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tests.py
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tests.py
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"""
KV Le
CSE 163 AG
Final Project
A script that does basic tests my data analysis for my final project
"""
import pandas as pd
import rq_one as rq1
def test_cleaned_data(anime, users, lists, anime_2019):
"""Tests if the data has been cleaned/retrieved properly
Parameters
----------
anime : DataFrame
Pandas DataFrame that contains anime show data
users : DataFrame
Pandas DataFrame that contains MAL user data
lists : DataFrame
Pandas DataFrame that contains information on MAL user's anime lists
anime_2019 : DataFrame
Pandas DataFrame that contains 2019 anime show data
Notes
-----
Throws an Assertion Error if any of the cleaned files have incorrect
columns of information
All values that are being tested are calculated and checked by hand
"""
assert set(["anime_id", "title", "image_url", "type",
"episodes", "duration_min", "score", "scored_by",
"rank", "popularity", "members", "favorites",
"related", "studio", "genre", "aired_from_year",
"source"]) == set(anime.columns)
assert set(["username", "user_id", "user_watching",
"user_completed", "user_onhold", "user_dropped",
"user_plantowatch", "user_days_spent_watching",
"gender", "location", "birth_date",
"stats_mean_score", "stats_episodes", "age"]) \
== set(users.columns)
assert set(["username", "anime_id", "my_score",
"my_status", "my_watched_episodes"]) == set(lists.columns)
assert set(["anime_id", "title", "type", "episodes", "duration_min",
"source", "score", "members", "favorites", "studio",
"genre"]) == set(anime_2019.columns)
print("All Cleaned Sets Have The Proper Columns")
def test_genre_calculations(anime, anime_2019):
"""Tests if common genre info maniplation techniques that I used are valid
Parameters
----------
anime : DataFrame
Pandas DataFrame that contains anime show data
anime_2019 : DataFrame
Pandas DataFrame that contains 2019 anime show data
Notes
-----
Expected all data sets to be a sub sample of the first 10 elements
Throws an Assertion Error if any of the testing fails
All values that are being tested are calculated and checked by hand
"""
# Testing mean calculation
assert abs(anime["score"].mean() - 7.954) < .001
assert abs(anime_2019["score"].mean() - 7.869) < .001
# Testing genre count calculations
genre_counts = anime["genre"].str.split(", ", expand=True).stack() \
.str.get_dummies().sum().sort_values(ascending=False).reset_index()
genres = ["Romance", "Comedy", "School", "Shoujo", "Shounen", "Magic",
"Drama", "Supernatural", "Fantasy", "Slice of Life",
"Parody", "Music", "Kids", "Josei", "Harem", "Action"]
amounts = [8, 8, 6, 4, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1]
for index in range(len(genres)):
assert (genre_counts["index"].iloc[index],
genre_counts[0].iloc[index]) == \
(genres[index], amounts[index])
# Testing yearly genre count calculations
yearly = pd.concat([anime.head(2)["aired_from_year"],
pd.get_dummies(anime.head(2)["genre"]
.str.split(", ", expand=True),
prefix="", prefix_sep="")], axis=1)
yearly = yearly.groupby("aired_from_year").sum() \
.groupby(level=0, axis=1).sum().reset_index()
yearly = pd.melt(yearly, ["aired_from_year"]) \
.rename(columns={"variable": "Genre"})
yearly = yearly[yearly["value"] > 0].sort_values("aired_from_year") \
.reset_index()
years = ([2007] * 5) + ([2012] * 4)
genres = ["Comedy", "Parody", "Romance", "School",
"Shounen", "Comedy", "Romance", "Shounen", "Supernatural"]
value = [1] * 9
for index in range(len(years)):
assert (yearly["aired_from_year"].iloc[index],
yearly["Genre"].iloc[index], yearly["value"].iloc[index]) == \
(years[index], genres[index], value[index])
print("Genre Information Manipulation is generally valid")
def test_studio_calculations(anime):
"""Tests if common studio info maniplation techniques that I used are valid
Parameters
----------
anime : DataFrame
Pandas DataFrame that contains anime show data
Notes
-----
Expected all data sets to be a sub sample of the first 10 elements
Throws an Assertion Error if any of the testing fails
All values that are being tested are calculated and checked by hand
"""
# Testing studio averages
avg = anime[["studio", "score"]].copy()
avg["studio"] = avg["studio"].str.split(", ")
avg = avg.explode("studio").groupby("studio").mean() \
.sort_values("score", ascending=False).reset_index()
studios = ["Bones", "Hal Film Maker", "J.C.Staff", "Studio Hibari",
"Studio Pierrot", "Gonzo", "David Production",
"Satelight", "Production Reed"]
score = [8.34, 8.21, 8.21, 8.03, 8.03, 7.89, 7.63, 7.55, 7.26]
for index in range(len(studios)):
assert (avg["studio"].iloc[index],
avg["score"].iloc[index]) == \
(studios[index], score[index])
# Testing genre count calculations
studio_counts = anime["studio"].str.split(", ", expand=True).stack() \
.str.get_dummies().sum().sort_values(ascending=False).reset_index()
studios = ["J.C.Staff", "Studio Pierrot", "Studio Hibari", "Satelight",
"Production Reed", "Hal Film Maker", "Gonzo",
"David Production", "Bones"]
amounts = [3] + ([1] * 8)
for index in range(len(studios)):
assert (studio_counts["index"].iloc[index],
studio_counts[0].iloc[index]) == \
(studios[index], amounts[index])
print("Studio Information Manipulation is generally valid")
def test_user_calculations(users):
"""Tests if common user info maniplation techniques that I used are valid
Parameters
----------
users : DataFrame
Pandas DataFrame that contains MAL user data
Notes
-----
Expected all data sets to be a sub sample of the first 10 elements
Throws an Assertion Error if any of the testing fails
All values that are being tested are calculated and checked by hand
Prints a success message if the tests pass
"""
avg1 = rq1.average_user(users)
avg2 = {
"age": 27.8,
"score": 8.214,
"days_watched": 48.9,
"episodes": 2962,
"completed": 109.5,
"watching": 20.7,
"planned": 36.3,
"onhold": 3.8,
"dropped": 5.3
}
for avg in avg1:
assert avg1[avg] - avg2[avg] < .01
print("User Information Manipulation is generally valid")
def main():
"""Runs all tests
Notes
-----
Throws an error if any tests are failed
"""
anime = pd.read_csv("data/animelist_cleaned.csv")
anime_2019 = pd.read_csv("data/animelist_2019.csv")
users = pd.read_csv("data/userlist_cleaned.csv")
lists = pd.read_csv("data/user_animelists_cleaned.csv")
test_cleaned_data(anime, users, lists, anime_2019)
anime_small = anime.head(10)
anime_2019_small = anime_2019.head(10)
users_small = users.head(10)
test_genre_calculations(anime_small, anime_2019_small)
test_studio_calculations(anime_small)
test_user_calculations(users_small)
if __name__ == "__main__":
main()