andreasvc/DH-crash-course-riddle.ipynb

## DH-crash-course-riddle.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:8993470560978cee21268daf5338413e6690b1aaef421ac66666d47210780284"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Genre Classification with a Bag-of-Words model\n",
      "==============================================\n",
      "\n",
      "This notebook demonstrates how the genre of a text can be automatically predicted from its word counts.\n",
      "\n",
      "The texts consist of 19th century English novels from project Gutenberg. The selection is based on the successful novels (high download counts) as used in Ashok et al (2013), [Success with style](http://aclweb.org/anthology/D13-1181).\n",
      "\n",
      "First some preliminaries. Import the external libraries we will use.\n",
      "Most importantly, we rely on Scikit-Learn to do machine learning. See http://scikit-learn.org/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import io, os, itertools\n",
      "import numpy, pandas\n",
      "import matplotlib.pyplot as plt\n",
      "from sklearn import feature_extraction, preprocessing, decomposition, cross_validation, svm, metrics\n",
      "\n",
      "# Tweak how tables are displayed\n",
      "pandas.set_option('display.precision', 4)\n",
      "pandas.set_option('display.max_colwidth', 30)\n",
      "pandas.set_option('display.colheader_justify', 'left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load the texts and convert to a bag-of-words table\n",
      "--------------------------------------------------\n",
      "The bag-of-words ([BOW](http://en.wikipedia.org/wiki/Bag-of-words_model)) table will contain a count for each text and every word.\n",
      "\n",
      "All letters are converted to lower case and we filter the words until we only have words with 3 or more alphabetic characters (no numbers or punctuation).\n",
      "\n",
      "Important parameters are ``max_features``, ``min_df``, and ``max_df``.\n",
      "\n",
      "-  ``max_features`` limits the model to consider only the top *n* most frequent words. More is often better but may be slow to compute.\n",
      "- ``min_df`` and ``max_df`` restrict the words to those that occur in a certain proportion of texts.\n",
      "\n",
      "For example, setting these to the values 0.2 and 0.8, respectively, restricts the model to words that are in at least 20%, and at most 80% of the texts. This removes rare words on the one hand, and ignores highly frequent words such as [function words](http://en.wikipedia.org/wiki/Function_word) on the other.\n",
      "\n",
      "- ``use_idf=True`` turns on [tf-idf](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) weighting; this incorporates not only the total frequency of a word, but also the number of texts in which it appears. This means that words which are frequent in one text but not in others will get a high score, while words that are frequent in all documents receive a lower score.\n",
      "- ``sublinear_tf=True`` is another variation which makes the word frequencies logarithmic."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a simple BOW model\n",
      "vectorizer = feature_extraction.text.TfidfVectorizer(\n",
      "        lowercase=True, token_pattern=r'\\b[-A-Za-z]{3,}\\b',\n",
      "        min_df=0.1, max_df=0.5, max_features=5000,\n",
      "        use_idf=True, sublinear_tf=True)\n",
      "\n",
      "# Get a list of all filenames in the 'train/' folder,\n",
      "# and add their text to the BOW table 'X'.\n",
      "filenames = os.listdir('train/')\n",
      "X = vectorizer.fit_transform((io.open('train/' + a, encoding='utf8').read() for a in filenames))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Inspect the vector representation\n",
      "---------------------------------\n",
      "To see what the vector representation looks like, we will look at how a simple [example](https://en.wikipedia.org/wiki/It_was_a_dark_and_stormy_night) is transformed to the BOW representation.\n",
      "\n",
      "Notice that because of the parameters before, many words are ignored."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vec = vectorizer.transform(['It was a dark and stormy night; '\n",
      "                            'the rain fell in torrents \u2014 except at occasional intervals, '\n",
      "                            'when it was checked by a violent gust of wind which swept '\n",
      "                            'up the streets (for it is in London that our scene lies), '\n",
      "                            'rattling along the housetops, and fiercely agitating the scanty '\n",
      "                            'flame of the lamps that struggled against the darkness.'])\n",
      "\n",
      "# we get back a large vector with a value for each possible word.\n",
      "# show a table with only the non-zero items in the vector:\n",
      "feature_names = vectorizer.get_feature_names()\n",
      "pandas.DataFrame([(feature_names[b], (a, b), vec[a, b])\n",
      "                        for a, b in zip(*vec.nonzero())],\n",
      "                       columns=['word', 'index', 'weight'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: left;\">\n",
        "      <th></th>\n",
        "      <th>word</th>\n",
        "      <th>index</th>\n",
        "      <th>weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     stormy</td>\n",
        "      <td> (0, 4269)</td>\n",
        "      <td> 0.440</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     scanty</td>\n",
        "      <td> (0, 3877)</td>\n",
        "      <td> 0.468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>   rattling</td>\n",
        "      <td> (0, 3539)</td>\n",
        "      <td> 0.418</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> occasional</td>\n",
        "      <td> (0, 2995)</td>\n",
        "      <td> 0.364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      lamps</td>\n",
        "      <td> (0, 2510)</td>\n",
        "      <td> 0.391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>    checked</td>\n",
        "      <td>  (0, 714)</td>\n",
        "      <td> 0.357</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "  word        index       weight\n",
        "0      stormy  (0, 4269)  0.440 \n",
        "1      scanty  (0, 3877)  0.468 \n",
        "2    rattling  (0, 3539)  0.418 \n",
        "3  occasional  (0, 2995)  0.364 \n",
        "4       lamps  (0, 2510)  0.391 \n",
        "5     checked   (0, 714)  0.357 "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get genre of each text\n",
      "----------------------\n",
      "The genre of each text is specified in a separate metadata file.\n",
      "\n",
      "Note that in the original data, a text may have multiple genres.\n",
      "We choose an arbitrary genre as the \"true\" genre.\n",
      "\n",
      "In a more careful study the single most appropriate genre would\n",
      "have to be seleceted by hand."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Print the first 5 lines to see what the metadata looks like:\n",
      "print(''.join(io.open('metadata.csv', encoding='utf8').readlines()[:5]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dataset,Fold,Success,FileName,Title,Author,Language,DownloadCount\n",
        "Adventure,1,SUCCESS,103.txt,around the world in 80 days,\"verne, jules, 1828-1905\",en,3260\n",
        "Adventure,1,SUCCESS,1145.txt,rupert of hentzau,\"hope, anthony, 1863-1933\",en,141\n",
        "Adventure,1,SUCCESS,1947.txt,scaramouche,\"sabatini, rafael, 1875-1950\",en,434\n",
        "Adventure,1,SUCCESS,18857.txt,a journey to the center of the earth,\"verne, jules, 1828-1905\",en,336\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the data; metadata.index will be the filename.\n",
      "metadata = pandas.read_csv('metadata.csv', index_col=3, encoding='utf8')\n",
      "genres = dict(zip(metadata.index, metadata['Dataset']))\n",
      "\n",
      "# convert the genre labels to integers\n",
      "encoder = preprocessing.LabelEncoder()\n",
      "y = encoder.fit_transform([genres[a] for a in filenames])\n",
      "\n",
      "# Create an abbreviated label \"Author_Title\" for each text\n",
      "authors = dict(zip(metadata.index, metadata['Author']))\n",
      "titles = dict(zip(metadata.index, metadata['Title']))\n",
      "labels = ['%s_%s' % (authors[a].split(',')[0].title(),\n",
      "        titles[a][:15].title()) for a in filenames]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Visualize how texts relate according to the bag-of-words model\n",
      "--------------------------------------------------------------\n",
      "By applying dimensionality reduction, it is possible to summarize the word counts for each text in two dimensions. Books which are close together are similar. The method used is [Latent Semantic Analysis](http://en.wikipedia.org/wiki/Latent_semantic_analysis)\n",
      "\n",
      "Note that for the purposes of visualization, we only show 2 dimensions; the classification model will use more. Books that are close together in this visualization may still be distinguished when more dimensions are taken into account."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reduce the BOW model to 2 dimensions\n",
      "dec = decomposition.TruncatedSVD(n_components=2)\n",
      "X_r = dec.fit_transform(X)\n",
      "print('Explained variance:', dec.explained_variance_ratio_)\n",
      "\n",
      "# Make a scatter plot with the author/title of each text as label\n",
      "plt.figure(figsize=(12, 8))\n",
      "for c, (i, target_name) in zip('rbmkycg', enumerate(encoder.classes_, 2)):\n",
      "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=target_name)\n",
      "    for n, xpos, ypos in zip(\n",
      "            (y == i).nonzero()[0], X_r[y == i, 0], X_r[y == i, 1]):\n",
      "        plt.annotate(labels[n], xy=(xpos, ypos), xytext=(5, 5),\n",
      "            textcoords='offset points', color=c,\n",
      "            fontsize='small', ha='left', va='top')\n",
      "\n",
      "plt.legend()\n",
      "plt.title('%s of dataset' % dec.__class__.__name__)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Explained variance:', array([ 0.04966917,  0.04049138]))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAv4AAAHmCAYAAAALCIrSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0af36afe50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Train a classifier\n",
      "------------------\n",
      "\n",
      "You can try different values for the parameter ``C``.\n",
      "This parameter controls the level of regularization;\n",
      "with higher values, the model will take more edge cases\n",
      "(datapoints close to datapoints of other classes) into account.\n",
      "This will give better scores on data that is similar to the training data,\n",
      "but if the training data is not representative, it may result in more errors.\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Randomly select 80% as training set, 20% as test/validation set,\n",
      "# but make sure that each genre is well-represented.\n",
      "train, test = next(iter(cross_validation.StratifiedShuffleSplit(\n",
      "        y, test_size=0.2, n_iter=1, random_state=42)))\n",
      "\n",
      "# Train an SVM classifier and predict the genre of the items in the test set.\n",
      "clf = svm.LinearSVC(C=1.0, random_state=42)\n",
      "clf.fit(X[train], y[train])\n",
      "pred = clf.predict(X[test])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Evaluate the classifier\n",
      "-----------------------\n",
      "The breakdown shows that not all genres are predicted as well;\n",
      "the f-score column is the most important.\n",
      "\n",
      "In the [confusion matrix](http://en.wikipedia.org/wiki/Confusion_matrix)\n",
      "we can see which genres were mistaken most often.\n",
      "The columns hold the number of times the model predicted a genre,\n",
      "while the rows show the true genres."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('Overall accuracy:\\t%4.1f %%\\n' % (100 * metrics.accuracy_score(y[test], pred)))\n",
      "print(metrics.classification_report(y[test], pred, target_names=encoder.classes_))\n",
      "pandas.DataFrame(metrics.confusion_matrix(y[test], pred),\n",
      "                       index=sorted(encoder.classes_),\n",
      "                       columns=sorted(encoder.classes_))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overall accuracy:\t68.3 %\n",
        "\n",
        "             precision    recall  f1-score   support\n",
        "\n",
        "  Adventure       0.40      0.25      0.31         8\n",
        "  Detective       1.00      0.89      0.94         9\n",
        "    Fiction       0.38      0.56      0.45         9\n",
        " Historical       0.90      0.90      0.90        10\n",
        "     Poetry       0.77      1.00      0.87        10\n",
        "     Sci-Fi       0.70      1.00      0.82         7\n",
        "      Short       0.50      0.20      0.29        10\n",
        "\n",
        "avg / total       0.67      0.68      0.66        63\n",
        "\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: left;\">\n",
        "      <th></th>\n",
        "      <th>Adventure</th>\n",
        "      <th>Detective</th>\n",
        "      <th>Fiction</th>\n",
        "      <th>Historical</th>\n",
        "      <th>Poetry</th>\n",
        "      <th>Sci-Fi</th>\n",
        "      <th>Short</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Adventure</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Detective</th>\n",
        "      <td> 0</td>\n",
        "      <td> 8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fiction</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Historical</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 9</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Poetry</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 10</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sci-Fi</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 7</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Short</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "            Adventure  Detective  Fiction  Historical  Poetry  Sci-Fi  Short\n",
        "Adventure   2          0          3        0            2      0       1    \n",
        "Detective   0          8          1        0            0      0       0    \n",
        "Fiction     1          0          5        1            1      0       1    \n",
        "Historical  0          0          1        9            0      0       0    \n",
        "Poetry      0          0          0        0           10      0       0    \n",
        "Sci-Fi      0          0          0        0            0      7       0    \n",
        "Short       2          0          3        0            0      3       2    "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which books were the hardest to classify?\n",
      "-----------------------------------------\n",
      "\n",
      "The following table lists the 10 books which had the least similarity to any of the genres.\n",
      "In the table, the numbers represent the distance of a text to a genre, where a negative value represents dissimilarity, and a positive value represents similarity. Values close to 0 represent uncertainty and are therefore the more difficult cases."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = sorted(zip(test, clf.decision_function(X[test])), key=lambda x: max(x[1]))[:10]\n",
      "pandas.DataFrame([a for _, a in data],\n",
      "                       index=[labels[n] for n, _ in data],\n",
      "                       columns=encoder.classes_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: left;\">\n",
        "      <th></th>\n",
        "      <th>Adventure</th>\n",
        "      <th>Detective</th>\n",
        "      <th>Fiction</th>\n",
        "      <th>Historical</th>\n",
        "      <th>Poetry</th>\n",
        "      <th>Sci-Fi</th>\n",
        "      <th>Short</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Hartley_The Gentlemen'S</th>\n",
        "      <td>-0.776</td>\n",
        "      <td>-0.885</td>\n",
        "      <td>-0.553</td>\n",
        "      <td>-0.563</td>\n",
        "      <td>-0.630</td>\n",
        "      <td>-1.237</td>\n",
        "      <td>-0.634</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Twain_A Double Barrel</th>\n",
        "      <td>-0.803</td>\n",
        "      <td>-0.613</td>\n",
        "      <td>-0.551</td>\n",
        "      <td>-0.905</td>\n",
        "      <td>-0.971</td>\n",
        "      <td>-0.741</td>\n",
        "      <td>-0.598</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Buchan_Huntingtower</th>\n",
        "      <td>-0.817</td>\n",
        "      <td>-0.558</td>\n",
        "      <td>-0.430</td>\n",
        "      <td>-0.903</td>\n",
        "      <td>-0.713</td>\n",
        "      <td>-1.058</td>\n",
        "      <td>-0.896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conrad_Tales Of Unrest</th>\n",
        "      <td>-0.426</td>\n",
        "      <td>-0.869</td>\n",
        "      <td>-0.475</td>\n",
        "      <td>-0.645</td>\n",
        "      <td>-0.996</td>\n",
        "      <td>-1.249</td>\n",
        "      <td>-0.614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hawthorne_Mosses From An </th>\n",
        "      <td>-0.789</td>\n",
        "      <td>-1.006</td>\n",
        "      <td>-0.756</td>\n",
        "      <td>-0.606</td>\n",
        "      <td>-0.656</td>\n",
        "      <td>-1.200</td>\n",
        "      <td>-0.391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>James_The Pupil</th>\n",
        "      <td>-0.933</td>\n",
        "      <td>-0.579</td>\n",
        "      <td>-0.383</td>\n",
        "      <td>-0.875</td>\n",
        "      <td>-0.789</td>\n",
        "      <td>-0.789</td>\n",
        "      <td>-0.651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Smith_A Gentleman Vag</th>\n",
        "      <td>-0.969</td>\n",
        "      <td>-0.571</td>\n",
        "      <td>-0.371</td>\n",
        "      <td>-0.968</td>\n",
        "      <td>-1.006</td>\n",
        "      <td>-0.978</td>\n",
        "      <td>-0.411</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conrad_The Arrow Of Go</th>\n",
        "      <td>-0.774</td>\n",
        "      <td>-0.446</td>\n",
        "      <td>-0.366</td>\n",
        "      <td>-0.567</td>\n",
        "      <td>-1.134</td>\n",
        "      <td>-1.301</td>\n",
        "      <td>-0.647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nesbit_The Wouldbegood</th>\n",
        "      <td>-0.603</td>\n",
        "      <td>-0.755</td>\n",
        "      <td>-0.697</td>\n",
        "      <td>-0.903</td>\n",
        "      <td>-0.897</td>\n",
        "      <td>-1.124</td>\n",
        "      <td>-0.363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Farmer_They Twinkled L</th>\n",
        "      <td>-0.920</td>\n",
        "      <td>-0.829</td>\n",
        "      <td>-0.787</td>\n",
        "      <td>-0.935</td>\n",
        "      <td>-0.854</td>\n",
        "      <td>-0.363</td>\n",
        "      <td>-0.379</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                           Adventure  Detective  Fiction  Historical  Poetry  \\\n",
        "Hartley_The Gentlemen'S   -0.776     -0.885     -0.553   -0.563      -0.630    \n",
        "Twain_A Double Barrel     -0.803     -0.613     -0.551   -0.905      -0.971    \n",
        "Buchan_Huntingtower       -0.817     -0.558     -0.430   -0.903      -0.713    \n",
        "Conrad_Tales Of Unrest    -0.426     -0.869     -0.475   -0.645      -0.996    \n",
        "Hawthorne_Mosses From An  -0.789     -1.006     -0.756   -0.606      -0.656    \n",
        "James_The Pupil           -0.933     -0.579     -0.383   -0.875      -0.789    \n",
        "Smith_A Gentleman Vag     -0.969     -0.571     -0.371   -0.968      -1.006    \n",
        "Conrad_The Arrow Of Go    -0.774     -0.446     -0.366   -0.567      -1.134    \n",
        "Nesbit_The Wouldbegood    -0.603     -0.755     -0.697   -0.903      -0.897    \n",
        "Farmer_They Twinkled L    -0.920     -0.829     -0.787   -0.935      -0.854    \n",
        "\n",
        "                           Sci-Fi  Short  \n",
        "Hartley_The Gentlemen'S   -1.237  -0.634  \n",
        "Twain_A Double Barrel     -0.741  -0.598  \n",
        "Buchan_Huntingtower       -1.058  -0.896  \n",
        "Conrad_Tales Of Unrest    -1.249  -0.614  \n",
        "Hawthorne_Mosses From An  -1.200  -0.391  \n",
        "James_The Pupil           -0.789  -0.651  \n",
        "Smith_A Gentleman Vag     -0.978  -0.411  \n",
        "Conrad_The Arrow Of Go    -1.301  -0.647  \n",
        "Nesbit_The Wouldbegood    -1.124  -0.363  \n",
        "Farmer_They Twinkled L    -0.363  -0.379  "
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which words are most strongly associated with each genre?\n",
      "---------------------------------------------------------\n",
      "For each genre, the top 10 words most strongly linked to each genre are shown.\n",
      "\n",
      "These words are not necessarily frequent, but if they occur, there is a high chance that it points to the given genre."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sort the weights of the classifier and take last 10 items\n",
      "data = []\n",
      "for n, target in enumerate(encoder.classes_):\n",
      "    data.append([(feature_names[m], clf.coef_[n][m])\n",
      "                 for m in numpy.argsort(clf.coef_[n])[-10:][::-1]])\n",
      "    \n",
      "pandas.DataFrame([itertools.chain(*a) for a in zip(*data)],\n",
      "                       columns=list(itertools.chain(*((target, '') for target in encoder.classes_))),\n",
      "                       index=range(1, 11))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: left;\">\n",
        "      <th></th>\n",
        "      <th>Adventure</th>\n",
        "      <th></th>\n",
        "      <th>Detective</th>\n",
        "      <th></th>\n",
        "      <th>Fiction</th>\n",
        "      <th></th>\n",
        "      <th>Historical</th>\n",
        "      <th></th>\n",
        "      <th>Poetry</th>\n",
        "      <th></th>\n",
        "      <th>Sci-Fi</th>\n",
        "      <th></th>\n",
        "      <th>Short</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>       buck</td>\n",
        "      <td> 0.460</td>\n",
        "      <td>  detective</td>\n",
        "      <td> 0.803</td>\n",
        "      <td>    billy</td>\n",
        "      <td> 0.453</td>\n",
        "      <td>   virginia</td>\n",
        "      <td> 0.491</td>\n",
        "      <td> poems</td>\n",
        "      <td> 0.789</td>\n",
        "      <td>   corrected</td>\n",
        "      <td> 0.667</td>\n",
        "      <td>     cats</td>\n",
        "      <td> 0.595</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   engineer</td>\n",
        "      <td> 0.444</td>\n",
        "      <td>     police</td>\n",
        "      <td> 0.559</td>\n",
        "      <td> chairman</td>\n",
        "      <td> 0.434</td>\n",
        "      <td>   sergeant</td>\n",
        "      <td> 0.480</td>\n",
        "      <td>   thy</td>\n",
        "      <td> 0.505</td>\n",
        "      <td>      errors</td>\n",
        "      <td> 0.626</td>\n",
        "      <td>      bob</td>\n",
        "      <td> 0.501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>       moat</td>\n",
        "      <td> 0.421</td>\n",
        "      <td>   revolver</td>\n",
        "      <td> 0.508</td>\n",
        "      <td>    helen</td>\n",
        "      <td> 0.420</td>\n",
        "      <td> lieutenant</td>\n",
        "      <td> 0.404</td>\n",
        "      <td>  thee</td>\n",
        "      <td> 0.467</td>\n",
        "      <td>        mars</td>\n",
        "      <td> 0.562</td>\n",
        "      <td>    model</td>\n",
        "      <td> 0.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    savages</td>\n",
        "      <td> 0.382</td>\n",
        "      <td>  inspector</td>\n",
        "      <td> 0.497</td>\n",
        "      <td>     anne</td>\n",
        "      <td> 0.420</td>\n",
        "      <td>     wagons</td>\n",
        "      <td> 0.403</td>\n",
        "      <td>  morn</td>\n",
        "      <td> 0.460</td>\n",
        "      <td>         car</td>\n",
        "      <td> 0.487</td>\n",
        "      <td>  dollars</td>\n",
        "      <td> 0.434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> adventures</td>\n",
        "      <td> 0.354</td>\n",
        "      <td>   criminal</td>\n",
        "      <td> 0.456</td>\n",
        "      <td>      guy</td>\n",
        "      <td> 0.397</td>\n",
        "      <td>       fort</td>\n",
        "      <td> 0.368</td>\n",
        "      <td>  poem</td>\n",
        "      <td> 0.416</td>\n",
        "      <td>    spelling</td>\n",
        "      <td> 0.470</td>\n",
        "      <td>   wizard</td>\n",
        "      <td> 0.432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>       tent</td>\n",
        "      <td> 0.345</td>\n",
        "      <td>   murderer</td>\n",
        "      <td> 0.445</td>\n",
        "      <td>    daddy</td>\n",
        "      <td> 0.391</td>\n",
        "      <td>   galloped</td>\n",
        "      <td> 0.360</td>\n",
        "      <td>   tis</td>\n",
        "      <td> 0.407</td>\n",
        "      <td> publication</td>\n",
        "      <td> 0.435</td>\n",
        "      <td>  chicago</td>\n",
        "      <td> 0.432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  attacking</td>\n",
        "      <td> 0.342</td>\n",
        "      <td> detectives</td>\n",
        "      <td> 0.437</td>\n",
        "      <td>    stove</td>\n",
        "      <td> 0.377</td>\n",
        "      <td>    treason</td>\n",
        "      <td> 0.354</td>\n",
        "      <td> skies</td>\n",
        "      <td> 0.400</td>\n",
        "      <td>      button</td>\n",
        "      <td> 0.434</td>\n",
        "      <td> magician</td>\n",
        "      <td> 0.424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>     wounds</td>\n",
        "      <td> 0.342</td>\n",
        "      <td>     drawer</td>\n",
        "      <td> 0.426</td>\n",
        "      <td>   saloon</td>\n",
        "      <td> 0.375</td>\n",
        "      <td>   nicholas</td>\n",
        "      <td> 0.353</td>\n",
        "      <td> sings</td>\n",
        "      <td> 0.373</td>\n",
        "      <td>   buildings</td>\n",
        "      <td> 0.426</td>\n",
        "      <td>    mamma</td>\n",
        "      <td> 0.416</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>    compass</td>\n",
        "      <td> 0.331</td>\n",
        "      <td>       card</td>\n",
        "      <td> 0.360</td>\n",
        "      <td>     cent</td>\n",
        "      <td> 0.373</td>\n",
        "      <td>     france</td>\n",
        "      <td> 0.350</td>\n",
        "      <td>  muse</td>\n",
        "      <td> 0.371</td>\n",
        "      <td>        onto</td>\n",
        "      <td> 0.417</td>\n",
        "      <td>    ruler</td>\n",
        "      <td> 0.415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>     camels</td>\n",
        "      <td> 0.329</td>\n",
        "      <td> collection</td>\n",
        "      <td> 0.354</td>\n",
        "      <td>   nothin</td>\n",
        "      <td> 0.359</td>\n",
        "      <td>    veteran</td>\n",
        "      <td> 0.349</td>\n",
        "      <td>  doth</td>\n",
        "      <td> 0.369</td>\n",
        "      <td>     section</td>\n",
        "      <td> 0.406</td>\n",
        "      <td>  tonight</td>\n",
        "      <td> 0.406</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "   Adventure          Detective          Fiction          Historical          \\\n",
        "1         buck  0.460   detective  0.803     billy  0.453    virginia  0.491   \n",
        "2     engineer  0.444      police  0.559  chairman  0.434    sergeant  0.480   \n",
        "3         moat  0.421    revolver  0.508     helen  0.420  lieutenant  0.404   \n",
        "4      savages  0.382   inspector  0.497      anne  0.420      wagons  0.403   \n",
        "5   adventures  0.354    criminal  0.456       guy  0.397        fort  0.368   \n",
        "6         tent  0.345    murderer  0.445     daddy  0.391    galloped  0.360   \n",
        "7    attacking  0.342  detectives  0.437     stove  0.377     treason  0.354   \n",
        "8       wounds  0.342      drawer  0.426    saloon  0.375    nicholas  0.353   \n",
        "9      compass  0.331        card  0.360      cent  0.373      france  0.350   \n",
        "10      camels  0.329  collection  0.354    nothin  0.359     veteran  0.349   \n",
        "\n",
        "   Poetry        Sci-Fi              Short             \n",
        "1   poems  0.789    corrected  0.667      cats  0.595  \n",
        "2     thy  0.505       errors  0.626       bob  0.501  \n",
        "3    thee  0.467         mars  0.562     model  0.481  \n",
        "4    morn  0.460          car  0.487   dollars  0.434  \n",
        "5    poem  0.416     spelling  0.470    wizard  0.432  \n",
        "6     tis  0.407  publication  0.435   chicago  0.432  \n",
        "7   skies  0.400       button  0.434  magician  0.424  \n",
        "8   sings  0.373    buildings  0.426     mamma  0.416  \n",
        "9    muse  0.371         onto  0.417     ruler  0.415  \n",
        "10   doth  0.369      section  0.406   tonight  0.406  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Can the model predict the genre of new texts?\n",
      "---------------------------------------------\n",
      "\n",
      "Finally, load new texts that the model has never seen before, and see what it predicts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Since we now evaluate on an external test set, we can use everything\n",
      "# as training data\n",
      "clf.fit(X, y)\n",
      "\n",
      "# Transform the new files to the format of the existing BOW table\n",
      "newfiles = os.listdir('test/')\n",
      "X1 = vectorizer.transform((io.open('test/' + a, encoding='utf8').read() for a in newfiles))\n",
      "predictions = encoder.inverse_transform(clf.predict(X1))\n",
      "\n",
      "pandas.DataFrame([\n",
      "            (authors[a].title(), titles[a].title(),\n",
      "            genres[a], b)\n",
      "        for a, b in zip(newfiles, predictions)],\n",
      "        index=newfiles,\n",
      "        columns=['Author', 'Title', 'actual', 'predicted'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: left;\">\n",
        "      <th></th>\n",
        "      <th>Author</th>\n",
        "      <th>Title</th>\n",
        "      <th>actual</th>\n",
        "      <th>predicted</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1027.txt</th>\n",
        "      <td>         Grey, Zane, 1872-1939</td>\n",
        "      <td> The Lone Star Ranger, A Ro...</td>\n",
        "      <td>    Fiction</td>\n",
        "      <td>    Fiction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10067.txt</th>\n",
        "      <td> Stevenson, Burton Egbert, ...</td>\n",
        "      <td> The Mystery Of The Boule C...</td>\n",
        "      <td>  Detective</td>\n",
        "      <td>  Detective</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12843.txt</th>\n",
        "      <td> Emerson, Ralph Waldo, 1803...</td>\n",
        "      <td>       Poems Household Edition</td>\n",
        "      <td>     Poetry</td>\n",
        "      <td>     Poetry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10150.txt</th>\n",
        "      <td>       Stoker, Bram, 1847-1912</td>\n",
        "      <td>               Dracula'S Guest</td>\n",
        "      <td>      Short</td>\n",
        "      <td>      Short</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>103.txt</th>\n",
        "      <td>       Verne, Jules, 1828-1905</td>\n",
        "      <td>   Around The World In 80 Days</td>\n",
        "      <td>  Adventure</td>\n",
        "      <td>  Detective</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18109.txt</th>\n",
        "      <td>     Piper, H. Beam, 1904-1964</td>\n",
        "      <td>           Graveyard Of Dreams</td>\n",
        "      <td>     Sci-Fi</td>\n",
        "      <td>     Sci-Fi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11228.txt</th>\n",
        "      <td> Chesnutt, Charles W. (Char...</td>\n",
        "      <td>       The Marrow Of Tradition</td>\n",
        "      <td> Historical</td>\n",
        "      <td> Historical</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "          Author                         Title                           \\\n",
        "1027.txt           Grey, Zane, 1872-1939  The Lone Star Ranger, A Ro...   \n",
        "10067.txt  Stevenson, Burton Egbert, ...  The Mystery Of The Boule C...   \n",
        "12843.txt  Emerson, Ralph Waldo, 1803...        Poems Household Edition   \n",
        "10150.txt        Stoker, Bram, 1847-1912                Dracula'S Guest   \n",
        "103.txt          Verne, Jules, 1828-1905    Around The World In 80 Days   \n",
        "18109.txt      Piper, H. Beam, 1904-1964            Graveyard Of Dreams   \n",
        "11228.txt  Chesnutt, Charles W. (Char...        The Marrow Of Tradition   \n",
        "\n",
        "          actual      predicted    \n",
        "1027.txt      Fiction     Fiction  \n",
        "10067.txt   Detective   Detective  \n",
        "12843.txt      Poetry      Poetry  \n",
        "10150.txt       Short       Short  \n",
        "103.txt     Adventure   Detective  \n",
        "18109.txt      Sci-Fi      Sci-Fi  \n",
        "11228.txt  Historical  Historical  "
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}