_map\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 897\u001b[1;33m                                                  no_recycling))
  \u001b[0m\u001b[0;32m    898\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthunks\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'lazy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    899\u001b[0m                     \u001b[1;31m# We don't want all ops maker to think about lazy Ops.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, node, storage_map, compute_map, no_recycling)\u001b[0m
  \u001b[0;32m    737\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Trying CLinker.make_thunk'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    738\u001b[0m                 outputs = cl.make_thunk(input_storage=node_input_storage,
  \u001b[1;32m--> 739\u001b[1;33m                                         output_storage=node_output_storage)
  \u001b[0m\u001b[0;32m    740\u001b[0m                 \u001b[0mfill_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_input_filters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_output_filters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    741\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[1;34m(self, input_storage, output_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1071\u001b[0m         cthunk, in_storage, out_storage, error_storage = self.__compile__(
  \u001b[0;32m   1072\u001b[0m             \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1073\u001b[1;33m             keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1074\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1075\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_CThunk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcthunk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit_tasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtasks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_storage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36m__compile__\u001b[1;34m(self, input_storage, output_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1013\u001b[0m                                     \u001b[0minput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1014\u001b[0m                                     \u001b[0moutput_storage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1015\u001b[1;33m                                     keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1016\u001b[0m         return (thunk,
  \u001b[0;32m   1017\u001b[0m                 [link.Container(input, storage) for input, storage in
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcthunk_factory\u001b[1;34m(self, error_storage, in_storage, out_storage, keep_lock)\u001b[0m
  \u001b[0;32m   1440\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1441\u001b[0m             module = get_module_cache().module_from_key(
  \u001b[1;32m-> 1442\u001b[1;33m                 key=key, lnk=self, keep_lock=keep_lock)
  \u001b[0m\u001b[0;32m   1443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1444\u001b[0m         \u001b[0mvars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morphans\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cmodule.pyc\u001b[0m in \u001b[0;36mmodule_from_key\u001b[1;34m(self, key, lnk, keep_lock)\u001b[0m
  \u001b[0;32m   1074\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1075\u001b[0m                 \u001b[0mlocation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdlimport_workdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1076\u001b[1;33m                 \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlnk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile_cmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1077\u001b[0m                 \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1078\u001b[0m                 \u001b[1;32massert\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcompile_cmodule\u001b[1;34m(self, location)\u001b[0m
  \u001b[0;32m   1352\u001b[0m                 \u001b[0mlib_dirs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlib_dirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1353\u001b[0m                 \u001b[0mlibs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlibs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1354\u001b[1;33m                 preargs=preargs)
  \u001b[0m\u001b[0;32m   1355\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1356\u001b[0m             \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfgraph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/gof/cmodule.pyc\u001b[0m in \u001b[0;36mcompile_str\u001b[1;34m(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module)\u001b[0m
  \u001b[0;32m   1986\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1987\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1988\u001b[1;33m             \u001b[0mp_out\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput_subprocess_Popen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1989\u001b[0m             \u001b[0mcompile_stderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp_out\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1990\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/site-packages/theano/misc/windows.pyc\u001b[0m in \u001b[0;36moutput_subprocess_Popen\u001b[1;34m(command, **params)\u001b[0m
  \u001b[0;32m     74\u001b[0m     \u001b[1;31m# we need to use communicate to make sure we don't deadlock around\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m     75\u001b[0m     \u001b[1;31m# the stdour/stderr pipe.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m---> 76\u001b[1;33m     \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m     77\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36mcommunicate\u001b[1;34m(self, input)\u001b[0m
  \u001b[0;32m    797\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m--> 799\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_communicate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m    800\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m    801\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36m_communicate\u001b[1;34m(self, input)\u001b[0m
  \u001b[0;32m   1407\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1408\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0m_has_poll\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1409\u001b[1;33m                 \u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_communicate_with_poll\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1410\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1411\u001b[0m                 \u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_communicate_with_select\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;32m/home/laboinfo/janod/.pyenv/versions/2.7.10/lib/python2.7/subprocess.pyc\u001b[0m in \u001b[0;36m_communicate_with_poll\u001b[1;34m(self, input)\u001b[0m
  \u001b[0;32m   1461\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[0mfd2file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1462\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[1;32m-> 1463\u001b[1;33m                     \u001b[0mready\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpoller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0m\u001b[0;32m   1464\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mselect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  \u001b[0;32m   1465\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0merrno\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEINTR\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m
  ",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: (KeyboardInterrupt(), <function _constructor_Function at 0x7fbb1b6e4140>, (<theano.compile.function_module.FunctionMaker object at 0x7fbb2603f5d0>, [<None>, <None>, <None>, <<CudaNdarray object at 0x7fbabf90ec70>>, <<CudaNdarray object at 0x7fbabf90eab0>>, <<CudaNdarray object at 0x7fbabf91c0b0>>, <<CudaNdarray object at 0x7fbabf90ef30>>, <<CudaNdarray object at 0x7fbabf91c4b0>>, <<CudaNdarray object at 0x7fbabf91c370>>, <<CudaNdarray object at 0x7fbabf91c8b0>>, <<CudaNdarray object at 0x7fbabf91c770>>], [None, None, None, <CudaNdarray object at 0x7fbabf90ec70>, <CudaNdarray object at 0x7fbabf90eab0>, <CudaNdarray object at 0x7fbabf91c0b0>, <CudaNdarray object at 0x7fbabf90ef30>, <CudaNdarray object at 0x7fbabf91c4b0>, <CudaNdarray object at 0x7fbabf91c370>, <CudaNdarray object at 0x7fbabf91c8b0>, <CudaNdarray object at 0x7fbabf91c770>]))"
       ]
      }
     ],
     "source": [
      "for key,table in data.iteritems():
  ",
      "    print key, np.argmax([x[0] for x in data[key][0] ])
  ",
      "    pandas.DataFrame(zip([x[0] for x in data[key][0][:200] ],[x[0] for x in data[key][1][:200]  ])).plot()
  ",
      "    plt.show()
  ",
      "data.close()"
     ]
    },
    {
     "cell_type": "code",
     "execution_count": null,
     "metadata": {
      "collapsed": true
     },
     "outputs": [],
     "source": []
    }
   ],
   "metadata": {
    "celltoolbar": "Raw Cell Format",
    "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"
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   "nbformat": 4,
   "nbformat_minor": 0
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