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AICup/Lectures/Student_lecture 1.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## First Lab\n",
"\n",
"What we are going to do today:\n",
"- read TSP data\n",
"- define euclidean distance function\n",
"- define a ProblemInstance python class \n",
"- store nodes in an instance of the class defined before\n",
"- plot raw data\n",
"- generate naive solution \n",
"- check if the solution is valid\n",
"- evaluate solution!#\n",
"\n",
"NOTE: I've marked all the code that you will have to fill with a `# TODO` comment\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This cell below is simply importing some useful stuff for later"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read TSP data\n",
"In this Cup you will have to deal with predefined set of problems. These problems are located in the `problems` folder.\n",
"\n",
"First lets get list them out"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ch130.tsp\n",
"d198.tsp\n",
"eil76.tsp\n",
"fl1577.tsp\n",
"kroA100.tsp\n",
"lin318.tsp\n",
"pcb442.tsp\n",
"pr439.tsp\n",
"rat783.tsp\n",
"u1060.tsp\n"
]
}
],
"source": [
"problems = glob.glob('../problems/*.tsp')\n",
"# example_problem = [\"../problems/eil76.tsp\"]\n",
"for prob in problems:\n",
" print(prob[12:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking by hand if all of the 10 problems are in the folder would be a waste of time so we can write a line of code just to check if they are all there"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.all([n[12:] in ['fl1577.tsp','pr439.tsp','ch130.tsp','rat783.tsp','d198.tsp', 'kroA100.tsp','u1060.tsp','lin318.tsp','eil76.tsp','pcb442.tsp'] for n in problems]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### File format\n",
"All the problems are stored in a `.tsp` (this file is actually a renamed `.txt` file, so you could open them with your favorite text editor)\n",
"\n",
"As we will see in a bit all the problems files are composed of different sections:\n",
"* `NAME`: the shortned name of the problem\n",
"* `COMMENT`: a comment area that can contain the full name of the problem\n",
"* `TYPE`: this defines the type of problem at hand, in our case is always TSP\n",
"* `DIMENSION`: this states the problem dimension\n",
"* `EDGE_WEIGHT_TYPE`: this section states the types of weights applied to edges, in our case it is always EUC_2D or the weights are giveng using the euclidean distance in 2 dimension\n",
"* `BEST_KNOWN`: this states the best known result obtained, note that as the Prof said, it is unlikely to get a better performance than this\n",
"* `NODE_COORD_SECTION`: finally we have the section that states the triplets that defines the problems points. These triplets are (point_number, x,y).\n",
"\n",
"Now that we know all of that, lets print the content of a single problem"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['NAME : eil76', 'COMMENT : 76-city problem (Christofides/Eilon)', 'TYPE : TSP', 'DIMENSION : 76', 'EDGE_WEIGHT_TYPE : EUC_2D', 'BEST_KNOWN : 538', 'NODE_COORD_SECTION', '1 22 22', '2 36 26', '3 21 45', '4 45 35', '5 55 20', '6 33 34', '7 50 50', '8 55 45', '9 26 59', '10 40 66', '11 55 65', '12 35 51', '13 62 35', '14 62 57', '15 62 24', '16 21 36', '17 33 44', '18 9 56', '19 62 48', '20 66 14', '21 44 13', '22 26 13', '23 11 28', '24 7 43', '25 17 64', '26 41 46', '27 55 34', '28 35 16', '29 52 26', '30 43 26', '31 31 76', '32 22 53', '33 26 29', '34 50 40', '35 55 50', '36 54 10', '37 60 15', '38 47 66', '39 30 60', '40 30 50', '41 12 17', '42 15 14', '43 16 19', '44 21 48', '45 50 30', '46 51 42', '47 50 15', '48 48 21', '49 12 38', '50 15 56', '51 29 39', '52 54 38', '53 55 57', '54 67 41', '55 10 70', '56 6 25', '57 65 27', '58 40 60', '59 70 64', '60 64 4', '61 36 6', '62 30 20', '63 20 30', '64 15 5', '65 50 70', '66 57 72', '67 45 42', '68 38 33', '69 50 4', '70 66 8', '71 59 5', '72 35 60', '73 27 24', '74 40 20', '75 40 37', '76 40 40', 'EOF']\n"
]
}
],
"source": [
"example_problem = \"../problems/eil76.tsp\"\n",
"with open(example_problem,\"r\") as exprob:\n",
" print(exprob.read().splitlines())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Euclidean Distance\n",
"Since all of our problems are using the euclidean distance between points for the edges weights.\n",
"We will now define a function that computes the euclidean distance. This distance will also be used to build the distance matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def distance_euc(point_i, point_j):\n",
" rounding = 0\n",
" x_i, y_i = point_i[0], point_i[1]\n",
" x_j, y_j = point_j[0], point_j[1]\n",
" distance = np.sqrt((x_i - x_j) ** 2 + (y_i - y_j) ** 2)\n",
" return round(distance, rounding)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's test it"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"point_1 = (2, 2)\n",
"point_2 = (5, 5)\n",
"distance_euc(point_1, point_2)\n",
"# Expected output is 4.0 with rounding to 0 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading and storing the data\n",
"We will now define a Class called `ProblemInstance`\n",
"\n",
"in the Constructor of the class (`__init__()`method of a class in Python) you will have to implement the code for:\n",
"* reading the raw data\n",
"* store the metadata\n",
"* read all the point and store them\n",
"* code the method that creates the distance matrix between points\n",
"* \\[optional\\] check if the problem loaded has an optimal and in that case store the optimal solution\n",
"* \\[optional\\] code the plotting method\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from src.utils import distance_euc\n",
"\n",
"class ProblemInstance:\n",
"\n",
" def __init__(self, name_tsp):\n",
" self.exist_opt = False\n",
" self.optimal_tour = None\n",
" self.dist_matrix = None\n",
" \n",
" # read raw data \n",
" # TODO\n",
" with open(name_tsp) as f_o:\n",
" data= f_o.read()\n",
" self.lines = data.splitlines()\n",
" \n",
"# file_object = open(name_tsp)\n",
"# data = file_object.read()\n",
"# file_object.close()\n",
"# self.lines = data.splitlines()\n",
"\n",
" # store metadata set information \n",
" # TODO\n",
" self.name = self.lines[0].split(' ')[2]\n",
" # here we expect the name of the problem\n",
" self.nPoints = np.int(self.lines[3].split(' ')[2])\n",
" self.best_sol = np.float(self.lines[5].split(' ')[2])\n",
" # here the lenght of the best solution\n",
" \n",
" # read all data points and store them \n",
" # TODO\n",
" self.points = np.zeros((self.nPoints, 3)) # this is the structure where we will store the pts data \n",
" for i in range(self.nPoints):\n",
" line_i = self.lines[7 + i].split(' ')\n",
" self.points[i, 0] = int(line_i[0])\n",
" self.points[i, 1] = line_i[1]\n",
" self.points[i, 2] = line_i[2]\n",
" \n",
" self.create_dist_matrix()\n",
" \n",
" # TODO [optional]\n",
" # if the problem is one with a optimal solution, that solution is loaded\n",
" if name_tsp in [\"../problems/eil76.tsp\", \"../problems/kroA100.tsp\"]:\n",
" self.exist_opt = True\n",
" file_object = open(name_tsp.replace(\".tsp\", \".opt.tour\"))\n",
" data = file_object.read()\n",
" file_object.close()\n",
" lines = data.splitlines()\n",
"\n",
" # read all data points and store them\n",
" self.optimal_tour = np.zeros(self.nPoints, dtype=np.int)\n",
" for i in range(self.nPoints):\n",
" line_i = lines[5 + i].split(' ')\n",
" self.optimal_tour[i] = int(line_i[0]) - 1\n",
"\n",
" def print_info(self):\n",
" print(\"\\n#############################\\n\")\n",
" print('name: ' + self.name)\n",
" print('nPoints: ' + str(self.nPoints))\n",
" print('best_sol: ' + str(self.best_sol))\n",
" print('exist optimal: ' + str(self.exist_opt))\n",
"\n",
" def plot_data(self,show_numbers=False): # todo [optional]\n",
" plt.figure(figsize=(8, 8))\n",
" plt.title(self.name)\n",
" plt.scatter(self.points[:, 1], self.points[:, 2])\n",
" if show_numbers:\n",
" for i, txt in enumerate(np.arange(self.nPoints)): # tour_found[:-1]\n",
" plt.annotate(txt, (self.points[i, 1], self.points[i, 2]))\n",
" plt.show()\n",
"\n",
" def create_dist_matrix(self): # TODO\n",
" self.dist_matrix = np.zeros((self.nPoints, self.nPoints))\n",
"\n",
" for i in range(self.nPoints):\n",
" for j in range(i, self.nPoints):\n",
" self.dist_matrix[i, j] = distance_euc(self.points[i][1:3], self.points[j][1:3])\n",
" self.dist_matrix += self.dist_matrix.T\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------------\n",
"Now we can test our Class with an example problem"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"example_problem = \"../problems/eil76.tsp\"\n",
"p_inst=ProblemInstance(example_problem)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"#############################\n",
"\n",
"name: eil76\n",
"nPoints: 76\n",
"best_sol: 538.0\n",
"exist optimal: True\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'\\n#############################\\n\\nname: eil76\\nnPoints: 76\\nbest_sol: 538.0\\nexist optimal: True\\n\\n'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p_inst.print_info()\n",
"p_inst.plot_data()\n",
"#Expected output\n",
"\"\"\"\n",
"#############################\n",
"\n",
"name: eil76\n",
"nPoints: 76\n",
"best_sol: 538.0\n",
"exist optimal: True\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"p_inst.plot_data(show_numbers=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------\n",
"### Random solver \n",
"Now we will code the random solver and test it with a class called `SolverTSP` that takes the solvers and the problem instance and act as a framework to compute the solution and gives us some additional information.\n",
"We will also need to code the `evaluate_solution` method of the the `SolverTSP` class"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"def random_method(instance_): # TODO\n",
" n = int(instance_.nPoints)\n",
" solution = np.random.choice(np.arange(n), size=n, replace=False)\n",
" return solution\n",
"\n",
"def marco_random_method(instance_):\n",
" solution = list(range(instance_.npoints))\n",
" for i in range(len(solution)):\n",
" rand = random.randint(i, len(solution))\n",
" solution[i], solution[rand] = solution[rand], solution[i]\n",
" return solution\n",
"\n",
"def numpy_random_method(insta_):\n",
" return np.random.permutation(np.arange(insta_.nPoints)) \n",
"available_methods = {\"random\": random_method,\n",
" \"marco\":marco_random_method,\n",
" \"numpy\":numpy_random_method}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from time import time as t\n",
"\n",
"class SolverTSP:\n",
" def __init__(self, algorithm_name, problem_instance):\n",
" self.duration = np.inf\n",
" self.found_length = np.inf\n",
" self.algorithm_name = algorithm_name\n",
" self.name_method = \"initialized with \" + algorithm_name\n",
" self.solved = False\n",
" self.problem_instance = problem_instance\n",
" self.solution = None\n",
"\n",
" def compute_solution(self, verbose=True, return_value=True):\n",
" self.solved = False\n",
" if verbose:\n",
" print(f\"### solving with {self.algorithm_name} ####\")\n",
" start_time = t()\n",
" self.solution = available_methods[self.algorithm_name](self.problem_instance)\n",
" assert self.check_if_solution_is_valid(self.solution), \"Error the solution is not valid\"\n",
" end_time = t()\n",
" self.duration = np.around(end_time - start_time, 3)\n",
" if verbose:\n",
" print(f\"### solved ####\")\n",
" self.solved = True\n",
" self.evaluate_solution()\n",
" self._gap()\n",
" if return_value:\n",
" return self.solution\n",
"\n",
" def plot_solution(self):\n",
" assert self.solved, \"You can't plot the solution, you need to compute it first!\"\n",
" plt.figure(figsize=(8, 8))\n",
" self._gap()\n",
" plt.title(f\"{self.problem_instance.name} solved with {self.name_method} solver, gap {self.gap}\")\n",
" ordered_points = self.problem_instance.points[self.solution]\n",
" plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')\n",
" plt.show()\n",
"\n",
" def check_if_solution_is_valid(self, solution):\n",
" rights_values = np.sum([self.check_validation(i, solution) for i in np.arange(self.problem_instance.nPoints)])\n",
" if rights_values == self.problem_instance.nPoints:\n",
" return True\n",
" else:\n",
" return False \n",
" \n",
" def check_validation(self, node , solution):\n",
" if np.sum(solution == node) == 1:\n",
" return 1\n",
" else:\n",
" return 0\n",
"\n",
" def evaluate_solution(self, return_value=False):\n",
" total_length = 0\n",
" from_node_id = self.solution[0] # starting_node\n",
" # TODO\n",
" for node_id in self.solution[1:]:\n",
" edge_distance = self.problem_instance.dist_matrix[from_node_id, node_id]\n",
" total_length += edge_distance\n",
" from_node_id = node_id\n",
" self.found_length = total_length\n",
" if return_value:\n",
" return total_length\n",
"\n",
" def _gap(self):\n",
" self.evaluate_solution(return_value=False)\n",
" self.gap = np.round(\n",
" ((self.found_length - self.problem_instance.best_sol) / self.problem_instance.best_sol) * 100, 2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------------------------\n",
"Now we will test our code"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"### solving with random ####\n",
"### solved ####\n",
"the total length for the solution found is 2522.0\n",
"while the optimal length is 538.0\n",
"the gap is 368.77%\n",
"the solution is found in 0.0 seconds\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'\\n### solving with random ####\\n### solved ####\\nthe total length for the solution found is 2424.0\\nwhile the optimal length is 538.0\\nthe gap is 350.56%\\nthe solution is found in 0.0 seconds\\n'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# here I'm repeating this two lines just to remind you which problem we are using\n",
"example_problem = \"../problems/eil76.tsp\"\n",
"p_inst = ProblemInstance(example_problem)\n",
"available_methods = {\"random\": random_method,\n",
" \"marco\":marco_random_method,\n",
" \"numpy\":numpy_random_method}\n",
"solver_name=\"random\"\n",
"# TODO\n",
"# 1. create an instance of SolverTSP\n",
"solver = SolverTSP(solver_name, p_inst)\n",
"# 2. compute a solution\n",
"solver.compute_solution()\n",
"# 3. print the information as for the output\n",
"# print(\"the total length for the solution found is\" + solver.solution)\n",
"print(f\"the total length for the solution found is {solver.found_length}\",\n",
" f\"while the optimal length is {solver.problem_instance.best_sol}\",\n",
" f\"the gap is {solver.gap}%\",\n",
" f\"the solution is found in {solver.duration} seconds\", sep=\"\\n\")\n",
"# 4. plot the solution\n",
"solver.plot_solution()\n",
"# this is the output expected and after that the solution's plot\n",
"\"\"\"\n",
"### solving with random ####\n",
"### solved ####\n",
"the total length for the solution found is 2424.0\n",
"while the optimal length is 538.0\n",
"the gap is 350.56%\n",
"the solution is found in 0.0 seconds\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------------\n",
"Finally since our example problem has an optimal solution we can plot it"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"solver = SolverTSP(\"optimal\", p_inst)\n",
"solver.solved = True\n",
"solver.solution = np.concatenate([p_inst.optimal_tour, [p_inst.optimal_tour[0]]])\n",
"solver.plot_solution()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "PyCharm (AI2020BsC)",
"language": "python",
"name": "pycharm-61970693"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}