Merge pull request #5751 from laurent-dinh/to_choice

theano/gpuarray/multinomial.py

 # TODO test dtype != float32
 from __future__ import absolute_import, print_function, division
 import os
+import warnings
 
 try:
     import pygpu
@@ -232,7 +233,7 @@ KERNEL void k_multi_warp_multinomial(
         return (3,)
 
 
-class GPUAMultinomialWOReplacementFromUniform(GpuKernelBase, Op):
+class GPUAChoiceFromUniform(GpuKernelBase, Op):
     """
     The output is transposed compared to MultinomialWOReplacementFromUniform.
     We must insert a Transpose op after it.
@@ -506,13 +507,22 @@ def local_gpua_multinomial(op, context_name, inputs, outputs):
 
 
 @register_opt('fast_compile')
-@op_lifter([theano.sandbox.multinomial.MultinomialWOReplacementFromUniform])
-@register_opt2([theano.sandbox.multinomial.MultinomialWOReplacementFromUniform], 'fast_compile')
+@op_lifter([theano.sandbox.multinomial.ChoiceFromUniform])
+@register_opt2([theano.sandbox.multinomial.ChoiceFromUniform], 'fast_compile')
 def local_gpua_multinomial_wor(op, context_name, inputs, outputs):
     # TODO : need description for function
     p, u, n = inputs
     m, = outputs
     if ((p.dtype == u.dtype == 'float32') and (m.dtype == 'int64')):
-        gpu_op = GPUAMultinomialWOReplacementFromUniform(op.odtype)
+        gpu_op = GPUAChoiceFromUniform(op.odtype)
         return GpuDimShuffle([False, False], [1, 0])(
             gpu_op(p, u, n))
+
+
+class GPUAMultinomialWOReplacementFromUniform(GPUAChoiceFromUniform):
+    def __init__(self, *args, **kwargs):
+        warnings.warn("GPUAMultinomialWOReplacementFromUniform is deprecated, "
+                      "use GPUAChoiceFromUniform instead.",
+                      DeprecationWarning,
+                      stacklevel=2)
+        super(GPUAMultinomialWOReplacementFromUniform, self).__init__(*args, **kwargs)

theano/gpuarray/tests/test_gpuarray_multinomial_wo_replacement.pkl

+ccopy_reg
+_reconstructor
+p1
+(ctheano.gpuarray.multinomial
+GPUAMultinomialWOReplacementFromUniform
+p2
+c__builtin__
+object
+p3
+NtRp4
+(dp5
+S'odtype'
+p6
+S'float32'
+p7
+sb.
\ No newline at end of file

theano/gpuarray/tests/test_multinomial.py

 from __future__ import absolute_import, print_function, division
 
+import os
 import numpy as np
 
 import unittest
 
 import theano
 from theano import config, function, tensor
+from theano.compat import PY3
+from theano.misc.pkl_utils import CompatUnpickler
 from theano.sandbox import multinomial
 from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
 
@@ -13,7 +16,7 @@ import theano.tests.unittest_tools as utt
 
 from .config import mode_with_gpu
 from ..multinomial import (GPUAMultinomialFromUniform,
-                           GPUAMultinomialWOReplacementFromUniform)
+                           GPUAChoiceFromUniform)
 
 
 def test_multinomial_output_dtype():
@@ -172,12 +175,12 @@ class test_OP_wor(unittest.TestCase):
 
     def test_select_distinct(self):
         """
-        Tests that MultinomialWOReplacementFromUniform always selects distinct elements
+        Tests that ChoiceFromUniform always selects distinct elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -195,13 +198,13 @@ class test_OP_wor(unittest.TestCase):
 
     def test_fail_select_alot(self):
         """
-        Tests that MultinomialWOReplacementFromUniform fails when asked to sample more
+        Tests that ChoiceFromUniform fails when asked to sample more
         elements than the actual number of elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -215,13 +218,13 @@ class test_OP_wor(unittest.TestCase):
 
     def test_select_proportional_to_weight(self):
         """
-        Tests that MultinomialWOReplacementFromUniform selects elements, on average,
+        Tests that ChoiceFromUniform selects elements, on average,
         proportional to the their probabilities
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -324,11 +327,11 @@ def test_gpu_opt_wor():
     p = tensor.fmatrix()
     u = tensor.fvector()
     n = tensor.iscalar()
-    m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+    m = multinomial.ChoiceFromUniform('auto')(p, u, n)
     assert m.dtype == 'int64', m.dtype
 
     f = function([p, u, n], m, allow_input_downcast=True, mode=mode_with_gpu)
-    assert any([type(node.op) is GPUAMultinomialWOReplacementFromUniform
+    assert any([type(node.op) is GPUAChoiceFromUniform
                 for node in f.maker.fgraph.toposort()])
     n_samples = 3
     pval = np.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
@@ -338,13 +341,24 @@ def test_gpu_opt_wor():
 
     # Test with a row, it was failing in the past.
     r = tensor.frow()
-    m = multinomial.MultinomialWOReplacementFromUniform('auto')(r, u, n)
+    m = multinomial.ChoiceFromUniform('auto')(r, u, n)
     assert m.dtype == 'int64', m.dtype
 
     f = function([r, u, n], m, allow_input_downcast=True, mode=mode_with_gpu)
-    assert any([type(node.op) is GPUAMultinomialWOReplacementFromUniform
+    assert any([type(node.op) is GPUAChoiceFromUniform
                 for node in f.maker.fgraph.toposort()])
     pval = np.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
     pval = pval / pval.sum(axis=1)[:, None]
     uval = np.ones_like(pval[:, 0]) * 0.5
     f(pval, uval, 1)
+
+
+def test_unpickle_legacy_op():
+    testfile_dir = os.path.dirname(os.path.realpath(__file__))
+    fname = 'test_gpuarray_multinomial_wo_replacement.pkl'
+
+    if not PY3:
+        with open(os.path.join(testfile_dir, fname), 'r') as fp:
+            u = CompatUnpickler(fp)
+            m = u.load()
+            assert isinstance(m, GPUAChoiceFromUniform)

theano/sandbox/multinomial.py

 from __future__ import absolute_import, print_function, division
 import numpy
+import warnings
 
 import theano
 from theano import Op, Apply
@@ -211,7 +212,7 @@ class MultinomialFromUniform(Op):
                 z[0][n, numpy.searchsorted(cumsum, unis_n)] += 1
 
 
-class MultinomialWOReplacementFromUniform(MultinomialFromUniform):
+class ChoiceFromUniform(MultinomialFromUniform):
     """
     Converts samples from a uniform into sample (without replacement) from a
     multinomial.
@@ -626,3 +627,12 @@ def local_gpu_multinomial(node):
             # The dimshuffle is on the cpu, but will be moved to the
             # gpu by an opt.
             return [gpu_from_host(ret)]
+
+
+class MultinomialWOReplacementFromUniform(ChoiceFromUniform):
+    def __init__(self, *args, **kwargs):
+        warnings.warn("MultinomialWOReplacementFromUniform is deprecated, "
+                      "use ChoiceFromUniform instead.",
+                      DeprecationWarning,
+                      stacklevel=2)
+        super(MultinomialWOReplacementFromUniform, self).__init__(*args, **kwargs)

theano/sandbox/rng_mrg.py

@@ -1513,7 +1513,7 @@ class MRG_RandomStreams(object):
 
         shape = p[:, 0].shape * size
         unis = self.uniform(size=shape, ndim=1, nstreams=nstreams)
-        op = multinomial.MultinomialWOReplacementFromUniform(dtype)
+        op = multinomial.ChoiceFromUniform(dtype)
         return op(p, unis, as_tensor_variable(size))
 
     def multinomial_wo_replacement(self, size=None, n=1, pvals=None,

theano/sandbox/tests/test_multinomial_wo_replacement.py

 from __future__ import absolute_import, print_function, division
 import numpy
+import os
 from theano import config, function, tensor
+from theano.compat import PY3
+from theano.misc.pkl_utils import CompatUnpickler
 from theano.sandbox import multinomial
 from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
 import unittest
@@ -10,12 +13,12 @@ class test_OP(unittest.TestCase):
 
     def test_select_distinct(self):
         """
-        Tests that MultinomialWOReplacementFromUniform always selects distinct elements
+        Tests that ChoiceFromUniform always selects distinct elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -43,13 +46,13 @@ class test_OP(unittest.TestCase):
 
     def test_fail_select_alot(self):
         """
-        Tests that MultinomialWOReplacementFromUniform fails when asked to sample more
+        Tests that ChoiceFromUniform fails when asked to sample more
         elements than the actual number of elements
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -63,13 +66,13 @@ class test_OP(unittest.TestCase):
 
     def test_select_proportional_to_weight(self):
         """
-        Tests that MultinomialWOReplacementFromUniform selects elements, on average,
+        Tests that ChoiceFromUniform selects elements, on average,
         proportional to the their probabilities
         """
         p = tensor.fmatrix()
         u = tensor.fvector()
         n = tensor.iscalar()
-        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)
+        m = multinomial.ChoiceFromUniform('auto')(p, u, n)
 
         f = function([p, u, n], m, allow_input_downcast=True)
 
@@ -164,3 +167,14 @@ class test_function(unittest.TestCase):
         avg_pvals /= avg_pvals.sum()
         avg_diff = numpy.mean(abs(avg_pvals - pvals))
         assert avg_diff < mean_rtol
+
+    def test_unpickle_legacy_op(self):
+        testfile_dir = os.path.dirname(os.path.realpath(__file__))
+        fname = 'test_sandbox_multinomial_wo_replacement.pkl'
+
+        if not PY3:
+            with open(os.path.join(testfile_dir, fname), 'r') as fp:
+                u = CompatUnpickler(fp)
+                m = u.load()
+                print(m)
+                assert isinstance(m, multinomial.ChoiceFromUniform)

theano/sandbox/tests/test_sandbox_multinomial_wo_replacement.pkl

+ccopy_reg
+_reconstructor
+p1
+(ctheano.sandbox.multinomial
+MultinomialWOReplacementFromUniform
+p2
+c__builtin__
+object
+p3
+NtRp4
+(dp5
+S'odtype'
+p6
+S'auto'
+p7
+sb.
\ No newline at end of file