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@jameskyle
Created May 17, 2016 17:27
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from __future__ import print_function, division
import datetime as dt
import pandas as pd
import numpy as np
import scipy.optimize as sopt
def gradebook():
columns = ("Assignment", "Due", "Grade", "Weight")
rows = [
("Assignment One", dt.datetime(2016, 5, 23), np.nan, 0.02),
("Assignment Two", dt.datetime(2016, 5, 23), np.nan, 0.02),
("Peer Feedback One", dt.datetime(2016, 5, 30), np.nan, 0.005),
("Peer Feedback Two", dt.datetime(2016, 5, 30), np.nan, 0.005),
("Assignment Three", dt.datetime(2016, 5, 30), np.nan, 0.065),
("Assignment Four", dt.datetime(2016, 5, 30), np.nan, 0.065),
("Peer Feedback Three", dt.datetime(2016, 6, 6), np.nan, 0.005),
("Peer Feedback Four", dt.datetime(2016, 6, 6), np.nan, 0.005),
("Assignment Five", dt.datetime(2016, 6, 6), np.nan, 0.065),
("Peer Feedback Five", dt.datetime(2016, 6, 13), np.nan, 0.005),
("Assignment Six", dt.datetime(2016, 6, 13), np.nan, 0.065),
("Assignment Seven", dt.datetime(2016, 6, 13), np.nan, 0.065),
("Peer Feedback Six", dt.datetime(2016, 6, 20), np.nan, 0.005),
("Peer Feedback Seven", dt.datetime(2016, 6, 20), np.nan, 0.005),
("Assignment Eight", dt.datetime(2016, 6, 20), np.nan, 0.065),
("Assignment Nine", dt.datetime(2016, 6, 20), np.nan, 0.065),
("Peer Feedback Eight", dt.datetime(2016, 6, 27), np.nan, 0.005),
("Peer Feedback Nine", dt.datetime(2016, 6, 27), np.nan, 0.005),
("Assignment Ten", dt.datetime(2016, 6, 27), np.nan, 0.065),
("Peer Feedback Ten", dt.datetime(2016, 7, 4), np.nan, 0.005),
("Assignment Eleven", dt.datetime(2016, 7, 4), np.nan, 0.065),
("Peer Feedback Eleven", dt.datetime(2016, 7, 11), np.nan, 0.005),
("Term Project Report", dt.datetime(2016, 7, 25), np.nan, 0.1),
("Peer Feedback Term Project Report", dt.datetime(2016, 8, 3), np.nan, 0.005),
("Exam", dt.datetime(2016, 8, 3), np.nan, 0.15),
("Final Portfolio", dt.datetime(2016, 8, 3), np.nan, 0.025),
("Piazza Participation", dt.datetime(2016, 8, 3), np.nan, 0.04),
]
return pd.DataFrame(rows, columns=columns)
def calc(grades):
df = gradebook()
df.loc[df['Grade'].isnull(), 'Grade'] = grades
return np.abs(check_grade(df) - .80)
def optimize(gb=None):
if gb is None:
gb = gradebook()
nullidxs = gb['Grade'].isnull()
nullsize = gb[nullidxs].shape[0]
guess = [.5] * nullsize
bounds = ((0.0, 1.0),) * nullsize
res = sopt.minimize(
calc,
guess,
method="SLSQP",
bounds=bounds,
)
optimized = gb.copy()
optimized.loc[nullidxs, 'Grade'] = res.x
return optimized
def check_grade(df):
return (df['Grade'] * df['Weight']).sum()
def main():
result = optimize()
print(result.to_string())
print("Optimized for final grade of: {0}".format(check_grade(result)))
if __name__ == "__main__":
main()
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