{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# The search.py module"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Introduction\n",
"============\n",
"\n",
"Hello!\n",
"In this IPython notebook, we'll study different kinds of search techniques used in [ search.py ]( https://github.com/aimacode/aima-python/blob/master/search.py ) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n",
"\n",
"The code in this IPython notebook, and the entire `aima-python` repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the `intro` IPython notebook. \n",
"\n",
"Now that you have all that sorted out, let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Uninformed Search Strategies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uninformed Search strategies are called `blind search`. In such search strategies, the only information we have about any state is generated by checking if a piece of data, or any of its successors, matches our `goal state` or not. THAT'S IT. NOTHING MORE. (Well ....not really. See the `value` method defined in the following section).\n",
"\n",
"First let's formulate the problem we intend to solve. So let's import everything from our module."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from search import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The search and other modules of the repository make use of several imports from the utils module. We will point the useful ones out if they are required to follow the material below. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n",
" \n",
"The `Problem` class is an abstract class on which we define our problems(*duh*).\n",
"Again, if you are confused about what `abstract class` means have a look at the `Intro` notebook.\n",
"The `Problem` class has six methods.\n",
"* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. In this and all of the below methods `self` refers to the object itself--the object whose method is called. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins his task of exploration to find the goal state(s) which is given in the `goal` parameter.\n",
"* `actions(self, state)` : This method returns all the possible actions our agent can make in state `state`.\n",
"* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n",
"* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else, of course, `False` is returned.\n",
"* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n",
"* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the above abstract class acts as a parent class, and there is another named called `GraphProblem` in our module. It creates a graph problem from an instance of the `Graph` class. To create a graph, simply type `graph = Graph(dict(...))`. The dictionary must contain nodes of the graph as keys, so make sure they are `hashable`. If you don't know what that means just use strings or numbers. Each node contains the adjacent nodes as keys and the edge length as its value. Each dictionary then should correspond to another dictionary in the graph. The `Graph` class creates a directed(edges allow only one way traffic) by default. If you want to make an undirected graph, use `UndirectedGraph` instead, but make sure to mention any edge in only one of its nodes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you didn't understand the above paragraph, `Fret not!`. Just think of the below code as a magicical method to create a simple undirected graph. I'll explain what it is about later."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"museum_graph = UndirectedGraph(dict(\n",
" Start = dict(Dog = 3, Cat = 9, Mouse = 4),\n",
" Dog = dict(Bear = 7),\n",
" Cat = dict(Monkey = 9, Fish = 8, Penguin = 3),\n",
" Mouse = dict(Penguin = 2),\n",
" Bear = dict(Monkey = 7),\n",
" Monkey = dict(Giraffe = 11, Fish = 6),\n",
" Fish = dict(Giraffe = 8),\n",
" Penguin = dict(Parrot = 4, Elephant = 6),\n",
" Giraffe = dict(Hen = 5),\n",
" Parrot = dict(Hen = 10),\n",
" Elephant = dict(Hen = 9)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imagine we are in a museum showcasing statues of various animals. To navigate through the museum there are paths between some statues and the entrance. We define the entrance and the statues as nodes in our graph with the path connecting them as edges. The cost/weight of an edge specifies is its length. So `Start = dict(Dog = 3, Cat = 9, Mouse = 4)` means that there are paths from `Start` to `Dog`, `Cat` and `Mouse` with path costs 3, 9 and 4 respectively. \n",
"\n",
"Here's an image below to better understand our graph."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"images/search_animal.svg\")
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Breadth First Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Breadth First Search, the `shallowest` unexpanded node is chosen for expansion. That means that all nodes of a given depth must be expanded before any node of the next depth level. This search strategy accomplishes this by using a `FIFO` meaning 'First In First Out' queue. Anything that gets in the queue first also gets out first just like the checkout queue in a supermarket. To use the algorithm, first we need to define our problem. Say we want to find the statue of `Monkey` and we start from the entrance which is the `Start` state. We'll define our problem using the `GraphProblem` class."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"monkey_problem = GraphProblem('Start', 'Monkey', museum_graph)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's find the solution for our problem using the `breadth_first_search` method. Note that it returns a `Node` from which we can find the solution by looking at the path that was taken to reach there."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Cat', 'Monkey']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bfs_node = breadth_first_search(monkey_problem)\n",
"bfs_node.solution()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get the output as `['Cat', 'Monkey']`. That is because first the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order when we are expanding the `Start` node. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that during a breadth first search, the goal test is done when the node is being added to the queue."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Uniform-cost Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Uniform-cost Search, we expand the node with the lowest path cost (the cost to reach that node from the start) instead of expanding the shallowest node. Rather than a `FIFO` queue, we use something called a `priority queue` which selects the element with the highest `priority` of all elements in the queue. For our problem, the shortest path between animals has the higher priority; the shortest path has the lowest path cost. Whenever we need to enqueue a node already in the queue, we will update its path cost if the newer path is better. This is a very important step, and it means that the path cost to a node may keep getting better until it is selected for expansion. This is the reason that we do a goal check only when a node is selected for expanion."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Dog', 'Bear', 'Monkey']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ucs_node = uniform_cost_search(monkey_problem)\n",
"ucs_node.solution()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We got the path`['Dog', 'Bear', 'Monkey']` instead of `['Cat', 'Monkey']`. Why? The path cost is lower! We can also see the path cost with the path_cost attribute. Let's compare the path cost of the Breadth first search solution and Uniform cost search solution"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(18, 17)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bfs_node.path_cost, ucs_node.path_cost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We were right! \n",
"\n",
"The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former passes through two roads compared to the three roads in the `ucs_node` solution."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Romania map visualisation\n",
"\n",
"Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem to reach 'Bucharest' starting from 'Arad'. This is how the problem is defined:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have a look at `romania_locations`. We will use these location values to draw the romania graph using **networkx**."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Lugoj': (165, 379), 'Hirsova': (534, 350), 'Urziceni': (456, 350), 'Bucharest': (400, 327), 'Timisoara': (94, 410), 'Oradea': (131, 571), 'Iasi': (473, 506), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Arad': (91, 492), 'Zerind': (108, 531), 'Neamt': (406, 537), 'Pitesti': (320, 368), 'Eforie': (562, 293), 'Drobeta': (165, 299), 'Vaslui': (509, 444), 'Mehadia': (168, 339), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Craiova': (253, 288)}\n"
]
}
],
"source": [
"romania_locations = romania_map.locations\n",
"print(romania_locations)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nAADAUo4QIfF0L7soAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G = nx.Graph()\n",
"\n",
"# use this while labeling nodes in the map\n",
"node_labels = dict()\n",
"\n",
"for n, p in romania_locations.items():\n",
" # add nodes from romania_locations\n",
" G.add_node(n)\n",
" # add nodes to node_labels\n",
" node_labels[n] = n\n",
"\n",
"# positions for node labels\n",
"node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n",
"\n",
"# use thi whiel labeling edges\n",
"edge_labels = dict()\n",
"\n",
"# add edges between cities in romania map - UndirectedGraph defined in search.py\n",
"for node in romania_map.nodes():\n",
" connections = romania_map.get(node)\n",
" for connection in connections.keys():\n",
" distance = connections[connection]\n",
" # add edges to the graph\n",
" G.add_edge(node, connection)\n",
" # add distances to edge_labels\n",
" edge_labels[(node, connection)] = distance\n",
"\n",
" \n",
"# initial colors for all the nodes\n",
"node_colors = [\"w\" for i in G.nodes()]\n",
" \n",
"# set the size of the plot\n",
"plt.figure(figsize=(18,13))\n",
"# draw the graph with locations from romania_locations\n",
"nx.draw(G, pos = romania_locations, node_color = node_colors)\n",
"\n",
"# draw labels for nodes\n",
"node_label_handles = nx.draw_networkx_labels(G, pos = node_label_pos, labels = node_labels, font_size = 14)\n",
"# add a white bounding box behind the node labels\n",
"[label.set_bbox(dict(facecolor='white', edgecolor='none')) for label in node_label_handles.values()]\n",
"\n",
"# add edge lables to the graph\n",
"nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n",
"\n",
"# show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
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