{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classy words: Exploring Term-Category Association Metrics\n",
    "### Jason S. Kessler ([@jasonkessler](http://www.twitter.com/JasonKessler))\n",
    "\n",
    "This notebook makes heavy use of the library Scattertext (https://github.com/JasonKessler/scattertext) for language processing and visualizations.\n",
    "\n",
    "The data used were scraped from Facebook by Max Woolf.  Please see his original notebook at https://github.com/minimaxir/clickbait-cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scattertext as st\n",
    "import sys\n",
    "import datetime\n",
    "import nltk\n",
    "import re\n",
    "import scipy\n",
    "import spacy\n",
    "import altair as alt\n",
    "\n",
    "from glob import glob\n",
    "from scipy.stats import rankdata\n",
    "from IPython.display import IFrame\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:98% !important; }</style>\"))\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([pd.read_csv(fn, sep='\\t')\n",
    "                .assign(publication=fn.split('/')[-1].split('_')[0]) \n",
    "                for fn in glob('./fb_headlines/*')]).reset_index()\n",
    "df['status_published'] = pd.to_datetime(df.status_published)\n",
    "df = df.loc[df.link_name.dropna().index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['parse'] = df.link_name.apply(nlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year_month'] = df.status_published.apply(lambda x: '%s-%s' % (x.year, str(x.month).zfill(2)))\n",
    "df['year'] = df.status_published.apply(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['reaction_percentile'] = (df.groupby(['publication', 'year'])['num_reactions']\n",
    "                             .apply(lambda x: pd.Series(scipy.stats.rankdata(x)/len(x), \n",
    "                                                        index=x.index)))\n",
    "df['reaction_bin'] = (df.reaction_percentile\n",
    "                      .apply(lambda x: 'Hi' if x > 2./3 else 'Lo' if x < 1./3 else 'Mid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_month_corpus = st.CorpusFromParsedDocuments(df, \n",
    "                                                 category_col='year_month', \n",
    "                                                 parsed_col='parse', \n",
    "                                                 feats_from_spacy_doc=st.PhraseMachinePhrases()).build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_month_corpus_compact = year_month_corpus.compact(st.CompactTerms(minimum_term_count=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scattertext.termranking import AbsoluteFrequencyRanker\n",
    "from scattertext.termscoring.RankDifference import RankDifference\n",
    "\n",
    "\n",
    "class GanttChart(object):\n",
    "    '''\n",
    "    Note: the Gantt charts listed here are inspired by\n",
    "    Dustin Arendt and Svitlana Volkova. ESTEEM: A Novel Framework for Qualitatively Evaluating and\n",
    "    Visualizing Spatiotemporal Embeddings in Social Media. ACL System Demonstrations. 2017.\n",
    "    http://www.aclweb.org/anthology/P/P17/P17-4005.pdf\n",
    "    '''\n",
    "    def __init__(self,\n",
    "                 term_doc_matrix,\n",
    "                 category_to_timestep_func,\n",
    "                 is_gap_between_sequences_func,\n",
    "                 timesteps_to_lag=4,\n",
    "                 num_top_terms_each_timestep=10,\n",
    "                 num_terms_to_include=40,\n",
    "                 starting_time_step=None,\n",
    "                 term_ranker=AbsoluteFrequencyRanker,\n",
    "                 term_scorer=RankDifference()):\n",
    "        '''\n",
    "        Parameters\n",
    "        ----------\n",
    "        term_doc_matrix : TermDocMatrix\n",
    "        category_to_timestep_func : lambda\n",
    "        is_gap_between_sequences_func : lambda\n",
    "        timesteps_to_lag : int\n",
    "        num_top_terms_each_timestep : int\n",
    "        num_terms_to_include : int\n",
    "        starting_time_step : object\n",
    "        term_ranker : TermRanker\n",
    "        term_scorer : TermScorer\n",
    "        '''\n",
    "        self.corpus = term_doc_matrix\n",
    "        self.timesteps_to_lag = timesteps_to_lag\n",
    "        self.num_top_terms_each_timestep = num_top_terms_each_timestep\n",
    "        self.num_terms_to_include = num_terms_to_include\n",
    "        self.is_gap_between_sequences_func = is_gap_between_sequences_func\n",
    "        self.category_to_timestep_func = category_to_timestep_func\n",
    "        self.term_ranker = term_ranker\n",
    "        self.term_scorer = term_scorer\n",
    "        categories = list(sorted(self.corpus.get_categories()))\n",
    "        if len(categories) <= timesteps_to_lag:\n",
    "            raise Exception(\"The number of categories in the term doc matrix is <= \"\n",
    "                            + str(timesteps_to_lag))\n",
    "        if starting_time_step is None:\n",
    "            starting_time_step = categories[timesteps_to_lag + 1]\n",
    "        self.starting_time_step = starting_time_step\n",
    "\n",
    "    def make_chart(self):\n",
    "        task_df = self.get_task_df()\n",
    "        import altair as alt\n",
    "        chart = alt.Chart(task_df, axisconfig = alt.AxisConfig(grid=True, ticks=len(set(task_df.start) | set(task_df.end)))).mark_bar().encode(\n",
    "            x='start',\n",
    "            x2='end',\n",
    "            y='term',\n",
    "        )\n",
    "        return chart\n",
    "\n",
    "    def _get_term_time_df(self):\n",
    "        data = []\n",
    "        tdf = self.term_ranker(self.corpus).get_ranks()\n",
    "        for cat in sorted(self.corpus.get_categories()):\n",
    "            if cat >= self.starting_time_step:\n",
    "                negative_categories = sorted([x for x in tdf.columns if x < cat])[-self.timesteps_to_lag:]\n",
    "                #print(negative_categories, cat)\n",
    "                scores = self.term_scorer.get_scores(\n",
    "                    tdf[sorted([x for x in tdf.columns if x < cat])[-self.timesteps_to_lag:]]\n",
    "                        .sum(axis=1),\n",
    "                    tdf[cat + ' freq'].astype(int)\n",
    "                )\n",
    "                for term in tdf.index[np.argsort(-scores)[:self.num_top_terms_each_timestep]]:\n",
    "                    data.append({'time': self.category_to_timestep_func(cat),\n",
    "                                 'term': term,\n",
    "                                 'top': 1})\n",
    "        return pd.DataFrame(data)\n",
    "\n",
    "    def get_task_df(self):\n",
    "        term_time_df = self._get_term_time_df()\n",
    "        terms_to_include = (\n",
    "            term_time_df\n",
    "                .groupby('term')['top']\n",
    "                .sum()\n",
    "                .sort_values(ascending=False)\n",
    "                .iloc[:self.num_terms_to_include].index\n",
    "        )\n",
    "        task_df = (\n",
    "            term_time_df[term_time_df.term.isin(terms_to_include)][['time', 'term']]\n",
    "                .groupby('term')\n",
    "                .apply(lambda x: pd.Series(self._find_sequences(x['time'])))\n",
    "                .reset_index()\n",
    "                .rename({0: 'sequence'}, axis=1)\n",
    "                .reset_index()\n",
    "                .assign(start=lambda x: x['sequence'].apply(lambda x: x[0]))\n",
    "                .assign(end=lambda x: x['sequence'].apply(lambda x: x[1]))\n",
    "            [['term', 'start', 'end']]\n",
    "        )\n",
    "        return task_df\n",
    "\n",
    "    def _find_sequences(self, time_steps):\n",
    "        min_timestep = None\n",
    "        last_timestep = None\n",
    "        sequences = []\n",
    "        cur_sequence = []\n",
    "        for cur_timestep in sorted(time_steps):\n",
    "            if min_timestep is None:\n",
    "                cur_sequence = [cur_timestep]\n",
    "                min_timestep = cur_timestep\n",
    "            elif not self.is_gap_between_sequences_func(last_timestep, cur_timestep):\n",
    "                cur_sequence.append(cur_timestep)\n",
    "                min_timestep = cur_timestep\n",
    "            else:\n",
    "                sequences.append([cur_sequence[0], cur_sequence[-1]])\n",
    "                cur_sequence = [cur_timestep]\n",
    "            last_timestep = cur_timestep\n",
    "        if len(cur_sequence) != []:\n",
    "            sequences.append([cur_sequence[0], cur_sequence[-1]])\n",
    "        return sequences\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart_start_year = '2015-01'\n",
    "category_to_datetime = lambda category: pd.to_datetime(datetime.datetime(*([int(t) for t in category.split('-')] + [1])))\n",
    "timesteps_to_lag = 4\n",
    "num_top_terms_each_timestep = 10 \n",
    "num_terms_to_include = 40 \n",
    "def is_gap_between_sequences(t1, t2): \n",
    "    return np.abs((t2 - t1).days) > 31\n",
    "corpus = year_month_corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[Timestamp('2015-02-01 00:00:00'), Timestamp('2015-04-01 00:00:00')],\n",
       " [Timestamp('2016-06-01 00:00:00'), Timestamp('2016-07-01 00:00:00')]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc = GanttChart(year_month_corpus_compact, category_to_datetime, is_gap_between_sequences, starting_time_step=chart_start_year)\n",
    "ttd = gc._get_term_time_df()\n",
    "gc._find_sequences(ttd[ttd['term'] == 'north korea'].time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2015-07-01 00:00:00'), Timestamp('2016-07-01 00:00:00')]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ttd[ttd['term'] == 'high school'].time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(GanttChart(year_month_corpus_compact, category_to_datetime, is_gap_between_sequences, starting_time_step=chart_start_year).get_task_df().start) | set(GanttChart(year_month_corpus_compact, category_to_datetime, is_gap_between_sequences, starting_time_step=chart_start_year).get_task_df().end))"
   ]
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   "execution_count": 101,
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"
     },
     "metadata": {
      "jupyter-vega": "#34c5ec27-8cb4-4d77-bb64-64b242b8aa24"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GanttChart(year_month_corpus_compact, category_to_datetime, is_gap_between_sequences, starting_time_step=chart_start_year).make_chart()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boston marathon</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2013-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bucket challenge</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>buzzfeed news</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2015-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buzzfeed video</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>charlie hebdo</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>climate change</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2016-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cover photo</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fashion week</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fox news</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2013-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>game of thrones</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2016-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gay marriage</td>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>2014-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>golden globes</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gun control</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2013-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>high school</td>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>2016-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hurricane sandy</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ice bucket</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ice bucket challenge</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>live blog</td>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>miley cyrus</td>\n",
       "      <td>2013-11-01</td>\n",
       "      <td>2014-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>minimum wage</td>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>2015-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new york city</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>2015-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>new york times</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2014-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>new york today</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>2015-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>north korea</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>2016-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>paris attacks</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>photos from cnn</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>2016-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pope francis</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>robin williams</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>san bernardino</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>sandra bland</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>2015-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>sex marriage</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>2015-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>state of the union</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>2016-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>super bowl</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>supreme court</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>2016-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>syrian refugees</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>taylor swift</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2015-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>thanksgiving recipe</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>united states</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>world cup</td>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>2015-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    term      start        end\n",
       "0        boston marathon 2013-05-01 2013-08-01\n",
       "1       bucket challenge 2014-09-01 2014-12-01\n",
       "2          buzzfeed news 2014-01-01 2015-10-01\n",
       "3         buzzfeed video 2015-02-01 2015-07-01\n",
       "4          charlie hebdo 2015-02-01 2015-05-01\n",
       "5         climate change 2013-01-01 2016-02-01\n",
       "6            cover photo 2013-01-01 2013-06-01\n",
       "7           fashion week 2013-10-01 2015-01-01\n",
       "8               fox news 2013-03-01 2013-09-01\n",
       "9        game of thrones 2014-07-01 2016-08-01\n",
       "10          gay marriage 2013-06-01 2014-07-01\n",
       "11         golden globes 2015-02-01 2015-05-01\n",
       "12           gun control 2013-03-01 2013-08-01\n",
       "13           high school 2013-08-01 2016-07-01\n",
       "14       hurricane sandy 2013-01-01 2013-12-01\n",
       "15            ice bucket 2014-09-01 2014-12-01\n",
       "16  ice bucket challenge 2014-09-01 2014-12-01\n",
       "17             live blog 2014-08-01 2014-10-01\n",
       "18           miley cyrus 2013-11-01 2014-02-01\n",
       "19          minimum wage 2014-07-01 2015-06-01\n",
       "20         new hampshire 2016-03-01 2016-06-01\n",
       "21         new york city 2014-02-01 2015-05-01\n",
       "22        new york times 2013-01-01 2014-03-01\n",
       "23        new york today 2014-10-01 2015-06-01\n",
       "24           north korea 2013-05-01 2016-07-01\n",
       "25         paris attacks 2015-12-01 2016-03-01\n",
       "26       photos from cnn 2015-06-01 2016-08-01\n",
       "27          pope francis 2015-10-01 2016-01-01\n",
       "28        robin williams 2014-09-01 2014-12-01\n",
       "29        san bernardino 2016-01-01 2016-04-01\n",
       "30          sandra bland 2015-08-01 2015-11-01\n",
       "31          sex marriage 2013-04-01 2015-09-01\n",
       "32    state of the union 2013-04-01 2016-05-01\n",
       "33            super bowl 2013-03-01 2016-06-01\n",
       "34         supreme court 2013-04-01 2016-07-01\n",
       "35       syrian refugees 2016-01-01 2016-03-01\n",
       "36          taylor swift 2015-03-01 2015-10-01\n",
       "37   thanksgiving recipe 2014-12-01 2015-03-01\n",
       "38         united states 2013-12-01 2014-09-01\n",
       "39             world cup 2014-08-01 2015-10-01"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GanttChart(year_month_corpus_compact, category_to_datetime, is_gap_between_sequences, starting_time_step=chart_start_year).get_task_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdf = AbsoluteFrequencyRanker(year_month_corpus_compact).get_ranks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term\n",
       "times square                                   1\n",
       "| upworthy                                     0\n",
       "mom reacts                                     0\n",
       "street art                                     0\n",
       "mel gibson                                     0\n",
       "cbs news                                       0\n",
       "good use                                       0\n",
       "joins twitter                                  0\n",
       "oil spill                                      0\n",
       "tech support                                   0\n",
       "bill nye on                                    0\n",
       "angry mom                                      0\n",
       "hail storm                                     0\n",
       "kung fu                                        0\n",
       "insane clown posse                             0\n",
       "kids reenact                                   0\n",
       "olivia munn                                    0\n",
       "goldman sachs                                  0\n",
       "earth day                                      0\n",
       "factory workers                                0\n",
       "human trafficking                              0\n",
       "tiger woods                                    0\n",
       "warning signs                                  0\n",
       "black parents                                  0\n",
       "law degree                                     0\n",
       "pie charts                                     0\n",
       "gay men                                        0\n",
       "action figure                                  0\n",
       "10 songs                                       0\n",
       "viral post                                     0\n",
       "                                              ..\n",
       "day in court                                   0\n",
       "political talk                                 0\n",
       "back pages                                     0\n",
       "salmon roasted in butter recipe                0\n",
       "home to roost                                  0\n",
       "episode 3 recap                                0\n",
       "carroll gardens                                0\n",
       "u.n.c. football player                         0\n",
       "medical bills                                  0\n",
       "sells out                                      0\n",
       "ukrainian military                             0\n",
       "podcast | episode                              0\n",
       "state dinner guest                             0\n",
       "democratic debate takeaways                    0\n",
       "tough path                                     0\n",
       "hangs over                                     0\n",
       "primary night                                  0\n",
       "vietnamese steak with cucumber salad recipe    0\n",
       "new york times fashion on instagram            0\n",
       "rocket company                                 0\n",
       "blue origin                                    0\n",
       "contested republican convention                0\n",
       "computer program                               0\n",
       "bernie sanders wins michigan                   0\n",
       "michigan primary                               0\n",
       "right way to stretch before exercise           0\n",
       "episode 7 recap                                0\n",
       "star candidate                                 0\n",
       "oregon occupier                                0\n",
       "five years                                     0\n",
       "Name: 1913-02 freq, Length: 23551, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdf['1913-02 freq'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>time</th>\n",
       "      <th>top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1918-06-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1919-06-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1926-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1926-05-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1928-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1933-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1933-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1934-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1934-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1940-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1942-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1942-08-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1946-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1947-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1947-08-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1951-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1954-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1955-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1958-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1959-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1963-08-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1963-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1964-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1964-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1965-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1967-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1997-10-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1998-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1999-05-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>1999-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2000-10-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2001-09-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2001-10-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2002-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2002-05-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2002-06-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2003-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2003-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2003-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2004-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2005-05-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2006-08-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2006-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2007-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2007-07-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2007-09-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2007-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1036</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2008-11-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2009-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>michigan primary</td>\n",
       "      <td>2009-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  term       time  top\n",
       "8     michigan primary 1918-06-01    1\n",
       "17    michigan primary 1919-06-01    1\n",
       "26    michigan primary 1926-02-01    1\n",
       "36    michigan primary 1926-05-01    1\n",
       "46    michigan primary 1928-11-01    1\n",
       "56    michigan primary 1933-04-01    1\n",
       "65    michigan primary 1933-12-01    1\n",
       "76    michigan primary 1934-01-01    1\n",
       "86    michigan primary 1934-02-01    1\n",
       "96    michigan primary 1940-04-01    1\n",
       "105   michigan primary 1942-02-01    1\n",
       "115   michigan primary 1942-08-01    1\n",
       "125   michigan primary 1946-11-01    1\n",
       "135   michigan primary 1947-04-01    1\n",
       "146   michigan primary 1947-08-01    1\n",
       "156   michigan primary 1948-01-01    1\n",
       "166   michigan primary 1951-01-01    1\n",
       "176   michigan primary 1954-01-01    1\n",
       "185   michigan primary 1955-03-01    1\n",
       "195   michigan primary 1958-01-01    1\n",
       "206   michigan primary 1959-01-01    1\n",
       "216   michigan primary 1960-09-01    1\n",
       "226   michigan primary 1962-03-01    1\n",
       "236   michigan primary 1963-08-01    1\n",
       "245   michigan primary 1963-11-01    1\n",
       "256   michigan primary 1964-01-01    1\n",
       "266   michigan primary 1964-03-01    1\n",
       "278   michigan primary 1965-11-01    1\n",
       "288   michigan primary 1966-01-01    1\n",
       "299   michigan primary 1967-02-01    1\n",
       "...                ...        ...  ...\n",
       "789   michigan primary 1997-10-01    1\n",
       "796   michigan primary 1998-04-01    1\n",
       "806   michigan primary 1999-05-01    1\n",
       "816   michigan primary 1999-12-01    1\n",
       "825   michigan primary 2000-10-01    1\n",
       "836   michigan primary 2001-01-01    1\n",
       "848   michigan primary 2001-04-01    1\n",
       "856   michigan primary 2001-09-01    1\n",
       "869   michigan primary 2001-10-01    1\n",
       "874   michigan primary 2002-04-01    1\n",
       "885   michigan primary 2002-05-01    1\n",
       "895   michigan primary 2002-06-01    1\n",
       "905   michigan primary 2003-02-01    1\n",
       "915   michigan primary 2003-03-01    1\n",
       "926   michigan primary 2003-12-01    1\n",
       "936   michigan primary 2004-12-01    1\n",
       "946   michigan primary 2005-05-01    1\n",
       "956   michigan primary 2006-08-01    1\n",
       "966   michigan primary 2006-11-01    1\n",
       "976   michigan primary 2007-04-01    1\n",
       "986   michigan primary 2007-07-01    1\n",
       "997   michigan primary 2007-09-01    1\n",
       "1006  michigan primary 2007-11-01    1\n",
       "1017  michigan primary 2007-12-01    1\n",
       "1026  michigan primary 2008-01-01    1\n",
       "1036  michigan primary 2008-02-01    1\n",
       "1046  michigan primary 2008-11-01    1\n",
       "1056  michigan primary 2008-12-01    1\n",
       "1066  michigan primary 2009-03-01    1\n",
       "1076  michigan primary 2009-04-01    1\n",
       "\n",
       "[107 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_time_df.groupby('term').apply(lambda x: len(list(x['time']))).sort_values(ascending=False)\n",
    "term_time_df[term_time_df.term == 'michigan primary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-37-57bf62f7605a>, line 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-37-57bf62f7605a>\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m    timesteps_to_lag = 4,\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "class GanttChart(object):\n",
    "    def __init__(self,\n",
    "                 corpus,\n",
    "                 category_to_datetime_func,\n",
    "                 is_gap_between_sequences_func,\n",
    "                 timesteps_to_lag = 4,\n",
    "                 num_top_terms_each_timestep = 10,\n",
    "                 num_terms_to_include = 40,\n",
    "                 term_ranker = st.AbsoluteFrequencyRanker,\n",
    "                 term_scorer = st.RankDifference):\n",
    "        self.corpus = corpus\n",
    "        self.timesteps_to_lag = timesteps_to_lag\n",
    "        self.num_top_terms_each_timestep = num_top_terms_each_timestep\n",
    "        self.num_terms_to_include = num_terms_to_include      \n",
    "        self.is_gap_between_sequences_func = is_gap_between_sequences_func\n",
    "        self.category_to_datetime_func = category_to_datetime_func\n",
    "        \n",
    "    def _find_sequences(self, time_steps):\n",
    "        min_timestep = None\n",
    "        max_timestep = None\n",
    "        last_timestep = None\n",
    "        gaps = []\n",
    "        for cur_timestep in sorted(time_steps):\n",
    "            if min_timestep is None:\n",
    "                min_timestep = cur_timestep\n",
    "            elif self.is_gap_between_sequences_func(cur_timestep, last_timestep):\n",
    "                gaps.append([min_timestep, last_timestep])\n",
    "                min_timestep = cur_timestep\n",
    "            last_timestep = cur_timestep\n",
    "        if gaps == [] or gaps[-1][1] != cur_timestep:\n",
    "            gaps.append([min_timestep, cur_timestep])\n",
    "        return gaps        \n",
    "\n",
    "    def make_chart(self):\n",
    "        data = []\n",
    "        tdf = self.term_ranker(corpus).get_ranks()\n",
    "        for cat in sorted(self.corpus.get_categories()):\n",
    "            if cat >= chart_start_year:\n",
    "                scores = st.RankDifference().get_scores(\n",
    "                    tdf[sorted([x for x in tdf.columns if x < cat])[-timesteps_to_lag:]].sum(axis=1), \n",
    "                    tdf[cat].astype(int))\n",
    "                for term in tdf.index[np.argsort(-scores)[:num_top_terms_each_timestep]]:\n",
    "                    data.append({'time': category_to_datetime(cat), \n",
    "                                 'term': term, \n",
    "                                 'top': 1})\n",
    "\n",
    "        term_time_df = pd.DataFrame(data)\n",
    "        terms_to_include = (term_time_df\n",
    "                            .groupby('term')\n",
    "                            ['top']\n",
    "                            .sum()\n",
    "                            .sort_values(ascending=False)\n",
    "                            .iloc[:num_terms_to_include].index)\n",
    "        task_df = (term_time_df[term_time_df\n",
    "                                .term.isin(terms_to_include)][['time', 'term']]\n",
    "                   .groupby('term')\n",
    "                       .apply(lambda x: pd.Series(find_sequences(x['time'])))\n",
    "                       .reset_index()\n",
    "                       .rename({0:'sequence'}, axis=1)\n",
    "                   .reset_index()\n",
    "                   .assign(start=lambda x: x['sequence'].apply(lambda x: x[0]))\n",
    "                   .assign(end=lambda x: x['sequence'].apply(lambda x: x[1]))\n",
    "                   [['term', 'start', 'end']])\n",
    "        #print(task_df)\n",
    "        chart = alt.Chart(task_df).mark_bar().encode(\n",
    "            x = 'start',\n",
    "            x2 = 'end',\n",
    "            y = 'term',\n",
    "        )\n",
    "        return chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> <ipython-input-18-1571ba5eb391>(54)generate_diachronic_chart()\n",
      "-> chart = alt.Chart(task_df).mark_bar().encode(\n",
      "(Pdb) c\n"
     ]
    }
   ],
   "source": [
    "chart = generate_diachronic_chart(category_to_datetime, \n",
    "                          timesteps_to_lag, \n",
    "                          num_top_terms_each_timestep, \n",
    "                          num_terms_to_include, \n",
    "                          is_gap_between_sequences,\n",
    "                          corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {
      "jupyter-vega": "#e35cb9f2-5b32-4246-b9f4-ddd06104ca13"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [py36]",
   "language": "python",
   "name": "Python [py36]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}