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update python code, remove __future__ imports, remove six, use absl-py for flags, update examples

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This commit is contained in:
Laurent Perron
2020-11-18 10:50:14 +01:00
parent c1b23ffd92
commit 4ececbe448
99 changed files with 246 additions and 340 deletions
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58
examples/python/shift_scheduling_sat.py
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@@ -12,21 +12,17 @@
# limitations under the License.
"""Creates a shift scheduling problem and solves it."""
from __future__ import print_function
import argparse
from ortools.sat.python import cp_model
from google.protobuf import text_format
from absl import app
from absl import flags
PARSER = argparse.ArgumentParser()
PARSER.add_argument(
'--output_proto',
default="",
help='Output file to write the cp_model'
'proto to.')
PARSER.add_argument('--params', default="", help='Sat solver parameters.')
FLAGS = flags.FLAGS
flags.DEFINE_string('output_proto', '',
'Output file to write the cp_model proto to.')
flags.DEFINE_string('params', '', 'Sat solver parameters.')
def negated_bounded_span(works, start, length):
@@ -92,13 +88,13 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,
# Forbid sequences that are too short.
for length in range(1, hard_min):
for start in range(len(works) - length + 1):
for start in range(len(works) - length - 1):
model.AddBoolOr(negated_bounded_span(works, start, length))
# Penalize sequences that are below the soft limit.
if min_cost > 0:
for length in range(hard_min, soft_min):
for start in range(len(works) - length + 1):
for start in range(len(works) - length - 1):
span = negated_bounded_span(works, start, length)
name = ': under_span(start=%i, length=%i)' % (start, length)
lit = model.NewBoolVar(prefix + name)
@@ -112,7 +108,7 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,
# Penalize sequences that are above the soft limit.
if max_cost > 0:
for length in range(soft_max + 1, hard_max + 1):
for start in range(len(works) - length + 1):
for start in range(len(works) - length - 1):
span = negated_bounded_span(works, start, length)
name = ': over_span(start=%i, length=%i)' % (start, length)
lit = model.NewBoolVar(prefix + name)
@@ -123,7 +119,7 @@ def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,
cost_coefficients.append(max_cost * (length - soft_max))
# Just forbid any sequence of true variables with length hard_max + 1
for start in range(len(works) - hard_max):
for start in range(len(works) - hard_max - 1):
model.AddBoolOr(
[works[i].Not() for i in range(start, start + hard_max + 1)])
return cost_literals, cost_coefficients
@@ -221,7 +217,7 @@ def solve_shift_scheduling(params, output_proto):
(3, 0, 5, -2),
# Employee 5 wants a night shift on the second Thursday.
(5, 3, 10, -2),
# Employee 2 does not want a night shift on the first Friday.
# Employee 2 does not want a night shift on the third Friday.
(2, 3, 4, 4)
]
@@ -308,8 +304,8 @@ def solve_shift_scheduling(params, output_proto):
works = [work[e, shift, d] for d in range(num_days)]
variables, coeffs = add_soft_sequence_constraint(
model, works, hard_min, soft_min, min_cost, soft_max, hard_max,
max_cost, 'shift_constraint(employee %i, shift %i)' % (e,
shift))
max_cost,
'shift_constraint(employee %i, shift %i)' % (e, shift))
obj_bool_vars.extend(variables)
obj_bool_coeffs.extend(coeffs)
@@ -332,8 +328,8 @@ def solve_shift_scheduling(params, output_proto):
for e in range(num_employees):
for d in range(num_days - 1):
transition = [
work[e, previous_shift, d].Not(),
work[e, next_shift, d + 1].Not()
work[e, previous_shift, d].Not(), work[e, next_shift,
d + 1].Not()
]
if cost == 0:
model.AddBoolOr(transition)
@@ -367,9 +363,9 @@ def solve_shift_scheduling(params, output_proto):
# Objective
model.Minimize(
sum(obj_bool_vars[i] * obj_bool_coeffs[i]
for i in range(len(obj_bool_vars)))
+ sum(obj_int_vars[i] * obj_int_coeffs[i]
for i in range(len(obj_int_vars))))
for i in range(len(obj_bool_vars))) +
sum(obj_int_vars[i] * obj_int_coeffs[i]
for i in range(len(obj_int_vars))))
if output_proto:
print('Writing proto to %s' % output_proto)
@@ -378,9 +374,8 @@ def solve_shift_scheduling(params, output_proto):
# Solve the model.
solver = cp_model.CpSolver()
solver.parameters.num_search_workers = 8
if params:
text_format.Merge(params, solver.parameters)
text_format.Parse(params, solver.parameters)
solution_printer = cp_model.ObjectiveSolutionPrinter()
status = solver.SolveWithSolutionCallback(model, solution_printer)
@@ -414,13 +409,16 @@ def solve_shift_scheduling(params, output_proto):
(var.Name(), solver.Value(var), obj_int_coeffs[i]))
print()
print(solver.ResponseStats())
print('Statistics')
print(' - status : %s' % solver.StatusName(status))
print(' - conflicts : %i' % solver.NumConflicts())
print(' - branches : %i' % solver.NumBranches())
print(' - wall time : %f s' % solver.WallTime())
def main(args):
"""Main."""
solve_shift_scheduling(args.params, args.output_proto)
def main(_):
solve_shift_scheduling(FLAGS.params, FLAGS.output_proto)
if __name__ == '__main__':
main(PARSER.parse_args())
app.run(main)