[+] Add starter files

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+"""CSC110 Fall 2020 Assignment 3, Part 3: Loops and Mutation Debugging Exercise
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This Python module contains the program and tests described in Part 3.
+Run this file to see the pytest report containing at least one failing test.
+
+While you are making small changes to this file, we will NOT run python_ta
+on this file when grading.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC110 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC110 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2020 David Liu and Mario Badr.
+"""
+import csv
+import math
+
+# Constant for different punctuation marks to remove from review strings.
+PUNCTUATION = '!\"#$%&()*+,-./:;<=>?@[]^_`{|}~'
+
+# Constant representing VADER word intensities.
+# (This is a small subset of the full VADER lexicon.)
+WORD_TO_INTENSITY = {
+    'adventure': 1.3,
+    'amusing': 1.6,
+    'brilliantly': 3.0,
+    'comedy': 1.5,
+    'excellent': 2.7,
+    'guilty': -1.8,
+    'magnificent': 2.9,
+    'succeeded': 1.8,
+    'terrible': -2.1
+}
+
+
+###############################################################################
+# Mario's Program
+###############################################################################
+def report_sentiment(review: str) -> tuple[str, float]:
+    """Return the VADER sentiment of the review in text.
+
+    The VADER sentiment is a tuple of the polarity, intensity of the review.
+    """
+    # First clean the review text and split into a list of words.
+    word_list = clean_text(review)
+
+    # Extract the number of times each VADER keyword appears in the review.
+    occurrences = count_keywords(word_list)
+
+    # Calculate the average intensity of the keywords in the review.
+    average_intensity = calculate_average_intensity(occurrences)
+
+    if average_intensity >= 0.05:
+        return ('positive', average_intensity)
+    elif average_intensity <= -0.05:
+        return ('negative', average_intensity)
+    else:
+        return ('neutral', average_intensity)
+
+
+def clean_text(text: str) -> list[str]:
+    """Return text as a list of words that have been cleaned up.
+
+    Cleaning up involves:
+        - removing punctuation
+        - converting all letters to lowercase (because our VADER keywords
+          are all written as lowercase)
+    """
+    # Remove punctuation from text
+    for p in PUNCTUATION:
+        text = str.replace(text, p, ' ')
+
+    # Convert text to lowercase
+    str.lower(text)
+
+    # Split text into words and return
+    return str.split(text)
+
+
+def count_keywords(word_list: list[str]) -> dict[str, int]:
+    """Return a frequency mapping of the VADER keywords in text.
+    """
+    # ACCUMULATOR: A mapping of keyword frequencies seen so far
+    occurrences_so_far = {}
+
+    for word in word_list:
+        if word in WORD_TO_INTENSITY:
+            if word not in occurrences_so_far:
+                occurrences_so_far[word] = 0
+                occurrences_so_far[word] = occurrences_so_far[word] + 1
+
+    return occurrences_so_far
+
+
+def calculate_average_intensity(occurrences: dict[str, int]) -> float:
+    """Return the average intensity of the given keyword occurrences.
+
+    Preconditions:
+        - occurrences != {}
+        - all({occurrences[keyword] >= 1 for keyword in occurrences})
+    """
+    num_keywords = sum([occurrences[word] for word in occurrences])
+    total_intensity = sum([WORD_TO_INTENSITY[word] * occurrences[word]
+                           for word in occurrences])
+    return total_intensity / num_keywords
+
+
+###############################################################################
+# Tests for report_sentiment (THERE ARE NO ERRORS IN THIS PART)
+###############################################################################
+def read_critic_data(filename: str) -> str:
+    """Return the movie review stored in the given file.
+
+    The file is a CSV file with two rows, a header row and a row of actual
+    movie review data. These files are based on real movie review data from Metacritic,
+    though they have been altered slightly to fit this assignment.
+
+    There ARE NO ERRORS in this function; it's used for testing purposes only.
+    """
+    with open(filename) as file:
+        reader = csv.reader(file)
+
+        # Skip header row
+        next(reader)
+
+        # Read the movie review, which is the last entry in the row
+        row = next(reader)
+        review = row[len(row) - 1]
+
+    return review
+
+
+def test_star_wars() -> None:
+    """Test that the review for Star Wars gives the correct polarity and intensity.
+
+    There is NO ERROR in this test.
+    """
+    review = read_critic_data('data/reviews/review_star_wars.csv')
+
+    actual = report_sentiment(review)
+    expected_polarity = 'positive'
+    expected_intensity = 2.25
+    assert actual[0] == expected_polarity
+    assert math.isclose(actual[1], expected_intensity)
+
+
+def test_legally_blonde() -> None:
+    """Test that the review for Legally Blonde gives the correct polarity and intensity.
+
+    There is NO ERROR in this test.
+    """
+    review = read_critic_data('data/reviews/review_legally_blonde.csv')
+
+    actual = report_sentiment(review)
+    expected_polarity = 'positive'
+    expected_intensity = 0.8333333333333333
+    assert actual[0] == expected_polarity
+    assert math.isclose(actual[1], expected_intensity)
+
+
+def test_transformers() -> None:
+    """Test that the review for Transformers gives the correct polarity and intensity.
+
+    There is NO ERROR in this test.
+    """
+    review = read_critic_data('data/reviews/review_transformers.csv')
+
+    actual = report_sentiment(review)
+    expected_polarity = 'negative'
+    expected_intensity = -0.9
+    assert actual[0] == expected_polarity
+    assert math.isclose(actual[1], expected_intensity)
+
+
+if __name__ == '__main__':
+    import python_ta.contracts
+    python_ta.contracts.DEBUG_CONTRACTS = False
+    python_ta.contracts.check_all_contracts()
+
+    import pytest
+    pytest.main(['a3_part3.py', '-v'])