{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dirichlet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "This notebook illustrates a regression task as a solution of the [Dirichlet problem](https://scikit-network.readthedocs.io/en/latest/reference/ranking.html#dirichlet) (heat diffusion with constraints)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sknetwork.data import karate_club, painters, movie_actor\n",
    "from sknetwork.regression import Dirichlet\n",
    "from sknetwork.visualization import visualize_graph, visualize_bigraph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:16.306451Z",
     "start_time": "2019-10-30T14:59:16.285271Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graph = karate_club(metadata=True)\n",
    "adjacency = graph.adjacency\n",
    "position = graph.position\n",
    "labels_true = graph.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:16.806199Z",
     "start_time": "2019-10-30T14:59:16.801162Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# heat diffusion\n",
    "dirichlet = Dirichlet()\n",
    "values = {0: 0, 33: 1}\n",
    "values_pred = dirichlet.fit_predict(adjacency, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:18.256044Z",
     "start_time": "2019-10-30T14:59:18.250280Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, scores=values_pred, seeds=values)\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Directed graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:20.928136Z",
     "start_time": "2019-10-30T14:59:20.921970Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "graph = painters(metadata=True)\n",
    "adjacency = graph.adjacency\n",
    "position = graph.position\n",
    "names = graph.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "picasso = 0\n",
    "monet = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:21.914536Z",
     "start_time": "2019-10-30T14:59:21.905073Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dirichlet = Dirichlet()\n",
    "values = {picasso: 0, monet: 1}\n",
    "values_pred = dirichlet.fit_predict(adjacency, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:23.258084Z",
     "start_time": "2019-10-30T14:59:23.253238Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_graph(adjacency, position, names, scores=values_pred, seeds=values)\n",
    "SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Bipartite graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:25.167485Z",
     "start_time": "2019-10-30T14:59:25.160764Z"
    },
    "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": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:25.606792Z",
     "start_time": "2019-10-30T14:59:25.600687Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dirichlet = Dirichlet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:25.852940Z",
     "start_time": "2019-10-30T14:59:25.849197Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "drive = 3\n",
    "aviator = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:26.164425Z",
     "start_time": "2019-10-30T14:59:26.153489Z"
    },
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "values_row = {drive: 0, aviator: 1}\n",
    "dirichlet.fit(biadjacency, values_row)\n",
    "values_row_pred = dirichlet.values_row_\n",
    "values_col_pred = dirichlet.values_col_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-30T14:59:27.324458Z",
     "start_time": "2019-10-30T14:59:27.319825Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=values_row_pred, scores_col=values_col_pred,\n",
    "                    seeds_row=values_row)\n",
    "SVG(image)\n"
   ]
  }
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