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examples/python: cleanup

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This commit is contained in:
Corentin Le Molgat
2022-10-07 18:21:23 +02:00
parent 261ce61ae1
commit 4f647a0d61
8 changed files with 91 additions and 102 deletions
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44
examples/python/appointments.py
View File

@@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# [START program]
"""Appointment selection.
@@ -29,11 +30,12 @@ from ortools.linear_solver import pywraplp
from ortools.sat.python import cp_model
# [END import]
FLAGS = flags.FLAGS
flags.DEFINE_integer('load_min', 480, 'Minimum load in minutes.')
flags.DEFINE_integer('load_max', 540, 'Maximum load in minutes.')
flags.DEFINE_integer('commute_time', 30, 'Commute time in minutes.')
flags.DEFINE_integer('num_workers', 98, 'Maximum number of workers.')
_LOAD_MIN = flags.DEFINE_integer('load_min', 480, 'Minimum load in minutes.')
_LOAD_MAX = flags.DEFINE_integer('load_max', 540, 'Maximum load in minutes.')
_COMMUTE_TIME = flags.DEFINE_integer('commute_time', 30,
'Commute time in minutes.')
_NUM_WORKERS = flags.DEFINE_integer('num_workers', 98,
'Maximum number of workers.')
class AllSolutionCollector(cp_model.CpSolverSolutionCallback):
@@ -118,7 +120,7 @@ def AggregateItemCollectionsOptimally(item_collections, max_num_collections,
"""
solver = pywraplp.Solver.CreateSolver('SCIP')
if not solver:
return []
return []
n = len(ideal_item_ratios)
num_distinct_collections = len(item_collections)
max_num_items_per_collection = 0
@@ -191,13 +193,13 @@ def GetOptimalSchedule(demand):
The same output type as EnumerateAllKnapsacksWithRepetition.
"""
combinations = EnumerateAllKnapsacksWithRepetition(
[a[2] + FLAGS.commute_time for a in demand], FLAGS.load_min,
FLAGS.load_max)
[a[2] + _COMMUTE_TIME.value for a in demand], _LOAD_MIN.value,
_LOAD_MAX.value)
print(('Found %d possible day schedules ' % len(combinations) +
'(i.e. combination of appointments filling up one worker\'s day)'))
selection = AggregateItemCollectionsOptimally(
combinations, FLAGS.num_workers, [a[0] / 100.0 for a in demand])
combinations, _NUM_WORKERS.value, [a[0] / 100.0 for a in demand])
output = []
for i in range(len(selection)):
if selection[i] != 0:
@@ -208,16 +210,16 @@ def GetOptimalSchedule(demand):
return output
def solve_appointments(_):
def main(_):
demand = [(45.0, 'Type1', 90), (30.0, 'Type2', 120), (25.0, 'Type3', 180)]
print('*** input problem ***')
print('Appointments: ')
for a in demand:
print(' %.2f%% of %s : %d min' % (a[0], a[1], a[2]))
print('Commute time = %d' % FLAGS.commute_time)
print('Commute time = %d' % _COMMUTE_TIME.value)
print('Acceptable duration of a work day = [%d..%d]' %
(FLAGS.load_min, FLAGS.load_max))
print('%d workers' % FLAGS.num_workers)
(_LOAD_MIN.value, _LOAD_MAX.value))
print('%d workers' % _NUM_WORKERS.value)
selection = GetOptimalSchedule(demand)
print()
installed = 0
@@ -239,15 +241,17 @@ def solve_appointments(_):
print()
print('%d installations planned' % installed)
for a in demand:
name = a[1]
per_type = installed_per_type[name]
if installed != 0:
print(f' {per_type} ({per_type * 100.0 / installed}%) installations of type {name} planned')
else:
print(f' {per_type} installations of type {name} planned')
name = a[1]
per_type = installed_per_type[name]
if installed != 0:
print(
f' {per_type} ({per_type * 100.0 / installed}%) installations of type {name} planned'
)
else:
print(f' {per_type} installations of type {name} planned')
# [END print_solution]
if __name__ == '__main__':
app.run(solve_appointments)
app.run(main)
# [END program]

36
examples/python/bus_driver_scheduling_sat.py
View File

@@ -31,13 +31,13 @@ from absl import flags
from google.protobuf import text_format
from ortools.sat.python import cp_model
FLAGS = flags.FLAGS
flags.DEFINE_string('output_proto', '',
'Output file to write the cp_model proto to.')
flags.DEFINE_string('params', 'num_search_workers:16,log_search_progress:true',
'Sat solver parameters.')
flags.DEFINE_integer('instance', 1, 'Instance to select (1, 2, 3).', 1, 3)
_OUTPUT_PROTO = flags.DEFINE_string(
'output_proto', '', 'Output file to write the cp_model proto to.')
_PARAMS = flags.DEFINE_string('params',
'num_search_workers:8,log_search_progress:true',
'Sat solver parameters.')
_INSTANCE = flags.DEFINE_integer('instance', 1, 'Instance to select (1, 2, 3).',
1, 3)
SAMPLE_SHIFTS_SMALL = [
#
@@ -1688,11 +1688,11 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
The objective value of the model.
"""
shifts = None
if FLAGS.instance == 1:
if _INSTANCE.value == 1:
shifts = SAMPLE_SHIFTS_SMALL
elif FLAGS.instance == 2:
elif _INSTANCE.value == 2:
shifts = SAMPLE_SHIFTS_MEDIUM
elif FLAGS.instance == 3:
elif _INSTANCE.value == 3:
shifts = SAMPLE_SHIFTS_LARGE
num_shifts = len(shifts)
@@ -1933,15 +1933,15 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
model.Minimize(
cp_model.LinearExpr.WeightedSum(delay_literals, delay_weights))
if not minimize_drivers and FLAGS.output_proto:
print('Writing proto to %s' % FLAGS.output_proto)
with open(FLAGS.output_proto, 'w') as text_file:
if not minimize_drivers and _OUTPUT_PROTO.value:
print('Writing proto to %s' % _OUTPUT_PROTO.value)
with open(_OUTPUT_PROTO.value, 'w') as text_file:
text_file.write(str(model))
# Solve model.
solver = cp_model.CpSolver()
if FLAGS.params:
text_format.Parse(FLAGS.params, solver.parameters)
if _PARAMS.value:
text_format.Parse(_PARAMS.value, solver.parameters)
status = solver.Solve(model)
@@ -1978,7 +1978,7 @@ def bus_driver_scheduling(minimize_drivers, max_num_drivers):
return int(solver.ObjectiveValue())
def solve_bus_driver_scheduling():
def main(_):
"""Optimize the bus driver allocation in two passes."""
print('----------- first pass: minimize the number of drivers')
num_drivers = bus_driver_scheduling(True, -1)
@@ -1989,9 +1989,5 @@ def solve_bus_driver_scheduling():
bus_driver_scheduling(False, num_drivers)
def main(_=None):
solve_bus_driver_scheduling()
if __name__ == '__main__':
app.run(main)

2
examples/python/gate_scheduling_sat.py
View File

@@ -28,7 +28,7 @@ from ortools.sat.python import visualization
from ortools.sat.python import cp_model
def main(_=None):
def main(_):
"""Solves the gate scheduling problem."""
model = cp_model.CpModel()

2
examples/python/hidato_sat.py
View File

@@ -189,7 +189,7 @@ def solve_hidato(puzzle, index):
print(' - wall time : %f s' % solver.WallTime())
def main(_=None):
def main(_):
for pb in range(1, 7):
solve_hidato(build_puzzle(pb), pb)

55
examples/python/knapsack_2d_sat.py
View File

@@ -28,14 +28,13 @@ from google.protobuf import text_format
from ortools.sat.python import cp_model
FLAGS = flags.FLAGS
flags.DEFINE_string('output_proto', '',
'Output file to write the cp_model proto to.')
flags.DEFINE_string('params', 'num_search_workers:16,log_search_progress:true',
'Sat solver parameters.')
flags.DEFINE_string('model', 'rotation',
'\'duplicate\' or \'rotation\' or \'optional\'')
_OUTPUT_PROTO = flags.DEFINE_string(
'output_proto', '', 'Output file to write the cp_model proto to.')
_PARAMS = flags.DEFINE_string('params',
'num_search_workers:16,log_search_progress:true',
'Sat solver parameters.')
_MODEL = flags.DEFINE_string('model', 'rotation',
'\'duplicate\' or \'rotation\' or \'optional\'')
def build_data():
@@ -130,15 +129,15 @@ def solve_with_duplicate_items(data, max_height, max_width):
model.Maximize(cp_model.LinearExpr.WeightedSum(is_used, item_values))
# Output proto to file.
if FLAGS.output_proto:
print('Writing proto to %s' % FLAGS.output_proto)
with open(FLAGS.output_proto, 'w') as text_file:
if _OUTPUT_PROTO.value:
print('Writing proto to %s' % _OUTPUT_PROTO.value)
with open(_OUTPUT_PROTO.value, 'w') as text_file:
text_file.write(str(model))
# Solve model.
solver = cp_model.CpSolver()
if FLAGS.params:
text_format.Parse(FLAGS.params, solver.parameters)
if _PARAMS.value:
text_format.Parse(_PARAMS.value, solver.parameters)
status = solver.Solve(model)
@@ -220,15 +219,15 @@ def solve_with_duplicate_optional_items(data, max_height, max_width):
model.Maximize(cp_model.LinearExpr.WeightedSum(is_used, item_values))
# Output proto to file.
if FLAGS.output_proto:
print('Writing proto to %s' % FLAGS.output_proto)
with open(FLAGS.output_proto, 'w') as text_file:
if _OUTPUT_PROTO.value:
print('Writing proto to %s' % _OUTPUT_PROTO.value)
with open(_OUTPUT_PROTO.value, 'w') as text_file:
text_file.write(str(model))
# Solve model.
solver = cp_model.CpSolver()
if FLAGS.params:
text_format.Parse(FLAGS.params, solver.parameters)
if _PARAMS.value:
text_format.Parse(_PARAMS.value, solver.parameters)
status = solver.Solve(model)
@@ -333,15 +332,15 @@ def solve_with_rotations(data, max_height, max_width):
model.Maximize(cp_model.LinearExpr.WeightedSum(is_used, item_values))
# Output proto to file.
if FLAGS.output_proto:
print('Writing proto to %s' % FLAGS.output_proto)
with open(FLAGS.output_proto, 'w') as text_file:
if _OUTPUT_PROTO.value:
print('Writing proto to %s' % _OUTPUT_PROTO.value)
with open(_OUTPUT_PROTO.value, 'w') as text_file:
text_file.write(str(model))
# Solve model.
solver = cp_model.CpSolver()
if FLAGS.params:
text_format.Parse(FLAGS.params, solver.parameters)
if _PARAMS.value:
text_format.Parse(_PARAMS.value, solver.parameters)
status = solver.Solve(model)
@@ -360,20 +359,16 @@ def solve_with_rotations(data, max_height, max_width):
print(data)
def solve_knapsack(model):
def main(_):
"""Solve the problem with all models."""
data, max_height, max_width = build_data()
if model == 'duplicate':
if _MODEL.value == 'duplicate':
solve_with_duplicate_items(data, max_height, max_width)
elif model == 'optional':
elif _MODEL.value == 'optional':
solve_with_duplicate_optional_items(data, max_height, max_width)
else:
solve_with_rotations(data, max_height, max_width)
def main(_=None):
solve_knapsack(FLAGS.model)
if __name__ == '__main__':
app.run(main)

15
examples/python/line_balancing_sat.py
View File

@@ -35,15 +35,12 @@ from absl import flags
from google.protobuf import text_format
from ortools.sat.python import cp_model
FLAGS = flags.FLAGS
flags.DEFINE_string('input', '', 'Input file to parse and solve.')
flags.DEFINE_string('params', '', 'Sat solver parameters.')
flags.DEFINE_string('output_proto', '',
'Output file to write the cp_model proto to.')
flags.DEFINE_string('model', 'scheduling',
'Model used: boolean, scheduling, greedy')
_INPUT = flags.DEFINE_string('input', '', 'Input file to parse and solve.')
_PARAMS = flags.DEFINE_string('params', '', 'Sat solver parameters.')
_OUTPUT_PROTO = flags.DEFINE_string(
'output_proto', '', 'Output file to write the cp_model proto to.')
_MODEL = flags.DEFINE_string('model', 'boolean',
'Model used: boolean, scheduling, greedy')
# pytype: disable=wrong-arg-types

12
examples/python/shift_scheduling_sat.py
View File

@@ -19,12 +19,10 @@ from absl import flags
from ortools.sat.python import cp_model
from google.protobuf import text_format
FLAGS = flags.FLAGS
flags.DEFINE_string('output_proto', '',
'Output file to write the cp_model proto to.')
flags.DEFINE_string('params', 'max_time_in_seconds:10.0',
'Sat solver parameters.')
_OUTPUT_PROTO = flags.DEFINE_string(
'output_proto', '', 'Output file to write the cp_model proto to.')
_PARAMS = flags.DEFINE_string('params', 'max_time_in_seconds:10.0',
'Sat solver parameters.')
def negated_bounded_span(works, start, length):
@@ -421,7 +419,7 @@ def solve_shift_scheduling(params, output_proto):
def main(_):
solve_shift_scheduling(FLAGS.params, FLAGS.output_proto)
solve_shift_scheduling(_PARAMS.value, _OUTPUT_PROTO.value)
if __name__ == '__main__':

27
examples/python/steel_mill_slab_sat.py
View File

@@ -24,12 +24,10 @@ from absl import flags
from ortools.linear_solver import pywraplp
from ortools.sat.python import cp_model
FLAGS = flags.FLAGS
flags.DEFINE_integer('problem', 2, 'Problem id to solve.')
flags.DEFINE_bool('break_symmetries', True,
'Break symmetries between equivalent orders.')
flags.DEFINE_string(
_PROBLEM = flags.DEFINE_integer('problem', 2, 'Problem id to solve.')
_BREAK_SYMMETRIES = flags.DEFINE_boolean(
'break_symmetries', True, 'Break symmetries between equivalent orders.')
_SOLVER = flags.DEFINE_string(
'solver', 'mip_column', 'Method used to solve: sat, sat_table, sat_column, '
'mip_column.')
@@ -737,15 +735,16 @@ def steel_mill_slab_with_mip_column_generation(problem):
print('No solution')
def main(_=None):
if FLAGS.solver == 'sat':
steel_mill_slab(FLAGS.problem, FLAGS.break_symmetries)
elif FLAGS.solver == 'sat_table':
steel_mill_slab_with_valid_slabs(FLAGS.problem, FLAGS.break_symmetries)
elif FLAGS.solver == 'sat_column':
steel_mill_slab_with_column_generation(FLAGS.problem)
def main(_):
if _SOLVER.value == 'sat':
steel_mill_slab(_PROBLEM.value, _BREAK_SYMMETRIES.value)
elif _SOLVER.value == 'sat_table':
steel_mill_slab_with_valid_slabs(_PROBLEM.value,
_BREAK_SYMMETRIES.value)
elif _SOLVER.value == 'sat_column':
steel_mill_slab_with_column_generation(_PROBLEM.value)
else: # 'mip_column'
steel_mill_slab_with_mip_column_generation(FLAGS.problem)
steel_mill_slab_with_mip_column_generation(_PROBLEM.value)
if __name__ == '__main__':