PWP Murder Mystery Feedback

[mackenzieyoung]

Rubric Score

Criteria 1: Valid Python Code

• Score Level: 4 (Exceeds expectations)
• Comment(s): Great job, your code runs without any errors.

Criteria 2: Implementation of Project Requirements

• Score Level: 4 (Exceeds expectations)
• Comment(s): Good, your code produces the suite of functions and classes required of it, and calls them in an appropriate order.

Criteria 3: Software Architecture

• Score Level: 4 (Exceeds expectations)
• Comment(s): Nice job, your code is separated into distinct classes and functions, each which are invoked for their own purposes.

Criteria 4: Uses Python Language Features

• Score Level: 4 (Exceeds expectations)
• Comment(s): Very good, your code uses Python features appropriately.

Criteria 5: Produces Accurate Output

• Score Level: 3 (Meets expectations)
• Comment(s): Good job, there are just a few small places where you did not get the intended output (see below).

Overall Score: 19/20

Great job overall! A couple places where you did not get the intended output 1) your get_average_sentence_length function computes average sentence length in terms of number of characters, but it was intended that you do so in terms of number of words. You can do this by splitting up the text by sentences on the ;, then splitting each sentence on the spaces to get a list of words, and then counting the length of that list. And 2) your frequency table comparison is a bit off. Instead of adding 1 to mutual_appearances and appearances, you want to add the values -- the smaller value should get added to mutual_appearances and the larger should get added to appearances. If the key doesn't exist in table2 the value for the key in table1 should be added to appearances.

[selthofer]

Thanks for your comments Mackenzie! Really appreciated!
Ok I understand those mistakes now and fixing them.
However, about get_average_sentence_length what do you think about this approach?:

"""I know the approach is to split text to sentences and then sentence to words by producing list of words and then count words in that list.
However, we already know that ';' is a sentence separator and that ' ' <blank> is a word separator.
Therefore we can count number of spaces and add 1 (since last word in text is not followed by a space) to get total number of words in the text.
Then we count number of ';' to get number sentences in the text sample.
So, in order to get average number of words per sentence we just divide num_words with num_sentences for the particular text sample"""