{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# PageRank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This notebook illustrates the ranking of the nodes of a graph by [PageRank](https://scikit-network.readthedocs.io/en/latest/reference/ranking.html#pagerank)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:50.554431Z",
     "start_time": "2019-07-15T12:29:50.414075Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sknetwork.data import karate_club, painters, movie_actor\n",
    "from sknetwork.ranking import PageRank\n",
    "from sknetwork.visualization import visualize_graph, visualize_bigraph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:51.261957Z",
     "start_time": "2019-07-15T12:29:51.249107Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graph = karate_club(metadata=True)\n",
    "adjacency = graph.adjacency\n",
    "position = graph.position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# PageRank\n",
    "pagerank = PageRank()\n",
    "scores = pagerank.fit_predict(adjacency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:55.341520Z",
     "start_time": "2019-07-15T12:29:55.026465Z"
    },
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, scores=np.log(scores))\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:53.130694Z",
     "start_time": "2019-07-15T12:29:52.704040Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# personalized PageRank\n",
    "weights = {1: 1, 10: 1}\n",
    "scores = pagerank.fit_predict(adjacency, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:58.518668Z",
     "start_time": "2019-07-15T12:29:58.152579Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, scores=np.log(scores), seeds=weights)\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Directed graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-15T12:29:58.542147Z",
     "start_time": "2019-07-15T12:29:58.529699Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graph = painters(metadata=True)\n",
    "adjacency = graph.adjacency\n",
    "names = graph.names\n",
    "position = graph.position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# PageRank\n",
    "pagerank = PageRank()\n",
    "scores = pagerank.fit_predict(adjacency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, scores=np.log(scores), names=names)\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# personalized PageRank\n",
    "cezanne = 11\n",
    "weights = {cezanne:1}\n",
    "scores = pagerank.fit_predict(adjacency, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, names, scores=np.log(scores + 1e-6), seeds=weights)\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Bipartite graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graph = movie_actor(metadata=True)\n",
    "biadjacency = graph.biadjacency\n",
    "names_row = graph.names_row\n",
    "names_col = graph.names_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pagerank = PageRank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "drive = 3\n",
    "aviator = 9\n",
    "weights_row={drive: 1, aviator: 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pagerank.fit(biadjacency, weights_row)\n",
    "scores_row = pagerank.scores_row_\n",
    "scores_col = pagerank.scores_col_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_bigraph(biadjacency, names_row, names_col,\n",
    "                    scores_row=np.log(scores_row), scores_col=np.log(scores_col), seeds_row=weights_row)\n",
    "SVG(image)\n"
   ]
  }
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