MAPF-ACO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Source Code for implementing ACO for the MAPF problem\n",
    "\n",
    "* Create algorithm for ACO (done)\n",
    "* Create test promles (done / more or less)\n",
    "* Create algorithm for time-expansion of the graph (working)\n",
    "* Create Multi-Agent-ACO solver\n",
    "\n",
    "Working Title: \"Cooperative Co-Evolution of Intent: Soving the Multi-Agent Path Finding Problem with Ant Colony Optimisation\"\n",
    "\n",
    "Abstract:\n",
    "* ACO is used to solve path finding problems in graphs\n",
    "* The pheromone-matrix represents the intent of an agent\n",
    "* A solution to the MAPF can be viewed as a state of consent of intentions\n",
    "* Two problems need to be solved: \n",
    "    * Time Extension of ACO\n",
    "    * Ants that consider other agents intent\n",
    "* Experiments: in progress\n",
    "* Works?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# install the required packages\n",
    "%pip install matplotlib pandas seaborn numpy networkx ipywidgets netgraph ipympl smac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "np.set_printoptions(threshold=np.inf, precision=3)\n",
    "import networkx as nx\n",
    "import ipywidgets as widgets\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NavigationProblem:\n",
    "    def __init__(self, start=0, goal=0, adjacency=None, pheromones=None):\n",
    "        self.adjacency = adjacency\n",
    "        self.pheromones = pheromones\n",
    "        self.start = start\n",
    "        self.goal = goal\n",
    "    \n",
    "    def set_pheromone_ones(self):\n",
    "        self.pheromones = np.ones_like(self.adjacency)\n",
    "        for i, j in np.ndindex(self.adjacency.shape):\n",
    "            if self.adjacency[i,j] == 0 and i != j:\n",
    "                self.pheromones[i,j] = 0\n",
    "    \n",
    "    def draw_adjacency(self):\n",
    "        G = nx.from_numpy_matrix(self.adjacency)\n",
    "        pos=nx.kamada_kawai_layout(G)\n",
    "        nx.draw(G,pos=pos,with_labels=True)\n",
    "        weight = nx.get_edge_attributes(G,'weight')\n",
    "        labels = { k : f\"{v:.1f}\" for k, v in weight.items()}\n",
    "        nx.draw_networkx_edge_labels(G,pos,edge_labels=labels) \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.watts_strogatz_graph(145,4, 0.01, seed=42)\n",
    "for e in G.edges():\n",
    "    G[e[0]][e[1]]['weight']= 0.5 + np.random.rand()\n",
    "problem_hard_1 = NavigationProblem(start=0, goal=75, adjacency=nx.adj_matrix(G).todense())\n",
    "problem_hard_1.set_pheromone_ones()\n",
    "\n",
    "G = nx.watts_strogatz_graph(42,4,0.05, seed=23)\n",
    "for e in G.edges():\n",
    "    G[e[0]][e[1]]['weight']= 0.5 + np.random.rand()\n",
    "problem_middle_1 = NavigationProblem(start=0, goal=27, adjacency=nx.adj_matrix(G).todense())\n",
    "problem_middle_1.set_pheromone_ones()\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_edge(0, 1, weight=1)\n",
    "G.add_edge(1, 2, weight=1.5)\n",
    "G.add_edge(1, 3, weight=1)\n",
    "G.add_edge(3, 5, weight=1)\n",
    "G.add_edge(1, 4, weight=3)\n",
    "G.add_edge(2, 5, weight=1)\n",
    "G.add_edge(4, 6, weight=1)\n",
    "G.add_edge(5, 7, weight=1)\n",
    "G.add_edge(6, 7, weight=3)\n",
    "G.add_edge(7, 8, weight=1)\n",
    "#G.add_edge(5, 9)\n",
    "problem_easy_1 = NavigationProblem(start = 0, goal = 8, adjacency = nx.adj_matrix(G).todense())\n",
    "problem_easy_1.set_pheromone_ones()\n",
    "\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_edge(0, 1, weight=1.0)\n",
    "G.add_edge(1, 2, weight=1.5)\n",
    "G.add_edge(1, 4, weight=1.0)\n",
    "problem_easy_2 = NavigationProblem(start = 0, goal = 3, adjacency = nx.adj_matrix(G).todense())\n",
    "problem_easy_2.set_pheromone_ones()\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_edge(0, 1, weight=1.0)\n",
    "G.add_edge(0, 2, weight=1.0)\n",
    "G.add_edge(1, 3, weight=1.0)\n",
    "G.add_edge(2, 3, weight=1.0)\n",
    "problem_easy_3 = NavigationProblem(start = 0, goal = 3, adjacency = nx.adj_matrix(G).todense())\n",
    "problem_easy_3.set_pheromone_ones()\n",
    "\n",
    "G = nx.Graph()\n",
    "G.add_edge(1, 2, weight=1.0)\n",
    "G.add_edge(2, 3, weight=1.0)\n",
    "G.add_edge(2, 4, weight=1.0)\n",
    "G.add_edge(4, 5, weight=1.0)\n",
    "G.add_edge(5, 7, weight=1.0)\n",
    "G.add_edge(7, 6, weight=1.0)\n",
    "G.add_edge(4, 6, weight=1.0)\n",
    "G.add_edge(6, 8, weight=1.0)\n",
    "problem_easy_4 = NavigationProblem(start = 0, goal = 7, adjacency = nx.adj_matrix(G).todense())\n",
    "problem_easy_4.set_pheromone_ones()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_pheromone_matrix(adjacency=None, pheromones=None, label=True, time=-1, width=15, height=15, thickness=3, show=True, prune=0, node_label=True, start=0, goal=0, size=1.0, cutoff=1e-10, pos=None):\n",
    "    plt.figure(figsize=(width,height))\n",
    "    matrix = None\n",
    "    if pheromones is not None:\n",
    "        matrix = pheromones.copy()\n",
    "    else:\n",
    "        matrix = adjacency.copy()\n",
    "    \n",
    "    for i, j in np.ndindex(matrix.shape):\n",
    "        if matrix[i, j] < cutoff:\n",
    "            matrix[i, j] = 0\n",
    "    G = nx.from_numpy_matrix(matrix, create_using=nx.DiGraph())\n",
    "    if pos is None:\n",
    "        Ga = nx.from_numpy_matrix(adjacency)\n",
    "        pos = nx.kamada_kawai_layout(Ga)\n",
    "    weight = nx.get_edge_attributes(G,'weight')\n",
    "    node_colors = [\"lightblue\" for _ in range(matrix.shape[0])]\n",
    "    node_colors[start] = \"red\"\n",
    "    node_colors[goal] = \"green\"\n",
    "    labels = { k : f\"{v:.2f}\" for k, v in weight.items()}\n",
    "    width = [(max(prune, w['weight']) - prune) * thickness /np.max(matrix) for u, v, w in G.edges(data=True)]\n",
    "    nx.draw_networkx_nodes(G,pos=pos, node_color=node_colors, node_size=[size * 300 * (1 + v) for v in np.diag(matrix)])\n",
    "    nx.draw_networkx_edges(G,pos, width=width, alpha=0.3, connectionstyle='arc3,rad=0.2', arrowstyle=\"-|>\", arraowsize=thickness*3)\n",
    "    if node_label:\n",
    "        node_labels=None\n",
    "        if label:\n",
    "            node_labels = {k : f\"{np.diag(matrix)[k]:.2f}\" for k in range(matrix.shape[0])}\n",
    "        nx.draw_networkx_labels(G, pos=pos, labels=node_labels)\n",
    "\n",
    "    if label:\n",
    "        nx.draw_networkx_edge_labels(G,pos,edge_labels=labels, label_pos=0.3)\n",
    "        \n",
    "    if show:\n",
    "        plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness_proportional_selection(probs, random=np.random.rand()):\n",
    "    s = sum(probs.values())\n",
    "    pick = random * s\n",
    "    current = 0\n",
    "    for key in sorted(probs):\n",
    "        current += probs[key]\n",
    "        if current >= pick:\n",
    "            return key\n",
    "    print(\"selection could not pick an item - should not happen!\")\n",
    "    return probs[-1]\n",
    "        \n",
    "\n",
    "class Ant:\n",
    "    def __init__(self, start = 0, goal = None, pheromones = None, adjacency = None, seed = None):\n",
    "        self.seed = seed\n",
    "        self.random = np.random.RandomState(seed=seed)\n",
    "        self.start = start\n",
    "        self.goal = goal\n",
    "        self.pheromones = pheromones\n",
    "        self.adjacency = adjacency\n",
    "        self.stuck = False\n",
    "        self.selection = self.ant_system_selection\n",
    "        self.forward = True\n",
    "        \n",
    "        self.setup()\n",
    "        \n",
    "        \n",
    "    def evaporate(self, gamma = .99):\n",
    "        self.pheromones = np.multiply(self.pheromones, gamma, out=self.pheromones)\n",
    "        \n",
    "    def normalize(self, value = 0.5):\n",
    "        factor = 1 * self.pheromones.shape[0] / np.sum(self.pheromones)\n",
    "        np.multiply(self.pheromones, factor, out=self.pheromones)\n",
    "        \n",
    "    def delayed_update(self):\n",
    "        amount = 1/self.distance\n",
    "        for i in range(len(self.path)-1):\n",
    "            self.pheromones[self.path[i], self.path[i+1]] += amount #/ self.adjacency[self.path[i], self.path[i+1]]\n",
    "    \n",
    "    def random_selection(self, new):\n",
    "        return self.random.choice(new)\n",
    "    \n",
    "    def value(self, i, j, alpha=0.5, beta=0.5, **kwargs):\n",
    "        if not self.forward:\n",
    "            i, j = j, i\n",
    "        d = self.adjacency[i,j]\n",
    "        return np.max([np.finfo(float).eps,self.pheromones[i,j]])**alpha * (1/d)**beta\n",
    "    \n",
    "    def ant_system_selection(self,state, new, **kwargs):\n",
    "        probs = {k : self.value(state, k, **kwargs) for k in new }\n",
    "        return fitness_proportional_selection(probs, self.random.rand())\n",
    "        \n",
    "    \n",
    "    def get_time(self):\n",
    "        return len(self.path)-1\n",
    "    \n",
    "    def roulette_wheel_selection(self, state ,new, probs = None):\n",
    "        if probs is None:\n",
    "            probs = {k : self.pheromones[state, k] for k in new }\n",
    "        return fitness_proportional_selection(probs, self.random.rand())\n",
    "\n",
    "    def setup(self, start=None, goal=None, invert_direction=False, **kwargs):\n",
    "        if start:\n",
    "            self.start = start\n",
    "        if goal:\n",
    "            self.goal = goal\n",
    "            \n",
    "        if invert_direction:\n",
    "            self.start, self.goal = self.goal, self.start\n",
    "            self.forward = not self.forward\n",
    "        self.stuck = False\n",
    "        self.state = self.start\n",
    "        self.path = [self.start]\n",
    "        self.distance = 0\n",
    "            \n",
    "    def step(self, selection=None, allow_revisit=False, allow_wait=False, **kwargs):\n",
    "        if selection:\n",
    "            self.selection = selection\n",
    "        if not self.selection:\n",
    "            self.selection = self.ant_system_selection\n",
    "        \n",
    "        # compute next possible states $new\n",
    "        new = [node for node in range(len(self.adjacency))]\n",
    "        if not (allow_revisit and self.forward):\n",
    "            new = [node for node in new if not (node in self.path)]\n",
    "        \n",
    "        new = [node for node in new if not (self.adjacency[self.state, node] == 0)]\n",
    "        \n",
    "        if allow_wait and self.forward:\n",
    "            new.append(self.state)\n",
    "        \n",
    "        # in case no more states are possible: return\n",
    "        if not new:\n",
    "            self.stuck=True\n",
    "            return\n",
    "        \n",
    "        # pick a new state from the list\n",
    "        self.state = self.selection(self.state, new, forward=self.forward, **kwargs)\n",
    "        self.path.append(self.state)\n",
    "        d = self.adjacency[self.path[-2], self.path[-1]]\n",
    "        #print(f\"current distance: {d}\")\n",
    "        self.distance += d\n",
    "        \n",
    "\n",
    "class ACO:\n",
    "    def __init__(self, start = 0, goal = None, pheromones = None, adjacency = None, seed = None, num_paths=100, **kwargs):\n",
    "        self._pos = None # for saving the graph layout (plotting)\n",
    "        self.seed = seed\n",
    "        self._num_paths = num_paths\n",
    "        self.random = np.random.RandomState(seed=seed)\n",
    "        self.start = start\n",
    "        self.goal = goal\n",
    "        self.adjacency = adjacency\n",
    "        self.paths = []\n",
    "        if pheromones is not None:\n",
    "            self.pheromones = pheromones\n",
    "        else:\n",
    "            self.pheromones = np.ones_like(self.adjacency)\n",
    "        self.best_distance = np.inf\n",
    "        self.best_path = [start]\n",
    "        self.kwargs = kwargs\n",
    "        assert self.pheromones.shape == self.adjacency.shape\n",
    "        for i in range(self.adjacency.shape[0]):\n",
    "            for j in range(self.adjacency.shape[1]):\n",
    "                if self.adjacency[i, j] == 0:\n",
    "                    self.pheromones[i, j] = 0\n",
    "        \n",
    "        self.pheromone_timeline = [self.pheromones.copy()]\n",
    "        self.distance_timeline = [self.best_distance]\n",
    "        self.create_ants()\n",
    "\n",
    "        \n",
    "    def create_ants(self, num_ants = 10):\n",
    "        self.ants = [Ant(start=self.start, goal=self.goal, pheromones=self.pheromones, adjacency=self.adjacency, seed=self.random.randint(2**32)) for _ in range(num_ants)]\n",
    "   \n",
    "    def online_pheromone_update(self, **kwargs):\n",
    "        for ant in self.ants:\n",
    "            amount = 1 / self.adjacency[ant.path[-2],ant.path[-1]]\n",
    "            if ant.forward:\n",
    "                self.pheromones[ant.path[-2], ant.path[-1]] += amount\n",
    "            else:\n",
    "                self.pheromones[ant.path[-1], ant.path[-2]] += amount\n",
    "                \n",
    "            \n",
    "    def delayed_online_pheromone_update(self, **kwargs):\n",
    "        for ant in self.ants:\n",
    "            if ant.state == ant.goal:\n",
    "                amount = 1 / ant.distance\n",
    "                path = ant.path\n",
    "                if not ant.forward:\n",
    "                    path.reverse()\n",
    "                for i in range(len(path) - 1):\n",
    "                    self.pheromones[path[i], path[i+1]] += amount\n",
    "    \n",
    "    def penalize_stuck(self, stuck_penalty=0.1, **kwargs):\n",
    "        for ant in self.ants:\n",
    "            if ant.stuck:\n",
    "                self.pheromones[ant.path[-2], ant.path[-1]] += -stuck_penalty\n",
    "        \n",
    "    def evaporate(self, gamma = .99):\n",
    "        self.pheromones = np.multiply(self.pheromones, gamma, out=self.pheromones)\n",
    "        \n",
    "    def normalize(self, gamma = 1, normalize_eps=0, **kwargs):\n",
    "        self.pheromones -= normalize_eps\n",
    "        np.clip(np.inf, 0, self.pheromones, out=self.pheromones)\n",
    "        factor = gamma * self.pheromones.shape[0] / np.sum(self.pheromones)\n",
    "        np.multiply(self.pheromones, factor, out=self.pheromones)\n",
    "        \n",
    "        \n",
    "    def step(self, **kwargs):\n",
    "        for ant in self.ants:\n",
    "            ant.step(**kwargs)\n",
    "\n",
    "    def daemon_actions(self, reverse_ants=True, **kwargs):\n",
    "        \"\"\"perform daemon actions\n",
    "        * Reset ants that have arrived\n",
    "        \n",
    "        Keyword Arguments:\n",
    "        reverse_ants -- if True the goal and start will be swapped before the ant is reset\n",
    "        \"\"\"\n",
    "        for ant in self.ants:\n",
    "            if ant.stuck:\n",
    "                ant.setup()\n",
    "            if ant.state == ant.goal:\n",
    "                if not ant.forward: \n",
    "                    ant.path.reverse()\n",
    "                self.paths.append(ant.path)\n",
    "                if len(self.paths) > self._num_paths:\n",
    "                    self.paths = self.paths[-self._num_paths:]\n",
    "                if ant.distance < self.best_distance:\n",
    "                    self.best_distance = ant.distance\n",
    "                    self.best_path = ant.path\n",
    "                ant.setup(invert_direction=reverse_ants)\n",
    "        self.distance_timeline.append(self.best_distance)\n",
    "    \n",
    "    \n",
    "    def update_pheromones(self, delayed_update=True, evaporation_method=\"normalize\", log_pheromones=True, penalize_stuck=False, elitist_update=False, **kwargs):\n",
    "        \"\"\"updates the pheromone matrix\n",
    "    \n",
    "        Keyword Arguments:\n",
    "        delayed_update -- bool telling to perform the pheromone update after each step or when the ant reaches the goal\n",
    "        evaporation_method -- tell the algorithm how to perform evaporation (\"normalize\" or \"evaporate\")\n",
    "        gamma -- rate of evaporation (depends on evaporation_method)\n",
    "        \"\"\"\n",
    "        if delayed_update:\n",
    "            self.delayed_online_pheromone_update(**kwargs)\n",
    "        else:\n",
    "            self.online_pheromone_update(**kwargs)\n",
    "        \n",
    "        if elitist_update:\n",
    "            amount = 1 / self.best_distance / len(self.ants)\n",
    "            for i in range(len(self.best_path) - 1):\n",
    "                self.pheromones[self.best_path[i], self.best_path[i+1]] += amount\n",
    "                \n",
    "        \n",
    "        # evaporation\n",
    "        if evaporation_method == \"normalize\":\n",
    "            self.normalize(**kwargs)\n",
    "        elif evaporation_method == \"evaporate\":\n",
    "            self.evaporate(**kwargs)\n",
    "        \n",
    "        if penalize_stuck:\n",
    "            self.penalize_stuck(**kwargs)\n",
    "        \n",
    "        #np.clip( self.pheromones,0, np.inf, out=self.pheromones)\n",
    "        if log_pheromones:\n",
    "            self.pheromone_timeline.append(self.pheromones.copy())\n",
    "            \n",
    "            \n",
    "    def run(self, generations=1000, **kwargs):\n",
    "        self.kwargs.update(kwargs)\n",
    "        for generation in range(generations):\n",
    "            self.step(**self.kwargs)\n",
    "            self.update_pheromones(**self.kwargs)\n",
    "            self.daemon_actions(**self.kwargs)\n",
    "    \n",
    "    def widget_pheromones(self):\n",
    "        w = widgets.interact(self.draw_pheromones,\n",
    "                 label=False,\n",
    "                 width=15, height=15,\n",
    "                 show=widgets.fixed(True),\n",
    "                 prune=widgets.FloatSlider(value=0.0, min=0.0, max=0.50, step=0.05),\n",
    "                 generation=widgets.IntSlider(min=-1,max=len(self.pheromone_timeline)-1,step=1,value=-1),\n",
    "                 thickness=widgets.FloatLogSlider(value=3),\n",
    "                 size=widgets.fixed(1)\n",
    "                );\n",
    "        display(w)\n",
    "    \n",
    "    def get_graph_layout(self):\n",
    "        if self._pos is None:\n",
    "            Ga = nx.from_numpy_matrix(self.adjacency)\n",
    "            self._pos = nx.kamada_kawai_layout(Ga)\n",
    "        return self._pos\n",
    "            \n",
    "    def draw_pheromones(self, generation=-1, **kwargs):\n",
    "        print(f\"Best Distance Found: {self.distance_timeline[generation]}\\n{self.best_path}\")\n",
    "        draw_pheromone_matrix(pheromones=self.pheromone_timeline[generation], adjacency=self.adjacency, start=self.start, goal=self.goal, pos=self.get_graph_layout(), **kwargs)\n",
    "        \n",
    "    \n",
    "    def draw_adjacency(self, **kwargs):\n",
    "        draw_pheromone_matrix(adjacency=self.adjacency, start=self.start, goal=self.goal, **kwargs)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Distance Found: 13.018509592196637\n",
      "[75, 77, 79, 80, 82, 123, 122, 121, 119, 120, 141, 143, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aco = ACO(start=problem_hard_1.start, goal=problem_hard_1.goal, pheromones=problem_hard_1.pheromones.copy(), adjacency=problem_hard_1.adjacency.copy(), seed=43)\n",
    "aco.run(generations=600, elitst_update=True, alpha=0.8, beta=0.2, reverse_ants=True, penalize_stuck=True)\n",
    "aco.draw_pheromones(label=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aco.widget_pheromones()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time extension and foreign Agent ... of ACO\n",
    "\n",
    "### Time Extension\n",
    "Time extension works by unrolling the pheromone matrix throughout time. In each time step t the pheromone matrix $\\mathcal{P}(t)$ is used for the pheromone update.\n",
    "The values $\\tau_{ij, t}$ are updated after an ant reaches the goal. In the time extended form, circular paths (in space) and waiting is also allowed.\n",
    "\n",
    "There are two decision rules governing the behaviour of the ants. On its way from start to goal the ant follows the (time-)global pheromone matrix $P$, the time extended pheromone matrix $\\mathcal{P}(t)$ and the foreign time extended pheromones at the current timestep $\\mathcal{P}_{f}(t)$.\n",
    "As paths can be simliar through time the actual matrix used instead of $\\mathcal{P}(t)$ is $\\sum_{i=t-k}^{t+k} \\mathcal{P}(i)$ with $k=3$.\n",
    "\n",
    "#### Decision Rule\n",
    "\n",
    "\\begin{equation}\n",
    "v = \\color{blue}{\\big(\\frac{1}{p_{c} + \\varepsilon_f}\\big)^{\\alpha_f}}\\cdot \\Big(\\sum_{i=t-k}^{t+k} \\mathcal{P}(i)\\Big)_{ij}^{\\alpha} \\cdot \\tau_{ij}^{\\beta} + \\varepsilon\n",
    "\\end{equation}\n",
    "\n",
    "#### Update Rule\n",
    "Each node in the path will get this amount of pheromones:\n",
    "\\begin{equation}\n",
    "v = \\big( \\frac{c_t |\\mathcal{T}|}{t_{goal}} + \\frac{c_d}{\\sum{d_e}} + \\color{blue}{\\frac{c_f}{\\sum{\\mathcal{P}_f(t)_{ij}}}} \\big)\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implementation of TimeExtendedACO\n",
    "class TimeExtendedACO(ACO):\n",
    "    def __init__(self, timeframe=100, prerun_generations=1000, problem=None, seed=None, **kwargs):\n",
    "        ACO.__init__(self, adjacency=problem.adjacency.copy(), pheromones=problem.pheromones.copy(), start=problem.start, goal=problem.goal, seed=seed, **kwargs)\n",
    "        self.node_count = np.zeros((self.adjacency.shape[0], timeframe), dtype=int)\n",
    "        self.node_sucess = np.zeros_like(self.node_count, dtype=int)\n",
    "        self.df_convergence = pd.DataFrame()\n",
    "        self.df_fitness = pd.DataFrame()\n",
    "        #print(f\"prerun for {prerun_generations} generations\")\n",
    "        self.run(generations=prerun_generations, **kwargs)\n",
    "        self.extended_pheromones = np.ones((timeframe, self.pheromones.shape[0], self.pheromones.shape[1]))\n",
    "        self.timeframe = timeframe\n",
    "        self.best_path = []\n",
    "        self.best_distance = np.inf\n",
    "        self.iteration = 0\n",
    "        # create new ants outside of the current graph\n",
    "        self.create_ants()\n",
    "        self.arrived = 0\n",
    "        self.timeout = 0\n",
    "        for ant in self.ants:\n",
    "            ant.forward = True\n",
    "            ant.selection = self.tAco_selection\n",
    "            ant.penalty = np.zeros_like(self.pheromones)\n",
    "        #for t in range(timeframe):\n",
    "            #self.extended_pheromones[t] = self.pheromones\n",
    "    \n",
    "    def edge_collision_pheromones(self, i, j, foreign_pheromones=None):\n",
    "        return foreign_pheromones[j, i] + foreign_pheromones[i, j]\n",
    "    \n",
    "    def node_collision_pheromones(self, j, foreign_pheromones=None):\n",
    "        return np.sum(foreign_pheromones[:,j])\n",
    "    \n",
    "    def compute_p_foreign(self, i, j, foreign_pheromones=None, edge_collision_factor=3.0, forward=True):\n",
    "        #ant has to go forward and foreign pheromones need to be present\n",
    "        if not forward or foreign_pheromones is None:\n",
    "            return 1.0\n",
    "        \n",
    "        p_foreign = self.node_collision_pheromones(j, foreign_pheromones=foreign_pheromones)\n",
    "        #backward edge- no edge collisions\n",
    "        p_foreign += edge_collision_factor * self.edge_collision_pheromones(i, j, foreign_pheromones=foreign_pheromones)\n",
    "        return p_foreign\n",
    "        \n",
    "    \n",
    "    def tAco_selection_value(self, i, j, edge_collision_factor=3, extended_pheromones=None, foreign_pheromones=None, alpha=0.5, eps=0.0001, beta=0.5, forward=True, alpha_foreign=1, eps_foreign=None, **kwargs):\n",
    "        \"\"\"computes the fitness value of a particular edge\n",
    "        parameters:\n",
    "        i -- from\n",
    "        j -- to\n",
    "        \n",
    "        extended_pheromones -- time extended pheromone matrix\n",
    "        alpha -- weight of the pheromone matrix\n",
    "        eps -- fixed value added to all the options (high eps -> random walk)\n",
    "        beta -- weight of the extended pheromone matrix\n",
    "        forward -- if False, i and j are swapped\n",
    "        \"\"\"\n",
    "        if not forward:\n",
    "            #print(\"test\")\n",
    "            i, j = j, i\n",
    "            \n",
    "        if eps_foreign is None:\n",
    "            eps_foreign = eps\n",
    "            \n",
    "        p = np.max([np.finfo(float).eps, self.pheromones[i, j]])\n",
    "        p_ext = np.max([np.finfo(float).eps, extended_pheromones[i, j]])\n",
    "        p_foreign = self.compute_p_foreign(i, j, foreign_pheromones=foreign_pheromones, edge_collision_factor=edge_collision_factor, forward=forward)\n",
    "        #print(f\"tAcoSelection_value- {i}, {j}: p: {p}, p_ext: {p_ext}, p_foreign:{p_foreign},  {p**alpha * p_ext**beta * (1 / (eps + p_foreign) ** alpha_foreign) + eps}\")\n",
    "        return  p**alpha * p_ext**beta * (1 / (eps_foreign + p_foreign**alpha_foreign)) + eps\n",
    "    \n",
    "    def extended_pheromones_sum(self):\n",
    "        \"\"\"normalized sum (over all time steps) of the time extended pheromone values\"\"\"\n",
    "        return self.normalize_(np.sum(self.extended_pheromones, axis=0))\n",
    "    \n",
    "    def extended_pheromone_convolution(self, t, length=3, function=lambda x: 2 / abs(x + 1), argument=None):\n",
    "        if argument is None:\n",
    "            argument = self.extended_pheromones\n",
    "        matrix = np.zeros_like(argument[t])\n",
    "        for x in range(max(0, t-length), t+length):\n",
    "            matrix += argument[x] * function(x)\n",
    "        return self.normalize_(matrix)\n",
    "    \n",
    "    def tAco_selection(self, state, new, time=0, forward=True, foreign_pheromones=None, **kwargs):\n",
    "        \"\"\"select the next action based on time extended pheromones\n",
    "        forward: select based on current time-step pheromone matrix\n",
    "        backward: select based on sum of time extended pheromones\n",
    "        \"\"\"\n",
    "        #print(f\"tAcoSelection- state: {state}, new: {new}, time: {time}\")\n",
    "        probs = {}\n",
    "        if forward:\n",
    "            if foreign_pheromones is None:\n",
    "                probs = {k : self.tAco_selection_value(state, k, extended_pheromones=self.extended_pheromone_convolution(time), **kwargs) for k in new }\n",
    "            else:\n",
    "                #print(\"there are foreign pheromones in tAco_selection\")\n",
    "                probs = {k : self.tAco_selection_value(state, k, extended_pheromones=self.extended_pheromone_convolution(time), foreign_pheromones=foreign_pheromones[time], **kwargs) for k in new }\n",
    "        else:\n",
    "            probs = {k : self.tAco_selection_value(state, k, extended_pheromones=self.extended_pheromones_sum(), forward=forward, **kwargs) for k in new }\n",
    "        \n",
    "        return fitness_proportional_selection(probs, random=self.random.rand())\n",
    "        #selected = fitness_proportional_selection(probs, random=self.random.rand())\n",
    "        #print(f\"{selected} -- {probs}\")\n",
    "        #return selected\n",
    "    \n",
    "    def evaporate(self, gamma = .99):\n",
    "        self.pheromones = np.multiply(self.pheromones, gamma, out=self.pheromones)\n",
    "        \n",
    "    def normalize_(self, pheromones, gamma = 1, normalize_eps=0.0, **kwargs):\n",
    "        x = np.clip(np.inf, 0, pheromones - normalize_eps)\n",
    "        factor = gamma * x.shape[0] / np.sum(x)\n",
    "        return x * factor\n",
    "    \n",
    "    def extended_step(self, **kwargs):\n",
    "        for ant in self.ants:\n",
    "            assert (ant.start == self.start) or not ant.forward\n",
    "            if ant.forward:\n",
    "                ant.step(selection=self.tAco_selection, allow_revisit=True, allow_wait=True, **kwargs)\n",
    "            else:\n",
    "                ant.step(selection=self.tAco_selection, allow_revisit=False, allow_wait=False, **kwargs)\n",
    "            \n",
    "    def run_extended(self, generations=1000, foreign_pheromones=None, show_progress=False,**kwargs):\n",
    "        if show_progress:\n",
    "            progress_bar = widgets.IntProgress(min=0, max=generations, description=f\"computing {generations} generations\")\n",
    "            display(progress_bar)\n",
    "        self.kwargs.update(kwargs)\n",
    "        data = [self.df_convergence]\n",
    "        for generation in range(generations):\n",
    "            if show_progress:\n",
    "                progress_bar.value = generation\n",
    "            self.iteration += 1\n",
    "            #if self.iteration % 1000 == 0:\n",
    "                #print(f\"iteration {self.iteration} --  arrived: {self.arrived}, timeout: {self.timeout}, ratio: {self.arrived / (self.arrived + self.timeout)}\")\n",
    "            self.extended_step(foreign_pheromones=foreign_pheromones, **self.kwargs)\n",
    "            self.extended_update_pheromones(foreign_pheromones=foreign_pheromones, **self.kwargs)\n",
    "            self.extended_daemon_actions(foreign_pheromones=foreign_pheromones, **self.kwargs)\n",
    "            data.append(pd.DataFrame({\"iteration\":self.iteration, \"arrived\": self.arrived, \"timout\":self.timeout, \"shortest\":self.best_distance}, index=[self.iteration]))\n",
    "        self.df_convergence = pd.concat(data)\n",
    "            \n",
    "    def extended_daemon_actions(self, reverse_ants=True, penalty_factor=0.01, depenalize_long_running=True, **kwargs):\n",
    "        \"\"\"perform daemon actions\n",
    "        * Reset ants that have arrived\n",
    "        \n",
    "        Keyword Arguments:\n",
    "        reverse_ants -- if True the goal and start will be swapped before the ant is reset\n",
    "        \"\"\"\n",
    "        for ant in self.ants:\n",
    "            if ant.get_time() >= self.timeframe:\n",
    "                #print(f\"ant did not arrive {ant.path}\")\n",
    "                if depenalize_long_running:\n",
    "                    self.depenalize_path(ant, penalty_factor)\n",
    "                ant.setup()\n",
    "                self.timeout += 1\n",
    "                \n",
    "            if ant.stuck:\n",
    "                if ant.forward:\n",
    "                    print(\"forward, should not be stuck\")\n",
    "                ant.setup()\n",
    "            if ant.state == ant.goal:\n",
    "                if ant.forward:\n",
    "                    self.arrived += 1\n",
    "                    self.paths.append(ant.path)\n",
    "                    if len(self.paths) > self._num_paths:\n",
    "                        self.paths = self.paths[-self._num_paths:]\n",
    "                    for t in range(len(ant.path)):\n",
    "                        self.node_count[ant.path[t],t] += 1\n",
    "                    #print(f\"ant arrived afer {ant.get_time()} steps: {ant.path} \")\n",
    "                if ant.distance < self.best_distance:\n",
    "                    self.best_distance = ant.distance\n",
    "                    self.best_path = ant.path\n",
    "                ant.setup(invert_direction=reverse_ants)\n",
    "        self.distance_timeline.append(self.best_distance)\n",
    "    \n",
    "    def been_there_done_that_penalty(self, path):\n",
    "        \"\"\"add fixed amount of penalty for every edge and every node visited\"\"\"\n",
    "        penalty = np.zeros_like(self.pheromones)\n",
    "        for n1, n2 in zip(path[1:], path[:-1]):\n",
    "            penalty[n1, n2] = -1\n",
    "            penalty[n2, n1] = -1\n",
    "            #penalty[n1, n1] = -1\n",
    "        return penalty\n",
    "            \n",
    "    \n",
    "    def extended_online_pheromone_update(self, penalty_factor = 0.01, **kwargs):\n",
    "        \"\"\"lower the pheromones in future nodes, where the ant has alreadd visited in the past\"\"\"\n",
    "        for ant in self.ants:\n",
    "            if ant.forward:\n",
    "                for t in range(ant.get_time(), self.timeframe):\n",
    "                    self.extended_pheromones[t] = self.extended_pheromones[t] + penalty_factor * self.been_there_done_that_penalty(ant.path)\n",
    "    \n",
    "    def depenalize_path(self, ant, penalty_factor, **kwargs):\n",
    "        \"\"\"reverse the penalty of an ant\"\"\"\n",
    "        for t in range(0,ant.get_time()):\n",
    "            for t2 in range(t, self.timeframe):\n",
    "                penalty = penalty_factor * self.been_there_done_that_penalty(ant.path[:t])\n",
    "                self.extended_pheromones[t2] -= penalty\n",
    "                    \n",
    "                    \n",
    "        \n",
    "    \n",
    "    def extended_delayed_online_pheromone_update(self, c_d=1.0, c_t=0.5, c_f=1.0, c_amount=10, penalty_factor = 0.01, foreign_pheromones = None, edge_collision_factor = 3.0, **kwargs):\n",
    "        \"\"\"reverse the penalty for moving backward and put more pheromones on the path\"\"\"\n",
    "        for ant in self.ants:\n",
    "            if ant.forward and ant.state==ant.goal:\n",
    "                # in case the goal is reached, reverse the penalty for moving backwards, for all time steps\n",
    "                self.depenalize_path(ant, penalty_factor)\n",
    "                path = ant.path + [ant.goal for _ in range(ant.get_time(), self.timeframe)]\n",
    "                foreign_pheromone_penalty = 0\n",
    "                if foreign_pheromones is not None:\n",
    "                    for t in range(0,self.timeframe - 1):\n",
    "                        foreign_pheromone_penalty += self.compute_p_foreign(path[t], path[t+1], foreign_pheromones=foreign_pheromones[t], edge_collision_factor=edge_collision_factor)\n",
    "                else:\n",
    "                    c_f = 0\n",
    "                amount = c_amount * (c_d / ant.distance +  c_t * self.timeframe/ant.get_time() + c_f / (1 + foreign_pheromone_penalty))\n",
    "                self.df_fitness = self.df_fitness.append({\"amount\": amount, \"distance_value\":ant.distance, \"time_value\":ant.get_time(), \"collision_value\":1/ (1 + foreign_pheromone_penalty), \"distance\": c_d / ant.distance, \"time\" : c_t * self.timeframe/ant.get_time(), \"collision\": c_f / (1 + foreign_pheromone_penalty), \"iteration\": self.iteration}, ignore_index=True)\n",
    "                for t in range(0,self.timeframe):\n",
    "                    # put pheromones on the path depending on path fitness\n",
    "                    self.extended_pheromones[t][path[t], path[t+1]] += amount\n",
    "            elif not ant.forward and ant.state==ant.goal:\n",
    "                amount = 1 / ant.distance\n",
    "                # put pheromone on the backward edges of the (backward) path\n",
    "                for i, j in zip(ant.path[:-1], ant.path[1:]):\n",
    "                    self.pheromones[j, i] += amount\n",
    "                \n",
    "\n",
    "    def extended_update_pheromones(self, evaporation_method=\"normalize\", normalize_eps=0.0, log_pheromones=True, penalize_stuck=False, elitist_update=False, **kwargs):\n",
    "        \"\"\"updates the pheromone matrix\n",
    "    \n",
    "        Keyword Arguments:\n",
    "        delayed_update -- bool telling to perform the pheromone update after each step or when the ant reaches the goal\n",
    "        evaporation_method -- tell the algorithm how to perform evaporation (\"normalize\" or \"evaporate\")\n",
    "        gamma -- rate of evaporation (depends on evaporation_method)\n",
    "        \"\"\"\n",
    "        \n",
    "        # the online pheromone update penalizes already visited nodes in future time-steps the penalty is lifted once the ant reaches the goal\n",
    "        self.extended_online_pheromone_update(**kwargs)\n",
    "        \n",
    "        # the delayed update reverses the penalty once the ant reaches the goal and puts extra pheromones on the travelled path (depending on fitness)\n",
    "        self.extended_delayed_online_pheromone_update(**kwargs)\n",
    "        \n",
    "        if elitist_update:\n",
    "            amount = 1 / self.best_distance / self.num_ants\n",
    "            for i in range(len(self.best_path) - 1):\n",
    "                if ant.forward:\n",
    "                    self.pheromones[self.best_path[i], self.best_path[i+1]] += amount\n",
    "        \n",
    "        # evaporation\n",
    "        if evaporation_method == \"normalize\":\n",
    "            self.pheromones = self.normalize_(self.pheromones, normalize_eps=normalize_eps)\n",
    "            for t in range(self.timeframe):\n",
    "                self.extended_pheromones[t] = self.normalize_(self.extended_pheromones[t], normalize_eps=normalize_eps)\n",
    "        elif evaporation_method == \"evaporate\":\n",
    "            self.evaporate(**kwargs)\n",
    "        \n",
    "        if penalize_stuck:\n",
    "            self.penalize_stuck(**kwargs)\n",
    "        \n",
    "        #np.clip( self.pheromones,0, np.inf, out=self.pheromones)\n",
    "        if log_pheromones:\n",
    "            self.pheromone_timeline.append(self.pheromones.copy())\n",
    "            \n",
    "        \n",
    "    def draw_extended_pheromones(self, time=-1, **kwargs):\n",
    "        if time >= 0:\n",
    "            matrix = self.extended_pheromones[time]\n",
    "        else:\n",
    "            matrix = self.extended_pheromones_sum()\n",
    "        \n",
    "        return draw_pheromone_matrix(adjacency=self.adjacency, pheromones=matrix, start=self.start, goal=self.goal, **kwargs)\n",
    "    \n",
    "    def plot_convergence(self, show=True, order=2):\n",
    "        sns.lmplot(data=self.df_convergence.loc[self.df_convergence.iteration % 100 == 0], x=\"iteration\", y=\"arrived\", order=order)\n",
    "        if show:\n",
    "            plt.show()\n",
    "    \n",
    "    def widget_extended_pheromones(self):\n",
    "        w = widgets.interact(self.draw_extended_pheromones,\n",
    "                 label=False,\n",
    "                 width=15, height=15,\n",
    "                 show=widgets.fixed(True),\n",
    "                 prune=widgets.FloatSlider(value=0.0, min=0.0, max=0.50, step=0.05),\n",
    "                 time=widgets.IntSlider(min=-1,max=self.timeframe-1,step=1,value=-1),\n",
    "                 thickness=widgets.FloatLogSlider(value=3, base=10, min=-5, max=5)\n",
    "                )\n",
    "        display(w)\n",
    "    \n",
    "    def path_to_dist(self, path):\n",
    "        value = 0.0\n",
    "        for n1, n2 in zip(path[:-1],path[1:]):\n",
    "            value += self.adjacency[n1, n2]\n",
    "        return value\n",
    "        \n",
    "        \n",
    "    def get_path_data(self):\n",
    "        data = [{\"path\": str(path), \"length\": len(path), \"distance\": self.path_to_dist(path)} for path in self.paths]\n",
    "        return pd.DataFrame(data)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Distance Found: 6.088383111981386\n",
      "[]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2ca3ca8703a46508f3ec1f0aad32ba6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=0, description='computing 1000 generations', max=1000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tAco = TimeExtendedACO(prerun_generations=100, problem=problem_middle_1, timeframe=40, normalize_eps=0.001, elitist_update=False, seed=43)\n",
    "tAco.draw_pheromones(label=False)\n",
    "tAco.run_extended(generations=1000, depenalize_long_running=False, alpha=0.5, beta=0.5, eps=0.001, penalty_factor=0.00, normalize_eps=0, elitist_update=False, show_progress=True)\n",
    "tAco.plot_convergence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3ac4af3f0724b59a16071b41a531ccc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntProgress(value=0, description='computing 1500 generations', max=1500)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tAco.run_extended(generations=1500, show_progress=True)\n",
    "sns.lmplot(data=tAco.df_convergence.loc[tAco.df_convergence.iteration % 100 == 0], x=\"iteration\", y=\"arrived\", order=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "771f6b9a500742c6a6b5376838923475",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=-1, description='generation', max=2600, min=-1), Checkbox(value=False, d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " tAco.widget_pheromones()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10a3698adc1142dd8b93ac6d4b0a06d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=-1, description='time', max=39, min=-1), Checkbox(value=False, descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function ipywidgets.widgets.interaction._InteractFactory.__call__.<locals>.<lambda>(*args, **kwargs)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " tAco.widget_extended_pheromones()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiAgentTACO:\n",
    "    def __init__(self, problem=None, agents=None, timeframe=100, seed=None, **kwargs):\n",
    "        self.seed = seed\n",
    "        self.random = np.random.RandomState(seed=seed)\n",
    "        self.agents = agents\n",
    "        self.adjacency = problem.adjacency.copy()\n",
    "        self.timeframe = timeframe\n",
    "        self.iteration = 0\n",
    "        self.kwargs = kwargs\n",
    "        for agent in self.agents:\n",
    "            agent[\"problem\"] = NavigationProblem(start=agent[\"start\"], goal=agent[\"goal\"], pheromones=problem.pheromones, adjacency=problem.adjacency)\n",
    "            agent[\"seed\"] = self.random.randint(2**32)\n",
    "            agent[\"tAco\"] = TimeExtendedACO(timeframe=timeframe, problem=agent[\"problem\"], seed=agent[\"seed\"], **kwargs)\n",
    "            for t in range(timeframe):\n",
    "                agent[\"tAco\"].extended_pheromones[t] = agent[\"tAco\"].pheromones.copy()\n",
    "        \n",
    "    def pheromone_sum(self):\n",
    "        pheromones = np.zeros_like(self.agents[0][\"tAco\"].extended_pheromones)\n",
    "        for agent in self.agents:\n",
    "            pheromones += agent[\"tAco\"].extended_pheromones\n",
    "        return pheromones\n",
    "        \n",
    "    \n",
    "    def run(self, show_progress=False, generations=1000, **kwargs):\n",
    "        self.kwargs.update(kwargs)\n",
    "        if show_progress:\n",
    "            progress_bar = widgets.IntProgress(min=0, max=generations, value=0)\n",
    "            display(progress_bar)\n",
    "        for generation in range(generations):\n",
    "            self.iteration += 1\n",
    "            if show_progress:\n",
    "                progress_bar.value=generation\n",
    "            self.step(**self.kwargs)\n",
    "        \n",
    "    def step(self, batch_size=10, **kwargs):\n",
    "        for agent in self.agents:\n",
    "            agent[\"tAco\"].run_extended(generations=batch_size, foreign_pheromones=self.pheromone_sum() - agent[\"tAco\"].extended_pheromones, **kwargs)\n",
    "    \n",
    "    def draw_extended_pheromones(self, label=True, time=-1, width=15, height=15, thickness=3, show=True, prune=0, node_label=True, agent=0):\n",
    "        tAco = self.agents[agent][\"tAco\"]\n",
    "        tAco.draw_extended_pheromones(label=label, time=time, width=width, height=height, thickness=thickness, show=show, prune=prune, node_label=node_label)\n",
    "    \n",
    "    def widget(self):\n",
    "        w = widgets.interact(mAco.draw_extended_pheromones,\n",
    "                 label=False,\n",
    "                 width=15, height=15,\n",
    "                 show=widgets.fixed(True),\n",
    "                 prune=widgets.FloatSlider(value=0.0, min=0.0, max=0.50, step=0.05),\n",
    "                 time=widgets.IntSlider(min=-1,max=mAco.timeframe-1,step=1,value=-1),\n",
    "                 agent=widgets.Dropdown(options=range(len(mAco.agents))),\n",
    "                 thickness=widgets.FloatLogSlider(value=3, base=10, min=-5, max=5)\n",
    "                );\n",
    "        display(w)\n",
    "    \n",
    "    def get_fitness_data(self):\n",
    "        fitness = []\n",
    "        for i in range(len(self.agents)):\n",
    "            df = self.agents[i][\"tAco\"].df_fitness\n",
    "            df[\"agent\"] = i\n",
    "            fitness.append(df)\n",
    "        return pd.concat(fitness)\n",
    "    \n",
    "    def get_path_data(self):\n",
    "        data = []\n",
    "        for i, a in enumerate(self.agents):\n",
    "            data.append(a[\"tAco\"].get_path_data())\n",
    "            data[-1][\"agent\"] = i\n",
    "        return pd.concat(data)\n",
    "    \n",
    "    def compute_node_collisions(self, paths1, paths2):\n",
    "        collisions = 0\n",
    "        for p in paths1:\n",
    "            for q in paths2:\n",
    "                short, long = p, q\n",
    "                if len(p) > len(q):\n",
    "                    long, short = p, q\n",
    "                for i, n1 in enumerate(short):\n",
    "                        if n1 == long[i]:\n",
    "                            collisions += 1\n",
    "        return collisions / len(paths1) / len(paths2)\n",
    "    \n",
    "    def compute_edge_collisions(self, paths1, paths2):\n",
    "        collisions = 0\n",
    "        for path1 in paths1:\n",
    "            for path2 in paths2:\n",
    "                short, long = path1, path2\n",
    "                if len(path1) > len(path2):\n",
    "                    long, short = path1, path2\n",
    "                for i, e1 in enumerate(zip(short[:-1], short[1:])):\n",
    "                    for e2 in zip(long[:-1], long[1:]):\n",
    "                        if e1 == e2:\n",
    "                            collisions += 1\n",
    "                        elif reversed(e1) == e2:\n",
    "                            collisions += 1\n",
    "        return collisions / len(paths1) / len(paths2)\n",
    "    \n",
    "    def compute_latest_collisions(self):\n",
    "        node_collisions = 0\n",
    "        edge_collisions = 0\n",
    "        for i, a in enumerate(self.agents):\n",
    "            for b in self.agents[i+1:]:\n",
    "                node_collisions += self.compute_node_collisions(a[\"tAco\"].paths, b[\"tAco\"].paths)\n",
    "                edge_collisions += self.compute_edge_collisions(a[\"tAco\"].paths, b[\"tAco\"].paths)\n",
    "        return node_collisions, edge_collisions\n",
    "            \n",
    "    \n",
    "    def validate_latest_paths(self):\n",
    "        df = self.get_path_data().groupby(\"agent\").mean().mean()\n",
    "        n, e = self.compute_latest_collisions()\n",
    "        df[\"node_collisions\"] = n\n",
    "        df[\"edge_collisions\"] = e\n",
    "        df[\"collisions\"] = n + e\n",
    "        df[\"iteration\"] = self.iteration\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = problem_easy_4\n",
    "mAco = MultiAgentTACO(problem=problem, agents=[\n",
    "    {\"start\":problem.start, \"goal\":problem.goal},\n",
    "    {\"start\":problem.goal, \"goal\":problem.start}]\n",
    "                      , timeframe=20, prerun_generations=40, seed=42,c_amount=30, c_f=300, c_t=0.1, c_d=1)\n",
    "#mAco.step(alpha_foreign=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAco.run(show_progress=True, generations=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAco.widget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAco.agents[1][\"tAco\"].draw_pheromones()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/Software/MAPF/lib/python3.7/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated since IPython 4.0. You should import from ipyparallel instead.\n",
      "  \"You should import from ipyparallel instead.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "from IPython.parallel import Client\n",
    "\n",
    "class Parameter:\n",
    "    def __init__(self, name, values=None, default=1.0):\n",
    "        self.name = name\n",
    "        if values is not None:\n",
    "            self.values = list(values)\n",
    "        else:\n",
    "            self.values = [default]\n",
    "        self.default = default\n",
    "\n",
    "def log_run_data(self, mAco, seed=0, parameters=None, **kwargs):\n",
    "    df = mAco.validate_latest_paths()\n",
    "    for param in parameters:\n",
    "        if param.values is not None and param.name in kwargs.keys():\n",
    "            df[param.name] = kwargs[param.name]\n",
    "    df[\"seed\"] = seed\n",
    "    return df\n",
    "\n",
    "def single_run(self, task, prameters=None, problem=None, agents=None, step_count=None, step_size=None):\n",
    "    run_data = []\n",
    "    mAco = MultiAgentTACO(problem=problem, agents=agents, seed=task[\"seed\"], **task[\"kwargs\"])\n",
    "    run_data.append(log_run_data(mAco, parameters=parameters, seed=task[\"seed\"], **task[\"kwargs\"]))\n",
    "    for step in range(step_count):\n",
    "        mAco.run(generations=step_size)\n",
    "        run_data.append(log_run_data(mAco, parameters=parameters, seed=task[\"seed\"], **task[\"kwargs\"]))\n",
    "    return pd.DataFrame(run_data)        \n",
    "\n",
    "class ExperimentRunner:\n",
    "    def __init__(self, problem=None, agents=None, parameters=[], step_size=50, step_count=3, independend_runs=31, seed=0, parallel_runs=4):\n",
    "        self.parameters = parameters\n",
    "        self.seed = seed\n",
    "        self.step_size = step_size\n",
    "        self.step_count = step_count\n",
    "        self.independend_runs = independend_runs\n",
    "        self.data = []\n",
    "        self.problem = problem\n",
    "        self.agents = agents\n",
    "        self.tasks = []\n",
    "    \n",
    "    def get_default_kwargs(self):\n",
    "        return {parameter.name : parameter.default for parameter in self.parameters}\n",
    "    \n",
    "    def log_run_data(self, mAco, seed=0, **kwargs):\n",
    "        df = mAco.validate_latest_paths()\n",
    "        for param in self.parameters:\n",
    "            if param.values is not None and param.name in kwargs.keys():\n",
    "                df[param.name] = kwargs[param.name]\n",
    "        df[\"seed\"] = seed\n",
    "        return df\n",
    "    \n",
    "         \n",
    "    def queue_defaults(self):\n",
    "        kwargs = self.get_default_kwargs()\n",
    "        for offset in range(self.independend_runs):\n",
    "            task = {\n",
    "                \"seed\": self.seed + offset,\n",
    "                \"kwargs\": kwargs\n",
    "            }\n",
    "            self.tasks.append(task)\n",
    "    \n",
    "    def queue_parameter(self, parameter):\n",
    "        if parameter.values is None:\n",
    "            return\n",
    "        for value in parameter.values:\n",
    "            if parameter in self.parameters and value == parameter.default:\n",
    "                continue\n",
    "            kwargs = self.get_default_kwargs()\n",
    "            kwargs.update({parameter.name : value})\n",
    "            for offset in range(self.independend_runs):\n",
    "                task = {\n",
    "                    \"seed\" : self.seed + offset,\n",
    "                    \"kwargs\" : kwargs\n",
    "                }\n",
    "                self.tasks.append(task)        \n",
    "            \n",
    "    def queue_non_defaults(self):\n",
    "        for parameter in self.parameters:\n",
    "            self.queue_parameter(parameter)\n",
    "    \n",
    "    def single_run(self, task, problem=None, agents=None, step_count=None, step_size=None):\n",
    "        if problem is None:\n",
    "            problem = self.problem\n",
    "        if agents is None:\n",
    "            agents = self.agents\n",
    "        if step_count is None:\n",
    "            step_count = self.step_count\n",
    "        if step_size is None:\n",
    "            step_size = self.step_size\n",
    "        run_data = []\n",
    "        mAco = MultiAgentTACO(problem=problem, agents=agents, seed=task[\"seed\"], **task[\"kwargs\"])\n",
    "        run_data.append(self.log_run_data(mAco, seed=task[\"seed\"], **task[\"kwargs\"]))\n",
    "        for step in range(step_count):\n",
    "            mAco.run(generations=step_size)\n",
    "            run_data.append(self.log_run_data(mAco, seed=task[\"seed\"], **task[\"kwargs\"]))\n",
    "        return pd.DataFrame(run_data)\n",
    "    \n",
    "    \n",
    "    def run_all(self):\n",
    "        runs = len(self.tasks)\n",
    "        progress = widgets.IntProgress(min=0, max=runs, value=0, description=f\"0 / {runs}\")\n",
    "        display(progress)\n",
    "        while self.tasks:\n",
    "            try:\n",
    "                task = self.tasks.pop()\n",
    "                \n",
    "                df = self.single_run(task)\n",
    "                self.data.append(df)\n",
    "            except KeyboardInterrupt:\n",
    "                print(\"interrupted execution, interrupting task\")\n",
    "                self.tasks.append(task)\n",
    "                raise KeyboardInterrupt\n",
    "            progress.value += 1\n",
    "            progress.description = f\"{progress.value} / {runs}\"\n",
    "    \n",
    "    def run_parallel(self):\n",
    "        \n",
    "        parallel_client = Client()\n",
    "        lview = parallel_client.load_balanced_view()\n",
    "        parallel_result = lview.map(self.single_run, self.tasks)\n",
    "        parallel_result.wait_interactive()\n",
    "        self.data.append(parallel_result.get())\n",
    "            \n",
    "    \n",
    "    def get_data(self):\n",
    "        return pd.concat(self.data, ignore_index=True)\n",
    "    \n",
    "    def store(self, filename=\"benchmark.pkl\"):\n",
    "        pd.to_pickle(self.get_data(), filename)\n",
    "        \n",
    "    def load(self, filename=\"benchmark.pkl\"):\n",
    "        return pd.read_pickle(filename)\n",
    "    \n",
    "    def store_parameters(self, filename=\"parameters.json\"):\n",
    "        parameters = [param.__dict__ for param in self.parameters]\n",
    "        with open(filename, 'w') as json_file:  \n",
    "            json.dump(parameters, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'problem_middle_1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-5d076021f1e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproblem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproblem_middle_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m agents=[\n\u001b[1;32m      3\u001b[0m     \u001b[0;34m{\u001b[0m\u001b[0;34m\"start\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"goal\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoal\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     {\"start\":problem.goal, \"goal\":problem.start}]\n\u001b[1;32m      5\u001b[0m \u001b[0mls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'problem_middle_1' is not defined"
     ]
    }
   ],
   "source": [
    "problem = problem_middle_1\n",
    "agents=[\n",
    "    {\"start\":problem.start, \"goal\":problem.goal},\n",
    "    {\"start\":problem.goal, \"goal\":problem.start}]\n",
    "ls = np.logspace(-1.2, 1.2, num=7)\n",
    "parameters = [\n",
    "    Parameter(\"prerun_generations\", default=800, values=[100,800]),\n",
    "    Parameter(\"alpha\", values=3.0*ls, default=3.0),\n",
    "    Parameter(\"beta\", default=1.0, values=ls),\n",
    "    Parameter(\"eps_foreign\", default=1.0, values=ls),\n",
    "    Parameter(\"show_progress\", default=False),\n",
    "    Parameter(\"timeframe\", default=40),\n",
    "    Parameter(\"eps\", default=0.1, values=ls*0.1),\n",
    "    Parameter(\"alpha_foreign\", values=ls),\n",
    "    Parameter(\"c_amount\", default=1),\n",
    "    Parameter(\"c_f\", default=1000, values=ls*1000),\n",
    "    Parameter(\"c_t\", default=500, values=ls*500),\n",
    "    Parameter(\"c_d\", default=500, values=ls*500),\n",
    "    Parameter(\"edge_collision_factor\", default=3, values=ls*3),\n",
    "]\n",
    "runner = ExperimentRunner(problem=problem, agents=agents, independend_runs=3, parameters=parameters, step_size=20, step_count=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'runner' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-f3d97c45b11d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue_defaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue_non_defaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'runner' is not defined"
     ]
    }
   ],
   "source": [
    "runner.queue_defaults()\n",
    "runner.queue_non_defaults()\n",
    "print(len(runner.tasks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 168/168 tasks finished after    2 s\n",
      "done\n"
     ]
    },
    {
     "ename": "CompositeError",
     "evalue": "one or more exceptions from call to method: single_run\n[2:apply]: AttributeError: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x1033f6390>\n[3:apply]: AttributeError: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x109115198>\n[1:apply]: AttributeError: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x10dd83780>\n[0:apply]: AttributeError: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x1101941d0>\n.... 164 more exceptions ...",
     "output_type": "error",
     "traceback": [
      "[2:apply]: ",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36munpack_apply_message\u001b[0;34m(bufs, g, copy)\u001b[0m",
      "\u001b[1;32m    203\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    204\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nargs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 205\u001b[0;31m         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_bufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeserialize_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    207\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36mdeserialize_object\u001b[0;34m(buffers, g)\u001b[0m",
      "\u001b[1;32m    143\u001b[0m     \u001b[0mbufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    144\u001b[0m     \u001b[0mpobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuffer_to_bytes_py2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mcanned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mistype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mMAX_ITEMS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcanned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x1033f6390>",
      "",
      "[3:apply]: ",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36munpack_apply_message\u001b[0;34m(bufs, g, copy)\u001b[0m",
      "\u001b[1;32m    203\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    204\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nargs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 205\u001b[0;31m         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_bufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeserialize_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    207\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36mdeserialize_object\u001b[0;34m(buffers, g)\u001b[0m",
      "\u001b[1;32m    143\u001b[0m     \u001b[0mbufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    144\u001b[0m     \u001b[0mpobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuffer_to_bytes_py2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mcanned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mistype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mMAX_ITEMS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcanned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x109115198>",
      "",
      "[1:apply]: ",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36munpack_apply_message\u001b[0;34m(bufs, g, copy)\u001b[0m",
      "\u001b[1;32m    203\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    204\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nargs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 205\u001b[0;31m         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_bufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeserialize_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    207\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36mdeserialize_object\u001b[0;34m(buffers, g)\u001b[0m",
      "\u001b[1;32m    143\u001b[0m     \u001b[0mbufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    144\u001b[0m     \u001b[0mpobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuffer_to_bytes_py2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mcanned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mistype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mMAX_ITEMS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcanned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x10dd83780>",
      "",
      "[0:apply]: ",
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36munpack_apply_message\u001b[0;34m(bufs, g, copy)\u001b[0m",
      "\u001b[1;32m    203\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    204\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nargs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 205\u001b[0;31m         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_bufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeserialize_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    206\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    207\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/ipyparallel/serialize/serialize.py\u001b[0m in \u001b[0;36mdeserialize_object\u001b[0;34m(buffers, g)\u001b[0m",
      "\u001b[1;32m    143\u001b[0m     \u001b[0mbufs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    144\u001b[0m     \u001b[0mpobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuffer_to_bytes_py2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbufs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mcanned\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mistype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanned\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mMAX_ITEMS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcanned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: Can't get attribute 'ExperimentRunner' on <IPython.core.interactiveshell.DummyMod object at 0x1101941d0>",
      "",
      "... 164 more exceptions ..."
     ]
    }
   ],
   "source": [
    "runner.run_parallel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate object of type \"<class 'ipyparallel.client.asyncresult.AsyncMapResult'>\"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-1aa72948e763>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-44-41e7219ce670>\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"benchmark.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    226\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                        copy=copy, sort=sort)\n\u001b[0m\u001b[1;32m    229\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    287\u001b[0m                        \u001b[0;34m' only pd.Series, pd.DataFrame, and pd.Panel'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m                        ' (deprecated) objs are valid'.format(type(obj)))\n\u001b[0;32m--> 289\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0;31m# consolidate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot concatenate object of type \"<class 'ipyparallel.client.asyncresult.AsyncMapResult'>\"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid"
     ]
    }
   ],
   "source": [
    "df = runner.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def explore_parameter(data=None, parameters=None, name=\"Name\"):\n",
    "    df = data\n",
    "    for parameter in parameters:\n",
    "        if parameter.name != name and parameter.name in df.keys():\n",
    "            df = df.loc[df[parameter.name] == parameter.default]\n",
    "    return df\n",
    "\n",
    "def plot_parameter(data=None, parameters=None, name=\"Name\", y=\"collisions\", **kwargs):\n",
    "    df_eps_f = explore_parameter(data=data, parameters=parameters, name=name)\n",
    "    sns.catplot(data=df_eps_f, x=\"iteration\", y=y, col=name, sharex=True, sharey=True, **kwargs)#, kind=\"box\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-13662740e4c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"eps\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"violin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "plot_parameter(data=df, parameters=parameters, name=\"eps\", kind=\"violin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'parameters' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-aaaf92fd287f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0mplot_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"edge_collisions\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"seed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'parameters' is not defined"
     ]
    }
   ],
   "source": [
    "for p in parameters:\n",
    "    if p.values is not None:\n",
    "        try:\n",
    "            plot_parameter(data=df, parameters=parameters, name=p.name, y=\"edge_collisions\", hue=\"seed\")\n",
    "            plt.show()\n",
    "        except ValueError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-557a5aa30ed2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"benchmark.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_pickle(\"benchmark.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can only compare identically-labeled DataFrame objects",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-4f5bd8acd943>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Software/MAPF/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   2094\u001b[0m             \u001b[0;31m# Another DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_indexed_same\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2096\u001b[0;31m                 raise ValueError('Can only compare identically-labeled '\n\u001b[0m\u001b[1;32m   2097\u001b[0m                                  'DataFrame objects')\n\u001b[1;32m   2098\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mdispatch_to_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Can only compare identically-labeled DataFrame objects"
     ]
    }
   ],
   "source": [
    "df2 == df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mAco.agents[0][\"tAco\"].draw_adjacency()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "c = Client()\n",
    "c.ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = c[:].apply_sync(lambda : \"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello World', 'Hello World', 'Hello World', 'Hello World']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import src.aco_mapf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import aco_mapf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package src.aco_mapf in src:\n",
      "\n",
      "NAME\n",
      "    src.aco_mapf\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    GraphWorld\n",
      "\n",
      "FILE\n",
      "    /Users/sebastian/Software/MAPF/src/aco_mapf/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(aco_mapf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.aco_mapf.GraphWorld import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1  = TestProblem().hard_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0.        , 0.64480945, 0.59804662, ..., 0.        , 0.51406955,\n",
       "         1.01497176],\n",
       "        [0.64480945, 0.        , 0.98639126, ..., 0.        , 0.        ,\n",
       "         0.95197678],\n",
       "        [0.59804662, 0.98639126, 0.        , ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        ...,\n",
       "        [0.        , 0.        , 0.        , ..., 0.        , 1.29011009,\n",
       "         1.03476866],\n",
       "        [0.51406955, 0.        , 0.        , ..., 1.29011009, 0.        ,\n",
       "         0.72829166],\n",
       "        [1.01497176, 0.95197678, 0.        , ..., 1.03476866, 0.72829166,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.adjacency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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