Description

30 pts
Non-standard (i.e., not built-in) python libraries you can use:
– pandas
In this assignment, we’re going back to homework 3 and computing each pony’s most frequent words using TF-IDF. Note that, throughout this assignment, we refer to “pony names” – use the canonical names we used for each of the main character ponies in HW3.
Task 1: Compute word counts (15 pts)
Write a script that computes word counts for each pony from all episodes of MLP. Your script, compile_word_counts.py should run as follows:
python compile_word_counts.py -o <word_counts_json> -d <clean_dialog.csv file>
Remember that -o and -d should refer to absolute paths, for example:
python compile_word_counts.py -o /path/to/word_counts.json -d path/to/clean_dialog.csv
For the output file, you should create directories if they do not exist.
The output file should be a dictionary with the following form:
{
“twilight sparkle”: {
“<word1>”: <# of times the word1 is used by twilight sparkle>, “<word2>”: <# of times the word2 is used by twilight sparkle>,
…
},
“pinkie pie”: {
…
}
… }
Make sure you have exactly the following keys for the pony names: “twilight sparkle”, “applejack”, “rarity”, “pinkie pie”, “rainbow dash”, “fluttershy”.
Indentation won’t matter in this exercise, you can have a one-line JSON file or a pretty-printed one.
For your analysis:
– Some of the words are going to be rare and will have a very small frequency. These words are not going to be very useful for your analysis. You should only keep words with a frequency higher than a specific threshold. For this homework, only keep words that occur at least 5 times across ALL valid speech acts.
– Also, to avoid boring results (like, the most frequent word being “the”), remove all the stopwords. Use the attached list of words. This file will also be provided in the HW assignment on mycourses. You should remove any words present in this file from your analysis. https://gist.githubusercontent.com/larsyencken/1440509/raw/53273c6c202b35ef00194d06751d8ef630e 53df2/stopwords.txt
– Use the same dialog file we used in HW3. (clean_dialog.csv from https://www.kaggle.com/liury123/my-little-pony-transcript). You must submit your generated file, which must be named word_counts.json and placed at the root of the submission_template folder. Please check the README.md file for HW8 for further instructions.
Other details and reminders:
– Valid speech acts – only consider speech acts where the speaker is an exact match for one of the main character ponies. Ignore any others. Also lines which involve multiple characters, i.e. “Twilight and Fluttershy” or inexact matches, such as “future Twilight Sparkle” should be ignored.
– Treat each word encountered as case insensitive. Store words in all lowercase form.
– Before processing text, replace punctuation characters with a space – a punctuation character is one of
these: ( ) [ ] , – . ? ! : ; # &
– A word must only include alphabetic characters. All other words should be ignored.
– Remove the stopwords (listed here https://gist.githubusercontent.com/larsyencken/1440509/raw/53273c6c202b35ef00194d06751d8ef630e 53df2/stopwords.txt)
– Tip: to keep your script performant, store your word counts in dictionaries.
Task 2: Compute most frequent & distinctive pony language (10 pts) Write the script compute_pony_lang.py which is run as follows:
python compute_pony_lang.py -c <pony_counts.json> -n <num_words>
The <pony_counts.json> file should have the same format output by your compile_word_counts.py script in Task 1. It should compute the <num_words> for each pony that has the highest TF-IDF score. Note that to compute the inverse document frequency, you should use the number of times the words were used by all 6 ponies (i.e., only use the counts in the pony_counts.json, not all speakers from the original script). The specific definition of TF-IDF you should implement is:
tf-idf(w, pony, script) = tf(w, pony) x idf(w, script) tf(w, pony) = the number of times pony uses the word w (which we compute in task1) idf(w, script) = log [
(total number of ponies) /
(number of ponies that use the word w)
]
Output should be written in JSON format to stdout with the following structure:
{
“<pony name>”: [ “highest-tfidf-word”, “second-highest-tfidf-word”, … ], “<pony name>”: …
}
Each pony word list should have <num_words> entries.
Use the same keys from Task 1 for the pony names: “twilight sparkle”, “applejack”, “rarity”, “pinkie pie”, “rainbow dash”, “fluttershy”.
As usual, the -c argument refers to an absolute path (and not just a file name). Indentation won’t matter in this exercise, you can have a one-line JSON file or a pretty-printed one.
Task 3: Write unit tests for your methods (5 pts)
Write two unit tests for your code – one test for task 1, one for task 2. For the first task, your unit test should pickup a mini script file and produce counts that are checked against a JSON file containing ground truth counts. For the second task, your unit test should pickup the ground truth counts and compute TF-IDF scores that are checked against a JSON file containing ground truth TF-IDF scores.
In both cases, we will provide the mini script file, ground truth counts, and ground truth TF-IDF counts – so you will only write the inputs and the assertions to match your code. Crucially, your tests should import your python code and run it, *NOT* invoke a new python3 process.
Follow these instructions carefully:
– You will find three files under the fixtures folder: mock_dialog.csv word_counts.true.json tf_idfs.true.json.
– mock_dialog.csv is populated with an example. That means you can modify its contents accordingly. It should contain just a small dialog example, so you can manually count words and calculate the TF-IDF.
– Manually create contents for word_counts.true.json tf_idfs.true.json (i.e. calculate them manually)
– DO NOT MODIFY THE NAME OF THESE FILES
– You’ll also find a test file named test_tasks.py under the test/ folder. We already load the fixtures files for you.
– Replace the assertions on test_task1 and test_task2 with any meaningful assertion. Just make sure you use the fixtures we provided; they will be under the variables self.mock_dialog, self.true_word_counts, self.true_tf_idfs. DELETE the self.assertTrue(True) you spot. – Your test suite MUST pass.