def get_raw_data():
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
# Get stats to normalize data
raw_data = np.array(example_set.to_float())
data_mean = np.average(raw_data, axis=0)
data_std = np.std(raw_data, axis=0)
data_std[np.nonzero(data_std == 0.0)] = 1.0
def normalizer(ex):
ex = np.array(ex)
normed = ((ex - data_mean) / data_std)
# The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label, as part of the
# normalization process
return normed[2:-1]
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
bags = [np.array(b.to_float(normalizer)) for b in bagset]
labels = [b.label for b in bagset]
tmp = list(zip(bags, labels))
random.seed(55)
random.shuffle(tmp)
bags, labels = zip(*tmp)
return bags, labels
def main():
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
table = re.findall(r"<(.*)>", str(example_set))
output.write(str(example_set))
print(len(table))
print(table)
# Get stats to normalize data
raw_data = np.array(example_set.to_float())
data_mean = np.average(raw_data, axis=0)
data_std = np.std(raw_data, axis=0)
data_std[np.nonzero(data_std == 0.0)] = 1.0
def normalizer(ex):
ex = np.array(ex)
normed = ((ex - data_mean) / data_std)
# The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label, as part of the
# normalization process
return normed[2:-1]
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
bags = [np.array(b.to_float(normalizer)) for b in bagset]
labels = np.array([b.label for b in bagset], dtype=float)
# Convert 0/1 labels to -1/1 labels
labels = 2 * labels - 1
# Spilt dataset arbitrarily to train/test sets
train_bags = bags[10:]
train_labels = labels[10:]
test_bags = bags[:10]
test_labels = labels[:10]
# Construct classifiers
classifiers = {}
classifiers['MissSVM'] = misvm.MissSVM(kernel='linear',
C=1.0,
max_iters=20)
classifiers['sbMIL'] = misvm.sbMIL(kernel='linear', eta=0.1, C=1e2)
classifiers['SIL'] = misvm.SIL(kernel='linear', C=1.0)
# Train/Evaluate classifiers
accuracies = {}
for algorithm, classifier in classifiers.items():
classifier.fit(train_bags, train_labels)
predictions = classifier.predict(test_bags)
accuracies[algorithm] = np.average(test_labels == np.sign(predictions))
for algorithm, accuracy in accuracies.items():
print('\n%s Accuracy: %.1f%%' % (algorithm, 100 * accuracy))
output.write('\n%s Accuracy: %.1f%%' % (algorithm, 100 * accuracy))
def main():
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
# Get stats to normalize data
raw_data = np.array(example_set.to_float())
data_mean = np.average(raw_data, axis=0)
data_std = np.std(raw_data, axis=0)
data_std[np.nonzero(data_std == 0.0)] = 1.0
def normalizer(ex):
ex = np.array(ex)
normed = ((ex - data_mean) / data_std)
# The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label, as part of the
# normalization process
return normed[2:-1]
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
bags = [np.array(b.to_float(normalizer)) for b in bagset]
labels = np.array([b.label for b in bagset], dtype=float)
# Convert 0/1 labels to -1/1 labels
labels = 2 * labels - 1
# Spilt dataset arbitrarily to train/test sets
train_bags = bags[10:]
train_labels = labels[10:]
test_bags = bags[:10]
test_labels = labels[:10]
# Construct classifiers
classifiers = {}
classifiers['MissSVM'] = misvm.MissSVM(kernel='linear', C=1.0, max_iters=20)
classifiers['sbMIL'] = misvm.sbMIL(kernel='linear', eta=0.1, C=1e2)
classifiers['SIL'] = misvm.SIL(kernel='linear', C=1.0)
# Train/Evaluate classifiers
accuracies = {}
for algorithm, classifier in classifiers.items():
classifier.fit(train_bags, train_labels)
predictions = classifier.predict(test_bags)
accuracies[algorithm] = np.average(test_labels == np.sign(predictions))
for algorithm, accuracy in accuracies.items():
print('\n%s Accuracy: %.1f%%' % (algorithm, 100 * accuracy))
def main():
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
# (The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label)
bags = [np.array(b.to_float())[:, 2:-1] for b in bagset]
labels = np.array([b.label for b in bagset], dtype=float)
# Convert 0/1 labels to -1/1 labels
labels = 2 * labels - 1
# Spilt dataset arbitrarily to train/test sets
train_bags = bags[10:]
train_labels = labels[10:]
test_bags = bags[:10]
test_labels = labels[:10]
# Construct classifiers
classifiers = {}
classifiers['MissSVM'] = misvm.MissSVM(kernel='linear',
C=1.0,
max_iters=10)
classifiers['sbMIL'] = misvm.sbMIL(kernel='linear', eta=0.1, C=1.0)
classifiers['SIL'] = misvm.SIL(kernel='linear', C=1.0)
# Train/Evaluate classifiers
accuracies = {}
for algorithm, classifier in classifiers.items():
classifier.fit(train_bags, train_labels)
predictions = classifier.predict(test_bags)
accuracies[algorithm] = np.average(test_labels == np.sign(predictions))
for algorithm, accuracy in accuracies.items():
print('\n%s Accuracy: %.1f%%' % (algorithm, 100 * accuracy))
def main():
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
# (The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label)
bags = [np.array(b.to_float())[:, 2:-1] for b in bagset]
labels = np.array([b.label for b in bagset], dtype=float)
# Convert 0/1 labels to -1/1 labels
labels = 2 * labels - 1
# Spilt dataset arbitrarily to train/test sets
train_bags = bags[10:]
train_labels = labels[10:]
test_bags = bags[:10]
test_labels = labels[:10]
print train_bags
print len(train_bags)
print "\n\n\n"
print train_labels
# Construct classifiers
classifiers = {}
classifiers['MissSVM'] = misvm.MissSVM(kernel='linear', C=1.0, max_iters=10)
classifiers['sbMIL'] = misvm.sbMIL(kernel='linear', eta=0.1, C=1.0)
classifiers['SIL'] = misvm.SIL(kernel='linear', C=1.0)
# Train/Evaluate classifiers
accuracies = {}
for algorithm, classifier in classifiers.items():
classifier.fit(train_bags, train_labels)
predictions = classifier.predict(test_bags)
accuracies[algorithm] = np.average(test_labels == np.sign(predictions))
for algorithm, accuracy in accuracies.items():
print '\n%s Accuracy: %.1f%%' % (algorithm, 100 * accuracy)
from scipy.sparse import identity
from scipy import sparse
from scipy.optimize import linprog
from numpy import inf
from scipy.optimize import fmin_bfgs
import sys
from misvmio import parse_c45, bag_set
from sklearn import cross_validation
from sklearn import metrics
###############
#LOAD DATA SET#
###############
# Load list of C4.5 Examples
example_set = parse_c45('musk1')
# Group examples into bags
bagset = bag_set(example_set)
# Convert bags to NumPy arrays
# (The ...[:, 2:-1] removes first two columns and last column,
# which are the bag/instance ids and class label)
bags = [np.array(b.to_float())[:, 2:-1] for b in bagset]
labels = np.array([b.label for b in bagset], dtype=float)
#SPLIT IN TRAINING AND TESTING
train_bags, test_bags, train_labels, test_labels = cross_validation.train_test_split(bags, labels, test_size=0.1, random_state=65)
