From aba1c6052c664c75b7ce03bd541b2e340cfae796 Mon Sep 17 00:00:00 2001 From: Harrison Deng Date: Wed, 10 Apr 2024 02:10:36 +0000 Subject: [PATCH] Completed EC3 task 1. --- A4/CSC_373_EC3.ipynb | 8941 ++++++++++++++++++++++++++++++++++++++++++ A4/task1.png | Bin 0 -> 224398 bytes 2 files changed, 8941 insertions(+) create mode 100644 A4/CSC_373_EC3.ipynb create mode 100644 A4/task1.png diff --git a/A4/CSC_373_EC3.ipynb b/A4/CSC_373_EC3.ipynb new file mode 100644 index 0000000..816afd5 --- /dev/null +++ b/A4/CSC_373_EC3.ipynb @@ -0,0 +1,8941 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "0744d366-ab31-4ed2-8845-e1d26ca90912", + "metadata": { + "cellView": "form", + "id": "0744d366-ab31-4ed2-8845-e1d26ca90912" + }, + "outputs": [], + "source": [ + "# @title Run to load helper functions\n", + "from dataclasses import dataclass\n", + "import networkx as nx\n", + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", + "import numpy as np\n", + "from scipy.optimize import linprog\n", + "import graphviz\n", + "pio.templates.default = \"plotly_white\"\n", + "\n", + "def plot_graph(g):\n", + " plot = graphviz.Digraph(engine=\"circo\")\n", + " for v in g.nodes:\n", + " plot.node(v)\n", + " for u, v in g.edges():\n", + " label = str(g.edges[(u, v)]['capacity']) if \"capacity\" in g.edges[(u, v)] else \"\"\n", + " plot.edge(u, v, label=label)\n", + " display(plot)\n", + "\n", + "def plot_bipartite(g, left, weights=False, matching=None):\n", + " fig = go.Figure()\n", + " right = [i for i in g.nodes if i not in left]\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=[0]*len(left),\n", + " y=list(range(len(left))),\n", + " text = list(map(str, left)),\n", + " textposition=\"middle left\",\n", + " mode='markers+text',\n", + " marker=dict(color=\"red\")\n", + " )\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=[1]*len(right),\n", + " y=list(range(len(right))),\n", + " text = list(map(str, right)),\n", + " textposition=\"middle right\",\n", + " mode='markers+text',\n", + " marker=dict(color=\"blue\")\n", + " )\n", + " )\n", + " for u,v,d in g.edges(data=True):\n", + " width = (1/3 + d['weight'])*3/4 if weights else 1\n", + " text = \"Weight: \" + str(d['weight']) if weights else \"\"\n", + " color = \"black\"\n", + " if matching is not None and (u, v) in matching:\n", + " color=\"pink\"\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " name=f\"{u}, {v}\",\n", + " x= np.linspace(0,1,101),\n", + " y=np.linspace(left.index(u), right.index(v), 101),\n", + " mode='lines',\n", + " marker=dict(color=color),\n", + " line=dict(width=width),\n", + " text= text\n", + " )\n", + " )\n", + " fig.update_layout(showlegend=False, width=700 , height=1000)\n", + " fig.update_xaxes(visible=False, range=(-0.1, 1.1))\n", + " fig.update_yaxes(visible=False)\n", + " return fig\n", + "\n", + "WORK_TIMES = [\n", + " \"Monday AM\"\n", + " , \"Tuesday AM\"\n", + " , \"Wednesday AM\"\n", + " , \"Thursday AM\"\n", + " , \"Friday_AM\"\n", + " , \"Saturday_AM\"\n", + " , \"Sunday_AM\"\n", + " , \"Monday_PM\"\n", + " , \"Tuesday_PM\"\n", + " , \"Wednesday_PM\"\n", + " , \"Thursday_PM\"\n", + " , \"Friday_PM\"\n", + " , \"Saturday_PM\"\n", + " , \"Sunday_PM\"\n", + "]\n", + "\n", + "\n", + "@dataclass\n", + "class Response:\n", + " student_id: str\n", + " availability: set\n", + "\n", + "def get_random_responses(n, seed):\n", + " seed_n = int.from_bytes(seed.encode('utf-8'), \"little\")\n", + " rng = np.random.default_rng(seed=seed_n)\n", + " cs_students = []\n", + " business_students = []\n", + " c = 0\n", + " p = [0.1, 0.3, 0.4, 0.2]\n", + " for _ in range(n//2):\n", + " num_days_available = rng.choice([1, 2, 3, 4], p=p)\n", + " times = set(rng.choice(WORK_TIMES, size=num_days_available, replace=False))\n", + " cs_students.append(Response(str(c), times))\n", + " c+=1\n", + " for _ in range(n//2):\n", + " num_days_available = rng.choice([1, 2, 3, 4], p=p)\n", + " times = set(rng.choice(WORK_TIMES, size=num_days_available, replace=False))\n", + " business_students.append(Response(str(c), times))\n", + " c+=1\n", + " return cs_students, business_students" + ] + }, + { + "cell_type": "markdown", + "id": "a07b9440-111c-41b9-8ef0-9fdacb4966b7", + "metadata": { + "id": "a07b9440-111c-41b9-8ef0-9fdacb4966b7" + }, + "source": [ + "# Basics: Graphs, Flow, and LP in Python" + ] + }, + { + "cell_type": "markdown", + "id": "47d48929-7e37-491c-a3ec-66ac07c6852b", + "metadata": { + "id": "47d48929-7e37-491c-a3ec-66ac07c6852b" + }, + "source": [ + "Before we get started with the exercises, let's get acquainted with graphs and LPs in Python, as well as some of the helper functions I've provided for you.\n", + "\n", + "## Directed graphs + Flow\n", + "\n", + "Here's how to define a directed graph using the networkx library (imported as nx), this is an example from the flow lecture.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "674a699c-22b5-42cf-859a-8ac38f68389e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + }, + "id": "674a699c-22b5-42cf-859a-8ac38f68389e", + "outputId": "ef511bdc-4144-42fd-c8d8-af5c576d8645" + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "s\n", + "\n", + "s\n", + "\n", + "\n", + "\n", + "u\n", + "\n", + "u\n", + "\n", + "\n", + "\n", + "s->u\n", + "\n", + "\n", + "20\n", + "\n", + "\n", + "\n", + "v\n", + "\n", + "v\n", + "\n", + "\n", + "\n", + "s->v\n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "\n", + "u->v\n", + "\n", + "\n", + "30\n", + "\n", + "\n", + "\n", + "t\n", + "\n", + "t\n", + "\n", + "\n", + "\n", + "u->t\n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "\n", + "v->t\n", + "\n", + "\n", + "20\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = nx.DiGraph()\n", + "g.add_node(\"s\")\n", + "g.add_node(\"u\")\n", + "g.add_node(\"v\")\n", + "g.add_node(\"t\")\n", + "g.add_edge(\"s\", \"u\", capacity=20)\n", + "g.add_edge(\"s\", \"v\", capacity=10)\n", + "g.add_edge(\"u\", \"v\", capacity=30)\n", + "g.add_edge(\"u\", \"t\", capacity=10)\n", + "g.add_edge(\"v\", \"t\", capacity=20)\n", + "plot_graph(g)" + ] + }, + { + "cell_type": "markdown", + "id": "52e31fec-eae4-4c55-9694-cf8082e897f7", + "metadata": { + "id": "52e31fec-eae4-4c55-9694-cf8082e897f7" + }, + "source": [ + "You can solve the network flow problem on the graph by calling `nx.maximum_flow`, you'll receive the flow value as well as some data structure from which you can extract the flow values along each edge!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "66347cb5-2752-4369-aa47-96f29c483688", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "66347cb5-2752-4369-aa47-96f29c483688", + "outputId": "2b49d0a5-0f0b-47a0-dcfc-b68c2b946c7c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "{'s': {'u': 20, 'v': 10}, 'u': {'v': 10, 't': 10}, 'v': {'t': 20}, 't': {}}\n" + ] + } + ], + "source": [ + "flow_value, flow_dict = nx.maximum_flow(g, \"s\", \"t\")\n", + "print(flow_value)\n", + "print(flow_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "6eb94084-61f8-4c96-8ea7-fed90dde702e", + "metadata": { + "id": "6eb94084-61f8-4c96-8ea7-fed90dde702e" + }, + "source": [ + "## Plotting bipartite graphs\n", + "\n", + "When your graph is bipartite, the `plot_graph` function doesn't always plot it in a way that \"looks bipartite\"." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "12758b83-6e78-4335-9fb3-e7b0f08485f2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 902 + }, + "id": "12758b83-6e78-4335-9fb3-e7b0f08485f2", + "outputId": "bfed8ffd-fd79-4436-87b0-a5620a47ba2b" + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", 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"\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = nx.DiGraph()\n", + "g.add_node(\"Earth\")\n", + "g.add_node(\"Water\")\n", + "g.add_node(\"Air\")\n", + "g.add_node(\"Fire\")\n", + "g.add_node(\"Aang\")\n", + "g.add_node(\"Katara\")\n", + "g.add_node(\"Toph\")\n", + "g.add_node(\"Zuko\")\n", + "g.add_node(\"Azula\")\n", + "g.add_edge(\"Aang\", \"Earth\", weight=1)\n", + "g.add_edge(\"Aang\", \"Air\", weight=2)\n", + "g.add_edge(\"Aang\", \"Water\", weight=1)\n", + "g.add_edge(\"Aang\", \"Fire\", weight=1)\n", + "g.add_edge(\"Katara\", \"Water\", weight=2)\n", + "g.add_edge(\"Toph\", \"Earth\", weight=3)\n", + "g.add_edge(\"Zuko\", \"Fire\", weight=1)\n", + "g.add_edge(\"Azula\", \"Fire\", weight=2)\n", + "plot_graph(g)" + ] + }, + { + "cell_type": "markdown", + "id": "8dff805a-cfe2-4376-8549-ef96a84f1e11", + "metadata": { + "id": "8dff805a-cfe2-4376-8549-ef96a84f1e11" + }, + "source": [ + "Instead, you can use the `plot_bipartite(g, left, weights=False, matching=None)` function. Here, `g` is a graph, and `left` is a list of vertices to be plotted on the left.\n", + "\n", + "Here are several examples showcasing the different parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "18438025-ce74-4abe-9ae7-c0b7cfefad8b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "18438025-ce74-4abe-9ae7-c0b7cfefad8b", + "outputId": "21ab529a-163e-42ed-b400-25f3accad5a0" + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "red" + }, + "mode": "markers+text", + "text": [ + "Aang", + "Katara", + "Toph", + "Zuko", + "Azula" + ], + "textposition": "middle left", + "type": "scatter", + "x": [ + 0, + 0, + 0, + 0, + 0 + ], + "y": [ + 0, + 1, + 2, + 3, + 4 + ] + }, + { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "matching = [(\"Aang\", \"Air\"), (\"Katara\", \"Water\"), (\"Zuko\", \"Fire\"), (\"Toph\", \"Earth\") ]\n", + "plot_bipartite(g, [\"Aang\", \"Katara\", \"Toph\", \"Zuko\", \"Azula\"], weights=True, matching=matching)" + ] + }, + { + "cell_type": "markdown", + "id": "c0138b47-0bfe-4016-911b-f5e18c4393c3", + "metadata": { + "id": "c0138b47-0bfe-4016-911b-f5e18c4393c3" + }, + "source": [ + "## Linear Programs\n", + "\n", + "For linear programs, we'll use the popular scientific computing library scipy. The documentation can be found [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linprog.html)\n", + "\n", + "Here's an example from the lecture...\n", + "\n", + "Note that `linprog` minimizes by default so you need to negate your objective function if you want to maximize." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "efc93e0c-e9ce-442f-9bcd-b952f600cc31", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "efc93e0c-e9ce-442f-9bcd-b952f600cc31", + "outputId": "15ee8f33-7fd8-461c-9c89-e42d0920f03a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + " message: Optimization terminated successfully. (HiGHS Status 7: Optimal)\n", + " success: True\n", + " status: 0\n", + " fun: -28.0\n", + " x: [ 8.000e+00 4.000e+00 0.000e+00]\n", + " nit: 2\n", + " lower: residual: [ 8.000e+00 4.000e+00 0.000e+00]\n", + " marginals: [ 0.000e+00 0.000e+00 1.667e-01]\n", + " upper: residual: [ inf inf inf]\n", + " marginals: [ 0.000e+00 0.000e+00 0.000e+00]\n", + " eqlin: residual: []\n", + " marginals: []\n", + " ineqlin: residual: [ 1.800e+01 0.000e+00 0.000e+00]\n", + " marginals: [-0.000e+00 -1.667e-01 -6.667e-01]\n", + " mip_node_count: 0\n", + " mip_dual_bound: 0.0\n", + " mip_gap: 0.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = [-3,-1,-2]\n", + "A = [\n", + " [1, 1, 3],\n", + " [2, 2, 5],\n", + " [4, 1, 2]\n", + " ]\n", + "b = [30, 24, 36]\n", + "linprog(c, A_ub=A, b_ub=b, bounds=(0,None))" + ] + }, + { + "cell_type": "markdown", + "id": "f7b32720-b8c8-427f-9e3d-3eb482ea5d63", + "metadata": { + "id": "f7b32720-b8c8-427f-9e3d-3eb482ea5d63" + }, + "source": [ + "# Match Making\n", + "\n", + "Imagine you are in the following scenario. You are a TA for an interdisciplinary course hosted by the CS and Business departments. As a part of the course, students need to pair up - one from the CS department and one from the Business department to develop an app.\n", + "\n", + "Since the CS students and Business students don't take that many other classes together, many of them would like some help finding a project partner. To facilitate this, the professor sent out a form asking for the student's home department and availability to work on a project. To simplify the problem a bit, let's suppose that every student is the class is either in the CS department or the Business department but not both. The prof, who hears that you have taken 373, tasks you with matching the students.\n", + "\n", + "Here is the data from the form responses." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "84ab68a4-8efa-48ba-83e3-26eb8323be7f", + "metadata": { + "id": "84ab68a4-8efa-48ba-83e3-26eb8323be7f" + }, + "outputs": [], + "source": [ + "cs_students, business_students = get_random_responses(30, seed=\"replace with your groups UTorIDs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "14557dcf-ec81-4618-9701-427c33268e1e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "14557dcf-ec81-4618-9701-427c33268e1e", + "outputId": "3cf304a8-f8b8-40fb-9e2d-5a13c8aabd8c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Response(student_id='0', availability={'Monday_PM', 'Saturday_AM', 'Sunday_PM'})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cs_students[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "_bFZl5lUEvg7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "_bFZl5lUEvg7", + "outputId": "49a2fdf6-181f-4e3a-b13c-9de080fb07ff" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cs_students[0].student_id" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dOa40fmkEyjh", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dOa40fmkEyjh", + "outputId": "fac07c07-94bb-4a29-bec1-218ddbe92741" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Monday_PM', 'Saturday_AM', 'Sunday_PM'}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cs_students[0].availability" + ] + }, + { + "cell_type": "markdown", + "id": "16096390-8421-4aaa-b75e-d581e99398eb", + "metadata": { + "id": "16096390-8421-4aaa-b75e-d581e99398eb" + }, + "source": [ + "As you can see, a response contains a student ID and a set of timeslots in which they are available.\n", + "\n", + "A CS student (one from the `cs_students` list) and a Business student can be paired up iff they overlap in their avaialability.\n", + "\n", + "## Task 1\n", + "Model this problem as a network flow problem. Solve the flow problem and extract a matching of the students. To submit, change the seed to a string containing your UTorIDs, run your code, and upload.\n", + "1. The block of code that generated the responses (above)\n", + "2. A screenshot of the flow dictionary you obtained\n", + "3. A screenshot of the graph you obtained along with the matching. I.e., it should be the output of `plot_bipartite` with the matching parameter equal to the matching you found." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "55b2c4b2-7dae-4b9d-a298-d67fa5173826", + "metadata": { + "id": "55b2c4b2-7dae-4b9d-a298-d67fa5173826" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Response(student_id='0', availability={'Monday_PM', 'Saturday_AM', 'Sunday_PM'})\n", + "Response(student_id='1', availability={'Monday_PM', 'Tuesday_PM'})\n", + "Response(student_id='2', availability={'Friday_AM', 'Sunday_PM', 'Thursday AM'})\n", + "Response(student_id='3', availability={'Friday_AM', 'Sunday_PM', 'Wednesday AM'})\n", + "Response(student_id='4', availability={'Monday_PM', 'Tuesday AM', 'Monday AM'})\n", + "Response(student_id='5', availability={'Friday_AM', 'Sunday_PM', 'Wednesday AM', 'Thursday AM'})\n", + "Response(student_id='6', availability={'Tuesday_PM', 'Sunday_PM', 'Sunday_AM'})\n", + "Response(student_id='7', availability={'Thursday_PM', 'Sunday_PM'})\n", + "Response(student_id='8', availability={'Monday AM'})\n", + "Response(student_id='9', availability={'Friday_PM', 'Sunday_AM', 'Wednesday_PM'})\n", + "Response(student_id='10', availability={'Saturday_AM', 'Monday AM'})\n", + "Response(student_id='11', availability={'Friday_PM', 'Saturday_PM', 'Wednesday_PM'})\n", + "Response(student_id='12', availability={'Friday_AM', 'Tuesday_PM', 'Thursday AM'})\n", + "Response(student_id='13', availability={'Tuesday AM', 'Thursday AM', 'Wednesday_PM'})\n", + "Response(student_id='14', availability={'Friday_AM', 'Saturday_AM', 'Saturday_PM'})\n" + ] + } + ], + "source": [ + "for cs_student in cs_students:\n", + " for " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ae561d1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9efa7d19", + "metadata": {}, + "source": [ + "## Task 2\n", + "Since you want students to put in a good effort on the project, you want to match students with more overlap in their availability. For two students, define their compatibility score as the number of timeslots they overlap. Your new goal is to find a matching of all the students that maximizes the total compatibility score. Model the problem as a Linear Program, solve it, and extract the matching.\n", + "\n", + "NOTE: If you find that you want to introduce some integrality constraints, try running the linear program without these constraints. It turns out that in the solution to this particular linear program, the integrality constraints are not needed and the optimal solution will in fact be integral anyway...\n", + "\n", + "To submit, upload\n", + "1. A screenshot of the output of `linprog` (it's okay if it is truncated)\n", + "2. A screenshot of the graph with this matching, again it should be the output of `plot_bipartite` with the matching parameter equal to the matching that you found, and weights=True.\n", + "3. Calculate the total weight of this matching and compare it to the matching obtained in Task 1.\n", + "\n", + "Another note: Networkx has some functions that find matchings directly (i.e. without reducing to flow). See [here](https://networkx.org/documentation/stable/reference/algorithms/matching.html). You shouldn't use these function for this exercise since one of the main points of this exercise is to have you reduce the matching problem to flow/LP. However, it's good to know that these things exist if you ever need to compute a matching in the future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39b97a8f-84a8-4fc1-8eaa-5487423f042e", + "metadata": { + "id": "39b97a8f-84a8-4fc1-8eaa-5487423f042e" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10c8e19d-2500-4013-803d-298da3de47ef", + "metadata": { + "id": "10c8e19d-2500-4013-803d-298da3de47ef" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b45b2fe-78d9-4b94-b224-f0061d4cd858", + "metadata": { + "id": "4b45b2fe-78d9-4b94-b224-f0061d4cd858" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fdccb5c-a1c3-404c-ab1e-13d01b8f21e9", + "metadata": { + "id": "9fdccb5c-a1c3-404c-ab1e-13d01b8f21e9" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + 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