首页 » Python代做 » Python代写 | COMP20008 Project 2

Python代写 | COMP20008 Project 2

这个作业是用Python对两个真实数据集执行数据链接并探索不同的分类算法
COMP20008 Project 2
The assignment is worth 25 marks, worth (25% of subject grade) and is due 8:00AM on
Monday 1 June 2020. Submission is via the LMS. Please ensure you get a submission
receipt via email. Late penalty structure is described at the end of this document.
Introduction
For this project, you will perform a data linkage on two real-world datasets (Part 1) and
explore different classification algorithms (Part 2).
The project is to be coded in Python 3. Eight (8) relevant data sets can be downloaded from
LMS and are available on Juptyer Hub:
ˆ datasets for Part 1: Data Linkage
– amazon.csv
– google.csv
– amazon google truth.csv
– amazon small.csv
– google small.csv
– amazon google truth small.csv
ˆ datasets for Part 2: Classification:
– life.csv
– world.csv
1
Part 1 – Data Linkage (12 marks)
Amazon and Google both have product databases. They would like to link the same products
in order to perform joint market research.
Na¨ıve data linkage without blocking (4 marks)
For this part, data linkage without blocking is performed on two smaller data sets:
amazon small.csv and google small.csv.
Task – 1A: Using amazon small.csv and google small.csv, implement the linkage between
the two data sets.
Your code for this question is to be contained in a single Python file called task1a.py and
produce a single csv file task1a.csv containing the following two column headings:
idAmazon, idGoogleBase
Each row in the datafile must contain a pair of matched products. For example, if your
algorithm only matched product b000hcz8ey from the Amazon dataset with product
http://www.google.com/base/feeds/snippets/11114112522015930440 from the Google
dataset your output task1a.csv would be as follows:
idAmazon, idGoogleBase
b000hcz8ey, http://www.google.com/base/feeds/snippets/11114112522015930440
The performance is evaluated in terms of recall and precision and the marks in this section
will be awarded based on the two measures of your algorithm.
recall = tp/(tp + fn)
precision = tp/(tp + f p)
where tp (true-positive) is the number of true positive pairs, f p the number of false positive
pairs, tn the number of true negatives, and fn the number of false negative pairs.
Note: The python package textdistance implements many similarity functions for strings
(https://pypi.org/project/textdistance/). You can use this package for the similarity
calculations for strings. You may also choose to implement your own similarity functions.
2
Blocking for efficient data linkage (4 marks)
Blocking is a method to reduce the computational cost for record linkage.
Task – 1B: Implement a blocking method for the linkage of the amazon.csv and google.csv
data sets.
Your code is be contained in a single Python file called task1b.py and must produce two csv
files amazon blocks.csv and google blocks.csv, each containing the following two column
headings:
block_key, product_id
The product id field corresponds to the idAmazon and idGoogleBase of the amazon.csv and
google.csv files respectively. Each row in the output files matches a product to a block. For
example, if your algorithm placed product b000hcz8ey from the Amazon dataset in blocks
with block keys x & y, your amazon blocks.csv would be as follows:
block_key, product_id
x, b000hcz8ey
y, b000hcz8ey
A block is uniquely identified by the block key. The same block key in the two block-files
(amazon blocks.csv and google blocks.csv) indicates that the corresponding products cooccur in the same block.
For example, if your algorithm placed the amazon product b000hcz8ey in block x and placed
google product http://www.google.com/base/feeds/snippets/11114112522015930440 in
block x, your amazon blocks.csv and google blocks.csv would be as follows respectively:
amazon_blocks.csv:
block_key, product_id
x, b000hcz8ey
google_blocks.csv:
block_key, product_id
x, http://www.google.com/base/feeds/snippets/11114112522015930440
The two products co-occur in the same block x.
To measure the quality of blocking, we assume that when comparing a pair of records, the
pair are always 100% similar and are a match. A pair of records are categorised as follows:
ˆ a record-pair is a true positive if the pair are found in the ground truth set and also the
pair co-occur in the same block.
ˆ a record-pair is a false positive if the pair co-occur in some block but are not found in
the ground truth set.
ˆ a record-pair is a false negative if the pair do not co-occur in any block but are found
in the ground truth set
3
ˆ a record-pair is a true negative if the pair do not co-occur in any block and are also not
found in the ground truth set.
Then, the quality of blocking can be evaluated using the following two measures:
P C (pair completeness) = tp/(tp + fn)
RR (reduction ratio) = 1 − (tp + fp)/n
where n is the total number of all possible record pairs from the two data sets.
(n = f p + fn + tp + tn)
The marks in this section will be awarded based on the pair completeness and reduction ratio
of your blocking algorithm.
Note: The time taken to produce your blocking implementation must be linear in the number
of items in the dataset. This means that you cannot, for example, compare each product to
every other product in the dataset in order to allocate it to a block. Implementations that
do so will be severely penalised.
Report on the Data Linkage project(4 marks)
Task – 1C Write a one-page report describing your algorithms and implementations of tasks
1a and 1b. You should discuss:
ˆ How your product comparison works, including your choice of similarity functions and
final scoring function and threshold.
ˆ An evaluation of the overall performance of your product comparison and what opportunities exist to improve it.
ˆ How your blocking implementation works.
ˆ An evaluation of the overall performance of your blocking method, how the method
relates to the performance measures and what opportunities exist to improve it.
Your report for this task should be contained in a single file called task1c.pdf or task1c.docx.
4
Part 2 – Classification (13 marks)
Each year, the World Bank publishes the World Development Indicators which provide high
quality and international comparable statistics about global development and the fight against
poverty [1]. As data scientists, we wish to understand how the information can be used to
predict average lifespan in different countries. To this end, we have provided the world.csv
file, which contains some of the World Development Indicators for each country and the
life.csv file containing information about the average lifespan for each country (based on
data from the World Health Organization) [2]. Each data file also contains a country name,
country code and year as identifiers for each record. These may be used to link the two
datasets but should not be considered features.
Comparing Classification Algorithms (3 marks)
Task – 2A Compare the performance of the following 3 classification algorithms: k-NN (k=5
and k=10) and Decision tree (with a maximum depth of 4) on the provided data. You may
use sklearn’s KNeighborsClassifier and DecisionTreeClassifier functions for this task. For the
k-NN classifier, all parameters other than k should be kept at their defaults. For the Decision tree classifier, all parameters other than the maximum depth should be kept at their
defaults. Use each classification algorithm to predict the class feature life expectancy at
birth(years) of the data (Low, Medium and High life expectancy) using the remaining features.
For each of the algorithms, fit a model with the following processing steps:
ˆ Split the dataset into a training set comprising 2/3 of the data and a test set comprising
the remaining 1/3 using the train test split function with a random state of 100.
ˆ Perform the same imputation and scaling to the training set:
– For each feature, perform median imputation to impute missing values.
– Scale each feature by removing the mean and scaling to unit variance.
ˆ Train the classifiers using the training set
ˆ Test the classifiers by applying them to the test set.
Your code must produce a CSV file called task2a.csv describing the median used for imputation for each feature, as well as the mean and variance used for scaling, all rounded to
three decimal places. The CSV file must have one row corresponding to each feature. The
first three lines of the output should be as follows (where x is a number calculated by your
program):
feature, median, mean, variance
Access to electricity, rural (% of rural population) [EG.ELC.ACCS.RU.ZS], x, x, x
Adjusted savings: particulate emission damage (% of GNI) [NY.ADJ.DPEM.GN.ZS], x, x, x
Your code must print the classification accuracy of each classifier to standard output. Your
output should look as follows (where the % symbol is replaced by the accuracy of each
algorithm, rounded to 3 decimal places):
5
Accuracy of decision tree: %
Accuracy of k-nn (k=5): %
Accuracy of k-nn (k=10): %
Your code for this question should be contained in a single Python file called task2a.py
Feature Engineering and Selection(6 marks)
Task – 2B This task will focus on k-NN with k=5 (from here on referred to as 5-NN). In
order to achieve higher prediction accuracy for 5-NN, one can investigate the use of feature
engineering and selection to predict the class feature of the data. Feature generation involves
the creation of additional features. Two possible methods are:
ˆ Interaction term pairs. Given a pair of features f1 and f2, create a new feature f12 =
f1 × f2. All possible pairs can be considered.
ˆ Clustering labels: apply k-means clustering to the data in world and then use the
resulting cluster labels as the values for a new feature fclusterlabel. You will need to
decide how many clusters to use. At test time, a label for a testing instance can be
created by assigning it to its nearest cluster.
Given a set of N features (the original features plus generated features), feature selection
involves selecting a smaller set of n features (n < N).
An alternative method of performing feature engineering & selection is to use Principal Component Analysis (PCA). The first n principal components can be used as features.
Your task in this question is to evaluate how the above methods for feature engineering and
selection affect the prediction accuracy compared to using 5-NN on a subset of the original
features in world. You should:
ˆ Implement feature engineering using interaction term pairs and clustering labels. This
should produce a dataset with 211 features (20 original features, 190 features generated
by interaction term pairs and 1 feature generated by clustering). You should (in some
principled manner) select 4 features from this dataset and perform 5-NN classification.
ˆ Implement feature engineering and selection via PCA by taking the first four principal
components. You should use only these four features to perform 5-NN classification.
ˆ Take the first four features (columns D-G, if the dataset is opened in Excel) from the
original dataset as a sample of the original 20 features. Perform 5-NN classification.
Your output for this question should include:
ˆ Any text, numbers, or other numerical data you reference in your report, printed to
standard output
ˆ Any graphs or charts as png files with the prefix task2b (e.g. task2bgraph1.png,
task2bgraph2.png)
6
ˆ The classification accuracy for the test set for of the three methods in the following
format, as the last three lines printed to standard output (where the % symbol is
replaced by the accuracy of 5-NN using each feature set, rounded to 3 decimal places):
Accuracy of feature engineering: %
Accuracy of PCA: %
Accuracy of first four features: %
Your code for this question should be contained in a single Python file called task2b.py
Your Report (4 marks)
Task – 2C Write a 1-2 page report describing your implementations. You should discuss:
ˆ Which algorithm (decision trees or k-nn) in Task-2A performed better on this dataset?
For k-nn, which value of k performed better? Explain your experiments and the results.
ˆ A description of the precise steps you took to perform the analysis in Task-2B.
ˆ The method you used to select the number of clusters for the clustering label feature
generation and a justification for the number of clusters you selected.
ˆ The method you used to select four features from the generated dataset of 211 features
for your analysis and a justification for this method.
ˆ Which of the three methods investigated in Task-2B produced the best results for classification using 5-NN and why this was the case.
ˆ What other techniques you could implement to improve classification accuracy with this
data.
ˆ How reliable you consider the classification model to be.
Your report for this task should be contained in a single file called task2c.pdf or task2c.docx.
Resources
The following are some useful resources, for refreshing your knowledge of Python, and for
learning about functionality of pandas.
ˆ Python tutorial
ˆ Python beginner reference
ˆ pandas 10min tutorial
ˆ Official pandas documentation
ˆ Official mathplotlib tutorials
ˆ Python pandas Tutorial by Tutorialspoint
7
ˆ pandas: A Complete Introduction by Learn Data Sci
ˆ pandas quick reference sheet
ˆ sklearn library reference
ˆ NumPy library reference
ˆ Textdistance library reference
ˆ Python Data Analytics by Fabio Nelli (available via University of Melbourne sign on)
Submission Instructions
Your code should be contained in four Python files, task1a.py, task1b.py, task2a.py and
task2b.py which are to be submitted to the LMS by the due date. Your two reports task1c
and task2c should be also be submitted to the LMS by the due date.
Other
Extensions and Late Submission Penalties: All requests for extensions must be made by email,
sent to Chris Ewin and CC’d to Pauline Lin and Anam Khan. If requesting an extension
due to illness, please attach a medical certificate or other supporting evidence. All extension
requests must be made at least 24 hours before the due date. Late submissions without an
approved extension will attract the following penalties
ˆ 0 < hourslate <= 24 (2 marks deduction)
ˆ 24 < hourslate <= 48 (4 marks deduction)
ˆ 48 < hourslate <= 72: (6 marks deduction)
ˆ 72 < hourslate <= 96: (8 marks deduction)
ˆ 96 < hourslate <= 120: (10 marks deduction)
ˆ 120 < hourslate <= 144: (12 marks deduction)
ˆ 144 < hourslate: (25 marks deduction)
where hourslate is the elapsed time in hours (or fractions of hours).
This project is expected to require 30-35 hours work.
Academic Honesty
You are expected to follow the academic honesty guidelines on the University website
https://academichonesty.unimelb.edu.au
8
Further Information
A project discussion forum has also been created on the Ed forum. Please use this in the
first instance if you have questions, since it will allow discussion and responses to be seen by
everyone. The teaching team will support the discussions on the forum until 24 hours prior
to the project deadline. There will also be a list of frequently asked questions on the project
page.
References
[1] The World Bank, “World development indicators,” 2016, data retrieved
from World Development Indicators, https://databank.worldbank.org/source/
world-development-indicators.
[2] World Health Organization, “Life expectancy and healthy life expectancy data by country,” 2016, data retrieved from Global Health Observatory data repository, https://apps.
who.int/gho/data/node.main.688?lang=en.
9


程序辅导定制C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


本网站支持 Alipay WeChatPay PayPal等支付方式

E-mail: vipdue@outlook.com  微信号:vipnxx


如果您使用手机请先保存二维码,微信识别。如果用电脑,直接掏出手机果断扫描。

blank

发表评论

您的电子邮箱地址不会被公开。