def test_register_courses_weight_invalid_negative():
tracker = pygtracker.GradeTracker()
df = generate_df_register_courses(
[523], ["lab4", "lab2", "lab1", "lab3"], [-0.5, 0.2, 0.7, 0.6]
)
with raises(ValueError):
tracker.register_courses(df)
def test_record_grades_grade_invalid_negative():
tracker = pygtracker.GradeTracker()
df = generate_df_record_grade(
[511], ["xiran"], ["lab4", "lab2", "lab1", "lab3"], [100.0, -77, 99, 88.4]
)
with raises(ValueError):
tracker.record_grades(df)
def test_record_grades_grade_invalid_over_100():
tracker = pygtracker.GradeTracker()
df = generate_df_record_grade(
[511], ["kevin"], ["lab4", "lab2", "lab1", "lab3"], [100.1, 77, 99, 88.4]
)
with raises(ValueError):
tracker.record_grades(df)
def generate_input_calculate_final_grade():
tracker = pygtracker.GradeTracker()
tracker.courses = pd.DataFrame(
np.array(
[
["511", 0.15, 0.15, 0.15, 0.15, 0.2, 0.2, 0, 0, 0, 0, 0],
["522", 0, 0, 0, 0, 0, 0, 0.1, 0.2, 0.2, 0.3, 0.2],
]
),
columns=[
"course_id",
"lab1",
"lab2",
"lab3",
"lab4",
"quiz1",
"quiz2",
"milestone1",
"milestone2",
"milestone3",
"milestone4",
"feedback",
],
)
tracker.grades = pd.DataFrame(
np.array(
[
["511", "tom", 100, 100, 79.2, 83.6, 75.6, 75.6, 0, 0, 0, 0, 0],
["511", "tiff", 87.6, 100, 81.2, 89.2, 100, 73.2, 0, 0, 0, 0, 0],
["511", "mike", 84.4, 79.6, 75.2, 98.8, 84.8, 100, 0, 0, 0, 0, 0],
["511", "joel", 100, 100, 99.6, 71.2, 96.8, 79.2, 0, 0, 0, 0, 0],
["522", "tom", 0, 0, 0, 0, 0, 0, 100, 97.6, 80, 100, 100],
["522", "tiff", 0, 0, 0, 0, 0, 0, 100, 77.2, 76.8, 100, 85.6],
["522", "mike", 0, 0, 0, 0, 0, 0, 92, 75.6, 97.6, 84.4, 98.8],
["522", "joel", 0, 0, 0, 0, 0, 0, 98.4, 85.6, 96.8, 100, 82.4],
]
),
columns=[
"course_id",
"student_id",
"lab1",
"lab2",
"lab3",
"lab4",
"quiz1",
"quiz2",
"milestone1",
"milestone2",
"milestone3",
"milestone4",
"feedback",
],
)
tracker.courses = convert_dtypes_to_float(tracker.courses)
tracker.grades = convert_dtypes_to_float(tracker.grades)
return tracker
def test_register_courses_weight_invalid_not_sum_to_one():
tracker = pygtracker.GradeTracker()
df = generate_df_register_courses(
[511],
["lab4", "lab2", "lab1", "lab3"],
[
0.2,
0.3,
0.4,
0.2,
],
)
with raises(ValueError):
tracker.register_courses(df)
def test_register_courses_expected_df():
tracker = pygtracker.GradeTracker()
df = generate_df_register_courses(
[523],
["lab1", "lab2", "lab3", "lab4", "quiz1", "quiz2"],
[0.15, 0.15, 0.15, 0.15, 0.2, 0.2],
)
tracker.register_courses(df)
res_df = generate_df_register_courses_output(
[523],
["lab1", "lab2", "lab3", "lab4", "quiz1", "quiz2"],
[0.15, 0.15, 0.15, 0.15, 0.2, 0.2],
)
assert_frame_equal(tracker.courses, res_df)
def test_record_grades_expected_df():
tracker = pygtracker.GradeTracker()
df = generate_df_record_grade(
[552],
["fiona"],
["lab1", "lab2", "lab3", "lab4", "quiz1", "quiz2"],
[66.6, 88.8, 77.7, 99.9, 90.9, 67.89],
)
tracker.record_grades(df)
grade_df = generate_df_record_grades_output(
[552],
["fiona"],
["lab1", "lab2", "lab3", "lab4", "quiz1", "quiz2"],
[66.6, 88.8, 77.7, 99.9, 90.9, 67.89],
)
assert_frame_equal(tracker.grades, grade_df)
def generate_input_suggest_grade_adjustment():
tracker = pygtracker.GradeTracker()
tracker.courses = pd.DataFrame(
np.array([["511", 0.15, 0.15, 0.15, 0.15, 0.2, 0.2]]),
columns=["course_id", "lab1", "lab2", "lab3", "lab4", "quiz1", "quiz2"],
)
tracker.grades = pd.DataFrame(
np.array([["511", "studentA", 90, 90, 90, 90, 85, 85]]),
columns=[
"course_id",
"student_id",
"lab1",
"lab2",
"lab3",
"lab4",
"quiz1",
"quiz2",
],
)
tracker.courses = convert_dtypes_to_float(tracker.courses)
tracker.grades = convert_dtypes_to_float(tracker.grades)
return tracker
def test_record_grades_course_id_invalid():
tracker = pygtracker.GradeTracker()
df = generate_df_record_grade([577], ["lab2"], [99.1], ["vaden"])
with raises(ValueError):
tracker.record_grades(df)
def test_integration_all_functions():
tracker = pygtracker.GradeTracker()
courses = pd.read_csv(os.path.join(".", "tests", "test_data", "course_info.csv"))
grades = pd.read_csv(os.path.join(".", "tests", "test_data", "student_info.csv"))
tracker.register_courses(courses)
tracker.record_grades(grades)
expected_tracker = generate_input_calculate_final_grade()
# courses are recorded correctly
assert_frame_equal(
tracker.courses, expected_tracker.courses[tracker.courses.columns]
)
# grades are recorded correctly
assert_frame_equal(
tracker.grades.sort_values(by=["course_id", "student_id"]),
expected_tracker.grades[tracker.grades.columns]
.sort_values(by=["course_id", "student_id"])
.reset_index(drop=True),
)
course_stat = tracker.generate_course_statistics(["511"])
expected_stat = convert_dtypes_to_float(
pd.DataFrame(
np.array([["511", 87.87, 86.91, 88.0, 88.96]]),
columns=["course_id", "mean", "1st-quantile", "median", "3rd-quantile"],
)
)
# stats are generated correctly
assert_frame_equal(course_stat, expected_stat)
courses_rank = tracker.rank_courses()
expected_courses_rank = convert_dtypes_to_float(
pd.DataFrame(
np.array([["522", 91.29], ["511", 87.87]]), columns=["course_id", "grade"]
)
)
# courses are ranked correctly
assert_frame_equal(courses_rank.reset_index(drop=True), expected_courses_rank)
students_rank = tracker.rank_students()
expected_students_rank = convert_dtypes_to_float(
pd.DataFrame(
np.array([["joel", 91.81, 1.0], ["tom", 90.09, 2.0], ["mike", 88.29, 3.0]]),
columns=["student_id", "grade", "rank"],
)
)
# students are ranked correctly
assert_frame_equal(students_rank, expected_students_rank)
adj_grades = tracker.suggest_grade_adjustment("511", benchmark_course=100)
expected_adj_grades = convert_dtypes_to_float(
pd.DataFrame(
np.array(
[
["511", "joel", 0, 100, 100, 100, 100, 0, 0, 0, 0, 100, 100],
["511", "mike", 0, 100, 100, 100, 100, 0, 0, 0, 0, 100, 100],
["511", "tiff", 0, 100, 100, 100, 100, 0, 0, 0, 0, 100, 100],
["511", "tom", 0, 100, 100, 100, 100, 0, 0, 0, 0, 100, 100],
]
),
columns=[
"course_id",
"student_id",
"feedback",
"lab1",
"lab2",
"lab3",
"lab4",
"milestone1",
"milestone2",
"milestone3",
"milestone4",
"quiz1",
"quiz2",
],
)
)
# grades are adjusted correctly
assert_frame_equal(adj_grades, expected_adj_grades)
final_grades = tracker.calculate_final_grade(["511"])
expected_final_grades = convert_dtypes_to_float(
pd.DataFrame(
np.array(
[
["511", "joel", 90.82],
["511", "mike", 87.66],
["511", "tiff", 88.34],
["511", "tom", 84.66],
]
),
columns=["course_id", "student_id", "grade"],
)
)
# final grades are correct
assert_frame_equal(final_grades, expected_final_grades)
def test_suggest_grade_adjustment_benchmark_quiz_less_than_zero():
tracker = pygtracker.GradeTracker()
with raises(ValueError):
tracker.suggest_grade_adjustment(
course_id="511", benchmark_course=90, benchmark_lab=90, benchmark_quiz=-2
)
def test_suggest_grade_adjustment_benchmark_quiz_not_float():
tracker = pygtracker.GradeTracker()
with raises(TypeError):
tracker.suggest_grade_adjustment(
course_id="511", benchmark_course=80, benchmark_lab=90, benchmark_quiz="90"
)
def test_suggest_grade_adjustment_course_id_not_string():
tracker = pygtracker.GradeTracker()
with raises(TypeError):
tracker.suggest_grade_adjustment(course_id=None)
def test_rank_students_course_input_not_string():
gradetracker = pygtracker.GradeTracker()
with raises(TypeError):
gradetracker.rank_students(course_id=511)
def test_rank_students_n_input_not_integer():
gradetracker = pygtracker.GradeTracker()
with raises(TypeError):
gradetracker.rank_students(n="4")
def test_rank_students_ascending_input_not_bool():
gradetracker = pygtracker.GradeTracker()
with raises(TypeError):
gradetracker.rank_students(course_id="511", ascending="True")
def test_record_grades_assess_invalid():
tracker = pygtracker.GradeTracker()
df = generate_df_record_grade([571], ["lab6"], [100], ["selina"])
with raises(ValueError):
tracker.record_grades(df)
def test_suggest_grade_adjustment_benchmark_quiz_more_than_one_hundred():
tracker = pygtracker.GradeTracker()
with raises(ValueError):
tracker.suggest_grade_adjustment(
course_id="511", benchmark_course=90, benchmark_lab=90, benchmark_quiz=150
)
def test_register_courses_assess_invalid():
tracker = pygtracker.GradeTracker()
df = generate_df_register_courses([522], ["lab5"], [1])
with raises(ValueError):
tracker.register_courses(df)