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jvesely/linear_combination_function_refactor #732

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Mar 19, 2018
Merged

jvesely/linear_combination_function_refactor #732

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2ebfef6
Function, LinearCombination: Refactor to improve code reuse
jvesely Mar 18, 2018
ccea48d
Function, LinearCombination: More code reuse refactoring
jvesely Mar 18, 2018
ab8df90
tests, LinearCombination: Use custom naming function instead of name …
jvesely Mar 18, 2018
d679fcb
test, LinearCombination Function: Add combination step tests
jvesely Mar 19, 2018
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71 changes: 29 additions & 42 deletions psyneulink/components/functions/function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2138,62 +2138,49 @@ def function(self,
# FIX FOR EFFICIENCY: CHANGE THIS AND WEIGHTS TO TRY/EXCEPT // OR IS IT EVEN NECESSARY, GIVEN VALIDATION ABOVE??
# Apply exponents if they were specified
if exponents is not None:
try:
variable = self._update_variable(variable ** exponents)
# Avoid divide by zero warning:
# make sure there are no zeros for an element that is assigned a negative exponent
# Allow during initialization because 0s are common in default_variable argument
if context is not None and INITIALIZING in context: # cxt-test
try:
variable = self._update_variable(variable ** exponents)
except ZeroDivisionError:
except ZeroDivisionError:
# Allow during initialization because 0s are common in
# default_variable argument
if context is not None and INITIALIZING in context: # cxt-test
variable = self._update_variable(np.ones_like(variable))
else:
# if this fails with ZeroDivisionError it should not be caught outside of initialization
variable = self._update_variable(variable ** exponents)
else:
# if this fails with ZeroDivisionError it should not be caught
# outside of initialization
raise

# Apply weights if they were specified
if weights is not None:
variable = self._update_variable(variable * weights)

# CALCULATE RESULT USING RELEVANT COMBINATION OPERATION AND MODULATION

if operation is SUM:
if isinstance(scale, numbers.Number):
# Scalar scale and offset
if isinstance(offset, numbers.Number):
result = np.sum(variable, axis=0) * scale + offset
# Scalar scale and Hadamard offset
else:
result = np.sum(np.append([variable * scale], [offset], axis=0), axis=0)
else:
# Hadamard scale, scalar offset
if isinstance(offset, numbers.Number):
result = np.product([np.sum([variable], axis=0), scale], axis=0) + offset
# Hadamard scale and offset
else:
hadamard_product = np.product([np.sum([variable], axis=0), scale], axis=0)
result = np.sum(np.append([hadamard_product], [offset], axis=0), axis=0)

combination = np.sum(variable, axis=0)
elif operation is PRODUCT:
product = np.product([variable], axis=0)
if isinstance(scale, numbers.Number):
# Scalar scale and offset
if isinstance(offset, numbers.Number):
result = product * scale + offset
# Scalar scale and Hadamard offset
else:
result = np.sum(np.append([product * scale], [offset], axis=0), axis=0)
else:
# Hadamard scale, scalar offset
if isinstance(offset, numbers.Number):
result = np.product([product, scale], axis=0) + offset
# Hadamard scale and offset
else:
hadamard_product = np.product(np.append([product], [scale], axis=0), axis=0)
result = np.sum(np.append([hadamard_product], [offset], axis=0), axis=0)

combination = np.product(variable, axis=0)
else:
raise FunctionError("Unrecognized operator ({0}) for LinearCombination function".
format(operation.self.Operation.SUM))
if isinstance(scale, numbers.Number):
product = combination * scale
# Scalar scale and offset
if isinstance(offset, numbers.Number):
result = product + offset
# Scalar scale and Hadamard offset
else:
result = np.sum([product, offset], axis=0)
else:
hadamard_product = np.product([combination, scale], axis=0)
# Hadamard scale, scalar offset
if isinstance(offset, numbers.Number):
result = hadamard_product + offset
# Hadamard scale and offset
else:
result = np.sum([hadamard_product, offset], axis=0)

return result

@property
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40 changes: 27 additions & 13 deletions tests/functions/test_combination.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@ def test_matrix(self):
SIZE=5
#This gives us the correct 2d array
test_var = np.random.rand(1, SIZE)
test_var2 = np.random.rand(2, SIZE)

RAND1_V = np.random.rand(1, SIZE)
RAND2_V = np.random.rand(1, SIZE)
Expand All @@ -88,24 +89,37 @@ def test_matrix(self):
(Function.LinearCombination, test_var, {'scale':RAND1_S, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, test_var * RAND1_S + RAND2_V),
(Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_S, 'operation':pnl.PRODUCT}, test_var * RAND1_V + RAND2_S),
(Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, test_var * RAND1_V + RAND2_V),
]

# use list, naming function produces ugly names
linear_combination_names = [
"COMBINE-1 SUM",
"COMBINE-1 SUM VECTOR OFFSET",
"COMBINE-1 SUM VECTOR SCALE",
"COMBINE-1 SUM VECTOR OFFSET SCALE",

"COMBINE-1 PRODUCT",
"COMBINE-1 PRODUCT VECTOR OFFSET",
"COMBINE-1 PRODUCT VECTOR SCALE",
"COMBINE-1 PRODUCT VECTOR OFFSET SCALE",
(Function.LinearCombination, test_var2, {'scale':RAND1_S, 'offset':RAND2_S, 'operation':pnl.SUM}, np.sum(test_var2, axis=0) * RAND1_S + RAND2_S),
# TODO: enable vector scale/offset when the validation is fixed
# (Function.LinearCombination, test_var2, {'scale':RAND1_S, 'offset':RAND2_V, 'operation':pnl.SUM}, np.sum(test_var2, axis=0) * RAND1_S + RAND2_V),
# (Function.LinearCombination, test_var2, {'scale':RAND1_V, 'offset':RAND2_S, 'operation':pnl.SUM}, np.sum(test_var2, axis=0) * RAND1_V + RAND2_S),
# (Function.LinearCombination, test_var2, {'scale':RAND1_V, 'offset':RAND2_V, 'operation':pnl.SUM}, np.sum(test_var2, axis=0) * RAND1_V + RAND2_V),

(Function.LinearCombination, test_var2, {'scale':RAND1_S, 'offset':RAND2_S, 'operation':pnl.PRODUCT}, np.product(test_var2, axis=0) * RAND1_S + RAND2_S),
# (Function.LinearCombination, test_var2, {'scale':RAND1_S, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, np.product(test_var2, axis=0) * RAND1_S + RAND2_V),
# (Function.LinearCombination, test_var2, {'scale':RAND1_V, 'offset':RAND2_S, 'operation':pnl.PRODUCT}, np.product(test_var2, axis=0) * RAND1_V + RAND2_S),
# (Function.LinearCombination, test_var2, {'scale':RAND1_V, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, np.product(test_var2, axis=0) * RAND1_V + RAND2_V),
]

# pytest naming function produces ugly names
def _naming_function(config):
_, var, params, _ = config
inputs = var.shape[0]
op = params['operation']
vector_string = ""
if not np.isscalar(params['scale']):
vector_string += " SCALE"
if not np.isscalar(params['offset']):
vector_string += " OFFSET"
if vector_string != "":
vector_string = " VECTOR" + vector_string
return "COMBINE-{} {}{}".format(inputs, op, vector_string)


@pytest.mark.function
@pytest.mark.combination_function
@pytest.mark.parametrize("func, variable, params, expected", test_linear_combination_data, ids=linear_combination_names)
@pytest.mark.parametrize("func, variable, params, expected", test_linear_combination_data, ids=list(map(_naming_function, test_linear_combination_data)))
@pytest.mark.benchmark
def test_linear_combination_function(func, variable, params, expected, benchmark):
f = func(default_variable=variable, **params)
Expand Down