def generate_rows(cls, coef_mat):
if is_pandas_dataframe(coef_mat):
row_gen = cls.generate_df_rows(coef_mat)
elif is_numpy_matrix(coef_mat):
row_gen = cls.generate_np_matrix_rows(coef_mat)
else:
row_gen = iter(coef_mat)
return row_gen
def matrix_ranges(self, coef_mat, dvars, lbs, ubs):
"""
Creates a list of range constraints
from a matrix of coefficients, a sequence of variables, and two sequence of numbers.
This method returns the list of range constraints built from
L <= Ax <= U
where A is the coefficient matrix (of size (M,N)), X is the variable sequence (size N),
L and B are sequence of numbers (resp. the lower and upper bounds of the ranges) both with size M.
:param coef_mat: A matrix of coefficients with M rows and N columns. This argument accepts
either a list of lists of numbers, a `numpy` array with size (M,N), or a `scipy` sparse matrix.
:param dvars: An ordered sequence of decision variables: accepts a Python list, `numpy` array,
or a `pandas` series. The size of the sequence must match the number of columns in the matrix.
:param lbs: A sequence of numbers: accepts a Python list, a `numpy` array,
or a `pandas` series. The size of the sequence must match the number of rows in the matrix.
:param ubs: A sequence of numbers: accepts a Python list, a `numpy` array,
or a `pandas` series. The size of the sequence must match the number of rows in the matrix.
:returns: A list of range constraints.
Example::
If A is a matrix of coefficients with 2 rows and 3 columns:
A = [[1, 2, 3],
[4, 5, 6]],
X = [x, y, z] where x, y, and z are decision variables (size 3), and
L = [101. 102], a sequence of numbers (size 2),
U = [201, 202]
then::
`mdl.range_constraints(A, X, L, U)` returns a list of two ranges
[(101 <= x + 2y+3z <= 102), (201 <= 4x + 5y +6z <= 202)].
Note:
If the dimensions of the matrix and variables or of the matrix and number sequence do not match,
an error is raised.
"""
checker = self._checker
if is_pandas_dataframe(coef_mat) or is_numpy_matrix(
coef_mat) or is_scipy_sparse(coef_mat):
nb_rows, nb_cols = coef_mat.shape
else:
# a sequence of sequences
a_mat = list(coef_mat)
nb_rows = len(a_mat)
nb_cols = None
try:
shared_len = None
for r in a_mat:
checker.check_ordered_sequence(r, 'matrix_constraints')
r_len = len(r)
if shared_len is None:
shared_len = r_len
elif r_len != shared_len:
self.fatal(
'All columns should have same length found {0} != {1}'
.format(shared_len, r_len))
nb_cols = shared_len if shared_len is not None else 0
except AttributeError:
self.fatal('All columns should have a len()')
s_dvars = self._to_list(dvars, caller='Model.range_constraints()')
s_lbs = self._to_list(lbs, caller='Model.range_constraints()')
s_ubs = self._to_list(ubs, caller='Model.range_constraints()')
# check
checker.typecheck_var_seq(s_dvars)
checker.typecheck_num_seq(s_lbs)
checker.typecheck_num_seq(s_ubs)
# ---
# check dimensions and whether to transpose or not.
# ---
nb_vars = len(s_dvars)
nb_lbs = len(s_lbs)
nb_ubs = len(s_ubs)
if nb_lbs != nb_rows:
self.fatal(
'Incorrect size for range lower bounds, expecting: {1}, got: {0},'
.format(nb_lbs, nb_rows))
if nb_ubs != nb_rows:
self.fatal(
'Incorrect size for range upper bounds, expecting: {1}, got: {0}'
.format(nb_ubs, nb_rows))
if nb_cols != nb_vars:
self.fatal(
'Incorrect number of variables, expecting: {1}, got: {0}, matrix is ({0},{1})'
.format(nb_vars, nb_cols, nb_rows, nb_cols))
if is_scipy_sparse(coef_mat):
return self._aggregator._sparse_matrix_ranges(
coef_mat, s_dvars, s_lbs, s_ubs)
else:
return self._aggregator._matrix_ranges(coef_mat, s_dvars, s_lbs,
s_ubs)
def matrix_constraints(self, coef_mat, dvars, rhs, sense='le'):
"""
Creates a list of linear constraints
from a matrix of coefficients, a sequence of variables, and a sequence of numbers.
This method returns the list of constraints built from
A.X <op> B
where A is the coefficient matrix (of size (M,N)), X is the variable sequence (size N),
and B is the sequence of right-hand side values (of size M).
<op> is the comparison operator that defines the sense of the constraint. By default, this generates
a 'less-than-or-equal' constraint.
Example:
`Model.scal_prod_vars_triple([x, y], [z, t], [2, 3])` returns the expression `2xz + 3yt`.
:param coef_mat: A matrix of coefficients with M rows and N columns. This argument accepts
either a list of lists of numbers, a `numpy` array with size (M,N), or a `scipy` sparse matrix.
:param dvars: An ordered sequence of decision variables: accepts a Python list, `numpy` array,
or a `pandas` series. The size of the sequence must match the number of columns in the matrix.
:param rhs: A sequence of numbers: accepts a Python list, a `numpy` array,
or a `pandas` series. The size of the sequence must match the number of rows in the matrix.
:param sense: A constraint sense \; accepts either a
value of type `ComparisonType` or a string (e.g 'le', 'eq', 'ge').
:returns: A list of linear constraints.
Example:
If A is a matrix of coefficients with 2 rows and 3 columns::
A = [[1, 2, 3],
[4, 5, 6]],
X = [x, y, z] where x, y, and z are decision variables (size 3), and
B = [100, 200], a sequence of numbers (size 2),
then::
`mdl.matrix_constraint(A, X, B, 'GE')` returns a list of two constraints
[(x + 2y+3z <= 100), (4x + 5y +6z <= 200)].
Note:
If the dimensions of the matrix and variables or of the matrix and number sequence do not match,
an error is raised.
"""
checker = self._checker
if is_pandas_dataframe(coef_mat) or is_numpy_matrix(
coef_mat) or is_scipy_sparse(coef_mat):
nb_rows, nb_cols = coef_mat.shape
else:
# a sequence of sequences
a_mat = list(coef_mat)
nb_rows = len(a_mat)
nb_cols = None
try:
shared_len = None
for r in a_mat:
checker.check_ordered_sequence(r, 'matrix_constraints')
r_len = len(r)
if shared_len is None:
shared_len = r_len
elif r_len != shared_len:
self.fatal(
'All columns should have same length found {0} != {1}'
.format(shared_len, r_len))
nb_cols = shared_len if shared_len is not None else 0
except AttributeError:
self.fatal('All columns should have a len()')
s_dvars = self._to_list(dvars, caller='Model.matrix-constraints()')
s_rhs = self._to_list(rhs, caller='Model.matrix-constraints()')
# check
checker.typecheck_var_seq(s_dvars)
for k in s_rhs:
checker.typecheck_num(k)
op = ComparisonType.parse(sense)
# ---
# check dimensions and whether to transpose or not.
# ---
nb_rhs = len(s_rhs)
nb_vars = len(s_dvars)
if (nb_rows, nb_cols) != (nb_rhs, nb_vars):
self.fatal(
'Dimension error, matrix is ({0},{1}), expecting ({3}, {2})'.
format(nb_rows, nb_cols, nb_vars, nb_rhs))
if is_scipy_sparse(coef_mat):
return self._aggregator._sparse_matrix_constraints(
coef_mat, s_dvars, s_rhs, op)
else:
return self._aggregator._matrix_constraints(
coef_mat, s_dvars, s_rhs, op)
