Naive-bayes-con-validation-sul-training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Naive bayes con feature selection specifica per ogni classe, per ogni classe facciamo\n",
    "l'estrazione delle features con la tecnica del chi2 e fittiamo i modelli\n",
    "su ogni classe separatamente \n",
    "'''\n",
    "\n",
    "import time\n",
    "import codecs\n",
    "import csv\n",
    "#import argparse\n",
    "import os\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "#from dill import dill\n",
    "#from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer, TfidfVectorizer\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "#from sklearn import cross_validation, svm\n",
    "from sklearn.svm import LinearSVC\n",
    "#from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "#from sklearn.tree.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.snowball import ItalianStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "\n",
    "# modulo per confrontare il risultato delle regex\n",
    "# richiede entrambi i db già etichettati\n",
    "import tortellino_parmigiano \n",
    "import crea_db\n",
    "\n",
    "\n",
    "import evaluation_report # stampa confusion_matrix e altro\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training sqlite e csv presenti!\n",
      "Carico training_regex.csv\n",
      "Test sqlite e csv presenti!\n",
      "Carico test_manina\n",
      "Trovato salvataggio training set\n",
      "Salvataggio caricato\n"
     ]
    }
   ],
   "source": [
    "ita_stopwords = set(stopwords.words('italian'))\n",
    "ita_stemmer = ItalianStemmer()\n",
    "ita_stemming = ita_stemmer.stem\n",
    "def get_tokenizer(doc): #(word_ngrams=None, char_ngrams=None, stopwords=None, additional_extractors=None):\n",
    "    doc = filter(lambda x: x not in ita_stopwords, word_tokenize(doc))\n",
    "    #doc = map(ita_stemming, doc)\n",
    "    #doc = [ita_stemmer.stem(a) for a in doc]\n",
    "    temp = ''\n",
    "    for par in doc:\n",
    "        temp = '{} {}'.format(temp, ita_stemming(par))        \n",
    "    return temp\n",
    "\n",
    "class CestinoTraining:\n",
    "    def __init__(self, features, labels, categories):\n",
    "        self.stemmed = list(map(get_tokenizer, texts))\n",
    "        self.features = features\n",
    "        self.labels = np.array(labels)\n",
    "        self.categories = categories\n",
    "\n",
    "DATA_FOLDER = \"/home/backup/dati_e_csv/\" # DATI\n",
    "# ci assicuriamo siano presenti i DB\n",
    "training_db_name = crea_db.build_training(OUTPUT=DATA_FOLDER)\n",
    "test_db_name = crea_db.build_test(OUTPUT=DATA_FOLDER) ################################\n",
    "\n",
    "    \n",
    "if os.path.exists('/home/backup/dati_e_csv/training_stemmed-bubu.pickle'):\n",
    "    print('Trovato salvataggio training set')\n",
    "    with open('/home/backup/dati_e_csv/training_stemmed-bubu.pickle', 'rb') as file:\n",
    "        salvataggio = pickle.load(file)\n",
    "    texts = salvataggio.features\n",
    "    stemmed_train = salvataggio.stemmed\n",
    "    Y = salvataggio.labels\n",
    "    etichette = ['attivazione', 'canone rai', 'cessazione', 'contratto', 'credito',\n",
    "                 'domiciliazione', 'fatturazione', 'gr-invito a chiamare - focus', 'non di competenza', 'richiamate']\n",
    "    print('Salvataggio caricato')\n",
    "else:\n",
    "    print('Creo il training set')\n",
    "    \n",
    "    texts = list()\n",
    "    labels = list()\n",
    "    names= []\n",
    "\n",
    "    # load training dataset # possiamo usare sqlite\n",
    "    with codecs.open(os.path.join(DATA_FOLDER, training_db_name), 'r', errors='ignore') as file:\n",
    "        reader = csv.reader(file, delimiter=';')\n",
    "        etichette = next(reader)\n",
    "        for row in reader:\n",
    "            # load documents\n",
    "            #print('Nome {}, etichette {}, conversazione{}'.format(row[10], row[:10], row[11]+row[12]))\n",
    "            #raise SystemExit\n",
    "            #row[11] + row[12])\n",
    "            unito = (row[11] + row[12]).replace('#O:', '').replace('#C:', '') #concatena il testo cliente con operatore\n",
    "            texts.append(unito)\n",
    "            # load row name\n",
    "            names.append(row[10])\n",
    "            # load labels\n",
    "            labels.append(list(map(lambda x: int(x), row[:10])))\n",
    "\n",
    "    print('dimensioni:',len(texts), len(labels))\n",
    "    # tokenization function # possiamo usare anche solo un canale cliente/operatore\n",
    "    print('Inizio lo stemming')\n",
    "\n",
    "    salvataggio = CestinoTraining(texts, labels, etichette)\n",
    "    stemmed_train = salvataggio.stemmed\n",
    "    Y = salvataggio.labels\n",
    "    with open('/home/backup/dati_e_csv/training_stemmed-bubu.pickle', 'wb') as file:\n",
    "        pickle.dump(salvataggio, file, protocol=0)\n",
    "    print('Training set creato e salvato')\n",
    "\n",
    "# ------------------------------------\n",
    "\n",
    "# DATA_FOLDER = \"/home/backup/dati_e_csv/\" # DATI\n",
    "\n",
    "\n",
    "# # ci assicuriamo siano presenti i DB\n",
    "# training_db_name = crea_db.build_training(OUTPUT=DATA_FOLDER)\n",
    "# test_db_name = crea_db.build_test(OUTPUT=DATA_FOLDER) \n",
    "\n",
    "# texts = list()\n",
    "# labels = list()\n",
    "# names= []\n",
    "# names_and_text = []\n",
    "\n",
    "# # load training dataset # possiamo usare sqlite\n",
    "# start_time = time.time()\n",
    "# with codecs.open(os.path.join(DATA_FOLDER, training_db_name), 'r', errors='ignore') as file:\n",
    "#     reader = csv.reader(file, delimiter=';')\n",
    "#     etichette = next(reader)\n",
    "#     for row in reader:\n",
    "#         # load documents\n",
    "#         #print('Nome {}, etichette {}, conversazione{}'.format(row[10], row[:10], row[11]+row[12]))\n",
    "#         #raise SystemExit\n",
    "#         unito = row[11]+row[12].replace('#O:', '').replace('#C:', '') #concatena il testo cliente con operatore\n",
    "#         texts.append(unito) \n",
    "#         # load row name\n",
    "#         names.append(row[10])\n",
    "#         # load labels\n",
    "#         labels.append(list(map(lambda x: int(x), row[:10])))\n",
    "#         names_and_text.append(tuple([row[10]]) + tuple([unito]) + tuple([row[:10]]))\n",
    "# Y = np.array(labels)\n",
    "# print(len(texts), len(labels))\n",
    "# # tokenization function # possiamo usare anche solo un canale cliente/operatore\n",
    "\n",
    "# ita_stopwords = set(stopwords.words('italian'))\n",
    "# ita_stemmer = ItalianStemmer()\n",
    "# ita_stemming = ita_stemmer.stem\n",
    "\n",
    "# def get_tokenizer(doc): #(word_ngrams=None, char_ngrams=None, stopwords=None, additional_extractors=None):\n",
    "#     doc = filter(lambda x: x not in ita_stopwords, word_tokenize(doc))\n",
    "#     #doc = map(ita_stemming, doc)\n",
    "#     #doc = [ita_stemmer.stem(a) for a in doc]\n",
    "#     temp = ''\n",
    "#     for par in doc:\n",
    "#         temp = '{} {}'.format(temp, ita_stemming(par))\n",
    "#     return temp\n",
    "\n",
    "# stemmed_train = list(map(get_tokenizer, texts))\n",
    "#print('Stemmati in -- %s seconds ----!'% (time.time() - start_time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GridSearch for tuning countvect parameters --> using a subset of all data due to Hardware resource\n",
    "pipeline = Pipeline([\n",
    "        ('vect': CountVectorizer()),\n",
    "        ('tfidf': TfidfTransformer()),\n",
    "    ])\n",
    "parameters = {'vect__ngram_range': ((1, 1),(1, 2),(1,3)),\n",
    "              'vect__max_df': (0.5,0.75,1.0),\n",
    "              'vect__min_df': (0,0.25,0.4),\n",
    "              'vect__max_features': (None,50,500,1000, 5000),\n",
    "              'tfidf__use_idf': (True,False),\n",
    "              'tfidf__smooth_idf': (True,False),\n",
    "              #'clf__estimator__alpha': (1.0,10.0,50.0,100.0),\n",
    "              #'clf__estimator__fit_prior': (True, False),\n",
    "              #'clf__estimator__penalty': ('l1','l2')\n",
    "             } \n",
    "\n",
    "gs = GridSearchCV(pipeline, parameters, scoring='f1_macro', n_jobs=-1, verbose=10)\n",
    "print('grid search in action...')\n",
    "start_time = time.time()\n",
    "gs.fit(stemmed_train[:5000], Y)\n",
    "\n",
    "print('\\n PARAMETRI STIMATI in -- %s ---. Algoritmo pronto per essere fittato' % (time.time() - start_time))\n",
    "print('BEST f1-macro SCORE: %0.3f' % gs.best_score_)\n",
    "print('BEST PARAMETRS SET:',gs.best_score_)     \n",
    "best_parameters = gs.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "     print('\\t%s: %r' % (param_name, best_parameters[param_name]))\n",
    "print('\\n --- TO-DO: fill Countvect with above parameters and run to rebuild data matrix.' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building DT matrix...\n",
      "DT matrix ceated in -- 3.186915397644043s ---.shape: 15000, 25875\n",
      "splitting data in 3 folds...\n",
      "splitted in -- 0.027646303176879883s---\n",
      "\n",
      "DATA DIMENSION:\n",
      "\t \n",
      "\ttrain:\t (9000, 25875)\n",
      " \ttest:\t (6000, 25875)\n"
     ]
    }
   ],
   "source": [
    "# selecting TS subset due to computational resource limitation\n",
    "stemmed_train = stemmed_train[:15000]\n",
    "Y = Y[:15000]\n",
    "\n",
    "\n",
    "# Build data matrix for training set: stemmed,stopword\n",
    "print('building DT matrix...')\n",
    "start_time = time.time()\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(stemmed_train)\n",
    "#tf_transformer = TfidfTransformer(use_idf=True)\n",
    "#X_train_counts = tf_transformer.fit_transform(X_train_counts)\n",
    "print('DT matrix ceated in -- {}s ---.shape: {}, {}'.format(\n",
    "        time.time() - start_time, X_train_counts.shape[0],X_train_counts.shape[1]))\n",
    "\n",
    "# creo training, validation e test per fare le valutazioni del modello sulle regex\n",
    "print('splitting data in 3 folds...')\n",
    "start_time = time.time()\n",
    "train, test, y_train, y_test = train_test_split(X_train_counts, Y, test_size=0.4, random_state=0)\n",
    "print('splitted in -- %ss---'%(time.time() - start_time))\n",
    "\n",
    "print('\\nDATA DIMENSION:\\n\\t \\n\\ttrain:\\t {}\\n \\ttest:\\t {}'.format(train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<15000x25875 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 1380811 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_counts.todense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models fitted in -- 0.18243169784545898s ---\n"
     ]
    }
   ],
   "source": [
    "# learn the model for testing on regex\n",
    "start_time = time.time()\n",
    "algo = OneVsRestClassifier(MultinomialNB())\n",
    "regex_model = algo.fit(train,y_train)\n",
    "print('models fitted in -- %ss ---'%(time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**attivazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.82      0.88      5450\n",
      "          1       0.24      0.58      0.34       550\n",
      "\n",
      "avg / total       0.89      0.79      0.83      6000\n",
      "\n",
      "[[4448 1002]\n",
      " [ 232  318]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**canone rai**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.90      0.94      5749\n",
      "          1       0.21      0.57      0.31       251\n",
      "\n",
      "avg / total       0.95      0.89      0.91      6000\n",
      "\n",
      "[[5201  548]\n",
      " [ 107  144]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**cessazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.93      0.96      5861\n",
      "          1       0.12      0.37      0.18       139\n",
      "\n",
      "avg / total       0.96      0.92      0.94      6000\n",
      "\n",
      "[[5464  397]\n",
      " [  87   52]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**contratto**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.84      0.88      5141\n",
      "          1       0.36      0.53      0.43       859\n",
      "\n",
      "avg / total       0.84      0.80      0.81      6000\n",
      "\n",
      "[[4339  802]\n",
      " [ 403  456]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**credito**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.72      0.81      0.76      3086\n",
      "          1       0.77      0.66      0.71      2914\n",
      "\n",
      "avg / total       0.74      0.74      0.74      6000\n",
      "\n",
      "[[2493  593]\n",
      " [ 983 1931]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**domiciliazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.90      0.94      5798\n",
      "          1       0.10      0.33      0.16       202\n",
      "\n",
      "avg / total       0.95      0.88      0.91      6000\n",
      "\n",
      "[[5223  575]\n",
      " [ 135   67]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**fatturazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.84      0.86      4801\n",
      "          1       0.44      0.49      0.46      1199\n",
      "\n",
      "avg / total       0.78      0.77      0.78      6000\n",
      "\n",
      "[[4047  754]\n",
      " [ 611  588]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**gr-invito a chiamare - focus**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.74      0.75      0.74      3250\n",
      "          1       0.70      0.68      0.69      2750\n",
      "\n",
      "avg / total       0.72      0.72      0.72      6000\n",
      "\n",
      "[[2446  804]\n",
      " [ 874 1876]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**non di competenza**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.92      0.95      5767\n",
      "          1       0.23      0.61      0.33       233\n",
      "\n",
      "avg / total       0.95      0.90      0.92      6000\n",
      "\n",
      "[[5288  479]\n",
      " [  92  141]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**richiamate**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.87      0.90      5404\n",
      "          1       0.25      0.39      0.30       596\n",
      "\n",
      "avg / total       0.86      0.82      0.84      6000\n",
      "\n",
      "[[4697  707]\n",
      " [ 363  233]]\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "def printmd(string):\n",
    "    display(Markdown(string))\n",
    "    \n",
    "for i,model in enumerate(regex_model.estimators_):\n",
    "    regex_predictions = model.predict(test)\n",
    "    printmd('**{}**'.format(etichette[i]))\n",
    "    print(classification_report(y_test[:,i], regex_predictions))\n",
    "    print(confusion_matrix(y_test[:,i], regex_predictions))\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PREDICTION sul training con alpha=1.0\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "                 attivazione       0.24      0.58      0.34       550\n",
      "                  canone rai       0.21      0.57      0.31       251\n",
      "                  cessazione       0.12      0.37      0.18       139\n",
      "                   contratto       0.36      0.53      0.43       859\n",
      "                     credito       0.77      0.66      0.71      2914\n",
      "              domiciliazione       0.10      0.33      0.16       202\n",
      "                fatturazione       0.44      0.49      0.46      1199\n",
      "gr-invito a chiamare - focus       0.70      0.68      0.69      2750\n",
      "           non di competenza       0.23      0.61      0.33       233\n",
      "                  richiamate       0.25      0.39      0.30       596\n",
      "\n",
      "                 avg / total       0.56      0.60      0.56      9693\n",
      "\n",
      "\n",
      " INDICI: \n",
      "\n",
      "accuracy 0.225166666667\n",
      "precision micro 0.465709473009\n",
      "recall micro 0.598988961106\n",
      "f1 micro 0.524007220217\n",
      "precision macro 0.341015516665\n",
      "recall macro 0.521986402075\n",
      "f1 macro 0.390990889535\n",
      "\n",
      " ERROR RATES\n",
      "\n",
      "error-rate for attivazione:  0.205666666667\n",
      "error-rate for canone rai:  0.109166666667\n",
      "error-rate for cessazione:  0.0806666666667\n",
      "error-rate for contratto:  0.200833333333\n",
      "error-rate for credito:  0.262666666667\n",
      "error-rate for domiciliazione:  0.118333333333\n",
      "error-rate for fatturazione:  0.2275\n",
      "error-rate for gr-invito a chiamare - focus:  0.279666666667\n",
      "error-rate for non di competenza:  0.0951666666667\n",
      "error-rate for richiamate:  0.178333333333\n"
     ]
    }
   ],
   "source": [
    "# predizioni sul training set \n",
    "regex_predictions = regex_model.predict(test)\n",
    "print('PREDICTION sul training con alpha=%s' % 1.0)\n",
    "print(classification_report(y_test, regex_predictions, target_names=etichette))\n",
    "\n",
    "print('\\n','INDICI: \\n')\n",
    "print('accuracy', accuracy_score(y_test, regex_predictions))\n",
    "print('precision micro',precision_score(y_test,regex_predictions, average='micro') )\n",
    "print('recall micro',recall_score(y_test, regex_predictions, average='micro'))\n",
    "print('f1 micro', f1_score(y_test, regex_predictions, average='micro'))\n",
    "print('precision macro', precision_score(y_test, regex_predictions, average='macro') )\n",
    "print('recall macro', recall_score(y_test,regex_predictions, average='macro'))\n",
    "print('f1 macro', f1_score(y_test, regex_predictions, average='macro'))\n",
    "\n",
    "# print error rate for each category\n",
    "print('\\n ERROR RATES\\n')\n",
    "for cat in range(Y.shape[1]):\n",
    "    print('error-rate for %s: ' % etichette[cat], 1 - accuracy_score(y_test[:,cat], regex_predictions[:,cat]))\n",
    "    \n",
    "#prior = np.array[[0.4,0.6],[],[],[],[],[],[],[],[],[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['attivazione', 'canone rai', 'cessazione', 'contratto', 'credito', 'domiciliazione', 'fatturazione', 'gr-invito a chiamare - focus', 'non di competenza', 'richiamate']\n"
     ]
    }
   ],
   "source": [
    "# importo e trasformo il test set annotato a mano\n",
    "unito_test = []\n",
    "names_test = []\n",
    "test_texts = list()\n",
    "y_true_ammano = list()\n",
    "\n",
    "# load test data\n",
    "# carica il test set\n",
    "with codecs.open(os.path.join(DATA_FOLDER, test_db_name), 'r', errors='ignore') as file:\n",
    "    reader = csv.reader(file, delimiter=';')\n",
    "    next(reader)\n",
    "    print(etichette)\n",
    "    for row in reader:\n",
    "        # \n",
    "        test_texts.append((row[1] + row[2]).replace('O:', '').replace('C:', ''))# \n",
    "        # load names\n",
    "        names_test.append(row[0])\n",
    "        # load labels\n",
    "        y_true_ammano.append(list(map(lambda x: int(x),row[-10:])))\n",
    "        \n",
    "stemmed_test = list(map(get_tokenizer, test_texts))\n",
    "print('\\ntest set caricato')\n",
    "\n",
    "# test set vectorization\n",
    "test_ammano = count_vect.transform(stemmed_test)\n",
    "print('\\nTest set vettorizzato. pronti')\n",
    "\n",
    "# prendo dal training solo i documenti che sono nel test_set\n",
    "# subset_X_train = []\n",
    "# y_true_train = []\n",
    "# for name,text,label in names_and_text:\n",
    "#     if name in names_test:\n",
    "#         subset_X_train.append(text)\n",
    "#         y_true_train.append(label)\n",
    "\n",
    "# stemmed_subset_train = list(map(get_tokenizer, subset_X_train))\n",
    "# subset_X_train_DTM = count_vect.transform(stemmed_subset_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**attivazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.87      0.91       351\n",
      "          1       0.56      0.78      0.65        73\n",
      "\n",
      "avg / total       0.88      0.86      0.86       424\n",
      "\n",
      "[[306  45]\n",
      " [ 16  57]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**canone rai**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.98      0.95       361\n",
      "          1       0.85      0.52      0.65        63\n",
      "\n",
      "avg / total       0.91      0.92      0.91       424\n",
      "\n",
      "[[355   6]\n",
      " [ 30  33]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**cessazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.93      0.96       414\n",
      "          1       0.12      0.40      0.19        10\n",
      "\n",
      "avg / total       0.96      0.92      0.94       424\n",
      "\n",
      "[[386  28]\n",
      " [  6   4]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**contratto**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.83      0.88       350\n",
      "          1       0.47      0.69      0.56        74\n",
      "\n",
      "avg / total       0.85      0.81      0.82       424\n",
      "\n",
      "[[292  58]\n",
      " [ 23  51]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**credito**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.69      0.76       262\n",
      "          1       0.62      0.81      0.70       162\n",
      "\n",
      "avg / total       0.76      0.74      0.74       424\n",
      "\n",
      "[[181  81]\n",
      " [ 31 131]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**domiciliazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.89      0.94       403\n",
      "          1       0.27      0.76      0.40        21\n",
      "\n",
      "avg / total       0.95      0.89      0.91       424\n",
      "\n",
      "[[360  43]\n",
      " [  5  16]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**fatturazione**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.77      0.82      0.79       300\n",
      "          1       0.48      0.40      0.43       124\n",
      "\n",
      "avg / total       0.68      0.70      0.69       424\n",
      "\n",
      "[[246  54]\n",
      " [ 75  49]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**gr-invito a chiamare - focus**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.48      0.64       401\n",
      "          1       0.08      0.78      0.14        23\n",
      "\n",
      "avg / total       0.93      0.50      0.62       424\n",
      "\n",
      "[[192 209]\n",
      " [  5  18]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**non di competenza**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.97      0.94       374\n",
      "          1       0.59      0.34      0.43        50\n",
      "\n",
      "avg / total       0.88      0.89      0.88       424\n",
      "\n",
      "[[362  12]\n",
      " [ 33  17]]\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "**richiamate**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.80      0.89       421\n",
      "          1       0.02      0.67      0.05         3\n",
      "\n",
      "avg / total       0.99      0.80      0.88       424\n",
      "\n",
      "[[338  83]\n",
      " [  1   2]]\n"
     ]
    }
   ],
   "source": [
    "for i,model in enumerate(regex_model.estimators_):\n",
    "    regex_predictions2 = model.predict(test_ammano)\n",
    "    printmd('**{}**'.format(etichette[i]))\n",
    "    print(classification_report(np.array(y_true_ammano)[:,i], regex_predictions2))\n",
    "    print(confusion_matrix(np.array(y_true_ammano)[:,i], regex_predictions2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPORT SENZA FEATURE SELECTION sul test set:\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "                 attivazione       0.55      0.70      0.61        73\n",
      "                  canone rai       0.71      0.43      0.53        63\n",
      "                  cessazione       0.13      0.40      0.20        10\n",
      "                   contratto       0.46      0.57      0.51        74\n",
      "                     credito       0.62      0.81      0.71       162\n",
      "              domiciliazione       0.21      0.62      0.31        21\n",
      "                fatturazione       0.48      0.35      0.41       124\n",
      "gr-invito a chiamare - focus       0.10      0.87      0.18        23\n",
      "           non di competenza       0.53      0.34      0.41        50\n",
      "                  richiamate       0.03      0.67      0.06         3\n",
      "\n",
      "                 avg / total       0.52      0.58      0.52       603\n",
      "\n",
      "\n",
      " INDICI: \n",
      "\n",
      "accuracy 0.122641509434\n",
      "precision micro 0.383025027203\n",
      "recall micro 0.583747927032\n",
      "f1 micro 0.462549277267\n",
      "precision macro 0.38160693756\n",
      "recall macro 0.575970216072\n",
      "f1 macro 0.392812690599\n",
      "\n",
      " ERROR RATES\n",
      "\n",
      "error-rate for attivazione:  0.150943396226\n",
      "error-rate for canone rai:  0.110849056604\n",
      "error-rate for cessazione:  0.0778301886792\n",
      "error-rate for contratto:  0.193396226415\n",
      "error-rate for credito:  0.259433962264\n",
      "error-rate for domiciliazione:  0.134433962264\n",
      "error-rate for fatturazione:  0.301886792453\n",
      "error-rate for gr-invito a chiamare - focus:  0.438679245283\n",
      "error-rate for non di competenza:  0.11320754717\n",
      "error-rate for richiamate:  0.14858490566\n"
     ]
    }
   ],
   "source": [
    "# predico sul test set e sul training set per controllare overfitting\n",
    "y_pred = regex_model.predict(test_ammano)\n",
    "#y_pred_train = main_clf.predict(X_train_counts)\n",
    "y_true_ammano = np.array(y_true_ammano)\n",
    "print('REPORT SENZA FEATURE SELECTION sul test set:')\n",
    "print(classification_report(y_true_ammano, y_pred, target_names=etichette))\n",
    "\n",
    "# accuracy_test = []\n",
    "# accuracy_train = []\n",
    "# for i in range(y_pred.shape[1]):\n",
    "#     accuracy_test.append(accuracy_score(y_true[:,i], y_pred[:,i]))\n",
    "#     accuracy_train.append(accuracy_score(Y[:,i], y_pred_train[:,i]))\n",
    "    \n",
    "\n",
    "# print ('ERROR RATE PER CLASSE:')\n",
    "# print('{:>35} {:>10}'.format('test','train'))\n",
    "# for n in range(len(etichette[:10])):\n",
    "#       print('{:>23}{:>12}{:>10}'.format(etichette[n],round(1-accuracy_test[n],3),round(1-accuracy_train[n],3)))\n",
    "    \n",
    "# print('''\n",
    "#     COME INTERPRETARE ERROR RATE:\n",
    "    \n",
    "#     Training error is low but is much lower than testing error - overfitting\n",
    "#     Both errors are low - ok\n",
    "#     Both errors are high - underfitting\n",
    "#     Training error is high but testing is low - error in implementation or very small dataset\n",
    "# ''')\n",
    "\n",
    "print('\\n','INDICI: \\n')\n",
    "print('accuracy', accuracy_score(y_true_ammano, y_pred))\n",
    "print('precision micro',precision_score(y_true_ammano, y_pred, average='micro') )\n",
    "print('recall micro',recall_score(y_true_ammano, y_pred, average='micro'))\n",
    "print('f1 micro', f1_score(y_true_ammano, y_pred, average='micro'))\n",
    "print('precision macro', precision_score(y_true_ammano, y_pred, average='macro') )\n",
    "print('recall macro', recall_score(y_true_ammano, y_pred, average='macro'))\n",
    "print('f1 macro', f1_score(y_true_ammano, y_pred, average='macro'))\n",
    "\n",
    "# print error rate for each category\n",
    "print('\\n ERROR RATES\\n')\n",
    "for cat in range(Y.shape[1]):\n",
    "    print('error-rate for %s: ' % etichette[cat], 1 - accuracy_score(y_true_ammano[:,cat], y_pred[:,cat]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "def pari(i):\n",
    "    if i%2 == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5), i=0):\n",
    "    \"\"\"\n",
    "    Generate a simple plot of the test and training learning curve.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
    "        An object of that type which is cloned for each validation.\n",
    "\n",
    "    title : string\n",
    "        Title for the chart.\n",
    "\n",
    "    X : array-like, shape (n_samples, n_features)\n",
    "        Training vector, where n_samples is the number of samples and\n",
    "        n_features is the number of features.\n",
    "\n",
    "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
    "        Target relative to X for classification or regression;\n",
    "        None for unsupervised learning.\n",
    "\n",
    "    ylim : tuple, shape (ymin, ymax), optional\n",
    "        Defines minimum and maximum yvalues plotted.\n",
    "\n",
    "    cv : int, cross-validation generator or an iterable, optional\n",
    "        Determines the cross-validation splitting strategy.\n",
    "        Possible inputs for cv are:\n",
    "          - None, to use the default 3-fold cross-validation,\n",
    "          - integer, to specify the number of folds.\n",
    "          - An object to be used as a cross-validation generator.\n",
    "          - An iterable yielding train/test splits.\n",
    "\n",
    "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
    "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
    "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
    "\n",
    "        Refer :ref:`User Guide <cross_validation>` for the various\n",
    "        cross-validators that can be used here.\n",
    "\n",
    "    n_jobs : integer, optional\n",
    "        Number of jobs to run in parallel (default 1).\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring='f1_macro')\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kjZtSShUfTVaUX2PHjg11EyKSxk0ppYqPJivKL707aXA0bkopVXw0WVFKKaVU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f483b9dc358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimators = regex_model\n",
    "#svm_all = VotingClassifier(estimators)\n",
    "#fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(25,50))\n",
    "\n",
    "#print(matrix_countvect.shape)\n",
    "#print(etichette_train_set.shape)\n",
    "#for i in range(Y.shape[1]):\n",
    "title = \"Learning Curves (NaiveBayes) \" #+ etichette[i]\n",
    "plot_learning_curve(estimators, title, X_train_counts, Y, (0, 1.1), cv=5, n_jobs=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZvLdd9/xzjvv8Ouvv+pTpWOdaExBCrcAbqB/MXU+BqYFlA0Hfg/Tpg1g6tSp\nY/r16+e3tG/f3rz77rt+07SWLl1q+vXrV2T61g033GBmz57tV7Zu3TrTr18/89tvv/mV33vvvebh\nhx/2K9uxY4fp16+f2bBhg1/59OnTzW233eZXdvjwYdOvXz/z6aef+pXPmTPHDB8+vIhtQ4cO1ePQ\n46iQ43j66aeLneoYiNvtLnHdSCnPvkeNGmXi4uKCbluyZIlxuVxm0aJFRbZlZ2ebW265xdSvX99U\nrVrVdOvWzWRkZJgOHTqYPn36eOtt3LjRuFyuIlOXjz/++CJ9jh8/3qSkpHjXjxw5Ylwul3nkkUf8\n6sTFxZnDhw/7tZ01a5ZxuVxmz5493rLDhw+b66+/3tSsWdNUq1bNDB482PznP/8xIlLs9PuZM2ea\nLl26mNq1a5vk5GTTsmVLc/fdd5usrCy/etu3bzf/93//Z44//niTnJxsTjzxRDNmzBi/c7Z582Yz\ncOBAk56eblJSUkynTp3M8uXL/foJ52tjjMnMzDR33HGHadGihUlKSjJ16tQx5557rpk+fXq5Xh9H\nA55pyZ4p9XPmzPHeGz33zHPPPTeqU5fFVLLwmIi4gQHGmJBzD0XkYaC3MeYMn7I5QLoxpk+INm2A\ndevWrfObIqcoSuRkZGTQtm1b9P109PHFF1/QsWNH3n77bS6++OJom6NUACV5P3vqAG2NMSWbe1+G\nVIqcFRFJFZEzRORMp6iZs97Q2f6QiLzi02SWU2eqiJwkIjcAgwF9trKiKEoJOXLkSJGyp556ioSE\nBDp37hwFixQlOJVCrABnAV8D67BhpseBDOA+Z3tdoKGnsjFmO3AR9vkq32CnLF9tjAmcIaT8SaZO\nnRptE2IS9ZsSCzzwwAMMGjSIp556iunTp9OjRw/mzZvHjTfeSO3ataNtnqJ4qRQJtsaYjwkjnIwx\nVwUp+wRoW552KfaHzpTIUb8psUDnzp356KOPuP/++zl8+DCNGzdmypQp5fqcFEUpDZVCrCiVl/vu\nu6/4SkoR1G9KLNC7d2969+4dbTMUpVgqyzCQoiiKoihKUFSsKIqiKIpSqVGxooRl79690TYhJlG/\nKYqilB0qVpSwjBgxItomxCTqN0VRlLJDxYoSlkmTJkXbhJhE/aYoilJ2qFhRwqJPJy0d6jdFUZSy\nQ8WKoiiKoiiVGhUriqIoSkRMmjQJl8v/9tGkSZMS5Wq9/PLLuFwudu7cWWb27NixA5fLxauvvlpm\nfSqVCxVM1nvjAAAgAElEQVQrSlheeOGFaJsQk6jfjg62bt3KyJEjad68OcnJyVSvXp3OnTszffr0\noL+rc6wgIoiIX5nL5SpSVtK2JeWNN97gqaeeCtmvcvSiT7BVwpKRkcHVV18dbTNiDvVb7LNo0SKG\nDh1KUlISV155Jaeeeiq5ubmsXr2acePGsX79embNmhVtMysNmzZtKhJtKWvmzJnDf/7zH26++Wa/\n8saNG5OdnU1CQkK57l+JHipWlLDMnDkz2ibEJOq3ohhjyu3bb1n3vX37di677DKaNm3KypUrOf74\n473brr/+eh544AEWLVoU1p7c3FwSExPLzKbKTrSFQpUqVaK6/2iSnZ1NcnJytM0oV3QYSFGUciMz\nM5PR40bTtE1TGp7dkKZtmjJ63GgyMzMrdd9Tp07l8OHDvPDCC35CxUOzZs246aabvOsul4vRo0cz\nZ84cTj31VJKSkli6dClgf9Ry7NixNGrUiKSkJFq1asXjjz9epM/ly5fTpUsXatSoQVpaGq1atWLC\nhAl+dWbMmMGpp55KamoqNWvWpF27drz55pthj+Xxxx/H5XKxa9euItvuvPNOEhMTOXjwIACrV69m\n6NChNG7cmKSkJBo1asStt95aoiGvYDkr69ev5/zzzyclJYWGDRsyZcoU3G53kbYffPABffv2pUGD\nBiQlJdGiRQsmT57sV7dbt24sWrTIm5/icrlo1qwZEDpnZeXKlXTp0oWqVatSo0YNBgwYwMaNG/3q\nePJvfvrpJ4YPH06NGjVIT09nxIgRJTruLVu2MGjQIOrVq0dycjINGzbksssuK3Idvv7665xzzjne\nc3feeeexYsUKvzrPPPOM9/pp0KABo0aN8p4bD127duX0008nIyODc889l9TUVL/rZPHixZx77rlU\nrVqVatWq0bdvX9avX1/scVR2NLKiKEq5kJmZSYceHdjQYgPu/m4QwMDMrTNZ2WMla5atIS0trdL1\nDbBw4UKaNWvGOeecU+I2H374IfPmzWPUqFHUqlWLJk2aANCvXz8+/vhjrrnmGs444wyWLl3K7bff\nzs8//+wVLevXr6dfv36ceeaZPPDAAyQmJrJlyxY+//xzb//PP/88N998M0OHDuWWW27hyJEjfPfd\nd6xdu5ZLL700pF1Dhw5l3LhxzJs3j7Fjx/pte+utt+jVqxfVq1f3rmdnZ3PDDTdw3HHH8eWXXzJj\nxgx2797N3Llzwx5/YGRrz549dO3aFbfbzV133UVKSgr/+Mc/SEpKKtL25ZdfJi0tjbFjx1K1alVW\nrlzJvffeS2ZmJlOnTgXg7rvv5uDBg+zevZsnn3wSYwxVq1YNac+KFSvo06cPzZs357777iM7O5vp\n06fTuXNnMjIyaNSokZ/dQ4cOpVmzZjz88MNkZGQwe/Zs6tSpw0MPPRRyH3l5efTo0YO8vDxGjx5N\n3bp12b17NwsXLuTAgQPea/C+++7jvvvuo1OnTjzwwANUqVKFtWvXsnLlSrp37w5Y0XT//ffTo0cP\nbrjhBjZt2sQzzzzDV199xWeffUZcXJzX3r1799KnTx8uvfRSrrzySurUqQPAa6+9xvDhw+nVqxeP\nPPIIWVlZPPvss3Tp0oWvv/7ae8wxiTHmmFiANoBZt26dURTlz7Fu3TpT3PvppttvMq5hLsMkiiyu\nYS4zetzoUu+/PPv+448/jIiYiy++uMRtRMTEx8ebjRs3+pW/9957RkTMQw895Fc+ZMgQExcXZ7Zu\n3WqMMebJJ580LpfL7N+/P+Q+BgwYYE477bQIjqSQjh07mnbt2vmVffnll0ZEzD//+U9v2ZEjR4q0\nffjhh01cXJzZtWuXt2zSpEnG5XL51WvSpIm56qqrvOu33HKLcblc5quvvvKW7d2716SnpxuXy2V2\n7NgRdr/XXXedqVq1qsnNzfWW9e3b1zRt2rRI3e3btxsRMa+88oq37MwzzzR169Y1Bw4c8JZ99913\nJi4uzgwfPtzvWETEXHvttX59Dhw40NSuXbvIvnz55ptvjIiYd955J2SdLVu2mLi4ODN48OCQdX77\n7TeTmJhoevfu7Vc+c+ZM43K5zMsvv+wt69q1q3G5XOb555/3q3vo0CFTo0YNc9111/mV//rrryY9\nPd2MHDky5P5L8n721AHamCjcw3UYSAlL//79o21CTKJ+gwUrFuBuXjTkD+Bu7mb+0vlk/C+jVMv8\npfPD9v3Big9Kbfcff/wBEHFkpmvXrpx00kl+ZYsXLyY+Pt5vyAhg7NixuN1uFi9eDEB6ejoA7777\nrufLVRHS09P573//y1dffRWRXQCXXHIJ69atY9u2bd6yuXPnkpSU5Het+ubYZGVlsW/fPjp06IDb\n7ebrr7+OaJ+LFy+mffv2tG3b1lt23HHHccUVVxSp67vfQ4cOsW/fPjp37kxWVlaRYZuS8Msvv/Dt\nt99y1VVXeaNGAKeddhoXXngh//rXv/zqiwgjR470K+vSpQv79u3j0KFDIffj6XvJkiVkZ2cHreM5\np/fee2/IflasWEFeXh633HKLX/m1115LWlpakfyoxMREhg8f7le2fPlyDh48yKWXXsq+ffu8i4hw\nzjnnsGrVqpD7jwV0GEgJy6hRo6JtQkxyrPvNGENeXJ4dngmGwM9Hfqbtc21D1wnZOZBD2L7zXHml\nTrqtVq0aQMS5L55hH1927NhB/fr1SU1N9Stv3bq1dztYMfHCCy9w7bXXMn78eC644AIGDhzI4MGD\nvcdwxx138OGHH3L22WfTokULevToweWXX07Hjh0BOySxf/9+v/3Url0bl8vFkCFDuPXWW5k7dy7j\nx48HYP78+fTp08dvKGXXrl3cc889LFiwgN9//91bLiJFcieKY8eOHbRv375IeaCgAzsMNmHCBFat\nWuUVi6Xdr2ffAC1btiyyrXXr1ixbtqxIUmrgEEmNGjUA+P3330MONzVp0oSxY8cybdo0Xn/9dbp0\n6UL//v0ZNmyY9zraunUrLpfLe84jsTchIYFmzZp5t3to0KAB8fH+t+/NmzdjjKFbt25F+hcRP9EW\ni6hYUcLSo0ePaJsQkxzrfhMREgoSrLAIphcM1Eusx8KRC0vVf993+/I/87+QfScUJJR6dlBaWhr1\n69fnhx9+iKjdn5mNkZSUxCeffMKqVatYtGgRS5YsYe7cuVxwwQUsW7YMEaFVq1Zs2rSJhQsXsmTJ\nEt555x2eeeYZJk6cyMSJE/n888/p1q0bIuIVatu2baNRo0bUq1ePLl26MG/ePMaPH8+aNWvYuXMn\njz76qNcGt9tN9+7dOXDgAHfeeScnnXQSqamp7N69m7/97W9BE2PLgoMHD3LuueeSnp7O5MmTadas\nGUlJSaxbt47x48eX234D8eSEBBIq0uXh0UcfZfjw4bz//vssW7aM0aNH89BDD7F27Vrq169fHqYG\nvdbcbjciwuuvv+7NYfElUNzEGrFtvaIolZZ+3fsxc+vMoMM1rp9cDOk1hDb1SvcbSoN7Dg7bd/8L\n/9wwXN++fXn++edZu3ZtREm2gTRu3JgPP/yQw4cP+0VXNmzY4N3uS7du3ejWrRuPPfYYDz30EHff\nfTerVq3i/PPPB+xNasiQIQwZMoT8/HwuvvhipkyZwp133skZZ5xRZHZJ3bp1vf9fcskl3HjjjWze\nvJm5c+eSmppK3759vdu///57Nm/ezGuvveY3VBPYZyTHvnnz5iLlgcM6H330Eb///jvvv/8+nTp1\n8pb/9NNPRdqWVIB6/Lpp06ag+69Vq1aZTvU95ZRTOOWUU7jrrrv44osv6NixI7NmzeL++++nefPm\nuN1u1q9fz+mnn16svb4Rury8PLZt28aFF15YrA3NmzfHGEPt2rW918vRhOasKIpSLky5ZwqtN7fG\ntcVlIywABlxbXLTe0prJd0+ulH0DjBs3jpSUFK655hp+/fXXItt/+uknpk+fXmw/ffr0IT8/n6ef\nftqv/IknnsDlctG7d28AvyEXD2eccQbGGHJycgCKDPHEx8fTunVrO+SWl0d6ejrnn3++3+L77JFB\ngwbhcrmYM2cO8+fPp2/fvn43bE9kITCS8eSTT5YqStWnTx+++OILvxyb3377jTlz5vjVi4uLwxjj\nt9/c3FyeeeaZIn2mpqaWaFiobt26nHnmmbzyyit+w0o//PADy5Yt46KLLor4eIKRmZlJQUGBX9kp\np5yCy+XynrcBAwYgItx///0hozTdu3cnISGhyDU1e/Zs/vjjDz9RGYqePXtSrVo1HnzwQfLz84ts\n37t3b0kPq1KikRUlLO+99x4DBgyIthkxh/rNDqesWbaGuyffzQcLPiDPlUeCO4H+3fsz+ZnJf2pq\ncXn2DfY5KnPmzOHSSy+ldevWfk+w/eyzz5g/fz5XXXVVsf3069ePbt26MWHCBLZt2+adurxgwQLG\njBlD06ZNAbj//vv55JNPuOiii2jcuDF79uzh2WefpVGjRnTu3BmwQ4t169alU6dO1KlTh/Xr1zNz\n5kz69u1bJCcmGLVr16Zbt25MmzaNQ4cOcckll/htb9WqFc2bN2fs2LH897//pVq1arz99tscOHCg\nFB60gu+1116jZ8+e3HzzzaSkpPD888/TpEkTvvvuO2+9jh07UqNGDa688kpGjx4N2GeSBBNIbdu2\n9U7BbteuHVWrVg15I3/00Ufp06cP7du35+qrryYrK4unn36aGjVqMHHixFIdUyArV65k1KhRDBky\nhJYtW5Kfn8+rr75KfHw8gwYNAmzEY8KECUyePJkuXbowcOBAEhMT+fe//02DBg2YMmUKtWrV4s47\n7+T++++nV69e9O/fn40bN/Lss89y9tlnB01KDiQtLY1nn32WK6+8kjZt2nDppZdSu3Ztdu7cyaJF\ni7w/ExGzRGMKUjQWdOpyqRg6dGi0TYhJjna/lWSqYyBut7vc7Cmvvrds2WJGjhxpmjVrZpKSkky1\natVMx44dzYwZM0xOTo63nsvlMqNHB58uffjwYTN27FhzwgknmMTERHPSSSeZadOm+dVZtWqVufji\ni80JJ5xgkpKSzAknnGCGDRtmtmzZ4q3z/PPPm65du5ratWub5ORkc+KJJ5rx48ebzMzMEh/P7Nmz\njcvlMunp6X72e9i4caPp0aOHqVatmjn++OPNddddZ77//nvjcrn8pgVPmjTJxMXF+bVt2rSpGTFi\nhF/ZDz/8YLp162ZSUlJMw4YNzYMPPmhefPHFIlOX16xZYzp27GhSU1PNCSecYO68806zfPly43K5\nzMcff+zny2HDhpmaNWsal8vlnca8ffv2IjYaY8zKlStNly5dTGpqqklPTzcDBgwoMr3cMw173759\nfuUvv/xyETsD2bZtm7nmmmvMiSeeaFJSUkytWrXMBRdcYFatWlWk7ssvv2zatm1rkpOTzXHHHWe6\ndetmPvzwQ786zzzzjDn55JNNYmKiqVevnhk1apQ5ePCgX52uXbua008/PaRNH3/8sendu7epUaOG\nSUlJMSeeeKIZMWKEycjICNkmFqYuiykmeehoQUTaAOvWrVtHmzalGydXFMWSkZFB27Zt0feTosQ+\nJXk/e+oAbY0xGRVqIJqzoiiKoihKJUfFiqIoiqIolRoVK4qiKIqiVGpUrChhKcmMB6Uo6jdFUZSy\nQ8WKEpZj/UmspUX9piiKUnaoWFHCctlll0XbhJhE/aYoilJ2qFhRFEVRFKVSo2JFURRFUZRKjT5u\nXwnL6tWrvY/7VkrOseI3zw/yKYoSu8TC+1jFihKWRx555Ji46ZY1R7vfatWqRUpKCsOGDYu2KYqi\nlAEpKSnUqlUr2maERMWKEpY333wz2ibEJEe73xo1asSGDRvK9Jdcs7Oz/X4FWCkZ6rfIUZ8VpVat\nWjRq1CjaZoRExYoSlpSUlGibEJMcC35r1KhRpf5wUxTl6EETbBVFURRFqdSoWFEURVEUpVKjYkUJ\ny+233x5tE2IS9VvkqM9Kh/otctRnsYeKFSUsmpNQOtRvkaM+Kx3qt8hRn8UeYoyJtg0Vgoi0Adat\nW7eONm3aRNscRVEURYkZMjIyaNu2LUBbY0xGRe9fIyuKoiiKolRqVKwoiqIoilKpUbGihGXjxo3R\nNiEmUb9FjvqsdKjfIkd9FnuoWFHCMm7cuGibEJOo3yJHfVY61G+Roz6LPVSsKGF5+umno21CTKJ+\nixz1WelQv0WO+iz2qDRiRURuFJFtIpItIl+ISLti6l8hIt+IyGER+VlEXhCRmhVl77GCTvErHeq3\nyFGflQ71W+Soz2KPSiFWROQS4HFgIvAX4FtgqYgE/QlIEekEvAI8D5wMDAbOBv5RIQYriqIoilJh\nVAqxAowBnjPGvGqM2QhcB2QBI0LUbw9sM8bMNMbsMMZ8DjyHFSyKoiiKohxFRF2siEgC0Bb40FNm\n7JPqVgAdQjRbAzQUkd5OH3WAIcCi8rX22GPq1KnRNiEmUb9FjvqsdKjfIkd9FntEXawAtYA4YE9A\n+R6gbrAGTiRlGDBXRHKB/wG/A6PK0c5jkqysrGibEJOo3yJHfVY61G+Roz6LPaL+uH0RqQfsBjoY\nY9b6lE8FzjXGFImuiMjJwHJsnssyoB7wGPBvY8w1Ifajj9tXFEVRlFKgj9uHvUABUCegvA7wS4g2\n44HPjDHTjDE/GGOWAzcAI5whoZD06dOH/v37+y0dOnTgvffe86u3bNky+vfvX6T9jTfeyAsvvOBX\nlpGRQf/+/dm7d69f+cSJE4uEG3fu3En//v2LPJRoxowZRX4JNCsri/79+/Ppp5/6lb/xxhtcddVV\nRWy75JJLKvVxrF69Wo9Dj0OPQ49Dj6OSH8cbb7zhvTfWrVuX/v37M2bMmCJtKpKoR1YAROQLYK0x\n5mZnXYCdwHRjzKNB6s8Hco0xl/uUdQBWAw2MMUVETqxFVjIzM3lswgQ+W7CA1Lw8Dick0KlfP26b\nMoW0tLRom6coiqIcQ2hkxTINuFZErhSRVsAsIAV4GUBEHhKRV3zqLwAGich1ItLUmcr8FFbwhIrG\nxAyZmZkM6tCBDjNnsnz7dt7fvZvl27fTYeZMBnXoQGZmZoXZEqj2lZKhfosc9VnpUL9Fjvos9qgU\nYsUYMw+4Dbgf+Bo4HehpjPnNqVIXaOhT/xXgVuBG4HtgLrABGFSBZpcbj02YwK0bNtDL7UacMgF6\nud2M2bCBx++6q8JsGTEi1OxxJRzqt8hRn5UO9VvkqM9ij0oxDFQRxNIwUPemTVm+fbtXqPhigB4N\nGrB8zRqoWhUSE+0SF1cutmRkZFR6f1VG1G+Roz4rHeq3yFGfRU60h4HiK3qHSniMMaTm5QUVKmAj\nLCl79mAmTkTatYOzzoKaNSElxS4e8SKheogMfUOXDvVb5KjPSof6LXLUZ7GHipVKhohwOCEBAyEj\nK4erVEEWL4aXXoL4eDjzTGjXDs4+G9q0sRGXtDRISrLCpUqVCj4KRVEURSk7VKxUQjr168fSmTPp\n5XYX2bbE5aLzxRfDxImwaxd88QV89hm88QY8+6wVKGedZcXLOefA6adDaqr/kFG8nnZFURQldqgU\nCbaKP7dNmcK01q1Z7HLhySgywGKXiydOOomxkydDQgI0aAB//Ss88gisXQsLF8Ltt9tts2bBpZdC\n+/Zw9dXw2GPw4YewbRvs3g0HDkB2NgQRRL4EPhNAKRnqt8hRn5UO9VvkqM9iDxUrlZC0tDTeXrOG\ntaNG0aNJE/7aoAE9mjRh7ahRvL12LWlNmkDjxtC0KTRqBMcfb4d6WrSAoUNhxgz46it46y249lo4\ndMiKlb/+FTp3huuvt3U++8yKlz174I8/ICcHAhKuMzIqPI/qqED9Fjnqs9Khfosc9VnsobOBYgBj\nDFJcwqwxkJtrlyNHrEDJzYW8PJtsW1AA330Ha9bA6tXw/fe2TcOGdrjonHOgY0eoX98OGXnyXRIS\nKuYgFUVRlEqLzgZSiqVYoWIrFeakpKVBrVqF4iU7Gw4ftnksZ54JN95oy9atszkvq1fD/Pm2n5NO\nsvXat4cOHaB27QqZIq0oiqIooVCxcrQSKF58Iy/Z2Tbyct55Nppy223w++926GjNGli1Cl5/3QqT\nU0+1s4zat7fRF880ac8sI5eOJCqKoijli4qVY4VA8VK7thUuOTl22CgpCXr2hAsusHV/+cWKl88/\nh3fegeeft+LkL3+x4qVjRztNOj0dkpMLh4zK6PkuiqIoiuJBvxYfq3jES7VqNkG3adPChN3ate3/\nf/0r/f/4Az7+GBYtgvHjbf2XXoLLLrNiZehQmDwZliyB7dth3z475JSfH+0jjCrBfnFVCY/6rHSo\n3yJHfRZ7aGRFsfhGXqpV80ZeRo0ebf9PToZmzWDIEDvdefNmO13688/hqafs9OmaNQvzXbp0sfkv\nvvkux9CQ0ahRo6JtQsyhPisd6rfIUZ/FHjobSCkZnpyXnJzChN3cXBtBycuD//ynULx8+62dfVS/\nfmG+S5cuVuz45rvokJGiKEpMoLOBlNggMPLidvsn7FarZp+cO3IkZGVZweIRL++9Z/to1qww36VL\nFzjhBJ0irSiKohSLihWldLhcVmgkJfkNG3nFy3HHWUGSn2+flpuRUShe3nzTip/Wre0Mo06d7FKn\njk6RVhRFUYpw7CQRKKXiPU9UpDg84qVaNSs6mjaFJk1swu6JJ8JFF8E998CCBbB8OTz0kM1p+de/\n4Jpr7G8YXXihnUY9b559su7Bg3amUjE/CVAZKbHfFC/qs9Khfosc9VnsoWJFCcsbb7xRuoahxEvD\nhvbZLYMHw5QpVrgsWAB33WWjM3PmwOWXwymnWIFz9912+88/h/xJgMpIqf12DKM+Kx3qt8hRn8Ue\nmmCrRAdPzktgwm5eHvz4I/z733bY6MsvC3Ni2rWz+S7nnWcjMb7Pd1EURVHKDU2wVY5NfHNeqlf3\nFy+1a9uHzw0fboeBfvihULw8/DA88IB9NszZZ9tcl65d7ZCS5rsoiqIclahYUSoHwcRLTo4VMHXr\n2ohKbi5kZsI33xT+NMCiRYU/yNi+vf1V6fPPt+se8aJTpBVFUWIaFStK5cTlssM8yclFxUvDhvZn\nAXJzYf9++4OM//63FS9vvWXbt2xZ+HyX88+30RrP810URVGUmEITbJWwXHXVVdE2weIRL9WrQ716\nhQm7p55qn6o7aZJNxF2xAqZOhdNOs/9fe60VLp07w803wxtv2GTdcv5JgErjtxhCfVY61G+Roz6L\nPTSyooSlR48e0TYhOIGRl4ICG2mpXx9OPhkGDbL5Ljt22KjLl1/aKdGzZhX+IKMn36VDB0hNLdOf\nBKi0fqvEqM9Kh/otctRnsYfOBlKOTjziJSfHPlE3O9sumzYVipcvv7QRlqpVC2cade9uhUxSUkQ/\nCWCMQTQ3RlGUoxSdDaQo5UFcXGHkJT29ULyccIKNpGRnw6FD/jONHn3UPvulZs3CZN3u3aFVKyte\nAqZIZ2Zm8tiECXy2YAGpeXkcTkigU79+3DZlCmlpaVE6cEVRlKMPjawoxya+kZfDh+2Q0cGD8PXX\nNuKydi18/71N7K1fv/D3jC68EBo3JjM3l0GdO3Prhg30dLsRwABLXS6mtW7N22vWqGBRFOWoQSMr\nSqVm9erVdO7cOdpmlD3BIi85OfbHFnv1spGXffsKh4y++ALmz7dtmzXjsYQEbv3xR3r5iH0Bernd\nmA0buHnECF70zExSSsRRe62VM+q3yFGfxR4aWVHC0r9/fz744INom1HxeMRLbq6NvGRn21lEa9fC\nv/9N93feYXlBAcGyVAxwfFwcv40aBSkpVhClpNihpJQUm8zrKfNdkpLstvj4wlwZkaL/R7oeI5Tn\ntXY05xQds+/RP4H6LHKiHVlRsaKEJSsri5SUlGibEX18xIs5dIgBZ53F+3v2hKx+kcvFwlq1kJwc\nO8SUk1PyfSUkFD4gLzHRChvf/z0/M+ApDyzzLfcIoECh5BFLcXGFosblKhthVFzdEJT1tXas5BQd\nLe/RihSUR4vPKpJoixUdBooBovmtUN/QDnFx3giIpKdzOCkJAyEjKzn16iHLl9un6xpT+FC77Gz7\neuRI4eKJ4PiuB9bxLNnZhfk1nnVPmWfJyyv5cYUTRYmJhds9S2B5oDgK/N8TMUpI8BctLlehOHLW\nU1wum/Ts2QalFk2Zhw4x6NxzuXXjRib55hTNnMmglSt5+/PPrWA5CqItsfwejZagjGWfHauoWKmk\nZGZmMuGBCSxYsYDcuFwSChK46IKLuO+u+6hWrZq3nji3S4+YEZ/bZ2DZ0RoGjwad+vdn6cyZ9HK7\ni2xb4nLRecAA++A6j1jxFS2B6wUF9tWz5OcX1oPQ7QIxxt588/IKk4d9F48Q8hVFvq8eIeUrgLKy\n7FOCfct8X0samY2L8xc4xYmiYGWBoiqYiEpOhvh4HnvwQW7duNHv/PjmFD0+ejST7rknuOgpzeLp\nozK9VnIyMzMZ1KEDt27YEFxQapJ6pcAjKBd7cvaihA4DVUIyMzPp0KMDG1pswN3cjedd7NrqotmG\nZrz51ptUrVo1rDDxLQtWx1PP5XyD9ZSJiLeey3nAcWAd3zLffYYTRyUpK+92ZYnng3bMhg308vmg\nXeJy8URZzQYqTuSEE0KBwsezFBQU37dnu6/vjPGPhHi25ef7R4xCiSJfgRNM9AQr840mRTKMlphI\n99xclhsTMvLVo0oVlp9xhi3wHJfnmvZ9DYwGBW4PVS9Uu8B137LA9uFeQ20LVq+0S2AfJV0XseI0\nVLnzOvGJJ+gwf35Qwb/Y5WLt3/7GpClTbFQuPr7wNT7ef8iyAvgz90lD6dpWhn1mZmZySecu3Lph\nI8e73Zxli3UYSLFMeGCCFSotfN7EAu7mbraarcx8aiaTJk3yu7A8F6enzPdi9ZY5r24K+zX5Jmh7\nT9mjkx7ltom3hezTt64EvTUUbgtWx7cskihRsLJgwiScQPOIMY9ACyfiAsuIh9kr/8Wz9z/IYwsX\nk4Fq4fgAACAASURBVJKXR1ZCAmf37c2LE+9mwr0TmPzw5KD+COWnoPYH1hW7SJwUrkTSr48oEUNQ\nsSKBIsipI24fEeQugIICJCEOEhMgLTV438YnQuSxMUAIidib2u2PPcaj48Z5b7riewN2u23EKFAI\nBRE7Jjub1IcfRg4dCuF/SHG5MA0bWo8FijYff5hAAWiMtcP32Nxuq4B822NfTbDtJmA/blNY7g69\nXdxujHHb8+MutOn2rCweTUoMsg+KClG3074S8BkwKcS2Xm430156CV56KWR7ExcHcXH2NT6ucD3e\n8xrvbHdBfDzGZ9udP//Cg00b23VnG3Guwjbe1zhMXLzt36c/v3p+dZz+4uMgLr7QloT4Qjs9beIT\nCtvG+baN9/ZhnH2Y+HhIiLfH4unXI9pC+acMBMvTd03m1g0b6e12U+HqJAAVK5WQBSsW4O4fJMyP\nFSwfzP+Azld2Jik+icS4RBLj7ZIUn0RSXJJdj7Pr8a74PxVdaNq4KdWTqpe6fSQECq2IxViwdgEC\nzd48St9noOAadu9NXHHvKIzbeMXMAXOA1Nqp7Dq4y//4nL7CCbbS1PXwp0RQmLre8jjsUqS+ODdG\nT1QGR6wAbmebJ8ph3IU3Y+8N1EBBAan1qrG14DdwG6TAV+T43mhBXECygSRAkoFkb1TQI4QOzkzF\nHDrkPUoDfv8frJHOrgk3YXzPujEY/xJbHGrIjYAbggnS1uBzHL71rUX+9Y29znzOgcH4GV9oq+N2\nA1XeWsj6IX29+xdx+bUXABHvNSxY8WewvhdA3FaYidNK3NYWcYSW4Fl3+nB84hGlttwUij+nL/EK\nMbvd5QaMG+M2JI6+Bzlw0MfWwnMkQGL1NHbfMwYpcNvrIT8fKShACgog321fnXVb5vN/gWd7PpJv\nxbW3PD+f+llZ5KckOe3ykJwc29bt26/b239hf77rvtsD9h3smikHjBOl8oqiuADx5bseKKo8osfl\n8hFY/kLth2WrmOx2MzoR5lcBMivksIKiYqWSYYwhLy6PEPcSENiXv4+r3786dB0fXOLyCho/MeMj\naDz/J8YnkhyfXCiA4hJJapPEP9b9w7Z32vqJpLhEkhKSCvvy6TPBlRCRUCoSPam4KG+pOJR5iKmP\nTmXZR8vIj8snviCeHl17cMftd3DTTTdVqC2hQsahvl1FUr+86gbWv/LWUT71KRLdMe6CohEfYzAm\nMPoBp194Hu+89hYfVzEsSIG8JEg4Av2y4Nxc4YxuncipIuATXXP+sVEdXyM9N3/P9emp63I5sS2f\n2iJ+YsHbLMgQpbd+wIUuAd+WBfFvH5CbMur2Vvg3CPLGCfU+jKRuGfeZNeER/jhwkLsTKXKOJufA\n4WrVkb8NB3y/LOAn/rz4lvkOZYbYfgWQWdI2vsOjHhESOGTqiDGPMCc/HwrcViz5CCny8wv/94ib\nvHwf0ZOP5Nv6Rcp8+/G0z/dp67sPH+FUZLvvvvPzrehy277kSK53m8nPJyknl45VYUN/cFcF/lHU\n9RWFipVKhoiQUJDg/zXDFwMNkhqw5IYlHMk/wpH8I+Tk55BTkENOfo5dLwh4zc/x1vG2KfAvO5R7\niH1Z+4LW8fyf747sV4oFKbFIChYVCqxfRCTF+4sn3z6rxFUpt3wVsEKl38X92NJyC+6/FuYVvbz1\nZVZfvJoF7y6galrVctt/ICEjIqEUXyUXgn+W4RPuoMui98jumYs5Ee/5mfkjvLAsgU8n3kVqvbrR\nNvOY5tTu3Th9/mvs6gfugHP0/kL464XdbNJ0jOONpYURT3+6zG+HZdf3d23O5n89s3C3BH4uuquK\nRMVKJaRf937M3DrTJtcG4PrJRa/ze1EzuWaF25Xvzie3ILeISAr8308M+YidnPwcjhQcKVKWlZvF\n/qz9wUWWUyfPHcF0XHyEUjmJpFefepXNLTdjWhjfneJu7maL2cK9D97L2DvHBhUFkQzjRCJCKrpu\nSduH6qM86z759JMc6Z2PaeG3AfdJcCQun+kzp3PPxHtC5j15h5VCbAu1b6XkHEyGHf2Alj6Fzjna\nCRxI/nP9G2dYrMBdgNu4S7UUmAKMMUHXC4ztN3DdUxZu/c8sofZbkvUCdwFufPpzu8Ou/5KWY4Vk\nJUBnA1VCQs4G+slF803NmTtvLmnV0rwfqL6vLnH5lf1Ztvy4hRYtWxRfsZzxFUqBYqekIiloFKqY\niFNIofQKcCUho1+8AFxTri45+vgNqF1GfRV3fl5ztpcR4QRNyG0Bw50lFUiB7Qt+KyDu+LiS7TNI\nwrnv/ovUKc4eJGz7cPb89NRP5F+RH/Icxf8znvo31rc3XLcjEnB7xUex6+ESTMvyWitDPJ/hLnER\nJ3GIBF+Pc8XhwlWq9ThXnO0zyLpLXLhc9lWM8NHDH5Ez1JmJ9zOeYSCdDaRY0tLSWLNsDXdPvpsP\nFnxAjivHmw9x20O3kZKW4lXAHvVsnAQ43/89f77fSj3rvoLGJa6gwkdEeODuB3jxzRfLVACVhnhX\nPPGueFISKv5hTgXuAnILcsnOz/aKnAHvD2Cv7A3eQCDhjwReuvilIv4qt1yRCHNTSlo3kn7/bN2Z\nY2ZywxM3lKh9yP0ZW3/CuxM4IAeCtkOgelp17u95PyKFSa6+ydTevr0vhds860WSsEMkbAdL+A7a\nXynbv3XPWwx+YHBouwKTgU3xtoazx9cnxe0rVB1jDLuSdpEvIYaWBRKTEunXsp/3Rhvn8rlZS5z3\nJi4iEa/PunUWNz15k7cs3BJWNDj9lsauYEtli9adM+0c/mv+WymGjVWsVFLS0tJ4aupTPMVThHqC\nrVeQOK+ebxchhYtPmUfsFLgLCkODQQTQhIcmcDj3sF8/oQgV3SmJOKrMxLniSHYlk5xQGJdOMklh\n84qOO+E4ujXtVmE2Hg38ZdZfaNCwQZn09ZA8xAFzIOT5SZM0Bp8yuEz2FW0GzB5QZn6rSJbFLyPL\nZIU8RzXia3BXl7vKZd9nzTorJn1W0fTo2oOXt74cNCWholGxEgOEm07qG4YtCwIFUOP0xmEFkK9A\ncrvt+GiBKfATQ77jp0FFFe6g0R/fY6xsAijcm9j1k4uLel5Urvs/GinLm0dx56fn/7d373F21PX9\nx19vCJcG5FJQog+JqCBapaiAGqwookFjPYq0Ru1PadJ6aZNK05pQ+/hp4hU2KmKICD9IjVpYsRe2\n8VKJqBUjIIVVSpVFuZlSJRBBIG6QQD6/P2YWz57dM7vfyebMzOb9fDzO47HnOzPnfOe9l/PZme93\n5sSTp+y9qtbUD90qv0dNzazXzlh6BhtO2cAtcQvbZ1ZbsHjMiu10qUeAOgugR7Y/MurIzyPbH/nt\noeUu2496f0afCpvo1NdkCqBRs4E6xhUd8ZMjWHfZup7OBqqjif62FB2lK9q2cCxCvu2WB7fwhlPf\nwC1Hjv3+HH7z4Vz2r5ex7+PSrwJtU8e/Q70zmWtJdWt78IEHOfvss/nq177KPT+9B3zX5Z3Lxcr0\nMtliZ7zlnae+Ro4GpRZAw1uGWXXOKv7jyv9g2+7b2OPRPTjxhBN595J3M3Of7mNrOounzmXQffZL\nt23b/7gULU9dNpn+dltW1J/HlhUUAzu63ZYtWzjnk+fwrSu/xSO7PcKM7TN4+Qkv5/Qlp7PvvtmH\nYNEf7Pb29v0cb58nWt5t3fZ96VYkdQ5Oncy67Sbabiret6wtD25h5cdXsv7b6x/7HZp74lyWvWfZ\nDhcqRReVLNPW2V60bLKv0229op+vybaNOkI9ye97UduPbvgRrz/p9eBiZedysVJOX18fZ5xxRtXd\n6IkdKoAefXTUH+9VZ6/i9L89fcI/6FV9kPdy28lu9/GPfZz3LH3PpLdNyXZk3NdEHy7d2if7oZO6\n/fa2K522X2V5ZP32W2OMrJv/FD7Wdv6nzuddp79r9G00xhvUvJP2IaU4K/qAbf8eTcWHddEH74Wr\nLuQdp7/jt8sLCrNxp8e3tY3ctgPG3sqjvS0/PjthW0p/pqIImez7DA4O8oLjXgCeDWR1NDw8XHUX\neqZ9DNDundeVT32tR8RBMw+aop7tGrY9tI29Z+xddTcaZ6/Yi8MOPGxU284ouFLWLVsIjpiKD96i\nD+O92ZunHvjUUW3jrdetrbN9V7D7bjv2N3FH+ciKmZmZFRocHOSYY46Bio6sdL9lo5mZmVkN1KZY\nkbRI0u2Stkq6RtJxE6y/p6SPSLpD0kOSbpP0pz3qrpmZmfVILYoVSfOBTwDLgecBNwCXSzq4YLN/\nAk4EFpDdXeLNwM07uau7nM2bu1yl1Qo5t3TOrBznls6ZNU8tihVgCXBBRHw+IoaAdwHDwMLxVpb0\nKuAlwLyI+HZEbIyI70fE1b3r8q5h4cJxvwU2AeeWzpmV49zSObPmKV2sSJoh6RWS3inpcXnbkyQl\nTY6XtAdwDPDNkbbIRv1eAczpstlrgeuAMyTdKelmSR+T5KkEU2zFihVVd6GRnFs6Z1aOc0vnzJqn\n1NRlSU8Bvg7MBvYCvgE8CJyRP39XwssdDOwObOpo3wQc2WWbp5EdWXkIeH3+Gp8Bfhf4s4T3tgl4\n5lQ5zi2dMyvHuaVzZs1T9sjKp8iObBwIbG1rvww4aUc7NQm7AduBt0TEdRHxdeBvgNMk7VW04bx5\n82i1WqMec+bMYWBgYNR669evp9Vqjdl+0aJFrFmzZlTb4OAgrVZrzHnQ5cuX09fXN6pt48aNtFot\nhoaGRrWfe+65LF26dFTb8PAwrVaLDRs2jGrv7+9nwYIFY/o2f/5874f3w/vh/fB+eD92aD/6+/sf\n+2ycNWsWrVaLJUuWjNmml0pdZ0XSL4HjI+JmSQ8CR0fEbZIOA34cEd2vNT72tfYgG59yakSsa2tf\nC+wfEaeMs83a/P2f0db2TOBHwDMi4tZxtvF1VszMzEpo6nVWdoNxL/H5ZLLTQZMWEduA62k7IqPs\n0oAnAVd12ex7wJMktRdFR5Idbbkz5f2tWOd/ATY5zi2dMyvHuaVzZs1TtlhZD/x12/PIB9Z+APha\nidc7G3i7pLflR0jOB2YCawEknSnpc23rXwL8EvispGdJOgFYCayJiN+UeH/rYnCw5wX0tODc0jmz\ncpxbOmfWPGVPAz0ZuJzspt5HkI1fOQLYDJwQEXeXeM2/BJYBhwA/BP4qIq7Ll30WeEpEvLxt/WcA\n5wIvJitcLgXe161Y8WkgMzOzcqo+DVRqNlBE3CnpaGA+cDSwL7AGuDgithZu3P01zwPO67JszGig\niPgJcHKZ9zIzM7PmSC5W8gGxFwAfioiLgYunvFdmZmZmueQxK/mA2FN3Ql/MzMzMxig7wHaA7GJs\nNs2Nd60Am5hzS+fMynFu6ZxZ85QaswL8FHi/pBeTTTv+dfvCiFi1ox2zeli8eHHVXWgk55bOmZXj\n3NI5s+YpOxvo9oLFERFPK9+lncOzgczMzMpp6mygp051R8zMzMzGU/quyyOUm4rOmJmZmXUqXazk\nV5u9kexGhlsl/Zekt05d16wOOm/QZZPj3NI5s3KcWzpn1jylihVJfwN8huzS+m/MH18HzpdU7a0Z\nbUr19/dX3YVGcm7pnFk5zi2dM2ueHRlguzwiPt/Rfhqwoo5jWjzA1szMrJyqB9iWPQ30RMa/I/JV\n+TIzMzOzKVG2WLmF7NRPp/lk12AxMzMzmxJlLwq3HLhU0gnA9/K2FwMnMX4RY2ZmZlZKqSMrEfEv\nwAuBzWSX3X99/vULIuKyqeueVW3BgjE3vLZJcG7pnFk5zi2dM2ueskdWiIjrgf8zhX2xGpo7d27V\nXWgk55bOmZXj3NI5s+YpOxtoHvBoRFze0X4ysFtE/PsU9W/KeDaQmZlZOU2dDXRWl3YVLDMzMzNL\nVrZYOQK4eZz2IeDw8t0xMzMzG61ssXI/MN6dlQ8Hfl2+O1Y3GzZsqLoLjeTc0jmzcpxbOmfWPGWL\nlX8DzpH09JEGSYcDnwDWTUXHrB5WrlxZdRcaybmlc2blOLd0zqx5yg6w3Z/sXkDHAnfmzYcCVwJv\niIhfTVkPp4gH2JYzPDzMzJkzq+5G4zi3dM6sHOeWzpmlq3qAbampyxFxv6TjgVcCR5PdefmGiPju\nVHbOqudf6HKcWzpnVo5zS+fMmifpNJCkOZL+ECAy64G7gfcA/yLp/0naayf008zMzHZRqWNW3g88\ne+SJpKOAC4FvkE1Zfi3w3inrnZmZme3yUouV5wLfbHv+JuDaiHh7RJwNvBvfG2haWbp0adVdaCTn\nls6ZlePc0jmz5kktVg4ENrU9fynQfrXa/yQbaGvTxOzZs6vuQiM5t3TOrBznls6ZNU/SbCBJPwPe\nGhFXStoT+BXw2oj4Zr78KOA7EfG7O6W3O8CzgczMzMqpejZQ6pGVrwFnSXoJcCYwDLTPAPp94NYp\n6puZmZlZ8tTl9wH/CnwH2AKcFhEPty1fCKyfor6ZmZmZpR1ZiYjNEXEC2diVAyPiso5V/hj4wFR1\nzqo3NDRUdRcaybmlc2blOLd0zqx5Sl1uPyLuj4hHx2m/t+NIizXcsmXLqu5CIzm3dM6sHOeWzpk1\nT9l7A9kuYvXq1VV3oZGcWzpnVo5zS+fMmsfFihXyFL9ynFs6Z1aOc0vnzJrHxYqZmZnVmosVMzMz\nqzUXK1aor6+v6i40knNL58zKcW7pnFnzuFixQsPDw1V3oZGcWzpnVo5zS+fMmifpcvtN5svtm5mZ\nldO0y+2bmZmZ9ZSLFTMzM6s1FytWaPPmzVV3oZGcWzpnVo5zS+fMmsfFihVauHBh1V1oJOeWzpmV\n49zSObPmcbFihVasWFF1FxrJuaVzZuU4t3TOrHlcrFghz5wqx7mlc2blOLd0zqx5XKyYmZlZrdWm\nWJG0SNLtkrZKukbScZPc7sWStknq+bxvMzMz2/lqUaxImg98AlgOPA+4Abhc0sETbLc/8Dngip3e\nyV3UmjVrqu5CIzm3dM6sHOeWzpk1Ty2KFWAJcEFEfD4ihoB3AcPAREO2zwcuBq7Zyf3bZQ0O+oBV\nGc4tnTMrx7mlc2bNU/nl9iXtQVaYnBoR69ra1wL7R8QpXbZbALwTOB54H/C6iOg6asqX2zczMyun\n6svtz+j1G47jYGB3YFNH+ybgyPE2kHQE8FHgDyJiu6Sd20MzMzOrTF1OA02apN3ITv0sj4hbR5or\n7JKZmZntRHUoVjYDjwKHdLQfAtw1zvqPA44FVuezgLaRnQZ6rqSHJb2s6M3mzZtHq9Ua9ZgzZw4D\nAwOj1lu/fj2tVmvM9osWLRozOGtwcJBWqzXmEs7Lly+nr69vVNvGjRtptVoMDQ2Naj/33HNZunTp\nqLbh4WFarRYbNmwY1d7f38+CBQvG9G3+/PneD++H98P74f3wfuzQfvT39z/22Thr1ixarRZLliwZ\ns00vVT5mBUDSNcD3I+L0/LmAjcCqiPhYx7oCntXxEouAE4FTgTsiYus47+ExKyW0Wi3WrVs38Yo2\ninNL58zKcW7pnFk6j1nJnA2slXQ9cC3Z7KCZwFoASWcCT4qI0yKrrn7cvrGku4GHIuKmnvZ6F7B4\n8eKqu9BIzi2dMyvHuaVzZs1Ti2IlIr6UX1Plg2Snf34InBwR9+SrzAIOrap/u7K5c+dW3YVGcm7p\nnFk5zi2dM2ueWhQrABFxHnBel2VjT7CNXv4B4AM7o19mZmZWrToMsDUzMzPrysWKFeoc5W6T49zS\nObNynFs6Z9Y8LlasUH9/f9VdaCTnls6ZlePc0jmz5qnF1OVe8NRlMzOzcqqeuuwjK2ZmZlZrLlbM\nzMys1lysmJmZWa25WLFC491Dwibm3NI5s3KcWzpn1jwuVqyQr/RYjnNL58zKcW7pnFnzeDaQmZmZ\nFfJsIDMzM7MCLlbMzMys1lysWKENGzZU3YVGcm7pnFk5zi2dM2seFytWaOXKlVV3oZGcWzpnVo5z\nS+fMmscDbK3Q8PAwM2fOrLobjePc0jmzcpxbOmeWzgNsrdb8C12Oc0vnzMpxbumcWfO4WDEzM7Na\nc7FiZmZmteZixQotXbq06i40knNL58zKcW7pnFnzuFixQrNnz666C43k3NI5s3KcWzpn1jyeDWRm\nZmaFPBvIzMzMrICLFTMzM6s1FytWaGhoqOouNJJzS+fMynFu6ZxZ87hYsULLli2ruguN5NzSObNy\nnFs6Z9Y8Llas0OrVq6vuQiM5t3TOrBznls6ZNY+LFSvkKX7lOLd0zqwc55bOmTWPixUzMzOrNRcr\nZmZmVmsuVqxQX19f1V1oJOeWzpmV49zSObPmcbFihYaHh6vuQiM5t3TOrBznls6ZNY8vt29mZmaF\nfLl9MzMzswIuVszMzKzWXKxYoc2bN1fdhUZybumcWTnOLZ0zax4XK1Zo4cKFVXehkZxbOmdWjnNL\n58yax8WKFVqxYkXVXWgk55bOmZXj3NI5s+ZxsWKFPHOqHOeWzpmV49zSObPmcbFiZmZmteZixczM\nzGrNxYoVWrNmTdVdaCTnls6ZlePc0jmz5nGxYoUGB3t+ocJpwbmlc2blOLd0zqx5fLl9MzMzK+TL\n7ZuZmZkVcLFiZmZmteZixczMzGqtNsWKpEWSbpe0VdI1ko4rWPcUSesl3S3pfklXSZrby/7uKlqt\nVtVdaCTnls6ZlePc0jmz5qlFsSJpPvAJYDnwPOAG4HJJB3fZ5ARgPfBq4PnAt4EvSzq6B93dpSxe\nvLjqLjSSc0vnzMpxbumcWfPUYjaQpGuA70fE6flzAf8DrIqIlZN8jf8GvhgRH+6y3LOBzMzMStjl\nZwNJ2gM4BvjmSFtkFdQVwJxJvoaAxwH37ow+mpmZWXUqL1aAg4HdgU0d7ZuAWZN8jaXAPsCXprBf\nZmZmVgN1KFZ2iKS3AO8D/jgiNk+0/rx582i1WqMec+bMYWBgYNR669evH3cQ1qJFi8ZcqnlwcJBW\nq8XmzaPffvny5fT19Y1q27hxI61Wi6GhoVHt5557LkuXLh3VNjw8TKvVYsOGDaPa+/v7WbBgwZi+\nzZ8/f8r3Y2BgYFrsB/T2+zEwMDAt9gN69/0YGBiYFvsBvf1+DAwMTIv9gN59P0bet+n7MWKq96O/\nv/+xz8ZZs2bRarVYsmTJmG16qfIxK/lpoGHg1IhY19a+Ftg/Ik4p2PZNwEXAH0XE1yd4H49ZKWH+\n/PlceumlVXejcZxbOmdWjnNL58zSVT1mpfJiBboOsN1INsD2Y122eTNZoTI/Ir4yifdwsWJmZlZC\n1cXKjF6/YRdnA2slXQ9cCywBZgJrASSdCTwpIk7Ln78lX/Zu4D8lHZK/ztaIeKC3XTczM7OdqRbF\nSkR8Kb+mygeBQ4AfAidHxD35KrOAQ9s2eTvZoNxP548RnwMW7vwem5mZWa/UolgBiIjzgPO6LFvQ\n8fzEnnTKzMzMKtf42UC2c403atwm5tzSObNynFs6Z9Y8Llas0Ny5vuVSGc4tnTMrx7mlc2bNU4vZ\nQL3g2UBmZmblVD0byEdWzMzMrNZcrJiZmVmtuVixQp2XarbJcW7pnFk5zi2dM2seFytWaOXKlVV3\noZGcWzpnVo5zS+fMmscDbK3Q8PAwM2fOrLobjePc0jmzcpxbOmeWzgNsrdb8C12Oc0vnzMpxbumc\nWfO4WDEzM7Nac7FiZmZmteZixQotXbq06i40knNL58zKcW7pnFnzuFixQrNnz666C43k3NI5s3Kc\nWzpn1jyeDWRmZmaFPBvIzMzMrICLFTMzM6s1FytWaGhoqOouNJJzS+fMynFu6ZxZ87hYsULLli2r\nuguN5NzSObNynFs6Z9Y8Llas0OrVq6vuQiM5t3TOrBznls6ZNY+LFSvkKX7lOLd0zqwc55bOmTWP\nixUzMzOrNRcrZmZmVmsuVqxQX19f1V1oJOeWzpmV49zSObPmcbFihYaHh6vuQiM5t3TOrBznls6Z\nNY8vt29mZmaFfLl9MzMzswIuVszMzKzWXKxYoc2bN1fdhUZybumcWTnOLZ0zax4XK1Zo4cKFVXeh\nkZxbOmdWjnNL58yax8WKFVqxYkXVXWgk55bOmZXj3NI5s+ZxsWKFPHOqHOeWzpmV49zSObPmcbFi\nZmZmteZixczMzGrNxYoVWrNmTdVdaCTnls6ZlePc0jmz5nGxYoUGB3t+ocJpwbmlc2blOLd0zqx5\nfLl9MzMzK+TL7ZuZmZkVcLFiZmZmteZixczMzGrNxYoVarVaVXehkZxbOmdWjnNL58yax8WKFVq8\neHHVXWgk55bOmZXj3NI5s+bxbCAzMzMr5NlAZmZmZgVcrJiZmVmtuVixQgMDA1V3oZGcWzpnVo5z\nS+fMmqc2xYqkRZJul7RV0jWSjptg/ZdJul7SQ5J+Ium0XvV1V9LX11d1FxrJuaVzZuU4t3TOrHlq\nUaxImg98AlgOPA+4Abhc0sFd1j8M+ArwTeBo4FPARZJe2Yv+7koe//jHV92FRnJu6ZxZOc4tnTNr\nnloUK8AS4IKI+HxEDAHvAoaBhV3W/wvgtohYFhE3R8SngX/OX8fMzMymkcqLFUl7AMeQHSUBILL5\n1FcAc7ps9qJ8ebvLC9Y3MzOzhqq8WAEOBnYHNnW0bwJmddlmVpf195O019R2z8zMzKo0o+oO9NDe\nADfddFPV/WiUa6+9lsHBnl//p/GcWzpnVo5zS+fM0rV9du5dxftXfgXb/DTQMHBqRKxra18L7B8R\np4yzzXeA6yPib9ra/hT4ZEQc2OV93gJcPLW9NzMz26X8SURc0us3rfzISkRsk3Q9cBKwDkCS8uer\numx2NfDqjra5eXs3lwN/AtwBPLQDXTYzM9vV7A0cRvZZ2nOVH1kBkPRGYC3ZLKBryWb1/BHwzIi4\nR9KZwJMi4rR8/cOAG4HzgH8gK2zOAeZFROfAWzMzM2uwyo+sAETEl/JrqnwQOAT4IXByRNyTrzIL\nOLRt/TskvQb4JPBu4E7gz1yomJmZTT+1OLJiZmZm1k0dpi6bmZmZdeVixczMzGptlyhWUm+Sx194\nfwAAC/tJREFUOF1Ieq+kayU9IGmTpMskPWOc9T4o6eeShiV9Q9LhHcv3kvRpSZslPSjpnyU9oWOd\nAyVdLOl+SfdJukjSPjt7H3tB0t9J2i7p7I5259ZG0pMkfSHf32FJN0h6fsc6zqyNpN0kfUjSbXkm\nt0j6v+Ost0vnJuklktZJ+t/8d7E1zjo9yUjSoZK+KunXku6StFJS7T5LizKTNENSn6T/krQlX+dz\nkp7Y8Rr1ySwipvUDmE82VfltwDOBC4B7gYOr7lsP9v1rwFuBZwFHkd388Q7gd9rWOSPP4w+B5wAD\nwK3Anm3rfCbf7qVkN5q8Cvhux3v9OzAIHAscD/wE+MeqM5iCDI8DbgN+AJzt3LrmdABwO3AR2e0z\nngK8AniqMyvM7e+Bu4FXAbOBNwAPAIud26i+v4psAsbrgEeBVsfynmRE9g/+jWTTd48CTs6/fx+u\nOqOUzID98n04FTgCeAFwDXBtx2vUJrPKA+3BN+wa4FNtz0U2e2hZ1X2rIIuDge3AH7S1/RxY0vZ8\nP2Ar8Ma2578BTmlb58j8dV6QP39W/vx5beucDDwCzKp6v3cgr32Bm4GXA99mdLHi3EZndRbwnQnW\ncWZjM/kycGFH2z8Dn3duXTPbzthipScZkV3faxtt/+wC7wTuA2ZUnU1KZuOscyxZUfPkOmZWu0NX\nU0nlbpI4nR0ABNl/IEh6Ktm08PZ8HgC+z2/zOZZsinv7OjcDG9vWeRFwX0T8oO29rsjf64U7Y0d6\n5NPAlyPiW+2Nzm1crwWuk/QlZaccByX9+chCZ9bVVcBJko4AkHQ08GKyo6LObRJ6nNGLgBsjYnPb\nOpcD+wPPnqJdqsrI58Ov8ufHUKPMpnWxQrmbJE5LkkR24bwNEfHjvHkW2Q9VUT6HAA/nv/zd1plF\ndljvMRHxKFlR1MicJb0JeC7w3nEWO7exngb8BdmRqLlkh49XSXprvtyZje8s4FJgSNLDwPXAORHx\nxXy5c5tYLzPqdhNdaHCOym4AfBZwSURsyZtnUaPManFROOuJ84DfI/uvzQpIejJZYfeKiNhWdX8a\nYjey893vy5/fIOk5ZFel/kJ13aq9+cBbgDcBPyYrkD8l6ecR4dxsp5M0A/gnsoLvLyvuTlfT/cjK\nZrJzcId0tB8C3NX77lRD0mpgHvCyiPhF26K7yMbwFOVzF7CnpP0mWKdzhPjuwO/SzJyPAR4PDEra\nJmkb2QCz0/P/fjfh3Dr9Aui8pflNZINGwT9r3awEzoqIf4qIH0XExWRX5h45oufcJtbLjO7q8j7Q\nwBzbCpVDgbltR1WgZplN62Il/6945CaJwKibJF5VVb96KS9UXgecGBEb25dFxO1kPyzt+exHdq5x\nJJ/ryQZLta9zJNmH0MiNI68GDpD0vLaXP4nsD8j3p3J/euQKslHrzwWOzh/XAf8IHB0Rt+HcOn2P\nbPBduyOBn4F/1grMJPuHqt128r/Nzm1iPc7oauAoZbeHGTEXuJ/syFhjtBUqTwNOioj7OlapV2ZV\nj1Le2Q/gjcAwo6cu/xJ4fNV968G+n0c24volZJXsyGPvtnWW5Xm8luwDegD4KaOn/J1HNi31ZWRH\nHb7H2OlrXyP7QD+O7FTTzcAXqs5gCrPsnA3k3Ebvx7FkMwfeCzyd7NTGg8CbnFlhbp8lG7A4j2y6\n9ylkYwA+6txG9X0fsn8anktWzP11/vzQXmZEVkTeQDZd9/fJZr5sAj5UdUYpmZENAfk3sn8mjmL0\n58Medcys8kB79E37S7K54lvJqrxjq+5Tj/Z7O9l/bZ2Pt3Wst4Js6t8w2SjtwzuW7wWcS3Za7UGy\navwJHescQHbk4X6yAulCYGbVGUxhlt+irVhxbuNmNA/4rzyPHwELx1nHmY3el32As/MPhF+TfcB+\ngI4pnbt6bmSnYcf7e/YPvc6I7MP+K8AWsg/dPmC3qjNKyYysMO5cNvL8hDpm5hsZmpmZWa1N6zEr\nZmZm1nwuVszMzKzWXKyYmZlZrblYMTMzs1pzsWJmZma15mLFzMzMas3FipmZmdWaixUzMzOrNRcr\nZrsISb+Q9I6E9U+W9KikPXdmv5pOUr+kS6ruh9l05mLFrCYkbc+Lg+3jPB6V9P4dfIvnAJ9LWP+b\nwBMj4uEdfF8zsx0yo+oOmNljZrV9/Saye8Q8g+wOppDdV2MMSbtHROede8eIiF+mdCYiHiG7qZ6Z\nWaV8ZMWsJiLi7pEH2U3BIiLuaWsfzk/NbJf0Skk/kPQb4BhJR0r6sqRNkh6QdLWkl7a/fvtpIEl7\n5a/ztny7X0sakvSqtvVH3mvP/Pk789d4Tb7uA/m2B7Vts4ekz0i6P+/L8smcJpF0oqTvSRqWdIek\nj0vaO1/2HElbJb2+bf23SXpQ0tPz53MkXSFps6T78q+Palt/ZH8XSPr3fH9vlDSS3XclbZF0paRD\n27Y7M89ysaQ783X+UdI+Bfuym6T3S7o9f5/rJbXalh8k6YuS7sn39yZJby7Kx2xX52LFrJk+SnbL\n92cBQ8C+wGVkd1p9PvAd4MuSDpngdVYAnyW7Tfy3gUsk7du2vPNOpwcAi4D5ZLeNPxI4q235+4FT\ngDcDJ5DdbfXVRR2Q9CxgHdmdW58N/AnwCuATABHx38B7gQslzZJ0GLAK+OuIuDV/mX3J7vb6IuB4\n4E7ga5L26ni79wPnA0cDG4GLgU/n7ccBvwOc07HNs4HXkN3a/jX563eu0+4DwKnAwnzb84BLJb0g\nX94HHAa8Engm8FfAvQWvZ2ZV38baDz/8GPsATgPuHaf9ZLLbuL9iEq/xU2Bh2/NfAO/Iv96L7Jbw\nf9e2/MC87YSO99ozf/7O/Pmstm2WALe1Pb8X+Iu25zOA/wUuKejnF4BPdrSdBPyGttvIA5cD64Er\ngX+dYN/3AIaBlxfs70vztvndcgfOBLYCB7W1vS7v2wH58/6R/QP2yd/36HH28aK2/fh01T9jfvjR\npIfHrJg10/XtTyTtB3yQrMCYRVYk7A3MnuB1bhz5IiLuk/Qw8ISC9e+NiLvanv9iZH1JTyA78vKf\nba/5iKQfTtCHo4HDJf15+y4Bu5MdmflZ3raA7CjSMNnRm9+uLD0R+Ajwkrw/uwF7Mnb/b2z7ehPZ\nkaP/7mjbX9KMyMbsANwao8f7XE1WDB3Rvq+5I8ly/64ktbXvAVyVf30e8EVJLwS+QVZ4db6OmbVx\nsWLWTL/ueL4KeCGwDLiN7GjAV8g+sIts63geFJ8eTl1/MvYFzgUuGGfZnW1fP5+sEJgBHAK0FxD9\nZAXBIuB/yI58/ICx+9/e/yhoK7tP++avcVJH/wAeAoiIf5M0m+yU0iuAKyV9LCJ2dLaX2bTlYsVs\nejgeuCAivgwg6QCyoxI9ExF3S/oV2diP6/J+zACeSzaGpptB4Pci4rZuK+RHbS4C/i/ZOJ1LJB0X\nESOFxhzgLRGxPl//cOBxO7hLI54u6aC2oytzgEfITrN1ujFfNrvoaElE3AOsBdZKuhb4e7JxM2Y2\nDhcrZtPDT4E/lrSe7Pf6w2TjS3ptNbBc0s+AW4G/BWYydqBuu48C35N0NtkH+Faya8KcEBFL8nUu\nAm6KiJWSHgf8kGw8yXvy5bcAp0m6ETgYWEl+JGMCmngVHgY+J+m9wEFkA38/HxG/6lwxP5W2Clid\nz2a6muzU2B8Ad0fEFyV9JG//MdkYl1fnX5tZFy5WzKaHd5N9oF9Ndm2Uj5ANmG3XWTCMV0AUFRWT\n8SGyYuESsg/588kGxHYtHCJiUNLLyAqsDXkfbiGbqYOkt5ONRfn9fP0HJb0N+Jakr0bEt8kGxn6G\nrIi5AziDsaeVyu7vj8gGxV4O7AcMkM3E6rY/SyX9nOwo0FOB+8jGGH04X+URsmLqKWSn874DvH0S\n/TDbZSliR/82mZmNT9JuZIXHhRFxZtX9SSXpTOClEXF81X0x25X5yIqZTRlJTyObEvxdstM/S8hm\nJ32xyn6ZWbP5onBmNpWC7JTGdWSnN54GnBgRt1faKzNrNJ8GMjMzs1rzkRUzMzOrNRcrZmZmVmsu\nVszMzKzWXKyYmZlZrblYMTMzs1pzsWJmZma15mLFzMzMas3FipmZmdWaixUzMzOrtf8PaFEPVk8u\nROUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f483b9dc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xxRUlbBs6dKjuh+7HIdmPxx9/vNSpjpEEg8Ey5y0vB7Pu0aNHm6SkpKjbFi1a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\ngZMyRkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48366489b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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88MMPzJo1y2/ORSho0aIF1113HU899RS///47f/nLX/jss8/Ytm0bUPgSAFdd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\niIiISF1TsSIiIiJ17f8DJBBdGFILyFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48366ee630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cuNBkZGQELN+68cYbzZQpU3xkq1atMhkZGeaPP/7wkd97773mkUce8ZFt3rzZ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\nzYokSRpoNiuSJGmg/X8AU/4/7JO/6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f483b9fd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsKHKyMhQPXr0UDk5OapTp06qT58+gXIbN25UlmWFDV0+8cQTw+qcOHGiSktL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\nXxvg+9AGPbYYsVITSEkp635x4rzgOCd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Vb+QMfPfv80pcwH3KR36J1zEV6M9SL/vTo7/M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\nIiIiTU3FioiIiDS1/w/czonNlUYjPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48366bcb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vvkjr1q0DuoDC2TVo0CA++uijkLMK7969u9yiSUrsiNfIinKYeOaZZ2JtQlyi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48365be7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n8NVXXzF37tyQMRfxoFOnTlx44YXcf//9/Pzzz/zxj3/k/fffZ/PmzUDpPwHw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f483651d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PPSomJsbnWDpy5IiqWbOmatKkiVcWytYjR45USUlJaufOnT7yoUOHqri4OLV/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48365357b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NGwJuLyLUrVuXzZs3+6zLyMhw2RkMEaFBgwZkZWVxxRVXhGy7IbIoE2NWikEN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\nXhR7o6OjadSokWu9k9q1a1O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UT3S0FjA7dhSkjb7+Gn77TbfRtClccoke89KiBdSoocVL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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48364d4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v2Y4dO0z37t3N+vXrveRTp041d911l5fsyJEjpnv37uaLL77wks+aNcsMGjTI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48364264e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(Y.shape[1]):\n",
    "    title = \"Learning Curves (NaiveBayes) \" + etichette[i]\n",
    "    plot_learning_curve(estimators, title, X_train_counts, Y[:,i], (0, 1.1), cv=5, n_jobs=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# esploriamo le features che selezione il modell"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}