class LogisticRegression(object):
def __init__(self, num_features, top_k_size):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CustomCountMinSketch(2, (1 << 15) - 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
class LogisticRegressionWithHeap(LogisticRegression):
'''
Logistic Regression with Top K Heap where in after each iteration we are maintaining
top k features in heap.
'''
def __init__(self, dimensions, train_file, test_file, size_topK):
super(LogisticRegressionWithHeap,
self).__init__(dimensions, train_file, test_file)
self.top_k = TopK(size_topK)
self.top_k_dict = {}
def train(self, feature_pos, features, label):
val = 0
for i in range(len(feature_pos)):
val += self.top_k.get_value_for_key(feature_pos[i]) * features[i]
sigm_val = self.sigmoid(val)
print("label {} sigmoid {}".format(label, sigm_val))
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(feature_pos)):
# updating the change only on previous values
grad_update = self.learning_rate * diff_label * features[i]
self.gradient_updates_dict[feature_pos[i]].append(grad_update)
self.gradients[feature_pos[i]] += grad_update
self.top_k.push(
Node(feature_pos[i], self.gradients[feature_pos[i]]))
return loss
def dump_top_K(self, filename):
with open(filename + ".json", 'w') as f:
for item in self.top_k.heap:
key = self.top_k.keys[item.value]
value = self.top_k.features[key]
f.write("{}:{}\n".format(key, value))
def __init__(self,
sparsity,
cms_type,
hash_func_counts,
batch_size,
count_sketch_size,
top_k,
dataset_dict,
top_k_dict={}):
random.seed(42)
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
self.load_dataset(dataset_dict)
self.batch_size = batch_size
self.sparsity = sparsity
self.recovered_weight = np.zeros(self.features, )
self.non_zero_indexes = np.nonzero(self.weight)
print("non zero indexes of weights {}".format(self.non_zero_indexes))
self.non_zero_weights = []
for index in self.non_zero_indexes:
self.non_zero_weights.append(self.weight[index])
print("non zero weights {}".format(self.non_zero_weights))
self.loss_val = 0
self.correctly_classified = 0
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CountSketch(3, (1 << 18) - 1)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK((1 << 14) - 1)
self.loss_val = 0
def __init__(self, num_features, top_k_size):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CustomCountMinSketch(2, (1 << 15) - 1)
# self.cms = CustomCountMinSketch(2, (1<<15) - 1)
self.top_k = TopK(top_k_size)
self.loss_val = 0
class LogisticRegression(object):
cms_dicts = {
"complementary_cms": ComplementaryCountMinSketch,
"complementary_cms_conservative":
ConservativeComplementaryCountMinSketch,
"mission_count_sketch": CountSketch,
"conservative_count_sketch": ConservativeCountSketch
}
def __init__(self,
cms_type,
hash_func_counts,
count_sketch_size,
top_k,
top_k_dict={}):
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
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
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]
logit += val
sigm_val = self.sigmoid(logit)
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(feature_pos)):
# updating the change only on previous values
grad_update = self.learning_rate * diff_label * features[i]
if feature_pos[i] in self.top_k_dict.keys():
self.top_k_dict[feature_pos[i]].append(grad_update)
value = self.cms.update(feature_pos[i], grad_update)
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
class LogisticRegression(object):
def __init__(self, count_sketch_size, top_k, feature_size):
self.learning_rate = 0.5
self.cms = CustomCountMinSketch(2, count_sketch_size)
self.top_k = TopK(top_k)
self.recovered_weight_vector = [0] * feature_size
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)
print("label {} sigmoid {}".format(label, sigm_val))
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(feature_pos)):
# updating the change only on previous values
grad_update = self.learning_rate * diff_label * features[i]
value = self.cms.update(feature_pos[i], grad_update)
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 sparse_recovery(self, feature_pos, feature_vals, label):
for i in range(len(feature_pos)):
cumulative_grad_val = self.cms.query(feature_pos[i])
self.recovered_weight_vector[feature_pos[i]-1] += cumulative_grad_val / feature_vals[i]
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 = CustomCountMinSketch(2, (1 << 15) - 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,
cms_type,
hash_func_counts,
count_sketch_size,
top_k,
top_k_dict={}):
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
class LogisticRegressionWithHeap(LogisticRegression):
def __init__(self, examples, features, sparsity, dataset_files_path):
super(LogisticRegressionWithHeap,
self).__init__(examples, features, sparsity, dataset_files_path)
self.top_k = TopK(sparsity)
def train(self, example, label):
val = 0
for i in range(len(example)):
val += self.top_k.get_value_for_key(i) * example[i]
sigm_val = self.sigmoid(val)
# print("label {} sigmoid {}".format(label, sigm_val))
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(example)):
# updating the change only on previous values
grad_update = self.learning_rate * diff_label * example[i]
self.recovered_weight[i] += grad_update
self.top_k.push(Node(i, self.recovered_weight[i]))
return loss
class MissionQuadreticLoss(object):
def __init__(self, top_k_size):
self.learning_rate = 0.2
self.cms = CountSketch(3, 1000)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK(top_k_size)
self.loss_val = 0
def train_with_sketch(self, feature_pos, features, label):
logit = 0
for i in range(len(feature_pos)):
val = self.top_k.get_value_for_key(feature_pos[i]) * features[i]
# calculating wTx
logit += val
# print("label {} wx {}".format(label, logit))
gradient = (label - logit)
print("loss {}".format(gradient))
if gradient != 0:
for i in range(len(feature_pos)):
updated_val = 2 * 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 gradient
def __init__(self, examples, features, sparsity, dataset_files_path):
super(LogisticRegressionWithHeap,
self).__init__(examples, features, sparsity, dataset_files_path)
self.top_k = TopK(sparsity)
def __init__(self, dimensions, train_file, test_file, size_topK):
super(LogisticRegressionWithHeap,
self).__init__(dimensions, train_file, test_file)
self.top_k = TopK(size_topK)
self.top_k_dict = {}
def __init__(self, count_sketch_size, top_k, feature_size):
self.learning_rate = 0.5
self.cms = CustomCountMinSketch(2, count_sketch_size)
self.top_k = TopK(top_k)
self.recovered_weight_vector = [0] * feature_size
def __init__(self, top_k_size):
self.learning_rate = 0.2
self.cms = CountSketch(3, 1000)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK(top_k_size)
self.loss_val = 0
class LogisticRegression(object):
cms_dicts = {
"complementary_cms": ComplementaryCountMinSketch,
"complementary_cms_conservative":
ConservativeComplementaryCountMinSketch,
"mission_count_sketch": CountSketch,
"conservative_count_sketch": ConservativeCountSketch
}
def __init__(self,
sparsity,
cms_type,
hash_func_counts,
batch_size,
count_sketch_size,
top_k,
dataset_dict,
top_k_dict={}):
random.seed(42)
self.learning_rate = 0.5
self.cms = self.cms_dicts[cms_type](hash_func_counts,
count_sketch_size)
self.top_k = TopK(top_k)
self.top_k_dict = top_k_dict
self.load_dataset(dataset_dict)
self.batch_size = batch_size
self.sparsity = sparsity
self.recovered_weight = np.zeros(self.features, )
self.non_zero_indexes = np.nonzero(self.weight)
print("non zero indexes of weights {}".format(self.non_zero_indexes))
self.non_zero_weights = []
for index in self.non_zero_indexes:
self.non_zero_weights.append(self.weight[index])
print("non zero weights {}".format(self.non_zero_weights))
self.loss_val = 0
self.correctly_classified = 0
def load_dataset(self, dataset_dict):
self.samples = np.loadtxt(dataset_dict['examples_path'], delimiter=',')
self.num_data, self.features = self.samples.shape
self.weight = np.loadtxt(dataset_dict['weights_path'], delimiter=',')
self.true_labels = np.loadtxt(dataset_dict['true_label_path'],
delimiter=',')
self.noisy_labels = np.loadtxt(dataset_dict['noisy_label_path'],
delimiter=',')
def train_dataset(self, epochs):
print("Dataset Training Started")
for epoch in range(epochs):
print("epoch {}".format(epoch))
for i in range(self.num_data):
if i % 500 == 0:
print("i {}".format(i))
label = self.true_labels[i]
example = self.samples[i]
loss = self.train_with_sketch(example, label)
self.loss_val += loss
self.reset_weight_sparsity()
print("Dataset Training Done")
def reset_weight_sparsity(self):
for item in self.top_k.heap:
key = self.top_k.keys[item.value]
value = lgr.top_k.features[key]
print("heap position {} value {}".format(key, value))
self.recovered_weight[key - 1] = value
def sigmoid(self, x):
if x >= 0:
return 1. / (1. + np.exp(-x))
else:
return np.exp(x) / (1. + np.exp(x))
def batch(self, iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
def loss(self, y, p):
if p == 1:
p = 0.999999
if p == 0:
p = 0.000001
return -(y * math.log(p) + (1 - y) * math.log(1 - p))
def train_dataset_batch(self, epochs, total_examples):
for epoch in range(epochs):
print("epoch {}".format(epoch))
for iteration in self.batch(range(0, total_examples),
self.batch_size):
self.train_batch(iteration)
def train_batch(self, examples_batch):
gradient = 0
loss = 0
feature_values = [0] * self.features
for example_index in examples_batch:
example, label = self.samples[example_index], self.true_labels[
example_index]
logit = 0
for i in range(len(example)):
val = self.top_k.get_value_for_key(i + 1) * example[i]
feature_values[i] += example[i]
logit += val
sigm_val = self.sigmoid(logit)
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(example)):
# updating the change only on previous values
if example[i] != 0:
grad_update = self.learning_rate * diff_label * example[
i]
if i + 1 in self.top_k_dict.keys():
self.top_k_dict[i + 1].append(grad_update)
value = self.cms.update(i, grad_update)
# self.recovered_weight[i] = value
self.top_k.push(Node(i + 1, value))
return loss
def train_with_sketch(self, example, label):
logit = 0
for i in range(len(example)):
val = self.top_k.get_value_for_key(i + 1) * example[i]
logit += val
sigm_val = self.sigmoid(logit)
loss = self.loss(y=label, p=sigm_val)
diff_label = (label - sigm_val) # difference in label
if diff_label != 0:
for i in range(len(example)):
# updating the change only on previous values
if example[i] != 0:
grad_update = self.learning_rate * diff_label * example[i]
if i + 1 in self.top_k_dict.keys():
self.top_k_dict[i + 1].append(grad_update)
value = self.cms.update(i, grad_update)
# self.recovered_weight[i] = value
self.top_k.push(Node(i + 1, value))
return loss
def accuracy_on_test(self):
print("Dataset Testing Started")
test_labels, test_features = self.true_labels, self.samples
for i in range(len(test_features)):
if i % 500 == 0:
print(i)
test_example = test_features[i]
pred_label = self.predict(test_example)
test_label = int(test_labels[i])
if pred_label == test_label:
self.correctly_classified += 1
# print("correctly classified test examples {}".format(self.correctly_classified))
print("Dataset Testing Done")
def predict(self, example):
logit = 0
for i in range(len(example)):
logit += self.top_k.get_value_for_key(i + 1) * example[i]
a = self.sigmoid(logit)
if a >= 0.5:
return 1
else:
return 0
def get_recovery_mse(self):
self.weight -= np.mean(self.weight)
self.weight /= np.std(self.weight)
self.recovered_weight -= np.mean(self.recovered_weight)
self.recovered_weight /= np.std(self.recovered_weight)
zip_object = zip(self.weight, self.recovered_weight)
difference = []
for list1_i, list2_i in zip_object:
difference.append(list1_i - list2_i)
return np.mean(np.square(difference))
def store_in_topk(self, non_zero_index, non_zero_values):
for i in range(len(non_zero_index)):
self.top_k.push(Node(non_zero_index[i] + 1, non_zero_values[i]))
def number_of_position_recovered(self):
topk_recovered = []
for item in self.top_k.heap:
key = self.top_k.keys[item.value]
topk_recovered.append(key - 1)
recovered = np.intersect1d(topk_recovered, self.non_zero_indexes[0])
print("recovered {}".format(recovered))
return len(recovered)
class LogisticRegression(object):
def __init__(self, num_features):
self.D = num_features
self.learning_rate = 5e-1
self.cms = CountSketch(3, (1 << 18) - 1)
# self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))
self.top_k = TopK((1 << 14) - 1)
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(self, X, y):
y_hat = np.dot(X, self.w) + self.b
loss = self.loss(y, self.sigmoid(y_hat))
dw, db = self.gradient(self.w, y, X, self.b)
self.w = self.w - self.learning_rate * dw
self.b = self.b - self.learning_rate * db
def train_with_sketch(self, feature_pos, features, label):
logit = 0
min_logit = float("inf")
max_logit = float("-inf")
print("number of features {}".format(len([i for i in range(0, len(features)) if features[i] > 0])))
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)
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))
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)):
# updating the change only on previous values
# if features[i] != 0 :
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)
if a > 0.5:
return 1
else:
return 0
def gradient_using_sketch(self, X):
for i in range(self.D):
self.cms.update(i, self.w[i])
dw, db = self.gradient(self.w, y, X, self.b)
for i in range(self.D):
self.cms.update(i, dw[i])
# todo: update in top K
def fit(self, X, y):
num_features = X.shape[1]
initial_wcb = np.zeros(shape=(2 * X.shape[1] + 1,))
params, min_val_obj, grads = fmin_l_bfgs_b(func=self.objective,
args=(X, y), x0=initial_wcb,
disp=10,
maxiter=500,
fprime=self.objective_grad)
print("params {}".format(params))
print("min val obj {}".format(min_val_obj))
print("grads dict {}".format(grads))
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 __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
