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 0x7fba14061a20>,\n",
       " <csp.CSP at 0x7fba14067a90>,\n",
       " <csp.CSP at 0x7fba14067b38>)"
      ]
     },
     "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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ss87KfffdlwceeCD9+vXLpZdemgULFpQ7WuGZCAWoP2eEAsA/9e7d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Lfeevnggw/SpInvqy8MKpVK/uu//it//OMfc88992TjjTcuOhIAwALXrOgAAAAsXFZZZZUs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\nyy6bYcOGpVOnT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fb982748>"
      ]
     },
     "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": [
    "eight_queens_csp = NQueensCSP(8)\n",
    "backtracking_instru_queen = make_instru(eight_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": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCYABAQTAAKCCQABwQSAwH8AzNAQrsdu/uMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fb75f710>"
      ]
     },
     "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": [
    "twelve_queens_csp = NQueensCSP(12)\n",
    "conflicts_instru_queen = make_instru(eight_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": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngIBgAkBAMAEgIJgAEBBMAAgIJgAE/gFsWRCumm1C3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fb75f3c8>"
      ]
     },
     "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": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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