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[ModelBuilder] Polish numpy code

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This commit is contained in:
Laurent Perron
2023-02-28 22:47:12 +04:00
parent f496dcbb17
commit ed006ad70b
2 changed files with 58 additions and 66 deletions
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122
ortools/linear_solver/python/model_builder.py
View File

@@ -42,7 +42,7 @@ from ortools.linear_solver.python import model_builder_helper as mbh
from ortools.linear_solver.python import pywrap_model_builder_helper as pwmb
# Custom types.
NumberT = Union[numbers.Number, np.number, int, float]
NumberT = Union[numbers.Number, np.number]
IntegerT = Union[numbers.Integral, np.integer]
LinearExprT = Union['LinearExpr', NumberT]
ConstraintT = Union['VarCompVar', 'BoundedLinearExpression', bool]
@@ -50,7 +50,7 @@ ShapeT = Union[IntegerT, Sequence[IntegerT]]
NumpyFuncT = Callable[[
'VariableContainer',
Optional[np.double],
Union[npt.NDArray[np.double], Sequence[NumberT], None],
Optional[Union[npt.NDArray[np.double], Sequence[NumberT]]],
], LinearExprT,]
# Forward solve statuses.
@@ -335,7 +335,7 @@ class _WeightedSum(LinearExpr):
coefficients: npt.NDArray[np.double],
constant: np.double = np.double(0.0),
):
LinearExpr.__init__(self)
super().__init__()
self.__variable_indices: npt.NDArray[np.int32] = variable_indices
self.__coefficients: npt.NDArray[
np.double] = mbh.assert_is_a_number_array(coefficients)
@@ -562,7 +562,6 @@ class Variable(LinearExpr):
return LinearExpr.weighted_sum([self], [arg], constant=0.0)
# TODO(user): Type slices.
_REGISTERED_NUMPY_UFUNCS: Dict[Any, NumpyFuncT] = {}
@@ -583,6 +582,7 @@ class VariableContainer(mixins.NDArrayOperatorsMixin):
pos: Union[slice, int, List[int], Tuple[Union[int, slice, List[int]],
...]],
) -> Union['VariableContainer', Variable]:
# delegate the treatment of the 'pos' query to __variable_indices.
index_or_slice: Union[np.int32, npt.NDArray[np.int32]] = (
self.__variable_indices[pos])
if mbh.is_integral(index_or_slice):
@@ -639,32 +639,31 @@ class VariableContainer(mixins.NDArrayOperatorsMixin):
return self.__variable_indices.shape[0]
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
if method == '__call__':
if ufunc not in _REGISTERED_NUMPY_UFUNCS:
return NotImplemented
container: Optional[VariableContainer] = None
scalar: Optional[NumberT] = None
coeffs: npt.NDArray[np.double] = None
for arg in inputs:
if mbh.is_a_number(arg):
scalar = mbh.assert_is_a_number(arg)
elif isinstance(arg, self.__class__):
container = arg
elif isinstance(arg, (Sequence, np.ndarray)):
coeffs = np.array(arg, dtype=np.double)
else:
print(f'not recognized {arg}')
return NotImplemented
if container is None:
return NotImplemented
if coeffs is not None:
assert container.shape == coeffs.shape
function = _REGISTERED_NUMPY_UFUNCS[ufunc]
return function(container, scalar, coeffs)
else:
if method != '__call__':
return NotImplemented
ufunc_impl = _REGISTERED_NUMPY_UFUNCS.get(ufunc)
if ufunc_impl is None:
return NotImplemented
container: Optional[VariableContainer] = None
scalar: Optional[NumberT] = None
coeffs: Optional[npt.NDArray[np.double]] = None
for arg in inputs:
if mbh.is_a_number(arg):
scalar = mbh.assert_is_a_number(arg)
elif isinstance(arg, self.__class__):
container = arg
elif isinstance(arg, (Sequence, np.ndarray)):
coeffs = np.array(arg, dtype=np.double)
else:
return NotImplemented
if container is None:
return NotImplemented
if coeffs is not None:
assert container.shape == coeffs.shape, (container.shape,
coeffs.shape)
return ufunc_impl(container, scalar, coeffs)
def __array_function__(self, func: Any, types: Any, args: Any,
kwargs: Any) -> LinearExprT:
@@ -673,7 +672,7 @@ class VariableContainer(mixins.NDArrayOperatorsMixin):
return _REGISTERED_NUMPY_UFUNCS[func](*args, **kwargs)
def implements(np_function: Any) -> Callable[[NumpyFuncT], NumpyFuncT]:
def _implements(np_function: Any) -> Callable[[NumpyFuncT], NumpyFuncT]:
"""Register an __array_function__ implementation for VariableContainer objects."""
def decorator(func: NumpyFuncT) -> NumpyFuncT:
@@ -683,7 +682,7 @@ def implements(np_function: Any) -> Callable[[NumpyFuncT], NumpyFuncT]:
return decorator
@implements(np.sum)
@_implements(np.sum)
def sum_variable_container(
container: VariableContainer,
scalar: Optional[np.double] = None,
@@ -700,7 +699,7 @@ def sum_variable_container(
)
@implements(np.multiply)
@_implements(np.multiply)
def multiply_variable_container(
container: VariableContainer,
scalar: Optional[np.double] = None,
@@ -716,17 +715,17 @@ def multiply_variable_container(
constant=0.0,
)
if coeffs is not None:
assert container.shape == coeffs.shape
assert container.shape == coeffs.shape, (container.shape, coeffs.shape)
return _WeightedSum(
variable_indices=indices.flatten(),
coefficients=coeffs.flatten(),
constant=0.0,
)
raise TypeError('Cannot call multiply_variable_container without argument')
raise ValueError('Cannot call multiply_variable_container without argument')
@implements(np.dot)
@_implements(np.dot)
def dot_variable_container(
container: VariableContainer,
scalar: Optional[np.double] = None,
@@ -736,7 +735,7 @@ def dot_variable_container(
indices: npt.NDArray[np.int32] = container.variable_indices
if coeffs is not None:
assert scalar is None
assert container.shape == coeffs.shape
assert container.shape == coeffs.shape, (container.shape, coeffs.shape)
return _WeightedSum(
variable_indices=indices.flatten(),
coefficients=coeffs.flatten(),
@@ -749,7 +748,7 @@ def dot_variable_container(
constant=0.0,
)
raise TypeError('Cannot call dot_variable_container without argument')
raise ValueError('Cannot call dot_variable_container without argument')
class VarCompVar:
@@ -975,31 +974,29 @@ class ModelBuilder:
shape = np.shape(lower_bounds)
elif shape != np.shape(lower_bounds):
raise ValueError(
'ModelBuilder.new_var_array: lower_bounds must be compatible'
' with shape')
'lower_bounds, upper_bounds, is_integral and shape must have'
' compatible shapes (when defined)')
if not np.isscalar(upper_bounds):
if shape is None:
shape = np.shape(upper_bounds)
elif shape != np.shape(upper_bounds):
raise ValueError(
'ModelBuilder.new_var_array: ubs must be compatible with shape'
' or lower_bounds')
'lower_bounds, upper_bounds, is_integral and shape must have'
' compatible shapes (when defined)')
if not np.isscalar(is_integral):
if shape is None:
shape = np.shape(is_integral)
elif shape != np.shape(is_integral):
raise ValueError(
'ModelBuilder.new_var_array: is_integral must be compatible'
' with shape, lower_bounds, or upper_bounds')
'lower_bounds, upper_bounds, is_integral and shape must have'
' compatible shapes (when defined)')
if shape is None:
raise ValueError(
'ModelBuilder.new_var_array: a shape must be defined')
raise ValueError('a shape must be defined')
if name is None:
name = ''
name = name or ''
if (np.isscalar(lower_bounds) and np.isscalar(upper_bounds) and
np.isscalar(is_integral)):
@@ -1038,23 +1035,21 @@ class ModelBuilder:
shape = np.shape(lower_bounds)
elif shape != np.shape(lower_bounds):
raise ValueError(
'ModelBuilder.new_num_var_array: lower_bounds must be compatible'
' with shape')
'lower_bounds, upper_bounds, and shape must have'
' compatible shapes (when defined)')
if not np.isscalar(upper_bounds):
if shape is None:
shape = np.shape(upper_bounds)
elif shape != np.shape(upper_bounds):
raise ValueError(
'ModelBuilder.new_num_var_array: ubs must be compatible with shape'
' or lower_bounds')
'lower_bounds, upper_bounds, and shape must have'
' compatible shapes (when defined)')
if shape is None:
raise ValueError(
'ModelBuilder.new_num_var_array: a shape must be defined')
raise ValueError('a shape must be defined')
if name is None:
name = ''
name = name or ''
if np.isscalar(lower_bounds) and np.isscalar(upper_bounds):
var_indices = self.__helper.add_var_array(shape, lower_bounds,
@@ -1089,23 +1084,21 @@ class ModelBuilder:
shape = np.shape(lower_bounds)
elif shape != np.shape(lower_bounds):
raise ValueError(
'ModelBuilder.new_int_var_array: lower_bounds must be compatible'
' with shape')
'lower_bounds, upper_bounds, and shape must have'
' compatible shapes (when defined)')
if not np.isscalar(upper_bounds):
if shape is None:
shape = np.shape(upper_bounds)
elif shape != np.shape(upper_bounds):
raise ValueError(
'ModelBuilder.new_int_var_array: upper_bounds must be compatible'
' with shape or lower_bounds')
'lower_bounds, upper_bounds, and shape must have'
' compatible shapes (when defined)')
if shape is None:
raise ValueError(
'ModelBuilder.new_int_var_array: a shape must be defined')
raise ValueError('a shape must be defined')
if name is None:
name = ''
name = name or ''
if np.isscalar(lower_bounds) and np.isscalar(upper_bounds):
var_indices = self.__helper.add_var_array(shape, lower_bounds,
@@ -1131,8 +1124,7 @@ class ModelBuilder:
if mbh.is_integral(shape):
shape = [shape]
if name is None:
name = ''
name = name or ''
var_indices = self.__helper.add_var_array(shape, 0.0, 1.0, True, name)
return VariableContainer(self.__helper, var_indices)

2
ortools/linear_solver/samples/bin_packing_mb.py
View File

@@ -67,7 +67,7 @@ def main():
# The amount packed in each bin cannot exceed its capacity.
for j in data['bins']:
model.add(
np.dot(x[:, j], data['weights']) <= y[j] * data['bin_capacity'])
np.dot(x[:, j], data['weights']) <= data['bin_capacity'] * y[j])
# [END constraints]
# [START objective]