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bag.py
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bag.py
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from collections import Counter
import numpy as np
from mldata import *
from ann import ANN, standardize, find_area_under_roc, evaluate_ann_performance, k_folds_stratified, flip_labels_with_probability
"""
Created by Nick Stevens
10/27/2016
"""
# Useful constants
CLASS_LABEL = -1
LEARNING_ALGORITHMS = {'ann': ANN}
def main(options):
assert options is not None
assert len(options) == 4
file_base = options[0]
example_set = parse_c45(file_base)
default_cv_option = 0
default_learning_algorithm = ANN
default_num_bagging_training_iters = 3
# If 0, use cross-validation. If 1, run algorithm on full sample.
cv_option = (1 if options[1] == '1' else default_cv_option)
try:
learning_algorithm = LEARNING_ALGORITHMS[options[2]] if options[2] in LEARNING_ALGORITHMS.keys() else\
default_learning_algorithm
except ValueError:
learning_algorithm = default_learning_algorithm
try:
num_bagging_training_iters = (int(options[3]) if int(options[3]) > 0 else default_num_bagging_training_iters)
except ValueError:
num_bagging_training_iters = default_num_bagging_training_iters
# Create a numpy array from the example set
example_set = np.array(example_set.to_float(), ndmin=2)
# Shuffle the set to ensure that it is not ordered by class label
np.random.seed(12345)
np.random.shuffle(example_set)
# Standardize the feature values in the example set
example_set = standardize(example_set)
if learning_algorithm is ANN:
num_hidden_units = 0 # Perceptron
weight_decay_coeff = 0.01
num_ann_training_iters = 0
p = 0.0 # Only increase to introduce noise
if cv_option == 1:
accuracy, precision, recall, fpr = ann_bag(example_set, example_set, num_hidden_units,
weight_decay_coeff, num_ann_training_iters,
num_bagging_training_iters)
print('Accuracy:\t' + str("%0.6f" % accuracy))
print('Precision:\t' + str("%0.6f" % precision))
print('Recall:\t\t' + str("%0.6f" % recall))
else:
num_folds = 5
fold_set = k_folds_stratified(example_set, num_folds)
accuracy_vals = np.empty(num_folds)
precision_vals = np.empty(num_folds)
recall_vals = np.empty(num_folds)
fpr_vals = np.empty(num_folds)
for i in xrange(0, num_folds):
validation_set = np.array(fold_set[i])
training_set = []
for j in xrange(1, 5):
k = (i + j) % 5
for example in fold_set[k]:
training_set.append(example)
training_set = np.array(training_set)
training_set = flip_labels_with_probability(training_set, p)
print('Fold ' + str(i + 1))
accuracy, precision, recall, fpr = ann_bag(training_set, validation_set, num_hidden_units,
weight_decay_coeff, num_ann_training_iters,
num_bagging_training_iters)
np.put(accuracy_vals, i, accuracy)
np.put(precision_vals, i, precision)
np.put(recall_vals, i, recall)
np.put(fpr_vals, i, fpr)
accuracy = np.mean(accuracy_vals)
accuracy_std = np.std(accuracy_vals, ddof=1)
precision = np.mean(precision_vals)
precision_std = np.std(precision_vals, ddof=1)
recall = np.mean(recall_vals)
recall_std = np.std(recall_vals, ddof=1)
aroc = find_area_under_roc(fpr_vals, recall_vals)
print('Accuracy:\t' + str("%0.6f" % accuracy) + '\t' + str("%0.6f" % accuracy_std))
print('Precision:\t' + str("%0.6f" % precision) + '\t' + str("%0.6f" % precision_std))
print('Recall:\t\t' + str("%0.6f" % recall) + '\t' + str("%0.6f" % recall_std))
print('Area Under ROC:\t' + str("%0.6f" % aroc) + '\n')
print('Fold Errors:\t' + str([float(str("%0.6f" % (1-x))) for x in accuracy_vals]) + '\n')
else:
raise NotImplementedError
def ann_bag(training_set, validation_set, num_hidden_units,
weight_decay_coeff, num_ann_training_iters, num_bagging_training_iters):
iter_labels = None
example_weights = np.full((training_set.shape[0], 1), 1.0 / len(training_set))
for i in xrange(0, num_bagging_training_iters):
print('\nBagging Iteration ' + str(i+1))
replicate_set = bootstrap_replicate(training_set, seed_value=i)
weighted_replicate_set = np.column_stack((example_weights, replicate_set))
ann = ANN(weighted_replicate_set, validation_set, num_hidden_units, weight_decay_coeff, weighted_examples=True)
ann.train(num_ann_training_iters, convergence_err=0.5)
if iter_labels is not None:
iter_labels = np.column_stack((iter_labels, ann.evaluate()[1]))
else:
iter_labels = ann.evaluate()[1]
voting_labels = np.apply_along_axis(most_common_label, 1, iter_labels)
assert ann is not None
actual_labels = ann.validation_labels
label_pairs = zip(actual_labels, voting_labels)
accuracy, precision, recall, fpr = evaluate_ann_performance(None, label_pairs)
return accuracy, precision, recall, fpr
def most_common_label(vector):
counter = Counter(vector)
return counter.most_common(1)[0][0]
def bootstrap_replicate(example_set, size=None, seed_value=12345):
"""
Creates a bootstrap replicate of example_set by sampling with replacement. If creating multiple replicates, input a
different seed_value for every call to produce different sets.
"""
num_examples = len(example_set)
if size is None:
size = num_examples
np.random.seed(seed_value)
replicate = np.empty([size, np.shape(example_set)[1]])
for i in xrange(0, size):
random_ex = example_set[np.random.randint(0, num_examples), :]
replicate[i, :] = random_ex
return replicate
if __name__ == "__main__":
main(sys.argv[1:])