def __init__(self, num_features, top_k_size, learning_rate):
self.D = num_features
self.w = np.array([0] * self.D)
self.b = 0
self.learning_rate = learning_rate
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(top_k_size)
self.loss_val = 0
class LogisticRegression(object):
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 0.5
# self.cms = CustomCountSketch(3, int(np.log(self.D) ** 2 / 3))
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(1 << 14 - 1)
def sigmoid(self, x):
if x >= 0:
return 1. / (1. + np.exp(-x))
else:
return np.exp(x) / (1. + np.exp(x))
def loss(self, y, p):
return -(y * math.log(p) + (1 - y) * math.log(1 - p))
def train_with_sketch(self, feature_pos, features, label):
logit = 0
min_logit = float("inf")
max_logit = float("-inf")
for i in range(len(feature_pos)):
# print("top k at pos {} value {}".format(feature_pos[i], self.top_k.get_item(feature_pos[i])))
val = self.top_k.get_value_for_key(feature_pos[i]) * features[i]
if val > max_logit:
max_logit = val
if val < min_logit:
min_logit = val
logit += val
if max_logit - min_logit == 0:
max_logit = 1
min_logit = 0
normalized_weights = (logit - min_logit) / (max_logit - min_logit)
print("normalized weights {}".format(normalized_weights))
sigm_val = self.sigmoid(normalized_weights)
if sigm_val == 1.0:
sigm_val = sigm_val - (1e-5)
print("label {} sigmoid {}".format(label, sigm_val))
loss = self.loss(y=label, p=sigm_val)
gradient = (label - sigm_val)
if gradient != 0:
for i in range(len(feature_pos)):
# updating the change only on previous values
updated_val = self.learning_rate * gradient * features[i]
value = self.cms.update(feature_pos[i], updated_val)
self.top_k.push(Node(feature_pos[i], value))
return loss
def predict(self, feature_pos, feature_val):
logit = 0
for i in range(len(feature_pos)):
logit += self.top_k.get_value_for_key(feature_pos[i]) * feature_val[i]
a = self.sigmoid(logit)
if a > 0.5:
return 1
else:
return 0
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 0.5
# self.cms = CustomCountSketch(3, int(np.log(self.D) ** 2 / 3))
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(1 << 14 - 1)
class LogisticRegression(object):
def __init__(self, num_features, top_k_size):
self.D = num_features
self.w = np.array([0] * self.D)
self.b = 0
self.learning_rate = 5e-1
self.cms = CustomCountSketch(3, (1 << 18) - 1)
# self.cms = CustomCountMinSketch(2, (1<<15) - 1)
self.top_k = TopK(top_k_size)
self.loss_val = 0
def sigmoid(self, x):
if x >= 0:
return 1. / (1. + np.exp(-x))
else:
return np.exp(x) / (1. + np.exp(x))
def loss(self, y, p):
return -(y * math.log(p) + (1 - y) * math.log(1 - p))
def train_with_sketch(self, feature_pos, features, label):
logit = 0
min_logit = float("inf")
max_logit = float("-inf")
for i in range(len(feature_pos)):
# print("top k at pos {} value {}".format(feature_pos[i], self.top_k.get_item(feature_pos[i])))
# multiplying w[i] with x[i]
val = self.top_k.get_value_for_key(feature_pos[i]) * features[i]
if val > max_logit:
max_logit = val
if val < min_logit:
min_logit = val
# calculating wTx
logit += val
if max_logit - min_logit == 0:
max_logit = 1
min_logit = 0
normalized_weights = (logit - min_logit) / (max_logit - min_logit)
sigm_val = self.sigmoid(normalized_weights)
if sigm_val == 1.0:
sigm_val = sigm_val - (1e-5)
# print("label {} sigmoid {}".format(label, sigm_val))
gradient = (label - sigm_val)
loss = self.loss(y=label, p=sigm_val)
self.loss_val += loss
if gradient != 0:
for i in range(len(feature_pos)):
updated_val = self.learning_rate * gradient * features[i]
value = self.cms.update(feature_pos[i], updated_val)
self.top_k.push(Node(feature_pos[i], value))
return loss
def negative_log_likelihood(self, y, x):
return -y * x / (1 + math.exp(y))
def predict(self, feature_pos, feature_val):
logit = 0
for i in range(len(feature_pos)):
logit += self.top_k.get_value_for_key(
feature_pos[i]) * feature_val[i]
a = self.sigmoid(logit)
return a
def __init__(self, num_features):
self.learning_rate = 5e-1
self.cms = CustomCountSketch(3, (1 << 18) - 1)
self.top_k = TopK(num_features)
self.loss_val = 0