Data_Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated Essay Checking - Data Visualization\n",
    "## Notebook 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring the data set to identify important trends in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the feature generated data saved after executing the last cell of CustomFeatureGeneration.ipynb - Notebook 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding the path to the folder which contains the saved data.\n",
    "import sys\n",
    "sys.path.append('./utils')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Importing necessary libraries and helper functions location in the utils folder.\n",
    "from requirements import *\n",
    "import helperfunctions as helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>essay</th>\n",
       "      <th>word_count</th>\n",
       "      <th>long_word_count</th>\n",
       "      <th>avg_word_length_per_essay</th>\n",
       "      <th>wrong_words</th>\n",
       "      <th>no_of_domain_words</th>\n",
       "      <th>word_to_sent_ratio</th>\n",
       "      <th>num_of_characters</th>\n",
       "      <th>sentence_count</th>\n",
       "      <th>noun_count</th>\n",
       "      <th>verb_count</th>\n",
       "      <th>comma_count</th>\n",
       "      <th>punctuation_count</th>\n",
       "      <th>adjective_count</th>\n",
       "      <th>adverb_count</th>\n",
       "      <th>quotation_mark_count</th>\n",
       "      <th>spelling_mistakes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dear local newspaper, I think effects computer...</td>\n",
       "      <td>236</td>\n",
       "      <td>54</td>\n",
       "      <td>4.83</td>\n",
       "      <td>67</td>\n",
       "      <td>169</td>\n",
       "      <td>14.75</td>\n",
       "      <td>1819</td>\n",
       "      <td>16</td>\n",
       "      <td>78</td>\n",
       "      <td>51</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dear @CAPS1 @CAPS2, I believe that using compu...</td>\n",
       "      <td>281</td>\n",
       "      <td>76</td>\n",
       "      <td>4.93</td>\n",
       "      <td>56</td>\n",
       "      <td>225</td>\n",
       "      <td>16.53</td>\n",
       "      <td>2205</td>\n",
       "      <td>17</td>\n",
       "      <td>94</td>\n",
       "      <td>66</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...</td>\n",
       "      <td>188</td>\n",
       "      <td>51</td>\n",
       "      <td>5.01</td>\n",
       "      <td>34</td>\n",
       "      <td>154</td>\n",
       "      <td>13.43</td>\n",
       "      <td>1482</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dear Local Newspaper, @CAPS1 I have found that...</td>\n",
       "      <td>346</td>\n",
       "      <td>109</td>\n",
       "      <td>5.38</td>\n",
       "      <td>76</td>\n",
       "      <td>270</td>\n",
       "      <td>13.31</td>\n",
       "      <td>2817</td>\n",
       "      <td>26</td>\n",
       "      <td>134</td>\n",
       "      <td>64</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dear @LOCATION1, I know having computers has a...</td>\n",
       "      <td>318</td>\n",
       "      <td>81</td>\n",
       "      <td>4.99</td>\n",
       "      <td>88</td>\n",
       "      <td>230</td>\n",
       "      <td>10.60</td>\n",
       "      <td>2533</td>\n",
       "      <td>30</td>\n",
       "      <td>107</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               essay  word_count  \\\n",
       "0  Dear local newspaper, I think effects computer...         236   \n",
       "1  Dear @CAPS1 @CAPS2, I believe that using compu...         281   \n",
       "2  Dear, @CAPS1 @CAPS2 @CAPS3 More and more peopl...         188   \n",
       "3  Dear Local Newspaper, @CAPS1 I have found that...         346   \n",
       "4  Dear @LOCATION1, I know having computers has a...         318   \n",
       "\n",
       "   long_word_count  avg_word_length_per_essay  wrong_words  \\\n",
       "0               54                       4.83           67   \n",
       "1               76                       4.93           56   \n",
       "2               51                       5.01           34   \n",
       "3              109                       5.38           76   \n",
       "4               81                       4.99           88   \n",
       "\n",
       "   no_of_domain_words  word_to_sent_ratio  num_of_characters  sentence_count  \\\n",
       "0                 169               14.75               1819              16   \n",
       "1                 225               16.53               2205              17   \n",
       "2                 154               13.43               1482              14   \n",
       "3                 270               13.31               2817              26   \n",
       "4                 230               10.60               2533              30   \n",
       "\n",
       "   noun_count  verb_count  comma_count  punctuation_count  adjective_count  \\\n",
       "0          78          51           15                 17               24   \n",
       "1          94          66           11                 17               18   \n",
       "2          68          43            8                 14               17   \n",
       "3         134          64           12                 26               38   \n",
       "4         107          57           12                 30               21   \n",
       "\n",
       "   adverb_count  quotation_mark_count  spelling_mistakes  \n",
       "0            10                     4                  0  \n",
       "1            17                     3                  1  \n",
       "2            10                     4                  6  \n",
       "3            21                     5                 14  \n",
       "4            24                     5                  3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the feature generated data and removing unwanted columns.\n",
    "X_all = pd.read_csv(\"model_and_visualization/features_set_1.csv\")\n",
    "y_all = pd.read_csv(\"model_and_visualization/target_set_1.csv\")\n",
    "\n",
    "X_all = X_all.drop('Unnamed: 0',axis=1)\n",
    "y_all = y_all.drop('Unnamed: 0',axis=1)\n",
    "\n",
    "X_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The X_all['essay'] will be the input for generating key features for our analyis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ```count``` of each of the following 6 features are selected for visualization considering the sentence structure and basic statistics of an essay.\n",
    "\n",
    "1. Domain Words: Does the word belong to the domain of the essay?\n",
    "2. Word to sentence ratio : Are the sentences short and concise or long and convoluted?\n",
    "3. Average Word Length: Are they using a sophisticated vocabulary?\n",
    "4. Noun Count: Number of nouns used?\n",
    "5. Long word count: Number of words with length > 6?\n",
    "6. Spelling Mistakes: How often is the author making spelling mistakes?\n",
    "\n",
    "These six key features should allow us to gain some insight into what is happening in our data. to calculate this, the **TextBlob, NLTK, Textmining etc** packages were used. Please refer ```Libraries.txt``` for a complete list of libraries used in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots below will allow you to view the underlying trends within the data. The plots below visualize how the 6 features above impact the grade of an essay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAANsCAYAAAAJKQrDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd30d754c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ((ax1, ax2),(ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2, figsize=(15, 15))\n",
    "ax1.scatter(X_all['long_word_count'],y_all)\n",
    "ax1.set_ylabel('Grade')\n",
    "ax1.set_xlabel('Word length > 6 count')\n",
    "ax1.set_title('Long word count v/s Essay Grade')\n",
    "\n",
    "ax2.scatter(X_all['avg_word_length_per_essay'],y_all)\n",
    "ax2.set_ylabel('Grade')\n",
    "ax2.set_xlabel('Average word length')\n",
    "ax2.set_title('Average word length v/s Essay Grade')\n",
    "\n",
    "ax3.scatter(X_all['no_of_domain_words'],y_all)\n",
    "ax3.set_ylabel('Grade')\n",
    "ax3.set_xlabel('Domain words')\n",
    "ax3.set_title('Domain words v/s Essay Grade')\n",
    "\n",
    "ax4.scatter(X_all['word_to_sent_ratio'],y_all)\n",
    "ax4.set_ylabel('Grade')\n",
    "ax4.set_xlabel('Word to sentence ratio')\n",
    "ax4.set_title('Word to sentence ratio v/s Essay Grade')\n",
    "\n",
    "ax5.scatter(X_all['spelling_mistakes'],y_all)\n",
    "ax5.set_ylabel('Grade')\n",
    "ax5.set_xlabel('Number of spelling mistakes')\n",
    "ax5.set_title('Number of spelling mistakes v/s Essay Grade')\n",
    "\n",
    "ax6.scatter(X_all['noun_count'],y_all)\n",
    "ax6.set_ylabel('Grade')\n",
    "ax6.set_xlabel('Noun count')\n",
    "ax6.set_title('Noun count v/s Essay Grade')\n",
    "\n",
    "fig.savefig('model_and_visualization/Essay123Features.png', bbox_inches = \"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we were able to see general trends between the sentence structure of an essay and the grade it received. What was unexpected was the fact that the faults of an essay did not carry much weight. Noteably, we see that it is not uncommon to see essays with a large number of spelling mistakes receive a high grade. One could make the assumption that techonology's (Word's Spell Check) influence on writing has made it less imperitive for students to spell correctly. It seems as though the message carries more weight than specific errors in the essay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Free form visualization :\n",
    "## Combines the word2vec model feature vectors and the heuristic feature vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will use Principal Component Analysis (PCA) for visualizing good essays (grade > 8) and poor essay (grade < 4) to see if any information can be obtained from the resultant graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting 100 good essays and 100 poor essays for performing PCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n"
     ]
    }
   ],
   "source": [
    "X_all['target'] = y_all\n",
    "goodessay = pd.DataFrame()\n",
    "pooressay = pd.DataFrame()\n",
    "\n",
    "for index,rows in X_all.iterrows():\n",
    "    if len(goodessay) == 100 and len(pooressay) == 100:\n",
    "        break\n",
    "    elif rows['target'] > 8 and len(goodessay) <= 100:\n",
    "        goodessay = goodessay.append(rows)\n",
    "    elif rows['target'] < 4 and len(pooressay) <= 100:\n",
    "        pooressay = pooressay.append(rows)\n",
    "print(len(pooressay))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Word2vec model already saved during the learning phase will be used for generating the feature vectors** \n",
    "Created a function ```essayvectors()``` that returns essay vectors for a given essay set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "essaygood = goodessay['essay']\n",
    "essaypoor = pooressay['essay']\n",
    "\n",
    "#Initializing variables for generating the word2vec model.\n",
    "num_features = 300 \n",
    "min_word_count = 40\n",
    "num_workers = 4\n",
    "context = 10\n",
    "downsampling = 1e-3\n",
    "\n",
    "#Function to generate essay vectors.\n",
    "def essayvectors(essaysv):\n",
    "        \n",
    "    sentences = [] \n",
    "    #Extract sentences that will be used for generating the word vectors.\n",
    "    for essay in essaysv:\n",
    "        sentences += helper.essay_to_sentences(unicode(essay,\"utf-8\",errors='ignore'), remove_stopwords = True)\n",
    "    \n",
    "    #Using saved word2vec model during the learning phase.\n",
    "    model = word2vec.Word2Vec(sentences, workers=num_workers, size=num_features, min_count = min_word_count, window = context, sample = downsampling)\n",
    "\n",
    "    clean_train_essays = []\n",
    "    \n",
    "    #Generate the essay vectors\n",
    "    for essay_v in essaysv:\n",
    "        clean_train_essays.append( helper.essay_to_wordlist( essay_v, remove_stopwords=True ))\n",
    "    essayvecs = helper.getAvgFeatureVecs( clean_train_essays, model, num_features )\n",
    "      \n",
    "    return essayvecs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing the PCA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3oAAAFNCAYAAABWsx1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZhwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd300739510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYbiYQjAMwzAA+P/hfvW3LXEM1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd300576b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "vecs2 = essayvectors(essaypoor)\n",
    "vecs1 = essayvectors(essaygood)\n",
    "\n",
    "#Remove \"nan\" values if occured.\n",
    "p = vecs2[~np.isnan(vecs2).any(axis=1)]\n",
    "\n",
    "#For good essays.\n",
    "pca1 = PCA(whiten=True) #Initialize PCA\n",
    "pca1.fit(vecs1) #Fit the good essay vectors.\n",
    "pcat1 = pca1.transform(vecs1) #Transform\n",
    "\n",
    "#For poor essays\n",
    "pca2 = PCA(whiten=True) #Initialize PCA\n",
    "pca2.fit(p) #Fit the poor essay vectors\n",
    "pcat2 = pca2.transform(p) #Transform\n",
    "#Plotting the PCA transformed data.\n",
    "\n",
    "#For Good essays\n",
    "ys = [i[1] for i in pcat1] #PCA Component y, NOTE: This can be accomplished by using pca.components_[1]\n",
    "xs = [i[0] for i in pcat1] #PCA Component x  NOTE: This can be accomplished by using pca.components_[0]\n",
    "\n",
    "#For poor essays\n",
    "ys2 = [i[1] for i in pcat2]\n",
    "xs2 = [i[0] for i in pcat2]\n",
    "\n",
    "fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "ax1.scatter(xs,ys,color='b')\n",
    "ax1.set_ylabel('PCA component y')\n",
    "ax1.set_xlabel('PCA component x')\n",
    "ax1.set_title('PCA For Good essays (score > 8)')\n",
    "\n",
    "ax2.scatter(xs2,ys2,color='r')\n",
    "ax2.set_ylabel('PCA component y')\n",
    "ax2.set_xlabel('PCA component x')\n",
    "ax2.set_title('PCA For Poor essays (score < 4)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('PCA component x')\n",
    "ax1.set_ylabel('PCA component y')\n",
    "ax1.set_title('PCA for good and poor essays')\n",
    "ax1.scatter(xs,ys, label='Good essay (grade > 8)')\n",
    "ax1.scatter(xs2,ys2, label = 'Poor essay (grade <4)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}