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example: fix linter in chemical_balance

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This commit is contained in:
Corentin Le Molgat
2022-12-19 15:51:57 +01:00
parent 6f12dd7c39
commit d6965013c7
2 changed files with 86 additions and 68 deletions
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80
examples/python/chemical_balance_lp.py Normal file → Executable file
View File

@@ -1,3 +1,4 @@
#!/usr/bin/env python3
# Copyright 2010-2022 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,59 +12,68 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# We are trying to group items in equal sized groups.
# Each item has a color and a value. We want the sum of values of each group to
# be as close to the average as possible.
# Furthermore, if one color is an a group, at least k items with this color must
# be in that group.
'''
We are trying to group items in equal sized groups.
Each item has a color and a value. We want the sum of values of each group to
be as close to the average as possible.
Furthermore, if one color is an a group, at least k items with this color must
be in that group.
'''
from ortools.linear_solver import pywraplp
import math
# Data
max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
max_quantities = [
["N_Total", 1944],
["P2O5", 1166.4],
["K2O", 1822.5],
["CaO", 1458],
["MgO", 486],
["Fe", 9.7],
["B", 2.4],
]
chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
["C", 61, 149, 384, 0, 30, 1,
0.2], ["D", 148, 70, 245, 0, 15, 1,
0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
chemical_set = [
["A", 0, 0, 510, 540, 0, 0, 0],
["B", 110, 0, 0, 0, 160, 0, 0],
["C", 61, 149, 384, 0, 30, 1, 0.2],
["D", 148, 70, 245, 0, 15, 1, 0.2],
["E", 160, 158, 161, 0, 10, 1, 0.2],
]
num_products = len(max_quantities)
all_products = range(num_products)
NUM_PRODUCTS = len(max_quantities)
ALL_PRODUCTS = range(NUM_PRODUCTS)
num_sets = len(chemical_set)
all_sets = range(num_sets)
NUM_SETS = len(chemical_set)
ALL_SETS = range(NUM_SETS)
# Model
max_set = [
min(max_quantities[q][1] / chemical_set[s][q + 1] for q in all_products
if chemical_set[s][q + 1] != 0.0) for s in all_sets
min(max_quantities[q][1] / chemical_set[s][q + 1] for q in ALL_PRODUCTS
if chemical_set[s][q + 1] != 0.0) for s in ALL_SETS
]
solver = pywraplp.Solver("chemical_set_lp",
pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
set_vars = [solver.NumVar(0, max_set[s], "set_%i" % s) for s in all_sets]
set_vars = [solver.NumVar(0, max_set[s], f"set_{s}") for s in ALL_SETS]
epsilon = solver.NumVar(0, 1000, "epsilon")
for p in all_products:
for p in ALL_PRODUCTS:
solver.Add(
sum(chemical_set[s][p + 1] * set_vars[s]
for s in all_sets) <= max_quantities[p][1])
for s in ALL_SETS) <= max_quantities[p][1])
solver.Add(
sum(chemical_set[s][p + 1] * set_vars[s]
for s in all_sets) >= max_quantities[p][1] - epsilon)
for s in ALL_SETS) >= max_quantities[p][1] - epsilon)
solver.Minimize(epsilon)
print(("Number of variables = %d" % solver.NumVariables()))
print(("Number of constraints = %d" % solver.NumConstraints()))
print(f"Number of variables = {solver.NumVariables()}")
print(f"Number of constraints = {solver.NumConstraints()}")
result_status = solver.Solve()
@@ -72,19 +82,17 @@ assert result_status == pywraplp.Solver.OPTIMAL
assert solver.VerifySolution(1e-7, True)
print(("Problem solved in %f milliseconds" % solver.wall_time()))
print(f"Problem solved in {solver.wall_time()} milliseconds")
# The objective value of the solution.
print(("Optimal objective value = %f" % solver.Objective().Value()))
print(f"Optimal objective value = {solver.Objective().Value()}")
for s in all_sets:
print(
" %s = %f" % (chemical_set[s][0], set_vars[s].solution_value()),
end=" ")
for s in ALL_SETS:
print(f" {chemical_set[s][0]} = {set_vars[s].solution_value()}", end=" ")
print()
for p in all_products:
for p in ALL_PRODUCTS:
name = max_quantities[p][0]
max_quantity = max_quantities[p][1]
quantity = sum(
set_vars[s].solution_value() * chemical_set[s][p + 1] for s in all_sets)
print("%s: %f out of %f" % (name, quantity, max_quantity))
quantity = sum(set_vars[s].solution_value() * chemical_set[s][p + 1]
for s in ALL_SETS)
print(f"{name}: {quantity} out of {max_quantity}")

74
examples/python/chemical_balance_sat.py Normal file → Executable file
View File

@@ -1,3 +1,4 @@
#!/usr/bin/env python3
# Copyright 2010-2022 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -11,31 +12,42 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# We are trying to group items in equal sized groups.
# Each item has a color and a value. We want the sum of values of each group to
# be as close to the average as possible.
# Furthermore, if one color is an a group, at least k items with this color must
# be in that group.
'''
We are trying to group items in equal sized groups.
Each item has a color and a value. We want the sum of values of each group to
be as close to the average as possible.
Furthermore, if one color is an a group, at least k items with this color must
be in that group.
'''
from ortools.sat.python import cp_model
import math
from ortools.sat.python import cp_model
# Data
max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]
max_quantities = [
["N_Total", 1944],
["P2O5", 1166.4],
["K2O", 1822.5],
["CaO", 1458],
["MgO", 486],
["Fe", 9.7],
["B", 2.4],
]
chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
["C", 61, 149, 384, 0, 30, 1,
0.2], ["D", 148, 70, 245, 0, 15, 1,
0.2], ["E", 160, 158, 161, 0, 10, 1, 0.2]]
chemical_set = [
["A", 0, 0, 510, 540, 0, 0, 0],
["B", 110, 0, 0, 0, 160, 0, 0],
["C", 61, 149, 384, 0, 30, 1, 0.2],
["D", 148, 70, 245, 0, 15, 1, 0.2],
["E", 160, 158, 161, 0, 10, 1, 0.2],
]
num_products = len(max_quantities)
all_products = range(num_products)
NUM_PRODUCTS = len(max_quantities)
ALL_PRODUCTS = range(NUM_PRODUCTS)
num_sets = len(chemical_set)
all_sets = range(num_sets)
NUM_SETS = len(chemical_set)
ALL_SETS = range(NUM_SETS)
# Model
@@ -46,40 +58,38 @@ max_set = [
int(
math.ceil(
min(max_quantities[q][1] * 1000 / chemical_set[s][q + 1]
for q in all_products if chemical_set[s][q + 1] != 0)))
for s in all_sets
for q in ALL_PRODUCTS if chemical_set[s][q + 1] != 0)))
for s in ALL_SETS
]
set_vars = [model.NewIntVar(0, max_set[s], "set_%i" % s) for s in all_sets]
set_vars = [model.NewIntVar(0, max_set[s], f"set_{s}") for s in ALL_SETS]
epsilon = model.NewIntVar(0, 10000000, "epsilon")
for p in all_products:
for p in ALL_PRODUCTS:
model.Add(
sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
for s in all_sets) <= int(max_quantities[p][1] * 10000))
for s in ALL_SETS) <= int(max_quantities[p][1] * 10000))
model.Add(
sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
for s in all_sets) >= int(max_quantities[p][1] * 10000) - epsilon)
for s in ALL_SETS) >= int(max_quantities[p][1] * 10000) - epsilon)
model.Minimize(epsilon)
# Creates a solver and solves.
solver = cp_model.CpSolver()
status = solver.Solve(model)
print("Status = %s" % solver.StatusName(status))
print(f"Status = {solver.StatusName(status)}")
# The objective value of the solution.
print("Optimal objective value = %f" % (solver.ObjectiveValue() / 10000.0))
print(f"Optimal objective value = {solver.ObjectiveValue() / 10000.0}")
for s in all_sets:
print(
" %s = %f" % (chemical_set[s][0], solver.Value(set_vars[s]) / 1000.0),
end=" ")
for s in ALL_SETS:
print(f" {chemical_set[s][0]} = {solver.Value(set_vars[s]) / 1000.0}", end=" ")
print()
for p in all_products:
for p in ALL_PRODUCTS:
name = max_quantities[p][0]
max_quantity = max_quantities[p][1]
quantity = sum(
solver.Value(set_vars[s]) / 1000.0 * chemical_set[s][p + 1]
for s in all_sets)
print("%s: %f out of %f" % (name, quantity, max_quantity))
for s in ALL_SETS)
print(f"{name}: {quantity} out of {max_quantity}")