SI 670 Final Project-FeatureSet1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SI 670 Final Project: Predicting Value of Jeopardy Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration & Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(216930, 7)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4680</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>HISTORY</td>\n",
       "      <td>$200</td>\n",
       "      <td>For the last 8 years of his life, Galileo was ...</td>\n",
       "      <td>Copernicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4680</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>ESPN's TOP 10 ALL-TIME ATHLETES</td>\n",
       "      <td>$200</td>\n",
       "      <td>No. 2: 1912 Olympian; football star at Carlisl...</td>\n",
       "      <td>Jim Thorpe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4680</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>EVERYBODY TALKS ABOUT IT...</td>\n",
       "      <td>$200</td>\n",
       "      <td>The city of Yuma in this state has a record av...</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4680</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>THE COMPANY LINE</td>\n",
       "      <td>$200</td>\n",
       "      <td>In 1963, live on \"The Art Linkletter Show\", th...</td>\n",
       "      <td>McDonald's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4680</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>EPITAPHS &amp; TRIBUTES</td>\n",
       "      <td>$200</td>\n",
       "      <td>Signer of the Dec. of Indep., framer of the Co...</td>\n",
       "      <td>John Adams</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Show Number    Air Date      Round                         Category Value  \\\n",
       "0         4680  2004-12-31  Jeopardy!                          HISTORY  $200   \n",
       "1         4680  2004-12-31  Jeopardy!  ESPN's TOP 10 ALL-TIME ATHLETES  $200   \n",
       "2         4680  2004-12-31  Jeopardy!      EVERYBODY TALKS ABOUT IT...  $200   \n",
       "3         4680  2004-12-31  Jeopardy!                 THE COMPANY LINE  $200   \n",
       "4         4680  2004-12-31  Jeopardy!              EPITAPHS & TRIBUTES  $200   \n",
       "\n",
       "                                            Question      Answer  \n",
       "0  For the last 8 years of his life, Galileo was ...  Copernicus  \n",
       "1  No. 2: 1912 Olympian; football star at Carlisl...  Jim Thorpe  \n",
       "2  The city of Yuma in this state has a record av...     Arizona  \n",
       "3  In 1963, live on \"The Art Linkletter Show\", th...  McDonald's  \n",
       "4  Signer of the Dec. of Indep., framer of the Co...  John Adams  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in and preview data\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "jeopardy_data = pd.read_csv('JEOPARDY_CSV.csv')\n",
    "# remove spaces from column names\n",
    "jeopardy_data.columns = [col.strip() for col in jeopardy_data.columns]\n",
    "print(jeopardy_data.shape)\n",
    "jeopardy_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 7)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>166403</th>\n",
       "      <td>4536</td>\n",
       "      <td>2004-05-03</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>DOUBLE A, B, Cs</td>\n",
       "      <td>$200</td>\n",
       "      <td>I'm this, you're glue, everything you say boun...</td>\n",
       "      <td>rubber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>4335</td>\n",
       "      <td>2003-06-06</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>MY PLACE?</td>\n",
       "      <td>$200</td>\n",
       "      <td>A Norman could say, \"I'm the king of the motte...</td>\n",
       "      <td>castle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119920</th>\n",
       "      <td>5224</td>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>POTPOURRI</td>\n",
       "      <td>$200</td>\n",
       "      <td>Shelley &amp; Eliot would be happy to know that Ap...</td>\n",
       "      <td>poetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33882</th>\n",
       "      <td>5668</td>\n",
       "      <td>2009-04-08</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>IT'S A COUNTRY THING</td>\n",
       "      <td>$200</td>\n",
       "      <td>Hat dance &amp; jumping bean</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186569</th>\n",
       "      <td>6247</td>\n",
       "      <td>2011-11-15</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>MELTING POTPOURRI</td>\n",
       "      <td>$200</td>\n",
       "      <td>\"Our actors\", says Prospero, \"were all spirits...</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45283</th>\n",
       "      <td>5687</td>\n",
       "      <td>2009-05-05</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>ARCHAEOLOGY</td>\n",
       "      <td>$200</td>\n",
       "      <td>In 1996 Franck Goddio discovered her palace un...</td>\n",
       "      <td>Cleopatra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184063</th>\n",
       "      <td>5023</td>\n",
       "      <td>2006-06-14</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>&amp; TAKIN' NAMES</td>\n",
       "      <td>$200</td>\n",
       "      <td>World poverty fighter, Time magazine Person of...</td>\n",
       "      <td>Bono</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85474</th>\n",
       "      <td>5139</td>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>BEAN</td>\n",
       "      <td>$200</td>\n",
       "      <td>This bean that shares the name of a South Amer...</td>\n",
       "      <td>lima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155694</th>\n",
       "      <td>5853</td>\n",
       "      <td>2010-02-10</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>BE TRUE TO YOUR SCHOOL</td>\n",
       "      <td>$200</td>\n",
       "      <td>The benefactor for whom this West Lafayette, I...</td>\n",
       "      <td>Purdue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147690</th>\n",
       "      <td>4293</td>\n",
       "      <td>2003-04-09</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>DECODE THE PERSONAL AD</td>\n",
       "      <td>$200</td>\n",
       "      <td>To start with, S. is for this</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Show Number    Air Date      Round                Category Value  \\\n",
       "166403         4536  2004-05-03  Jeopardy!         DOUBLE A, B, Cs  $200   \n",
       "781            4335  2003-06-06  Jeopardy!               MY PLACE?  $200   \n",
       "119920         5224  2007-05-03  Jeopardy!               POTPOURRI  $200   \n",
       "33882          5668  2009-04-08  Jeopardy!    IT'S A COUNTRY THING  $200   \n",
       "186569         6247  2011-11-15  Jeopardy!       MELTING POTPOURRI  $200   \n",
       "45283          5687  2009-05-05  Jeopardy!             ARCHAEOLOGY  $200   \n",
       "184063         5023  2006-06-14  Jeopardy!          & TAKIN' NAMES  $200   \n",
       "85474          5139  2007-01-04  Jeopardy!                    BEAN  $200   \n",
       "155694         5853  2010-02-10  Jeopardy!  BE TRUE TO YOUR SCHOOL  $200   \n",
       "147690         4293  2003-04-09  Jeopardy!  DECODE THE PERSONAL AD  $200   \n",
       "\n",
       "                                                 Question     Answer  \n",
       "166403  I'm this, you're glue, everything you say boun...     rubber  \n",
       "781     A Norman could say, \"I'm the king of the motte...     castle  \n",
       "119920  Shelley & Eliot would be happy to know that Ap...     poetry  \n",
       "33882                            Hat dance & jumping bean    Mexican  \n",
       "186569  \"Our actors\", says Prospero, \"were all spirits...        air  \n",
       "45283   In 1996 Franck Goddio discovered her palace un...  Cleopatra  \n",
       "184063  World poverty fighter, Time magazine Person of...       Bono  \n",
       "85474   This bean that shares the name of a South Amer...       lima  \n",
       "155694  The benefactor for whom this West Lafayette, I...     Purdue  \n",
       "147690                      To start with, S. is for this     single  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract subset of the data using the following criteria:\n",
    "# (1) only 'Jeopardy!' round\n",
    "# (2) no answers with numbers or questions containing images/links\n",
    "# (3) only shows after 4000 (to limit amount of data)\n",
    "# (4) only questions with 5 or more words\n",
    "# (5) only questions with a value of $200 or $1000\n",
    "\n",
    "jeopardy_data_sub = jeopardy_data[jeopardy_data['Round'] == 'Jeopardy!']\n",
    "jeopardy_data_sub = jeopardy_data_sub[jeopardy_data_sub.Answer.str.isalpha() == True]\n",
    "jeopardy_data_sub = jeopardy_data_sub[~jeopardy_data_sub.Question.str.contains(\"<a href=\")]\n",
    "jeopardy_data_sub = jeopardy_data_sub[jeopardy_data_sub['Show Number'] >= 4000]\n",
    "jeopardy_data_sub = jeopardy_data_sub[jeopardy_data_sub['Question'].str.split().str.len() >= 5]\n",
    "\n",
    "import random\n",
    "# get a sample of 1,000 for each Value\n",
    "jeopardy_data_sub_200 = jeopardy_data_sub[jeopardy_data_sub['Value']=='$200'].sample(2000, random_state=670)\n",
    "jeopardy_data_sub_1000 = jeopardy_data_sub[jeopardy_data_sub['Value']=='$1000'].sample(2000, random_state=670)\n",
    "jeopardy_data_sub = pd.concat([jeopardy_data_sub_200, jeopardy_data_sub_1000])\n",
    "\n",
    "\n",
    "print(jeopardy_data_sub.shape)\n",
    "jeopardy_data_sub.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# use word embeddings to find the similarity between the question/topic and the answer\n",
    "\n",
    "import gensim \n",
    "\n",
    "# source: https://github.com/eyaler/word2vec-slim\n",
    "# example of pre-trained word embeddings, trained on Google News documents\n",
    "model = gensim.models.KeyedVectors.load_word2vec_format(\"GoogleNews-vectors-negative300-SLIM.bin\", binary=True)\n",
    "\n",
    "# source: https://stackoverflow.com/questions/22129943/how-to-calculate-the-sentence-similarity-using-word2vec-model-of-gensim-with-pyt\n",
    "\n",
    "import numpy as np\n",
    "from scipy import spatial\n",
    "\n",
    "index2word_set = set(model.wv.index2word)\n",
    "\n",
    "def avg_feature_vector(sentence, model, num_features, index2word_set):\n",
    "    words = sentence.split()\n",
    "    feature_vec = np.zeros((num_features, ), dtype='float32')\n",
    "    n_words = 0\n",
    "    for word in words:\n",
    "        if word in index2word_set:\n",
    "            n_words += 1\n",
    "            feature_vec = np.add(feature_vec, model[word])\n",
    "    if (n_words > 0):\n",
    "        feature_vec = np.divide(feature_vec, n_words)\n",
    "    return feature_vec\n",
    "\n",
    "# function to calculate the similarity between two sentences\n",
    "def calculate_sim(text1, text2):\n",
    "    s1_afv = avg_feature_vector(text1, model=model, num_features=300, index2word_set=index2word_set)\n",
    "    s2_afv = avg_feature_vector(text2, model=model, num_features=300, index2word_set=index2word_set)\n",
    "    sim = 1 - spatial.distance.cosine(s1_afv, s2_afv)\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to count the average word length in the sentence \n",
    "import numpy as np\n",
    "\n",
    "def avg_word_length(text):\n",
    "    lens = []\n",
    "    for word in text.split():\n",
    "        lens.append(len(word))\n",
    "    return np.mean(lens)\n",
    "\n",
    "# function to count the max word length in the sentence \n",
    "def longest_word(text):\n",
    "    lens = []\n",
    "    for word in text.split():\n",
    "        lens.append(len(word))\n",
    "    try:\n",
    "        return np.max(lens)\n",
    "    except:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/scipy/spatial/distance.py:720: RuntimeWarning: invalid value encountered in float_scalars\n",
      "  dist = 1.0 - uv / np.sqrt(uu * vv)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flesch Reading Ease Score</th>\n",
       "      <th>Flesch-Kincaid Grade Level</th>\n",
       "      <th>Longest Word (Question)</th>\n",
       "      <th>Longest Word (Answer)</th>\n",
       "      <th>Average Answer Word Length (Cleaned)</th>\n",
       "      <th>Answer Len (Cleaned)</th>\n",
       "      <th>QA Similarity</th>\n",
       "      <th>TA Similarity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1000</th>\n",
       "      <td>70.390675</td>\n",
       "      <td>6.98215</td>\n",
       "      <td>9.8085</td>\n",
       "      <td>7.0475</td>\n",
       "      <td>7.060150</td>\n",
       "      <td>0.9975</td>\n",
       "      <td>0.275571</td>\n",
       "      <td>0.165164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$200</th>\n",
       "      <td>72.825295</td>\n",
       "      <td>6.51860</td>\n",
       "      <td>9.5480</td>\n",
       "      <td>6.5100</td>\n",
       "      <td>6.533434</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>0.275524</td>\n",
       "      <td>0.168144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Flesch Reading Ease Score  Flesch-Kincaid Grade Level  \\\n",
       "Value                                                          \n",
       "$1000                  70.390675                     6.98215   \n",
       "$200                   72.825295                     6.51860   \n",
       "\n",
       "       Longest Word (Question)  Longest Word (Answer)  \\\n",
       "Value                                                   \n",
       "$1000                   9.8085                 7.0475   \n",
       "$200                    9.5480                 6.5100   \n",
       "\n",
       "       Average Answer Word Length (Cleaned)  Answer Len (Cleaned)  \\\n",
       "Value                                                               \n",
       "$1000                              7.060150                0.9975   \n",
       "$200                               6.533434                0.9945   \n",
       "\n",
       "       QA Similarity  TA Similarity  \n",
       "Value                                \n",
       "$1000       0.275571       0.165164  \n",
       "$200        0.275524       0.168144  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import textstat as ts # source: https://pypi.org/project/textstat/\n",
    "\n",
    "# how many words are in the question?\n",
    "jeopardy_data_sub['Question Len'] = jeopardy_data_sub['Question'].apply(lambda x: ts.lexicon_count(x, removepunct=True))\n",
    "# what is the readability of the question?\n",
    "jeopardy_data_sub['Flesch Reading Ease Score'] = jeopardy_data_sub['Question'].apply(lambda text: ts.flesch_reading_ease(text))\n",
    "# what is the grade level associated the question?\n",
    "jeopardy_data_sub['Flesch-Kincaid Grade Level'] = jeopardy_data_sub['Question'].apply(lambda text:ts.flesch_kincaid_grade(text))\n",
    "# longest word in question? \n",
    "jeopardy_data_sub['Longest Word (Question)'] = jeopardy_data_sub['Question'].apply(lambda text:longest_word(text))\n",
    "# longest word in answer?\n",
    "jeopardy_data_sub['Longest Word (Answer)'] = jeopardy_data_sub['Answer'].apply(lambda text:longest_word(text))\n",
    "\n",
    "\n",
    "# clean questions/answers by lowercasing and removing stop words\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "\n",
    "jeopardy_data_sub['Question_cleaned'] = jeopardy_data_sub['Question'].apply(lambda x: \" \".join([word.lower() for word in x.split() if word.lower() not in stopwords]))\n",
    "jeopardy_data_sub['Answer_cleaned'] = jeopardy_data_sub['Answer'].apply(lambda x: \" \".join([word.lower() for word in x.split() if word.lower() not in stopwords]))\n",
    "jeopardy_data_sub['Category_cleaned'] = jeopardy_data_sub['Category'].apply(lambda x: \" \".join([word.lower() for word in x.split() if word.lower() not in stopwords]))\n",
    "\n",
    "# what is the average length of a word in the answer?\n",
    "jeopardy_data_sub['Average Answer Word Length (Cleaned)'] = jeopardy_data_sub['Answer_cleaned'].apply(lambda text: avg_word_length(text))\n",
    "# how many words are in the answer?\n",
    "jeopardy_data_sub['Answer Len (Cleaned)'] = jeopardy_data_sub['Answer_cleaned'].apply(lambda x: ts.lexicon_count(x, removepunct=True))\n",
    "\n",
    "# similarity between the question and answer\n",
    "jeopardy_data_sub['QA Similarity'] = jeopardy_data_sub.apply(lambda x: calculate_sim(x.Question_cleaned, x.Answer_cleaned), axis=1)\n",
    "# similarity between the topic and answer\n",
    "jeopardy_data_sub['TA Similarity'] = jeopardy_data_sub.apply(lambda x: calculate_sim(x.Category_cleaned, x.Answer_cleaned), axis=1)\n",
    "\n",
    "jeopardy_data_sub.groupby('Value')[['Flesch Reading Ease Score', \n",
    "         'Flesch-Kincaid Grade Level', 'Longest Word (Question)', 'Longest Word (Answer)',\n",
    "         'Average Answer Word Length (Cleaned)', 'Answer Len (Cleaned)', 'QA Similarity', 'TA Similarity']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14ae55590>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "sns.violinplot(x=\"Value\", y=\"TA Similarity\", data=jeopardy_data_sub)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 7)\n",
      "(3000,)\n",
      "(1000, 7)\n",
      "(1000,)\n"
     ]
    }
   ],
   "source": [
    "# split data into training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cols_sub = ['Flesch Reading Ease Score', \n",
    "         'Flesch-Kincaid Grade Level', 'Longest Word (Question)', 'Longest Word (Answer)',\n",
    "         'Average Answer Word Length (Cleaned)', 'QA Similarity', 'TA Similarity']\n",
    "\n",
    "X = jeopardy_data_sub[cols_sub]\n",
    "y = jeopardy_data_sub['Value']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)\n",
    "# checks\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "scaler = ColumnTransformer(        \n",
    "        remainder='passthrough', \n",
    "        transformers=[\n",
    "            ('scaler', MinMaxScaler() , ['Flesch Reading Ease Score', \n",
    "         'Flesch-Kincaid Grade Level', 'Longest Word (Question)', 'Longest Word (Answer)',\n",
    "         'Average Answer Word Length (Cleaned)'])])\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X_train_imp = imputer.fit_transform(X_train_scaled)\n",
    "X_test_imp = imputer.transform(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.5656666666666667\n",
      "Testing Score: 0.55\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       $1000       0.56      0.47      0.51       500\n",
      "        $200       0.54      0.63      0.58       500\n",
      "\n",
      "    accuracy                           0.55      1000\n",
      "   macro avg       0.55      0.55      0.55      1000\n",
      "weighted avg       0.55      0.55      0.55      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train_imp, y_train)\n",
    "preds = gnb.predict(X_test_imp)\n",
    "print(\"Training Score:\", gnb.score(X_train_imp, y_train))\n",
    "print(\"Testing Score:\", gnb.score(X_test_imp, y_test))\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.571\n",
      "Testing Score: 0.564\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       $1000       0.57      0.55      0.56       500\n",
      "        $200       0.56      0.58      0.57       500\n",
      "\n",
      "    accuracy                           0.56      1000\n",
      "   macro avg       0.56      0.56      0.56      1000\n",
      "weighted avg       0.56      0.56      0.56      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svc = LinearSVC()\n",
    "svc.fit(X_train_imp, y_train)\n",
    "preds = svc.predict(X_test_imp)\n",
    "print(\"Training Score:\", svc.score(X_train_imp, y_train))\n",
    "print(\"Testing Score:\", svc.score(X_test_imp, y_test))\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:   11.5s finished\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': 1e-05}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'alpha': [0.00001, 0.0001, 0.001, 0.01, 0.1],\n",
    "}\n",
    "\n",
    "mlp = MLPClassifier(alpha=0.00001, random_state=0)\n",
    "grid_search = GridSearchCV(estimator = mlp, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(X_train_imp, y_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.5806666666666667\n",
      "Testing Score: 0.57\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       $1000       0.58      0.52      0.55       500\n",
      "        $200       0.56      0.62      0.59       500\n",
      "\n",
      "    accuracy                           0.57      1000\n",
      "   macro avg       0.57      0.57      0.57      1000\n",
      "weighted avg       0.57      0.57      0.57      1000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(alpha=1e-05, random_state=0)\n",
    "mlp.fit(X_train_imp, y_train)\n",
    "preds = mlp.predict(X_test_imp)\n",
    "print(\"Training Score:\", mlp.score(X_train_imp, y_train))\n",
    "print(\"Testing Score:\", mlp.score(X_test_imp, y_test))\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 45 candidates, totalling 135 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    9.7s\n",
      "[Parallel(n_jobs=-1)]: Done 135 out of 135 | elapsed:   41.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'max_depth': 2, 'max_features': 2, 'n_estimators': 100}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [1,2,3,4,5],\n",
    "    'max_features': [2, 3, 4],\n",
    "    'n_estimators': [100, 200, 500]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier(random_state=670)\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, \n",
    "                          cv = 3, n_jobs = -1, verbose = 2)\n",
    "grid_search.fit(X_train_imp, y_train)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.5856666666666667\n",
      "Testing Score: 0.58\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       $1000       0.59      0.55      0.57       500\n",
      "        $200       0.58      0.61      0.59       500\n",
      "\n",
      "    accuracy                           0.58      1000\n",
      "   macro avg       0.58      0.58      0.58      1000\n",
      "weighted avg       0.58      0.58      0.58      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(max_depth=2, max_features=2, n_estimators=100, random_state=670)\n",
    "rf.fit(X_train_imp, y_train)\n",
    "preds = rf.predict(X_test_imp)\n",
    "print(\"Training Score:\", rf.score(X_train_imp, y_train))\n",
    "print(\"Testing Score:\", rf.score(X_test_imp, y_test))\n",
    "\n",
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize feature importances from RF model\n",
    "# source: https://stackoverflow.com/questions/44101458/random-forest-feature-importance-chart-using-python\n",
    "import matplotlib.pyplot as plt\n",
    "features = X_train.columns\n",
    "importances = rf.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "font = {'family' : 'arial',\n",
    "        'size'   :14}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}