Assignment 2 - Clustering¶ Learning Outcomes¶ In this assignment, you will do the following: · Explore a dataset and carry out clustering using k-means algorithm · Identify the optimum number of...

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Hello,I have an assignment on clustering that I think I will not finish on time due to other pressing work. I have included the word doc and theassignment2.ipynb file of which the work has to be completed. The data file is large, I have included the link to it on the doc file. Best Regards, Ed


Assignment 2 - Clustering¶ Learning Outcomes¶ In this assignment, you will do the following: · Explore a dataset and carry out clustering using k-means algorithm · Identify the optimum number of clusters for a given dataset Data: https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014 Problem Description¶ In this assignment, you will study the electricity demand from clients in Portugal, during 2013 and 2014. You have been provided with the data file, which you should download when you download this assignment file. The data11 available contains 370 time series, corresponding to the electric demand22 for 370 clients, between 2011 and 2014. In this guided exercise, you will use clustering techniques to understand the typical usage behaviour during 2013-2014. Both these datasets are publicly available, and can be used to carry out experiments. Their source is below: 1. Data: https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014# 2. Electric Demand: http://www.think-energy.net/KWvsKWH.htm We will start by exploring the data set and continue on to the assignment. Consider this as a working notebook, you will add your work to the same notebook. In this assignment we will use the sklearn package for k-means. Please refer here for the documentation https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html (https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html). The sklearn package for k-means is one of the many clustering algorithms found in the module "sklearn.cluster". These come with a variety of functions that you can call by importing the package. For example from sklearn.cluster import AgglomerativeClustering from sklearn.cluster import KMeans Work to be completed in the workbook provide - assignment2.ipynb – the questions are at the 2nd half of the workbook. Questions (15 marks total)¶ Q1: (7 marks) a. Determine what a convenient number of clusters. Justify your choice. Make use of the sklearn's package for k-means for this. You may refer to the module to figure out how to come up with the optimal number of clusters. b. Make a plot for each cluster, that includes: - The number of clients in the cluster (you can put this in the title of the plot) - All the curves in the cluster - The curve corresponding to the center of the cluster (make this curve thicker to distinguish it from the individual curves). The center is also sometimes referred to as "centroid". You have 2 separate plots for each cluster if you prefer (one for the individual curves, one for the centroid) Q2: (8 marks) In this exercise you work with the daily curves of 1 single client. First, create a list of arrays, each array containing a curve for a day. You may use X from the cells above. X = average_curves_norm.copy() The list contains 730 arrays, one for each of the days of 2013 and 2014. a. Determine the optimal value of k ( number of clusters). This time you may also perform silhoutte analysis as stated in the module. Carrying out silhoutte analysis is left as an exercise. What do you understand about the clusters? b. Based on your results from your analyses of both methods, what do understand? Interpret it perhaps with different perspectives of timelines like weeks or months.
assignment-2-ga4l2skl.docx assignment2-gy2obatm.ipynb
Answered Same DayJun 22, 2021

Answer To: Assignment 2 - Clustering¶ Learning Outcomes¶ In this assignment, you will do the following: ·...

Sandeep Kumar answered on Jun 26 2021
133 Votes
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "D9UboDIvnAKI"
},
"source": [
"## Assignment 2 - Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5AibtmcInAKK"
},
"source": [
"## Learning Outcomes\n",
"\n",
"In this assignment, you will do the following:\n",
"\n",
"* Explore a dataset and carry out clustering using k-means algorithm\n",
"\n",
"* Identify the optimum number of clusters for a given dataset\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "TJBMBFfAnAKK"
},
"source": [
"## Problem Description\n",
"\n",
"In this assignment, you will study the electricity demand from clients in Portugal, during 2013 and 2014. You have been provided with the data file, which you should download when you download this assignment file.\n",
"\n",
"The data$^1$ available contains 370 time series, corresponding to the electric demand$^2$ for 370 clients, between 2011 and 2014. \n",
"\n",
"In this guided exercise, you will use clustering techniques to understand the typical usage behaviour during 2013-2014.\n",
"\n",
"Both these datasets are publicly available, and can be used to carry out experiments. Their source is below:\n",
"\n",
" 1. Data:\n",
"https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014#\n",
"\n",
" 2. Electric Demand:\n",
"http://www.think-energy.net/KWvsKWH.htm\n",
"\n",
"We will start by exploring the data set and continue on to the assignment. Consider this as a working notebook, you will add your work to the same notebook.\n",
"\n",
"In this assignment we will use the sklearn package for k-means. Please refer here for the documentation https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n",
"(https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html).\n",
"\n",
"The sklearn package for k-means is one of the many clustering algorithms found in the module \"sklearn.cluster\". These come with a variety of functions that you can call by importing the package.\n",
"\n",
"For example \n",
" \n",
" from sklearn.cluster import AgglomerativeClustering\n",
" from sklearn.cluster import KMeans\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "u0fHlteBnAKL"
},
"source": [
"## Data Preparation\n",
"\n",
"Start by downloading the data to a local directory and modify the \"pathToFile\" and \"fileName\" variables, if needed. The data file has been provided with this assignment. It is also available at the links provided above."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0DJsRL9_nAKM"
},
"outputs": [],
"source": [
"pathToFile = r\"\"\n",
"#pathToFile = r\"C:\\\\Users\\\\\\\\Downloads\\\\\"\n",
"\n",
"fileName = 'LD2011_2014.txt'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "yLxHF5B-nAKP"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.cluster import KMeans\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"from sklearn.metrics import silhouette_score\n",
"from sklearn.cluster import AgglomerativeClustering\n",
"random.seed(42)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "6c6CmGbYnAKR"
},
"outputs": [],
"source": [
"# Replace \",\" by \".\", otherwise the numbers will be in the form 2,3445 instead of 2.3445\n",
"import fileinput\n",
"\n",
"with fileinput.FileInput(pathToFile+fileName, inplace=True, backup='.bak') as file:\n",
" for line in file:\n",
" print(line.replace(\",\", \".\"), end='')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ACTUfls8nAKU"
},
"outputs": [],
"source": [
"# Create dataframe\n",
"import pandas as pd\n",
"data = pd.read_csv(pathToFile+fileName, sep=\";\", index_col=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "CfULOBctnAKW"
},
"source": [
"### Quick data inspection"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "x3OHI8vRnAKX",
"outputId": "c821694f-6e8f-48ad-ff42-336915cb4da4"
},
"outputs": [
{
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MT_001MT_002MT_003MT_004MT_005MT_006MT_007MT_008MT_009MT_010...MT_361MT_362MT_363MT_364MT_365MT_366MT_367MT_368MT_369MT_370
2011-01-01 00:15:000.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
2011-01-01 00:30:000.00.00.00.00.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.00.0
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2 rows × 370 columns

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" MT_001 MT_002 MT_003 MT_004 MT_005 MT_006 MT_007 \\\n",
"2011-01-01 00:15:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2011-01-01 00:30:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" MT_008 MT_009 MT_010 ... MT_361 MT_362 MT_363 \\\n",
"2011-01-01 00:15:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"2011-01-01 00:30:00 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
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" MT_364 MT_365 MT_366 MT_367 MT_368 MT_369 MT_370 \n",
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"2011-01-01 00:30:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
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"[2 rows x 370 columns]"
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},
"execution_count": 18,
"metadata": {},
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"source": [
"data.head(2)"
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{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "BtQvkYF_nAKd",
"outputId": "9598f8f3-b53a-49ce-d97f-51d562f582d0"
},
"outputs": [
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MT_001MT_002MT_003MT_004MT_005MT_006MT_007MT_008MT_009MT_010...MT_361MT_362MT_363MT_364MT_365MT_366MT_367MT_368MT_369MT_370
2014-12-31 23:45:001.26903621.3371271.737619166.66666785.365854285.71428610.17524225.58922664.68531572.043011...246.25267728000.01443.037975909.09090926.0756194.095963664.618086146.911519646.6275666540.540541
2015-01-01 00:00:002.53807119.9146511.737619178.86178984.146341279.76190510.17524249.15824962.93706369.892473...188.43683127800.01409.282700954.54545527.3794004.095963628.621598131.886477673.0205287135.135135
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" MT_001 MT_002 MT_003 MT_004 MT_005 \\\n",
"2014-12-31 23:45:00 1.269036 21.337127 1.737619 166.666667 85.365854 \n",
"2015-01-01 00:00:00 2.538071 19.914651 1.737619 178.861789 84.146341 \n",
"\n",
" MT_006 MT_007 MT_008 MT_009 MT_010 \\\n",
"2014-12-31 23:45:00 285.714286 10.17524 225.589226 64.685315 72.043011 \n",
"2015-01-01 00:00:00 279.761905 10.17524 249.158249 62.937063 69.892473 \n",
"\n",
" ... MT_361 MT_362 MT_363 MT_364 \\\n",
"2014-12-31 23:45:00 ... 246.252677 28000.0 1443.037975 909.090909 \n",
"2015-01-01 00:00:00 ... 188.436831 27800.0 1409.282700 954.545455 \n",
"\n",
" MT_365 MT_366 MT_367 MT_368 MT_369 \\\n",
"2014-12-31 23:45:00 26.075619 4.095963 664.618086 146.911519 646.627566 \n",
"2015-01-01 00:00:00 27.379400 4.095963 628.621598 131.886477 673.020528 \n",
"\n",
" MT_370 \n",
"2014-12-31 23:45:00 6540.540541 \n",
"2015-01-01 00:00:00 7135.135135 \n",
"\n",
"[2 rows x 370 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail(2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Ydr4AkL9nAKg",
"outputId": "1b4b50f0-3fc4-4805-8160-d8872a35efae"
},
"outputs": [
{
"data": {
"text/plain": [
"(140256, 370)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "j4xzOoTunAKj"
},
"source": [
"#### As it can be seen, the dataframe contains a row for each interval of 15 minutes between Jan 1, 2011 to Dec 31 2014. There are 370 columns corresponding 370 clients. The dataframe is indexed by the timestamp.\n",
"\n",
"Since the frequency is 15 minutes, each day provides $24\\times 4 = 96$ datapoints, which multiplied by 365 days and 4 years (plus 1 day in Feb 29, 2012) gives: $96 \\times 365 \\times 4 + 96 = 140256$, as observed in data.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "K1RXKoAwnAKj",
"outputId": "a7cfcc67-0a4c-4ee9-8cc9-b6ea80a74d3a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Index: 140256 entries, 2011-01-01 00:15:00 to 2015-01-01 00:00:00\n",
"Columns: 370 entries, MT_001 to MT_370\n",
"dtypes: float64(370)\n",
"memory usage: 397.0+ MB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "LqyOk3X6nAKm",
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"outputs": [
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MT_001MT_002MT_003MT_004MT_005MT_006MT_007MT_008MT_009MT_010...MT_361MT_362MT_363MT_364MT_365MT_366MT_367MT_368MT_369MT_370
count140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000...140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000140256.000000
mean3.97078520.7684802.91830882.18449037.240309141.2273854.521338191.40147639.97535442.205152...218.21370137607.9875371887.4273662940.03173465.4131509.269709424.26290494.704717625.2517348722.355145
std5.98396513.27241511.01445658.24839226.46132798.4399846.485684121.98118729.81459533.401251...204.83353238691.9548321801.4864882732.25196765.00781810.016782274.33712280.297301380.6560429195.155777
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.0000002.8449500.00000036.58536615.85365971.4285710.565291111.11111113.9860149.677419...5.7102070.0000000.0000000.00000013.0378100.0000000.00000030.05008383.9442820.000000
50%1.26903624.8933141.73761987.39837439.024390157.7380952.826456222.22222240.20979040.860215...131.33476124100.0000001050.6329112136.36363631.2907437.021650525.89991276.794658758.0645160.000000
75%2.53807129.8719771.737619115.85365954.878049205.3571434.522329279.46127957.69230861.290323...403.28336954800.0000003312.2362875363.636364108.21382011.702750627.743635151.919866875.36656917783.783784
max48.223350115.220484151.172893321.138211150.000000535.71428644.657999552.188552157.342657198.924731...852.962170192800.0000007751.05485212386.363636335.07170860.2691631138.718174362.2704511549.12023530918.918919
\n",
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8 rows × 370 columns

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" MT_001 MT_002 MT_003 MT_004 \\\n",
"count 140256.000000 140256.000000 140256.000000 140256.000000 \n",
"mean 3.970785 20.768480 2.918308 82.184490 \n",
"std 5.983965 13.272415 11.014456 58.248392 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 2.844950 0.000000 36.585366 \n",
"50% 1.269036 24.893314 1.737619 87.398374 \n",
"75% 2.538071 29.871977 1.737619 115.853659 \n",
"max 48.223350 115.220484 151.172893 321.138211 \n",
"\n",
" MT_005 MT_006 MT_007 MT_008 \\\n",
"count 140256.000000 140256.000000 140256.000000 140256.000000 \n",
"mean 37.240309 141.227385 4.521338 191.401476 \n",
"std 26.461327 98.439984 6.485684 121.981187 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 15.853659 71.428571 0.565291 111.111111 \n",
"50% 39.024390 157.738095 2.826456 222.222222 \n",
"75% 54.878049 205.357143 4.522329 279.461279 \n",
"max 150.000000 535.714286 44.657999 552.188552 \n",
"\n",
" MT_009 MT_010 ... MT_361 MT_362 \\\n",
"count 140256.000000 140256.000000 ... 140256.000000 140256.000000 \n",
"mean 39.975354 42.205152 ... 218.213701 37607.987537 \n",
"std 29.814595 33.401251 ... 204.833532 38691.954832 \n",
"min 0.000000 0.000000 ... 0.000000 0.000000 \n",
"25% 13.986014 9.677419 ... 5.710207 0.000000 \n",
"50% 40.209790 40.860215 ... 131.334761 24100.000000 \n",
"75% 57.692308 61.290323 ... 403.283369 54800.000000 \n",
"max 157.342657 198.924731 ... 852.962170 192800.000000 \n",
"\n",
" MT_363 MT_364 MT_365 MT_366 \\\n",
"count 140256.000000 140256.000000 140256.000000 140256.000000 \n",
"mean 1887.427366 2940.031734 65.413150 9.269709 \n",
"std 1801.486488 2732.251967 65.007818 10.016782 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 13.037810 0.000000 \n",
"50% 1050.632911 2136.363636 31.290743 7.021650 \n",
"75% 3312.236287 5363.636364 108.213820 11.702750 \n",
"max 7751.054852 12386.363636 335.071708 60.269163 \n",
"\n",
" MT_367 MT_368 MT_369 MT_370 \n",
"count 140256.000000 140256.000000 140256.000000 140256.000000 \n",
"mean 424.262904 94.704717 625.251734 8722.355145 \n",
"std 274.337122 80.297301 380.656042 9195.155777 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 30.050083 83.944282 0.000000 \n",
"50% 525.899912 76.794658 758.064516 0.000000 \n",
"75% 627.743635 151.919866 875.366569 17783.783784 \n",
"max 1138.718174 362.270451 1549.120235 30918.918919 \n",
"\n",
"[8 rows x 370 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bEz7tlUqnAKp"
},
"source": [
"### Plot the first 2 days of 2012 for the first 2 clients"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "DVIRDmI2nAKp",
"outputId": "f76887ae-1b6d-40ab-f9a8-63ef83264ae2"
},
"outputs": [
{
"data": {
"image/png":...
SOLUTION.PDF
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