}
c2_vect_options = {
'ngram_range': (1,1),
'sublinear_tf': True,
'smooth_idf': True,
'preprocessor': pr.no_usernames,
'use_idf': True,
'stop_words': None
}
c1_default_options = {
'penalty': 'l1',
'C': 1.0
}
c2_default_options = {
'C': 1.0
}
c1 = MaxEnt(docs_train_subjectivity, y_train_subjectivity, default_options=c1_default_options, vect_options=c1_vect_options)
c2 = SVM(docs_train_polarity, y_train_polarity, default_options=c2_default_options, vect_options=c2_vect_options)
clf = Combined(c1, c2)
if len(sys.argv) > 1:
y_predicted = clf.predict(docs_test)
sys.stdout.write(pe.predictions_as_str(y_predicted))
else:
s.test_clf(clf, docs_test, y_test)
rect = ax.bar(ind+(width * x) + 4, res[x], width, color=colors[x])
autolabel(rect)
# fig.autofmt_xdate()
# Shink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend( ['Precision', 'Recall', 'F1 Score', 'Accuracy'], loc='center left', bbox_to_anchor=(1, 0.5) )
savefig("plots/round2/maxent_extra.png", format="png")
plt.clf()
fig = plt.figure(2)
ax = fig.add_subplot(111)
plt.title('Confusion Matrix For MaxEnt, w/NTNU')
res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest')
for i, cas in enumerate(conf_arr):
for j, c in enumerate(cas):
if c>0:
plt.text(j-.2, i+.2, c, fontsize=14)
cb = fig.colorbar(res)
savefig("plots/round2/maxent_confuse_extra.png", format="png")
stat.test_clf(clf, docs_test, y_test)
for i, l in enumerate(labels):
for x, y in enumerate(res):
rect = ax.bar(ind + (width * x) + 4, res[x], width, color=colors[x])
autolabel(rect)
# fig.autofmt_xdate()
# Shink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(['Precision', 'Recall', 'F1 Score', 'Accuracy'],
loc='center left',
bbox_to_anchor=(1, 0.5))
savefig("plots/round2/svm_orig.png", format="png")
plt.clf()
fig = plt.figure(2)
ax = fig.add_subplot(111)
plt.title('Confusion Matrix For SVM')
res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest')
for i, cas in enumerate(conf_arr):
for j, c in enumerate(cas):
if c > 0:
plt.text(j - .2, i + .2, c, fontsize=14)
cb = fig.colorbar(res)
savefig("plots/round2/svm_confuse_orig.png", format="png")
stat.test_clf(clf, docs_test, y_test)
