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Use NumPy randomness for random walks, not Python #1666

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Use NumPy randomness for random walks, not Python #1666

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Use NumPy randomness for random walks, not Python
huonw Jun 10, 2020
4ad45a1
Fixes
huonw Jun 10, 2020
3bf3330
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huonw Jun 10, 2020
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24 changes: 12 additions & 12 deletions stellargraph/data/explorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ def __init__(self, graph, seed=None):
raise TypeError("Graph must be a StellarGraph or StellarDiGraph.")

self.graph = graph
self._random_state, self._np_random_state = random_state(seed)
_, self._np_random_state = random_state(seed)

def _get_random_state(self, seed):
"""
Expand All @@ -72,10 +72,10 @@ def _get_random_state(self, seed):
"""
if seed is None:
# Restore the random state
return self._random_state
return self._np_random_state
# seed the random number generator
require_integer_in_range(seed, "seed", min_val=0)
rs, _ = random_state(seed)
_, rs = random_state(seed)
return rs

@staticmethod
Expand Down Expand Up @@ -107,7 +107,7 @@ def __init__(self, graph, graph_schema=None, seed=None):

# Initialize the random state
self._check_seed(seed)
self._random_state, self._np_random_state = random_state(seed)
_, self._np_random_state = random_state(seed)

# We require a StellarGraph for this
if not isinstance(graph, StellarGraph):
Expand Down Expand Up @@ -154,9 +154,9 @@ def _get_random_state(self, seed):
"""
if seed is None:
# Use the class's random state
return self._random_state
return self._np_random_state
# seed the random number generator
rs, _ = random_state(seed)
_, rs = random_state(seed)
return rs

def neighbors(self, node):
Expand Down Expand Up @@ -669,7 +669,7 @@ def run(self, nodes, n_size, n=1, seed=None):
neighbours = [-1] * _size
else:
# sample with replacement
neighbours = rs.choices(neighbours, k=n_size[cur_depth])
neighbours = rs.choice(neighbours, size=n_size[cur_depth])

# add them to the back of the queue
q.extend((sampled_node, depth) for sampled_node in neighbours)
Expand Down Expand Up @@ -721,7 +721,7 @@ def run(self, nodes, n_size, n=1, seed=None):
q.extend([(node, node_type, 0)])

# add the root node to the walks
walk.append([node])
walk.append(np.array([node]))
while len(q) > 0:
# remove the top element in the queue and pop the item from the front of the list
frontier = q.pop(0)
Expand All @@ -743,10 +743,10 @@ def run(self, nodes, n_size, n=1, seed=None):
# In case of no neighbours of the current node for et, neigh_et == [None],
# and samples automatically becomes [None]*n_size[depth-1]
if len(neigh_et) > 0:
samples = rs.choices(neigh_et, k=n_size[depth - 1])
samples = rs.choice(neigh_et, size=n_size[depth - 1])
else: # this doesn't happen anymore, see the comment above
_size = n_size[depth - 1]
samples = [-1] * _size
samples = np.full(_size, -1)

walk.append(samples)
q.extend(
Expand Down Expand Up @@ -886,7 +886,7 @@ def _sample_neighbours(self, rs, node, idx, size):
# Sampling from empty neighbourhood
return [-1] * size
# Sample with replacement
return rs.choices(neighbours, k=size)
return rs.choice(neighbours, size=size)

def _check_neighbourhood_sizes(self, in_size, out_size):
"""
Expand Down Expand Up @@ -1034,7 +1034,7 @@ def run(
f"max_walk_length: maximum walk length should not be less than the context window size, found {max_walk_length}"
)

np_rs = self._np_random_state if seed is None else np.random.RandomState(seed)
np_rs = self._get_random_state(seed)
walks = []
num_cw_curr = 0

Expand Down
12 changes: 6 additions & 6 deletions stellargraph/mapper/sampled_link_generators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -439,19 +439,19 @@ def sample_features(self, head_links, batch_num):
[
(
nt,
reduce(
operator.concat,
(samples[ks] for samples in node_samples for ks in indices),
[],
),
np.concatenate(
[samples[ks] for samples in node_samples for ks in indices]
)
if indices
else np.array([], dtype=np.uint8),
)
for nt, indices in self._sampling_schema[ii]
]
)

# Interlace the two lists, nodes_by_type[0] (for src head nodes) and nodes_by_type[1] (for dst head nodes)
nodes_by_type = [
tuple((ab[0][0], reduce(operator.concat, (ab[0][1], ab[1][1]))))
(ab[0][0], np.concatenate([ab[0][1], ab[1][1]]))
for ab in zip(nodes_by_type[0], nodes_by_type[1])
]

Expand Down
10 changes: 5 additions & 5 deletions stellargraph/mapper/sampled_node_generators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -476,11 +476,11 @@ def sample_features(self, head_nodes, batch_num):
nodes_by_type = [
(
nt,
reduce(
operator.concat,
(samples[ks] for samples in node_samples for ks in indices),
[],
),
np.concatenate(
[samples[ks] for samples in node_samples for ks in indices]
)
if indices
else np.array([], dtype=np.uint8),
)
for nt, indices in self._sampling_schema[0]
]
Expand Down
4 changes: 2 additions & 2 deletions tests/data/test_heterogeneous_breadth_first_walker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,7 +199,7 @@ def test_walk_generation_single_root_node_self_loner(self):
assert len(subgraphs) == 1
assert len(subgraphs[0]) == 9
for level in subgraphs[0]:
assert type(level) == list
assert type(level) == np.ndarray
if len(level) > 0:
# All values should be rood_node_id or None
for value in level:
Expand All @@ -219,7 +219,7 @@ def test_walk_generation_single_root_node_self_loner(self):
)

for level in subgraphs2[0]:
assert type(level) == list
assert type(level) == np.ndarray
if len(level) > 0:
for value in level:
assert (value == nodes[0]) or (value == -1)
Expand Down