def test_LogisticRegression(self):
clf = sklearn.linear_model.LogisticRegression()
clf.fit(self.X_train, self.y_train)
lr = LogisticRegression()
lr.train(Dataset(self.X_train, self.y_train))
assert_array_equal(
clf.predict(self.X_train), lr.predict(self.X_train))
assert_array_equal(
clf.predict(self.X_test), lr.predict(self.X_test))
self.assertEqual(
clf.score(self.X_train, self.y_train),
lr.score(Dataset(self.X_train, self.y_train)))
self.assertEqual(
clf.score(self.X_test, self.y_test),
lr.score(Dataset(self.X_test, self.y_test)))
def main():
global pos_filepath, dataset_filepath, csv_filepath, vectors_list, ids_list
dataset_filepath = "/Users/dndesign/Desktop/active_learning/vecteurs_et_infos/vectors_2015.txt"
csv_filepath = "/Users/dndesign/Desktop/active_learning/donnees/corpus_2015_id-time-text.csv"
pos_filepath = "/Users/dndesign/Desktop/active_learning/donnees/oriane_pos_id-time-text.csv"
vectors_list, ids_list = get_vectors_list(dataset_filepath)
timestr = time.strftime("%Y%m%d_%H%M%S")
text_file = codecs.open("task_" + str(timestr) + ".txt", "w", "utf-8")
print("Loading data...")
text_file.write("Loading data...\n")
# Open this file
t0 = time.time()
file = openfile_txt(dataset_filepath)
num_lines = sum(1 for line in file)
print("Treating " + str(num_lines) + " entries...")
text_file.write("Treating : %s entries...\n" % str(num_lines))
# Number of queries to ask human to label
quota = 10
E_out1, E_out2, E_out3, E_out4, E_out6, E_out7 = [], [], [], [], [], []
trn_ds, tst_ds = split_train_test(csv_filepath)
model = SVM(kernel='linear')
# model = LogisticRegression()
''' UncertaintySampling (Least Confident)
UncertaintySampling : it queries the instances about which
it is least certain how to label
Least Confident : it queries the instance whose posterior
probability of being positive is nearest 0.5
'''
qs = UncertaintySampling(trn_ds, method='lc', model=LogisticRegression(C=.01))
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
''' UncertaintySampling (Max Margin)
'''
trn_ds2 = copy.deepcopy(trn_ds)
qs2 = USampling(trn_ds2, method='mm', model=SVM(kernel='linear'))
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
''' CMB Sampling
Combination of active learning algorithms (distance-based (DIST), diversity-based (DIV))
'''
trn_ds3 = copy.deepcopy(trn_ds)
qs3 = CMBSampling(trn_ds3, model=SVM(kernel='linear'))
model.train(trn_ds3)
E_out3 = np.append(E_out3, 1 - model.score(tst_ds))
''' Random Sampling
Random : it chooses randomly a query
'''
trn_ds4 = copy.deepcopy(trn_ds)
qs4 = RandomSampling(trn_ds4, random_state=1126)
model.train(trn_ds4)
E_out4 = np.append(E_out4, 1 - model.score(tst_ds))
''' QueryByCommittee (Vote Entropy)
QueryByCommittee : it keeps a committee of classifiers and queries
the instance that the committee members disagree, it also examines
unlabeled examples and selects only those that are most informative
for labeling
Vote Entropy : a way of measuring disagreement
Disadvantage : it does not consider the committee members’ class
distributions. It also misses some informative unlabeled examples
to label
'''
trn_ds6 = copy.deepcopy(trn_ds)
qs6 = QueryByCommittee(trn_ds6, disagreement='vote',
models=[LogisticRegression(C=1.0),
LogisticRegression(C=0.01),
LogisticRegression(C=100)],
random_state=1126)
model.train(trn_ds6)
E_out6 = np.append(E_out6, 1 - model.score(tst_ds))
''' QueryByCommittee (Kullback-Leibler Divergence)
QueryByCommittee : it examines unlabeled examples and selects only
those that are most informative for labeling
Disadvantage : it misses some examples on which committee members
disagree
'''
trn_ds7 = copy.deepcopy(trn_ds)
qs7 = QueryByCommittee(trn_ds7, disagreement='kl_divergence',
models=[LogisticRegression(C=1.0),
LogisticRegression(C=0.01),
LogisticRegression(C=100)],
random_state=1126)
model.train(trn_ds7)
E_out7 = np.append(E_out7, 1 - model.score(tst_ds))
with sns.axes_style("darkgrid"):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
query_num = np.arange(0, 1)
p1, = ax.plot(query_num, E_out1, 'red')
p2, = ax.plot(query_num, E_out2, 'blue')
p3, = ax.plot(query_num, E_out3, 'green')
p4, = ax.plot(query_num, E_out4, 'orange')
p6, = ax.plot(query_num, E_out6, 'black')
p7, = ax.plot(query_num, E_out7, 'purple')
plt.legend(('Least Confident', 'Max Margin', 'Distance Diversity CMB', 'Random Sampling', 'Vote Entropy', 'KL Divergence'), loc=1)
plt.ylabel('Accuracy')
plt.xlabel('Number of Queries')
plt.title('Active Learning - Query choice strategies')
plt.ylim([0, 1])
plt.show(block=False)
for i in range(quota):
print("\n#################################################")
print("Query number " + str(i) + " : ")
print("#################################################\n")
text_file.write("\n#################################################\n")
text_file.write("Query number %s : " % str(i))
text_file.write("\n#################################################\n")
ask_id = qs.make_query()
print("\033[4mUsing Uncertainty Sampling (Least confident) :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using Uncertainty Sampling (Least confident) :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
ask_id = qs2.make_query()
print("\033[4mUsing Uncertainty Sampling (Max Margin) :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using Uncertainty Sampling (Smallest Margin) :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds2.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
ask_id = qs3.make_query()
print("\033[4mUsing CMB Distance-Diversity Sampling :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using Uncertainty Sampling (Entropy) :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds3.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds3)
E_out3 = np.append(E_out3, 1 - model.score(tst_ds))
ask_id = qs4.make_query()
print("\033[4mUsing Random Sampling :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using Random Sampling :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds4.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds4)
E_out4 = np.append(E_out4, 1 - model.score(tst_ds))
ask_id = qs6.make_query()
print("\033[4mUsing QueryByCommittee (Vote Entropy) :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using QueryByCommittee (Vote Entropy) :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds6.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds6)
E_out6 = np.append(E_out6, 1 - model.score(tst_ds))
ask_id = qs7.make_query()
print("\033[4mUsing QueryByCommittee (KL Divergence) :\033[0m")
print("Tweet :" + define_tweet_by_id(ask_id), end='', flush=True)
print("Simulating human response : " + str(simulate_human_decision(ask_id)) + " \n")
text_file.write("Using QueryByCommittee (KL Divergence) :\n")
text_file.write("Tweet : %s \n" % str(define_tweet_by_id(ask_id)))
text_file.write("Simulating human response : %s \n\n" % str(simulate_human_decision(ask_id)))
trn_ds7.update(ask_id, simulate_human_decision(ask_id))
model.train(trn_ds7)
E_out7 = np.append(E_out7, 1 - model.score(tst_ds))
ax.set_xlim((0, i + 1))
ax.set_ylim((0, max(max(E_out1), max(E_out2), max(E_out3), max(E_out4), max(E_out6), max(E_out7)) + 0.2))
query_num = np.arange(0, i + 2)
p1.set_xdata(query_num)
p1.set_ydata(E_out1)
p2.set_xdata(query_num)
p2.set_ydata(E_out2)
p3.set_xdata(query_num)
p3.set_ydata(E_out3)
p4.set_xdata(query_num)
p4.set_ydata(E_out4)
p6.set_xdata(query_num)
p6.set_ydata(E_out6)
p7.set_xdata(query_num)
p7.set_ydata(E_out7)
plt.draw()
t2 = time.time()
time_total = t2 - t0
print("\n\n\n#################################################\n")
print("Execution time : %fs \n\n" % time_total)
text_file.write("\n\n\n#################################################\n")
text_file.write("Execution time : %fs \n" % time_total)
text_file.close()
input("Press any key to save the plot...")
plt.savefig('task_' + str(timestr) + '.png')
print("Done")
def main():
quota = 10 # ask human to label 10 samples
n_classes = 5
E_out1, E_out2 = [], []
trn_ds, tst_ds, ds = split_train_test(n_classes)
trn_ds2 = copy.deepcopy(trn_ds)
# print(trn_ds.get_entries())
# print(len(trn_ds))
qs = UncertaintySampling(trn_ds, method='lc', model=LogisticRegression())
qs2 = RandomSampling(trn_ds2)
model = LogisticRegression()
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.set_xlabel('Number of Queries')
ax.set_ylabel('Error')
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
query_num = np.arange(0, 1)
p1, = ax.plot(query_num, E_out1, 'g', label='qs Eout')
p2, = ax.plot(query_num, E_out2, 'k', label='random Eout')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.05),
fancybox=True,
shadow=True,
ncol=5)
plt.show(block=False)
img_ax = fig.add_subplot(2, 1, 2)
box = img_ax.get_position()
img_ax.set_position(
[box.x0, box.y0 - box.height * 0.1, box.width, box.height * 0.9])
# Give each label its name (labels are from 0 to n_classes-1)
lbr = InteractiveLabeler(label_name=[str(lbl) for lbl in range(n_classes)])
for i in range(quota):
ask_id = qs.make_query()
print("asking sample from Uncertainty Sampling")
# reshape the image to its width and height
lb = lbr.label(trn_ds.data[ask_id][0].reshape(8, 8))
trn_ds.update(ask_id, lb)
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
ask_id = qs2.make_query()
print("asking sample from Random Sample")
lb = lbr.label(trn_ds2.data[ask_id][0].reshape(8, 8))
trn_ds2.update(ask_id, lb)
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
ax.set_xlim((0, i + 1))
ax.set_ylim((0, max(max(E_out1), max(E_out2)) + 0.2))
query_num = np.arange(0, i + 2)
p1.set_xdata(query_num)
p1.set_ydata(E_out1)
p2.set_xdata(query_num)
p2.set_ydata(E_out2)
plt.draw()
input("Press any key to continue...")
def test_VarianceReduction(self):
trn_ds = Dataset(
self.X, np.concatenate([self.y[:2], [None] * (len(self.y) - 2)]))
qs = VarianceReduction(trn_ds, model=LogisticRegression(), sigma=0.1)
qseq = run_qs(trn_ds, qs, self.y, self.quota)
assert_array_equal(qseq, np.array([4, 5, 2, 3]))
def main():
quota = 10 # ask human to label 30 samples
n_classes = 5
E_out1, E_out2 = [], []
trn_ds, tst_ds, ds = split_train_test(n_classes)
trn_ds2 = copy.deepcopy(trn_ds)
qs = UncertaintySampling(trn_ds, method='lc', model=LogisticRegression())
qs2 = RandomSampling(trn_ds2)
model = LogisticRegression()
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.set_xlabel('Number of Queries')
ax.set_ylabel('Error')
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
query_num = np.arange(0, 1)
p1, = ax.plot(query_num, E_out1, 'g', label='qs Eout')
p2, = ax.plot(query_num, E_out2, 'k', label='random Eout')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True,
shadow=True, ncol=5)
plt.show(block=False)
img_ax = fig.add_subplot(2, 1, 2)
box = img_ax.get_position()
img_ax.set_position([box.x0, box.y0 - box.height * 0.1, box.width,
box.height * 0.9])
# Give each label its name (labels are from 0 to n_classes-1)
lbr = InteractiveLabeler(label_name=[str(lbl) for lbl in range(n_classes)])
for i in range(quota):
ask_id = qs.make_query()
print("asking sample from Uncertainty Sampling")
# reshape the image to its width and height
lb = lbr.label(trn_ds.data[ask_id][0].reshape(8, 8))
trn_ds.update(ask_id, lb)
model.train(trn_ds)
E_out1 = np.append(E_out1, 1 - model.score(tst_ds))
ask_id = qs2.make_query()
print("asking sample from Random Sample")
lb = lbr.label(trn_ds2.data[ask_id][0].reshape(8, 8))
trn_ds2.update(ask_id, lb)
model.train(trn_ds2)
E_out2 = np.append(E_out2, 1 - model.score(tst_ds))
ax.set_xlim((0, i + 1))
ax.set_ylim((0, max(max(E_out1), max(E_out2)) + 0.2))
query_num = np.arange(0, i + 2)
p1.set_xdata(query_num)
p1.set_ydata(E_out1)
p2.set_xdata(query_num)
p2.set_ydata(E_out2)
plt.draw()
input("Press any key to continue...")
def test_quire(self):
trn_ds = init_toyexample(self.X, self.y)
qs = QUIRE(trn_ds)
model = LogisticRegression()
qseq = run_qs(trn_ds, self.lbr, model, qs, self.quota)
assert_array_equal(qseq, np.array([6, 7, 9, 8]))
def main():
test_size = 0.25 # the percentage of samples in the dataset that will be
# randomly selected and assigned to the test set
result = {'E1': [], 'E2': [], 'E3': [], 'E4': [], 'E5': [], 'E6': []}
for i in range(10): # repeat experiment
trn_ds, tst_ds, fully_labeled_trn_ds = split_train_test(test_size)
trn_ds2 = copy.deepcopy(trn_ds)
trn_ds3 = copy.deepcopy(trn_ds)
trn_ds4 = copy.deepcopy(trn_ds)
trn_ds5 = copy.deepcopy(trn_ds)
trn_ds6 = copy.deepcopy(trn_ds)
lbr = IdealLabeler(fully_labeled_trn_ds)
model = BinaryRelevance(LogisticRegression())
quota = 150 # number of samples to query
qs = MMC(trn_ds, br_base=LogisticRegression())
_, E_out_1 = run(trn_ds, tst_ds, lbr, model, qs, quota)
result['E1'].append(E_out_1)
qs2 = RandomSampling(trn_ds2)
_, E_out_2 = run(trn_ds2, tst_ds, lbr, model, qs2, quota)
result['E2'].append(E_out_2)
qs3 = MultilabelWithAuxiliaryLearner(trn_ds3,
BinaryRelevance(
LogisticRegression()),
BinaryRelevance(SVM()),
criterion='hlr')
_, E_out_3 = run(trn_ds3, tst_ds, lbr, model, qs3, quota)
result['E3'].append(E_out_3)
qs4 = MultilabelWithAuxiliaryLearner(trn_ds4,
BinaryRelevance(
LogisticRegression()),
BinaryRelevance(SVM()),
criterion='shlr')
_, E_out_4 = run(trn_ds4, tst_ds, lbr, model, qs4, quota)
result['E4'].append(E_out_4)
qs5 = MultilabelWithAuxiliaryLearner(trn_ds5,
BinaryRelevance(
LogisticRegression()),
BinaryRelevance(SVM()),
criterion='mmr')
_, E_out_5 = run(trn_ds5, tst_ds, lbr, model, qs5, quota)
result['E5'].append(E_out_5)
qs6 = BinaryMinimization(trn_ds6, LogisticRegression())
_, E_out_6 = run(trn_ds6, tst_ds, lbr, model, qs6, quota)
result['E6'].append(E_out_6)
E_out_1 = np.mean(result['E1'], axis=0)
E_out_2 = np.mean(result['E2'], axis=0)
E_out_3 = np.mean(result['E3'], axis=0)
E_out_4 = np.mean(result['E4'], axis=0)
E_out_5 = np.mean(result['E5'], axis=0)
E_out_6 = np.mean(result['E6'], axis=0)
print("MMC: ", E_out_1[::5].tolist())
print("Random: ", E_out_2[::5].tolist())
print("MultilabelWithAuxiliaryLearner_hlr: ", E_out_3[::5].tolist())
print("MultilabelWithAuxiliaryLearner_shlr: ", E_out_4[::5].tolist())
print("MultilabelWithAuxiliaryLearner_mmr: ", E_out_5[::5].tolist())
print("BinaryMinimization: ", E_out_6[::5].tolist())
query_num = np.arange(1, quota + 1)
fig = plt.figure(figsize=(9, 6))
ax = plt.subplot(111)
ax.plot(query_num, E_out_1, 'g', label='MMC')
ax.plot(query_num, E_out_2, 'k', label='Random')
ax.plot(query_num, E_out_3, 'r', label='AuxiliaryLearner_hlr')
ax.plot(query_num, E_out_4, 'b', label='AuxiliaryLearner_shlr')
ax.plot(query_num, E_out_5, 'c', label='AuxiliaryLearner_mmr')
ax.plot(query_num, E_out_6, 'm', label='BinaryMinimization')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
plt.legend(loc=2, bbox_to_anchor=(1.05, 1), borderaxespad=0.)
plt.xlabel('Number of Queries')
plt.ylabel('Loss')
plt.title('Experiment Result (Hamming Loss)')
plt.show()
labeled_train = list(set(range(labeled_entries)).difference(test_index))
trn_ds = Dataset(X[train_index], Y.iloc[labeled_train].values.tolist() + [None]*(len(train_index) - len(labeled_train)))
tst_ds = Dataset(X[test_index],Y.iloc[test_index].values.tolist())
data_CV_train = data_CV_concat.iloc[train_index]
'''
MAIN FUNCTION
'''
result = {'Hamming': [],'F1': []}
model = BinaryRelevance(LogisticRegression())
quota = 20 # number of samples to query
#EXECUTE FROM HERE FOR ITERATIONS
qs1 = MultilabelWithAuxiliaryLearner(
trn_ds,
BinaryRelevance(LogisticRegression()),
BinaryRelevance(SVM()),
criterion='hlr')
run(data_CV_train,trn_ds, qs1, quota)
model.train(trn_ds)
def initialModelSetup(modelID, modelParams=None, fixRandomState=False):
if modelID == 0:
# Single random forest
if modelParams is None:
modelParams = (1000, )
model=[SklearnProbaAdapter(RandomForestClassifier(random_state=42 \
if fixRandomState else None, n_estimators=modelParams[0]))]
elif modelID == 1:
# List of random forests
if modelParams is None:
modelParams = (100, 15)
model=[SklearnProbaAdapter(RandomForestClassifier(random_state=i \
if fixRandomState else None, n_estimators=modelParams[0]))
for i in range(modelParams[1])]
elif modelID == 2:
# Small varied committee
# SVC can be made a probabilistic model with probability=true,
# but that slows down the fit
model=[LogisticRegression(C=1.0, random_state=0 \
if fixRandomState else None),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=100,
random_state=1 if fixRandomState else None)),
SklearnAdapter(SVC(random_state=2 \
if fixRandomState else None)),
SklearnProbaAdapter(DecisionTreeClassifier(random_state=3 \
if fixRandomState else None)),
SklearnProbaAdapter(KNeighborsClassifier(n_neighbors=19))]
elif modelID == 3:
# Large varied committee
# SVC can be made a probabilistic model with probability=true,
# but that slows down the fit
model=[LogisticRegression(C=1.0,
random_state=0 if fixRandomState else None),
LogisticRegression(C=0.1, random_state=1 \
if fixRandomState else None),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=100,
random_state=2 if fixRandomState else None)),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=200,
random_state=3 if fixRandomState else None)),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=300,
random_state=4 if fixRandomState else None)),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=400,
random_state=5 if fixRandomState else None)),
SklearnProbaAdapter(RandomForestClassifier(n_estimators=500,
random_state=6 if fixRandomState else None)),
SklearnAdapter(SVC(C=1.0,#kernel='rbf',
random_state=7 if fixRandomState else None)),
SklearnAdapter(SVC(C=0.1,kernel='rbf',
random_state=8 if fixRandomState else None)),
SklearnAdapter(SVC(C=1.0,kernel='poly', degree=3,
random_state=9 if fixRandomState else None)),
SklearnAdapter(SVC(C=0.1,kernel='poly', degree=3,
random_state=10 if fixRandomState else None)),
SklearnProbaAdapter(DecisionTreeClassifier(random_state=11
if fixRandomState else None)),
SklearnProbaAdapter(KNeighborsClassifier(n_neighbors=19,
weights='distance')),
SklearnProbaAdapter(KNeighborsClassifier(n_neighbors=19)),
SklearnProbaAdapter(KNeighborsClassifier(n_neighbors=9))
]
return model
def main(args):
acc_reviewer, acc_train, acc_test = [], [], []
trn_ds, tst_ds, y_train = split_train_test()
# query strategy
# https://libact.readthedocs.io/en/latest/libact.query_strategies.html
# #libact-query-strategies-uncertainty-sampling-module
qs = UncertaintySampling(trn_ds, method='lc', model=LogisticRegression())
# The passive learning model. The model given in the query strategy is not
# the same. Have a look at this one.
model = LogisticRegression()
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.set_xlabel('Number of Queries')
ax.set_ylabel('Error')
oracle = y_train[get_indices_labeled_entries(trn_ds)]
review = [label for feat, label in trn_ds.get_labeled_entries()]
reviewer_acc = accuracy_score(oracle, review)
# Train the model on the train dataset.
# Append the score (error).
model.train(trn_ds)
acc_reviewer = np.append(acc_reviewer, reviewer_acc)
acc_train = np.append(
acc_train,
model.model.score([x[0] for x in trn_ds.get_entries()], y_train))
acc_test = np.append(acc_test, model.score(tst_ds))
query_num = np.arange(0, 1)
p0, = ax.plot(query_num, acc_reviewer, 'g', label='Acc reviewer')
p1, = ax.plot(query_num, acc_reviewer, 'b', label='Acc train')
p2, = ax.plot(query_num, acc_test, 'r', label='Acc test')
plt.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.05),
fancybox=True,
shadow=True,
ncol=5)
plt.show(block=False)
img_ax = fig.add_subplot(2, 1, 2)
box = img_ax.get_position()
img_ax.set_position(
[box.x0, box.y0 - box.height * 0.1, box.width, box.height * 0.9])
# Give each label its name (labels are from 0 to n_classes-1)
lbr = InteractiveLabeler(label_name=["0", "1"])
# lbr = InteractivePaperLabeler(label_name=["0", "1"])
for i in range(args.quota):
# make a query from the pool
ask_id = qs.make_query()
print("asking sample from Uncertainty Sampling")
# reshape the image to its width and height
data_point = trn_ds.data[ask_id][0].reshape(8, 8)
lb = lbr.label(data_point)
# update the label in the train dataset
trn_ds.update(ask_id, lb)
# train the model again
model.train(trn_ds)
# compute accuracy of the reviewer
oracle = y_train[get_indices_labeled_entries(trn_ds)]
review = [label for feat, label in trn_ds.get_labeled_entries()]
reviewer_acc = accuracy_score(oracle, review)
# append the score to the model
acc_reviewer = np.append(acc_reviewer, reviewer_acc)
acc_train = np.append(
acc_train,
model.model.score([x[0] for x in trn_ds.get_entries()], y_train))
acc_test = np.append(acc_test, model.score(tst_ds))
# adjust the limits of the axes
ax.set_xlim((0, i + 1))
ax.set_ylim((0, max(acc_test) + 0.2))
query_num = np.arange(0, i + 2)
p0.set_xdata(query_num)
p0.set_ydata(acc_reviewer)
p1.set_xdata(query_num)
p1.set_ydata(acc_train)
p2.set_xdata(query_num)
p2.set_ydata(acc_test)
plt.draw()
input("Press any key to continue...")