def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
model = RandomForestClassifier(n_estimators=20, criterion="entropy")
model.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = model.predict(test_data[0])
predicted_proba = model.predict_proba(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
result.add_record(model.get_params(), key=token, confusion_matrix=confusion_matrix)
# In case any label is missing, populate it
if predicted_proba.shape[1] != num_classes:
temp_array = np.zeros((predicted_proba.shape[0], num_classes), np.float32)
for i in range(len(model.classes_)):
temp_array[:, model.classes_[i]] = predicted_proba[:, i]
predicted_proba = temp_array
return predicted_y, predicted_proba
def training_and_test(token, train_data, test_data, num_classes, result, model, log_dir):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
train_y = np.zeros((train_data[1].shape[0], num_classes))
test_y = np.zeros((test_data[1].shape[0], num_classes))
for i in range(train_data[1].shape[0]):
train_y[i, train_data[1].flatten()[i]] = 1
for i in range(test_data[1].shape[0]):
test_y[i, test_data[1].flatten()[i]] = 1
model.fit(train_data[0], train_y, iter_num=5000, summaries_dir=log_dir, test_x=test_data[0], test_y=test_y,
criterion='monitor_based')
# Test
predicted_y = mlp.predict(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
variable_file = os.path.join(log_dir, token + '_save.ckpt')
saver.save(mlp.sess, variable_file)
result.add_record(variable_file, key=token, confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result, model, log_dir):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
train_y = np.zeros((train_data[1].shape[0], num_classes))
test_y = np.zeros((test_data[1].shape[0], num_classes))
for i in range(train_data[1].shape[0]):
train_y[i, train_data[1].flatten()[i]] = 1
for i in range(test_data[1].shape[0]):
test_y[i, test_data[1].flatten()[i]] = 1
model.fit(train_data[0], train_y, pretrain_iter_num=5000, tuning_iter_num=5000,
summaries_dir=log_dir, test_x=test_data[0], test_y=test_y,
summary_interval=50)
# Test
predicted_y = model.predict(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
variable_file = os.path.join(log_dir, token + '_save.ckpt')
saver.save(model.sess, variable_file)
result.add_record(variable_file, key=token, confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
model = RandomForestClassifier(n_estimators=20, criterion="entropy")
model.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = model.predict(test_data[0])
predicted_proba = model.predict_proba(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(),
predicted=predicted_y)
result.add_record(model.get_params(),
key=token,
confusion_matrix=confusion_matrix)
# In case any label is missing, populate it
if predicted_proba.shape[1] != num_classes:
temp_array = np.zeros((predicted_proba.shape[0], num_classes),
np.float32)
for i in range(len(model.classes_)):
temp_array[:, model.classes_[i]] = predicted_proba[:, i]
predicted_proba = temp_array
return predicted_y, predicted_proba
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
svm_model = sklearn.svm.SVC(kernel='rbf')
svm_model.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = svm_model.predict(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
result.add_record(svm_model, key=token, confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
decision_tree = DecisionTree(train_data[0].shape[1], num_classes, log_level=logging.WARNING)
decision_tree.build(train_data[0], train_data[1].flatten())
# Test
predicted_y = decision_tree.classify(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
result.add_record(decision_tree.export_to_dict(), key=token, confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
svm_model = sklearn.svm.SVC(kernel='rbf')
svm_model.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = svm_model.predict(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(),
predicted=predicted_y)
result.add_record(svm_model, key=token, confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
decision_tree = sklearn.tree.DecisionTreeClassifier(
min_samples_leaf=4, min_samples_split=4
)
decision_tree.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = decision_tree.predict(test_data[0])
predicted_proba = decision_tree.predict_proba(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(), predicted=predicted_y)
result.add_record(decision_tree.get_params(), key=token, confusion_matrix=confusion_matrix)
return predicted_y, predicted_proba
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
decision_tree = DecisionTree(train_data[0].shape[1],
num_classes,
log_level=logging.WARNING)
decision_tree.build(train_data[0], train_data[1].flatten())
# Test
predicted_y = decision_tree.classify(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(),
predicted=predicted_y)
result.add_record(decision_tree.export_to_dict(),
key=token,
confusion_matrix=confusion_matrix)
return predicted_y
def training_and_test(token, train_data, test_data, num_classes, result):
"""Train and test
Args:
token (:obj:`str`): token representing this run
train_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of training feature and label
test_data (:obj:`tuple` of :obj:`numpy.array`): Tuple of testing feature and label
num_classes (:obj:`int`): Number of classes
result (:obj:`pyActLearn.performance.record.LearningResult`): LearningResult object to hold learning result
"""
decision_tree = sklearn.tree.DecisionTreeClassifier(min_samples_leaf=4,
min_samples_split=4)
decision_tree.fit(train_data[0], train_data[1].flatten())
# Test
predicted_y = decision_tree.predict(test_data[0])
predicted_proba = decision_tree.predict_proba(test_data[0])
# Evaluate the Test and Store Result
confusion_matrix = get_confusion_matrix(num_classes=num_classes,
label=test_data[1].flatten(),
predicted=predicted_y)
result.add_record(decision_tree.get_params(),
key=token,
confusion_matrix=confusion_matrix)
return predicted_y, predicted_proba