5. HopRank (empirical-lcc).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# !{sys.executable} -m pip install tldextract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align:center;'>\n",
    "<h1>HopRank</h1>\n",
    "<h2>Empirical Analysis</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Lisette Espin-Noboa\"\n",
    "__copyright__ = \"Copyright 2018, HopRank\"\n",
    "__credits__ = [\"Florian Lemmerich\", \"Markus Strohmaier\", \"Simon Walk\", \"Mark Musen\"]\n",
    "__license__ = \"GPL\"\n",
    "__version__ = \"1.0.3\"\n",
    "__maintainer__ = \"Lisette Espin-Noboa\"\n",
    "__email__ = \"Lisette.Espin@gesis.org\"\n",
    "__status__ = \"Developing\"\n",
    "\n",
    "########################################################################################\n",
    "# Warnings\n",
    "########################################################################################\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Dependencies</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################################################\n",
    "# System\n",
    "########################################################################################\n",
    "import os\n",
    "import importlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from collections import Counter\n",
    "from joblib import Parallel, delayed\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt \n",
    "from collections import defaultdict\n",
    "\n",
    "########################################################################################\n",
    "# Local Dependencies\n",
    "########################################################################################\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "from org.gesis.libs.bioportal.ontology import Ontology\n",
    "from org.gesis.libs.bioportal.transition import Transition\n",
    "from org.gesis.libs.bioportal.clickstream import NAVITYPES\n",
    "from org.gesis.libs.bioportal.clickstream import load_clickstream\n",
    "from org.gesis.libs.bioportal.clickstream import preprocess_clickstream\n",
    "from org.gesis.libs.bioportal.clickstream import preprocess_sessions\n",
    "from org.gesis.libs.bioportal.clickstream import clean_clickstream\n",
    "from org.gesis.libs.bioportal.submission import get_submissions\n",
    "from org.gesis.libs.utils import log\n",
    "from org.gesis.libs.utils import weighted_to_undirected\n",
    "from org.gesis.libs.utils import to_symmetric\n",
    "\n",
    "########################################################################################\n",
    "# Plot's style\n",
    "########################################################################################\n",
    "sns.set_context(context=\"paper\", font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Constants</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SM = 'submission'\n",
    "SUBMISSIONS_FN = '/bigdata/lespin/datasets/bioportal/submissions.json'\n",
    "\n",
    "ON = 'ontology'\n",
    "ONTOLOGY_ROOT = '/bigdata/lespin/datasets/bioportal/ontologies'\n",
    "ONTOLOGY_GRAPH_OUTPUT = '/bigdata/lespin/bioportal/ontologies/graph'\n",
    "ONTOLOGY_ADJACENCY_OUTPUT = '/bigdata/lespin/bioportal/ontologies/matrix'\n",
    "ONTOLOGY_NODES_OUTPUT = '/bigdata/lespin/bioportal/ontologies/nodes'\n",
    "ONTOLOGY_HOPS_OUTPUT = '/bigdata/lespin/bioportal/ontologies/hops'\n",
    "\n",
    "CS = 'clickstream'\n",
    "CS_FN = 'BP_webpage_requests_<YEAR><POSTFIX>.csv.bz2'\n",
    "CLICKSTREAM_ROOT = '/bigdata/lespin/datasets/bioportal/clickstream/'\n",
    "CLICKSTREAM_DF = '/bigdata/lespin/bioportal/clickstreams'\n",
    "\n",
    "TR = 'transitions'\n",
    "TRANS_GRAPH_OUTPUT = '/bigdata/lespin/bioportal/transitions/graph'\n",
    "TRANS_ADJACENCY_OUTPUT = '/bigdata/lespin/bioportal/transitions/matrix'\n",
    "TRANS_NODES_OUTPUT = '/bigdata/lespin/bioportal/transitions/nodes'\n",
    "LCC_TRANS_GRAPH_OUTPUT = '/bigdata/lespin/bioportal/transitions_in_lcc/graph'\n",
    "LCC_TRANS_ADJACENCY_OUTPUT = '/bigdata/lespin/bioportal/transitions_in_lcc/matrix'\n",
    "LCC_TRANS_NODES_OUTPUT = '/bigdata/lespin/bioportal/transitions_in_lcc/nodes'\n",
    "\n",
    "SU = 'summary'\n",
    "LOG_OUTPUT = '/bigdata/lespin/bioportal/logs'\n",
    "PLOTS_OUTPUT = '/bigdata/lespin/bioportal/plots'\n",
    "SUMMARY_OUTPUT = '/bigdata/lespin/bioportal/summary'\n",
    "YEAR = '2015'\n",
    "\n",
    "LAG_BREAK_IN_MINUTES = 60\n",
    "MIN_SESSION_LENGTH = 2\n",
    "MIN_REQ = 1000\n",
    "MIN_TRANSITIONS = 1000\n",
    "MAXKHOPS = 32\n",
    "\n",
    "LCC = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align:center;'>\n",
    "<h1>Functions</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_by(data, x, y, hue, hue_order, height, width, legend_top, legend_ncol, legend_box, palette, xlog, ylog, grid, fn):\n",
    "\n",
    "    sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8, \n",
    "                            'xtick.color': 'black', 'ytick.color': 'black',\n",
    "                           'axes.edgecolor': 'black'})\n",
    "    \n",
    "    fg = sns.catplot(data=data,\n",
    "                 kind='point',\n",
    "                 x=x,\n",
    "                 y=y,\n",
    "                 hue=hue,\n",
    "                 hue_order=hue_order,\n",
    "                 height=height,\n",
    "                 aspect=width/height,\n",
    "                 legend=not legend_top,\n",
    "                 legend_out=not legend_top,\n",
    "                 palette=sns.color_palette(palette, data[hue].nunique()),\n",
    "                )\n",
    "    \n",
    "    if xlog: fg.ax.set_xscale('log')\n",
    "    if ylog: fg.ax.set_yscale('log')\n",
    "        \n",
    "    if legend_top:\n",
    "        _ = fg.ax.legend(frameon=False, \n",
    "                         fancybox=False,\n",
    "                         bbox_to_anchor=legend_box, \n",
    "                         loc=\"lower left\", \n",
    "                         ncol=legend_ncol, \n",
    "                         mode=\"expand\", \n",
    "                         borderaxespad=0)\n",
    "    \n",
    "#     plt.xticks(list(plt.xticks()[0]))\n",
    "    \n",
    "    fg.ax.grid(grid)\n",
    "    plt.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "    plt.tight_layout()\n",
    "    if fn is not None:\n",
    "        plt.savefig(fn, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align:center;'>\n",
    "<h1>Overlaps</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading submissions...\n",
      "2019-02-25 10:47:18\t/bigdata/lespin/datasets/bioportal/submissions.json loaded!\n",
      "2019-02-25 10:47:18\t- 642 ontologies\n",
      "2019-02-25 10:47:18\t- 13 years\n",
      "loading ontologies...\n",
      "generating k-hop overlaps...\n",
      "loading summary overlaps...\n",
      "CPT already loaded.\n",
      "MEDDRA already loaded.\n",
      "NDFRT already loaded.\n",
      "LOINC already loaded.\n",
      "ICD9CM already loaded.\n",
      "WHO-ART already loaded.\n",
      "MESH already loaded.\n",
      "ICD10 already loaded.\n",
      "CHMO already loaded.\n",
      "HL7 already loaded.\n",
      "OMIM already loaded.\n"
     ]
    }
   ],
   "source": [
    "print('loading submissions...')\n",
    "submissions_dict = get_submissions(SUBMISSIONS_FN)\n",
    "\n",
    "print('loading ontologies...')\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_ontologies_transitions_{}.{}'.format(YEAR,'csv'))\n",
    "df_onto = pd.read_csv(fn, index_col=False)\n",
    "tmp = df_onto.loc[df_onto[\"T'\"] >= MIN_TRANSITIONS].copy()\n",
    "ontology_order = df_onto.ontology.unique()\n",
    "del(df_onto)\n",
    "\n",
    "print('generating k-hop overlaps...')\n",
    "columns = ['ontology','diameter','navitype','khop','coverage','transitions','%coverage','%transitions','dyads_in_khop','total_transitions_ontology_navitype']\n",
    "df_overlap = pd.DataFrame(columns=columns)\n",
    "\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_overlaps_{}.{}'.format(YEAR,'csv'))\n",
    "if os.path.exists(fn):\n",
    "    print('loading summary overlaps...')\n",
    "    df_overlap = pd.read_csv(fn, index_col=None)\n",
    "    \n",
    "for index,row in tmp.iterrows():\n",
    "    onto_name = row['ontology']\n",
    "     \n",
    "    if onto_name in ['SNOMEDCT']:\n",
    "        continue\n",
    "        \n",
    "    if df_overlap.query(\"ontology == @onto_name\").size > 0:\n",
    "        print(\"{} already loaded.\".format(onto_name))\n",
    "        continue\n",
    "        \n",
    "    submission = submissions_dict[onto_name][YEAR]\n",
    "    onto = Ontology(name=onto_name, year=YEAR, submission_id=submission['submissionId'], root_folder=ONTOLOGY_ROOT)\n",
    "    \n",
    "    for khop in np.arange(1,MAXKHOPS+1,1):    \n",
    "        \n",
    "        if khop > row[\"d'\"]:\n",
    "            continue\n",
    "          \n",
    "        try:\n",
    "            M = onto.get_khop_matrix(path=ONTOLOGY_HOPS_OUTPUT, k=khop, lcc=LCC)\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "        try:\n",
    "            for navitype in [None]+NAVITYPES:            \n",
    "                # all transitions\n",
    "                cs = Transition(onto_name, navitype, YEAR)            \n",
    "                cs.load_adjacency(path=LCC_TRANS_ADJACENCY_OUTPUT)        \n",
    "                \n",
    "                if cs.T is not None:\n",
    "                    total_dydas_in_khop = M.sum()\n",
    "                    total_transitions = cs.get_undirected_adjacency().sum()\n",
    "                    coverage_in_khop = M.multiply(cs.get_undirected_adjacency() > 0)\n",
    "                    transitions_in_khop = M.multiply(cs.get_undirected_adjacency())\n",
    "                    \n",
    "                    df_overlap = df_overlap.append({'ontology':onto_name,\n",
    "                                                    'diameter':row[\"d'\"],\n",
    "                                                    'navitype':'ALL' if navitype is None else navitype,\n",
    "                                                    'khop':khop,\n",
    "                                                    'coverage':coverage_in_khop.sum() / 2.,\n",
    "                                                    'transitions':transitions_in_khop.sum() / 2.,\n",
    "                                                    '%coverage':100*coverage_in_khop.sum() / total_dydas_in_khop,\n",
    "                                                    '%transitions':100*transitions_in_khop.sum() / total_transitions,\n",
    "                                                    'dyads_in_khop':total_dydas_in_khop,\n",
    "                                                    'total_transitions_ontology_navitype':total_transitions,\n",
    "                                                   }\n",
    "                                                   , ignore_index=True)\n",
    "                del(cs)\n",
    "                del(coverage_in_khop)\n",
    "                del(transitions_in_khop)\n",
    "            del(M)\n",
    "        except Exception as ex:\n",
    "            print(ex)\n",
    "            df_overlap = df_overlap.append({'ontology':onto_name,\n",
    "                                            'diameter':row[\"d'\"],\n",
    "                                            'navitype':'ALL' if navitype is None else navitype,\n",
    "                                            'khop':khop,\n",
    "                                            'coverage':0,\n",
    "                                            'transitions':0,\n",
    "                                            '%coverage':0.0,\n",
    "                                            '%transitions':0.0,\n",
    "                                            'dyads_in_khop':0,\n",
    "                                            'total_transitions_ontology_navitype':0,\n",
    "                                           }\n",
    "                                           , ignore_index=True)\n",
    "\n",
    "df_overlap.to_csv(fn, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ontology</th>\n",
       "      <th>diameter</th>\n",
       "      <th>navitype</th>\n",
       "      <th>khop</th>\n",
       "      <th>coverage</th>\n",
       "      <th>transitions</th>\n",
       "      <th>%coverage</th>\n",
       "      <th>%transitions</th>\n",
       "      <th>dyads_in_khop</th>\n",
       "      <th>total_transitions_ontology_navitype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CPT</td>\n",
       "      <td>15</td>\n",
       "      <td>ALL</td>\n",
       "      <td>1</td>\n",
       "      <td>3565.0</td>\n",
       "      <td>7763.0</td>\n",
       "      <td>27.192982</td>\n",
       "      <td>17.385949</td>\n",
       "      <td>26220</td>\n",
       "      <td>89302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPT</td>\n",
       "      <td>15</td>\n",
       "      <td>DC</td>\n",
       "      <td>1</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>8.451564</td>\n",
       "      <td>26.307780</td>\n",
       "      <td>26220</td>\n",
       "      <td>10514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPT</td>\n",
       "      <td>15</td>\n",
       "      <td>DE</td>\n",
       "      <td>1</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>2736.0</td>\n",
       "      <td>14.698703</td>\n",
       "      <td>26.214429</td>\n",
       "      <td>26220</td>\n",
       "      <td>20874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CPT</td>\n",
       "      <td>15</td>\n",
       "      <td>EX</td>\n",
       "      <td>1</td>\n",
       "      <td>1267.0</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>9.664378</td>\n",
       "      <td>28.187135</td>\n",
       "      <td>26220</td>\n",
       "      <td>23940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPT</td>\n",
       "      <td>15</td>\n",
       "      <td>DU</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.541571</td>\n",
       "      <td>1.952333</td>\n",
       "      <td>26220</td>\n",
       "      <td>7888.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ontology  diameter navitype  khop  coverage  transitions  %coverage  \\\n",
       "0      CPT        15      ALL     1    3565.0       7763.0  27.192982   \n",
       "1      CPT        15       DC     1    1108.0       1383.0   8.451564   \n",
       "2      CPT        15       DE     1    1927.0       2736.0  14.698703   \n",
       "3      CPT        15       EX     1    1267.0       3374.0   9.664378   \n",
       "4      CPT        15       DU     1      71.0         77.0   0.541571   \n",
       "\n",
       "   %transitions  dyads_in_khop  total_transitions_ontology_navitype  \n",
       "0     17.385949          26220                              89302.0  \n",
       "1     26.307780          26220                              10514.0  \n",
       "2     26.214429          26220                              20874.0  \n",
       "3     28.187135          26220                              23940.0  \n",
       "4      1.952333          26220                               7888.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overlap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 999\n",
    "#print(df_overlap.query(\"navitype!='ALL'\").groupby(['navitype','khop'])['%transitions'].mean())\n",
    "# df_overlap.groupby(['khop'])['%transitions'].mean().reset_index().sort_values(['%transitions'],ascending=False).round(2)\n",
    "df_overlap.groupby(['navitype','khop'])['%transitions'].mean().reset_index().sort_values(['navitype','%transitions','khop'],ascending=False).round(2)\n",
    "# df_overlap.ontology.nunique()\n",
    "# df_overlap.query('transitions>0').groupby('navitype')['khop'].max().reset_index().khop.mean()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>k-hop Overlap</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### SORTED BY # TRANSITIONS in LCC\n",
    "\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_ontologies_transitions_{}.{}'.format(YEAR,'csv'))\n",
    "df_onto = pd.read_csv(fn, index_col=False)\n",
    "tmp = df_onto.loc[df_onto[\"T'\"] >= MIN_TRANSITIONS].copy()\n",
    "ontology_order = df_onto.sort_values(\"T'\", ascending=False).ontology.unique()\n",
    "\n",
    "intersect = np.intersect1d(ontology_order, df_overlap.ontology.unique(), assume_unique=True, return_indices=True)\n",
    "intersect = np.sort(intersect[1])\n",
    "ontology_order = ontology_order[intersect]\n",
    "\n",
    "tmp = df_overlap.query(\"navitype=='ALL' & coverage > 0\").copy()\n",
    "tmp.sort_values('transitions',ascending=False,inplace=True)\n",
    "fn = os.path.join(PLOTS_OUTPUT,'lcc_summary_coverage_per_khop_sorted_by_transitions.pdf')\n",
    "plot_by(tmp, 'khop', '%coverage', 'ontology', ontology_order, 3, 6, True, 4, (-0.09, 1.02, 1.1, 0.2), \"RdBu_r\", False, True, False, fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x201.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SORTED BY DIAMETER in LCC\n",
    "\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_ontologies_transitions_{}.{}'.format(YEAR,'csv'))\n",
    "df_onto = pd.read_csv(fn, index_col=False)\n",
    "tmp = df_onto.loc[df_onto[\"T'\"] >= MIN_TRANSITIONS].copy()\n",
    "ontology_order = df_onto.sort_values(\"d'\", ascending=False).ontology.unique()\n",
    "\n",
    "intersect = np.intersect1d(ontology_order, df_overlap.ontology.unique(), assume_unique=True, return_indices=True)\n",
    "intersect = np.sort(intersect[1])\n",
    "ontology_order = ontology_order[intersect]\n",
    "\n",
    "tmp = df_overlap.query(\"navitype=='ALL' & coverage > 0\").copy()\n",
    "tmp.sort_values('transitions',ascending=False,inplace=True)\n",
    "fn = os.path.join(PLOTS_OUTPUT,'lcc_summary_coverage_per_khop_sorted_by_diameter.pdf')\n",
    "plot_by(tmp, 'khop', '%coverage', 'ontology', ontology_order, 2.8, 6, True, 4, (-0.09, 1.02, 1.1, 0.2), \"RdBu_r\", False, True, False, fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### SORTED BY NODES in LCC\n",
    "\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_ontologies_transitions_{}.{}'.format(YEAR,'csv'))\n",
    "df_onto = pd.read_csv(fn, index_col=False)\n",
    "tmp = df_onto.loc[df_onto[\"T'\"] >= MIN_TRANSITIONS].copy()\n",
    "ontology_order = df_onto.sort_values(\"N'\", ascending=False).ontology.unique()\n",
    "\n",
    "intersect = np.intersect1d(ontology_order, df_overlap.ontology.unique(), assume_unique=True, return_indices=True)\n",
    "intersect = np.sort(intersect[1])\n",
    "ontology_order = ontology_order[intersect]\n",
    "\n",
    "tmp = df_overlap.query(\"navitype=='ALL' & coverage > 0\").copy()\n",
    "tmp.sort_values('transitions',ascending=False,inplace=True)\n",
    "fn = os.path.join(PLOTS_OUTPUT,'lcc_summary_coverage_per_khop_sorted_by_nodes.pdf')\n",
    "plot_by(tmp, 'khop', '%coverage', 'ontology', ontology_order, 3, 6, True, 4, (-0.09, 1.02, 1.1, 0.2), \"RdBu_r\", False, True, False, fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### SORTED BY EDGES in LCC\n",
    "\n",
    "fn = os.path.join(SUMMARY_OUTPUT,'lcc_summary_ontologies_transitions_{}.{}'.format(YEAR,'csv'))\n",
    "df_onto = pd.read_csv(fn, index_col=False)\n",
    "tmp = df_onto.loc[df_onto[\"T'\"] >= MIN_TRANSITIONS].copy()\n",
    "ontology_order = df_onto.sort_values(\"E'\", ascending=False).ontology.unique()\n",
    "\n",
    "intersect = np.intersect1d(ontology_order, df_overlap.ontology.unique(), assume_unique=True, return_indices=True)\n",
    "intersect = np.sort(intersect[1])\n",
    "ontology_order = ontology_order[intersect]\n",
    "\n",
    "tmp = df_overlap.query(\"navitype=='ALL' & coverage > 0\").copy()\n",
    "tmp.sort_values('transitions',ascending=False,inplace=True)\n",
    "fn = os.path.join(PLOTS_OUTPUT,'lcc_summary_coverage_per_khop_sorted_by_edges.pdf')\n",
    "plot_by(tmp, 'khop', '%coverage', 'ontology', ontology_order, 3, 6, True, 4, (-0.09, 1.02, 1.1, 0.2), \"RdBu_r\", False, True, False, fn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Transitions per k-hop</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ssd/lespin/virtualenv/python3.5/lib/python3.5/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x187.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = df_overlap.query(\"coverage > 0\").copy()\n",
    "navitypes_order = ['DE','DC','DU','EX','EL','ES','LS','ALL']\n",
    "fn = os.path.join(PLOTS_OUTPUT,'lcc_summary_transitions_per_khop.pdf')\n",
    "plot_by(tmp, 'khop', '%transitions', 'navitype', navitypes_order, 2.6, 6, True, 4, (0, 1.02, 1, 0.2), None, False, True, False, fn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Transitions per ontology and navitype (betas)</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  plot_betas(onto_name, df, navitypes_order, fn=None):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(5,3))\n",
    "    cbar_ax = fig.add_axes([.93, .26, .02, .47])\n",
    "\n",
    "    tmp = df.query(\"navitype.isin(@navitypes_order)\").copy()\n",
    "    tmp = tmp.pivot(index='navitype', columns='khop', values='%transitions')\n",
    "    \n",
    "    tmp.loc[:,0] = 0\n",
    "    tmp = tmp.reindex(sorted(tmp.columns), axis=1)\n",
    "    \n",
    "    tmp = tmp + 1 #0.001\n",
    "    tmp = tmp.div(tmp.sum(axis=1), axis='index')*100\n",
    "    tmp.rename(columns={c:r'$\\beta{}$'.format(c) for c in tmp.columns}, inplace=True)\n",
    "    tmp = tmp.reindex(navitypes_order)\n",
    "      \n",
    "    ax = sns.heatmap(tmp, \n",
    "                     ax = ax,\n",
    "                     cbar_ax = cbar_ax,\n",
    "                     cbar = True,\n",
    "                     cmap = \"Blues\",\n",
    "                     square = False)\n",
    "\n",
    "    _ = ax.set_title(onto_name)\n",
    "    _ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0 if len(tmp.columns) <= 10 else 90)\n",
    "    _ = ax.set_yticklabels(ax.get_yticklabels(), rotation=0)\n",
    "    _ = ax.set_xlabel('')\n",
    "    _ = ax.hlines([y-0.03 for y in np.arange(len(navitypes_order))], *ax.get_xlim(), color='white')\n",
    "    \n",
    "    if fn is not None:\n",
    "        plt.savefig(fn, bbox_inches='tight' )\n",
    "        print('{} saved!'.format(fn))\n",
    "    plt.show()\n",
    "    plt.clf()     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "navitypes_order = ['DE','DC','DU','EX','EL','ES','LS','ALL']\n",
    "tmp = df_overlap.query(\"khop <= diameter\").copy()\n",
    "for onto_name, df in tmp.groupby('ontology'):\n",
    "    fn = os.path.join(PLOTS_OUTPUT,'lcc_betas_{}_{}.pdf'.format(onto_name,YEAR))\n",
    "    _ = plot_betas(onto_name, df, navitypes_order, fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_betas_zero(onto_name, df, navitypes_order, fn=None):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(5,3))\n",
    "    cbar_ax = fig.add_axes([.93, .26, .02, .47])\n",
    "\n",
    "    tmp = df.query(\"navitype.isin(@navitypes_order)\").copy()\n",
    "    tmp = tmp.pivot(index='navitype', columns='khop', values='%transitions')\n",
    "\n",
    "    tmp.loc[:,0] = 0\n",
    "    tmp = tmp.reindex(sorted(tmp.columns), axis=1)\n",
    "    \n",
    "    tmp = tmp + 1\n",
    "    tmp = tmp.div(tmp.sum(axis=1), axis='index')*100 #row norm\n",
    "    tmp.rename(columns={c:r'$\\beta{}$'.format(c) for c in tmp.columns}, inplace=True)\n",
    "    tmp = tmp.reindex(navitypes_order)\n",
    "                \n",
    "    ax = sns.heatmap(tmp, \n",
    "                     ax = ax,\n",
    "                     cbar_ax = cbar_ax,\n",
    "                     cbar = True,\n",
    "                     cmap = \"Blues\",\n",
    "                     square = False)\n",
    "\n",
    "    _ = ax.set_title(onto_name)\n",
    "    _ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0 if len(tmp.columns) <= 10 else 90)\n",
    "    _ = ax.set_yticklabels(ax.get_yticklabels(), rotation=0)\n",
    "    _ = ax.set_xlabel('')\n",
    "    _ = ax.hlines([y-0.03 for y in np.arange(len(navitypes_order))], *ax.get_xlim(), color='white')\n",
    "    \n",
    "    if fn is not None:\n",
    "        plt.savefig(fn, bbox_inches='tight' )\n",
    "        print('{} saved!'.format(fn))\n",
    "    plt.show()\n",
    "    plt.clf()     \n",
    "\n",
    "    \n",
    "navitypes_order = ['DE','DC','DU','EX','EL','ES','LS','ALL']\n",
    "tmp = df_overlap.query(\"khop <= diameter\").copy()\n",
    "for onto_name, df in tmp.groupby('ontology'):\n",
    "\n",
    "    if onto_name != 'MEDDRA':\n",
    "        continue\n",
    "    \n",
    "    fn = os.path.join(PLOTS_OUTPUT,'lcc_betas_{}_{}.pdf'.format(onto_name,YEAR))\n",
    "    _ = plot_betas_zero(onto_name, df, navitypes_order, fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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