Python代写｜Python SE Assignment

本次美国代写是一个Python搜索引擎的assignment

Before you start, make sure to read the Search Engine Specification

(1) Information analyst: small collection of web pages

(2) Algorithms and data structures developer: larger collection of web pages

Now that you have been provided the HTML files to index, you may build your inverted index off of them. The inverted index is simply a map with the token as a key and a list of its corresponding postings. A posting is the representation of the token’s occurrence in a document. The posting typically (not limited to) contains the following info (you are encouraged to think of other attributes that you could add to the index):

The document name/id the token was found in.

Its tf-idf score for that document (for MS1, add only the term frequency)

When designing your inverted index, you will think about the structure of your posting first.

You would normally begin by implementing the code to calculate/fetch the elements which will constitute your posting.

Use scripts/classes that will perform a function or a set of closely related functions. This helps in keeping track of your progress, debugging, and also dividing work amongst teammates if you’re in a group.

We strongly recommend you use GitHub as a mechanism to work with your team members on this project, but please make the p
roject private.

Deliverables: Submit your code and a report (in PDF format) with a table containing some analytics about your index. The minimum analytics are:

The number of indexed documents;

The number of unique tokens;

The total size (in KB) of your index on disk.

Note for the developer option: at this time, you do not need to have the optimized index, but you may save time if you do.

No late submissions will be accepted for this milestone.

Did your report show up on time?

Are the reported numbers plausible?

Important note: You can only change teams between the milestones of Assignment 3 if there are strong reasons for doing so. Please, write an email to the teaching team explaining why you need to change teams and we will assess your situation.

At least the following queries should be used to test your retrieval:

1 – cristina lopes

2 – machine learning

4 – master of software engineering

Once you have built the inverted index, you are ready to test document retrieval with queries. At the very least, the search should be able to deal with boolean queries: AND only. If you wish, you can sort the retrieved documents based on tf-idf scoring (you are not required to do so now, but it will be required for the final search engine). This can be done using the cosine similarity method. Feel free to use a library to compute cosine similarity once you have the term frequencies and inverse document frequencies (although it should be very easy for you to write your own implementation). You may also add other weighting/scoring mechanisms to help refine the search results.

Deliverables: Submit your code and a report (in PDF format) with the following content:

the top 5 URLs for each of the queries above

a screenshot of your search interface in action (text or web-based)

No late submissions will be accepted for this milestone.

Did your report show up on time?

Are the reported URLs plausible?

Important note: You can only change teams between the milestones of Assignment 3 if there are strong reasons for doing so. Please, write an email to the teaching team explaining why you need to change teams and we will assess your situation.

During this last stretch, you will improve and finalize your search engine. Come up with a set of at least 20 queries that guide you in evaluating how well your search engine performs, both in terms of ranking performance (effectiveness) and in terms of runtime performance (efficiency). At least half of those queries should be chosen because they do poorly on one or both criteria; the other half should do well. Then change your code to make it work better for the queries that perform poorly, while preserving the good performance of the other ones, and while being as general as possible.

Note for the developer option: at the end of the project, you should have the optimized index that allows you to run both the indexer and the search with small memory footprint, smaller than the index size.