sandeshregmi/classifying-rareshare-posts.ipynb

## classifying-rareshare-posts.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to train and test two kinds of classifiers on the labeled questions from Rareshare forum posts. We'll do so in the following steps.\n",
    "\n",
    "1. Load the data and make sure it's in the right format\n",
    "2. Create training and test sets from the data\n",
    "3. Transform text from the questions for train and test sets\n",
    "4. Fit classifiers and evaluate their performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rareshare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the data and make sure it's in the right format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_RS = pd.read_csv('labeled-questions.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look some of the questions to get a sense for what other information we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>whProb</th>\n",
       "      <th>Primary</th>\n",
       "      <th>Secondary</th>\n",
       "      <th>Tertiary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hi All, How is everyone doing?</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What kind of tape are we talking about?</td>\n",
       "      <td>0.999756</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>So, ginbo43, what's the diagnosis for which th...</td>\n",
       "      <td>0.999668</td>\n",
       "      <td>Treatment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>How do we find out what NIH is doing to solve ...</td>\n",
       "      <td>0.999574</td>\n",
       "      <td>Literature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HI All, so what is everyone's story?</td>\n",
       "      <td>0.999443</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text    whProb     Primary  \\\n",
       "0                     Hi All, How is everyone doing?  0.999897  Irrelevant   \n",
       "1            What kind of tape are we talking about?  0.999756  Irrelevant   \n",
       "2  So, ginbo43, what's the diagnosis for which th...  0.999668   Treatment   \n",
       "3  How do we find out what NIH is doing to solve ...  0.999574  Literature   \n",
       "4               HI All, so what is everyone's story?  0.999443  Irrelevant   \n",
       "\n",
       "  Secondary Tertiary  \n",
       "0       NaN      NaN  \n",
       "1       NaN      NaN  \n",
       "2       NaN      NaN  \n",
       "3       NaN      NaN  \n",
       "4       NaN      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_RS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also summarize the primary labels we gave each of the questions and their counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Irrelevant        718\n",
       "Symptom           177\n",
       "Treatment         171\n",
       "Disease           102\n",
       "Drug               68\n",
       "Diagnosis          48\n",
       "Resource           36\n",
       "Literature         17\n",
       "Location           16\n",
       "Gene                6\n",
       "Clinical Trial      4\n",
       "Test                4\n",
       "Nutrition           3\n",
       "treatment           3\n",
       "Side Effect         3\n",
       "Payment             3\n",
       "Drugs               2\n",
       "Prevention          1\n",
       "Community           1\n",
       "Treament            1\n",
       "Network             1\n",
       "Risk                1\n",
       "Treatement          1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_RS['Primary'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like some of these are mistakes in spelling or capitalization. We can come back to correct this later, but for now we'll leave those out and only use labels with more than five instances. We don't want to know how to classify irrelevant questions, so we don't include them, but it might be useful to construct a classifier to distinguish relevant versus irrelevant questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Symptom', u'Treatment', u'Disease', u'Drug', u'Diagnosis',\n",
       "       u'Resource', u'Literature', u'Location', u'Gene'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = data_RS['Primary'].value_counts().keys()[1:10]\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the primary labels here, but we can come back and use multiple labels later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_RS = data_RS[data_RS['Primary'].isin(labels)]\n",
    "data_RS = data_RS[['Text', 'Primary']]\n",
    "data_RS = data_RS.dropna() # Exclude row where questions is blank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the data that we have now is in the following format. Once we've removed irrelevant questions, we only have a couple hundred questions to test. This is fairly small, so we'll want to go back to the Rareshare data we have directly from the database to see if we can get more questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Primary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>So, ginbo43, what's the diagnosis for which th...</td>\n",
       "      <td>Treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>How do we find out what NIH is doing to solve ...</td>\n",
       "      <td>Literature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>If so, how long, which treatment and where/whom?</td>\n",
       "      <td>Treatment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>What about stress levels in the year leading u...</td>\n",
       "      <td>Symptom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I was wondering how many people who are member...</td>\n",
       "      <td>Location</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text     Primary\n",
       "2  So, ginbo43, what's the diagnosis for which th...   Treatment\n",
       "3  How do we find out what NIH is doing to solve ...  Literature\n",
       "7   If so, how long, which treatment and where/whom?   Treatment\n",
       "8  What about stress levels in the year leading u...     Symptom\n",
       "9  I was wondering how many people who are member...    Location"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_RS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_RS.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create training and test sets from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the whole data set in the correct format, we're going to split it into training and test sets so that we can build classifiers and see how well they actually do on classifying questions. In this case we're going to use 80% of the data to train the model and test it on the other 20% that we've held out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "train, test = train_test_split(data_RS, test_size = 0.2, random_state=0) # Set seed for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the size of each of the resulting sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Transform text from the questions for train and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data split, we want to transform it into a format that can be used to fit and test the classifier. We start by importing functions that allow us different ways of counting and weighting the words that show up in a given questions, and dealing with the fact that the feature vectors are sparse: most words don't show up in any given question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(stop_words='english') # Exclude words like 'and', 'not', etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we transform the text to use in fitting the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train = tfidf_vectorizer.fit_transform(train['Text'])\n",
    "y_train = train['Primary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each training example we have a description of the questions in terms of the vocabulary across all questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<512x1197 sparse matrix of type '<type 'numpy.float64'>'\n",
       "\twith 2192 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, let's take the first question in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Is RUS generic/whats the likelihood that it would be passed down to my future children?'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Text'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vectorizer we used to transform these text weights terms by a combination of how often words occur within the question versus how often they occur across all questions. We can look at the non-zero weights for this question in particular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 184)\t0.358553914449\n",
      "  (0, 449)\t0.376174320915\n",
      "  (0, 765)\t0.358553914449\n",
      "  (0, 616)\t0.401008888992\n",
      "  (0, 1170)\t0.401008888992\n",
      "  (0, 456)\t0.401008888992\n",
      "  (0, 912)\t0.344886465376\n"
     ]
    }
   ],
   "source": [
    "print X_train[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If we take a look at the training data feature vectors we can see the benefit of using a sparse matrix. There are millions of potential values to remember, but we're only using a very small fraction of them. In fact, less than 1% of the values are something other than zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0035766499582289054"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.nonzero()[0]) / float(X_train.shape[0] * X_train.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do the exact same thing for the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test = tfidf_vectorizer.transform(test['Text'])\n",
    "y_test = test['Primary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the test data is defined by the same size vocabulary. That is, the number of columns across the train and test sets is exactly the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<128x1197 sparse matrix of type '<type 'numpy.float64'>'\n",
       "\twith 343 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Fit classifiers and evaluate their performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data in the correct format, we want to see how well we can classify the different kinds of questions. Here we'll use two kinds of classifiers: a Multinomial Naive Bayes Classifier and a Support Vector Machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomail Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both models we'll want to see how well the classifiers do for each of the different categories, and what kinds of mistakes the classifiers make."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report # Different measures of how well the classifier does\n",
    "from sklearn.metrics import confusion_matrix # Kinds of errors the classifier makes\n",
    "from sklearn.metrics import accuracy_score # The overall accuracy of the classifier\n",
    "from time import time # Function for timing code execution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we fit a multinomial naive bayes classifier. This can be done very quickly given the amount of data we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done training in 0.004543s\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "parameters = {'alpha': 0.01} #\n",
    "clf = MultinomialNB(**parameters).fit(X_train, y_train)\n",
    "print(\"done training in %fs\" % (time() - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we predict the labels for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done testing in 0.000840s\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "pred = clf.predict(X_test)\n",
    "print(\"done testing in %fs\" % (time() - t0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third, we can look at different evaluation measures. Note that the categories the classifier does poorly on are those which have very few examples. For example, both `Literature` and `Location` are some of the least frequent categories. Here's a quick summary of the different measures, where a measure closer to one indicates better performance.\n",
    "\n",
    "\\begin{equation}\n",
    "    Precision = \\frac{\\text{True positives}}{\\text{True positives} + \\text{False positives}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    Recall = \\frac{\\text{True positives}}{\\text{True positives} + \\text{False negatives}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    F = 2 \\cdot \\frac{Precision \\cdot Recall}{Precision + Recall}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Diagnosis       0.40      0.36      0.38        11\n",
      "    Disease       0.33      0.42      0.37        19\n",
      "       Drug       0.11      0.20      0.14         5\n",
      "       Gene       0.00      0.00      0.00         1\n",
      " Literature       0.00      0.00      0.00         8\n",
      "   Location       0.67      0.33      0.44         6\n",
      "   Resource       0.27      0.50      0.35         6\n",
      "    Symptom       0.45      0.45      0.45        33\n",
      "  Treatment       0.53      0.51      0.52        39\n",
      "\n",
      "avg / total       0.41      0.41      0.41       128\n",
      "\n",
      "Overall accuracy:\n",
      "0.4140625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cahern/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.py:958: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "clf_report = classification_report(y_test, pred)\n",
    "print(clf_report)\n",
    "print(\"Overall accuracy:\")\n",
    "print(accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One point of comparison for how well the classifier is doing is to see how well we could do by simply just guessing the most frequent category in the training set (Note that this is also the most frequent category in the testing set as well). So, it looks like the classifier is capturing some kind of signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent category in training:\n",
      "0.28125\n",
      "Most frequent category in testing:\n",
      "0.3046875\n"
     ]
    }
   ],
   "source": [
    "print(\"Most frequent category in training:\")\n",
    "print(max(y_train.value_counts()) / float(y_train.shape[0]))\n",
    "print(\"Most frequent category in testing:\")\n",
    "print(max(y_test.value_counts()) / float(y_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get more insight as well by looking at the kinds of mistakes that the classifier makes, and visualizing them. The kinds of mistakes might also suggest a better set of labels, or using multiple labels. For example, the scientific literature could be thought of as a resource for patients; using multiple labels might allow us to capture the fact that people tend to ask questions about symptoms and treatment in the same question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Diagnosis     11\n",
       "Disease       19\n",
       "Drug           5\n",
       "Gene           1\n",
       "Literature     8\n",
       "Location       6\n",
       "Resource       6\n",
       "Symptom       33\n",
       "Treatment     39\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  2  0  0  0  1  0  3  1]\n",
      " [ 3  5  0  0  0  0  0  7  4]\n",
      " [ 0  1  1  0  0  0  0  1  2]\n",
      " [ 0  0  0  0  0  0  0  0  1]\n",
      " [ 1  2  0  0  0  0  3  2  0]\n",
      " [ 0  0  0  0  0  3  1  2  0]\n",
      " [ 0  1  0  0  0  0  2  0  3]\n",
      " [ 0  4  2  0  0  0  0 18  9]\n",
      " [ 3  2  4  0  0  0  1  6 23]]\n",
      "[11 19  5  1  8  6  6 33 39]\n"
     ]
    }
   ],
   "source": [
    "confusion_mtx = confusion_matrix(y_test, pred)\n",
    "print(confusion_mtx)\n",
    "print(confusion_mtx.sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Diagnosis', 'Disease', 'Drug', 'Gene', 'Literature', 'Location',\n",
       "       'Resource', 'Symptom', 'Treatment'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = np.unique(y_test)\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cahern/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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omgdjRE1EJUmSRpFd85IkSVJ/VkQlSZJGkV3zkiRJ6sQ8SETtmpckSVIn1vhE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\nghVRY4wxxhhTC/8PQxs6vctnDOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f498a1a39d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.imshow(confusion_mtx, interpolation = 'nearest', cmap = 'hot');\n",
    "plt.colorbar(orientation = 'vertical', shrink = 0.6);\n",
    "plt.xticks(np.arange(len(np.unique(label_names))), label_names, rotation=45);\n",
    "plt.yticks(np.arange(len(np.unique(label_names))), label_names);\n",
    "plt.tick_params(labelsize = 14);\n",
    "plt.tight_layout();\n",
    "plt.ylabel('True category', fontsize = 14, fontweight = 'semibold');\n",
    "plt.xlabel('Predicted category', fontsize = 14, fontweight = 'semibold');\n",
    "plt.title('Label confusion matrix', fontsize = 16, fontweight = 'semibold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look into the ten best features for each class, and these seem to make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagnosis: scan, testing, tests, confirm, did, doctors, disease, tested, diagnosis, diagnosed\n",
      "Disease: kidney, maybe, people, common, stones, like, amn, dent, disease, scls\n",
      "Drug: using, steroids, rituximab, did, does, lipitor, started, help, effects, ivig\n",
      "Gene: regarding, tested, readers, mutated, sdhb, wondered, cyclic, wnk4, wnk1, gene\n",
      "Literature: yes, scientists, listen, medical, abstract, literature, consist, stemline, does, premeds\n",
      "Location: state, live, uk, port, thoughts, locations, support, location, tested, hospital\n",
      "Resource: know, disease, hematologist, good, going, patients, idea, doctors, nih, doctor\n",
      "Symptom: rash, feel, wondering, disease, episode, attacks, remission, does, did, symptoms\n",
      "Treatment: medications, cellcept, treatments, surgery, help, did, effects, long, ivig, treatment\n"
     ]
    }
   ],
   "source": [
    "feature_names = tfidf_vectorizer.get_feature_names()\n",
    "for i, label in enumerate(label_names):\n",
    "    top10 = np.argsort(clf.coef_[i])[-10:]\n",
    "    print(\"%s: %s\" % (label, \", \".join(feature_names[j] for j in top10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, now let's see what the classifier predicts for a given question. Test out your own, and see if the results make sense to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_question = [\"What is the best treatment for Hailey-Hailey disease?\"]\n",
    "new_vector = tfidf_vectorizer.transform(new_question) # Convert question to proper format\n",
    "prob_values = clf.predict_proba(new_vector) # Predict probabilities of classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3wf9Q0yALYqDNg6RpY4lG0gF0DTl/hJqexpj/qeMkYpzDAKKq5maiWm0tosBx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49885b4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(len(np.unique(y_train))), prob_values.ravel(), align='center') # Plot result\n",
    "plt.xticks(np.arange(len(np.unique(y_train)))-.3, np.unique(y_train), rotation=45)\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's try a question that has multiple potential responses. In this case it looks like `Literature` is the most probable category according to the classifier, but `Literature`, `Gene`, and `Disease` are also fairly probable. This is close to what we want if we are only using a single label for each question. We can simply return information if the probability of that category applying exceeds a given threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PkaaNJRpJB9A15PxRanoaY+6njpOIcQ4DiKqaW4hqtTWJAsf91NcTqVziXp3o\nZXUY8UR6T1q/PjGi9Fa6Bo8Nqvm82Ivo5fMssFlaNzpdw2cV63L9aXkG5vPEWC5djMUgmx2Jx59P\np+U1gN1qSn/9FKTXTxfqUaTuf0SJ5ttEnXPTAmgpb5WOCszlh7mrCYYR7zj8JTGvStE9b1K62d4H\nbFFD2uWuqt8tHY9ikFIxYnDZJuyP49L1MKq0bl9i+t0fEMO+awtUpX1RzMZ4Qum7HwD/lz5vSlRr\nnkdN83Izd1XW8ukm9m2iIFbML78u0R7ySWrsJln7cW91Bno7GRrWXUz0LCkayQ4iStyHdnfg+pH2\n+kTXoV3pKnGflk7CLYr8pSD+BGmkmH6adm6UL87lin+J6qsplErYxECipWtKu5hQaigxI95nS98d\nRQxmqqVkydwl7kEpIG5FNNqfTHSVPYCYT2VlUmNhnceDKFA9T9Tt/4xUr5y++zHwYPo8jjT5VM3n\nxjpEoWoQ0VY0ldRISrSXjSVNB53rT9u9Pb7crc/MVgLecPd/WLzRegPg6+4+w8w2IboGbkj05Z5W\nQdp7EyfW80Rjx/pEd6bfEKWbsUSAmJ7S/pq7P9HfdGXBWbyMdhwxUOl+ooHwmvT5Mnd/uca0JxLB\n8XmiWuRG4DaiL/NM4nH9SHd/vIa0G7usYmbHEnXuvyeqKoyoKjmizv1QSn83osH2CqIL7QkpD3e4\n+8/TNlu7+8Pp81Je46jF1FX0YGLmxJfd/VyLl3GPI6rUNiNmVPxjXXloilbfPXq5k+9F1CV+nTQ7\nHfG4dS3Rsf8p4pH1i6TXJvUz3QnEwd65tG4ycYFumpZPoqsKpa1mGFucfoiucHcTpasb6BosNZIo\n9Z1JTb0YiAbax4hZK3cj2mHOJkr5pxLVNbXMDsjcJe4TiW54lxEl7LXpms9mF6KqpLaBSKV8DCS6\nis4GxqR1mxGFoPPp6tlh1DDKuZv8HJbOgVVSrHgcOD99txFRCGvJ6N/K/9ZWZ6C005cofd6Z6Ha3\nUboQ/wJcUjoxPpQC6K5Ed7l+TaJP1I3NpqshpZyXKUT/6mFEv9WJVDzns376PD4DG5aPIp64jiNG\ntg5OQWT5dNHWdnyIftwnpc9GtLfc2t9zcAHzcDwxwdQ66UbyjdJ3pxJPA5W9cKOXfKwHXJ0+T03X\nyTJpeQxRnVPrbImU2iCI3l8HEF01T0jnxhZEVc436sxHK37aYmKq9FbqL5tZMdnRUOIiGUm0mO8I\nbGlmV7t+hLvAAAAGs0lEQVT74+7+30Rd1sXE28h/15/03f2vRPXIl8xsuLv/K00YhLufDbxA9Kp4\nlXi/3gv9SU/mn5kNJ+pzMbMjzWwt4iK9j5jed3eP0XBHEXPNvFLV8bF4s/ucf5MBwMdTt0P3GCn5\nOnFjr4WZDWlYtQpRLfB+oprmE2a2RMrndKKP92M15KOYdKuIGwMAN7Ol3f144mnoodRFcRpwjrs/\nXXU+SvkZ4ClyE/Xbs9z9JmIk587ARz1GHv8OWNXMVqkrL63QLv28BxCPWCuY2Qbu/qM0nPl0osrk\nSTPrBPY2szHuPi2t28ndX6kiA+5+q5nNJk6+Ld39r2Y2xN3fIhpGZ6Xt3qkiPelb6qs7mwiWSxCl\n7Vvd/WIzG0OcLysQVRknEkGrkuOTbhqPmNl73P1VMxvs7rPc/VozGw38NM1KtyHRe6GS87CbfIwj\nZox8FHjCY2bAVYnqmmeA/d19VqqHf8vdL6spH6sRN81niQbiV939N+mG8U3gcHc/2syuAR4zs/WI\nmfpq41397E8mehoNMbMb3P376fhtb2bDiEFbH6kqVrSNVhb76arfLv79JDELXPFygUuIR+P9iEfT\nUeXta8rTBOIELYbMHk7UhdfWYq+fbo/DaOC49PkwYjrcs9PyIOLR+DKiD/f/UsPb6Inudk/T1euo\n/CaYU4mRizdS34jeoh1mElGHfE3aL6OIp8HT0nZHEH2ra+lzT9ygniQaQTdJ+/sMos//ikTBa3Rp\n+2YMvS+qS44muoquQkwG9v20/gNED6S7qLCraDv9tC7heV8uMIKou5yUTo4NicEgVxGPyB9oPHA1\n5m0C0d2qeCNOS2bJW5x/UpBYMZ0H+xDzktxBVI0U9arFBVznlKbFnDJzBXCiLnUr6usOWLTDFC8X\nWZ1oqD8gLW9GdFG8hqjTrfzmldJZm2h/Kl7gsGw6NjcSs0remo5LeTj+XG9irzo/pc9LEe1fa6Xz\n4naiynVI6Ryp7F217fbTkq6CFi8XuIGYg+EdIpAfQJR0phG9OnYkBuE8WQzzLXcjbEIe9ya6XY1x\ndQdsmoYh7ysQJc7XiNeIrUnMJfM9ov/yDsQ58+86zwkzm0C0r2zlUYVyIjHMelev6CUOPaS7FzGF\n6nvT+X87cUO7mRhj8BjxogfcvZah3Wb2UeIaOCnVdY8hbiQrEvNhjyWeTo0I4LVNk5Cuya8RN4+j\niTax4vVqD7j7QWm7jxHnxxc8h9kBF1az7xb0/nKBl4jW+wHEY9ntRP1abXfyPvLalq8/WlR/6HpZ\nwODSuvcSAWwKETA2IZ7WbqKJj8N0PY19guimOqZJ6e4JzCCeTm8jqgOOJbrRXk5Nk12V0t+ZKNmP\nJwpb1xE3jitT+sX7W88nbjJ15WMP0stNiJvHj+gaTXs30S1wSaI32BMsIt0Be/tpesnbzHYg5jsY\nkJbndNg3s/OJO+ZhxIW6pLs/19QMSksUDYRA0UBYNBYXc7cfSPTquMbdn7OuOc2bmce9ieHWW3gN\nvTl6SXccMQhpVU+DbsxsINEuU2sjXGo0PpZ40fQM4snnCSKAfpKoTnnLzK4DZrj7WTXkYXdiiPu9\nRDvY8UR8mOjuT5jZ0kT99itEY+4p7v5k1floN62qNikeQ+d6uYCZHUyUyA9t2L72WfOk9cxsX2JW\nwO089fYhun8Vk/ofQ4wivNDd/92iPNY6OrCXdCcQg9R28SaMmuwm/RU8usoWyx3EALlDiInirgfO\ndPdfV5zubkQf8slEo+RgooS9FtG54Seeuoamap0lPb2/dVHXkq6CnqY2JbrlbV06Kf4NvJYu2rc9\ndQVS4F48uPstZjaL6KK3VQrgQ4nzYjAxWu7mVgXulMeWvIw2XTNDgDtSV9bZff6natN/FcDMBhOT\nT51DBOvn0/oDvJ765deIYf73mtlGxARTbxBz+b+P6Gfe6e7PpX2yWARuaFHJe07iqQTu7uuY2QbE\nI+nJ7n5HyzIlLddNA+EkooGww2ucLz0HZraMu/+jRWkPJqYFmAJckG62TelEYGYDPeZK34DoYfIG\n0fNlXaKL4Pe8xvnB21HLJ6ZKF+pNRN/q09z9NlWTSDovvkp0FT2W6MnQ78nHpH9SAB/u7n9oZu+v\nhjxsQPQwGUh0p7zM3f/QzDy0g5YHbwAzG0vMe3xzq04IaT+taiCU+dPKQpbF23H2JQJ3S1543Wpt\nEbwLqcHBFbil0KoGQml/5R5Ji6O2Ct4iIjJ/2mJWQRERWTAK3iIiGVLwFhHJkIK3iEiGFLxFRDKk\n4C0ikiEFbxGRDCl4i4hk6P8D0f0FlsdsESEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f498a1a3850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_question = [\"Has a genetic mutation been associated with Hailey-Hailey disease in the research literature?\"]\n",
    "new_vector = tfidf_vectorizer.transform(new_question) # Convert question to proper format\n",
    "prob_values = clf.predict_proba(new_vector) # Predict probabilities of classes\n",
    "plt.bar(np.arange(len(label_names)), prob_values.ravel(), align='center') # Plot result\n",
    "plt.xticks(np.arange(len(label_names))-.3, label_names, rotation=45)\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One last thing to consider is if the question is either not in English or is nonsense. In this case, the classifier just yields the prior probability over labels in the training set. Basically, the question that's been input has no information in it, so it guesses according to how often it has seen the different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fahpkQQy0eZA0bSzRSDqIriHnj1DT3Rjz3nWcSIxzGERU1dxMVKutQRQ47qO+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4984843e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_question = [\"asdf asdf asdf j 2erjwn\"]\n",
    "new_vector = tfidf_vectorizer.transform(new_question) # Convert question to proper format\n",
    "prob_values = clf.predict_proba(new_vector) # Predict probabilities of classes\n",
    "plt.bar(np.arange(len(label_names)), prob_values.ravel(), align='center') # Plot result\n",
    "plt.xticks(np.arange(len(label_names))-.3, label_names, rotation=45)\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test for this two ways. First, we can simply count up the number of non-zero elements in the vectorized question. Second, we can calculate the amount of information gained from the question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_vector.nonzero()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.07226562,  0.16015625,  0.12304688,  0.00976563,  0.01757812,\n",
       "        0.01953125,  0.05859375,  0.28125   ,  0.2578125 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the question entered is random typing, there should be zero information carried by the question. This is exactly what we find, which is a good thing. One way of deciding whether or not to present a given kind of information might be to set a threshold for determining how much of a shift is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3368086899420177e-17"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_counts = np.array(y_train.value_counts().sort_index().tolist())\n",
    "prior_prob = train_counts / float(train_counts.sum())\n",
    "entropy(prob_values[0], prior_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do the same thing for a support vector machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier # A class of models trained using Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done training in 0.031745s\n",
      "done testing in 0.000454s\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Diagnosis       0.36      0.36      0.36        11\n",
      "    Disease       0.29      0.26      0.28        19\n",
      "       Drug       0.14      0.20      0.17         5\n",
      "       Gene       0.00      0.00      0.00         1\n",
      " Literature       0.00      0.00      0.00         8\n",
      "   Location       0.75      0.50      0.60         6\n",
      "   Resource       0.29      0.33      0.31         6\n",
      "    Symptom       0.46      0.55      0.50        33\n",
      "  Treatment       0.53      0.59      0.56        39\n",
      "\n",
      "avg / total       0.41      0.44      0.42       128\n",
      "\n",
      "Overall accuracy:\n",
      "0.4375\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "parameters = {\n",
    "    'loss': 'log', # This makes it a linear SVM, can change to 'log' for logistic regression\n",
    "    'penalty': 'l2',\n",
    "    'n_iter': 50,\n",
    "    'alpha': 0.00001,\n",
    "    'fit_intercept': True,\n",
    "}\n",
    "clf = SGDClassifier(**parameters).fit(X_train, y_train)\n",
    "print(\"done training in %fs\" % (time() - t0))\n",
    "t0 = time()\n",
    "pred = clf.predict(X_test)\n",
    "print(\"done testing in %fs\" % (time() - t0))\n",
    "print(classification_report(y_test, pred))\n",
    "print(\"Overall accuracy:\")\n",
    "print(accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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omgdjRE1EJUmSRpFd85IkSVJ/VkQlSZJGkV3zkiRJ6sQ8SETtmpckSVIn1vhE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\nghVRY4wxxhhTC/8PQxs6vctnDOoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f498842e6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "plt.imshow(confusion_mtx, interpolation = 'nearest', cmap = 'hot');\n",
    "plt.colorbar(orientation = 'vertical', shrink = 0.6);\n",
    "plt.xticks(np.arange(len(np.unique(label_names))), label_names, rotation=45);\n",
    "plt.yticks(np.arange(len(np.unique(label_names))), label_names);\n",
    "plt.tick_params(labelsize = 14);\n",
    "plt.tight_layout();\n",
    "plt.ylabel('True category', fontsize = 14, fontweight = 'semibold');\n",
    "plt.xlabel('Predicted category', fontsize = 14, fontweight = 'semibold');\n",
    "plt.title('Label confusion matrix', fontsize = 16, fontweight = 'semibold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diagnosis: confirm, sour, whatsoever, proof, scan, syrinx, attention, testing, diagnosis, diagnosed\n",
      "Disease: lgl, women, ill, ra, desease, 30, peter, stones, guess, children\n",
      "Drug: maxalon, narcotic, octagam, john, life, rituximab, singulair, lipitor, steroids, thalidomide\n",
      "Gene: regarding, variability, mutated, readers, sdhb, gene, wondered, cyclic, wnk4, wnk1\n",
      "Literature: medical, say, http, abstract, stemline, literature, consist, listen, scientists, premeds\n",
      "Location: live, state, members, uk, port, locations, support, tested, location, hospital\n",
      "Resource: cleveland, june, idea, misreading, referral, new, hematologist, educate, nih, doctor\n",
      "Symptom: attacks, remission, rash, episode, guys, unpleasant, itching, feeling, symptoms, telling\n",
      "Treatment: chemo, pills, treatments, difference, controlled, used, chemotherapy, surgery, treatment, everyday\n"
     ]
    }
   ],
   "source": [
    "feature_names = tfidf_vectorizer.get_feature_names()\n",
    "for i, label in enumerate(label_names):\n",
    "    top10 = np.argsort(clf.coef_[i])[-10:]\n",
    "    print(\"%s: %s\" % (label, \", \".join(feature_names[j] for j in top10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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foaanMeZ/6jiJGOcwgKiquZmoVluLKHDcR309kcol7jWJXlaHE0+kd6f1GxAj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\nIiIZ+v9IjhUqxJ0kMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4989751fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_question = [\"What is the best treatment for Hailey-Hailey disease?\"]\n",
    "new_vector = tfidf_vectorizer.transform(new_question)\n",
    "prob_values = clf.predict_proba(new_vector)\n",
    "plt.bar(np.arange(len(label_names)), prob_values.ravel(), align='center')\n",
    "plt.xticks(np.arange(len(label_names))-.3, label_names, rotation=45)\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RSDqIriHnD1PT0xjzP3WcQIxzGERU1dxEVKutQRQ47qW+nkjlEvfqRC+rQ4kn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\npOAtIpIhBW8RkQz9f1EtG+eLm0qVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49884351d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_question = [\"Has a genetic mutation been associated with Hailey-Hailey disease in the research literature?\"]\n",
    "new_vector = tfidf_vectorizer.transform(new_question) # Convert question to proper format\n",
    "prob_values = clf.predict_proba(new_vector) # Predict probabilities of classes\n",
    "plt.bar(np.arange(len(label_names)), prob_values.ravel(), align='center') # Plot result\n",
    "plt.xticks(np.arange(len(label_names))-.3, label_names, rotation=45)\n",
    "plt.ylim(0,1)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}