# 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)
afm.fit(X, y)
yAFM = afm.predict_proba(X)
afms = BoundedLogistic(first_bounds=bounds, first_l2=l2)
afms.fit(X, 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):
plt.figure(plot_id+1)
#if len(plotkcs) > 1:
# p = subplots[plot_id]
y = np.array(y)
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])]
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()
if args.model == "AFM":
m = CustomLogistic(bounds=bounds, l2=l2, fit_intercept=False)
m.fit(X, y)
yHat = 1 - m.predict_proba(X)
elif args.model == "AFM+S":
m = BoundedLogistic(first_bounds=bounds, first_l2=l2)
m.fit(X, X2, y)
yHat = 1 - m.predict_proba(X, X2)
else:
raise ValueError("Model type not supported")
headers = original_headers + ["Predicted Error Rate (%s)" % kc_model]
outfilePath = args.workingDir + "/output.txt"
outfile = open(outfilePath, 'w')
outfile.write("\t".join(headers) + "\n")
cntRowMissOpp = 0
for i, row in enumerate(original_step_data):
oppCell = row[len(row) - 1]
if oppCell is None or oppCell == "":
# 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)
afm.fit(X, y)
yAFM = afm.predict_proba(X)
afms = BoundedLogistic(first_bounds=bounds, first_l2=l2)
afms.fit(X, 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):
plt.figure(plot_id+1)
#if len(plotkcs) > 1:
# p = subplots[plot_id]