def rfc_learner(option):
accuracy = []
data = pool_reader()
true_labels = oracle.read_mat()
[row_size, col_size] = data.shape
points = np.empty([0, col_size])
labels = []
used = set()
flag = True
predictions = np.zeros(row_size)
for i in xrange(0, 256):
if option == 'select':
if flag:
pick = random.sample(range(row_size), 1)[0]
else:
# pick = get_next(data, points, used)
clf = RandomForestClassifier(n_estimators=10, criterion='entropy')
clf.fit(points, np.array(labels))
prob = clf.predict_proba(data)
weight = np.abs(prob[:, 0] - 0.5)
rank = np.argsort(weight)
for x in xrange(len(rank)):
if rank[x] not in used:
pick = rank[x]
break
else:
while 1:
pick = random.sample(range(row_size), 1)[0]
if pick not in used:
break
used.add(pick)
points = np.vstack([points, data[pick]])
if oracle.oracle1(true_labels, pick) == 1:
flag = False
labels.append(oracle.oracle1(true_labels, pick))
clf = RandomForestClassifier(n_estimators=10, criterion='entropy')
clf.fit(points, np.array(labels))
predictions = clf.predict(data)
cur_acc = err.generalization_error(predictions, true_labels)
accuracy.append(cur_acc)
plt.plot(accuracy)
plt.show()
print "f1 ", f1_score(predictions, true_labels)
return accuracy
def lrc_learner(option):
accuracy = []
data = pool_reader()
true_labels = oracle.read_mat()
[row_size, col_size] = data.shape
predictions = np.zeros(row_size)
points = np.empty([0, col_size])
labels = []
used = set()
flag = True
for i in xrange(0, 256):
if option == "select":
pick = -1
if flag:
while 1:
pick = random.sample(range(row_size), 1)[0]
if pick not in used:
used.add(pick)
points = np.vstack([points, data[pick]])
label = oracle.oracle1(true_labels, pick)
labels.append(label)
if label == 1:
flag = False
break
else:
clf = LogisticRegression()
clf.fit(points, np.array(labels))
prob = clf.predict_proba(data)
weight = np.abs(prob[:, 0] - 0.5)
rank = np.argsort(weight)
for x in xrange(len(rank)):
if rank[x] not in used:
pick = rank[x]
break
used.add(pick)
points = np.vstack([points, data[pick]])
label = oracle.oracle1(true_labels, pick)
labels.append(label)
clf.fit(points, np.array(labels))
predictions = clf.predict(data)
cur_acc = err.generalization_error(predictions, true_labels)
accuracy.append(cur_acc)
else:
while 1:
pick = random.sample(range(row_size), 1)[0]
if pick not in used:
break
used.add(pick)
points = np.vstack([points, data[pick]])
label = oracle.oracle1(true_labels, pick)
labels.append(label)
if label == 1:
flag = False
if not flag:
clf = LogisticRegression()
clf.fit(points, np.array(labels))
predictions = clf.predict(data)
cur_acc = err.generalization_error(predictions, true_labels)
accuracy.append(cur_acc)
plt.plot(accuracy)
plt.show()
print "f1 ", f1_score(predictions, true_labels)
return accuracy
