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import main as mn
import theano
from theano import tensor as tsr
import blocks
import blocks.algorithms, blocks.main_loop, blocks.extensions.monitoring
import picklable_itertools
import numpy as np
from six.moves import range
import fuel
import fuel.datasets
import collections
class LearnedDataset(fuel.datasets.Dataset):
"""
Dynamically-created dataset (for active learning)
-compatible with ConstantScheme with request corresponding to a
batch_size
"""
provides_sources = ('x', 's')
def __init__(self, node_p, graph, source=mn.var_source, **kwargs):
super(LearnedDataset, self).__init__(**kwargs)
self.node_p = node_p
self.graph = graph
self.n_cascades = 1 # nbr of cascades of total size approx = request
self.source = lambda graph, t : source(graph, t, self.node_p)
def get_data(self, state=None, request=None):
floatX = 'int8'
x_obs = np.empty((request, len(self.graph)), dtype=floatX)
s_obs = np.empty((request, len(self.graph)), dtype=floatX)
i = 0
while i < request:
x_tmp, s_tmp = mn.build_cascade_list(
mn.simulate_cascades(self.n_cascades, graph, self.source),
collapse=True
)
x_obs[i:i + len(x_tmp)] = x_tmp[:request - i]
s_obs[i:i + len(x_tmp)] = s_tmp[:request - i]
i += len(x_tmp)
self.n_cascades += 1 # learn optimal nbr in loop
self.n_cascades = max(1, self.n_cascades - 2)
return (x_obs, s_obs)
class ActiveLearning(blocks.extensions.SimpleExtension):
"""
Extension which updates the node_p array passed to the get_data method of
LearnedDataset
"""
def __init__(self, dataset, **kwargs):
super(ActiveLearning, self).__init__(**kwargs)
self.dataset = dataset
def do(self, which_callback, *args):
out_degree = np.sum(self.dataset.graph, axis=1)
self.dataset.node_p = out_degree / np.sum(out_degree)
print(self.dataset.node_p)
class ShuffledBatchesScheme(fuel.schemes.ShuffledScheme):
"""
Iteration scheme over finite dataset:
-shuffles batches but not within batch
-arguments: dataset_size (int) ; batch_size (int)
"""
def get_request_iterator(self):
indices = list(self.indices) # self.indices = xrange(dataset_size)
start = np.random.randint(self.batch_size)
batches = list(map(
list,
picklable_itertools.extras.partition_all(self.batch_size,
indices[start:])
))
if indices[:start]:
batches.append(indices[:start])
batches = np.asarray(batches)
return iter(batches[np.random.permutation(len(batches))])
def create_mle_model(graph):
"""
return cascade likelihood theano computation graph
"""
n_nodes = len(graph)
g_shared = theano.shared(value=graph, name='graph')
x = tsr.matrix(name='x', dtype='int8')
s = tsr.matrix(name='s', dtype='int8')
params = theano.shared(
.5 + .01 *
np.random.normal(size=(n_nodes, n_nodes)).astype(theano.config.floatX),
name='params'
)
y = tsr.maximum(tsr.dot(x, params), 1e-5)
infect = tsr.log(1. - tsr.exp(-y[0:-1]))
lkl_pos = tsr.sum(infect * (x[1:] & s[1:]))
lkl_neg = tsr.sum(-y[0:-1] * (~x[1:] & s[1:]))
lkl_mle = lkl_pos + lkl_neg
lkl_mle.name = 'cost'
diff = (g_shared - params) ** 2
subarray = tsr.arange(g_shared.shape[0])
tsr.set_subtensor(diff[subarray, subarray], 0)
rmse = tsr.sum(diff) / (n_nodes ** 2)
rmse.name = 'rmse'
return x, s, params, lkl_mle, rmse
def create_fixed_data_stream(n_cascades, graph, batch_size, shuffle=True):
"""
creates a datastream for a fixed (not learned) dataset:
-shuffle (bool): shuffle minibatches but not within minibatch, else
sequential (non-shuffled) batches are used
"""
cascades = mn.build_cascade_list(mn.simulate_cascades(n_cascades, graph),
collapse=True)
x_obs, s_obs = cascades[0], cascades[1]
data_set = fuel.datasets.base.IndexableDataset(collections.OrderedDict(
[('x', x_obs), ('s', s_obs)]
))
if shuffle:
scheme = ShuffledBatchesScheme(len(x_obs), batch_size=batch_size)
else:
scheme = fuel.schemes.SequentialScheme(len(x_obs),
batch_size=batch_size)
return fuel.streams.DataStream(dataset=data_set, iteration_scheme=scheme)
def create_learned_data_stream(graph, batch_size):
node_p = np.ones(len(graph)) / len(graph)
data_set = LearnedDataset(node_p, graph)
scheme = fuel.schemes.ConstantScheme(batch_size)
return fuel.streams.DataStream(dataset=data_set, iteration_scheme=scheme)
if __name__ == "__main__":
batch_size = 1000
#graph = mn.create_random_graph(n_nodes=1000)
graph = mn.create_star(1000)
print('GRAPH:\n', graph, '\n-------------\n')
x, s, params, cost, rmse = create_mle_model(graph)
alg = blocks.algorithms.GradientDescent(
cost=-cost, parameters=[params], step_rule=blocks.algorithms.AdaDelta()
)
data_stream = create_learned_data_stream(graph, batch_size)
#n_cascades = 10000
#data_stream = create_fixed_data_stream(n_cascades, graph, batch_size,
# shuffle=False)
loop = blocks.main_loop.MainLoop(
alg, data_stream,
extensions=[
blocks.extensions.FinishAfter(after_n_batches = 10**4),
blocks.extensions.monitoring.TrainingDataMonitoring([cost, params,
rmse], after_batch=True),
blocks.extensions.Printing(every_n_batches = 10)#,
#ActiveLearning(data_stream.dataset)
]
)
loop.run()
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