def main():
"""
Doc
"""
data = load_data()
humanppi_G = nx.Graph(data.humanppi, name="HumanPPI")
write_to_hdf5(humanppi_G)
return 0
def main():
data = load_data()
hgraph = nx.Graph(data.humanppi)
fgraph = nx.Graph(data.functions)
# plot_degree(hgraph)
# plot_cp_degree(hgraph)
#plot_fn_degree(hgraph, fgraph)
plot_fn_cp_weight(hgraph, fgraph)
def main():
data = load_data()
if args.edges == 0:
ppiGraph = nx.Graph(data.humanppi, name="HumanPPI")
else:
ppiGraph = nx.Graph(data.humanppi[0:args.edges], name="HumanPPI")
draw_kwargs = dict(node_size=5, font_size=6,
with_labels=False, linewidths=0.1, width=0.2)
layout_kw = dict(iterations=args.iterations)
img_title = 'Nodes: %i Edges: %i Iterations: %i Runtime: %s'
if args.iterations is None:
# Number of iters will be set to 10, check the get_iterations method.
iters = get_iterations()
for iteration in iters:
start_time = time.time()
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
nx.draw(ppiGraph, pos=nx.spring_layout(
ppiGraph, iterations=iteration), **draw_kwargs)
run_time = time.time() - start_time
runtime_str = str(round(run_time, 2)) + ' seconds'
print("Iterations: %i Runt-time: %s" % (iteration, runtime_str))
subs = (len(ppiGraph), args.edges, iteration, runtime_str)
ax.set_title(img_title % subs)
filepath = 'report/graphs' + '/' + \
zeros(iteration, padlength=4) + '.png'
plt.savefig(filepath, bbox_inches='tight')
print("Done! Saved at : " + filepath)
fig.clf()
plt.close()
else:
start_time = time.time()
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
nx.draw(ppiGraph, pos=nx.spring_layout(
ppiGraph, **layout_kw), **draw_kwargs)
runtime = time.time() - start_time
runtime_str = str(round(runtime, 2)) + ' seconds'
subs = (len(ppiGraph), args.edges, args.iterations, runtime_str)
ax.set_title(img_title % subs)
filepath = 'report/graphs' + '/' + \
zeros(args.iterations, padlength=4) + '.png'
plt.savefig(filepath, bbox_inches='tight')
print("Done! Saved at " + filepath)
fig.clf()
plt.close()
def main():
if (len(sys.argv) != 3):
print('Usage: $ python %s in.pickle outmax' % sys.argv[0])
quit()
data_file = sys.argv[1]
outmax = int(sys.argv[2])
all_dataset = misc.load_data(data_file)
html_dataset = {}
c_dataset = {}
for doc in all_dataset:
if re.match(r'.+\.(c|h)$', doc):
c_dataset[doc] = all_dataset[doc]
elif re.match(r'.+\.(html)$', doc):
html_dataset[doc] = all_dataset[doc]
pp = pprint.PrettyPrinter(indent=4)
for doc,val in sorted(html_dataset.items(), key=lambda x:(x[0])[::-1], reverse=True):
neighbor = get_neighbor_c(html_dataset[doc], c_dataset, outmax)
print '--%s--' % doc
pp.pprint(neighbor)
print ''
start = datetime.now()
clf = linear_model.LogisticRegression()
clf.fit(X, y)
score = np.mean(clf.predict(T) == valid)
return score, datetime.now() - start
if __name__ == '__main__':
import sys, misc
# don't bother me with warnings
import warnings; warnings.simplefilter('ignore')
np.seterr(all='ignore')
print __doc__ + '\n'
if not len(sys.argv) == 2:
print misc.USAGE % __file__
sys.exit(-1)
else:
dataset = sys.argv[1]
print 'Loading data ...'
data = misc.load_data(dataset)
print 'Done, %s samples with %s features loaded into ' \
'memory' % data[0].shape
res_skl = misc.bench(bench_skl, data)
print 'MLPy: mean %.2f, std %.2f\n' % (
np.mean(res_skl), np.std(res_skl))
import misc
# don't bother me with warnings
import warnings
warnings.simplefilter('ignore')
np.seterr(all='ignore')
print __doc__ + '\n'
if not len(sys.argv) == 2:
print misc.USAGE % __file__
sys.exit(-1)
else:
dataset = sys.argv[1]
print 'Loading data ...'
data = misc.load_data(dataset)
# set sigma to something useful
from milk.unsupervised import pdist
sigma = np.median(pdist(data[0]))
print 'Done, %s samples with %s features loaded into ' \
'memory' % data[0].shape
score, res_shogun = misc.bench(bench_shogun, data)
print 'Shogun: mean %.2f, std %.2f' % (
np.mean(res_shogun), np.std(res_shogun))
print 'Score: %.2f\n' % score
score, res_mdp = misc.bench(bench_mdp, data)
print 'MDP: mean %.2f, std %.2f' % (
model.add(BatchNormalization((256,)))
model.add(Dropout(0.5))
model.add(Dense(256, n_classes, init='glorot_uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer="adam")
return model
model_name = 'dump_keras_ensemble.0.5.0.5.0.5'
n_models = 20
print("Loading data...")
X, labels = load_data(paths.train_file, train=True)
y = preprocess_labels(labels)
X_test, ids = load_data(paths.test_file, train=False)
X = preprocess_data(X)
X_test = preprocess_data(X_test)
n_classes = y.shape[1]
n_dims = X.shape[1]
print("Training %d models..." % n_models)
proba = 0
models = range(1, n_models+1)
for i in models:
print("\n-------------- Model %d --------------\n" % i)
model = model_factory(n_classes, n_dims)
"""PCA benchmarks"""
import numpy as np
from datetime import datetime
#
# .. Load dataset ..
#
from misc import load_data, bench
print 'Loading data ...'
X, y, T = load_data()
print 'Done, %s samples with %s features loaded into ' \
'memory' % X.shape
n_components = 9
def bench_skl():
#
# .. scikits.learn ..
#
from scikits.learn import pca as skl_pca
start = datetime.now()
clf = skl_pca.RandomizedPCA(n_components=n_components)
clf.fit(X)
return datetime.now() - start
def bench_pybrain():
#
# .. pybrain ..
def main():
data = load_data()
if args.edges == 0:
ppiGraph = nx.Graph(data.humanppi, name="HumanPPI")
else:
ppiGraph = nx.Graph(data.humanppi[0:args.edges], name="HumanPPI")
draw_kwargs = dict(node_size=5,
font_size=6,
with_labels=False,
linewidths=0.1,
width=0.2)
layout_kw = dict(iterations=args.iterations)
img_title = 'Nodes: %i Edges: %i Iterations: %i Runtime: %s'
if args.iterations == None:
iters = get_iterations()
for iteration in iters:
starttime = time.time()
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
nx.draw(ppiGraph, pos=nx.spring_layout(ppiGraph, iterations=iteration ), **draw_kwargs )
runtime = time.time() - starttime
runtime_str = str(round(runtime, 2))+' seconds'
print "Iterations: %i Run-time: %s "%(iteration, runtime_str)
subs = (len(ppiGraph), args.edges, iteration, runtime_str)
ax.set_title(img_title%subs)
filepath = 'graphs'+'/'+zeros(iteration, padlength=4)+'.png'
plt.savefig(filepath, bbox_inches='tight')
print 'Written to: '+filepath
fig.clf()
plt.close()
else:
starttime = time.time()
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
nx.draw(ppiGraph, pos=nx.spring_layout(ppiGraph, **layout_kw ), **draw_kwargs )
runtime = time.time() - starttime
runtime_str = str(round(runtime, 2))+' seconds'
subs = (len(ppiGraph), args.edges, args.iterations, runtime_str)
ax.set_title(img_title%subs)
filepath = 'graphs'+'/'+zeros(args.iterations, padlength=4)+'.png'
plt.savefig(filepath, bbox_inches='tight')
print 'Written to: '+filepath
fig.clf()
plt.close()
misc.msg_log[len(misc.msg_log) - config.logsize:] = []
for i in self.client_roster:
if i != except_jid and self.client_roster[i]['to'] and self.client_roster[i]['subscription'] == 'both' and self.client_roster[i].resources:
misc.check_time(self, misc.data['quiet'], i)
if misc.check_time(self, misc.data['stop'], i) and (i not in misc.data['block'] or except_jid not in misc.data['block'][i]):
try:
self.send_message(mto=i, mbody=body, mtype='chat')
except Exception:
pass
if __name__ == '__main__':
signal.signal(signal.SIGTERM, lambda signal, frame: sys.exit()) # handle SIGTERM, graceful exit
misc.restarting = False
misc.quiting = False
misc.load_data()
logging.basicConfig(level=config.loglevel)
for i in ('stop', 'quiet', 'block'):
if i not in misc.data:
misc.data[i] = {}
if config.store_log:
if 'msg_log' in misc.data:
misc.msg_log = misc.data['msg_log']
else:
misc.data['msg_log'] = misc.msg_log
if 'cmd_log' in misc.data:
misc.cmd_log = misc.data['cmd_log']
else:
misc.data['cmd_log'] = misc.cmd_log
