def get_attributes(self):
""" return attributes by tornado Attributes type
@return:
attributes: Attributes
"""
attributes = []
dim_index = 0
dataset_attributes = Dataset.get_attributes(self.dataset)
for attr in dataset_attributes:
for index in range(attr[1]):
attribute = Attribute()
attribute.set_name('{}_{}'.format(attr[0], index))
attribute.set_type(TornadoDic.NUMERIC_ATTRIBUTE)
attribute.set_possible_values([])
if self.split:
range_dim = self.configuration['split']['range'][
self.cur_data_slit][dim_index]
else:
range_dim = self.configuration['all']['range'][dim_index]
attribute.set_bounds_values(range_dim[0], range_dim[1])
if attr[0] != 'cast':
attributes.append(attribute)
dim_index += 1
return [0, 1], attributes
def construct_attribute(self):
for attr in [('warning_level', self.le, [0, 1, 2])]:
for index in range(attr[1]):
attribute = Attribute()
attribute.set_name('{}_{}'.format(attr[0], index))
attribute.set_type(TornadoDic.NOMINAL_ATTRIBUTE)
attribute.set_possible_values(attr[2])
self.attributes.append(attribute)
def construct_attribute():
nb_property = []
for attr in [('warning_level', 1, [1, 2, 3])]:
for index in range(attr[1]):
att = Attribute()
att.set_name('{}_{}'.format(attr[0], index))
att.set_type(TornadoDic.NOMINAL_ATTRIBUTE)
att.set_possible_values(attr[2])
nb_property.append(att)
return nb_property
class Perceptron(SuperClassifier):
"""This is the implementation of a single perceptron for learning from data streams."""
LEARNER_NAME = TornadoDic.PERCEPTRON
LEARNER_TYPE = TornadoDic.TRAINABLE
LEARNER_CATEGORY = TornadoDic.NUM_CLASSIFIER
__BIAS_ATTRIBUTE = Attribute()
__BIAS_ATTRIBUTE.set_name("bias")
__BIAS_ATTRIBUTE.set_type(TornadoDic.NUMERIC_ATTRIBUTE)
__BIAS_ATTRIBUTE.set_possible_values(1)
def __init__(self, labels, attributes, learning_rate=1):
super().__init__(labels, attributes)
attributes.append(self.__BIAS_ATTRIBUTE)
self.WEIGHTS = OrderedDict()
self.__initialize_weights()
self.LEARNING_RATE = learning_rate
def __initialize_weights(self):
for c in self.CLASSES:
self.WEIGHTS[c] = OrderedDict()
for a in self.ATTRIBUTES:
self.WEIGHTS[c][a.NAME] = 0.2 * random.random() - 0.1
def train(self, instance):
x = instance[0:len(instance) - 1]
x.append(1)
y_real = instance[len(instance) - 1]
predictions = OrderedDict()
for c in self.CLASSES:
predictions[c] = self.predict(x, c)
for c in self.CLASSES:
actual = 1 if c == y_real else 0
delta = (actual -
predictions[c]) * predictions[c] * (1 - predictions[c])
for i in range(0, len(instance)):
self.WEIGHTS[c][self.ATTRIBUTES[i].
NAME] += self.LEARNING_RATE * delta * x[i]
self._IS_READY = True
def predict(self, x, c):
s = 0
for i in range(0, len(x)):
s += self.WEIGHTS[c][self.ATTRIBUTES[i].NAME] * x[i]
p = 1 / (1 + math.exp(-s))
return p
def test(self, instance):
if self._IS_READY:
x = instance[0:len(instance) - 1]
y = instance[len(instance) - 1]
x.append(1)
predictions = OrderedDict()
for c in list(self.CLASSES):
predictions[c] = self.predict(x, c)
y_predicted = max(predictions.items(),
key=operator.itemgetter(1))[0]
self.update_confusion_matrix(y, y_predicted)
return y_predicted
else:
print("Please train a Perceptron classifier first!")
exit()
def get_prediction_prob_list(self, instance):
# Just return a one-hot array
if self._IS_READY:
x = instance[0:len(instance) - 1]
y = instance[len(instance) - 1]
x.append(1)
predictions = OrderedDict()
for c in list(self.CLASSES):
predictions[c] = self.predict(x, c)
y_predicted = max(predictions.items(),
key=operator.itemgetter(1))[0]
prob = []
for i, c in enumerate(self.CLASSES):
prob.append(int(c == y_predicted))
return prob
else:
print("Please train a Perceptron classifier first!")
exit()
def reset(self):
super()._reset_stats()
self.WEIGHTS = OrderedDict()
self.__initialize_weights()
def read(file_path):
labels = []
attributes = []
attributes_min_max = []
records = []
data_flag = False
reader = open(file_path, "r")
for line in reader:
if line.strip() == '':
continue
if line.startswith("@attribute") or line.startswith("@ATTRIBUTE"):
line = line.strip('\n\r\t')
line = line.split(' ')
attribute_name = line[1]
attribute_value_range = line[2]
attribute = Attribute()
attribute.set_name(attribute_name)
if attribute_value_range.lower() in ['numeric', 'real', 'integer']:
attribute_type = TornadoDic.NUMERIC_ATTRIBUTE
attribute_value_range = []
attributes_min_max.append([0, 0])
else:
attribute_type = TornadoDic.NOMINAL_ATTRIBUTE
attribute_value_range = attribute_value_range.strip('{}').replace("'", "")
attribute_value_range = attribute_value_range.split(',')
attributes_min_max.append([None, None])
attribute.set_type(attribute_type)
attribute.set_possible_values(attribute_value_range)
attributes.append(attribute)
elif line.startswith("@data") or line.startswith("@DATA"):
data_flag = True
labels = attributes[len(attributes) - 1].POSSIBLE_VALUES
attributes.pop(len(attributes) - 1)
continue
elif data_flag is True:
line = re.sub('\s+', '', line)
elements = line.split(',')
for i in range(0, len(elements) - 1):
if attributes[i].TYPE == TornadoDic.NUMERIC_ATTRIBUTE:
elements[i] = float(elements[i])
min_value = attributes_min_max[i][0]
max_value = attributes_min_max[i][1]
if elements[i] < min_value:
min_value = elements[i]
elif elements[i] > max_value:
max_value = elements[i]
attributes_min_max[i] = [min_value, max_value]
records.append(elements)
for i in range(0, len(attributes)):
if attributes[i].TYPE == TornadoDic.NUMERIC_ATTRIBUTE:
attributes[i].set_bounds_values(attributes_min_max[i][0], attributes_min_max[i][1])
return labels, attributes, records
class Logistic(SuperClassifier):
"""basic logistic"""
LEARNER_NAME = TornadoDic.LOGISTIC
LEARNER_TYPE = TornadoDic.TRAINABLE
LEARNER_CATEGORY = TornadoDic.NUM_CLASSIFIER
__BIAS_ATTRIBUTE = Attribute()
__BIAS_ATTRIBUTE.set_name("bias")
__BIAS_ATTRIBUTE.set_type(TornadoDic.NUMERIC_ATTRIBUTE)
__BIAS_ATTRIBUTE.set_possible_values(1)
def __init__(self, labels, attributes, learning_rate=0.8):
super().__init__(labels, attributes)
attributes.append(self.__BIAS_ATTRIBUTE)
self.seen_label = labels
self.WEIGHTS = OrderedDict()
self.__initialize_weights()
self.LEARNING_RATE = learning_rate
self.birthday = 0
self.dataOfDeath = 0
self.mse_it = 0
self.squareErrors = 0
self.id = 0
random.seed(1)
def __initialize_weights(self):
for a in self.ATTRIBUTES:
self.WEIGHTS[a.NAME] = 0.2 * random.random() - 0.1
# self.WEIGHTS[a.NAME] = 1.0
def train(self, instance, drift_status):
x = instance[0:len(instance) - 1]
x.append(1)
y_real = instance[len(instance) - 1]
prediction = self.predict(x)
p = np.clip(prediction, 0.00001, 1-0.00001)
err = y_real - p
# update the weights and bias
for i in range(len(instance)):
self.WEIGHTS[self.ATTRIBUTES[i].NAME] += self.LEARNING_RATE * x[i] * err
self._IS_READY = True
def predict(self, x):
s = 0
for i in range(0, len(x)):
s += self.WEIGHTS[self.ATTRIBUTES[i].NAME] * x[i]
# print("value of s ==>", s)
if s >= 0:
return 1.0 / (1 + np.exp(-s))
else:
return np.exp(s) / (1 + np.exp(s))
# p = 1.0 / (1 + np.exp(-s))
# return p
def getLoss(self, instance):
x = instance[0:len(instance) - 1]
y = instance[len(instance) - 1]
x.append(1)
y_predicted = self.predict(x)
y_predicted = np.clip(y_predicted, 0.00001, 1-0.00001)
loss = - y * np.log(y_predicted) - (1 - y) * np.log(1 - y_predicted)
return loss
def test(self, instance):
x = instance[0:len(instance) - 1]
x.append(1)
y_predicted = self.predict(x)
return [1-y_predicted, y_predicted]
def reset(self):
super()._reset_stats()
self.WEIGHTS = OrderedDict()
self.__initialize_weights()