def find_best_child_train(self, children):
best_loss = sys.maxint
best_child = None
for child_1 in children:
loss_1 = calculate_loss(self.scoring_function, child_1, self.true_output, self.number_of_labels)
if loss_1 < best_loss:
best_loss = loss_1
best_child = child_1
# Compare only with best
best_attributes = construct_sparse_attributes(self.attributes, best_child)
for child_1 in children:
if child_1 == best_child:
continue
loss_1 = calculate_loss(self.scoring_function, child_1, self.true_output, self.number_of_labels)
if loss_1 == best_loss:
continue
if random.uniform(0.0, 1.0) <= self.reduction:
attributes_1 = construct_sparse_attributes(self.attributes, child_1)
self.h_training_examples.append(attributes_1 - best_attributes)
self.h_training_labels.append(np.sign(loss_1 - best_loss))
self.h_training_examples.append(best_attributes - attributes_1)
self.h_training_labels.append(np.sign(best_loss - loss_1))
return best_child
def generate_examples_c(self,
fitted_h_classifier,
x_train,
y_train,
verbose=0):
c_start_time = time.clock()
c_training_x = []
c_training_y = []
for i in xrange(len(x_train)):
flipbit = FlipBit(x_train[i],
self.number_of_labels,
self.scoring_function,
'test',
initial_br=self.initial_br,
fitted_classifier=fitted_h_classifier)
outputs = flipbit.greedy_search(
self.depth_of_search) # Get outputs using fitted H heuristic
best_loss = sys.maxint
best_output = None
for output in outputs:
loss = calculate_loss(self.scoring_function, output,
y_train[i], self.number_of_labels)
if loss < best_loss:
best_loss = loss
best_output = output
output_1_attributes = construct_sparse_attributes(
x_train[i], best_output)
for output in outputs:
if best_output == output:
continue
loss_2 = calculate_loss(self.scoring_function, output,
y_train[i], self.number_of_labels)
if best_loss == loss_2:
continue
output_2_attributes = construct_sparse_attributes(
x_train[i], output)
c_training_x.append(output_1_attributes - output_2_attributes)
c_training_y.append(np.sign(best_loss - loss_2))
c_training_x.append(output_2_attributes - output_1_attributes)
c_training_y.append(np.sign(loss_2 - best_loss))
c_construction_end_time = time.clock()
if verbose > 0:
print("C construction time: {0:.4f}, Examples: {1}".format(
c_construction_end_time - c_start_time, len(c_training_x)))
return c_training_x, c_training_y
def generate_examples_c(self, fitted_h_classifier, x_train, y_train, verbose=0):
c_start_time = time.clock()
c_training_x = []
c_training_y = []
for i in xrange(len(x_train)):
flipbit = FlipBit(
x_train[i],
self.number_of_labels,
self.scoring_function,
"test",
initial_br=self.initial_br,
fitted_classifier=fitted_h_classifier,
)
outputs = flipbit.greedy_search(self.depth_of_search) # Get outputs using fitted H heuristic
best_loss = sys.maxint
best_output = None
for output in outputs:
loss = calculate_loss(self.scoring_function, output, y_train[i], self.number_of_labels)
if loss < best_loss:
best_loss = loss
best_output = output
output_1_attributes = construct_sparse_attributes(x_train[i], best_output)
for output in outputs:
if best_output == output:
continue
loss_2 = calculate_loss(self.scoring_function, output, y_train[i], self.number_of_labels)
if best_loss == loss_2:
continue
output_2_attributes = construct_sparse_attributes(x_train[i], output)
c_training_x.append(output_1_attributes - output_2_attributes)
c_training_y.append(np.sign(best_loss - loss_2))
c_training_x.append(output_2_attributes - output_1_attributes)
c_training_y.append(np.sign(loss_2 - best_loss))
c_construction_end_time = time.clock()
if verbose > 0:
print(
"C construction time: {0:.4f}, Examples: {1}".format(
c_construction_end_time - c_start_time, len(c_training_x)
)
)
return c_training_x, c_training_y
def fit_simplified(self, x_train, y_train):
c_training_examples = []
c_training_scores = []
h_training_examples = []
h_training_scores = []
start_time = time.clock()
print "Number of examples in training set: " + str(len(x_train))
for i in xrange(len(x_train)):
flipbit = FlipBit(x_train[i], self.number_of_labels, self.scoring_function, true_output=y_train[i])
outputs = flipbit.greedy_search(self.depth_of_search)
h_training_examples.extend(flipbit.get_training_examples())
h_training_scores.extend(flipbit.get_training_scores())
for j in xrange(len(outputs)):
example = construct_sparse_attributes(x_train[i], outputs[j])
score = calculate_loss(self.scoring_function, outputs[j], y_train[i], self.number_of_labels)
c_training_examples.append(example)
c_training_scores.append(score)
generating_end_time = time.clock()
self.h_regressor.fit(vstack(h_training_examples, format='csr'), h_training_scores)
print "Number of H regression learning examples: " + str(len(h_training_examples))
self.c_regressor.fit(vstack(c_training_examples, format='csr'), c_training_scores)
print "Number of C regression learning examples: " + str(len(c_training_examples))
fit_time = time.clock()
construction_time = (generating_end_time - start_time)
learning_time = (fit_time - generating_end_time)
print("Construction time: {0:.4f}, Learning HC time: {1:.4f}".format(construction_time, learning_time))
def predict_best_output(attributes, outputs, classifier):
result_dict = {}
for i in range(len(outputs)):
pretendent_attributes = construct_sparse_attributes(attributes, outputs[i])
result_dict[i] = classifier.decision_function(pretendent_attributes)[0]
# print result_dict
index_of_best = min(result_dict.iteritems(), key=operator.itemgetter(1))[0]
return outputs[index_of_best]
def predict_best_output(self, example, outputs):
best_score = sys.maxint
best_output = None
for output in outputs:
attributes = construct_sparse_attributes(example, output)
score = self.c_regressor.predict(attributes)
if score < best_score:
best_score = score
best_output = output
return best_output
def predict_best_output(attributes, outputs, classifier):
result_dict = {}
for i in range(len(outputs)):
pretendent_attributes = construct_sparse_attributes(
attributes, outputs[i])
result_dict[i] = classifier.decision_function(pretendent_attributes)[0]
# print result_dict
index_of_best = min(result_dict.iteritems(), key=operator.itemgetter(1))[0]
return outputs[index_of_best]
def find_best_child(self, children):
best_score = sys.maxint
best_child = None
for child in children:
example = construct_sparse_attributes(self.attributes, child)
if (self.regressor is not None) and (self.true_output is None):
score = self.regressor.predict(example)
elif (self.true_output is not None) and (self.regressor is None):
score = calculate_loss(self.scoring_function, child, self.true_output, self.number_of_labels)
self.h_training_examples.append(example)
self.h_training_scores.append(score)
else:
raise ValueError("Either regressor or true_output must not be None.")
if score < best_score:
best_score = score
best_child = child
return best_child
def fit(self, x_train, y_train):
c_training_examples = []
c_training_scores = []
h_training_examples = []
h_training_scores = []
start_time = time.clock()
for i in xrange(len(x_train)):
flipbit = FlipBit(x_train[i], self.number_of_labels, self.scoring_function, true_output=y_train[i])
flipbit.greedy_search(self.depth_of_search) # Run greedy_search to construct H training examples
h_training_examples.extend(flipbit.get_training_examples())
h_training_scores.extend(flipbit.get_training_scores())
h_construction_end_time = time.clock()
print("H training examples construction time: {0:.4f}".format(h_construction_end_time-start_time))
self.h_regressor.fit(vstack(h_training_examples, format='csr'), h_training_scores)
h_fit_end_time = time.clock()
print("H heuristic train time: {0:.4f}".format(h_fit_end_time-h_construction_end_time))
for i in xrange(len(x_train)):
flipbit = FlipBit(x_train[i], self.number_of_labels, self.scoring_function, fitted_regressor=self.h_regressor)
outputs = flipbit.greedy_search(self.depth_of_search) # Get outputs using fitted H heuristic
for j in xrange(len(outputs)):
example = construct_sparse_attributes(x_train[i], outputs[j])
score = calculate_loss(self.scoring_function, outputs[j], y_train[i], self.number_of_labels)
c_training_examples.append(example)
c_training_scores.append(score)
c_construction_end_time = time.clock()
print("C training examples construction time: {0:.4f}".format(c_construction_end_time-h_fit_end_time))
self.c_regressor.fit(vstack(c_training_examples, format='csr'), c_training_scores)
c_fit_end_time = time.clock()
print("C heuristic train time: {0:.4f}".format(c_fit_end_time-c_construction_end_time))
print("Training examples - Total: {0}, H: {1}, C: {2}".format(len(x_train), len(h_training_examples),
len(c_training_examples)))
