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 0x7fd20c13ca58>,\n",
       " <csp.CSP at 0x7fd20c142ac8>,\n",
       " <csp.CSP at 0x7fd20c0cc080>)"
      ]
     },
     "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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m4IMPzl577ZW33norm2++eY455pj89Kc/zTbbbJOnnnoqbdu2TZKy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qXKafw/xmvAgAFyc3MzOjYAPBSLxaLVq1dr1KhRatu2rT766COVLVvW6FgObe/evRoyZIji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\nIhSAeWvXrl35256elkbQJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1ec07cac8>"
      ]
     },
     "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)"
   ]
  }
 ],
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