def test_symbol():
xt = theano_code(x)
assert isinstance(xt, (tt.TensorVariable, ts.ScalarVariable))
assert xt.name == x.name
assert theano_code(x, broadcastables={x: (False,)}).broadcastable == (False,)
assert theano_code(x, broadcastables={x: (False,)}).name == x.name
def test_MatrixSlice():
n = sympy.Symbol('n', integer=True)
X = sympy.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
from theano.scalar import Scalar
from theano import Constant
s = Scalar('int64')
assert tuple(Yt.owner.op.idx_list) == (slice(s, s, s), slice(s, s, s))
assert Yt.owner.inputs[0] == theano_code(X)
# == doesn't work in theano like it does in SymPy. You have to use
# equals.
assert [i.equals(j) for i, j in zip(Yt.owner.inputs[1:],[
Constant(s, 1),
Constant(s, 2),
Constant(s, 3),
Constant(s, 4),
Constant(s, 5),
Constant(s, 6),
])]
k = sympy.Symbol('k')
kt = theano_code(k, dtypes={k: 'int32'})
start, stop, step = 4, k, 2
Y = X[start:stop:step]
Yt = theano_code(Y, dtypes={n: 'int32', k: 'int32'})
def test_complexfunctions():
xt, yt = theano_code(x, dtypes={x:'complex128'}), theano_code(y, dtypes={y: 'complex128'})
from sympy import conjugate
from theano.tensor import as_tensor_variable as atv
from theano.tensor import complex as cplx
assert theq(theano_code(y*conjugate(x)), yt*(xt.conj()))
assert theq(theano_code((1+2j)*x), xt*(atv(1.0)+atv(2.0)*cplx(0,1)))
def test_add():
expr = x + y
comp = theano_code(expr)
assert comp.owner.op == theano.tensor.add
comp = theano_code(expr, broadcastables={x: (False,), y: (False,)})
assert comp.broadcastable == (False,)
comp = theano_code(expr, broadcastables={x: (False, True), y: (False, False)})
assert comp.broadcastable == (False, False)
def test_Piecewise():
# A piecewise linear
xt, yt = theano_code(x), theano_code(y)
expr = sy.Piecewise((0, x < 0), (x, x < 2), (1, True)) # ___/III
result = theano_code(expr)
assert result.owner.op == tt.switch
expected = tt.switch(xt < 0, 0, tt.switch(xt < 2, xt, 1))
assert theq(result, expected)
expr = sy.Piecewise((x, x < 0))
result = theano_code(expr)
expected = tt.switch(xt < 0, xt, np.nan)
assert theq(result, expected)
def test_MatrixSlice():
n = sympy.Symbol('n', integer=True)
X = sympy.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
assert tuple(Yt.owner.op.idx_list) == (slice(1, 2, 3), slice(4, 5, 6))
assert Yt.owner.inputs[0] == theano_code(X)
k = sympy.Symbol('k')
kt = theano_code(k, dtypes={k: 'int32'})
start, stop, step = 4, k, 2
Y = X[start:stop:step]
Yt = theano_code(Y, dtypes={n: 'int32', k: 'int32'})
def test_MatrixSlice():
n = sympy.Symbol('n', integer=True)
X = sympy.MatrixSymbol('X', n, n)
Y = X[1:2:3, 4:5:6]
Yt = theano_code(Y)
assert tuple(Yt.owner.op.idx_list) == (slice(1,2,3), slice(4,5,6))
assert Yt.owner.inputs[0] == theano_code(X)
k = sympy.Symbol('k')
kt = theano_code(k, dtypes={k: 'int32'})
start, stop, step = 4, k, 2
Y = X[start:stop:step]
Yt = theano_code(Y, dtypes={n: 'int32', k: 'int32'})
def test_Piecewise():
# A piecewise linear
xt, yt = theano_code(x), theano_code(y)
expr = sy.Piecewise((0, x<0), (x, x<2), (1, True)) # ___/III
result = theano_code(expr)
assert result.owner.op == tt.switch
expected = tt.switch(xt<0, 0, tt.switch(xt<2, xt, 1))
assert theq(result, expected)
expr = sy.Piecewise((x, x < 0))
result = theano_code(expr)
expected = tt.switch(xt < 0, xt, np.nan)
assert theq(result, expected)
def test_add():
expr = x + y
comp = theano_code(expr)
assert comp.owner.op == theano.tensor.add
comp = theano_code(expr, broadcastables={x: (False, ), y: (False, )})
assert comp.broadcastable == (False, )
comp = theano_code(expr,
broadcastables={
x: (False, True),
y: (False, False)
})
assert comp.broadcastable == (False, False)
def test_global_cache():
""" Test use of the global cache. """
from sympy.printing.theanocode import global_cache
backup = dict(global_cache)
try:
# Temporarily empty global cache
global_cache.clear()
for s in [x, X, f_t]:
st = theano_code(s)
assert theano_code(s) is st
finally:
# Restore global cache
global_cache.update(backup)
def test_global_cache():
""" Test use of the global cache. """
from sympy.printing.theanocode import global_cache
backup = dict(global_cache)
try:
# Temporarily empty global cache
global_cache.clear()
for s in [x, X, f_t]:
st = theano_code(s)
assert theano_code(s) is st
finally:
# Restore global cache
global_cache.update(backup)
def test_DenseMatrix():
t = sy.Symbol('theta')
for MatrixType in [sy.Matrix, sy.ImmutableMatrix]:
X = MatrixType([[sy.cos(t), -sy.sin(t)], [sy.sin(t), sy.cos(t)]])
tX = theano_code(X)
assert isinstance(tX, tt.TensorVariable)
assert tX.owner.op == tt.join
def test_BlockMatrix():
n = sympy.Symbol('n', integer=True)
A = sympy.MatrixSymbol('A', n, n)
B = sympy.MatrixSymbol('B', n, n)
C = sympy.MatrixSymbol('C', n, n)
D = sympy.MatrixSymbol('D', n, n)
At, Bt, Ct, Dt = map(theano_code, (A, B, C, D))
Block = sympy.BlockMatrix([[A, B], [C, D]])
Blockt = theano_code(Block)
solutions = [tt.join(0, tt.join(1, At, Bt), tt.join(1, Ct, Dt)),
tt.join(1, tt.join(0, At, Ct), tt.join(0, Bt, Dt))]
assert any(theq(Blockt, solution) for solution in solutions)
def test_cache_types_distinct():
"""
Test that symbol-like objects of different types (Symbol, MatrixSymbol,
AppliedUndef) are distinguished by the cache even if they have the same
name.
"""
symbols = [sy.Symbol('f_t'), sy.MatrixSymbol('f_t', 4, 4), f_t]
cache = {} # Single shared cache
printed = {}
for s in symbols:
st = theano_code_(s, cache=cache)
assert st not in printed.values()
printed[s] = st
# Check all printed objects are distinct
assert len(set(map(id, printed.values()))) == len(symbols)
# Check retrieving
for s, st in printed.items():
assert theano_code(s, cache=cache) is st
def test_cache_types_distinct():
"""
Test that symbol-like objects of different types (Symbol, MatrixSymbol,
AppliedUndef) are distinguished by the cache even if they have the same
name.
"""
symbols = [sy.Symbol('f_t'), sy.MatrixSymbol('f_t', 4, 4), f_t]
cache = {} # Single shared cache
printed = {}
for s in symbols:
st = theano_code_(s, cache=cache)
assert st not in printed.values()
printed[s] = st
# Check all printed objects are distinct
assert len(set(map(id, printed.values()))) == len(symbols)
# Check retrieving
for s, st in printed.items():
assert theano_code(s, cache=cache) is st
def test_many():
expr = sy.exp(x**2 + sy.cos(y)) * sy.log(2*z)
comp = theano_code(expr)
expected = tt.exp(xt**2 + tt.cos(yt)) * tt.log(2*zt)
def theano_code_(expr, **kwargs):
""" Wrapper for theano_code that uses a new, empty cache by default. """
kwargs.setdefault('cache', {})
with warns_deprecated_sympy():
return theano_code(expr, **kwargs)
def test_AppliedUndef():
t = sy.Symbol('t')
f = sy.Function('f')
ft = theano_code(f(t))
assert isinstance(ft, tt.TensorVariable)
assert ft.name == 'f_t'
def test_Derivative():
assert theq(theano_code(sy.Derivative(sy.sin(x), x, evaluate=False)),
theano.grad(tt.sin(xt), xt))
def theano_code_(expr, **kwargs):
""" Wrapper for theano_code that uses a new, empty cache by default. """
kwargs.setdefault('cache', {})
return theano_code(expr, **kwargs)
def test_Derivative():
simp = lambda expr: theano_simplify(fgraph_of(expr))
assert theq(simp(theano_code(sy.Derivative(sy.sin(x), x, evaluate=False))),
simp(theano.grad(tt.sin(xt), xt)))
def test_slice():
assert theano_code(slice(1, 2, 3)) == slice(1, 2, 3)
assert str(theano_code(slice(1, x, 3), dtypes={x: 'int32'})) ==\
str(slice(1, xt, 3))
def test_Integers():
assert theano_code(sympy.Integer(3)) == 3
def test_factorial():
n = sympy.Symbol('n')
assert theano_code(sympy.factorial(n))
def test_Rationals():
assert theq(theano_code(sympy.Integer(2) / 3), tt.true_div(2, 3))
assert theq(theano_code(S.Half), tt.true_div(1, 2))
def test_cache():
sx = sy.Symbol('x')
cache = {}
tx = theano_code(sx, cache=cache)
assert theano_code(sx, cache=cache) is tx
assert theano_code(sx, cache={}) is not tx
def test_Transpose():
X = sympy.MatrixSymbol('X', 4, 4)
assert isinstance(theano_code(X.T).owner.op, tt.DimShuffle)
def test_MatrixSymbol():
X = sympy.MatrixSymbol('X', 4, 5)
Xt = theano_code(X)
assert isinstance(Xt, tt.TensorVariable)
assert Xt.broadcastable == (False, False)
def test_Relationals():
xt, yt = theano_code(x), theano_code(y)
assert theq(theano_code(x > y), xt > yt)
assert theq(theano_code(x < y), xt < yt)
assert theq(theano_code(x >= y), xt >= yt)
assert theq(theano_code(x <= y), xt <= yt)
def test_trig():
assert theq(theano_code(sympy.sin(x)), tt.sin(xt))
assert theq(theano_code(sympy.tan(x)), tt.tan(xt))
def test_cache():
sx = sy.Symbol('x')
cache = {}
tx = theano_code(sx, cache=cache)
assert theano_code(sx, cache=cache) is tx
assert theano_code(sx, cache={}) is not tx
def test_dtype():
assert theano_code(x, dtypes={x: 'float32'}).type.dtype == 'float32'
assert theano_code(x, dtypes={x: 'float64'}).type.dtype == 'float64'
assert theano_code(x+1, dtypes={x: 'float32'}).type.dtype == 'float32'
assert theano_code(x+y, dtypes={x: 'float64', y: 'float32'}).type.dtype == 'float64'
def test_Relationals():
xt, yt = theano_code(x), theano_code(y)
assert theq(theano_code(x > y), xt > yt)
assert theq(theano_code(x < y), xt < yt)
assert theq(theano_code(x >= y), xt >= yt)
assert theq(theano_code(x <= y), xt <= yt)
def test_MatMul():
X = sympy.MatrixSymbol('X', 4, 4)
Y = sympy.MatrixSymbol('X', 4, 4)
Z = sympy.MatrixSymbol('X', 4, 4)
expr = X*Y*Z
assert isinstance(theano_code(expr).owner.op, tt.Dot)
def test_symbols_are_created_once():
expr = x**x
comp = theano_code(expr)
assert theq(comp, xt**xt)
def test_MatAdd():
X = sympy.MatrixSymbol('X', 4, 4)
Y = sympy.MatrixSymbol('X', 4, 4)
Z = sympy.MatrixSymbol('X', 4, 4)
expr = X+Y+Z
assert isinstance(theano_code(expr).owner.op, tt.Elemwise)
def theano_code_(expr, **kwargs):
""" Wrapper for theano_code that uses a new, empty cache by default. """
kwargs.setdefault('cache', {})
return theano_code(expr, **kwargs)
