Useful CSE 163 Resources

Learning objective: Implement specialized data types with Python classes for tf-idf information retrieval.

  • is the file representing the SearchEngine class as well as a command-line interface for using the SearchEngine.

  • is the file representing a single document in the SearchEngine.

  • is the file for you to put your own tests. The Run button executes this program.

  • and index.html provide a web app for you to run your completed search engine. You do not need to look at or modify these files ever.

  • is a helper file that has code to help you test your code.


This assessment is very information-dense. Plan ahead by taking notes of what you need to do and where you will need to do it. It will probably help to get a global view of the entire assessment by reading all of the components before starting any coding—we expect students to spend at least 40 minutes reading, synthesizing, and planning before starting any coding.


A search engine is an algorithm that takes a query and retrieves the most relevant documents for that query. In order to identify the most relevant documents, our search engine will use term frequency–inverse document frequency (tf–idf), a text information statistic for determining the relevance of a term to each document from a corpus consisting of many documents.

The tf-idf statistic consists of two components: term frequency and inverse document frequency. Term frequency computes the number of times that a term appears in a document (such as a single Wikipedia page). If we were to use only the term frequency in determining the relevance of a term to each document, then our search result might not be helpful since most documents contain many common words such as “the” or “a”. In order to down-weight these common terms, the document frequency computes the number of times that a term appears across the corpus of all documents. The tf-idf statistic takes a term and a document and returns the term frequency divided by the document frequency.

In this assessment, we’ll implement two classes: a Document class to represent individual web pages and a SearchEngine class that aggregates the corpus of all Document objects. The SearchEngine builds on the Document, so be sure to fully complete the Document class before moving onto the SearchEngine.


The Document class defined in represents the data in a single web page and includes methods to compute term frequency. (But not document frequency since that would require access to all of the documents in the corpus.)

Task: Write an initializer __init__ that takes a path to a document and initializes the document data. Assume that the file exists, but that it could be empty. In order to implement term_frequency later, we’ll need to precompute the term frequency for each term in the document in the initializer by constructing a dictionary that maps each str term to its float term frequency tf\textit{tf} in the given document.

tf(t,d)=count of term t in dcount of words in d \textit{tf}\,(t, d) = \frac{\text{count of term }t\text{ in }d}{\text{count of words in }d}

Consider the term frequencies for this short document containing 4 total words.

the cutest cutest dog
  • 'the' appears 1 time out of 4 total words, so the tf\textit{tf} is 0.25.

  • 'cutest' appears 2 times out of 4 total words, so the tf\textit{tf} is 0.5.

  • 'dog' appears 1 time out of 4 total words, so the tf\textit{tf} is 0.25.

When constructing this dictionary, normalize all terms by lowercasing text and ignoring punctuation so that 'corgi', 'CoRgi', and 'corgi!!' are considered the same. We have provided a function called normalize_token in that you can import to normalize a single token from a string. For example, the following code cells show how to use the function.

token = 'CoRgi!!'
token = normalize_token(token)
print(token)  # corgi

token = '<div>Hi!</div>'
token = normalize_token(token)
print(token)  # divhidiv


If you’re familiar with HTML, you might have noticed that a lot of the files in our provided small_wiki corpus contain HTML code, including tags that look like <div>. Don’t worry about handling the html. Sticking with what we provide above for normalization is enough.

Task: Write a method term_frequency that returns the term frequency of a given term by looking it up in the precomputed dictionary. Remember to normalize the term. If a term does not appear in a given document, it should have a term frequency of 0.

Task: Write a method get_path that returns the path of the file that this document represents.

Task: Write a method get_words that returns a list of the unique, normalized words in this document.

Task: Write at least 3 assert_equals tests for Document in using your own test corpus.


Create new files in your workspace for each additional test case. When specifying file names, use absolute paths such as /home/song.txt.


Bugs in the Document implementation will really hurt when implementing SearchEngine. Make sure to fully test your Document before moving on.


The SearchEngine class defined in represents a corpus of Document objects and includes methods to compute the tf–idf statistic between each document and a given query.

Task: Write an initializer that takes a str path to a directory such as /course/small_wiki/ and constructs an inverted index associating each term in the corpus to the list of documents that contain the term. Assume the string represents a valid directory, and that the directory contains only valid files. Do not recreate any behavior that is already done in the Document class—call the Document.get_words method! Create at most one Document object for each file.

In order to implement search later, it will be necessary to find all documents which contain the given term. The initializer for SearchEngine should precompute the inverted index, a dictionary associating each str term to the list of Document objects that include the term. Consider this demonstration corpus of 3 str documents and the inverted index for the corpus.

doc1 = Document("/home/files/corgis.txt")   # File contents: 'I love corgis'
doc2 = Document("/home/files/puppies.txt")  # File contents: 'I love puppies'
doc3 = Document("/home/files/dogs.txt")     # File contents: 'I love dogs'

inverted_index = {
    'i': [doc1, doc2, doc3],
    'love': [doc1, doc2, doc3],
    'corgis': [doc1],
    'puppies': [doc2],
    'dogs': [doc3]

The inverted index will help in implementing the search method below by providing a way to answer the question, “Which documents contain the term, 'corgis'?”

To iterate over all the files in a directory, call os.listdir to list all the file names and join the path with os.path.join.


import os

directory = '/course/small_wiki/'
for filename in os.listdir(directory):
    print(os.path.join(directory, filename))

Task: Write a method _calculate_idf that takes a str term and returns the inverse document frequency of that term. If the term is not in the corpus, return 0. Otherwise, if the term is in the corpus, compute the inverse document frequency idf\textit{idf} as follows.

idf(t)=ln(total number of documents in corpusnumber of documents containing term t) \textit{idf}\,(t) = \ln\left(\frac{\text{total number of documents in corpus}}{\text{number of documents containing term } t}\right)

Call math.log to compute the natural logarithm ln\ln of a given number.


_calculate_idf is a private method, which should not be called by the client. Because it’s defined in the SearchEngine class, you should use it to help you implement the search function. We will expect that you have this private method with the behavior described so that we can test your code.

Task: Write a method search that takes a str query that contains one or more terms. The search method returns a list of document paths sorted in descending order by tf–idf statistic. Normalize the terms before processing. If there are no matching documents, return an empty list.

Subgoal: Start by implementing the search method for queries that contain only a single term. To generate a ranked list of documents, first collect all the documents that contain the given term. Then, compute the tf–idf statistic for each document.

tfidf(t,d)=tf(t,d)idf(t) \textit{tfidf}\,(t, d) = \textit{tf}\,(t, d) \cdot \textit{idf}\,(t)

Store these (Document, float) pairs as a list of tuples. Finally, return the sorted list of documents in descending order according to the tf–idf statistic.

Subgoal: Start writing your first assert_equals test for search with a single term in using your own test corpus.

Subgoal: Extend the search method to queries that contain multiple terms. The output of a multi-term query are all the documents that match at least one term in the query. The tf–idf statistic for multi-term queries is the sum of the single-term tf–idf statistics.

tfidf(‘love corgis’,d)=tfidf(‘love’,d)+tfidf(‘corgis’,d) \textit{tfidf}\,(\text{`love corgis'}, d) = \textit{tfidf}\,(\text{`love'}, d) + \textit{tfidf}\,(\text{`corgis'}, d)

Finding the relevant documents for a multi-word query is a bit more challenging. Instead of looking at a single entry in the dictionary, we must look at all Document objects that contain at least one word in the query.

Task: Write at least 3 assert_equals tests for SearchEngine in using your own test corpus.

Let’s walk through a full example of the search process. Say we have a corpus named 'other_files' containing 3 documents with the following contents:

  • /home/other_files/doc1Dogs are the greatest pets.

  • /home/other_files/doc2Cats seem pretty okay

  • /home/other_files/doc3I love dogs!

This corpus would have the following inverted index

    "dogs": [doc1, doc3],
    "are": [doc1],
    "the": [doc1],
    "greatest": [doc1],
    "pets": [doc1],
    "cats": [doc2],
    "seem": [doc2],
    "pretty": [doc2],
    "okay": [doc2],
    "i": [doc3],
    "love": [doc3],

Searching this corpus for the query 'love dogs' returns a list in the order ['/home/other_files/doc3', '/home/other_files/doc1'].

  1. Find all matching documents with at least one query word. doc3 contains the word 'love' while both doc1 and doc3 contain the word 'dogs'. Both doc1 and doc3 contain at least one word in the query.

  2. Compute the tf–idf statistic for each matching document. For each matching document, the tf–idf statistic for a multi-word query 'love dogs' is the sum of the tf–idf statistics for 'love' and 'dogs' individually.

    1. tfidf(’love dogs’,doc1)=0+0.081=0.081\textit{tfidf}\,(\text{'love dogs'}, \texttt{doc1}) = 0 + 0.081 = 0.081 since 'love' doesn’t appear in doc1.

    2. tfidf(’love dogs’,doc3)=0.366+0.135=0.501\textit{tfidf}\,(\text{'love dogs'}, \texttt{doc3}) = 0.366 + 0.135 = 0.501.

  3. Associate each matching document with its tf–idf statistic in a list of tuples to sort by descending tf–idf statistic. Return the matching document paths in descending tf–idf order ['/home/other_files/doc3', '/home/other_files/doc1'].


Sorting by tf-idf with a list of tuples is just how we decided to do it. If you find that another method of storing and sorting makes more sense to you, like storing a dictionary mapping documents to their respective scores, feel free to try it out!


You might find that field variables are helpful as you implement your SearchEngine; think carefully about if a variable needs to be used outside of one function and hence wether or not it needs to be stored with self.


Assessment submissions should pass these checks: flake8 and code quality guidelines. The code quality guidelines are very thorough. For this assessment, the most relevant rules can be found in these sections (new sections bolded):


Submit your work by pressing the Mark button. Submit as often as you want until the deadline for the initial submission. Note that we will only grade your most recent submission. You can view your past submissions using the “Submissions” button.

Please make sure you are familiar with the resources and policies outlined in the syllabus and the take-home assessments page.

A4 - Search

Initial Submission by Friday 05/06 at 11:59 pm.

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