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提交 b8fad4ca authored 作者: Iban Harlouchet's avatar Iban Harlouchet
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flake8 theano/sparse/basic.py

上级 11a78c73
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theano/sparse/basic.py
浏览文件 @ b8fad4ca
......@@ -16,7 +16,7 @@ from six.moves import xrange
import scipy.sparse
import theano
from theano import gof, tensor, compile, scalar, config
from theano import gof, tensor, scalar, config
from theano.gradient import DisconnectedType
from theano.sparse.utils import hash_from_sparse
import theano.tests.unittest_tools as utt
......@@ -28,10 +28,10 @@ sparse_formats = ['csc', 'csr']
""" Types of sparse matrices to use for testing """
_mtypes = [scipy.sparse.csc_matrix, scipy.sparse.csr_matrix]
#_mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,
# _mtypes = [sparse.csc_matrix, sparse.csr_matrix, sparse.dok_matrix,
# sparse.lil_matrix, sparse.coo_matrix]
#* new class ``dia_matrix`` : the sparse DIAgonal format
#* new class ``bsr_matrix`` : the Block CSR format
# * new class ``dia_matrix`` : the sparse DIAgonal format
# * new class ``bsr_matrix`` : the Block CSR format
_mtype_to_str = {scipy.sparse.csc_matrix: "csc",
scipy.sparse.csr_matrix: "csr"}
......@@ -153,7 +153,8 @@ def verify_grad_sparse(op, pt, structured=False, *args, **kwargs):
:return: None
"""
conv_none = lambda x: x
def conv_none(x):
return x
def conv_csr(ind, indptr, shp):
def f(spdata):
......@@ -358,9 +359,9 @@ class SparseVariable(_sparse_py_operators, gof.Variable):
def __str__(self):
return '%s{%s,%s}' % (
self.__class__.__name__,
self.format,
self.dtype)
self.__class__.__name__,
self.format,
self.dtype)
def __repr__(self):
return str(self)
......@@ -369,11 +370,11 @@ class SparseVariable(_sparse_py_operators, gof.Variable):
class SparseConstantSignature(tuple):
def __eq__(self, other):
(a, b), (x, y) = self, other
return a == x \
and (b.dtype == y.dtype)\
and (type(b) == type(y))\
and (b.shape == y.shape)\
and (abs(b - y).sum() < 1e-6 * b.nnz)
return a == x and\
(b.dtype == y.dtype) and\
(type(b) == type(y)) and\
(b.shape == y.shape) and\
(abs(b - y).sum() < 1e-6 * b.nnz)
def __hash__(self):
(a, b) = self
......@@ -394,11 +395,11 @@ class SparseConstant(gof.Constant, _sparse_py_operators):
def __str__(self):
return '%s{%s,%s,shape=%s,nnz=%s}' % (
self.__class__.__name__,
self.format,
self.dtype,
self.data.shape,
self.data.nnz)
self.__class__.__name__,
self.format,
self.dtype,
self.data.shape,
self.data.nnz)
def __repr__(self):
return str(self)
......@@ -490,7 +491,8 @@ class CSMProperties(gof.Op):
data = tensor.TensorType(dtype=csm.type.dtype,
broadcastable=(False,)).make_variable()
return gof.Apply(self, [csm],
[data, tensor.ivector(), tensor.ivector(), tensor.ivector()])
[data, tensor.ivector(),
tensor.ivector(), tensor.ivector()])
def perform(self, node, inputs, out):
(csm,) = inputs
......@@ -658,7 +660,7 @@ class CSM(gof.Op):
if len(shape) != 2:
raise ValueError('Shape should be an array of length 2')
if (data.shape != indices.shape and numpy.size(data) !=
numpy.size(self.kmap)):
numpy.size(self.kmap)):
errmsg = ('Data (shape ' + repr(data.shape) +
' must have the same number of elements ' +
'as indices (shape' + repr(indices.shape) +
......@@ -684,7 +686,7 @@ class CSM(gof.Op):
g_data, g_indices, g_indptr, g_shape = csm_properties(g_out)
# unpack the data vector and wrap it as a 1d TensorType
g_data = csm_grad(self.kmap)(x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape)
g_data, g_indices, g_indptr, g_shape)
return [g_data, DisconnectedType()(), DisconnectedType()(), DisconnectedType()()]
def infer_shape(self, node, shapes):
......@@ -776,14 +778,14 @@ class CSMGrad(gof.op.Op):
self.kmap)
def make_node(self, x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape):
g_data, g_indices, g_indptr, g_shape):
gout_data = g_data.type()
return gof.Apply(self, [x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape], [gout_data])
g_data, g_indices, g_indptr, g_shape], [gout_data])
def perform(self, node, inputs, outputs):
(x_data, x_indices, x_indptr, x_shape,
g_data, g_indices, g_indptr, g_shape) = inputs
g_data, g_indices, g_indptr, g_shape) = inputs
(g_out,) = outputs
if len(x_indptr) - 1 == x_shape[0]:
sp_dim = x_shape[1]
......@@ -919,7 +921,7 @@ class DenseFromSparse(gof.op.Op):
[x],
[tensor.TensorType(dtype=x.type.dtype,
broadcastable=(False, False)
).make_variable()])
).make_variable()])
def perform(self, node, inputs, outputs):
(x,) = inputs
......@@ -990,8 +992,8 @@ class SparseFromDense(gof.op.Op):
x = tensor.as_tensor_variable(x)
if x.ndim > 2:
raise TypeError(
"Theano does not have sparse tensor types with more "
"than 2 dimensions, but %s.ndim = %i" % (x, x.ndim))
"Theano does not have sparse tensor types with more "
"than 2 dimensions, but %s.ndim = %i" % (x, x.ndim))
elif x.ndim == 1:
x = x.dimshuffle('x', 0)
elif x.ndim == 0:
......@@ -1003,7 +1005,7 @@ class SparseFromDense(gof.op.Op):
[x],
[SparseType(dtype=x.type.dtype,
format=self.format
).make_variable()])
).make_variable()])
def perform(self, node, inputs, outputs):
(x,) = inputs
......@@ -1243,7 +1245,7 @@ class GetItem2d(gof.op.Op):
# def infer_shape(self, node, i0_shapes):
# return i0_shapes
def make_node(self, x, index):
scipy_ver = [ int(n) for n in scipy.__version__.split('.')[:2]]
scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]
x = as_sparse_variable(x)
assert x.format in ["csr", "csc"]
assert len(index) in [1, 2]
......@@ -1260,11 +1262,11 @@ class GetItem2d(gof.op.Op):
# If start or stop or step are None, make them a Generic
# constant. Else, they should be converted to Tensor Variables
# of dimension 1 and int/uint dtype.
if scipy_ver < [0, 14] and ind.step != None:
if scipy_ver < [0, 14] and ind.step is not None:
raise ValueError(
'Slice with step is not support with current'
' version of Scipy.')
if ind.step is None or ind.step == 1:
if ind.step is None or ind.step == 1:
step = generic_None
else:
if not isinstance(step, gof.Variable):
......@@ -1301,8 +1303,8 @@ class GetItem2d(gof.op.Op):
stop.ndim, stop.dtype)
elif ((isinstance(ind, gof.Variable) and
getattr(ind, 'ndim', -1) == 0)
or numpy.isscalar(ind)):
getattr(ind, 'ndim', -1) == 0) or
numpy.isscalar(ind)):
raise NotImplementedError(
'Theano has no sparse vector' +
'Use X[a:b, c:d], X[a:b, c:c+1] or X[a:b] instead.')
......@@ -1439,7 +1441,7 @@ class Transpose(gof.op.Op):
[x],
[SparseType(dtype=x.type.dtype,
format=self.format_map[x.type.format]
).make_variable()])
).make_variable()])
def perform(self, node, inputs, outputs):
(x,) = inputs
......@@ -1832,11 +1834,11 @@ class SquareDiagonal(gof.op.Op):
raise TypeError('data argument must be a vector', diag.type)
return gof.Apply(self, [diag],
[SparseType(dtype=diag.dtype, format='csc')()])
[SparseType(dtype=diag.dtype, format='csc')()])
def perform(self, node, inputs, outputs):
(z,) = outputs
diag, o_shape = inputs[0], inputs[0].shape * 2
diag = inputs[0]
N = len(diag)
data = diag[:N]
......@@ -1960,7 +1962,7 @@ class AddSS(gof.op.Op):
[x, y],
[SparseType(dtype=out_dtype,
format=x.type.format
).make_variable()])
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2001,7 +2003,8 @@ class AddSSData(gof.op.Op):
return gof.Apply(self,
[x, y],
[SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()])
format=x.type.format
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2067,10 +2070,10 @@ class AddSD(gof.op.Op):
[x, y],
[tensor.TensorType(dtype=out_dtype,
broadcastable=y.type.broadcastable
).make_variable()])
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
(x, y) = inputs
(out,) = outputs
assert _is_dense(y)
......@@ -2110,7 +2113,8 @@ class StructuredAddSV(gof.op.Op):
return gof.Apply(self,
[x, y],
[SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()])
format=x.type.format
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2222,10 +2226,10 @@ class MulSS(gof.op.Op):
assert x.format in ["csr", "csc"]
assert y.format in ["csr", "csc"]
out_dtype = scalar.upcast(x.type.dtype, y.type.dtype)
return gof.Apply(self, [x, y],
return gof.Apply(self,
[x, y],
[SparseType(dtype=out_dtype,
format=x.type.format
)()])
format=x.type.format)()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2300,7 +2304,6 @@ class MulSD(gof.op.Op):
out_dtype = node.outputs[0].dtype
if x.format == 'csc':
x_data = x.data
indices = x.indices
indptr = x.indptr
if x.dtype == out_dtype:
......@@ -2315,7 +2318,6 @@ class MulSD(gof.op.Op):
z_data[i_idx] *= y[i, j]
out[0] = z
elif x.format == 'csr':
x_data = x.data
indices = x.indices
indptr = x.indptr
if x.dtype == out_dtype:
......@@ -2363,12 +2365,13 @@ class MulSV(gof.op.Op):
if x.type.dtype != y.type.dtype:
raise NotImplementedError(
"MulSV not implemented for differing dtypes."
"Got %s and %s." % (str(x.type.dtype), str(y.type.dtype)))
"MulSV not implemented for differing dtypes."
"Got %s and %s." % (str(x.type.dtype), str(y.type.dtype)))
return gof.Apply(self,
[x, y],
[SparseType(dtype=x.type.dtype,
format=x.type.format).make_variable()])
format=x.type.format
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2483,7 +2486,8 @@ class __ComparisonOpSS(gof.op.Op):
return gof.Apply(self,
[x, y],
[SparseType(dtype='uint8',
format=x.type.format).make_variable()])
format=x.type.format
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2527,7 +2531,8 @@ class __ComparisonOpSD(gof.op.Op):
return gof.Apply(self,
[x, y],
[SparseType(dtype='uint8',
format=x.type.format).make_variable()])
format=x.type.format
).make_variable()])
def perform(self, node, inputs, outputs):
(x, y) = inputs
......@@ -2765,9 +2770,11 @@ class HStack(gof.op.Op):
for x in var:
assert x.format in ["csr", "csc"]
return gof.Apply(
self, var,
[SparseType(dtype=self.dtype, format=self.format).make_variable()])
return gof.Apply(self,
var,
[SparseType(dtype=self.dtype,
format=self.format
).make_variable()])
def perform(self, node, block, outputs):
(out,) = outputs
......@@ -2852,7 +2859,7 @@ class VStack(HStack):
def grad(self, inputs, gout):
(gz,) = gout
is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
for i in range(len(inputs))]
for i in range(len(inputs))]
if _is_sparse_variable(gz):
gz = dense_from_sparse(gz)
......@@ -3729,11 +3736,10 @@ def structured_dot_grad(sparse_A, dense_B, ga):
sdgcsx = sdg_csr
CSx = CSR
g_A_data = sdgcsx(csm_indices(sparse_A), \
g_A_data = sdgcsx(csm_indices(sparse_A),
csm_indptr(sparse_A), dense_B, ga)
return CSx(g_A_data, csm_indices(sparse_A), \
csm_indptr(sparse_A), \
csm_shape(sparse_A))
return CSx(g_A_data, csm_indices(sparse_A),
csm_indptr(sparse_A), csm_shape(sparse_A))
else:
raise NotImplementedError()
......@@ -3756,7 +3762,7 @@ class SamplingDot(gof.op.Op):
raise TypeError(p)
# TODO: use it.
dtype_out = scalar.upcast(x.type.dtype, y.type.dtype, p.type.dtype)
dtype_out = scalar.upcast(x.type.dtype, y.type.dtype, p.type.dtype) # noqa
return gof.Apply(self, [x, y, p], [p.type()])
......@@ -3857,7 +3863,8 @@ class Dot(gof.op.Op):
y_is_sparse_var = _is_sparse_variable(y)
if not x_is_sparse_var and not y_is_sparse_var:
raise TypeError("Sparse dot product should have at least one "
raise TypeError(
"Sparse dot product should have at least one "
"sparse variable as inputs, but the inputs are "
"%s (%s) and %s (%s)." % (x, x.type, y, y.type))
......
theano/tests/test_flake8.py
浏览文件 @ b8fad4ca
......@@ -231,7 +231,6 @@ whitelist_flake8 = [
"sparse/type.py",
"sparse/__init__.py",
"sparse/opt.py",
"sparse/basic.py",
"sparse/tests/test_utils.py",
"sparse/tests/test_opt.py",
"sparse/tests/test_basic.py",
......
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