Incorporated bmcfee + cjacoby feedback.

examples/frameworks/keras_example.py

@@ -139,7 +139,7 @@ def sampler(X, y):
     '''
     X = np.atleast_2d(X)
     # y's are binary vectors, and should be of shape (10,) after this.
-    y = np.atleast_2d(y)
+    y = np.atleast_1d(y)
 
     n = X.shape[0]
 

examples/mux_files_example.py

@@ -61,7 +61,7 @@ def npz_generator(npz_path):
     """Generate data from an npz file."""
     npz_data = np.load(npz_path)
     X = npz_data['X']
-    # y's are binary vectors, and should be of shape (10,) after this.
+    # Y is a binary maxtrix with shape=(n, k), each y will have shape=(k,)
     y = npz_data['Y']
 
     n = X.shape[0]

pescador/buffered.py

@@ -74,7 +74,7 @@ def iterate(self, max_iter=None, partial=True):
             not the number of individual samples.
 
         partial : bool, default=True
-            Return buffers smaller than the requested size.
+            If True, will return a final batch smaller than the requested size.
         """
         with core.StreamActivator(self):
             for n, batch in enumerate(buffer_stream(self.stream_,

pescador/maps.py

@@ -134,10 +134,13 @@ def keras_tuples(stream, inputs=None, outputs=None):
     ------
     DataError if the stream contains items that are not data-like.
     """
+    flatten_inputs, flatten_outputs = False, False
     if inputs and isinstance(inputs, six.string_types):
         inputs = [inputs]
+        flatten_inputs = True
     if outputs and isinstance(outputs, six.string_types):
         outputs = [outputs]
+        flatten_outputs = True
 
     inputs, outputs = (inputs or []), (outputs or [])
     if not inputs + outputs:
@@ -147,11 +150,11 @@ def keras_tuples(stream, inputs=None, outputs=None):
     for data in stream:
         try:
             x = list(data[key] for key in inputs) or None
-            if len(inputs) == 1:
+            if len(inputs) == 1 and flatten_inputs:
                 x = x[0]
 
             y = list(data[key] for key in outputs) or None
-            if len(outputs) == 1:
+            if len(outputs) == 1 and flatten_outputs:
                 y = y[0]
 
             yield (x, y)

pescador/mux.py

@@ -48,7 +48,7 @@ def __init__(self, streamers, k,
         k : int > 0
             The number of streams to keep active at any time.
 
-        lam : float > 0 or None
+        rate : float > 0 or None
             Rate parameter for the Poisson distribution governing sample counts
             for individual streams.
             If ``None``, sample infinitely from each stream.

tests/test_buffered.py

@@ -1,4 +1,5 @@
 #!/usr/bin/env python
+# TODO: Remove these tests with the `buffered.py` submodule at 2.0 release.
 
 import pytest
 import numpy as np
@@ -11,7 +12,7 @@
 @pytest.mark.parametrize('dimension', [1, 2, 3])
 @pytest.mark.parametrize('batch_size', [1, 2, 5, 17])
 @pytest.mark.parametrize('buf_size', [1, 2, 5, 17, 100])
-def test_buffer_streamer(dimension, batch_size, buf_size):
+def test_BufferedStreamer(dimension, batch_size, buf_size):
 
     key = 'X'
 
@@ -44,7 +45,7 @@ def __unpack_stream(stream):
 @pytest.mark.parametrize('dimension', [1, 2, 3])
 @pytest.mark.parametrize('batch_size', [1, 2, 5, 17])
 @pytest.mark.parametrize('buf_size', [1, 2, 5, 17, 100])
-def test_buffer_streamer_tuple(dimension, batch_size, buf_size, items):
+def test_BufferedStreamer_tuples(dimension, batch_size, buf_size, items):
 
     gen_stream = pescador.Streamer(T.md_generator, dimension, 50,
                                    size=batch_size, items=items)

tests/test_maps.py

@@ -84,6 +84,11 @@ def test_keras_tuples(sample_data):
         assert n == x ** 0.5
         assert y is None
 
+    stream = pescador.maps.keras_tuples(sample_data, inputs=["bang"])
+    for n, (x, y) in enumerate(stream):
+        assert n == x[0] ** 0.5
+        assert y is None
+
     stream = pescador.maps.keras_tuples(sample_data, inputs=["foo", "bang"],
                                         outputs=["bar", "whiz"])
     for n, (x, y) in enumerate(stream):