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from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
import argparse
import numpy as np
from scipy.sparse import hstack
from sklearn.feature_extraction import DictVectorizer
import matplotlib.pyplot as plt
from custom_logistic import CustomLogistic
from bounded_logistic import BoundedLogistic
from process_datashop import read_datashop_student_step
from roll_up import transaction_to_student_step
def avg_y_by_x(x,y):
x = np.array(x)
y = np.array(y)
xs = sorted(list(set(x)))
xv = []
yv = []
for v in xs:
ys = [y[i] for i,e in enumerate(x) if e == v]
if len(ys) > 0:
yv.append(sum(ys) / len(ys))
return xv, yv
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Process datashop file.')
parser.add_argument('-ft', choices=["student_step", "transaction"],
help='the type of file to load (default="student_step")',
parser.add_argument('student_data', type=argparse.FileType('r'),
help="the student data file in datashop format")
args = parser.parse_args()
if args.ft == "transaction":
ssr_file = transaction_to_student_step(args.student_data)
ssr_file = open(ssr_file,'r')
ssr_file = args.student_data
kcs, opps, y, stu, student_label, item_label = read_datashop_student_step(ssr_file)
# Get everything in the right matrix format
sv = DictVectorizer()
qv = DictVectorizer()
ov = DictVectorizer()
S = sv.fit_transform(stu)
Q = qv.fit_transform(kcs)
O = ov.fit_transform(opps)
X = hstack((S, Q, O))
y = np.array(y)
# Regularize the student intercepts
l2 = [1.0 for i in range(S.shape[1])]
l2 += [0.0 for i in range(Q.shape[1])]
l2 += [0.0 for i in range(O.shape[1])]
# Bound the learning rates to be positive
bounds = [(None, None) for i in range(S.shape[1])]
bounds += [(None, None) for i in range(Q.shape[1])]
bounds += [(0, None) for i in range(O.shape[1])]
X = X.toarray()
X2 = Q.toarray()
afm = CustomLogistic(bounds=bounds, l2=l2, fit_intercept=False), y)
yAFM = afm.predict_proba(X)
afms = BoundedLogistic(first_bounds=bounds, first_l2=l2), X2, y)
yAFMS = afms.predict_proba(X, X2)
#plotkcs = ['All Knowledge Components']
plotkcs = list(set([kc for row in kcs for kc in row])) + ['All Knowledge Components']
#f, subplots = plt.subplots(len(plotkcs))
for plot_id, plotkc in enumerate(plotkcs):
#if len(plotkcs) > 1:
# p = subplots[plot_id]
# p = subplots
xs = []
y1 = []
y2 = []
y3 = []
for i in range(len(y)):
for kc in opps[i]:
if not (kc == plotkc or plotkc == 'All Knowledge Components'):
x, y1 = avg_y_by_x(xs, y1)
x, y2 = avg_y_by_x(xs, y2)
x, y3 = avg_y_by_x(xs, y3)
y1 = [1-v for v in y1]
y2 = [1-v for v in y2]
y3 = [1-v for v in y3]
human_line, = plt.plot(x, y1, color='red', label="Actual Data")
afm_line, = plt.plot(x, y2, color='blue', label="AFM")
afms_line, = plt.plot(x, y3, color='green', label="AFM+S")
plt.legend(handles=[human_line, afm_line, afms_line])
#p.plot(x, y1)
#p.plot(x, y2)
#p.plot(x, y3)