def svm_learner(budget):
accuracy = []
data = csv_reader('resources/pool.csv')
testset = csv_reader('resources/testSet.csv')
true_labels = oracle.read_mat()
used = {}
# do nothing about model until reasonable training subset achieved
[row, col] = data.shape
preds = np.zeros(row)
selected = []
labels = []
query = 0
# query each point until get one with label 1
while 1:
r = compound.next_compound(data)
r_str = np.array_str(np.char.mod('%d', r))
if r_str[1: (len(r_str) - 1)] not in used:
r_label = oracle.oracle2(r, data)
query += 1
used[r_str[1: (len(r_str) - 1)]] = r_label
selected.append(r.tolist())
labels.append(r_label)
accuracy.append(error.generalization_error(preds, true_labels))
if np.sum(labels) == 1 and len(labels) > 1:
accuracy.pop()
break
x = np.array(selected)
y = np.array(labels)
clf = SVC(kernel='linear')
clf.fit(x, y)
preds = clf.predict(data)
accuracy.append(error.generalization_error(preds, true_labels))
num = 2543 - len(used)
i = 0
while i < num and query < budget:
r = compound.next_compound(data)
r_str = np.array_str(np.char.mod('%d', r))
if r_str[1: (len(r_str) - 1)] not in used:
i += 1
distance = clf.decision_function(r)
if np.abs(distance[0]) <= 0.78:
x = np.vstack([x, r])
r_label = oracle.oracle2(r, data)
y = np.hstack([y.tolist(), r_label])
query += 1
clf.fit(x, y)
preds = clf.predict(testset)
accuracy.append(error.test_error(preds, true_labels))
plt.plot(accuracy)
plt.show()
print f1_score(preds, true_labels[0:250])
return
def stream_learner(method, option, budget):
features = csv_reader('resources/pool.csv')
[row, col] = features.shape
testset = csv_reader('resources/testSet.csv')
true_labels = oracle.read_mat()
if method == "rf":
clf = RandomForestClassifier(n_estimators=10, criterion='entropy')
if method == "lr":
clf = LogisticRegression(penalty='l2')
accuracy = []
points = []
labels = []
used = {}
flag = True
query_count = 0
i = 0
pred = np.zeros(250)
if option == "select":
# active learner
while i < 2543 and query_count < budget:
if flag:
# call next compound until get one point with label 1
cur_point = compound.next_compound(features)
cur_str = np.array_str(np.char.mod('%d', cur_point))
if cur_str[1: (len(cur_str) - 1)] not in used:
i += 1
points.append(cur_point)
cur_label = oracle.oracle2(cur_point, features)
labels.append(cur_label)
used[cur_str[1: (len(cur_str) - 1)]] = cur_label
query_count += 1
if cur_label == 1:
flag = False
else:
clf.fit(np.asarray(points), np.array(labels))
cur_point = compound.next_compound(features)
cur_str = np.array_str(np.char.mod('%d', cur_point))
if cur_str[1: (len(cur_str) - 1)] not in used:
# decide if ask oracle for help
i += 1
prob = clf.predict_proba(cur_point)
if 0.1 <= prob[0][0] <= 0.9:
points.append(cur_point)
cur_label = oracle.oracle2(cur_point, features)
labels.append(cur_label)
query_count += 1
used[cur_str[1: (len(cur_str) - 1)]] = cur_label
clf.fit(np.asarray(points), np.array(labels))
pred = clf.predict(testset)
cur_acc = error.test_error(pred, true_labels)
print cur_acc, " ", query_count, " ", cur_label, " ", prob[0][0], " ", prob[0][1]
accuracy.append(cur_acc)
else:
# random learner
while i < budget:
cur_point = compound.next_compound(features)
cur_str = np.array_str(np.char.mod('%d', cur_point))
if cur_str[1: (len(cur_str) - 1)] not in used:
points.append(cur_point)
cur_label = oracle.oracle2(cur_point, features)
if cur_label == 1:
flag = False
labels.append(cur_label)
used[cur_str[1: (len(cur_str) - 1)]] = cur_label
query_count += 1
i += 1
if not flag:
clf.fit(np.asarray(points), np.array(labels))
pred = clf.predict(testset)
cur_acc = error.test_error(pred, true_labels)
print cur_acc, " ", query_count, " ", cur_label
accuracy.append(cur_acc)
plt.plot(accuracy)
plt.show()
print "f1", f1_score(pred, true_labels[0:250])
return
