Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Personalized Medicine: Redefining Cancer Treatment\n",
    "\n",
    "\n",
    "\n",
    "#### Matt Shaffer https://github.com/planetceres \n",
    "#### Kaggle Competition: https://www.kaggle.com/c/msk-redefining-cancer-treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to [discussion boards](https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35810#202604) on Kaggle, the classes we are trying to predict appear to be as follows:\n",
    "\n",
    "1. Likely Loss-of-function\n",
    "2. Likely Gain-of-function\n",
    "3. Neutral\n",
    "4. Loss-of-function\n",
    "5. Likely Neutral\n",
    "6. Inconclusive\n",
    "7. Gain-of-function\n",
    "8. Likely Switch-of-function\n",
    "9. Switch-of-function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import glob\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sps\n",
    "import Bio\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')\n",
    "from itertools import islice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_version = '001'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_name = 'model_' + model_version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_directory = '/Data/kaggle/Personalized Medicine'\n",
    "model_directory = data_directory + '/saved_models'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_path = os.path.join(model_directory, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create model directory if it does not exist\n",
    "if not os.path.isdir(model_directory):\n",
    "    print(\"creating directory for saved models\")\n",
    "    os.mkdir(model_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load model to resume training or perform inference\n",
    "def load_model_from_json(model_path):\n",
    "    model = model_from_json(open(model_path + '.json').read())\n",
    "    model.load_weights(model_path + '.h5')\n",
    "    #model.compile(optimizer=rmsprop, loss='mse')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "# Load model to resume training or perform inference\n",
    "def load_recent_model(model_path):\n",
    "    # Locate the most recent model the folder to resume training from\n",
    "    model_recent = max(glob.iglob(model_path + '*.hdf5'), key=os.path.getctime)\n",
    "    print(\"Using model at checkpoint: {}\".format(model_recent))\n",
    "    #model = model_from_json(open(model_path + '.json').read())\n",
    "    model = load_model(model_recent)\n",
    "    #model.compile(optimizer=rmsprop, loss='mse')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save model\n",
    "def save_model_to_json(m, model_path):    \n",
    "    json_string = m.model.to_json()\n",
    "    open(model_path + '.json', 'w').write(json_string)\n",
    "    m.model.save_weights(model_path + '.h5', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_op_str(data_type):\n",
    "    p = \"Done processing \" + data_type + \" data in {:.2f} seconds\"\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_blank(n):\n",
    "    print(\" \"*n, end=\"\\r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_variants = pd.read_csv(data_directory + \"/input/training_variants\")\n",
    "test_variants = pd.read_csv(data_directory + \"/input/test_variants\")\n",
    "train_text = pd.read_csv(data_directory + \"/input/training_text\", sep=\"\\|\\|\", engine='python', header=None, skiprows=1, names=[\"ID\",\"Text\"])\n",
    "test_text = pd.read_csv(data_directory + \"/input/test_text\", sep=\"\\|\\|\", engine='python', header=None, skiprows=1, names=[\"ID\",\"Text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test set has no labels and is used only for score submission. This will be a challenge since the sample size is small, and it will be hard to learn the properties of the population needed to perform inference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ID', 'Gene', 'Variation', 'Class']\n",
      "['ID', 'Gene', 'Variation']\n"
     ]
    }
   ],
   "source": [
    "# Test set has no labels and is used \n",
    "print(list(train_variants.columns))\n",
    "print(list(test_variants.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the gene variant data, we also have a text corpus for each example that provides the clinical evidence that human experts used to classify the genetic mutations. This is essentially an unstructured feature set, and our first task will be to map this noisy data to a set of features that can more easily be used for prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ID', 'Text']\n",
      "['ID', 'Text']\n"
     ]
    }
   ],
   "source": [
    "print(list(train_text.columns))\n",
    "print(list(test_text.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merge the text with the variant data, and separate the target values (`Class`) from the features\n",
    "train = pd.merge(train_variants, train_text, how='left', on='ID')\n",
    "y_train = train['Class'].values\n",
    "X_train = train.drop('Class', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Do the same thing with the test data, but note that there are no classes to separate as targets\n",
    "X_test = pd.merge(test_variants, test_text, how='left', on='ID')\n",
    "test_index = X_test['ID'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create mini data sets for model building\n",
    "train_mini = train.sample(frac=0.05)\n",
    "y_train_mini = train_mini['Class'].values\n",
    "X_train_mini = train_mini.drop('Class', axis=1)\n",
    "X_test_mini = X_test.sample(frac=0.05)\n",
    "test_index_mini = X_test_mini['ID'].values\n",
    "\n",
    "# Create mini dev set for model building\n",
    "dev_mini = train.sample(frac=0.05)\n",
    "y_dev_mini = dev_mini['Class'].values\n",
    "X_dev_mini = dev_mini.drop('Class', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_mini.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create dataset with all variants\n",
    "all_variants = pd.concat([train_variants, test_variants], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "\n",
    "We need some way to create learnable features out of the text, so applying a TFIDF vectorizer seems like a logical first strategy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "corpus_train = X_train['Text']\n",
    "corpus_test = X_test['Text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Cyclin-dependent kinases (CDKs) regulate a var...\n",
       "1     Abstract Background  Non-small cell lung canc...\n",
       "2     Abstract Background  Non-small cell lung canc...\n",
       "3    Recent evidence has demonstrated that acquired...\n",
       "4    Oncogenic mutations in the monomeric Casitas B...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2. This mutation resulted in a myeloproliferat...\n",
       "1     Abstract The Large Tumor Suppressor 1 (LATS1)...\n",
       "2    Vascular endothelial growth factor receptor (V...\n",
       "3    Inflammatory myofibroblastic tumor (IMT) is a ...\n",
       "4     Abstract Retinoblastoma is a pediatric retina...\n",
       "Name: Text, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5668,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus_train_mini = X_train_mini['Text']\n",
    "corpus_test_mini = X_test_mini['Text']\n",
    "corpus_dev_mini = X_dev_mini['Text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Functions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sparse_metrics(transformer_type, sparse_mtx):\n",
    "    print(\"Vocabulary length: {}\".format(len(transformer_type.vocabulary_)))\n",
    "    print('sparse matrix shape: {}'.format(sparse_mtx.shape))\n",
    "    print('nonzero count: {}'.format(sparse_mtx.nnz))\n",
    "    print('sparsity: {:.2f}'.format((100.0 * sparse_mtx.nnz / (sparse_mtx.shape[0] * sparse_mtx.shape[1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tfidf_func(X, Y, ngrams=1, plotting=False):\n",
    "    t = time.time()\n",
    "    tfidf = TfidfVectorizer(stop_words = 'english', ngram_range=(1, ngrams), sublinear_tf=True, use_idf=True)\n",
    "    tfidf_train = tfidf.fit_transform(X)\n",
    "    print(print_op_str(\"train\").format(time.time()-t), end=\"\\r\")\n",
    "    tfidf_test = tfidf.transform(Y)\n",
    "    print(print_op_str(\"test\").format(time.time()-t), end=\"\\r\")\n",
    "    print_blank(len(print_op_str(\"test\")))\n",
    "    \n",
    "    print_op = \"Shape: {}\\nNon-zero mean: {}\\nNon-zero median: {}\"\n",
    "    mean_nnz = int(round(np.mean(tfidf_train.getnnz(1))))\n",
    "    median_nnz = int(round(np.median(tfidf_train.getnnz(1))))\n",
    "    print(print_op.format(tfidf_train.shape, mean_nnz,median_nnz))\n",
    "    print(\"\\nDone in {:.2f} seconds\".format(time.time()-t))\n",
    "    \n",
    "    if plotting == True:     \n",
    "        fig, ax = plt.subplots(figsize=(12, 6))\n",
    "        plt.plot(np.sort(tfidf_train.getnnz(1))[::-1])\n",
    "\n",
    "        # Log transformed\n",
    "        plt.figure(figsize=(12,6))\n",
    "        plt.plot(np.sort(np.log(tfidf_train.getnnz(1)))[::-1])\n",
    "        \n",
    "    return tfidf, tfidf_train, tfidf_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (166, 40669)                        \n",
      "Non-zero mean: 1628\n",
      "Non-zero median: 1385\n",
      "Done in 4.14 seconds\n"
     ]
    }
   ],
   "source": [
    "# TFIDF on mini dataset\n",
    "tf_1, tf_train_1, tf_test_1 = tfidf_func(corpus_train_mini, corpus_test_mini, ngrams=1, plotting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary length: 40669\n",
      "sparse matrix shape: (166, 40669)\n",
      "nonzero count: 270168\n",
      "sparsity: 4.00\n"
     ]
    }
   ],
   "source": [
    "sparse_metrics(tf_1, tf_train_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Ngram == 2 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(stop_words = 'english', ngram_range=(1, 2), sublinear_tf=True, use_idf=True)\n",
    "tfidf_train = tfidf.fit_transform(corpus_train)\n",
    "tfidf_test = tfidf.transform(corpus_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (3321, 2887033)\n",
      "Non-zero mean: 6015\n",
      "Non-zero median: 4555\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape: {}\\nNon-zero mean: {}\\nNon-zero median: {}\".format(tfidf_train.shape, \n",
    "                                                                 int(round(np.mean(tfidf_train.getnnz(1)))),\n",
    "                                                                 int(round(np.median(tfidf_train.getnnz(1))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4240"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Non zero values for first entry in training set\n",
    "tfidf_train[0].nnz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot non-zero values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20da83da0>]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1AADAEnLOYG1mq8zsMTPbY2a7zezjXnutmf3UzPZ7X2vGXfNJMztgZvvM7E3j2q8wsxe8\n175gZua1R83sO17702a2tvg/6vx577ZVkqS9J9mFEQAAYKmYTo91RtIfOec2S7pG0kfNbLOkuyQ9\n4pzbIOkR77m8126TdJGkWyR9ycyC3nt9WdIHJW3wHrd47XdI6nbOrZf0t5I+W4SfzTevWV8nSTrV\nO+xzJQAAAJgv5wzWzrmTzrlfe8f9kvZKapJ0q6R7vNPukfR27/hWSfc655LOuUOSDki6ysxWSKp0\nzj3lnHOSvj7pmtH3+p6kG0d7sxeiFVVxSVJrz4jPlQAAAGC+FDTG2huisVXS05KWOedOei+dkrTM\nO26SdGzcZce9tibveHL7hGuccxlJvZLqCqmtlNQkwqqIhfTckS6/SwEAAMA8mXawNrNySf8s6Q+c\nc33jX/N6oF2Ra5uqhg+ZWYuZtXR0dMz1t5sxM9NFKyvV0c+SewAAAEvFtIK1mYWVD9XfdM79H6+5\nzRveIe9ru9feKmnVuMubvbZW73hy+4RrzCwkqUpS5+Q6nHN3O+e2Oee2NTQ0TKd031TEwhpIsioI\nAADAUjGdVUFM0lck7XXOfX7cSw9Jut07vl3Sg+Pab/NW+lin/CTFZ7xhI31mdo33nh+YdM3oe71L\n0qNeL/iCVRENqW847XcZAAAAmCfT6bG+TtL7Jb3BzHZ4j7dI+oykm81sv6SbvOdyzu2WdJ+kPZJ+\nJOmjzrnRrtuPSPpH5Sc0vizpYa/9K5LqzOyApE/IW2FkIVu/rFytPcNq72cCIwAAwFIQOtcJzrl/\nlXS2FTpuPMs1n5b06SnaWyRtmaJ9RNK7z1XLQrJ5RaUk6WjnkBorYj5XAwAAgLnGzotzZHVtQpJ0\ntGvI50oAAAAwHwjWc6SpJi4zgjUAAMBSQbCeI9FQUE3VcW0/2uN3KQAAAJgHBOs5dN359Xr2cJeS\nGZbdAwAAWOwI1nPodRsbNJTK6oNff87vUgAAADDHCNZz6K2XrND7r1mjX+zv0FAq43c5AAAAmEME\n6zn22g31ck566uAZG0kCAABgESFYz7Hr1tdLEpMYAQAAFjmC9Rwri4ZUXx7VC629fpcCAACAOUSw\nngdv3rJcj+/r0IM7Wv0uBQAAAHOEYD0PPvXWC3XRykp9+fGX/S4FAAAAc4RgPQ9i4aCuX1+vgx2D\ncs75XQ4AAADmAMF6njRURJXK5nS4ky3OAQAAFiOC9Ty5pLlakvS5n+zzuRIAAADMBYL1PLlqXa3W\n1iV0rHvY71IAAAAwBwjW8+iGTY3ae7JPpweSfpcCAACAIiNYz6N/c+kKpTI5PfkyuzACAAAsNgTr\neXRxU7WCAdN/+f4epbM5v8sBAABAERGs51EkFNB7tjWrvT+pO7+1naX3AAAAFhGC9Tz7z2/brEtX\nVetHu0/p8Zc6/C4HAAAARUKwnmeJSEjf/b1rtaYuoT/57k7tPtHrd0kAAAAoAoK1DyKhgP7xA9tk\nJv3Jd3f6XQ4AAACKgGDtkw3LKvRbV6/WnpN9uq/lmN/lAAAAYJYI1j76D68/XyurYvrT7+3Up+5/\nQSPprN8lAQAAYIYI1j6KhYP64cdfq8tWVeubTx/VW77wCx3pHGS1EAAAgAXIFmqI27Ztm2tpafG7\njKL51/2n9eF/ek79yYwkqbEiqm/ccbU2La/wuTIAAIClzcyec85tO9d59FiXiOs31Osrv32lPnHz\nRt35hvU6PZDUn37veSUzDA8BAABYCEJ+F4BXXLWuVletq5UkVcXD+q8/2KtP3Pe8/u59W2VmPlcH\nAACAV0OPdYn6f157nq5fX68f7Dyph54/4Xc5AAAAOAeCdQn7379zpSpjIf2cHRoBAABKHsG6hIWD\nAd2wqVEPbG/VgDepEQAAAKWJYF3i3rG1STkn3fS5n+to55Df5QAAAOAsCNYl7oZNDfrsOy9W91BK\nn/7hHr/LAQAAwFkQrEucmem9V67W6zc2aPeJPqUyOb9LAgAAwBQI1gvErZc16Xj3sO6451l2ZgQA\nAChBBOsF4q2XrNAfv3GjfrH/tD78T78mXAMAAJQYgvUC8pEb1uvfbm3Sj3af0vWffUyHTg/6XRIA\nAAA8BOsFJBAwfe49l+qPbt6o1p5h3fp3/6pjXawUAgAAUAoI1guMmenOGzfooY9dp1Q2p49969dK\nZ5nQCAAA4DeC9QJ1SXO1/urWLXr+eK8+9i3GXAMAAPiNYL2AvfuKZv3RzRv1491t+sPv7FCGnmsA\nAADfEKwXMDPTx96Qn9D4wI4Teuj5E36XBAAAsGQRrBc4M9PfvPtSSdL921s1nMr6XBEAAMDSRLBe\nBIIB0+3XrtEv9p/WW77wC7X3j/hdEgAAwJJDsF4k/uI3L9Jfv/MSHTo9qN/84i/15w/u0u4TvX6X\nBQAAsGQQrBcJM9N7rlyle373Kq2ojumeJ4/otruf0ssdA36XBgAAsCTYQl2mbdu2ba6lpcXvMkrW\ngfYBvfcfnlTfSFpbV9eouTquP3vbZtWWRfwuDQAAYEExs+ecc9vOdR491ovU+sZy/fOHX6M3XNCo\nZDqrB3a06qbP/1xffGS/ugdTfpcHAACw6NBjvUTsPN6jT92/Sy+09qoiFtL/uO0yveGCZX6XBQAA\nUPLoscYElzRX61/uvF4Pfew6ra5N6He/1qI/uHe7WnuG/S4NAABgUSBYLzGXNFfrvt+7Vh987Tr9\ncNcp3fS5n+ultn6/ywIAAFjwCNZLUFk0pE+9dbPu/dA1yuRyeuPfPqF3fflXeviFk8rlFubQIAAA\nAL8RrJewy1fX6Pt3vlYfueF8dQ2m9OFv/lrX/PdH9OiLbX6XBgAAsOAweRGSpGzO6QcvnNTfPbpf\nL7UN6LYrV+kTN29UY2XM79IAAAB8Nd3JiwRrTJDMZPX5n7ykf3jioAImrakr0yXNVfqvb9+iiljY\n7/IAAADm3XSDdWg+isHCEQ0F9cm3XKi3XbJSD+xo1aHTg3pwxwk9c6hLH77hfL3vqtUKBxlBBAAA\nMBnBGlO6uLlKFzdXSZIe39euv3hot/7fB3friZdO64vv26p4JOhzhQAAAKWFrkec0w2bGvX4n/yG\n/vCmjfrZ3ja9/e9/qZ+/1OF3WQAAACWFYI1p+/hNG/S/f/tK9Y+kdftXn9Hr/voxffnxl9XFFukA\nAABMXkThkpms/uHnB/XA9lYdPD2oSCigf3fVat121SpdsLzS7/IAAACKilVBMOecc9rV2qcvPrpf\nj7zYLuec3nfVar1ja5MuX12jQMD8LhEAAGDWihaszeyrkt4mqd05t8Vrq5X0HUlrJR2W9B7nXLf3\n2icl3SEpK+n3nXM/9tqvkPQ1SXFJP5T0ceecM7OopK9LukJSp6T3OucOn6twgnVp6R5M6W9+sk/f\nazmuVDan6kRY77y8We/Y2qQtTVV+lwcAADBjxQzWr5M0IOnr44L1X0vqcs59xszuklTjnPuPZrZZ\n0rclXSVppaSfSdronMua2TOSfl/S08oH6y845x42s49IusQ59x/M7DZJ73DOvfdchROsS1P/SFrf\n33lSP93Tpl8eOK1kJqfNKyr1765erd+6erXM6MUGAAALy3SD9TknLzrnnpDUNan5Vkn3eMf3SHr7\nuPZ7nXNJ59whSQckXWVmKyRVOueecvkk//VJ14y+1/ck3WikrwWrIhbW+65ara/+9pV65j/dpL/8\nzYskSX/2wC69638+qZc7BnyuEAAAYG7MdFWQZc65k97xKUnLvOMmScfGnXfca2vyjie3T7jGOZeR\n1CupbqpvamYfMrMWM2vp6GC5t1JXlQjr9tes1Q9+/3r91a0XaefxHt34uZ/r/V95WvdvP66hVMbv\nEgEAAIpm1hvEeOOk52UGpHPubkl3S/mhIPPxPTF7ZqYPXLtW16+v1wPbW3X/jlb94XeeV2Vst163\nsUFr6hK6bn29XnN+vd+lAgAAzNhMg3Wbma1wzp30hnm0e+2tklaNO6/Za2v1jie3j7/muJmFJFUp\nP4kRi8x5DeX6xBs36Q9v3qhnDnXp288c1XNHu/XwrlP6+8de1hsuaNQNmxp0+eoarW8sVyzM7o4A\nAGDhmGmwfkjS7ZI+4319cFz7t8zs88pPXtwg6Rlv8mKfmV2j/OTFD0j64qT3elLSuyQ96hbqGoCY\nFjPT1efV6erz8iN+RtJZ/fcf7tV9Lcf16Iv539FCAdNlq6r1mvPrtHVNjVbVJNRUHWcrdQAAULKm\nsyrItyXdIKleUpukP5f0gKT7JK2WdET55fa6vPM/Jel3JWUk/YFz7mGvfZteWW7vYUl3esNIYpK+\nIWmr8pMkb3POHTxX4awKsvjkck4n+0bUcrhLe0726ZcHTmtXa9/Y68GAaduaGv2bS1dqbV2Z4pGA\n4uGQNi2vUJA1swEAwBxhgxgsCr3DaW0/2q3e4bT2nerXj3ef0ssdgxPOqUmEddebL9A7tjYrEprp\nfFwAAICpEayxKDnn9HLHoDoHkhrJ5HSyZ1h3P3FQB08PqiYR1vuvXasr19ZobV2ZmmvirJsNAABm\nbbrBetarggDzycy0vrFc6xvLx9reeskKPfpiu7733HF94ZH9Y+3nN5RpfWO5rj2vTlesqdUFKyoU\nDtKjDQAA5gY91lhUeofS2nWiVzuO9ajlcJcOdAzoWNewpPyEyOpERKtq4zqvvly/9/rztKGxnF5t\nAADwqhgKAig/dKS1Z1g7jvVoz4k+dQ6kdKRrUM8e7lY251QRC2nzikpdva5Wb7t0pdY3lCvAREgA\nADAOwRp4FYdOD+qpg53a1dqrncd79UJrryRpWWVU59WXKxYOqLEipg3LyrV5ZaXW1pWptizC2toA\nACxBjLEGXsW6+jKtqy8be97aM6xfvNShJ/Z3qL0vqf5kWi+09uo7LcfGzjGTKmNhralLaOuqat1w\nQaO2rKxSQ0XUjx8BAACUGHqsgVfROZDUjmM9au9P6lTviLqHUtp3ql87j/dqOJ2VJK2simnrmhpd\n0lSl6zfUa/OKSsZtAwCwiNBjDRRBXXlUN1647Iz2oVRGzx/r1e4Tvdp+tEfbj3TrBztPSg9LtWWR\nsR7xd17erGvPr/OhcgAAMN/osQaKpK1vRI++2K6dx3t0sGNQL57qV+9wWqtq49q2plaXr67WpuWV\nWlEVY41tAAAWECYvAj7rG0nruy3H9eyhLrUc6dbpgeTYa2WRoF6/qUEXrazStjU1uqS5WvEIEyMB\nAChFBGughDjndLx7WIdOD+pY95CeO9KtJ17q0OmBlKT8GtublldoVU1CzTVxnddQrktXVSkeDqoi\nFlZ9eYQebgAAfMIYa6CEmJlW1Sa0qjYhSfqtq9dIym9o88zhLu041q2dx3t1oGNAP9vbpkxu4i+8\nsXBATdVxrasv09bVNVpXX6bXb2xQWZT/hAEAKBX8rQz4qCoR1s2bl+nmza9MkMzlnH59ND90ZCSd\nU89QSse6h9XaPaz97f362d52SVIiEtRvbGrUFWtqVFMWVnU8oktXVau2LOLXjwMAwJJGsAZKTCBg\n2ra29qyv942k9eyhLv1kd5se29euH7xwcsLr9eURNVTEtKwyqqbquFZWx1VXFtFFK6u0pYmlAAEA\nmCsEa2CBqYyFdeOFy3TjhcvknFP3UFq9w2md6h3RjmM9Oto1qPa+pNr68897htJj125aVqHXbqjX\nLVuWa+vqGgXZvh0AgKJh8iKwyI2ks2rrG9EvD3Tq/u3HteNYj9JZp0gwoMp4SPXlUa2oiqmuPKpY\nOKBoKKgLllfoHVubFAoG/C4fAADfsSoIgCl1DiT1y5c7tbu1V30jGXX0J3Wyd1hdgymlMjkNpbIa\nTmdVEQ1p88pKXdxUpfqKqC5bVa3LVlUrFmZZQADA0kKwBjAjzjk9srddj7/Url2tfdp9olfp7Cv/\nn6gti2jTsgptaarUhSsqtawy5m16k1AkRA83AGDxYbk9ADNiZrpp8zLd5K1U4pxT30hGT77cqZc7\nBnSsa0h7T/bpniePKJXJjV0XDpouX12jjcsqtKYuoc0rK7WlqUqVsbBfPwoAAPOKYA3gVZmZquJh\n3bJl+YT2dDanI51DOj2QVGv3sPa19evpg516cEer+kYykqRgwPS6DfXauLxCTdVxbWmq0qXN1Uya\nBAAsSgShiIYbAAATW0lEQVRrADMSDga0vrFc6xvLz3jt9EBSu0/06fvPn1DLkW49sf+0st6mN/Xl\nEa2qTeiWi5br0lXV2ramhkmSAIBFgTHWAOacc06n+kb0qwOd+uWB09rZ2qsD7QOSpEgwoCvW1OjK\ndbVaV5/QBcsrtbwypsp4mJ5tAEBJYPIigJLlnFN7f1JPHezU9qM9euZQl/ac7JtwTlU8rIubqrS8\nKqbllTGtrI5rfWO5ahJhRUL5ZQFryyJMmAQAzDmCNYAFZTiVVWvPkPac7Nfp/qSeP96jI51Dausb\nUXt/cmwoyXjBgGnLykq9fWuTrltfr3g4qOaaOLtLAgCKilVBACwo8UhQ6xsrtL6x4ozXsjmnk73D\nOtA+oP6RjFKZnEYyWZ3oGdZPdrfpL/9lz9i51Ymw6soiKo+GVBYNjfV2BwKmgEkBy381s7HjgJnM\npBVV+V7x5ZUxVSVYzQQAUBiCNYCSFwyYmmsSaq5JnPHaH79xk5462KXuoZQ6+pPa396vrsGUBpNZ\nDSYzenRfu/qG05qiw/tVLa+MaUtTlS5prtKauoSioaCioYCioYDW1JdpRWVMAcaAAwDGIVgDWNDM\nTNeeXzetc51zyjkp55xyzsmNHUvZrNOx7iEdPD2oU73D2nOiTy+09uqRF9s01Yi5aCig5VUxrasv\n09q6Mq2tS+iy1TWqiIUUDwfVUBFVmNVOAGBJIVgDWDLMTEGTgpq6p7kqUaUtTVUT2vpH0mrrG1Ey\nkxvb8v1w56AOnx7Uyd4RHWgfUMvhbg0kMxOuCwZMq2ri2risQletq9WVa2u1tq6MISYAsIgRrAHg\nVVTEwqqYtHvkdevrJzx3zulE74h2t/ZqOJ3VUCqr1u5hHTo9qF0nevWTPW2SpFDAdNOFy3Rxc5Wu\nPb9Oq2sTqklEWFYQABYJgjUAzJKZqak6rqbq+JSvt/WN6Lkj3Xr2cJd+tOuUfrT71Lhrpep4WHXl\nUdWWRVRXFlFdeUS1ZdGx4/ryqJqq4woHAwoHTbVlEVY+AYASxHJ7ADDPugdTevpQp9r6kuocTKlr\nMKnOgZQ6B1PqHEiqazClnuH0lGO7pfymOqM7WJZFQ1peFVNzTVyJcFDxSFCxcFCV8bAqY2GdV1+m\nRDSoSDBAGAeAGWK5PQAoUTVlEd2yZcWrnpPJ5tQ9lFbXYEqn+kbU1jeibM5pKJVVe/+IjncNq2Mg\nOdYb3jucfvXvmQjrijW1uvHCRl25tkZ1ZdGxEA4AKA6CNQCUoFAwoIaKqBoqotq0/My1vcdzzmkk\nndNwOqsRb4x373A+lB8+PaiRdFbHu4f1yItt+tnetgnX1iTCaqqJq7k6oVW1cVXGwgqHAoqHg1pV\nG1dFLKxwMKBIMKCqRFgrq2L0fAPAWRCsAWCBMzPFI/lhIK8mlcnpSOegth/t0WAqo8FkRid6R9Ta\nPaz97f16bF+7kpncq75HeTSk5pq4qhNhVcXD2ryiStWJsBKRoBKRkOKR/HbzkVBA1fH89vPBgI09\nQoGAgl69bEcPYLEhWAPAEhEJBbRhWYU2LJu6B9w5p2zOKZXNaTCZX1Ywmc4pnc0plc2pvT+pl9sH\ndLx7SH3DGb3UNqAf726b8r2mIxw0bwfM0R0x8ztgjt8RMxgw1ZdHlYgEtaWpSusbyxUNBRQLB9VY\nEVVjZUxlkaCi4aASkSBrhwPwFcEaACAp3/MdCppCwYASkZAaKqLnvGZ06MlgMqOhVH4oSjKT00g6\nq57htDLZnDK5fGAffaSzOQ2nshpKZ+Xc6MY9r2ze48Zt4pPK5NQ5kNLpgaS+9qvD56ynPBpSbVlE\n6+rLdNmqajXVxBUPB1UeDSkaDigcDHg95/nQHgkGVBnP974z3hzAbBGsAQAzFgvnJ0DWlkXm/Hv1\njaQ1ksoH9+F0Vu19+cmbo2PLB5NZ9QyndHogpf1t/fofj+wv6P3zK6dM7DE3kwIBG2sb38NeFQ/r\n9RsbdHFzlRrK85NBy6MhRUKB/CMYUFk0RC86sIQQrAEAC0JlLL+E4KiNZxnSMqp3OK3+kbSGU1n1\njWSUyuSUzTllcjllsk4Zr/e8dzit7sGUhtLZV3rMc+N70F85zo3rYT/SOaSv/vKQ0tlXX7Y2GDCZ\nXgnqTdVxVSXCinu/kDRURFURDak8FlJlLKwNy8pVFY8oHgmqKh5WWSTIhFFggSBYAwAWpSpviMdc\nSmVyeqmtX73D+QDfn0wrnXFKZnNKZ/KhPZubONTlSOeghlJZDaeyeqG1V10DKQ2kMmddtzwcNFXG\nwmPrk29oLFdDRVTBcT3pwUC+B31NXZkqYyHVlUfGhr2MPbwx66P/ygCg+AjWAADMUCQU0Jamqlm/\nTy7nNJTOqmsgpQMd/eofyWjYWzaxeyjf8z6SzqlzMKntx7rVNZBS1jnlcvmwnvV62qcjFDBdsKJC\nq2oS+X8FiIdUnYgoFs6v1FIZC2lNXZlCXggf/QWFVVyAcyNYAwDgs0DAVB4NqTwa0uq6xIzeo71/\nRG29yfwa5kOpsYmjuVx+2EvOOWWyTu39Se0+0asD7QPqHU7nx66nX32ZRUmqToTVXBNXWSSkmkRE\nK6vjWlOXGFt6sSoe1urahGrLIgxdwZJFsAYAYBForIipsSI2o2uTmaxGUjkls1l1DabU2j2sTM5p\nJJ3vNe8ZSutU34hO9gxrMJnVgY4B/fylDg2ns2e8VzBgKvMmciaiIZVFQyqLBMe+1pTle8eDZgqM\nW6Elv7xifiz6WJs3hKWxMqrVtQlVJyKKe0NZggHCO0oPwRoAgCUuGgoqGgpKCquxIqYLllee85pc\nzun0YFJ9w2lvAmhaR7qG1DmQ1GAyo0FvGcbBVFZDyYy6Boc0mMqocyDlTR7NKTfN4StTaaiIao3X\nQz7aa16diIz1nlcnwqqO51+rjIdVEQ0pQBjHHCNYAwCAggUCNqtecumVTYlGx4tnvee5sbb8MJbj\n3cM62TusnqG0RtJZDaezOto1pJM9IzraNaQXWvO96lP1oI/Va69MaK1KRFQdD08YP95UHVfC2xE0\nHMw/IiEbOw4HTcFAYKw3vSoeHus9j4UDDH+BJII1AADwydimROc4b2V1fFrvl8zkh670DqXV4w1h\nyQ9lSY0NaekdHn0tpcOdg2MruiQz5x5nfjaRYEArq2NqrklMWE6xMh7WssqoahIRxcIBxcP5jZfq\nyxmHvlgRrAEAwKIQDQXVWBEsuBc9l3PqHEwpnc2NPVIZN+74lR1Ex69/PpLOaiSdU89wflz68e5h\nPbqvfWwVl7OpLYto07IKVcZD3sos4bEVWipiYVXGQkpEQlpeFdWaujI2GVpACNYAAGBJCwRMDRXR\nor5nLufUO5xWe39SPUMpjWRyGkpm1NY3ohda+3Skc1CHTw+pbyStvuG0BlNTD2MJB00VsbDKo6EJ\nPd3BgHlDUQKqLYuovjyqVTUJrayOa3lVTOvqy5jg6QOCNQAAQJEFAqaasohqyiLTOj+Tzal/JKP+\nkUx+eEo6qyOdgzp4elADIxn1eDuESpKTUzrr1DOU0nA6q+eOdKtrMDVhMmgsHFBZJDS2sdDoEJSG\niqgayqNqqIiquSahDcvKVRENMTSlSAjWAAAAPgsFA2cE8avW1U77+mzO6VjXkE71jehY15D2nerX\nsDfRs284rY6BlF5uH1BHf1Kp7MRhKuGgqSoe0Zq6hK5fX6/XbazXpuWVKo8SEwtlbrpbNZWYbdu2\nuZaWFr/LAAAAWDCcc+obzqi9f0SHO4d0sGNgbDLn3pP92nm8Z6zne21dQmvryxQJBpSIBFWdiOg1\n59fp4uYqraia3oTSxcLMnnPObTvXefwqAgAAsESYmaoSYVUlwtqwrELSsgmv9wyl9OTLnTp4elDb\nj/aoo39EyUxOg6mM2vuS+tqvDkvKDzXZvKJSv3PdOl1zXl3Rx6gvVARrAAAASJKqExG9+eIVU742\nks5q+9Ee7WrtVVvfiB7YcUJ3fnu7JOmKNTW6el2trt9Qr2vPq1uyY7YZCgIAAICCZbI57TrRpyde\n6tCPd5/S7hN9kvLrel+6qkp1ZVHVlke8cdsNC3rM9nSHghCsAQAAMGu9Q2ndv/24Xu4Y1M7WXg2n\nMjrePayhVFbRUED15VHdsKlBH79xgxorZ75jpx8YYw0AAIB5U5UI67evWzehLZnJ6smXO/X4vg6d\n6BnWfS3H9MD2Vl2wolK/salBTTVxXbaqRmvrEoti+AjBGgAAAHMiGgrqhk2NumFToyRp78k+ffVf\nD2n7sR79fz95aey88xrK9NaLV+gTN29c0AGbYA0AAIB5ceGKSv3Nuy+VJLX3j+hY17B2Hu/Rt54+\nqi8+ekD7TvXr7g+cc8RFyWLzeQAAAMy7xoqYrlhTo9+5bp3+5c7rtaGxXD/Z06ZjXUN+lzZjBGsA\nAAD4KhYO6n95PdX3b2/1uZqZI1gDAADAd2vry3TFmhr9ePcpv0uZsZIJ1mZ2i5ntM7MDZnaX3/UA\nAABgfl17Xp12n+hTMpP1u5QZKYlgbWZBSX8v6c2SNkt6n5lt9rcqAAAAzKcNy8olSf/2S7/SN546\nokOnBxdUyC6VVUGuknTAOXdQkszsXkm3Strja1UAAACYN2+7ZKVePNWvbz51RP/5gV2SpEgooP/2\njov1riuafa7u3EolWDdJOjbu+XFJV/tUCwAAAHwQDJj+4y0X6E/ftEmHTg/q6UNd+ubTR/TH331e\nX3/ysB762PV+l/iqSiVYT4uZfUjShyRp9erVPlcDAACAuWBmOq+hXOc1lOvGCxv1Z/fvUjKT87us\ncyqVYN0qadW4581e2wTOubsl3S1J27Ztc/NTGgAAAPzSWBFbMJvGlMTkRUnPStpgZuvMLCLpNkkP\n+VwTAAAAMG0l0WPtnMuY2cck/VhSUNJXnXO7fS4LAAAAmLaSCNaS5Jz7oaQf+l0HAAAAMBOlMhQE\nAAAAWNAI1gAAAEAREKwBAACAIiBYAwAAAEVAsAYAAACKgGANAAAAFAHBGgAAACgCgjUAAABQBARr\nAAAAoAgI1gAAAEARmHPO7xpmxMw6JB3x6dvXSzrt0/dearjX84v7Pb+43/OHez2/uN/zi/s999Y4\n5xrOddKCDdZ+MrMW59w2v+tYCrjX84v7Pb+43/OHez2/uN/zi/tdOhgKAgAAABQBwRoAAAAoAoL1\nzNztdwFLCPd6fnG/5xf3e/5wr+cX93t+cb9LBGOsAQAAgCKgxxoAAAAoAoJ1AczsFjPbZ2YHzOwu\nv+tZDMzssJm9YGY7zKzFa6s1s5+a2X7va8248z/p3f99ZvYm/ypfGMzsq2bWbma7xrUVfH/N7Arv\nz+mAmX3BzGy+f5aF4Cz3+y/MrNX7jO8ws7eMe437PUNmtsrMHjOzPWa228w+7rXz+Z4Dr3K/+XzP\nATOLmdkzZva8d7//0mvn813qnHM8pvGQFJT0sqTzJEUkPS9ps991LfSHpMOS6ie1/bWku7zjuyR9\n1jve7N33qKR13p9H0O+foZQfkl4n6XJJu2ZzfyU9I+kaSSbpYUlv9vtnK8XHWe73X0j64ynO5X7P\n7l6vkHS5d1wh6SXvnvL5nt/7zed7bu63SSr3jsOSnvbuGZ/vEn/QYz19V0k64Jw76JxLSbpX0q0+\n17RY3SrpHu/4HklvH9d+r3Mu6Zw7JOmA8n8uOAvn3BOSuiY1F3R/zWyFpErn3FMu/3/pr4+7BuOc\n5X6fDfd7FpxzJ51zv/aO+yXtldQkPt9z4lXu99lwv2fB5Q14T8Pew4nPd8kjWE9fk6Rj454f16v/\nTwXT4yT9zMyeM7MPeW3LnHMnveNTkpZ5x/wZFEeh97fJO57cjum708x2ekNFRv/plvtdJGa2VtJW\n5Xv1+HzPsUn3W+LzPSfMLGhmOyS1S/qpc47P9wJAsIbfrnfOXSbpzZI+amavG/+i9xs2S9fMEe7v\nvPiy8kPILpN0UtLn/C1ncTGzckn/LOkPnHN941/j8118U9xvPt9zxDmX9f5+bFa+93nLpNf5fJcg\ngvX0tUpaNe55s9eGWXDOtXpf2yXdr/zQjjbvn6/kfW33TufPoDgKvb+t3vHkdkyDc67N+wsyJ+l/\n6ZXhS9zvWTKzsPIh75vOuf/jNfP5niNT3W8+33PPOdcj6TFJt4jPd8kjWE/fs5I2mNk6M4tIuk3S\nQz7XtKCZWZmZVYweS3qjpF3K39fbvdNul/Sgd/yQpNvMLGpm6yRtUH5SBgpT0P31/tmxz8yu8WaT\nf2DcNTiH0b8EPe9Q/jMucb9nxbs3X5G01zn3+XEv8fmeA2e733y+54aZNZhZtXccl3SzpBfF57vk\nhfwuYKFwzmXM7GOSfqz8CiFfdc7t9rmshW6ZpPu9lX9Ckr7lnPuRmT0r6T4zu0PSEUnvkSTn3G4z\nu0/SHkkZSR91zmX9KX1hMLNvS7pBUr2ZHZf055I+o8Lv70ckfU1SXPlZ5Q/P44+xYJzlft9gZpcp\n/0+2hyX9nsT9LoLrJL1f0gveOFRJ+k/i8z1Xzna/38fne06skHSPmQWV7wS9zzn3fTN7Uny+Sxo7\nLwIAAABFwFAQAAAAoAgI1gAAAEAREKwBAACAIiBYAwAAAEVAsAYAAACKgGANAAAAFAHBGgAAACgC\ngjUAAABQBP8XWLepz/Q056UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c97f08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "plt.plot(np.sort(tfidf_train.getnnz(1))[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a8de2c88>]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Go9NlLBlhGAAAAEvmhUwN/04ME4YBAACwdGFmhgEAABBUoZCpHtSeYTPrNrPP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\nBBZhGAAAAIH1/wGAcQOS7++r7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e8ea3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log transformed\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(np.sort(np.log(tfidf_train.getnnz(1)))[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 193.55342793,  138.98355951,  121.01724619,   98.93547919,\n",
       "         91.42839175,   85.64171263,   84.84211607,   84.70932757,\n",
       "         84.5755022 ,   84.44620146])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(np.array(tfidf_train.sum(axis=0))[0])[::-1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1814889"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(np.array(tfidf_train.sum(axis=0))[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top words overall that contribute to TF-IDF scores for the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] mutations  [2] brca1  [3] cells  [4] variants  [5] fig  [6] mutation  [7] al  [8] cell  [9] et al  [10] et  [11] cancer  [12] pten  [13] egfr  [14] figure  [15] patients  [16] p53  [17] protein  [18] activity  [19] kinase  [20] mutant  [21] tumor  [22] type  [23] domain  [24] mutants  [25] expression  [26] brct  [27] binding  [28] vus  [29] ras  [30] alk  [31] data  [32] using  [33] wild  [34] tumors  [35] wild type  [36] analysis  [37] gene  [38] dna  [39] table  [40] raf  [41] deleterious  [42] supplementary  [43] 10  [44] activation  [45] braf  [46] exon  [47] imatinib  [48] variant  [49] kit  [50] brca2  [51] resistance  [52] pdgfra  [53] functional  [54] results  [55] genes  [56] function  [57] associated  [58] tsc2  [59] missense  [60] flt3  [61] wt  [62] identified  [63] clinical  [64] used  [65] growth  [66] shown  [67] signaling  [68] proteins  [69] human  [70] residues  [71] akt  [72] mtor  [73] assay  [74] study  [75] breast  [76] neutral  [77] assays  [78] phosphorylation  [79] gefitinib  [80] lines  [81] fgfr2  [82] erbb2  [83] smad4  [84] structure  [85] melanoma  [86] levels  [87] sequence  [88] treatment  [89] cases  [90] amino  [91] cell lines  [92] observed  [93] receptor  [94] fusion  [95] family  [96] studies  [97] pathway  [98] patient  [99] tsc1  [100] 20  [101] different  [102] disease  [103] nrf2  [104] samples  [105] lung  [106] kras  [107] response  [108] number  [109] 12  [110] reported  [111] transcriptional  [112] p110α  [113] showed  [114] expressing  [115] author manuscript  [116] inhibitors  [117] loop  [118] previously  [119] tyrosine  [120] site  [121] ret  [122] based  [123] 11  [124] her2  [125] smad2  [126] specific  [127] classification  [128] 15  [129] sequencing  [130] mice  [131] control  [132] fgfr3  [133] pcr  [134] loss  [135] brca  [136] described  [137] high  [138] anti  [139] met  [140] mm  [141] pik3ca  [142] risk  [143] phosphatase  [144] domains  [145] smad3  [146] 14  [147] cbl  [148] performed  [149] compared  [150] notch  [151] pathogenic  [152] pi3k  [153] erk  [154] cancers  [155] region  [156] inhibitor  [157] supplementary fig  [158] effect  [159] genetic  [160] somatic  [161] egfr mutations  [162] interaction  [163] mutated  [164] line  [165] survival  [166] acid  [167] catalytic  [168] brca1 brct  [169] including  [170] kinase domain  [171] vitro  [172] yeast  [173] ba f3  [174] increased  [175] 13  [176] ba  [177] transfected  [178] inhibition  [179] jak2  [180] crizotinib  [181] individuals  [182] known  [183] odds  [184] 50  [185] similar  [186] mek  [187] nsclc  [188] proliferation  [189] normal  [190] likely  [191] oncogenic  [192] 18  [193] terminal  [194] activating  [195] author  [196] class  [197] resistant  [198] p16ink4a  [199] sensitivity  [200] f3  "
     ]
    }
   ],
   "source": [
    "top_features = np.argsort(np.array(tfidf_train.sum(axis=0))[0])[::-1][:200]\n",
    "for i, idx in enumerate(top_features):\n",
    "    print(\"[{}] {}\".format(i+1, tfidf.get_feature_names()[idx]), end=\" \"*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The words that contribute least overall to TF-IDF scores for the training set are listed below. Many of these appear to be words with citations following them, such as `identified16`, `treatments6`, `tumours18`, `immunological26`, etc. Unfortunately, this presents a challenge because some of the genes also end with numbers(such as `RUNX1`, `BRCA2`), which means we can't just truncate the words. We also see things like gene sequences (i.e. `aagggaccagtacag`) and measurements (`333nm`, `200µg`) that likely refer to results discussed in specific papers. These might be relevant in very specific context, but probably won't help generalize predictions during inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bottom_features = np.argsort(np.array(tfidf_train.sum(axis=0))[0])[::-1][-200:-1]\n",
    "for i, idx in enumerate(bottom_features):\n",
    "    print(\"[{}] {}\".format(i+1, tfidf.get_feature_names()[idx]), end=\" \"*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(np.sort(np.array(np.log(tfidf_train.sum(axis=0)))[0])[::-1][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#tfidf_train[0,1]\n",
    "pd.DataFrame(tfidf_train.sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(tfidf_train.sum(axis=0)).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Text Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create list of unique `Gene` and `Variation` values that are in the training and test sets. This will be used for text preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10116"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_obj = [\n",
    "    X_train['Gene'],\n",
    "    X_train['Variation'],\n",
    "    X_test['Gene'],\n",
    "    X_test['Variation']\n",
    "]\n",
    "\n",
    "tt_corpus = []\n",
    "[tt_corpus.extend(df.values.tolist()) for df in corpus_obj]\n",
    "tt_corpus = pd.Series(tt_corpus)\n",
    "tt_corpus = [s.lower() for s in list(tt_corpus.unique())]\n",
    "len(tt_corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to correct our text for tokens that appear as different due to text formatting of things like citations. Ideally, we would compare our features to a dictionary list that contains common terms for the domain of genomics research. Using just the list created from the `Gene` and ` Variation` data, we are at risk of missing important tokens that might be related, or not in the observations.\n",
    "\n",
    "One example, is `p16ink4a`, which was one of the highest predictors for tfidf, but does not appear in our list of terms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p16ink4a'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_x = \"p16ink4a\"\n",
    "if str_x.lower() not in tt_corpus:\n",
    "    str_x = re.sub(r'^[^a-zA-z]*|[^a-zA-Z]*$', \"\", str_x, 0)\n",
    "str_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('l155h', 4413),\n",
       " ('pkhd1', 6115),\n",
       " ('y570h', 9994),\n",
       " ('nipa1', 5536),\n",
       " ('s1297del', 7950),\n",
       " ('r299h', 7058),\n",
       " ('r216c', 6832),\n",
       " ('r108l', 6441),\n",
       " ('chst3', 1119),\n",
       " ('y514c', 9977),\n",
       " ('bbs9', 662),\n",
       " ('y599_d600inseyeyeyey', 10002),\n",
       " ('t14p', 8627),\n",
       " ('w372s', 9647),\n",
       " ('cyp21a2', 1193),\n",
       " ('s199c', 8047),\n",
       " ('y374c', 9923),\n",
       " ('t599_v600insv', 8882),\n",
       " ('y568_l576delinsvn', 9993),\n",
       " ('r69c', 7610)]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_gene = CountVectorizer(lowercase=True, ngram_range=(1,1))\n",
    "cv_gene.fit(tt_corpus)\n",
    "list(islice(cv_gene.vocabulary_.items(), 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Custom Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sw = stopwords\n",
    "stopwrds = sw.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def text_preprocessor(s, action=1):\n",
    "    \n",
    "    # lowercase (this is already defaulted in Count Vectorizer)\n",
    "    s = s.lower()\n",
    "    \n",
    "    # Regex functions\n",
    "    def regexer(s, keep=None):\n",
    "        \n",
    "        # remove numbers at end of words that might be citations or other misleading modifiers\n",
    "        if keep == \"no_citations\":\n",
    "            if s.lower() not in tt_corpus:\n",
    "                regex = r\"^[^a-zA-z]*|[^a-zA-Z]*$\"\n",
    "                \n",
    "        # remove all numbers in words that might be citations or other misleading modifiers\n",
    "        if keep == \"no_numbers\":\n",
    "            if s.lower() not in tt_corpus:\n",
    "                regex = r\"[^a-zA-Z ]\"\n",
    "            \n",
    "        # Apply Regex transformation\n",
    "        s = re.sub(regex, \"\", s, 0)\n",
    "        return s\n",
    "    \n",
    "    # Remove stop words\n",
    "    def stopwords(s):\n",
    "        \n",
    "        # Use nltk function for tokenization\n",
    "        tokens = word_tokenize(s) \n",
    "        \n",
    "        # Get stopwords list from nltk.corpus and remove words in the list\n",
    "        s_bin = [w for w in tokens if not w in stopwrds]\n",
    "        s = ' '.join(s_bin)\n",
    "        return s\n",
    "    \n",
    "    # Lemmatize or stem function\n",
    "    def lemmatize(s, func=\"stem\"):\n",
    "        # Split string into individual tokens\n",
    "        tokens = word_tokenize(s) \n",
    "        s_bin = []\n",
    "        \n",
    "        # Stem tokens\n",
    "        if func == \"stem\":\n",
    "            fn = PorterStemmer().stem\n",
    "        # Lemmatization\n",
    "        if func == \"lemma\":\n",
    "            fn = WordNetLemmatizer().lemmatize\n",
    "        \n",
    "        for t in tokens:\n",
    "            s_bin.append(fn(t))\n",
    "        s = ' '.join(s_bin)\n",
    "        return s\n",
    "    \n",
    "    # Select transformation based on `action` parameter\n",
    "    if 0 in action:\n",
    "        s = regexer(s, keep=\"no_citations\")\n",
    "    if 1 in action:\n",
    "        s = regexer(s, keep=\"no_numbers\")\n",
    "    if 2 in action:\n",
    "        s = stopwords(s)\n",
    "    if 3 in action:\n",
    "        s = lemmatize(s, \"lemma\")\n",
    "    if 4 in action:\n",
    "        s = lemmatize(s, \"stem\")\n",
    "\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.1, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 2),\n",
       "        preprocessor=<function <lambda> at 0x14cd481e0>,\n",
       "        stop_words='english', strip_accents=None,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer(preprocessor=lambda x: text_preprocessor(x, action=[0, 2, 3]), \n",
    "                     lowercase=True, \n",
    "                     stop_words='english', \n",
    "                     min_df=1, \n",
    "                     max_df=.1, \n",
    "                     ngram_range=(1,2))\n",
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv_train_mini = cv.fit_transform(corpus_train_mini)\n",
    "cv_dev_mini = cv.transform(corpus_dev_mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_cv = cv.fit_transform(corpus_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_cv = cv.transform(corpus_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some sample bigrams from the count vectorizer show terms that intuitively seem to be relevant to determining the class of gene-variant pairings. For example, `methylation mutational` describes the process of methylation, which regulates gene expression, and the pairing with `mutational` provides context. `resistance clinical` seems important in the context of gene pairings, as well as `site frequency`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('128 MN1', 37587),\n",
       " ('problem genetic', 2394330),\n",
       " ('size inversely', 2649165),\n",
       " ('Invitrogen catalog', 669650),\n",
       " ('methylation mutational', 2115605),\n",
       " ('hotspot SNPs', 1850576),\n",
       " ('Endogenous MyD88', 500475),\n",
       " ('supplement 1B', 2732633),\n",
       " ('Recently NUP98', 921101),\n",
       " ('phase data', 2322665),\n",
       " ('resistance clinical', 2533397),\n",
       " ('site frequency', 2646231),\n",
       " ('underline 712', 2843950),\n",
       " ('pGBT9 TRP1', 2276264),\n",
       " ('Fgfr3 Viable', 533516),\n",
       " ('pY869 detected', 2282312),\n",
       " ('665752 JNJ38877605', 225464),\n",
       " ('Research Inc', 926139),\n",
       " ('key advance', 1985754),\n",
       " ('site Their', 2645338)]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample bi-grams\n",
    "list(islice(cv.vocabulary_.items(), 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tri-grams might also be relevant, and there are some examples below, but due to computational limitations, using CountVectorizer or TFIDF with a range of 1-3 doesn't seem possible without moving this analysis to a cloud computing platform. Even when selecting for only 10% of the possible vocabulary, the number of features grows to over 20 million when tokenizing with `range(1,3)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('day 43 005', 2358384),\n",
       " ('differently result combined', 2583154),\n",
       " ('man 191100 clinical', 4757053),\n",
       " ('ssa hdr', 7250466),\n",
       " ('10 dl hematocrit', 37545),\n",
       " ('medulloblastomas 68 demonstrated', 4851125),\n",
       " ('functioning different pathway', 3452747),\n",
       " ('analtech newark developed', 1070313),\n",
       " ('rasq61r control', 6362798),\n",
       " ('tumor tissue sts', 7876878),\n",
       " ('nk granulo monocytic', 5343760),\n",
       " ('block cdh1', 1433634),\n",
       " ('vivo study pk', 8146749),\n",
       " ('containing p53 dna', 2137352),\n",
       " ('deficiency promotes differentiated', 2392257),\n",
       " ('pvhl213 lysine', 6268396),\n",
       " ('project bi78d3 santa', 6135025),\n",
       " ('counted study property', 2214788),\n",
       " ('hdmec hemec transfected', 3747309),\n",
       " ('amplification 11q22 subgroup', 1054921)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample tri-grams\n",
    "list(islice(cv.vocabulary_.items(), 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sparse matrix shape: (3321, 2690998)\n",
      "nonzero count: 15229698\n",
      "sparsity: 0.17\n"
     ]
    }
   ],
   "source": [
    "len(cv.vocabulary_)\n",
    "print('sparse matrix shape: {}'.format(train_cv.shape))\n",
    "print('nonzero count: {}'.format(train_cv.nnz))\n",
    "print('sparsity: {:.2f}'.format((100.0 * train_cv.nnz / (train_cv.shape[0] * train_cv.shape[1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sparse matrix shape: (3321, 8277612)\n",
      "nonzero count: 33126986\n",
      "sparsity: 0.12\n"
     ]
    }
   ],
   "source": [
    "len(cv.vocabulary_)\n",
    "print('sparse matrix shape: {}'.format(train_cv.shape))\n",
    "print('nonzero count: {}'.format(train_cv.nnz))\n",
    "print('sparsity: {:.2f}'.format((100.0 * train_cv.nnz / (train_cv.shape[0] * train_cv.shape[1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occurrences</th>\n",
       "      <th>term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2645842</th>\n",
       "      <td>13532</td>\n",
       "      <td>vus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874194</th>\n",
       "      <td>12129</td>\n",
       "      <td>pdgfra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753241</th>\n",
       "      <td>11573</td>\n",
       "      <td>notch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546317</th>\n",
       "      <td>11073</td>\n",
       "      <td>tsc2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179301</th>\n",
       "      <td>11056</td>\n",
       "      <td>gist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326362</th>\n",
       "      <td>10944</td>\n",
       "      <td>smad4</td>\n",
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       "      <th>410356</th>\n",
       "      <td>10589</td>\n",
       "      <td>ar</td>\n",
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       "    <tr>\n",
       "      <th>2354408</th>\n",
       "      <td>10078</td>\n",
       "      <td>spop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1760727</th>\n",
       "      <td>9014</td>\n",
       "      <td>nrf2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324983</th>\n",
       "      <td>8603</td>\n",
       "      <td>smad2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747697</th>\n",
       "      <td>8522</td>\n",
       "      <td>crizotinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325665</th>\n",
       "      <td>8456</td>\n",
       "      <td>smad3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426972</th>\n",
       "      <td>8202</td>\n",
       "      <td>jak2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073212</th>\n",
       "      <td>7350</td>\n",
       "      <td>fgfr2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569909</th>\n",
       "      <td>7301</td>\n",
       "      <td>cbl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545863</th>\n",
       "      <td>6955</td>\n",
       "      <td>tsc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564333</th>\n",
       "      <td>6804</td>\n",
       "      <td>catenin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2177510</th>\n",
       "      <td>6632</td>\n",
       "      <td>ret</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733203</th>\n",
       "      <td>6601</td>\n",
       "      <td>nf1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753592</th>\n",
       "      <td>6349</td>\n",
       "      <td>notch1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         occurrences        term\n",
       "2645842        13532         vus\n",
       "1874194        12129      pdgfra\n",
       "1753241        11573       notch\n",
       "2546317        11073        tsc2\n",
       "1179301        11056        gist\n",
       "2326362        10944       smad4\n",
       "410356         10589          ar\n",
       "2354408        10078        spop\n",
       "1760727         9014        nrf2\n",
       "2324983         8603       smad2\n",
       "747697          8522  crizotinib\n",
       "2325665         8456       smad3\n",
       "1426972         8202        jak2\n",
       "1073212         7350       fgfr2\n",
       "569909          7301         cbl\n",
       "2545863         6955        tsc1\n",
       "564333          6804     catenin\n",
       "2177510         6632         ret\n",
       "1733203         6601         nf1\n",
       "1753592         6349      notch1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ = np.asarray(train_cv.sum(axis=0)).ravel().tolist()\n",
    "counts_df = pd.DataFrame({'term': cv.get_feature_names(), 'occurrences': occ})\n",
    "counts_df.sort_values(by='occurrences', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occurrences</th>\n",
       "      <th>term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>481169</th>\n",
       "      <td>1</td>\n",
       "      <td>bgj398 patient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844442</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827008</th>\n",
       "      <td>1</td>\n",
       "      <td>described hepatocellular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481156</th>\n",
       "      <td>1</td>\n",
       "      <td>bgj398 crc334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249173</th>\n",
       "      <td>1</td>\n",
       "      <td>hinted egfrviii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844460</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter modulating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481144</th>\n",
       "      <td>1</td>\n",
       "      <td>bgiii sensitive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844458</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter mm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844457</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter mlh1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844456</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter minimum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844454</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter mg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844452</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481154</th>\n",
       "      <td>1</td>\n",
       "      <td>bgj398 cetuximab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844451</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter metacore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844450</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter mentioned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249174</th>\n",
       "      <td>1</td>\n",
       "      <td>hinted greater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249175</th>\n",
       "      <td>1</td>\n",
       "      <td>hinted her2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481152</th>\n",
       "      <td>1</td>\n",
       "      <td>bgj398 86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844446</th>\n",
       "      <td>1</td>\n",
       "      <td>parameter measurable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345499</th>\n",
       "      <td>1</td>\n",
       "      <td>indeterminate altered</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         occurrences                      term\n",
       "481169             1            bgj398 patient\n",
       "1844442            1       parameter malignant\n",
       "827008             1  described hepatocellular\n",
       "481156             1             bgj398 crc334\n",
       "1249173            1           hinted egfrviii\n",
       "1844460            1      parameter modulating\n",
       "481144             1           bgiii sensitive\n",
       "1844458            1              parameter mm\n",
       "1844457            1            parameter mlh1\n",
       "1844456            1         parameter minimum\n",
       "1844454            1              parameter mg\n",
       "1844452            1          parameter method\n",
       "481154             1          bgj398 cetuximab\n",
       "1844451            1        parameter metacore\n",
       "1844450            1       parameter mentioned\n",
       "1249174            1            hinted greater\n",
       "1249175            1               hinted her2\n",
       "481152             1                 bgj398 86\n",
       "1844446            1      parameter measurable\n",
       "1345499            1     indeterminate altered"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_df.sort_values(by='occurrences', ascending=False).tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tfidf_fn(X, Y, ngrams=1, mn=1, mx=0.1, test_transform=False, plotting=False):\n",
    "    t = time.time()\n",
    "    tfidf = TfidfVectorizer(stop_words = 'english', \n",
    "                            ngram_range=(1, ngrams), \n",
    "                            sublinear_tf=True, \n",
    "                            use_idf=True, \n",
    "                            preprocessor=lambda x: text_preprocessor(x, action=[0, 2, 3]), \n",
    "                            lowercase=True, \n",
    "                            min_df=mn, \n",
    "                            max_df=mx)\n",
    "    print(\"Beginning tf-idf on training data\", end=\"\\r\")\n",
    "    tfidf_train = tfidf.fit_transform(X)\n",
    "    print(print_op_str(\"train\").format(time.time()-t), end=\"\\r\")\n",
    "    \n",
    "    if test_transform == True:\n",
    "        print(\"Beginning tf-idf on test data\", end=\"\\r\")\n",
    "        tfidf_test = tfidf.transform(Y)\n",
    "        print(print_op_str(\"test\").format(time.time()-t), end=\"\\r\")\n",
    "        print_blank(len(print_op_str(\"test\")))\n",
    "    else:\n",
    "        tfidf_test = sps.csr_matrix((1,1))\n",
    "        print_blank(len(print_op_str(\"train\")))\n",
    "    \n",
    "    print_op = \"Shape: {}\\nNon-zero mean: {}\\nNon-zero median: {}\"\n",
    "    mean_nnz = int(round(np.mean(tfidf_train.getnnz(1))))\n",
    "    median_nnz = int(round(np.median(tfidf_train.getnnz(1))))\n",
    "    print(print_op.format(tfidf_train.shape, mean_nnz,median_nnz))\n",
    "    print(\"\\nDone in {:.2f} seconds\".format(time.time()-t))\n",
    "    \n",
    "    if plotting == True:     \n",
    "        fig, ax = plt.subplots(figsize=(12, 6))\n",
    "        plt.plot(np.sort(tfidf_train.getnnz(1))[::-1])\n",
    "\n",
    "        # Log transformed\n",
    "        plt.figure(figsize=(12,6))\n",
    "        plt.plot(np.sort(np.log(tfidf_train.getnnz(1)))[::-1])\n",
    "        \n",
    "    return tfidf, tfidf_train, tfidf_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (166, 31358)                        \n",
      "Non-zero mean: 478\n",
      "Non-zero median: 394\n",
      "\n",
      "Done in 81.18 seconds\n"
     ]
    }
   ],
   "source": [
    "tf_mini, tf_train_mini, tf_dev_mini = tfidf_fn(corpus_train_mini, \n",
    "                                                corpus_dev_mini, \n",
    "                                                ngrams=1, \n",
    "                                                mn=1, \n",
    "                                                mx=0.1, \n",
    "                                                test_transform=True, \n",
    "                                                plotting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occurrences</th>\n",
       "      <th>term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34791</th>\n",
       "      <td>1.558993</td>\n",
       "      <td>vus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19955</th>\n",
       "      <td>1.315848</td>\n",
       "      <td>l858r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31120</th>\n",
       "      <td>1.314083</td>\n",
       "      <td>stat5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15606</th>\n",
       "      <td>1.272930</td>\n",
       "      <td>gist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24631</th>\n",
       "      <td>1.190774</td>\n",
       "      <td>p110α</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25284</th>\n",
       "      <td>1.139664</td>\n",
       "      <td>pdgfra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30669</th>\n",
       "      <td>1.104860</td>\n",
       "      <td>smad4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7597</th>\n",
       "      <td>1.079173</td>\n",
       "      <td>brca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10394</th>\n",
       "      <td>1.026186</td>\n",
       "      <td>crizotinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21587</th>\n",
       "      <td>1.024441</td>\n",
       "      <td>mek2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>1.019304</td>\n",
       "      <td>dasatinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8511</th>\n",
       "      <td>1.012527</td>\n",
       "      <td>causality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30665</th>\n",
       "      <td>1.008039</td>\n",
       "      <td>smad2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8932</th>\n",
       "      <td>0.969727</td>\n",
       "      <td>cfc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>0.945359</td>\n",
       "      <td>brct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30662</th>\n",
       "      <td>0.936103</td>\n",
       "      <td>smad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5931</th>\n",
       "      <td>0.914855</td>\n",
       "      <td>appendix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22251</th>\n",
       "      <td>0.907693</td>\n",
       "      <td>mmr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>0.903591</td>\n",
       "      <td>cdk4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11183</th>\n",
       "      <td>0.897954</td>\n",
       "      <td>dbd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       occurrences        term\n",
       "34791     1.558993         vus\n",
       "19955     1.315848       l858r\n",
       "31120     1.314083       stat5\n",
       "15606     1.272930        gist\n",
       "24631     1.190774       p110α\n",
       "25284     1.139664      pdgfra\n",
       "30669     1.104860       smad4\n",
       "7597      1.079173        brca\n",
       "10394     1.026186  crizotinib\n",
       "21587     1.024441        mek2\n",
       "11142     1.019304   dasatinib\n",
       "8511      1.012527   causality\n",
       "30665     1.008039       smad2\n",
       "8932      0.969727         cfc\n",
       "7609      0.945359        brct\n",
       "30662     0.936103        smad\n",
       "5931      0.914855    appendix\n",
       "22251     0.907693         mmr\n",
       "8753      0.903591        cdk4\n",
       "11183     0.897954         dbd"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ = np.asarray(tf_train_mini.sum(axis=0)).ravel().tolist()\n",
    "comp_df = pd.DataFrame({'term': tf_mini.get_feature_names(), 'occurrences': occ})\n",
    "comp_df.sort_values(by='occurrences', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mini data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd_metrics(s, n):\n",
    "    explained_variance = s.explained_variance_ratio_.sum()\n",
    "    #     print_op = \"Explained variance of SVD with {} features: {}%\"\n",
    "    #     print(print_op.format(n, int(explained_variance * 100)))\n",
    "    return explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd_fn(data_train, data_test=None, target=None, n_features=20, test_transform=False, plotting=False):\n",
    "    t = time.time()\n",
    "    print(\"Beginning svd on training data\", end=\"\\r\")\n",
    "    svd = TruncatedSVD(n_features, algorithm='arpack')\n",
    "    svd_train = svd.fit_transform(data_train)\n",
    "    print(print_op_str(\"train\").format(time.time()-t), end=\"\\r\")\n",
    "    \n",
    "    if test_transform == True:\n",
    "        print(\"Beginning svd on test data\", end=\"\\r\")\n",
    "        svd_test = svd.transform(data_test)\n",
    "        print(print_op_str(\"test\").format(time.time()-t), end=\"\\r\")\n",
    "        print_blank(len(print_op_str(\"test\")))\n",
    "    else:\n",
    "        svd_test = sps.csr_matrix((1,1))\n",
    "        print_blank(len(print_op_str(\"train\")))\n",
    "        \n",
    "    total_explained_variance = svd_metrics(svd, n_features)\n",
    "        \n",
    "    if plotting == True:\n",
    "        data2D_svd = svd_train\n",
    "        plt.scatter(data2D_svd[:,0], data2D_svd[:,1], c=target)\n",
    "        plt.show()  \n",
    "        \n",
    "    return svd, svd_train, svd_test, total_explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_n_svd = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning svd on training data\r",
      "Done processing train data in 0.02 seconds\r",
      "                                            \r",
      "Explained variance of SVD with 2 features: 5%\n"
     ]
    }
   ],
   "source": [
    "svd_mini, svd_train_mini, svd_test_mini, tev_mini = svd_fn(tf_train_mini, \n",
    "                                                 tf_dev_mini, \n",
    "                                                 y_train_mini, \n",
    "                                                 features_n_svd, \n",
    "                                                 test_transform=False, \n",
    "                                                 plotting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Vt = svd_mini.components_[:20, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x147ee7c50>]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1rZTXz57I6ysnMru8iNSKahE5VgoKGTfcU6HxyoEWmtsTHGnvZF9TG9tfbWbH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144c8f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(svd_mini.explained_variance_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of SVD and hyperparameter search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the processing time necessary for preprocessing steps, we'll try to identify optimal values of hyperparameters by using smaller subsets of the the training data. We'll use a sampling method to randomly select (without replacement) from the training set to obtain samples that range in size from 4%-12% of the total data size, just to make sure the size of the samples doesn't impact the dimensionality reduction significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd_comp_analysis(cv_train_subset, n_comp, plotting=True, printout=False):\n",
    "\n",
    "    t0 = time.time()\n",
    "    \n",
    "    analysis = {}\n",
    "    params_svd = {'n_components': range(1,n_comp+1)}\n",
    "    for i, n in enumerate(params_svd['n_components']):\n",
    "        \n",
    "        config_n = i\n",
    "        config_params = {}\n",
    "        \n",
    "        message0 = \"SVD n_components: {}\".format(n)\n",
    "        if printout == True:\n",
    "            print(message0)\n",
    "    \n",
    "        # Apply SVD transformation\n",
    "        t1 = time.time()\n",
    "        message3 = \"\\tBeginning svd component: {}\".format(n)\n",
    "        \n",
    "        if printout == True:\n",
    "            print(message3, end=\"\\r\")\n",
    "            \n",
    "        svd = TruncatedSVD(n_components=n, algorithm='arpack')\n",
    "        svd.fit_transform(cv_train_subset)\n",
    "        config_params[\"explained_variance_ratio\"] = svd.explained_variance_ratio_\n",
    "              \n",
    "        if printout == True:\n",
    "            print(print_blank(len(message3)), end=\"\\r\")\n",
    "        message4 = \"\\tSVD done in {:.2f} seconds\".format(time.time() - t1)\n",
    "              \n",
    "        evar = svd_metrics(svd, n)\n",
    "        config_params[\"explained_variance\"] = evar\n",
    "              \n",
    "        analysis[config_n] = config_params\n",
    "        if printout == True:\n",
    "            print(\"\\t{} components completed in: {:.2f} seconds\".format(n, time.time()-t1))\n",
    "        message5 = \"{} components in {:.2f}(iter)/{:.2f}(total) secs\".format(n, time.time()-t1, time.time()-t0)\n",
    "        message6 = \"{} {}\".format(message5, \". \"*n)\n",
    "        print(message6, end=\"\\r\")\n",
    "    \n",
    "    print(print_blank(len(message6)), end=\"\\r\")\n",
    "    print(\"\\nTotal time to completion for {} components: {:.2f} seconds\".format(n_comp, time.time()-t0))\n",
    "    \n",
    "    return analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd_batch_analysis(n_epoch=1, n_comp=20, plotting=True):\n",
    "    t0 = time.time()\n",
    "    \n",
    "    if plotting == True:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    batch = {}\n",
    "    epochs = {'n_epoch': range(1,n_epoch+1)}\n",
    "    for i, n in enumerate(epochs['n_epoch']):\n",
    "        \n",
    "        t1 = time.time()\n",
    "        message0 = \"Epoch: {}\".format(n)\n",
    "        print(message0, end=\"\\r\")\n",
    "    \n",
    "        comp_results = {}\n",
    "        # create subset with `p_data` percent of training data in range(4,12)\n",
    "        p_data = np.random.randint(4, 12)/100\n",
    "        comp_results[\"p_data\"] = p_data\n",
    "        \n",
    "        # Create mini data sets for model building\n",
    "        train_subset = train.sample(frac=p_data)\n",
    "        y_train_subset = train_subset['Class'].values\n",
    "        X_train_subset = train_subset.drop('Class', axis=1)\n",
    "\n",
    "        # Create mini dev set for model building\n",
    "        dev_subset = train.sample(frac=p_data)\n",
    "        y_dev_subset = dev_subset['Class'].values\n",
    "        X_dev_subset = dev_subset.drop('Class', axis=1)\n",
    "        \n",
    "        corpus_train_subset = X_train_subset['Text']\n",
    "        corpus_dev_subset = X_dev_subset['Text']\n",
    "\n",
    "        comp_results[\"x_shape\"] = X_train_subset.shape\n",
    "    \n",
    "        # Apply tokenization\n",
    "        print(print_blank(len(message0)), end=\"\\r\")\n",
    "        message1 = \"Epoch: {} Applying tokenization...\".format(n)\n",
    "        print(message1, end=\"\\r\")\n",
    "              \n",
    "        cv = CountVectorizer(preprocessor=lambda x: text_preprocessor(x, action=[0, 2, 3]), \n",
    "                     lowercase=True, \n",
    "                     stop_words='english', \n",
    "                     min_df=1, \n",
    "                     max_df=.1, \n",
    "                     ngram_range=(1,2))\n",
    "        comp_results[\"count_vectorizer\"] = cv\n",
    "        \n",
    "        cv_train_subset = cv.fit_transform(corpus_train_subset)\n",
    "        #cv_dev_subset = cv.transform(corpus_dev_subset)\n",
    "              \n",
    "        print(print_blank(len(message1)), end=\"\\r\")\n",
    "        message2 = \"\\tTokenization done in {:.2f} seconds\".format(time.time() - t0)\n",
    "        print(message2, end=\"\\r\")\n",
    "        \n",
    "        # Number of components must be less than number of observations\n",
    "        if X_train_subset.shape[0] < n_comp:\n",
    "            n_comp = X_train_subset.shape[0]-1\n",
    "            \n",
    "        batch_analysis = svd_comp_analysis(cv_train_subset, n_comp, plotting)\n",
    "        comp_results[\"results\"] = batch_analysis\n",
    "        batch[i] = comp_results\n",
    "        \n",
    "        if plotting == True:\n",
    "            ax.plot(batch[i]['results'][n_comp-1]['explained_variance_ratio'])\n",
    "        \n",
    "        print(print_blank(len(message2)), end=\"\\r\")\n",
    "        print(\"Epoch {} completed in: {:.2f} seconds\".format(n, time.time()-t1))\n",
    "    \n",
    "    print(\"\\nTotal time to completion for {} epochs: {:.2f} seconds\".format(n_epoch, time.time()-t0))\n",
    "    \n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 242.61 seconds\n",
      "Epoch 1 completed in: 340.64 seconds\n",
      "None                                                                                                                                                                                      \n",
      "Total time to completion for 70 components: 60.97 seconds\n",
      "Epoch 2 completed in: 95.63 seconds \n",
      "\n",
      "Total time to completion for 2 epochs: 436.37 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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iHAoRNiMSNqJh45TZ9Sw7YRYNCfVxyMgU/BJs2TS8+SRs/bk3+fzOF70DQTEL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f962710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svd_comp0 = svd_batch_analysis(2, 70, plotting=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 312.05 seconds\n",
      "Epoch 1 completed in: 413.75 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 270.86 seconds\n",
      "Epoch 2 completed in: 371.01 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 183.55 seconds\n",
      "Epoch 3 completed in: 253.86 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 197.14 seconds\n",
      "Epoch 4 completed in: 282.28 seconds \n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 197.08 seconds\n",
      "Epoch 5 completed in: 286.48 seconds \n",
      "None                                                                                                                                                                                      \n",
      "Total time to completion for 70 components: 87.12 seconds\n",
      "Epoch 6 completed in: 135.96 seconds \n",
      "None                                                                                                                                                                                      \n",
      "Total time to completion for 70 components: 84.82 seconds\n",
      "Epoch 7 completed in: 129.14 seconds \n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 104.60 seconds\n",
      "Epoch 8 completed in: 157.00 seconds \n",
      "\n",
      "Total time to completion for 8 epochs: 2029.55 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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t9QkgHq1S3T1K155RvBaTjI9QPnSQQ3vHGBsJqcUtBATkrEqeIWqBUQ2dWpBQ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\n50rlgdxL2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16480d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svd_comp1 = svd_batch_analysis(8, 70, plotting=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 60)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vnoDdS8AWBmfPgmE/B0fUQVUDOsB1n11HYX0hC6Yv2P98BMBTX+bw+BfZfPo/\n59AnMfSAzWue/yaPvH8+xQ07PmfjH57msRwfbqeXPyTHMqrGD01eLCkhhF/SA0tiMFprXnnuHfL2\nZdFdjWfcFanUV+ymeOd2inbuoGZfEQAmi5VuQ85m8i/uwNTOm+6EEKKjJByOpHgDLH8Csj4Csx2G\n3Ai9p7YESMAH2k92QyFXbnmCi6LP4qHeN0NwLATHUuJRTHzyIwZ3M3LLmCQy4zMxKRsPfLCNd9YX\ncWmPEGY9/WtsPXtiGDCQjQW1bN1Xj9lkYny3fiRa0jGGBxH/2+Eog6KsrIxnnnmGcHc6Qc5kkjMi\niEkJJiY1hODwADWluRRs28LmLz6h77iJnD/7V9IvIYT4wSQcOqJ8J6z4J2x5G7S/TfE/IsKYGx5G\nZrOLKqORCpORxkMuY1mUCZu3P2Wlfblt6IXcObEvVc/8h+r/zm3ps9AaHdD4/AF0IIAtcTD2obdi\nPz+eyPHpALzwwgs0NTnpG3Yu5XsbqS1t2n8HblCImZiUEALeVeSu+ZjxN97G4MlTD22qEEJ0iITD\nsagrgoqdYDC3dF4bTKCMNGsfv9r8JE3eRuKMQcRgJNavCXe7qdpTRldjFWuC3HzucFBrNBBssnNu\nl0lM6TaFs+PPxmQ4+KHzb3IqePHlRfy+ShOwhvLu+C5cNbo71QXZzJ8/nxtvvJG0tDS8bj+VRY1U\nFDRQUVBPSW4dteVO4lO/oWDbBi67/yFS+w08PscuhDijSDicYPe+t4W31u7lIsdO/pC8huzy5Xzi\nsPFlSAiNtPyOCoVRGTEoA0aDEaNqmWbuGMT53itZs28Vdyb1YVyPSLqVLqV3RgaXXTajzb4aql3M\nu38lgybFk7PiKZrqarn2L08QFht/sg9bCNHJ/ZBwOJVjK3U6v57YE5vZyKwxE4kODyK6oZSRG1/B\nvX4e33gryA6Lwd/3EgL2SPzaTyAQwK/9ePwe3rN8y6BvdpEZ2YcbDHP5qG4ETlco3m3b6DFoGIN6\nJB+0r5BIG8kZEexeX8vFd97P6w/8hvmPPczVDz2O2WY7Rb+AEOJMIWcOx0MgADlfwEf/A56mlofv\nMg5+hYXWmp2bNhDylhPnrg+5Y+RCGiNjOKfoHMDI1CkXcnbmkIM6nnetLmXxizuYfsdZeF35vP/X\nB0kfNpKLfn2PdFALITrs9H8I7qfKYGgJg1uXQlQPeOMqWPGvg8Z4UkrR+6whWHsEE9T9Ah5dlMKk\nLsP5JvVcU0BIAAAgAElEQVQbaqzlfPbJx/z5mT+zrfj7Iaa6nRWD2WZk57clpA0czJif3Uj2quWs\n+fCdU3GUQogziFxWOp7CkmDmZzD/F7Dof6FsB0z9F5i/vwwUPrUnZbsbCQo/mxveKePOf3/C7Pdf\npbBwHQMqYnn9v69T3aWasWePZUq3KfQYEkvOunLOucrHkIsuoXxvPsvfegVPs5Pw+ETsYeE4wiNa\nP8PlwTkhxHEh4XC8Wexw2YsQ2weW/hmqd8OVr0FIHADmOAf2wXHAuTR+fi+2517kmVm3ceGTPfnW\nWsYERx6WfAtflX7F8wnP82Dvv+Fb4SdvYwW9RiQwadbtNFZXsWb+u+3u3hYSSmJ6BmkDB5M2cDDh\n8YlyCUoIccykz+FE2jEfPpgNQRFw9Rsto8gCvloXpY+tA38BDR8+ROzdd1M4cToznv2Wqf3juTLN\nzeIli3FrN9uitjGlcA6xsRFMv2Pw/k37PB6cdbU01dXQVFuLs66Gptoa6isqKNyxhbqyUgDCYuNI\nGziYLgMHk9p3AFa7o92mCiFOX3Ir609RyRZ442poroZLn2t5Khuo/TiPxhXF0PQ5DV+8T8j55zP/\nvJk89k0R/7pqEKNTbLz7/ruUFJfgVD5SykZzw5/GEhod1KHd1pTuY8/mDezZvIHC7VvxuloGHwyJ\niiEyKZnIxGQiEpOITEwmMimZ4IgoOcMQ4jQl4fBT1VAGb14Dxevg3P+F0b/B3+Sl9LF1WHuEozzf\nUv73JzCnpfH4qJms8Ibw+a/HkBBq5esVX7P0y6UY/GYiY5O5/Zc3HPMfcb/Py77snRS3jt1U3Tp5\nmr8frbbH0BFcfOd9EhBCnIYkHH7KvC5YcDtsfQcGXAUXP0n9V6XULy4g6sa+BOpyKf7Nb/A7nfx9\n0OU0DB/H67cOx2hQ7Cvbxwv/mUdAubAkWPmfn80hODj4RzVHa01TTTXV+4rIXbuKjZ9/xEW/voeM\nEeccpwMWQvxUyK2sP2VmG1z6PIx/ALa8CS9PJXiwDXNSMNWvZWGKy6Dr++8R1KsXd347jwELXuK5\npbsASIxL5NLzf4ajoSuukmYe/9fjrFq9iqamph/cHKUUwZFRpPYbyLgbbiE2rTtfvfw8nmbn8Tpi\nIUQnJmcOp8L2D1s6qh0x+Ke/TsX7bvz1bmKujMBsLqfsmXnUfPot5ZHhLDrvVi6ZeQkD4kN58e7l\nNCYXkO9bQ5Q7Co3GEG0gtlssGb0y6BbVjaSQJMyGY7+ddV/2Tt74/V0MuegSxl1389FXEEJ0GnJZ\nqTMp3tDSD9FUiU/HUNH8MBozMZZ7MBv2UbfXRtnGMPwuIxUJ4Xw5aRZ9wwdTk11L1K3VrN7zLc5C\nJ/YqO3avHb/yUxpUyr7gfUR1iWJU8ihGJY2iT1QfDKpjJ4gLn32S7csWc/3fniQ6Ne3EHr8Q4qSR\ncOhs6vfByn+D0YxXp1CxIh1lNhBzVQSmhAQCRTuo/NeD1HxTTMCrKEgfSG7SbfSalsaEC7pS3eSh\nqNrJ5pxt5O/ehre8ApMvgMvoYVdYFvkh+VjNDvpHZTIyYTTndxtLUmjsYZvjrK/jxTtmE53ShSv+\n8FfpnBbiNCHh0Ml5ihupeG4LxlALMbMHYnS0XB7y522i4rH7qPk6n5WZf8RCNZssBWTreLwGE16D\nEa/BhMliJjEcIqgkinpcQLatmryojfgttaBN/Oas+5g58PLDtmHL4s9Z9PxTTL79TvqcM/4kHbkQ\n4kSScDgNuPPqqJi7DXO8nZhb+mOwff8Qu2/PTpb95X12qrMZuep/sblr2t1G6NSpuG+6iWVr1rI3\nfzdGswVzYhzvOD/HE7yTa3pdw11D72q3b0IHArz++7uoryjnpn/+nzw0J8RpQMLhNNG8s5qqeTsw\nRdowRtpQRoUyGVBGhdenyV5fRoTaSZJxBYYRV6JCgsDvRXu9uHftomrui5hTkkl64glqIyNZvnw5\n27dvB6WoMLgpD8khMimSh6c8TIwjps3+S3fn8Nr9v+GsCy5iwo23nYJfQAhxPEk4nEaat1XSuHIf\n2htA+wJofwDt0+AL4G70Ygoc8M/NqDBFB2GOs2OOtaMMpZT+8R781dXE/va3RFx3LVVVVWzcuIlF\nq7cQ5KtHAT6Djy5pXRjUexC9evUiJCRk/yYXv/Aftiz+nGsf+Sexad1O/g8ghDhuJBzOEPWVzbz9\np9V0jfMz3PU0vrDReKPPw1vRjL/aBRos3YJxb/2IxkVvEDxhAgl/fhhTRAS7KxqZ9q8vGZ1UifKs\nJaQhBLvPjtlsZuSYkQT3CCanNofskh3YXtpMncND8dRYJiWeyzlRw3FoG+6mRtzOJkwWK6n9BkrH\ntRA/cRIOZ5DNSwpZ/k4OE8dVkbHzFhh0LUx7Cn+Tl6bVpTSu2kegwYuyeGhe/x66OZekR/+CPTOT\n/3yVy6Of7+LxK7vzednj7CjcwZC6IUQ1RlFrqWVD9AZ0qObsihQSl9ejFajD/Gsy8vKfMeKyq0/u\nwQshjomEwxkkENB88Ph6asqcXDNxNfbVD8PYe2D8fQBoXwDn1koalxfjLW5E+5rx7F6KOaKS0Ouv\n5drNUNrg5fNfj+Sd3S+TV5NHSnMKjVsb8Tg9DB06lAnjx7N10ad4mptpNLjIceaxqX47hd4SfGbN\niNJUInd7cJ+bhiMznVBLKKGWUMKsYZgMJpp9zTi9Tpw+5/7PZl8zJoMJh9lBsDkYu9mOw+Qg2BJM\nnD2OjMiMU/zLCnH6OWHhoJS6APgXYARe0Fo/cki5ai2fAjiBG7XWG1rL5gIXAeVa634HrBMJvAWk\nAXuAK7TW7d9+00rC4WDVJU289ec1dB0QzQVx/wcbX2l5udCQG/fX0Vrj2VtPw7ICXFm1+OvycS5/\nEp0QzX8ih2K/aCqPXnv2/voul4svv/ySNWvWEBwczOTJk+natStKqf2Xj/Lq8lhcsJi1JWtI+bKW\nyBLNsiHV7IlrPGJ7zQYzQaYgfAEfTl/7w3T8fvjvuSLjih//4wgh9jsh4aCUMgLZwCSgCFgLXK21\n3nFAnSnAHFrCYRjwL631sNayMUAjMO+QcHgUqNZaP6KUuheI0Frfc6S2SDi0te7TPaxekMfkW3vT\nLWsO7F4C5/8Vzp7V8vrSAzg3lVP9TjbK7MOz81Vcm1ZQb7ZjnHYpA2ZdhzklZX8AFBcX88H8BVSW\nlx1230op4mJjad5XgLeilPNvvomYXt1p8DbgDXixm+3YTXbsZjtBpqCDbp0N6ABOr5Mmb9P+6cmN\nT7KxfCNvXvgmPSJ6nJgfTIgz0IkKhxHAg1rr81vnfwegtf7rAXX+D/hKa/1G6/wuYJzWuqR1Pg34\n+JBw2F9HKZXQuv4RrylIOLTl9wd495F1OOs8XP27ftg+vRVyvoBu42Da0xCWfFB99546ql7ZgQ6A\ndbBi0dP/YsCezRjQEBtHTXo/tkV3Z6E5kQ1eO2nGGmzKh0JjNCjCg8xE2M2E283E2A2E+OopLi7C\n6/UC4LDb6ZKWRkpKCqmpqcTHx2M0Gjt0LJXNlcxYMINIWyRvXPgGNpPt6CsJIY7qRIXDZcAFWutb\nWuevA4ZprW8/oM7HwCNa6+Wt80uAe7TW61rn02gbDrVa6/DW7wqo+W7+kP3PAmYBpKamDtm7d++x\nHN8ZoaKggXceWUfGsDjOvb43rH8JFt4PBhNc+Dj0v5ymeg91Fc1EJQVjdHqpfHk7vkoXdWMTuPmT\nFYyoyKZXWQ79q/KIcLdcHnKHR2EcdBa+yGhqtZkqv4Fyn4FSDxS5FOVYuexn53HdmHTydmax4IVn\n8FqDsMQkUN/QAIDZbCY5OZnU1FRSU1NJTk7GarUe9liWFy/n54t/zhU9r+D3I35/Mn4+IU57PyQc\nfhLvkNZaa6Xavx9Ga/0c8By0nDmc1IZ1EjGpIZx1XiobPt9LemYcqZkzCaSNoezVh9n78lIKDFDR\nEL2/fnicnfhkB119mrCl+3i0b38+COtPSNcoYtLCSWkop3ndOpxr1uLcuBF/TQ02l4t4oO8h+/as\nfJ6cQYOIHnsOV1w4jQ9ffYFgTxPX/vb3lFdVU1BQQEFBAV9//TVaa5RSjB07lnHjxrV7LKOTRnND\nnxt4ecfLjEgcwcQuE0/Y7yaEODy5rHSa8Hn9vP3ntXjdfhK6h1Gwoxq304dSmnjzLlKDdxF1zkVU\n6XTK99ZTvqceZ52H/kEGulqNlCnFnjArtlArQSFmgoItBIWYsYdaSOgRTnCYmUCzi4CzCd3cTMDp\npCK/iDef+4AhlbkkVBYCUB0bxZqEcEIcIQwaNY5+02dgi4rG7XZTVFTEhg0b2L59OxdccAHDhw9v\n91i8fi/XfnYthQ2FvDf1PRKCE07mTynEaedEXVYy0dIhfS5QTEuH9DVa6+0H1LkQuJ3vO6Sf1Fqf\nfUB5Gm3D4TGg6oAO6Uit9d1HaouEw5GV7K7jw79vwBpspkvfSFL7RpHSOxJbw054/zaoyIJbv4TE\nswBoqnVTvqcO57clhO2tp9ZqZLvJiLPRi6vBS+CAp7BjUkPodlYM3QbFEJnw/XhL8zcV86s3N/H7\nUXFcpspo+nYl+etWs92qaAiyYvL76eKFjJRuxPQfiKVXBh9nZZFdWsqUnj3JCA1Du10E3G6U0UTY\n1Isw2O0U1Bdw+UeXkxGZwdzz52Iy/CROcoXolE7kraxTgH/ScivrXK31n5VSswG01s+29hk8BVxA\ny62sMw/ob3gDGAdEA2XAH7TW/1VKRQFvA6nAXlpuZa0+UjskHI7O1eTFGmRCGQ55atlVB//OhPAU\nuHlxmzuZGleXUPthLtZuYURd3xdlMeB2+miscVOwvYq8TRWU5dcDEBFvp+ugGHoMjiU6JZhfvLaB\nJVnlfDRnNBnxIWit8ZaVsXfpYrasWMbeihI0EFPvpEtlHZFNLr4ZO4aKmBjO+fobEkpL97fD0qUL\niY89StCAAXy0+yPuW34fswfO5peDfnmifzohTlvyEJw4ss1vwge3wdQnYcgNbYqbNpZT8/YuLKmh\nRM/se9CIsACNNW7yN1eQt6mC4uxadEDTpV8Uvc9L4Yq31hMfZuPDX47CbDwkeGqq2bL4c7Ys+pSm\nulqiomM565xzWVFSTk1jI9dceBEpqSm4c3LYd9/9+MrLibn9l0TdeisPrPoDn+Z8zFOj/smonjKE\nuBA/hISDODKt4cXJULEL5qwHe2SbKs6tlVS/uRNzvIPom/rtf6fEoVyNXnas2MeGhXtxN/sI7hnG\n30vKuOm8Hvx6Ys921/H7fGSvWs7Kt1+jtqyEhN79KQ2Owuv3c9NNNxETE4O/vp7SP/6J+k8+wXvW\nQEpGDGbj2mWYPYqgqEi69TuL5N79SO7Tn7DYOBnXSYgOkHAQR1e2HZ49BwZfD1P/2W6V5p3VVL26\nA1NUEDG39McYYgFA+zW+SifeUife0ib89R5UnJ2cwkY2fFuK1x9go8XHnNuHcFZ61GGb4Pd52bJk\nIavee5NGZzPu9P7Y7A5unTULe5CNnFUr2PD265RVlKK0JiEpiSWRewitgrT6cLxNLU9XB0dGkdy7\nHwMnTSa5d7/D7k+IM52Eg+iYz++DVf+BW5dA0pB2q7hya6matx1jiAVLl1C8JU14K5zga/33xQAG\nm4mA0weACjZToxR5ZU5K/Zo+56bSZ3gCkYmHf1mQp9nJuo8/ZNXCT6lP6IrVZMRUXUqgvo7I8HD6\nDx5K5OdL8K/fgOncc7h/WAF7DNX8ufd9xFSaKcraRsH2LbgaGxjzs5kMuXC6nEkI0Q4JB9Exrnp4\nKhNCE+GWJWBo/wlm9956ql7dAUphjndgjre3fjowx9jBpPBVNuPOq8O9uxZ3Xh2BxpYnpT0BjUuD\nz2zAFh1EWEow9ngHxhAL1u7h+89GAJpqa/j89XlsLyknYPz+MlZQUBCxMTGEVlZhWb6cCLeLRaNh\nQVo5fx39Vy7oegFup5PP//MPctd+S69RYznvtjmYrfJktRAHknAQHbflHXj/FrjoH5B502Grfffg\nWkdorfGVO1mwYBcVuTWkBAzEYSAEhc0AxtbtaKPCPCCa0HEpBMXaD9p+Y2MjFRUVlJeXHzS53e7v\ndoLV46QgtJp+PQczqf8kUlNS2Pz5Ryx/6xViUrpw8V0PEB4X/8N/GyFOMxIOouO0hpenQulWmLMB\nHIfvI/ghGt0+NhfWsmFvDVtzq2ja3UB6E/TASDerkRSzwg/s9WpKgkxYImzYQyygIODXaN36GQjg\n92sMVg9BcT6a89ZSnrODmvBwnI6WS1YWi4We/XpiUA3sefcjAgQomxDF3sgGkkOS6R3Zm16Rvegd\n1ZvooOgjN1yI05CEgzg25Vnw7GgYdA1c/O8TuiutNXmVTazNqiArp5qmwgZGNmkyDUb8wE5/gHyt\n0EYDZpMBs9mA1WzAYjZiMCgaalw0VrecPYRHmYkoWot993J2ptaxq1cXQgLJGDBQYSkjsria0Mom\nqoeEkhdZS0lzKVqBVpowWzhdI7rTNTadpOguJDgSiHfEkxCcQIg5RPosxGlJwkEcuy8egJX/huvn\nQ5dRYGz/1tUTod7lZfu2cvwr9pFS4iKAJocAu/Dvn4qNEB8RRGyIheiAgagGTWitD2utFxUAg9+N\nzVWN3+ilMbSOhtB6/CY/Ro8fU3UxpoZaDD5Pu/tvsvmoCvVQHeqhKsxDc5SJ8KhYou0xRFgjiLBF\nEG4LJ9IaSbgtnHBrOG6/m0ZPI43eRho8Dfs/AzpAj/Ae9I7qTUZEBnaz/aT9jkIcjYSDOHbuBnhq\nKDSUAArsURAcByFxrZ8JLS8PiuhyQpvhq2ymcW0proJ6vPsaUe4AAH4F5TYD+UZNrlGTrfxk+3w4\n3X6inAEymn3Eej0kBSmiTBq/20WtoYYqex2uoJbOcYPPQITFRlp6MrGR4TisFpob6ijavZOy/N00\nlVW0XGYD/DYDLofCbfThMnhpNnjwmjReUwCfURNQGhRoQCswoLCYrPiMAYoc9dSEetAGRZfQLi2X\ns6J60SeqD32j+hJiCTmhv6EQhyPhIH6Ymr0tLwlqLIeG0pbPxtbPhhKwR8P1H0Js75PSHB3Q+Ktd\neIob8RQ34i1uwFPciHb5WyoYFeYEB5akYDzRNl7Lr+D/dpSQGO3gkUv7M6xbS/9JcVYWa99dSG6d\nm6YgH9rYEhbBwcF07dqV7t270717d/BC/qadFGXtoqIgD4+zHrSHQMBNwOfG53Xh87gI+HxHbbsy\nGVFxIdRGBsh3VJNjK6UxyA8KuoZ1pX90f/pF96N/dH96RvTEYrQcdZtC/FgSDuL4K8+CedPB74Zr\n3zvscxEnmtYaf9XhA0MDZQbNroAPR1IwY0amEpIaiik6CO12k/3SJ6xeV0lFiAO/uRqvrQ6foWVd\nk9eB2R2JxR2B1W3DpDUeo5WA0Y3f6CZgdOE3uvAb3CgUJoMZs8mCxWTBarFgsVpx2I2kpBlprNxL\nae4uynbn4vO2XM6yhIUQ6BLOvhgXG2z5lOoqoOW1qekR6fSJ6rN/Sg9Pl8AQx52EgzgxqvNh3jRw\nVsHVb0LXc051i4DvzzC8pU14S5toLmmkMq+W4GY/Rlo6lg0hFoJ6RWLLiMDSPZSCz75l/ed7qQxE\nYKIUn62aZoeLelsArRRmrbEEAjgNBvSBndNaY9FGtMmIL+BD085/N1oRbAujS/cUEhLjCFIaf001\nZdk72LttE+6mJlCKqLQ0jN1iKI/zkWUtZkdNFg2elpcjmQwm0sPT6Rfdj3Ep4xiRMALzSewHEqcn\nCQdx4tTvg1cugZo9cMU86Hn+qW7RYW3Kr+bpd7ZhqXYxHBPDlAm7VvgVVEdYcKeGENM/hm4ZUShT\nyyCBLpeL/Px8du/ejcfjITw8nBCDgaCKCqx792LakYVn82YCzc2EXXE5Eb/4BR6rFZfLhcvlorKs\nms3f5lBaWorP3EjA8H0nuNVqxWazYVQQ8LjxNjXibqhH+f0YzWYckVGYQoJxWzWNXidN7ibqffVk\nh2TTFNrEuNRxnJd2HiMSR2A1Hv4tekIcjoSDOLGaquDVS6FsG1zyf9D/srblJRth30awhMDg68By\n+OEzTiSPL8AnW/exu7yJoqomTKVOUmq8DPQqetDyRHhAgTkqCHOcHXNsy2SKtWOOs+8PjQP56+qo\n+PdT1Lz+OsaQEGLu+DXhl1+OOuAd2VX7Gvn2/d3kby/BHOklvr8ZS2gAt9u9f/ouUJqbmvD7vPg9\nHvD7Ws5OrFaCgkPwKUVDYxOBiABrQtZQbCzGYXYwNnks41LGMShmEPGOeLn1VnSIhIM48Vz18MZV\nsHclTLgfDOaWMNi3AWoLDq7riIUxd7Xc7WT6afwfr9Pjo7igjmVL8inNr+Usu5WBNhvUuqDlBilU\nkAn7wBjsg2OxpLR99sG1K5uyhx/GuXYt1j69iX/gAeyDBx9Up3BnNSvfy6WysBFHuJXIRAcR8XYi\n4h1EJtgJj3MQFGJGKYUOBCjNyyFvw1ryNqylPH83GoVKTKU5PBaf1sQkR1KRVs2SyiXUumsBiA2K\nZUDMAAbGDGRAzAD6RPXBZpKhQ0RbEg7i5PA2w9vXQ84XLfMRaS1vl/tuShgI5Tvhy4dgzzcQmgxj\n72552K696+eBANTkQ+1eCEmEyK4nJUzmbyrm/g+2YVDw6PT+nJsQhrfUSXNWFa7tVWhvAFN0EPbB\nsdjPisUU8f0fXq01DZ99Rtmjj+ErLSX04qnE3nUX5tjY7+sENNlryyjYXkVNqZOa0iZ8nsD+cqvd\nhNnWdlwrv68BX3MeBIpwNu7BGRyMJyoOlIGE4CCSeydTFe9jR+NONldupqipCACTMhFhi8BqtGIz\n2QgyBR30Pc4eR4IjgcTgRBKCE0hwJBBhjZCzjzOAhIM4efw+KN3SEgztvBcCaHl2IO+rlpAoXg+R\n3WDc71qenSjbDuXbWz+zwOs8YEUF/9/emQfJddz3/dPvnGtndvaaxWIB7C4AggRAigQoHiZFUQcl\nUmEkxU7JkkvUFVtWxXI5iVOJpKo4VlKuyFVJynLFZVlRKEuWYxVj3RIl6iBFUQdB4qIBEARB7C64\nF/aanXve3fnjzR7ALkCAJEhQ25+qRveb12/mvR+A+U73r3+/zm2CziHo2Aqd2yC3MZ6iMtNgJlvt\nFFgpSLTDi/yCe36+wR9+9RBPjZX4nZs38yf37iRh6kROQPPIHPWDM3gjZQCswSxGZxLN0hGmhjA1\nIKS+71fUfvoDouIzdP3rj9Fx330Ia/WKIxlJaiWXhak6C2calKYbBH645n0FfkRpukFxsk7gzRBE\nozSyC/hZe9Wz6oaO0AShFiI7JM6gg2M4OKGDEzi4gUvNrzHdmKYZNM+6Nmkk2ZDewMbMRvrb+unP\n9NPf1r90nDZfnWlBxcuLEgfFlYmUcOL78Mifxf6KRVKdUNgFPbviumMQKlNQPAXzz8H8qbjtlC/8\n/ukeGLwDht4Ig2+85IA9L4j4Hz86wd88OsxVhQx/8KZtZBMmSUsnZemkGgGJk2W0Z0vQDJB+hPTD\n5fTli48ZFGn84vMY7ZLCpz5F5g23X9J9rEUUSSqzTYpTdYpTdcZHJnj++ado1MaJoiKICHSTRLaH\nVGc3s7V4mewtt9zC7bffTjKZXL4/KSm7ZabqU0zWJ5mqxfVkbZKJ2gTj1XFqfu2sz9/Wvo17Bu/h\nnoF72JTd9JKfR/HqoMRBcWUTRXDq4fiXb2E3ZHpe+Be/lNAoQnUSvAb49Xhaa7Ht1mDqMAw/CvWZ\n+Jr8QCwSA2+IP8NMxaMNM7nctjKgn70N6s+eneXfPXCYudra6TYAujIW127McW1/O9dtyLK7t43u\nhIl7qkz5e8OEFY9g/jDOvi+TvuNmCp/4BFZ//0s03Gqcms/kc/M884t9jB17glrxeBy4Z1gEG7bg\nprPommCoK8/O7dtp7ylgp1IYlo2ZSGDaCUzbxrQTaC2HupSSildhvDrOWG2MscoYP5/4OQdnDgKw\nu3M39wzew9sH3k4hXVh1T37kU3bLSCnpTnW/7M+sePEocVCsX6SE2WdikRh5FEZ/Dm7l/P01E7p3\nxCOWwq5YrAq7qVudjC00aHghjhfS8EIaftyuOD4nzlQ5MlHm2ekqUeu/Tk+bzVWFNlIC7pwLuW0h\nJIx8vGNfwzv9c07s/g38ga2YW7eSu+YqNmzZSF8+SUfaetnm+91mk8M//CWHf3iIZmUOq9OnnDJx\nrQTCc7HnJtGadUQYIMIQsSJOw7BtMu0dpPMdZPIdZDo6yeQ7SHd00n/1Lmq2z0OjD/HgyIMcLx5H\nILi+53ps3absluPilan79aX37Ev3sbewlxt7b2RvYS+b2zYr38ariBIHhWKRMIDZ4/HqKr8Z+zSW\nSjNODTLzNJw5Go9KFkl3Q/fVsX+kc2vL57EV8oNgLjukG17A8akK/zRe5sh4mVNz9aX8TBsCeM+C\n5CoXKs1ZomPfxFg4jWzMgwypmClOZwtM5HpZ6Oil1lmg2d2H37OBZFuatoRBLmnSn0+ypSPNls4U\nG3IJDH318tpz8b2QX33tOY48OkFHX5rtb02y7+DPmJ2bO6ufoWuYuoGpCxKaRl6TiGqJRqlItThP\n0No/w7BsbvnN32bvvf8CwzQZLY/y/dHv87Oxn6FrOjk7R87KxbWdo91ux498Ds0c4sD0AYpOEYDu\nZDd7C3vZ0bGDzkQnHYkOOpNx3ZHoUKusLjNKHBSKF0OjGDvGp4/GYjH3bOzraMyv6CQg1x+LR6oD\nkh2tOh+3M93QszMWE91ASknzqVlK3xsmqvqtt5CEmo/vlgjKUzA7gtGsIGUUC4uMaFgJFuwM02aK\n7+cKPNqxGSk0DE2wMZ9kc0eK7oyNpgk0AZoQCLHctg2NlKWTLPrIffNIN6T99V1ktkXkzJCkCGg2\nmzSbTRqNBs1mk4mJCZrNJr29vdx6663s2rWL0PMoz5zh8a9/lZP7fkl+w0be/OHfZ+B1e9Y04VpI\nKfJ/nycAABjRSURBVBmpjHBg+gD7z+xn//R+Zhoza/ZNm2mu67qON/S/gTv672BL9vImelxvKHFQ\nKF5OmqWWc3w4rovDUJ+DZjEWlGYJ3HOc5UYiHnkUdkNhF1Hnbnw5RFAzCeabBPNOXM81lxMJXoDI\nq1C3fUb7ujmcsDjgOEzVY7GJpCSSrY2RZHzs+vE0mJSQiOCupsnVvsGkHvGcGdIwoa07wYa+NoY2\nZtnWk2FHd5K550/y+OOPMzc3RyaT4aabbmLv3r2k02lGDh/g4S9+jtKZKa66+Tbu/ODv0db54jZN\nagZNik6RYrNI0Sky78xTdIqcqZ/hyTNPMlweBmBz2+ZYKDbewd7evSoy/CWixEGheKUJ/VgkqpPx\nktwzR5ZHIfXZ5X75wThp4cY90LcH2XsdMrSIvCj+Zm99yS+2w0qT6sMHaB6bBJlDS7ccvEJiFtIY\nPSmMriRGZxKjO4nZlURLxTEkUkocP6LuBdSdgJGDM5z80Tje4gimhSMkJU1S0SR22qS/N82Gjgbz\n5ZOcmR9H13W2Dm5naHCIwYEtjDz+CPu++f8QQnDzb/42G6/eiZVILjm2Fx3dmr72nuQXw1g1doI/\nNv4YT5x5Ajd0MTWTjZmNbGzbGC+1zfQvtXtSPWSsjBKPF0CJg0JxJVGbicVi6qk4gnziEFTigDWE\nHqdAv/pe2POBOI7jPLgjIyw88C3qjx0Fqwe9fTNapgeR7ECI5S9iGTZBughNgi4QhojjMSwDLWWS\nuGUT3oYC1XmH8myT+ek605N1yvNN3LqPEUi0VsLCwKjTTE3i2fNEerx6Sw+SWH4GvTIKpWcR0doj\nH9NOkO3uIddTINtdINezWHpJZDLISAISGUlkqxYCcoVeNG35eZzA4YkzT7B/ej/j1fGl5bYVb/VC\nA1MzyZgZ0maajJVZaqeMFCkzFbfNFGkjTdpKk7fzS/6OfCJP1sr+WjvMlTgoFFc61emWUByE538V\nr6oSAq66B278CGx9M2hrO56l51F9+BGcY0eRnkfoeEjXQAYWhAmkTCJDHRnKOBVIpMWrsnQLLZED\nwyacPYyRPUNqzzUkb9hD4uodCDMecZwpNfnhU1M8euQMz54uY4fQkzTZ2SnotCqE7izF2jSRjEVB\nlwZaoIEHWghaAFoQYsoQM6gShWUCt0QUOBdlmrbObna/6a3svvMust095+1X8SpMVCeYqE0w25yl\n7teXduer+TVqXrw7XzNoUvfrNIIGdb++KgBwJYZmkLfjnf+WRKZVL7bziTzdqW56kj10p7rpTHZi\naq+NjLlKHBSK1xrFETj4JTj4d9CYg/YtcS6q1703jskInLj4znLbTELXjrNWT62FlBLpOITlMt7E\nDNVHxghm08goxDv5Q7yTDyEsjcSOHRgbejF7ChiFAkZPD06ug19VNH4wJ/jZqSINLyRp6ty2Nc+t\nBShoVYJmjUqlQrlcoVIu4wfL01Z5q48uriao6TTLFXy3hIzKSOkCIt4Xw9ZJZCwSaQs7ISlNH6E0\ndQKAzs3XMHDdHfTt2IOVtNB0gaZrsSNeXy6mbZBpt9HNF17JFcmIZtCk6lUpuaXY7+Eu+z+KTpGS\nW6LhN6j5tVh0WvVawiIQdCQ6KKQL3D1wN7911W+RtbKX9Nf/SnHZxEEIcTfwWUAHviCl/Mw550Xr\n/DuABvAhKeXBC10rhPhT4PeAxYnZT0kpH7zQfShxUPzaEnjwzHdg/xfjfFQvhNDjtCK9u1fEaeyC\n7MYLBhYGRYfyQ6M0n5pFmBFCP4U/8hjB9Bn8mRlko3FWf6NvA9kPfIhn9r6Jh4fL/Pj4DBOl+Isy\nZel0Ziw60zZdGYuOhCBvhmSa05RHjiBlxG233cZtt92GkDrNqke95FEtOtQWHGpFl+qCQ63oUC26\n+E5AFFQIvKOE7lGQNRBJdHM7QCws0kVKr1W7CC2Jbl5FqmM32a4CmXyCTN4mk7fp6MtQGMiSyr70\nzZOCKKDklphpzDDbmGWm2aobM5wqneLw7GGSRpJ3b3s377/m/WzObn7Jn/lyclnEQcSTms8CdwHj\nwJPA+6SUT6/o8w7gD4nF4Wbgs1LKmy90bUscalLK/36xN6vEQbEumD0RJzUUWrz6yUjEowQjGSck\ndCsw/XTs9J4+enY23J5dcPNH4dr3xHmnzoP7fIXy90bwTlfQOxMkd3aS2JHH6NII52YJZmbwJycp\nfeObNA8cQM/nyd/3fvLvex+nXIPHTs4yXXGYr3nM1T3may7zNY/5uosfSrKaz935GVL1Sdrasrzt\nbXexe/fupXl9KSWVSoWJiQkmJiaYmpoCwDRMDMPE0A0aCwuUJk9Tm5nCjEJsoZGwLEwrhWknMewk\n1dkpFqZOAZBo24Sd2UnEVnxneVSV7UpQGMxRGMhSGMrS3d92USONS+H4/HG+cvwrPDjyIGEUcuem\nO7lv533cWLjxivBlXC5xuBX4Uynl21vHnwSQUv63FX3+BviplPIfWscngDuBgfNdq8RBoXiZcMrx\nSqnJQ3Do72H6SJyMcM8H4PW/e95cU1JKmkfnqe+bwh0pQygRlo69rZ3EjjyJHR0Y7TaNAweY/99f\noPbTnyJSKfLveQ8dH/ogZm/vmu/57HSNrx8c5xuHJqA2x632GHnqdBT6uPaaHTw/Ns6ZqUmajVZE\ntdAw0u0ITUdEATL0kWFA1CorMU2Tzs5Ouru76e7uJp/PowUBZ04cY+zAPuaeH0ETGv07r2Xoxreh\nG4NMj5aZHqlQW4gD+zRD0LUxHlX0tEq+kEJoL/1LfLYxy1dPfJUHTjxAyS2xNbeVvYW9S/uGD+YG\n0bUXv5rrxXK5xOFfAndLKX+3dXwfcLOU8uMr+nwX+IyU8uet458A/5FYHNa8tiUOHwbKwH7gj6WU\nCxe6FyUOCsULIGXs6N73OTj+XUDGzu7XfwQ6t0MiC3YWzvmCitwQ91QJ50QR58QCYakVId2TIrG9\nHXtbOzKcZeHL91P53oMgBKkbbiB16y2kb7mF5LXXLjm2FwnCiF+cmudr+8c4efwI12ljJEVAOUow\nJ9PMRmnmojQLMkXI2r/kBRKLgJxw2JaN2JQMyIkmwq3iNmqr+uu6jqkJwkYd2ajR2dXF695wJ/0D\ng9h6muqZgJnRKjOnK8ycruK7sXPdTOj0bGmjozeNmTQwbX1VyeRtcj0pTOuFv9ydwOE7w9/hodGH\nODZ3bCmhYcpIsatrF7u7dlNIFfBCDz/y4xL6eJFHJCOGckPs6dnDUPsQmnjpo5zXmjgUgDniveH/\nK7BBSvmRNT7/o8BHATZv3rz39OnTl/J8CsX6pTwO+++HA397TrQ38U59iVwsFsl8HPmdKUCmB5nu\nIQh6cWZzOFMW7ukGBBFoYG3KYvRoeM/+gsaTP8I9fhykRKRSpG7cS/rmW0jd9HrsrVvRUsvTWhXH\n58HD45wpN+jMpmlPWXSkLdpTZlwnLSIpcfwQJ4jiulUW6j7Hpyocm6xwdLLM+ELs8zAI6UuGdNqS\nnBGS0QNSwsfGxwgdqM0jo/AsH4zUDKSVwUhlSWTaabOzpMIUVt3Gn/VxFlwCN0RGnJdMh02+N017\nIUW+kCLXnUQztHgBQCRbwe5xrWmCZNZgXp/h2eZxjs4d4ejcUZ5ZeIYgOmdUpJlYeuwfWcxTlbWy\n3NBzA3sKe9jTs4ednTuX+lwKr6lppXM+YwD4rpRy94XuRY0cFIoXge/EyQjrs3GuKacc+y2cclya\nC3FMRm1mdcQ3INu34ba9FTfag1PpxZ/TQIKet0le1w7BKM6hx6k/vg9veHjpOrOvD2vrVuyhIayt\nQ0uCERSLhK0SzBcJF4oECwtodgI9n0dvb0fPt2O02kZvL9bg4NLcfanhxUIxUWZsoUHVCag0fcpN\nn0qrXXF8IgnZoMpvlJ9goztFM5llrn0QaVkkoyZp3LN89zVpUZMWodTiqS5Nj/NPteqEZpMkQzJI\nYjUsZBki/wIqcg6aJkjlLFI5m2TWQLfjkY6u6+iahqZrCA2EJnBEnelwktPeCCcbzzDmjeIaDTyz\nSSJt0plq5adakaeq3W4/a+ntWctwk/nLIg4GsVP5LcAEsVP5d6SUx1b0+WfAx1l2SP+llPKmC10r\nhNggpZxqXf9viUcU773QvShxUCguM74Tpz6vzUBtOnaOTz0Vp0VfGAUgkmkc6y3U3TfhetsBn2Ti\nMJnUYwjvOZwZcCsGXgncYoRX9JHBeb5nNIGRNtBTBlFkEDYCovrqZaPW0BC5d76T3D+/F3Pj+QMG\nz8fY00d4+P7PMTd2mv5rdtPe24cfhlS9kJrn0wgi3DDERyB1i1DTCRFEYRiPPmSIKc/+pd+UBvXI\nxouSFIMcs2E7AQYSiJBIQEeQjiAjBZlIkG7VmUhgIdAADRCSpbYGGPL8/o+ICM92cK06TatC1ShS\nNRZomFUco45j1lp1HceoEeg+Rz909LItZX0H8BfEy1Hvl1L+mRDiYwBSys+1lrL+L+Bu4qWsH5ZS\n7j/fta3X/w64nnhaaRT4/UWxOB9KHBSKV5HmwnLE9/QxCD38Zjv12R3U57chQwsjuUCqcxhdK6PJ\nGkJWEWGZsFIiKJaQfhUzGaGnNIyUQLM1hKaDjOJVVzKe0gmNHsLsNYSpQdx6mvIvn6Z5JI6BSL3+\n9eTe9U7a3v529La2Vbe5GN+BpiGs5bToURhy+IcPcuj73ybw196zw6lWl85ZySQ9g1spDG2nd2gb\n2cIGAt1kplJjanqO2bl5SgtF6uUioeeA0Eh09pHo3ozZ0Q+GRbg4zcRiTZwqhTgXVhgt1nFZbPt+\nSOiEBG5I5ERIL0R6EZET4tV8DF+SXiE2KclSdPsqjIiP/9VdKghOoVC88kReSPOpWWqPT+FPrHYU\nLyFAz9no+QRGRwIjb6N3JOL9uX2HaHoYOXOaaG4cuTCDrFWJpIVAEvrglnTckkHoAiLCSHhE1XHC\n8iSR6xJ5IdILWLFdRSwQto2wbTTLQiQS6LncmlNYWmcntXSS+WqZmdPDTJ96jpnTw4T+coCflUyS\n7eqhrat7qY6SGWbqDYZPP0+5XEbTNAYGBhgaGkLTWv6IFSWKIizLIpPJnFWSyeRFLX0tN33Gig3G\nFxqMFZuMFeuMTtUYnawSNkOSEaQR9CVtNiYs/tOf3K7EQaFQvLqEdR/pBERuGNdOiHRDIicgrHqE\nRYdgwSUoOkTV8++6t4QO0EoJIoG1fiHLCNwphDuC5o2i+SNo/hTSK0EkiPQMUs8gtRSRSBCGCcJm\nRFiuEC4sIL2z70PYNtbAAPbWIYyBQRodOapRSK1WiUu1QrVcplpZwG0uT4Nppkmmf4CovYtyJGh4\nF/F8K9A0bUkkEokEiUTirLZlWQRBsFR8319qJ5NJtmzZgp0vMLwQ8HTLif/0ZIVffvItShwUCsVr\nB+mHBAtuvHRWgLB1NFtHJIy4tvRV8QdycX5GQlh18SfqeJM1/Mka/kSVsLIi+6yQ6AkHwyiji2n0\nYAzDew5Tew7TnEFsuRk5+EZk362Edj/+zAze8DDuqWHc4VN4p4bxJyaWNnJaC1/TqCVMagmLmm1R\nTVrUEjZNUwdNRwN6dJu+XJ5NvRvJFnox83kC06Ie+DQ8n7rvU/c9GkFA3ffxLRvX0HE9j2azieM4\neOcIjWEYmKaJYRgYhkGtVsNvjXC6u7vZsmULAwMDbNmyhWw2q8RBoVCsb8Kahz9VJyg6hAsuQSmu\nw5JDWPGWppyEFmCap7GCQ9jaCazUDPqmwTgOxEqD3QZWhkgk8OY9Ii2HbOtDJjohipBhiAxD8H0i\nxyVqNIiaDWSzSdRo4tZqzCzMMVGaY8pvUm9pXFvTpafSINt0MaIIPZRxHUUYkUSPIvRIIkwTe/s2\nkrt2k9i1C+uaa9CGBjGTSXRdXzX9FIYhk5OTjI6Ocvr0aZ5//vklQfn0pz+txEGhUCjOhwwjgqKD\nP17DG6vijlXxJ6rxlBUghIMQARAgZAj4CEIgRKOBJhbQ9TpaWkfPZdC6OtELfWg9vWiFfrRc+5qR\n1lJKipPjDB98kuEnH2fi5DPI6MLLYHVNQ48kehCgB2EsGjLe89tIpTAzbVi5HGZ7HqujEzubJdmW\nJZnNkmrLYmfaqLous8USt99xxyWLg3EpnRUKheK1jNA1zO4UZneK1A1xWnAZRHiTsViERSfebyKU\nrTpCBgH4HlGtQVh18BoQlSwoCViKyS21SoimNdBMDz0RoSUkutlA06sk9DLXigWu31TELwhqPngy\nRSBTBDKJL22CyCaIDDwBjh7gI/AjHbfu4FbqeNUaQbOJ02wQVitEZyaIhCAUglDXkC9jHiclDgqF\nYl0jDA17cxZ788Wn25ahJGr4hLNzRJOjRMUiYalCVG0Q1QNCB6K6jl/J4MgeJAPnfS+jVdZKwC6o\noYsiuphHT86jp200M0BYEmFIoiAiqAf41SZ+1cVzI5pehOtKnEDihhIvenGzQ0ocFAqF4hIRukBv\ns9Db+mCo7/wdpQQhiNyQsOoRVbx4xVbFQ4YRwtDi3fqM5YIGUaVKOF8mKumE5QRhrYBbF4SuAYEG\n58YJJuNiEpdV/PiCuyGsiRIHhUKhuFy0pnk0W0ezk9CVvMgLO9d8VUqJ9CNkMyByAqJmqzgh0g+X\nndRi8Q8JUQB/fum3rsRBoVAoXiMIIRCWDpaOnrMv62e9vDteKBQKheLXAiUOCoVCoViFEgeFQqFQ\nrEKJg0KhUChWocRBoVAoFKtQ4qBQKBSKVShxUCgUCsUqlDgoFAqFYhVKHBQKhUKxCiUOCoVCoViF\nEgeFQqFQrEKJg0KhUChWocRBoVAoFKtQ4qBQKBSKVShxUCgUCsUqlDgoFAqFYhVKHBQKhUKxCiUO\nCoVCoViFEgeFQqFQrOKixEEIcbcQ4oQQ4jkhxCfWOC+EEH/ZOv9PQog9L3StEKJDCPEjIcTJVp1/\neR5JoVAoFC+VFxQHIYQO/BVwD7ATeJ8QYuc53e4BtrfKR4G/vohrPwH8REq5HfhJ61ihUCgUVwAX\nM3K4CXhOSjkspfSArwLvOqfPu4Avy5jHgXYhxIYXuPZdwJda7S8B736Jz6JQKBSKlwnjIvpsBMZW\nHI8DN19En40vcG1BSjnVap8BCmt9uBDio8SjEQBXCHH0Iu55PdAFzL3aN3GFoGyxjLLFMsoWy+y4\n1AsuRhwuO1JKKYSQ5zn3eeDzAEKI/VLKG1/Rm7tCUbZYRtliGWWLZZQtlhFC7L/Uay5mWmkC2LTi\nuL/12sX0udC1062pJ1r1zMXftkKhUCguJxcjDk8C24UQg0IIC3gv8O1z+nwb+EBr1dItQLk1ZXSh\na78NfLDV/iDwrZf4LAqFQqF4mXjBaSUpZSCE+DjwEKAD90spjwkhPtY6/zngQeAdwHNAA/jwha5t\nvfVngAeEEP8KOA285yLu9/OX8nC/5ihbLKNssYyyxTLKFstcsi2ElGtO9SsUCoViHaMipBUKhUKx\nCiUOCoVCoVjFFSsOQoj7hRAzK+Ma1mPKDSHEJiHEI0KIp4UQx4QQf9R6fT3aIiGEeEII8VTLFp9u\nvb7ubLGIEEIXQhwSQny3dbwubSGEGBVCHBFCHF5ctrmObdEuhPhHIcQzQojjQohbX4wtrlhxAP4W\nuPuc19Zjyo0A+GMp5U7gFuAPWilI1qMtXODNUsrXAdcDd7dWx61HWyzyR8DxFcfr2RZvklJevyK2\nYb3a4rPAD6SUVwOvI/73cem2kFJesQUYAI6uOD4BbGi1NwAnXu17fBVs8i3grvVuCyAFHCSOuF+X\ntiCOG/oJ8Gbgu63X1qstRoGuc15bd7YAcsAIrcVGL8UWV/LIYS0uKuXGrytCiAHgBmAf69QWrWmU\nw8RBkz+SUq5bWwB/AfwHIFrx2nq1hQR+LIQ40Eq5A+vTFoPALPDF1nTjF4QQaV6ELV5r4rCEjCVw\n3azDFUJkgK8B/0ZKWVl5bj3ZQkoZSimvJ/7VfJMQYvc559eFLYQQ9wIzUsoD5+uzXmzR4vbWv4t7\niKde71h5ch3ZwgD2AH8tpbwBqHPOFNLF2uK1Jg7rMuWGEMIkFoa/l1J+vfXyurTFIlLKEvAIsV9q\nPdriNuCdQohR4mzHbxZCfIX1aQuklBOtegb4BnFG6PVoi3FgvDWiBvhHYrG4ZFu81sRh3aXcEEII\n4P8Ax6WU/3PFqfVoi24hRHurnST2vTzDOrSFlPKTUsp+KeUAcVqah6WU72cd2kIIkRZCtC22gbcB\nR1mHtpBSngHGhBCLWVjfAjzNi7DFFRshLYT4B+BO4rS708B/Br4JPABsppVyQ0pZfLXu8ZVACHE7\n8BhwhOW55U8R+x3Wmy2uI977Qyf+YfOAlPK/CCE6WWe2WIkQ4k7g30sp712PthBCDBGPFiCeVvm/\nUso/W4+2ABBCXA98AbCAYeJ0RhqXaIsrVhwUCoVC8erxWptWUigUCsUrgBIHhUKhUKxCiYNCoVAo\nVqHEQaFQKBSrUOKgUCgUilUocVAoFArFKpQ4KBQKhWIV/x+C7EeEFj+g2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c0fe7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for i in range(len(svd_comp1)):\n",
    "    ax.plot(svd_comp1[i]['results'][len(svd_comp1[i]['results'])-1]['explained_variance_ratio'])\n",
    "plt.ylim([0, 0.03])\n",
    "plt.xlim([10,60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11, 0.1, 0.08, 0.09, 0.09, 0.05, 0.05, 0.06]\n",
      "0.07875\n"
     ]
    }
   ],
   "source": [
    "p_frac_comp1 = [svd_comp1[i]['p_data'] for i in range(len(svd_comp1))]\n",
    "print(p_frac_comp1)\n",
    "print(np.mean(p_frac_comp1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sanity check performing same analysis on TF-IDF Vectorizer transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd2_batch_analysis(n_epoch=1, n_comp=20, plotting=True):\n",
    "    t0 = time.time()\n",
    "    \n",
    "    if plotting == True:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    batch = {}\n",
    "    epochs = {'n_epoch': range(1,n_epoch+1)}\n",
    "    for i, n in enumerate(epochs['n_epoch']):\n",
    "        \n",
    "        t1 = time.time()\n",
    "        message0 = \"Epoch: {}\".format(n)\n",
    "        print(message0, end=\"\\r\")\n",
    "    \n",
    "        comp_results = {}\n",
    "        # create subset with `p_data` percent of training data in range(4,12)\n",
    "        p_data = np.random.randint(4, 12)/100\n",
    "        comp_results[\"p_data\"] = p_data\n",
    "        \n",
    "        # Create mini data sets for model building\n",
    "        train_subset = train.sample(frac=p_data)\n",
    "        y_train_subset = train_subset['Class'].values\n",
    "        X_train_subset = train_subset.drop('Class', axis=1)\n",
    "\n",
    "        # Create mini dev set for model building\n",
    "        dev_subset = train.sample(frac=p_data)\n",
    "        y_dev_subset = dev_subset['Class'].values\n",
    "        X_dev_subset = dev_subset.drop('Class', axis=1)\n",
    "        \n",
    "        corpus_train_subset = X_train_subset['Text']\n",
    "        corpus_dev_subset = X_dev_subset['Text']\n",
    "\n",
    "        comp_results[\"x_shape\"] = X_train_subset.shape\n",
    "    \n",
    "        # 2. Apply tokenization\n",
    "        print(print_blank(len(message0)), end=\"\\r\")\n",
    "        message1 = \"Epoch: {} Applying tokenization...\".format(n)\n",
    "        print(message1, end=\"\\r\")\n",
    "        \n",
    "        tf, tf_train_subset, tf_dev_subset = tfidf_fn(corpus_train_subset, \n",
    "                                                corpus_dev_subset, \n",
    "                                                ngrams=2, \n",
    "                                                mn=1, \n",
    "                                                mx=0.1, \n",
    "                                                test_transform=True, \n",
    "                                                plotting=False)\n",
    "              \n",
    "        comp_results[\"tfidf_vectorizer\"] = tf_subset\n",
    "        \n",
    "        tf_train_subset = cv.fit_transform(corpus_train_subset)\n",
    "        #cv_dev_subset = cv.transform(corpus_dev_subset)\n",
    "              \n",
    "        print(print_blank(len(message1)), end=\"\\r\")\n",
    "        message2 = \"\\tTokenization done in {:.2f} seconds\".format(time.time() - t0)\n",
    "        print(message2, end=\"\\r\")\n",
    "        \n",
    "        # Number of components must be less than number of observations\n",
    "        if X_train_subset.shape[0] < n_comp:\n",
    "            n_comp = X_train_subset.shape[0]-1\n",
    "            \n",
    "        batch_analysis = svd_comp_analysis(tf_train_subset, n_comp, plotting)\n",
    "        comp_results[\"results\"] = batch_analysis\n",
    "        batch[i] = comp_results\n",
    "        \n",
    "        if plotting == True:\n",
    "            ax.plot(batch[i]['results'][n_comp-1]['explained_variance_ratio'])\n",
    "        \n",
    "        print(print_blank(len(message2)), end=\"\\r\")\n",
    "        print(\"Epoch {} completed in: {:.2f} seconds\".format(n, time.time()-t1))\n",
    "    \n",
    "    print(\"\\nTotal time to completion for {} epochs: {:.2f} seconds\".format(n_epoch, time.time()-t0))\n",
    "    \n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 315.89 seconds\n",
      "Epoch 1 completed in: 433.12 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 109.92 seconds\n",
      "Epoch 2 completed in: 163.44 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 143.72 seconds\n",
      "Epoch 3 completed in: 207.37 seconds\n",
      "None                                                                                                                                                                                       \n",
      "Total time to completion for 70 components: 191.29 seconds\n",
      "Epoch 4 completed in: 269.07 seconds\n",
      "\n",
      "Total time to completion for 4 epochs: 1073.07 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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y73sfkihnxM8YE3cWCBOsJ++xbX8XT+zcxoMb97GvI8HU2lpuWDYHv/4xvrf5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\nYwwA/z8Kl3KjFmCl4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cbeac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf_comp0 = svd_batch_analysis(4, 70, plotting=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 60)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xLDbnhhvIvORi44Y6IcRPTq+Eg1LKDCwHTgAqgbnA+VrrJR3KTAKuwwiHccDj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\nDaI4hBBCaBDFIYQQQoMoDiGEEBr8B50CKMsyIE6WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154419f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for i in range(len(tf_comp0)):\n",
    "    ax.plot(tf_comp0[i]['results'][len(tf_comp0[i]['results'])-1]['explained_variance_ratio'])\n",
    "plt.ylim([0, 0.03])\n",
    "plt.xlim([10,60])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Number of SVD Components\n",
    "\n",
    "From the figure, we can see that as we add components, we start to see a diminishing return on explained variance, and start to add significant computation time. We varied the batch size to be just around 8% of the total training set, and since it looks like the explained variance starts to stabilize somewhere between around 20 and 40 components, we might consider scaling that by about 10x for the full dataset. This would give us about 200 components, which isn't exactly a precise measurement, but it coincides with general guidance on using SVD from external [resources](http://www.kdnuggets.com/2016/08/approaching-almost-any-machine-learning-problem.html/2) and gives us an estimate to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (3321, 2690998)                     s\n",
      "Non-zero mean: 4586\n",
      "Non-zero median: 3328\n",
      "\n",
      "Done in 2075.80 seconds\n"
     ]
    }
   ],
   "source": [
    "tfidf, tfidf_train, tfidf_test = tfidf_fn(corpus_train,\n",
    "                                corpus_test,\n",
    "                                ngrams=2, \n",
    "                                mn=1, \n",
    "                                mx=0.1, \n",
    "                                test_transform=True, \n",
    "                                plotting=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.feature_extraction.text.TfidfVectorizer"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save tokenized vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_sparse_csr(filename,array):\n",
    "    np.savez(filename,data = array.data ,indices=array.indices,\n",
    "             indptr =array.indptr, shape=array.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_sparse_csr(filename):\n",
    "    loader = np.load(filename + '.npz')\n",
    "    return sps.csr_matrix((  loader['data'], loader['indices'], loader['indptr']),\n",
    "                         shape = loader['shape'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 406 ms, sys: 599 ms, total: 1.01 s\n",
      "Wall time: 1.23 s\n"
     ]
    }
   ],
   "source": [
    "save_sparse_csr(data_directory + '/data/train_bigram_vocabulary', tfidf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 587 ms, sys: 797 ms, total: 1.38 s\n",
      "Wall time: 1.63 s\n"
     ]
    }
   ],
   "source": [
    "save_sparse_csr(data_directory + '/data/test_bigram_vocabulary', tfidf_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load tfidf from previous session if applicable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 911 ms, sys: 345 ms, total: 1.26 s\n",
      "Wall time: 1.31 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "tfidf = TfidfVectorizer()\n",
    "tfidf_train = load_sparse_csr(data_directory + '/data/train_bigram_vocabulary')\n",
    "tfidf_test = load_sparse_csr(data_directory + '/data/test_bigram_vocabulary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to reduce the dimensionality of the transformed text, we can use truncated singular value decomposition (SVD), which is similar to PCA, but for sparse matrices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Choose index of number of features where we will truncate dimensionality\n",
    "features_n_svd = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load previously transformed data if applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.load(data_directory + '/data/train_svd_200')\n",
    "np.load(data_directory + '/data/test_svd_200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "svd = TruncatedSVD(features_n_svd, algorithm='arpack')\n",
    "svd_train = svd.fit_transform(tfidf_train)\n",
    "svd_test = svd.transform(tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3321, 200)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save(data_directory + '/data/train_svd_200', svd_train)\n",
    "np.save(data_directory + '/data/test_svd_200', svd_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FO9fGFuG2ySdx6qjR3f59uK7LG/OqqWtuY8pBoynOS/6mrfZ+qSyoekgsqE4m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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1242465f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2D_svd = svd_train\n",
    "plt.scatter(data2D_svd[:,0], data2D_svd[:,1], c=y_train)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MFpHZRUVFLZe5VjRn6xbCtQr/3VyWxbqS4lbIkVIqGSSiCmgz0K3W567V2+oQkcHAU8Cp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x274b24b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2D_svd = svd_train\n",
    "plt.scatter(data2D_svd[:,0], data2D_svd[:,2], c=y_train)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HMctTcS0tJZyTQtWgXMIDUqDYwVPmkD2ohPb9itlxYxVOXgq7+nYl553lSNhB\nDBiXYFwWFX1zSFu2AzFgZ3jY+LtDcfyRrj5WpzLcbiLXDNX9+LO2+OkY6IS/g5fB+Xmc228Iv5z4\nLhXBIJVBG7/Hjd/r5uwxh/Di0vlU2jZ+t4tfjDyKnx4+QscDKKUOyP5MBheTBCAiZwCPAS7gGWPM\n/fVel+rXxwEVwFXGmLn72m9zEsBJt/waf68qyh8MYpWG2DVhCE6GF6zqXj5ETtghP6TuqGDAuDUU\nnldOOMODu3jPjV/jFqqGpBLs6adodFdSZxdReXoeToUHACsthDsr1NDkngQL0qh9g9m7W/AU73ku\ngJMCFXl7hoSluN3cesxxXHno4Y1+tslrV3LTxx8QCEW6leak+nnngsvolpl9QMdKKdV2xHU2UBFx\nAU8CpwIFwHciMskYs7RWsbFAv+qfI4F/Vv/bIu57+0Vc6z0Ep+zGU1BB0RXDcTK91R3zq+Ou/tcd\ngLKuqWz8tCup7bcjdnlNmarh6RTd0q2ml6gnxaEyNw+nzFuzB7EaT6CSXYXZvWc1p2CWwVVqcJfa\n+JcXYpWHCHbLpCI3C1yR/VXaNv/31VR6Z7fjuB49o/a5cmchP39vUp1PsauiipNfeJblP78Jl8u1\n38dLKZWcYtHgPApYbYxZY4wJAq8A4+uVGQ+8YCJmANki0jkGdTfo000zSZuwi1CRi3C7FMKZvjon\n/z0EAdzlwu7UNKRLOyBywg5nuij6bXdMqguT5sKkuhABd1aozlGzvE7Ut/8fuPz1F/M2BNOqSJu2\nivRvNpA+ZzPZH6yky5NLkfIQ2JErgZDjcO2kt/nPguiLpJs+eb8m9tqfI+wYHp/7bROOjlJKRcQi\nAXQFNtZ6XlC9bX/LACAi14nIbBGZXVhYeEABuSM7AsfBeF1IA4tt12HA8VmI7cbqkBvp/XNMFqaR\nE7uVYtc8doIuGmtFsyvrfRsXIdzOz7af9KdiSKS5xgo5+DaWkzF7B7j3/O8IGYf7vppGZShUZxcb\ni0uoP27hB9MLNuz1YyqlVG2trsuJMWaiMWaEMWZEhw4dDmgfqSlZBD/MwJdXibuwfK9lDRBOMaRu\nCUQ2eNyE072E013gbuBEK3WbfcKlHqie0XnPZ6h+UNzwYt7G62LHOT1rZo+2Qg4Zc3ZElQsFQqws\nqru9c2Y6ja0qM7xTi11UKaXaoFgkgE1At1rP86u37W+ZmPn3BbcTzgqSeksmwS7pZEz5HkLhBs7S\nJtK8L2FkGN1QAAAcL0lEQVSy50cSQDDbzY5rjqBiWCMDtww4VbUOmyOEtqfUvPYDuygyr1Bjlwfh\nTC/Gu2c/xhWdbAzQPjWtzrYHTx5b69U9JUWEW0ed0HDMSinVgFgkgO+AfiLSS0S8wEXApHplJgFX\nSMRRQLExZksM6m5QTkYG/7nnHooXppBxC6T0ryTt63V41u1CqoJgHIwYDDYphaXkTyrGFQLHguJu\nPnAM4R3ZkaNT6wRe89CCH4b7isfB7YRwf52OtdxPuMKFCVgYifQSkmDDc/xLKIyEIq85XovSo/Lq\nFrAdfLuCdM3MrLN5eMcu3DtmDC5rT4JJDbo4zt2NW174gC+XfK+LyiilmqTZvYCMMbaI/BKYTKQb\n6HPGmCUicn31608BHxLpArqaSDfQq5tb7750bpfLlw/9PfLkV2DbNru2FmP5La773UOUm0348lNw\nVmdhUj0MPKQ7V95wOt8UFTDt/bkUbSyk4P1ynJNzIDWSJ6XCwTvDxfInbuOjVYv4cuUaPpy8Glwu\nMNBHcvH50pm3Zc/KXv7lu6kamI3x7bkfIIEwWVO3RsYYiFA+JJvSw3KRQBgcAy7Bu7GcX51yfNTn\nArh48HAuHjycimCAO17+hG9XrGfOlsgF1bcrN/Cjwwfyh/PH1JQvLC/HMQ4bSor5btMm2qemckJe\nPpXby+jYswMpaTpHkVLJqE0PBIsVx3EwxjSpi+W2sjIufes1tpWXAVAZCOBfsovKflk1YxB8K4tx\nlYdwVzlU9skkmJ9Kyrpi8p5aiZ2XgvFajP3Nj7jzunP3WtecNQX8/Ol3qAzWvVHs87h5+dcX4/Zb\n3PjRe6wqKsKu/v9sEVmn2AmG6fX893i2VND35hMYe9Fx9M5px/+WLGJ7eRkn9+zNj/sPxOdOisHi\nSrUZcR8I1lJaSwLYX8YY5mzZzNayUoZ17ET3rGzWbN7Gs+9N5dzjR5DVOZNrP3mNDUXlWAJH9ezC\n86deRHlVgOKyCrp1bN+keh7/6BuenjIr6pawx2Xxi7Gj+dvy6ZTX60UUCRDcJeApEcRA2BXGs3UX\nErSp7J+Fnesn1e2hR3Y2b5x/MSkeT/MPilIqLuI6EExFExFGdKnby7V3l47c+7MLap5/ccENUe/L\nTEslM63pi8JkpvjxuF0E7brjDdwui6Ul2xs++QOeIvBUCGIEz9ZS/B8tJaXcVA9NNhQf15GdZ/Vg\n7e5dvLx4Adcc1qTfJaXUQabVdQNVTTf2sAFYDYxCE4RlZdHdSoHINNWAGMEeXULluQ67nhnI5v8N\nYvMLA7FsQ9Y320lZXkyVbfPhqpUt+yGUUgmjCeAglpeVzl+v+BFpPg9pPi9pPi8ZKT6euGZ8g4nh\nB8YN9ugSkOrJ6374cQsFLw/CCjpkTt8GQLrXG6+Po5SKM20COsidMLg3U/90PXPWbMJlCYf36orH\n7WLcrv4s3dHwSGqriurLgHpJQgSqxyZI0CHF7ebyYcNb9gMopRJGrwDaAJ/HzegBPTiyX3c87khP\npeuOGEWaJ/rbu8sSMGV73Z/jtSg7vD1XHHoYp/Tq0yIxK6UST68A2ii3ZfHN1T/lpskf8s3G9QjC\n0d26MdesINhp7++1Mzy89JdfMKxnt0bLbNxSyNOTvqQyGOTCE49kxCF9a14zxlC2u5yUdD9uj/6K\nKdVaaTfQJLK6pJCLpj5DuV293kH1NNc1jAHHYebY28hMS2toFwD849XJPLxxYaS8CAjkTt3CUUV+\nRp81knce/4jiHaW43Bbjrh3DdQ9drolAqTjRbqCqQXn+DEJOrS6j9e8Ti7DsvHv2uo/CncU8vHEB\nxlt3UFzR8Z2Y/9dFLPvdqpptdhA+fHoKgcog/+9fP2tu+EqpGNN7AEkk0+vnrG6H4nfVzft+l4f/\nHn81y875wz738fx7U5EGpjcy7sigsvoClUE+fXEq5SUVBxq2UqqF6BVAkrl7+DjS3F5eXTcH2wmT\n58/grkPHcXhu9ya93w7XX+TmB4KrvOGBZ263i11bd5OW2fRBbk31/dYi7n/nS+asKSDV5+X8ow/h\nhtOOrrkZrpRqnCaAJOOxXNw+7HR+M/RUAuEQqW7vfi1Af+kZx/LM/1ZHbZeQg2lkP8YY8rrvfXqL\nst1lTPztS8z7fBHtOudw7f2Xcsixg/b6nq27S7ns8VcorwpigOKKKl6cNo/1hbt55Mozm/yZlEpW\n2gSUpNyWRZrHt18nf4DuXTpwSVoPJBiOLGHpGCQQJn3uDlzldlR5f6qPS+48F6+/8QFlOzbv5PxO\nP+WjZz9j69rtLP12BTcffzevPvjOXmP571fzCITCdeZCCoRspi5dw6adxfv1uZRKRnoFoPbbn6+/\ngLMWruKG2/9B2CWkLdmFb11ZzT3lXsO6s339DnI6ZnPx787h1Cv2vlDNfZc+hh2MTh7P3vEy5958\nJu5GZiRdsnEboQaapLxuF2u27aRru6z9/WhRyquCvDFzEVOXrKFDZhqXHncYw3roymuqbdAEoA7I\nyGH9mPbm/dw46nbWr4sMLPP43Nz12s2M/vHI/drX0ukNzzdkHMN3H83j6Hr7M8Ywf2cBoZQQliU4\n9dZ8DoUdenTI2a8YGlJWFeDCR19me3EZVSEbEfh88ff87pyTmHDk0GbvX6lE0wSgDlhKio9nFj3a\n7P243BY/DE2oLz2n7tKctuPwyxmvMGvHOoIpDkIqtfuz+twuRvXNp3v77GbH9co3C9i2u5RA9Wyr\nxkBVyOaBd75k3OED8evYBnWQ03sAKuFOvPCYBrd7/Z6oG8FvrJvLrB3rqAyHCPvC2MPKcdIjzUc+\nj5tzjhzKI1f+OCZxfbZodc3JvzbLEpYVbItJHUolkn6FUQl307+uY+G0pWz5fs9J1XJZ/N+Hd0aV\nfXP9PCrDtbqbpjs4wyvwWh7+d+K19Muqu7ayMYYFuzaxcGcBnVIyObFTfywDG5YWkNe9PenZ6TQm\nJz2lwe122CEzRZfRVAc/TQAq4dxuNy+seoLZny7gqzdm0LlXHhNuPhOvN3olMqeRqUtEwNTr0BR0\nwtww/X/MLdpA2Dh4LBfWV0VkProeCUM41cIZks5Tb/yRgd2i5z269NjDmP19AZW1blBbInTLzaJP\np9zmfWilWgFNAKrVGHHqoYw49dC9ljm7x6GsWbKDqnDdQWeZHj/9MjrU2fbi6pnMKVpPVbj6BL60\nlPZ/3wDAzp93oeL4bMQ2nDP9GboszeGTMTfWWff5mIE9+dmYI/nnJzPwuF2EHUOn7HSevPbsGHxa\npRKvWQlARNoBrwI9gXXABcaYXfXKdANeADoSmX5sojHmsebUq5LXhb1GMGXzchbv2kRFOITf5cYl\nFo+OOj9qTMMb6+fuOfkDGW9sR4KG4qs7UXFcNngtTPXwhM3lu7l02r955aRr6+zjmlNGcd7Rw1iy\ncRvZaX4Gdc3b77ETtQVtm1e/Xcik75ZiWcKEUUM596hDcLv0dpyKv+ZeAdwOfGaMuV9Ebq9+flu9\nMjZwizFmrohkAHNE5FNjzNJm1q2SkNdy8fyxVzC9cC2zd6ynvT+dcflDyfZGt9eHTd1JizybA2BB\n+antwFfvhCvCgt2bGqwzK9XP6AE9mh274xh+NvFtFm/YSlUokpjWbJvGV8vX8vhPxjcrsSh1IJr7\ntWM88J/qx/8Boq6NjTFbjDFzqx+XAsuArvXLKdVUIsLovN78avBJXNJ7ZIMnf4Az8w/BZ+35jhPs\nnYKTYmFciTnRzli1gaUbt9Wc/CHSrXTW6o0s3LA1ITGp5NbcBNDRGLOl+vFWIs08jRKRnsBhwMxm\n1qvUPl3T/xh6ZbQn1RVp56k4vyPYBteuhietc0nz/hwcY/h24wZeXrSA2Zs3UX+tjdnfF1ARjK47\nFHaYu6bhqw+lWtI+m4BEZArQ0BpSdfroGWOMSEMTAtfsJx14E7jJGFOyl3LXAdcBdO/etBkqlWpI\nmtvL6yf+lC+2rmBu0Ua6HJLFltXzePv1Oey+pgt4BCypWdjmxkEnHnBduyorufDNV9lSWoJjDCJC\n/9z2vHj2eaR5IwmoQ2YaXreLYL2xBV6Xi/YZe58pddPOYmas3ECa38sJg3uT0kAPKaX2V7NWBBOR\nFcCJxpgtItIZ+NIYM6CBch7gfWCyMeaRpu5fVwRTLeWBRZN56ftZ2MbB53Lz28GncmnfUQe8v198\nOIkpa74n5Oy57+B1ubh4yDD+cOLJ7C6v5DcvfMDM1Ruj3puR4mPK73/KsmlLefq2l9i4fDMduuVy\n5Z8u5KSLjuHvH37Nf6bOxWUJIoIgPPXTcxjeq8sBx6varv1ZEay5CeAhoKjWTeB2xphb65URIvcH\ndhpjbtqf/WsCUAeDsOMw6B+PYTvRK+WkYHH5bBefdbLYYcJRZfKy0njymrOpWLaVu89+gEDFnjkx\nfKk+Tv+/83lxy4Y6YxEAMlN8fPmnn+FxRa97UFxRxdszF7Now1Z6dMjGn+tlbdlu+rXL5dxBQ8jy\n6yC2tiyeS0LeD7wmItcA64ELqgPoAjxjjBkHHANcDiwSkfnV77vDGPNhM+tWqlVwjGl0gFpVMMTH\nH81n58WHYDx1T9aWCCP65/Piivm8M3MezrV9yflsC2lLIj2pAxUBXvlyLpW9ouc1CjuG71YXRPVO\n2rKrhAsffZmKYIhAyAaJ9L2u7OTgS3Xz+KzpvHH+xfRppwPZVDMTgDGmCDilge2bgXHVj78mevVZ\npdoMj8vFyC5dmbWpoM7aBIQNqYt3Ec70QdhAvWZ723J4a/tywtsNZLghI5OtPdJp9/5Gcr6I9K0I\nOo2twEaDU2E/OGkqxRVVexKSifzHVyRUeW0Cts0dn3/Kq+dd1KzPrNoGHQmsVAzce/KpnPfa/wiE\nbSptGysYRirDtH8nMu2EcUf3MLJzoO5yNoDLYudZ3Sk9sgPpc4rImryK9Lyt7D6+B3bennmLwo7D\niN75Ufv8Zvn6qKsRQbCCBkxkuow5WzYTDIfx1mo++mr1Wn7/xicUFVXg8liMGzmAv4w/DcvSAWpt\nmf7fVSoGeue048urruG3o4/j4qGH0P2LHXS/dz7ukhCu8hApi7dBcM83dpdlYac0cv9NhFCnVHad\n2oXNNw7BtamE3NcW49pViduy8Hnc/OH8MaQ1sMqarwlrIVsiWLUGnc3ZUMAN/3qHHYXl4EA44DDp\nm2Vc9uxr+38g1EFFE4BSMZLp83PV8MO59+TT+MlRI/G79lxgZ36xlqyv15Ne5dAxK50JRw7Zd1dO\nr4tQez/lh+RgOYah6yu56qQjeOOWyzjziIbXS55w5NCoJGAwhKuXTfBYFmN69cFd65v9H96egnEM\nUqulVgwsXLGFrcWl+38g1EFDE4BSLeDC285m+IlD8KV68af5SE330z/g4sM/XMuUu3/K3eeNYVTX\nfQ+IN34XlX0zMWFDWlElvx53LD33strZDacfzYg++fg9blJ9HsQCkwr0DeDrVEFKpwBjBnevM0ht\n0/biyMnfMVi7KyPjIkxkree/vjg5FodDtVJ6D0CpFuDxerj3gztYs3A9q+etpVOvPA45blCd+X5+\nf8LJfPPS83XGDkQJOrh3B7AsoefQfQ+M9HncPHXdBFZu2cGqLTton53CLcveYHfQJoxDgAB/WfQh\nBZW7+NXgkwDITPdTVFWOVRHCyfJH5tYG8LqYvGYDJ345jzNPPKxZx0O1TnoFoFQL6j2sB6ddeSLD\njh8cNdlbr+wc3r7wUkZ26YpbpMGucmIMGbN24PF7uOj2pk9D3b9ze350+EAW2QWU2VWE2ZNkKsMh\nnlv1LbsCFQDcePoxSGUIk+bZc/Kv5b53vmxyvergoglAqQQa3CGPV8+7iJU33synl19N/9xcfC43\nHgfcxUG6TFxB3x6duO+ju+jVhCuA+r7atpqAY0dt91guFu/eDMB5hw/l0C4dosoAIEIZe7lCUQc1\nbQJSqpXondOOjy+9io3FxYScMD2zsnFud3A3Y/H5zilZWAhOve6mYePQ3hfpVhqwbcqygZ0N7MAY\nUk18vyeWllbw2afzyMvLYfSxg+Nad7LRBKBUK9MtK6vmcXP74V/e90gmb15SZ2EcF0K3tBwGZkUm\n731i1gxWSAnuYBi8rqhmoLzcjGbFsD/u/8N/mXL/e5Gho47B1SGdRybfzZChzV+PQUXTJiCl2rAh\n2Z259/DxZHh8pLm9+Cw3Q3K68PQxl9Xck3ht6WIC4TDGF33yR4Q1xaUE7ehmpFh7f9IMptw3CQmF\nkWAYsR3CW0v4f6f8EWdvN8rVAdMrAKXauHH5Qzm1yyBWlxSS6fXTNbXu3EL2Xqab+EFhSTld22Xt\ns1xzvPjgO2DXPdGLAWd3BZ9OnsvpY5s0v5naD3oFoFQS8FguBmV3ijr5A5zau29kYFgjM3YJ0DGz\n5ZuByneUNhyCJWzdVNTi9ScjTQBKJbnfjD6OvLQ0aGdh6s9NBIw9bADuBuYyirUhpwxtcM4kbIdT\nzjiixetPRtoEpFSSa5+ayqeXXc17K5fzxjcLWbV6B3bYwW1ZnHvUUO46N2rC35irCIXIPncAG9vv\nAtshbU4hvq1BPDsqOeK04eTnt2/xGJKRJgClFCkeDxcMOYQLhhwS97ptx+HCN15h9c4iQu18eIoh\nOLQHwaGAgcleh+WbtzGwS6TX0p23PsWs12Zi+dxcescErrhybNxjbiuatSJYS9MVwZRq+z75fhW3\nfPIR5aEQ7jLw7hTE7LkbYMRgpxiGeXzseGIG1o5Anfenj+7KO1//Ld5ht1r7syKY3gNQSiXU7M2b\nKA+FAHCX1D35A4gR3BXCivnrsHYEEKjzU/btJr7+dmG8w24TNAEopRKqS0ZmzdTZ0liPVIGM+Q0N\nVY549HfPxz6wJKAJQCmVUOMHDKpZn8Dx0WBPJGOBVIUa3YfoymUHRI+aUiqhclJSeHHC+fTIysZp\nJ9UL2UeSgMFgxBDMMZQc17nRfdzxyLWNvra7vJLPFq1m+sr12GEdUVyb9gJSSiXcoR078fkVP2FT\naQn/+WYuL389H8c2GI8hlBW5Mig9Oo+Mmdvxbyiv8952Y3pz+GEDG9zvi9Pm8tgHX+OuXv/Y53Hx\np9OOZenrs/Cn+jnvNz8ms1385jpqbZrVC0hE2gGvAj2BdcAFxphdjZR1AbOBTcaYM5uyf+0FpFTy\nmr1oNde9/gbF2S4wkFoa5tHTxrLo22VMfvZzXD43v7jvCsaNPSrqvUWlFTzw7pd8PG9FVIOSVATJ\nmzgbqX7h+keu5NybmnRKOijsTy+g5iaAB4Gdxpj7ReR2IMcYc1sjZW8GRgCZmgCUUk21rmA7djhM\n3x6NNwHVtru8kgl/fZEdJeUN3E0ACdhkv7cC38bimm2vbX2anLzoaTIORvHsBjoe+E/14/8ADS5Z\nJCL5wI+AZ5pZn1IqyfTMz2vyyR/gf1/Pp7iiqsGT/w+q0oVdx3ekeFR7dp7Qib/d8XTzAz0INTcB\ndDTGbKl+vBXo2Ei5vwG3gi4tpJRqWd+sWE/QbnyGU1NRSeqWCrJmFpIxp4js6dv54sNFnHv9fXGM\nsnXYZwIQkSkisriBn/G1y5lIW1JU0hWRM4Htxpg5TQlIRK4TkdkiMruwsLCpn0MppQDonJPR0NLG\nEbZD1pdrcZcEsQIOVthgBR08hVVs/nZNXONsDfbZC8gYM6ax10Rkm4h0NsZsEZHOwPYGih0DnCUi\n4wA/kCkiLxljLmukvonARIjcA2jKh1BKqR9cfvzhfLlkDVWhPYvYCJCV6if88QL8G8qQem0RVtiQ\n8n1ps+r9v8df5b8LF+KkuZGqMIM9GbzxeIO3RFuN5jYBTQKurH58JfBu/QLGmN8ZY/KNMT2Bi4DP\nGzv5K6VUcw3r0Zk/nj+GDL+PVJ8Hr9vF4b278u5tVzL6nL1MK92Mr5sX3/Qw/y5eQ1X/LAK9M6nq\nl8W87sKIy3534DuNg+aOA7gfeE1ErgHWAxcAiEgX4BljzLhm7l8ppfbbj44YxGmH9mdt4S4yU3x0\nyo709f/rHT/luH9+jW9zRU03UIiMNK7qmX7A9c13igmnZsAP6xl4LAxQ0iutGZ+i5TXrCsAYU2SM\nOcUY088YM8YYs7N6++aGTv7GmC+b2gVUKaWaw+N20b9z+5qT/w/KB2fhpLhwvNXTT3gt7Ewv1sCc\nA64r1Dl1z8m/FjvHx233//uA99vSdCoIpVRSmTv57+RcdiglR+RSOiyHkpEdOOGusXz91kMHvE+x\nG28/6tk574D329J0KgilVNJ586nYts17C8qoSsnCeF17NtoOvoJyfn7zj2JaVyzpFYBSSjXTxGsu\nxbu5AgmGkSobCYRx7wxwcmqnRIe2V3oFoJRSzXTMqMEsG/VHjr3i9xS7w3iCDl88+TuysrISHdpe\naQJQSqkY+fqFPyc6hP2iTUBKKZWkNAEopVSS0gSglFJJShOAUkolKU0ASimVpDQBKKVUktIEoJRS\nSapZawK3NBEpJDLLaHO1B3bEYD9tiR6TaHpMoukxidbaj0kPY0yHphRs1QkgVkRkdlMXSU4Wekyi\n6TGJpsckWls6JtoEpJRSSUoTgFJKJalkSQATEx1AK6THJJoek2h6TKK1mWOSFPcAlFJKRUuWKwCl\nlFL1tKkEICJniMgKEVktIrc38LqIyN+rX18oIocnIs54acLxuLT6OCwSkW9F5NBExBlP+zomtcqN\nFBFbRM6LZ3yJ0JRjIiInish8EVkiIlPjHWO8NeFvJ0tE3hORBdXH5OpExNlsxpg28QO4gO+B3oAX\nWAAMrldmHPARIMBRwMxEx53g4zEayKl+PLYtH4+mHpNa5T4HPgTOS3TciT4mQDawFOhe/Twv0XG3\ngmNyB/BA9eMOwE7Am+jY9/enLV0BjAJWG2PWGGOCwCvA+HplxgMvmIgZQLaIdI53oHGyz+NhjPnW\nGLOr+ukMID/OMcZbU35HAG4E3gS2xzO4BGnKMbkEeMsYswHAGNPWj0tTjokBMkREgHQiCcCOb5jN\n15YSQFdgY63nBdXb9rdMW7G/n/UaIldHbdk+j4mIdAXOAf4Zx7gSqSm/J/2BHBH5UkTmiMgVcYsu\nMZpyTJ4ABgGbgUXAr40xTnzCix1dElIhIicRSQDHJjqWVuBvwG3GGCfy5U4ROU8cAZwCpADTRWSG\nMWZlYsNKqNOB+cDJQB/gUxH5yhhTktiw9k9bSgCbgG61nudXb9vfMm1Fkz6riAwDngHGGmOK4hRb\nojTlmIwAXqk++bcHxomIbYx5Jz4hxl1TjkkBUGSMKQfKRWQacCjQVhNAU47J1cD9JnITYLWIrAUG\nArPiE2JstKUmoO+AfiLSS0S8wEXApHplJgFXVPcGOgooNsZsiXegcbLP4yEi3YG3gMuT5NvcPo+J\nMaaXMaanMaYn8AZwQxs++UPT/m7eBY4VEbeIpAJHAsviHGc8NeWYbCByRYSIdAQGAGviGmUMtJkr\nAGOMLSK/BCYTuYv/nDFmiYhcX/36U0R6dYwDVgMVRLJ4m9TE43E3kAv8o/obr23ayCRXDWniMUkq\nTTkmxphlIvIxsBBwgGeMMYsTF3XLauLvyZ+B50VkEZFehbcZY1rzDKEN0pHASimVpNpSE5BSSqn9\noAlAKaWSlCYApZRKUpoAlFIqSWkCUEqpJKUJQCmlkpQmAKWUSlKaAJRSKkn9fwtfi3mWvyM3AAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x274c01eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2D_svd = svd_train\n",
    "plt.scatter(data2D_svd[:,0], data2D_svd[:,3], c=y_train)\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:W207-final]",
   "language": "python",
   "name": "conda-env-W207-final-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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}