ENH: using sklearn.utils for create RNGs and generating minibatches

spotlight/cross_validation.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from sklearn.utils import murmurhash3_32
+from sklearn.utils import murmurhash3_32, check_random_state
 
 from spotlight.interactions import Interactions
 
@@ -36,10 +36,7 @@ def shuffle_interactions(interactions,
     interactions: :class:`spotlight.interactions.Interactions`
         The shuffled interactions.
     """
-
-    if random_state is None:
-        random_state = np.random.RandomState()
-
+    random_state = check_random_state(random_state)
     shuffle_indices = np.arange(len(interactions.user_ids))
     random_state.shuffle(shuffle_indices)
 

spotlight/datasets/synthetic.py

@@ -6,6 +6,8 @@
 
 import numpy as np
 
+from sklearn.utils import check_random_state
+
 from spotlight.interactions import Interactions
 
 
@@ -108,8 +110,7 @@ def generate_sequential(num_users=100,
         instance of the interactions class
     """
 
-    if random_state is None:
-        random_state = np.random.RandomState()
+    random_state = check_random_state(random_state)
 
     transition_matrix = _build_transition_matrix(
         num_items - 1,

spotlight/factorization/implicit.py

@@ -4,6 +4,8 @@
 
 import numpy as np
 
+from sklearn.utils import check_random_state
+
 import torch
 
 import torch.optim as optim
@@ -104,7 +106,7 @@ def __init__(self,
         self._representation = representation
         self._sparse = sparse
         self._optimizer_func = optimizer_func
-        self._random_state = random_state or np.random.RandomState()
+        self._random_state = check_random_state(random_state)
         self._num_negative_samples = num_negative_samples
 
         self._num_users = None

spotlight/sampling.py

@@ -3,6 +3,7 @@
 """
 
 import numpy as np
+from sklearn.utils import check_random_state
 
 
 def sample_items(num_items, shape, random_state=None):
@@ -27,10 +28,7 @@ def sample_items(num_items, shape, random_state=None):
     items: np.array of shape [shape]
         Sampled item ids.
     """
-
-    if random_state is None:
-        random_state = np.random.RandomState()
-
+    random_state = check_random_state(random_state)
     items = random_state.randint(0, num_items, shape, dtype=np.int64)
 
     return items

spotlight/sequence/implicit.py

@@ -5,6 +5,8 @@
 
 import numpy as np
 
+from sklearn.utils import check_random_state
+
 import torch
 
 import torch.optim as optim
@@ -119,7 +121,7 @@ def __init__(self,
         self._use_cuda = use_cuda
         self._sparse = sparse
         self._optimizer_func = optimizer_func
-        self._random_state = random_state or np.random.RandomState()
+        self._random_state = check_random_state(random_state)
         self._num_negative_samples = num_negative_samples
 
         self._num_items = None

spotlight/torch_utils.py

@@ -1,5 +1,7 @@
 import numpy as np
 
+from sklearn.utils import check_random_state, gen_batches
+
 import torch
 
 
@@ -20,16 +22,15 @@ def cpu(tensor):
 
 
 def minibatch(*tensors, **kwargs):
-
     batch_size = kwargs.get('batch_size', 128)
 
     if len(tensors) == 1:
         tensor = tensors[0]
-        for i in range(0, len(tensor), batch_size):
-            yield tensor[i:i + batch_size]
+        for minibatch_indices in gen_batches(len(tensor), batch_size):
+            yield tensor[minibatch_indices]
     else:
-        for i in range(0, len(tensors[0]), batch_size):
-            yield tuple(x[i:i + batch_size] for x in tensors)
+        for minibatch_indices in gen_batches(len(tensors[0]), batch_size):
+            yield tuple(x[minibatch_indices] for x in tensors)
 
 
 def shuffle(*arrays, **kwargs):
@@ -40,8 +41,7 @@ def shuffle(*arrays, **kwargs):
         raise ValueError('All inputs to shuffle must have '
                          'the same length.')
 
-    if random_state is None:
-        random_state = np.random.RandomState()
+    random_state = check_random_state(random_state)
 
     shuffle_indices = np.arange(len(arrays[0]))
     random_state.shuffle(shuffle_indices)

tests/test_torch_utils.py

@@ -0,0 +1,16 @@
+import numpy as np
+import torch
+from spotlight.torch_utils import minibatch
+
+
+def test_minibatch():
+    for n in [1, 12]:
+        data = torch.randn(n, 6)
+        s = 0
+        ss = 0
+        for x in minibatch(data, batch_size=3):
+            s += np.prod(x.size())
+            ss += x.sum()
+            assert x.size(1) == data.size(1)
+        assert s == np.prod(data.size())
+        assert ss == data.sum()