csp.ipynb

{
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   "source": [
    "# Constraint Satisfaction Problems (CSPs)\n",
    "\n",
    "This IPy notebook acts as supporting material for topics covered in **Chapter 6 Constraint Satisfaction Problems** of the book* Artificial Intelligence: A Modern Approach*. We make use of the implementations in **csp.py** module. Even though this notebook includes a brief summary of the main topics familiarity with the material present in the book is expected. We will look at some visualizations and solve some of the CSP problems described in the book. Let us import everything from the csp module to get started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from csp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review\n",
    "\n",
    "CSPs are a special kind of search problems. Here we don't treat the space as a black box but the state has a particular form and we use that to our advantage to tweak our algorithms to be more suited to the problems. A CSP State is defined by a set of variables which can take values from corresponding domains. These variables can take only certain values in their domains to satisfy the constraints. A set of assignments which satisfies all constraints passes the goal test. Let us start by exploring the CSP class which we will use to model our CSPs. You can keep the popup open and read the main page to get a better idea of the code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%psource CSP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __ _ _init_ _ __ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as dict where key specify the variables and value specify the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list its value which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph Coloring\n",
    "\n",
    "We use the graph coloring problem as our running example for demonstrating the different algorithms in the **csp module**. The idea of map coloring problem is that the adjacent nodes (those connected by edges) should not have the same color throughout the graph. The graph can be colored using a fixed number of colors. Here each node is a variable and the values are the colors that can be assigned to them. Given that the domain will be the same for all our nodes we use a custom dict defined by the **UniversalDict** class. The **UniversalDict** Class takes in a parameter which it returns as value for all the keys of the dict. It is very similar to **defaultdict** in Python except that it does not support item assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R', 'G', 'B']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = UniversalDict(['R','G','B'])\n",
    "s[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this what we need is that the neighbors must not have the same color. This is defined in the function **different_values_constraint** of the module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource different_values_constraint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows to take input in the form of strings and return a Dict of the form compatible with the **CSP Class**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%pdoc parse_neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables our the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **australia**, **usa** and **france** are three CSPs that have been created using **MapColoringCSP**. **australia** corresponds to ** Figure 6.1 ** in the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource MapColoringCSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<csp.CSP at 0x7fbc28536a20>,\n",
       " <csp.CSP at 0x7fbc2853ca90>,\n",
       " <csp.CSP at 0x7fbc2853cb38>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "australia, usa, france"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions\n",
    "\n",
    "We will now implement few helper functions that will help us visualize the Coloring Problem. We will make some modifications to the existing Classes and Functions for additional book keeping. To begin with we modify the **assign** and **unassign** methods in the **CSP** to add a copy of the assignment to the **assingment_history**. We call this new class **InstruCSP**. This would allow us to see how the assignment evolves over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "class InstruCSP(CSP):\n",
    "    \n",
    "    def __init__(self, variables, domains, neighbors, constraints):\n",
    "        super().__init__(variables, domains, neighbors, constraints)\n",
    "        self.assingment_history = []\n",
    "        \n",
    "    def assign(self, var, val, assignment):\n",
    "        super().assign(var,val, assignment)\n",
    "        self.assingment_history.append(copy.deepcopy(assignment))\n",
    "    \n",
    "    def unassign(self, var, assignment):\n",
    "        super().unassign(var,assignment)\n",
    "        self.assingment_history.append(copy.deepcopy(assignment)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define **make_instru** which takes an instance of **CSP** and returns a **InstruCSP** instance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_instru(csp):\n",
    "    return InstruCSP(csp.variables, csp.domains, csp.neighbors,\n",
    "               csp.constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes are they are connected to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neighbors = {\n",
    "    0: [6, 11, 15, 18, 4, 11, 6, 15, 18, 4], \n",
    "    1: [12, 12, 14, 14], \n",
    "    2: [17, 6, 11, 6, 11, 10, 17, 14, 10, 14], \n",
    "    3: [20, 8, 19, 12, 20, 19, 8, 12], \n",
    "    4: [11, 0, 18, 5, 18, 5, 11, 0], \n",
    "    5: [4, 4], \n",
    "    6: [8, 15, 0, 11, 2, 14, 8, 11, 15, 2, 0, 14], \n",
    "    7: [13, 16, 13, 16], \n",
    "    8: [19, 15, 6, 14, 12, 3, 6, 15, 19, 12, 3, 14], \n",
    "    9: [20, 15, 19, 16, 15, 19, 20, 16], \n",
    "    10: [17, 11, 2, 11, 17, 2], \n",
    "    11: [6, 0, 4, 10, 2, 6, 2, 0, 10, 4], \n",
    "    12: [8, 3, 8, 14, 1, 3, 1, 14], \n",
    "    13: [7, 15, 18, 15, 16, 7, 18, 16], \n",
    "    14: [8, 6, 2, 12, 1, 8, 6, 2, 1, 12], \n",
    "    15: [8, 6, 16, 13, 18, 0, 6, 8, 19, 9, 0, 19, 13, 18, 9, 16], \n",
    "    16: [7, 15, 13, 9, 7, 13, 15, 9], \n",
    "    17: [10, 2, 2, 10], \n",
    "    18: [15, 0, 13, 4, 0, 15, 13, 4], \n",
    "    19: [20, 8, 15, 9, 15, 8, 3, 20, 3, 9], \n",
    "    20: [3, 19, 9, 19, 3, 9]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coloring_problem = MapColoringCSP('RGBY', neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "coloring_problem1 = make_instru(coloring_problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtracking Search\n",
    "\n",
    "For solving a CSP the main issue with Naive search algorithms is that they can continue expanding obviously wrong paths. In backtracking search, we check constraints as we go. Backtracking is just the above idea combined with the fact that we are dealing with one variable at a time. Backtracking Search is implemented in the repository as the function **backtracking_search**. This is the same as **Figure 6.5** in the book. The function takes as input a CSP and few other optional parameters which can be used to further speed it up. The function returns the correct assignment if it satisfies the goal. We will discuss these later. Let us solve our **coloring_problem1** with **backtracking_search**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = backtracking_search(coloring_problem1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'R',\n",
       " 1: 'R',\n",
       " 2: 'R',\n",
       " 3: 'R',\n",
       " 4: 'G',\n",
       " 5: 'R',\n",
       " 6: 'G',\n",
       " 7: 'R',\n",
       " 8: 'B',\n",
       " 9: 'R',\n",
       " 10: 'G',\n",
       " 11: 'B',\n",
       " 12: 'G',\n",
       " 13: 'G',\n",
       " 14: 'Y',\n",
       " 15: 'Y',\n",
       " 16: 'B',\n",
       " 17: 'B',\n",
       " 18: 'B',\n",
       " 19: 'G',\n",
       " 20: 'B'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result # A dictonary of assingments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also check the number of assingments made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coloring_problem1.nassigns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us check the total number of assingments and unassingments which is the lentgh ofour assingment history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(coloring_problem1.assingment_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "\n",
    "Next, we define some functions to create the visualisation from the assingment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which return such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. This will be more clear with the example below. If this sounds confusing do not worry this is not the part of the core material and our only goal is to help you visualize how the process works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_update_step_function(graph, instru_csp):\n",
    "    \n",
    "    def draw_graph(graph):\n",
    "        # create networkx graph\n",
    "        G=nx.Graph(graph)\n",
    "        # draw graph\n",
    "        pos = nx.spring_layout(G,k=0.15)\n",
    "        return (G, pos)\n",
    "    \n",
    "    G, pos = draw_graph(graph)\n",
    "    \n",
    "    def update_step(iteration):\n",
    "        # here iteration is the index of the assingment_history we want to visualize.\n",
    "        current = instru_csp.assingment_history[iteration]\n",
    "        #  We convert the particular assingment to a default dict so that the color for nodes which \n",
    "        # have not been assigned defaults to black.\n",
    "        current = defaultdict(lambda: 'Black', current)\n",
    "\n",
    "        # Now we use colors in the list and default to black otherwise.\n",
    "        colors = [current[node] for node in G.node.keys()]\n",
    "        # Finally drawing the nodes.\n",
    "        nx.draw(G, pos, node_color=colors, node_size=500)\n",
    "\n",
    "        labels = {label:label for label in G.node}\n",
    "        # Labels shifted by offset so as to not overlap nodes.\n",
    "        label_pos = {key:[value[0], value[1]+0.03] for key, value in pos.items()}\n",
    "        nx.draw_networkx_labels(G, label_pos, labels, font_size=20)\n",
    "\n",
    "        # show graph\n",
    "        plt.show()\n",
    "\n",
    "    return update_step  # <-- this is a function\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let us plot our problem. We first use the function above to obtain a step function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_func = make_update_step_function(neighbors, coloring_problem1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we set the canvas size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (18.0, 18.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hhx1y8skn56677sr8+fOLHRcAYJOlCAUACsrReP7RrFmz0r1795x11lm57LLL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc0845d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assingment_history)-1, step=1, value=0)\n",
    "w=widgets.interactive(step_func,iteration=iteration_slider)\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NQueens Visualization\n",
    "\n",
    "Just like the Graph Coloring Problem we will start with defining a few helper functions to help us visualize the assignments as they evolve over time. The **make_plot_board_step_function** behaves similar to the **make_update_step_function** introduced earlier. It initializes a chess board in the form of a 2D grid with alternating 0s and 1s. This is used by **plot_board_step** function which draws the board using matplotlib and adds queens to it. This function also calls the **label_queen_conflicts** which modifies the grid placing 3 in positions in a position where there is a conflict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_queen_conflicts(assingment,grid):\n",
    "    ''' Mark grid with queens that are under conflict. '''\n",
    "    for col, row in assingment.items(): # check each queen for conflict\n",
    "        row_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                         if temp_row == row and temp_col != col}\n",
    "        up_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                        if temp_row+temp_col == row+col and temp_col != col}\n",
    "        down_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n",
    "                          if temp_row-temp_col == row-col and temp_col != col}\n",
    "        \n",
    "        # Now marking the grid.\n",
    "        for col, row in row_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "        for col, row in up_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "        for col, row in down_conflicts.items():\n",
    "                grid[col][row] = 3\n",
    "\n",
    "    return grid\n",
    "\n",
    "def make_plot_board_step_function(instru_csp):\n",
    "    '''ipywidgets interactive function supports\n",
    "       single parameter as input. This function\n",
    "       creates and return such a function by taking\n",
    "       in input other parameters.\n",
    "    '''\n",
    "    n = len(instru_csp.variables)\n",
    "    \n",
    "    \n",
    "    def plot_board_step(iteration):\n",
    "        ''' Add Queens to the Board.'''\n",
    "        data = instru_csp.assingment_history[iteration]\n",
    "        \n",
    "        grid = [[(col+row+1)%2 for col in range(n)] for row in range(n)]\n",
    "        grid = label_queen_conflicts(data, grid) # Update grid with conflict labels.\n",
    "        \n",
    "        # color map of fixed colors\n",
    "        cmap = matplotlib.colors.ListedColormap(['white','lightsteelblue','red'])\n",
    "        bounds=[0,1,2,3] # 0 for white 1 for black 2 onwards for conflict labels (red).\n",
    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
    "        \n",
    "        fig = plt.imshow(grid, interpolation='nearest', cmap = cmap,norm=norm)\n",
    "\n",
    "        plt.axis('off')\n",
    "        fig.axes.get_xaxis().set_visible(False)\n",
    "        fig.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "        # Place the Queens Unicode Symbol\n",
    "        for col, row in data.items():\n",
    "            fig.axes.text(row, col, u\"\\u265B\", va='center', ha='center', family='Dejavu Sans', fontsize=32)\n",
    "        plt.show()\n",
    "    \n",
    "    return plot_board_step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us visualize a solution obtained via backtracking. We use of the previosuly defined **make_instru** function for keeping a history of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "twelve_queens_csp = NQueensCSP(12)\n",
    "backtracking_instru_queen = make_instru(twelve_queens_csp)\n",
    "result = backtracking_search(backtracking_instru_queen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "backtrack_queen_step = make_plot_board_step_function(backtracking_instru_queen) # Step Function for Widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiIOKDBFREQcUGCKiIg4oMAUERFx4CsfXLBtV+tQ1eFY6ayB+ebsgaa1ciYW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\nRBxQYIqIiDigwBQREXFAgSkiIuLA/wGx9HtR0bJVGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc083825c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n",
    "matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n",
    "\n",
    "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assingment_history)-1, step=0, value=0)\n",
    "w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us finally repeat the above steps for **min_conflicts** solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conflicts_instru_queen = make_instru(twelve_queens_csp)\n",
    "result = min_conflicts(conflicts_instru_queen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conflicts_step = make_plot_board_step_function(conflicts_instru_queen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization has same features as the above. But here it also highlights the conflicts by labeling the conflicted queens with a red background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YB3QOXGmSlFUGpkLbgWKgAnimZ+wIkACCnu9TZMJyJjALuBPoGvBKJWngGZgK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\nAJeCsXdLtrMjTjqdpuH9d8n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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc083fae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assingment_history)-1, step=0, value=0)\n",
    "w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n",
    "display(w)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  },
  "widgets": {
   "state": {
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