├── .gitignore
├── README.md
├── diet-ml
├── diet-model.gz
├── solution.csv
├── diet_nutrients.csv
├── kpis.csv
├── stats.csv
├── diet_food.csv
├── diet_food_nutrients.csv
├── model.py
├── ml-deploy.py
├── ml-submit.py
└── main.py
├── warehouse-do
├── warehouse.pdf
├── warehouses.csv
├── supplyCosts.csv
└── warehouse_ml.mod
├── warehouse-org
├── warehouse.pdf
├── .project
├── .oplproject
├── warehouse_data.mod
├── warehouse_cloud.dat
└── warehouse_cloud.mod
├── warehouse-python-batch
├── mk-zip.sh
├── warehouses.csv
├── warehouse-model.tar.gz
├── submit-log.txt
├── supplyCosts.csv
├── deploy-log.txt
├── ml-deploy.py
├── warehouse_ml.mod
└── ml-submit.py
├── warehouse-excel
├── warehouse_data.xlsx
└── warehouse_ml.mod
├── mp-sample
├── solution.csv
├── diet_nutrients.csv
├── kpis.csv
├── diet_food.csv
├── diet_food_nutrients.csv
├── solution.json
├── model.py
└── main.py
├── diet-python
├── diet_nutrients.csv
├── diet_food.csv
├── diet_food_nutrients.csv
├── model.py
└── main.py
├── house-building
├── house-building1.ipynb
└── house-building3.ipynb
└── LICENSE
/.gitignore:
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1 | /local
2 |
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/README.md:
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1 | # cplex-samples
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/diet-ml/diet-model.gz:
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https://raw.githubusercontent.com/makaishi2/cplex-samples/master/diet-ml/diet-model.gz
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/warehouse-do/warehouse.pdf:
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https://raw.githubusercontent.com/makaishi2/cplex-samples/master/warehouse-do/warehouse.pdf
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/warehouse-org/warehouse.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/makaishi2/cplex-samples/master/warehouse-org/warehouse.pdf
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/warehouse-python-batch/mk-zip.sh:
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1 | export COPYFILE_DISABLE=1
2 | tar czvf warehouse-model.tar.gz warehouse_ml.mod
3 |
4 |
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/warehouse-do/warehouses.csv:
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1 | name,capacity,fixedCost
2 | Bonn,1,30
3 | Bordeaux,4,30
4 | London,2,30
5 | Paris,1,30
6 | Rome,3,30
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/warehouse-excel/warehouse_data.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/makaishi2/cplex-samples/master/warehouse-excel/warehouse_data.xlsx
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/warehouse-python-batch/warehouses.csv:
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1 | name,capacity,fixedCost
2 | Bonn,1,30
3 | Bordeaux,4,30
4 | London,2,30
5 | Paris,1,30
6 | Rome,3,30
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/warehouse-python-batch/warehouse-model.tar.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/makaishi2/cplex-samples/master/warehouse-python-batch/warehouse-model.tar.gz
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/diet-ml/solution.csv:
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1 | name,value
2 | Spaghetti W/ Sauce,2.1551724137931036
3 | Chocolate Chip Cookies,10.0
4 | Lowfat Milk,1.8311671008899097
5 | Hotdog,0.9296975991385925
6 |
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/mp-sample/solution.csv:
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1 | name,value
2 | Spaghetti W/ Sauce,2.1551724137931036
3 | Chocolate Chip Cookies,10.0
4 | Lowfat Milk,1.8311671008899097
5 | Hotdog,0.9296975991385925
6 |
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/diet-ml/diet_nutrients.csv:
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1 | name,qmin,qmax
2 | Calories," 2000"," 2500"
3 | Calcium," 800"," 1600"
4 | Iron," 10"," 30"
5 | Vit_A," 5000"," 50000"
6 | Dietary_Fiber," 25"," 100"
7 | Carbohydrates," 0"," 300"
8 | Protein," 50"," 100"
9 |
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/diet-python/diet_nutrients.csv:
--------------------------------------------------------------------------------
1 | name,qmin,qmax
2 | Calories," 2000"," 2500"
3 | Calcium," 800"," 1600"
4 | Iron," 10"," 30"
5 | Vit_A," 5000"," 50000"
6 | Dietary_Fiber," 25"," 100"
7 | Carbohydrates," 0"," 300"
8 | Protein," 50"," 100"
9 |
--------------------------------------------------------------------------------
/mp-sample/diet_nutrients.csv:
--------------------------------------------------------------------------------
1 | name,qmin,qmax
2 | Calories," 2000"," 2500"
3 | Calcium," 800"," 1600"
4 | Iron," 10"," 30"
5 | Vit_A," 5000"," 50000"
6 | Dietary_Fiber," 25"," 100"
7 | Carbohydrates," 0"," 300"
8 | Protein," 50"," 100"
9 |
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/diet-ml/kpis.csv:
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1 | Name,Value
2 | Total Calories,2000.0
3 | Total Calcium,800.0000000000001
4 | Total Iron,11.278317739831891
5 | Total Vit_A,8518.432542485823
6 | Total Dietary_Fiber,25.0
7 | Total Carbohydrates,256.80576358904455
8 | Total Protein,51.17372234135308
9 | Minimal cost,2.690409171696264
10 |
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/diet-ml/stats.csv:
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1 | Name,Value
2 | STAT.cplex.size.integerVariables,0
3 | STAT.cplex.size.continousVariables,9
4 | STAT.cplex.size.linearConstraints,7
5 | STAT.cplex.size.booleanVariables,0
6 | STAT.cplex.size.constraints,7
7 | STAT.cplex.size.quadraticConstraints,0
8 | STAT.cplex.size.variables,9
9 | STAT.cplex.modelType,LP
10 |
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/mp-sample/kpis.csv:
--------------------------------------------------------------------------------
1 | "NAME","VALUE"
2 | "Total Calories","2000.0"
3 | "Total Calcium","800.0000000000001"
4 | "Total Iron","11.278317739831891"
5 | "Total Vit_A","8518.432542485823"
6 | "Total Dietary_Fiber","25.0"
7 | "Total Carbohydrates","256.80576358904455"
8 | "Total Protein","51.17372234135308"
9 | "Minimal cost","2.690409171696264"
10 |
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/warehouse-org/.project:
--------------------------------------------------------------------------------
1 |
2 |
3 | warehouse_cloud
4 |
5 |
6 |
7 |
8 |
9 |
10 | ilog.odms.ide.core.opl.project.nature
11 |
12 |
13 |
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/warehouse-org/.oplproject:
--------------------------------------------------------------------------------
1 |
2 | 1.0
3 |
4 |
5 | [
6 | ]
7 | [
8 | ]
9 |
10 |
11 |
12 |
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/diet-ml/diet_food.csv:
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1 | name,unit_cost,qmin,qmax
2 | Roasted Chicken," 0.84"," 0"," 10"
3 | Spaghetti W/ Sauce," 0.78"," 0"," 10"
4 | "Tomato,Red,Ripe,Raw"," 0.27"," 0"," 10"
5 | "Apple,Raw,W/Skin"," .24"," 0"," 10"
6 | Grapes," 0.32"," 0"," 10"
7 | Chocolate Chip Cookies," 0.03"," 0"," 10"
8 | Lowfat Milk," 0.23"," 0"," 10"
9 | Raisin Brn," 0.34"," 0"," 10"
10 | Hotdog," 0.31"," 0"," 10"
11 |
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/diet-python/diet_food.csv:
--------------------------------------------------------------------------------
1 | name,unit_cost,qmin,qmax
2 | Roasted Chicken," 0.84"," 0"," 10"
3 | Spaghetti W/ Sauce," 0.78"," 0"," 10"
4 | "Tomato,Red,Ripe,Raw"," 0.27"," 0"," 10"
5 | "Apple,Raw,W/Skin"," .24"," 0"," 10"
6 | Grapes," 0.32"," 0"," 10"
7 | Chocolate Chip Cookies," 0.03"," 0"," 10"
8 | Lowfat Milk," 0.23"," 0"," 10"
9 | Raisin Brn," 0.34"," 0"," 10"
10 | Hotdog," 0.31"," 0"," 10"
11 |
--------------------------------------------------------------------------------
/mp-sample/diet_food.csv:
--------------------------------------------------------------------------------
1 | name,unit_cost,qmin,qmax
2 | Roasted Chicken," 0.84"," 0"," 10"
3 | Spaghetti W/ Sauce," 0.78"," 0"," 10"
4 | "Tomato,Red,Ripe,Raw"," 0.27"," 0"," 10"
5 | "Apple,Raw,W/Skin"," .24"," 0"," 10"
6 | Grapes," 0.32"," 0"," 10"
7 | Chocolate Chip Cookies," 0.03"," 0"," 10"
8 | Lowfat Milk," 0.23"," 0"," 10"
9 | Raisin Brn," 0.34"," 0"," 10"
10 | Hotdog," 0.31"," 0"," 10"
11 |
--------------------------------------------------------------------------------
/warehouse-org/warehouse_data.mod:
--------------------------------------------------------------------------------
1 |
2 | tuple TWarehouse {
3 | key string name;
4 | int capacity;
5 | float fixedCost;
6 | }
7 |
8 | tuple TSupplyCost {
9 | key string warehouseName;
10 | key int storeId;
11 | float cost;
12 | }
13 |
14 | tuple TPlan {
15 | int nbStores;
16 | }
17 |
18 | TPlan plan = ...;
19 | {TWarehouse} warehouses = ...;
20 | {TSupplyCost} supplyCosts = ...;
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/warehouse-python-batch/submit-log.txt:
--------------------------------------------------------------------------------
1 | $ python ml-submit.py
2 | 401c79b4-c4b5-4ad9-b1b3-9f6b013fbeee
3 | queued...
4 | queued...
5 | queued...
6 | queued...
7 | queued...
8 | {
9 | "completed_at": "2020-07-21T04:12:41.537Z",
10 | "running_at": "2020-07-21T04:12:40.449Z",
11 | "state": "completed"
12 | }
13 | warehouseName storeId
14 | 0 Bonn 4
15 | 1 Bordeaux 2
16 | 2 Bordeaux 6
17 | 3 Bordeaux 7
18 | 4 Bordeaux 9
19 | 5 London 8
20 | 6 London 10
21 | 7 Rome 1
22 | 8 Rome 3
23 | 9 Rome 5
24 |
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/diet-ml/diet_food_nutrients.csv:
--------------------------------------------------------------------------------
1 | Food,Calories,Calcium,Iron,Vit_A,Dietary_Fiber,Carbohydrates,Protein
2 | Roasted Chicken," 277.4"," 21.9"," 1.8"," 77.4"," 0"," 0"," 42.2"
3 | Spaghetti W/ Sauce," 358.2"," 80.2"," 2.3"," 3055.2"," 11.6"," 58.3"," 8.2"
4 | "Tomato,Red,Ripe,Raw"," 25.8"," 6.2"," 0.6"," 766.3"," 1.4"," 5.7"," 1"
5 | "Apple,Raw,W/Skin"," 81.4"," 9.7"," 0.2"," 73.1"," 3.7"," 21"," 0.3"
6 | Grapes," 15.1"," 3.4"," 0.1"," 24"," 0.2"," 4.1"," 0.2"
7 | Chocolate Chip Cookies," 78.1"," 6.2"," 0.4"," 101.8"," 0"," 9.3"," 0.9"
8 | Lowfat Milk," 121.2"," 296.7"," 0.1"," 500.2"," 0"," 11.7"," 8.1"
9 | Raisin Brn," 115.1"," 12.9"," 16.8"," 1250.2"," 4"," 27.9"," 4"
10 | Hotdog," 242.1"," 23.5"," 2.3"," 0"," 0"," 18"," 10.4"
11 |
--------------------------------------------------------------------------------
/diet-python/diet_food_nutrients.csv:
--------------------------------------------------------------------------------
1 | Food,Calories,Calcium,Iron,Vit_A,Dietary_Fiber,Carbohydrates,Protein
2 | Roasted Chicken," 277.4"," 21.9"," 1.8"," 77.4"," 0"," 0"," 42.2"
3 | Spaghetti W/ Sauce," 358.2"," 80.2"," 2.3"," 3055.2"," 11.6"," 58.3"," 8.2"
4 | "Tomato,Red,Ripe,Raw"," 25.8"," 6.2"," 0.6"," 766.3"," 1.4"," 5.7"," 1"
5 | "Apple,Raw,W/Skin"," 81.4"," 9.7"," 0.2"," 73.1"," 3.7"," 21"," 0.3"
6 | Grapes," 15.1"," 3.4"," 0.1"," 24"," 0.2"," 4.1"," 0.2"
7 | Chocolate Chip Cookies," 78.1"," 6.2"," 0.4"," 101.8"," 0"," 9.3"," 0.9"
8 | Lowfat Milk," 121.2"," 296.7"," 0.1"," 500.2"," 0"," 11.7"," 8.1"
9 | Raisin Brn," 115.1"," 12.9"," 16.8"," 1250.2"," 4"," 27.9"," 4"
10 | Hotdog," 242.1"," 23.5"," 2.3"," 0"," 0"," 18"," 10.4"
11 |
--------------------------------------------------------------------------------
/mp-sample/diet_food_nutrients.csv:
--------------------------------------------------------------------------------
1 | Food,Calories,Calcium,Iron,Vit_A,Dietary_Fiber,Carbohydrates,Protein
2 | Roasted Chicken," 277.4"," 21.9"," 1.8"," 77.4"," 0"," 0"," 42.2"
3 | Spaghetti W/ Sauce," 358.2"," 80.2"," 2.3"," 3055.2"," 11.6"," 58.3"," 8.2"
4 | "Tomato,Red,Ripe,Raw"," 25.8"," 6.2"," 0.6"," 766.3"," 1.4"," 5.7"," 1"
5 | "Apple,Raw,W/Skin"," 81.4"," 9.7"," 0.2"," 73.1"," 3.7"," 21"," 0.3"
6 | Grapes," 15.1"," 3.4"," 0.1"," 24"," 0.2"," 4.1"," 0.2"
7 | Chocolate Chip Cookies," 78.1"," 6.2"," 0.4"," 101.8"," 0"," 9.3"," 0.9"
8 | Lowfat Milk," 121.2"," 296.7"," 0.1"," 500.2"," 0"," 11.7"," 8.1"
9 | Raisin Brn," 115.1"," 12.9"," 16.8"," 1250.2"," 4"," 27.9"," 4"
10 | Hotdog," 242.1"," 23.5"," 2.3"," 0"," 0"," 18"," 10.4"
11 |
--------------------------------------------------------------------------------
/warehouse-do/supplyCosts.csv:
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1 | warehouseName,storeId,cost
2 | Bonn,1,20
3 | Bonn,2,28
4 | Bonn,3,74
5 | Bonn,4,2
6 | Bonn,5,46
7 | Bonn,6,42
8 | Bonn,7,1
9 | Bonn,8,10
10 | Bonn,9,93
11 | Bonn,10,47
12 | Bordeaux,1,24
13 | Bordeaux,2,27
14 | Bordeaux,3,97
15 | Bordeaux,4,55
16 | Bordeaux,5,96
17 | Bordeaux,6,22
18 | Bordeaux,7,5
19 | Bordeaux,8,73
20 | Bordeaux,9,35
21 | Bordeaux,10,65
22 | London,1,11
23 | London,2,82
24 | London,3,71
25 | London,4,73
26 | London,5,59
27 | London,6,29
28 | London,7,73
29 | London,8,13
30 | London,9,63
31 | London,10,55
32 | Paris,1,25
33 | Paris,2,83
34 | Paris,3,96
35 | Paris,4,69
36 | Paris,5,83
37 | Paris,6,87
38 | Paris,7,59
39 | Paris,8,43
40 | Paris,9,85
41 | Paris,10,71
42 | Rome,1,30
43 | Rome,2,74
44 | Rome,3,70
45 | Rome,4,61
46 | Rome,5,4
47 | Rome,6,59
48 | Rome,7,56
49 | Rome,8,96
50 | Rome,9,46
51 | Rome,10,95
--------------------------------------------------------------------------------
/warehouse-python-batch/supplyCosts.csv:
--------------------------------------------------------------------------------
1 | warehouseName,storeId,cost
2 | Bonn,1,20
3 | Bonn,2,28
4 | Bonn,3,74
5 | Bonn,4,2
6 | Bonn,5,46
7 | Bonn,6,42
8 | Bonn,7,1
9 | Bonn,8,10
10 | Bonn,9,93
11 | Bonn,10,47
12 | Bordeaux,1,24
13 | Bordeaux,2,27
14 | Bordeaux,3,97
15 | Bordeaux,4,55
16 | Bordeaux,5,96
17 | Bordeaux,6,22
18 | Bordeaux,7,5
19 | Bordeaux,8,73
20 | Bordeaux,9,35
21 | Bordeaux,10,65
22 | London,1,11
23 | London,2,82
24 | London,3,71
25 | London,4,73
26 | London,5,59
27 | London,6,29
28 | London,7,73
29 | London,8,13
30 | London,9,63
31 | London,10,55
32 | Paris,1,25
33 | Paris,2,83
34 | Paris,3,96
35 | Paris,4,69
36 | Paris,5,83
37 | Paris,6,87
38 | Paris,7,59
39 | Paris,8,43
40 | Paris,9,85
41 | Paris,10,71
42 | Rome,1,30
43 | Rome,2,74
44 | Rome,3,70
45 | Rome,4,61
46 | Rome,5,4
47 | Rome,6,59
48 | Rome,7,56
49 | Rome,8,96
50 | Rome,9,46
51 | Rome,10,95
--------------------------------------------------------------------------------
/mp-sample/solution.json:
--------------------------------------------------------------------------------
1 | {"CPLEXSolution": {"version": "1.0", "header": {"problemName": "diet", "objectiveValue": "2.6904091716962637", "solved_by": "cplex_local"}, "variables": [{"index": "1", "name": "Spaghetti W/ Sauce", "value": "2.1551724137931036", "reducedCost": 0}, {"index": "5", "name": "Chocolate Chip Cookies", "value": "10.0", "reducedCost": -0.06964156898661943}, {"index": "6", "name": "Lowfat Milk", "value": "1.8311671008899097", "reducedCost": 0}, {"index": "8", "name": "Hotdog", "value": "0.9296975991385925", "reducedCost": 0}], "linearConstraints": [{"name": null, "index": 0, "slack": -500.0, "dual": 0.0012549782286529975}, {"name": null, "index": 1, "slack": -800.0, "dual": 0.0002625434401323112}, {"name": null, "index": 2, "slack": -18.721682260168105}, {"name": null, "index": 3, "slack": -41481.56745751418}, {"name": null, "index": 4, "slack": -75.0, "dual": 0.026673346086024577}, {"name": null, "index": 5, "slack": -43.194236410955455}, {"name": null, "index": 6, "slack": -48.82627765864693}]}}
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/diet-ml/model.py:
--------------------------------------------------------------------------------
1 | #dd-cell
2 | food = inputs['diet_food']
3 | nutrients = inputs['diet_nutrients']
4 | food_nutrients = inputs['diet_food_nutrients']
5 | food_nutrients.set_index('Food', inplace=True)
6 | #dd-cell
7 | from docplex.mp.model import Model
8 |
9 | # Model
10 | mdl = Model(name='diet')
11 |
12 | # Create decision variables, limited to be >= Food.qmin and <= Food.qmax
13 | qty = food[['name', 'qmin', 'qmax']].copy()
14 | qty['var'] = qty.apply(lambda x: mdl.continuous_var(lb=x['qmin'],
15 | ub=x['qmax'],
16 | name=x['name']),
17 | axis=1)
18 | # make the name the index
19 | qty.set_index('name', inplace=True)
20 |
21 | # Limit range of nutrients, and mark them as KPIs
22 | for n in nutrients.itertuples():
23 | amount = mdl.sum(qty.loc[f.name]['var'] * food_nutrients.loc[f.name][n.name]
24 | for f in food.itertuples())
25 | mdl.add_range(n.qmin, amount, n.qmax)
26 | mdl.add_kpi(amount, publish_name='Total %s' % n.name)
27 |
28 | # Minimize cost
29 | obj = mdl.sum(qty.loc[f.name]['var'] * f.unit_cost for f in food.itertuples())
30 | mdl.add_kpi(obj, publish_name="Minimal cost");
31 | mdl.minimize(obj)
32 |
33 | mdl.print_information()
34 | #dd-markdown Solve
35 | #dd-cell
36 | ok = mdl.solve()
37 | #dd-cell
38 | mdl.print_solution()
39 | #dd-markdown Make dataframe from solution
40 | #dd-cell
41 | import pandas
42 | import numpy
43 |
44 | solution_df = pandas.DataFrame(columns=['name', 'value'])
45 |
46 | for index, dvar in enumerate(mdl.solution.iter_variables()):
47 | solution_df.loc[index,'name'] = dvar.to_string()
48 | solution_df.loc[index,'value'] = dvar.solution_value
49 | #dd-cell
50 | solution_df
51 | #dd-cell
52 | outputs['solution'] = solution_df
53 |
--------------------------------------------------------------------------------
/mp-sample/model.py:
--------------------------------------------------------------------------------
1 | #dd-cell
2 | food = inputs['diet_food']
3 | nutrients = inputs['diet_nutrients']
4 | food_nutrients = inputs['diet_food_nutrients']
5 | food_nutrients.set_index('Food', inplace=True)
6 | #dd-cell
7 | from docplex.mp.model import Model
8 |
9 | # Model
10 | mdl = Model(name='diet')
11 |
12 | # Create decision variables, limited to be >= Food.qmin and <= Food.qmax
13 | qty = food[['name', 'qmin', 'qmax']].copy()
14 | qty['var'] = qty.apply(lambda x: mdl.continuous_var(lb=x['qmin'],
15 | ub=x['qmax'],
16 | name=x['name']),
17 | axis=1)
18 | # make the name the index
19 | qty.set_index('name', inplace=True)
20 |
21 | # Limit range of nutrients, and mark them as KPIs
22 | for n in nutrients.itertuples():
23 | amount = mdl.sum(qty.loc[f.name]['var'] * food_nutrients.loc[f.name][n.name]
24 | for f in food.itertuples())
25 | mdl.add_range(n.qmin, amount, n.qmax)
26 | mdl.add_kpi(amount, publish_name='Total %s' % n.name)
27 |
28 | # Minimize cost
29 | obj = mdl.sum(qty.loc[f.name]['var'] * f.unit_cost for f in food.itertuples())
30 | mdl.add_kpi(obj, publish_name="Minimal cost");
31 | mdl.minimize(obj)
32 |
33 | mdl.print_information()
34 | #dd-markdown Solve
35 | #dd-cell
36 | ok = mdl.solve()
37 | #dd-cell
38 | mdl.print_solution()
39 | #dd-markdown Make dataframe from solution
40 | #dd-cell
41 | import pandas
42 | import numpy
43 |
44 | solution_df = pandas.DataFrame(columns=['name', 'value'])
45 |
46 | for index, dvar in enumerate(mdl.solution.iter_variables()):
47 | solution_df.loc[index,'name'] = dvar.to_string()
48 | solution_df.loc[index,'value'] = dvar.solution_value
49 | #dd-cell
50 | solution_df
51 | #dd-cell
52 | outputs['solution'] = solution_df
53 |
--------------------------------------------------------------------------------
/diet-python/model.py:
--------------------------------------------------------------------------------
1 | #dd-cell
2 | food = inputs['diet_food']
3 | nutrients = inputs['diet_nutrients']
4 | food_nutrients = inputs['diet_food_nutrients']
5 | food_nutrients.set_index('Food', inplace=True)
6 | #dd-cell
7 | from docplex.mp.model import Model
8 |
9 | # Model
10 | mdl = Model(name='diet')
11 |
12 | # Create decision variables, limited to be >= Food.qmin and <= Food.qmax
13 | qty = food[['name', 'qmin', 'qmax']].copy()
14 | qty['var'] = qty.apply(lambda x: mdl.continuous_var(lb=x['qmin'],
15 | ub=x['qmax'],
16 | name=x['name']),
17 | axis=1)
18 | # make the name the index
19 | qty.set_index('name', inplace=True)
20 |
21 | # Limit range of nutrients, and mark them as KPIs
22 | for n in nutrients.itertuples():
23 | amount = mdl.sum(qty.loc[f.name]['var'] * food_nutrients.loc[f.name][n.name]
24 | for f in food.itertuples())
25 | mdl.add_range(n.qmin, amount, n.qmax)
26 | mdl.add_kpi(amount, publish_name='Total %s' % n.name)
27 |
28 | # Minimize cost
29 | obj = mdl.sum(qty.loc[f.name]['var'] * f.unit_cost for f in food.itertuples())
30 | mdl.add_kpi(obj, publish_name="Minimal cost");
31 | mdl.minimize(obj)
32 |
33 | mdl.print_information()
34 | #dd-markdown Solve
35 | #dd-cell
36 | ok = mdl.solve()
37 | #dd-cell
38 | mdl.print_solution()
39 | #dd-markdown Make dataframe from solution
40 | #dd-cell
41 | import pandas
42 | import numpy
43 |
44 | solution_df = pandas.DataFrame(columns=['name', 'value'])
45 |
46 | for index, dvar in enumerate(mdl.solution.iter_variables()):
47 | solution_df.loc[index,'name'] = dvar.to_string()
48 | solution_df.loc[index,'value'] = dvar.solution_value
49 | #dd-cell
50 | solution_df
51 | #dd-cell
52 | outputs['solution'] = solution_df
53 |
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/warehouse-org/warehouse_cloud.dat:
--------------------------------------------------------------------------------
1 | // --------------------------------------------------------------------------
2 | // Licensed Materials - Property of IBM
3 | //
4 | // 5725-A06 5725-A29 5724-Y48 5724-Y49 5724-Y54 5724-Y55
5 | // Copyright IBM Corporation 1998, 2013. All Rights Reserved.
6 | //
7 | // Note to U.S. Government Users Restricted Rights:
8 | // Use, duplication or disclosure restricted by GSA ADP Schedule
9 | // Contract with IBM Corp.
10 | // --------------------------------------------------------------------------
11 |
12 | // 10 stores to be opened.
13 | plan = <10>;
14 |
15 | warehouses =
16 | {
17 |
18 | ,
19 | ,
20 | ,
21 | ,
22 | };
23 |
24 | supplyCosts =
25 | {
26 |
27 | ,
28 | ,
29 | ,
30 | ,
31 | ,
32 | ,
33 | ,
34 | ,
35 | ,
36 | //
37 | ,
38 | ,
39 | ,
40 | ,
41 | ,
42 | ,
43 | ,
44 | ,
45 | ,
46 | ,
47 | //
48 | ,
49 | ,
50 | ,
51 | ,
52 | ,
53 | ,
54 | ,
55 | ,
56 | ,
57 | ,
58 | //
59 | ,
60 | ,
61 | ,
62 | ,
63 | ,
64 | ,
65 | ,
66 | ,
67 | ,
68 | ,
69 | //
70 | ,
71 | ,
72 | ,
73 | ,
74 | ,
75 | ,
76 | ,
77 | ,
78 | ,
79 | ,
80 |
81 | };
82 |
--------------------------------------------------------------------------------
/warehouse-org/warehouse_cloud.mod:
--------------------------------------------------------------------------------
1 | // --------------------------------------------------------------------------
2 | // Licensed Materials - Property of IBM
3 | //
4 | // 5725-A06 5725-A29 5724-Y48 5724-Y49 5724-Y54 5724-Y55
5 | // Copyright IBM Corporation 1998, 2013. All Rights Reserved.
6 | //
7 | // Note to U.S. Government Users Restricted Rights:
8 | // Use, duplication or disclosure restricted by GSA ADP Schedule
9 | // Contract with IBM Corp.
10 | // --------------------------------------------------------------------------
11 |
12 | include "warehouse_data.mod";
13 |
14 |
15 | range stores = 1..plan.nbStores;
16 |
17 | dvar boolean Open[ warehouses ];
18 | dvar boolean Supply[ stores ][ warehouses ];
19 |
20 | // expression
21 | dexpr float totalOpeningCost = sum( w in warehouses ) w.fixedCost * Open[w];
22 | dexpr float totalSupplyCost = sum( w in warehouses , s in stores, k in supplyCosts : k.storeId == s && k.warehouseName == w.name )
23 | Supply[s][w] * k.cost;
24 |
25 | minimize
26 | totalOpeningCost + totalSupplyCost;
27 |
28 | subject to {
29 | forall( s in stores )
30 | ctEachStoreHasOneWarehouse:
31 | sum( w in warehouses ) Supply[s][w] == 1;
32 |
33 | forall( w in warehouses, s in stores )
34 | ctUseOpenWarehouses:
35 | Supply[s][w] <= Open[w];
36 |
37 | forall( w in warehouses )
38 | ctMaxUseOfWarehouse:
39 | sum( s in stores) Supply[s][w] <= w.capacity;
40 | }
41 |
42 |
43 | {int} StoresSupplied[w in warehouses] = { s | s in stores : Supply[s][w] == 1 };
44 | {string} OpenWarehouses = { w.name | w in warehouses : Open[w] == 1 };
45 | tuple TSuppliedStore {
46 | string warehouseName;
47 | int storeId;
48 | }
49 | {TSuppliedStore} network;
50 |
51 | execute DISPLAY_RESULTS{
52 | network.clear();
53 | writeln("* Open Warehouses=", OpenWarehouses);
54 | for ( var w in warehouses) {
55 | if ( Open[w] ==1) {
56 | writeln("* stores supplied by ", w.name, ": ", StoresSupplied[w]);
57 | for (var s in stores) {
58 | if (Supply[s][w] == 1) {
59 | network.addOnly(w.name, s);
60 | }
61 | }
62 | }
63 | }
64 | }
65 |
--------------------------------------------------------------------------------
/warehouse-python-batch/deploy-log.txt:
--------------------------------------------------------------------------------
1 | $ python ml-deploy.py
2 | List Models
3 | ------------------------------------ ------------------------------------- ------------------------ ----------------
4 | GUID NAME CREATED TYPE
5 | b862c975-dc26-4b0a-94d5-06f099fb5a51 Auto generated DO opl model 2020-07-19T06:59:38.305Z do-opl_12.10
6 | ------------------------------------ ------------------------------------- ------------------------ ----------------
7 | List Deployments
8 | ------------------------------------ ------------------------------------------ ----- ------------------------ -------------
9 | GUID NAME STATE CREATED ARTIFACT_TYPE
10 | 00616f70-64b0-46fb-97f1-3284bc8a6640 Auto generated DO opl deployment ready 2020-07-19T06:59:38.400Z model
11 | ------------------------------------ ------------------------------------------ ----- ------------------------ -------------
12 | 211f73af-6a7c-445c-a5b8-ffa526fd703f
13 |
14 |
15 | #######################################################################################
16 |
17 | Synchronous deployment creation for uid: '211f73af-6a7c-445c-a5b8-ffa526fd703f' started
18 |
19 | #######################################################################################
20 |
21 |
22 | ready.
23 |
24 |
25 | ------------------------------------------------------------------------------------------------
26 | Successfully finished deployment creation, deployment_uid='cf184900-bce6-4737-bfdb-ec8902057f35'
27 | ------------------------------------------------------------------------------------------------
28 |
29 |
30 | cf184900-bce6-4737-bfdb-ec8902057f35
31 | ------------------------------------ ------------------------------------------ ----- ------------------------ -------------
32 | GUID NAME STATE CREATED ARTIFACT_TYPE
33 | cf184900-bce6-4737-bfdb-ec8902057f35 WAREHOUSE OPL Deployment ready 2020-07-21T04:06:42.233Z model
34 | 00616f70-64b0-46fb-97f1-3284bc8a6640 Auto generated DO opl deployment ready 2020-07-19T06:59:38.400Z model
35 | ------------------------------------ ------------------------------------------ ----- ------------------------ -------------
36 |
--------------------------------------------------------------------------------
/warehouse-python-batch/ml-deploy.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | # コマンドによる事前準備
4 | # $ pip install watson-machine-learning-client-V4
5 | # $ MACでは次が重要
6 | # $ export COPYFILE_DISABLE=1
7 | # $ tar czvf warehouse-model.tar.gz warehouse_ml.mod
8 |
9 | import sys
10 |
11 | # Watson ML credentails
12 | apikey = 'xxxx'
13 | instance_id = 'xxxx'
14 |
15 | tarfile = 'warehouse-model.tar.gz'
16 |
17 | # --------------------------------------------------------
18 | # メインルーチン
19 | # --------------------------------------------------------
20 | if __name__ == '__main__':
21 |
22 | # 引数の受け取り
23 | argv = sys.argv
24 | argc = len(argv)
25 |
26 | wml_credentials = {
27 | "apikey": apikey,
28 | "instance_id": instance_id,
29 | "url": 'https://us-south.ml.cloud.ibm.com'
30 | }
31 |
32 | from watson_machine_learning_client import WatsonMachineLearningAPIClient
33 | client = WatsonMachineLearningAPIClient(wml_credentials)
34 |
35 | print('List Models')
36 | client.repository.list_models()
37 |
38 | print('List Deployments')
39 | client.deployments.list()
40 |
41 | # 登録に必要な情報の設定
42 | mdl_07_metadata = {
43 | client.repository.ModelMetaNames.NAME: "WAREHOUSE OPL",
44 | client.repository.ModelMetaNames.DESCRIPTION: "WAREHOUSE OPL",
45 | #client.repository.ModelMetaNames.TYPE: "do-docplex_12.10",
46 | client.repository.ModelMetaNames.TYPE: "do-opl_12.10",
47 | client.repository.ModelMetaNames.RUNTIME_UID: "do_12.10"
48 | }
49 |
50 | # モデルの登録
51 | model_details = client.repository.store_model(model=tarfile, meta_props=mdl_07_metadata)
52 |
53 | # モデルUIDの取得
54 | model_uid = client.repository.get_model_uid(model_details)
55 | print( model_uid )
56 |
57 | # Webサービス化に必要な情報
58 | meta_props = {
59 | client.deployments.ConfigurationMetaNames.NAME: "WAREHOUSE OPL Deployment",
60 | client.deployments.ConfigurationMetaNames.DESCRIPTION: "WAREHOUSE OPL Deployment",
61 | client.deployments.ConfigurationMetaNames.BATCH: {},
62 | client.deployments.ConfigurationMetaNames.COMPUTE: {'name': 'S', 'nodes': 1}
63 | }
64 |
65 | # Webサービス化
66 | deployment_details = client.deployments.create(model_uid, meta_props=meta_props)
67 |
68 | deployment_uid = client.deployments.get_uid(deployment_details)
69 | print( deployment_uid )
70 |
71 | client.deployments.list()
72 |
73 |
--------------------------------------------------------------------------------
/diet-ml/ml-deploy.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | # コマンドによる事前準備
4 | # $ pip install -U ibm-watson-machine-learning
5 | # $ MACでは次が重要
6 | # $ export COPYFILE_DISABLE=1
7 | # $ tar czvf diet-model.gz main.py model.py
8 | # main.py 共通に使われるmodel呼び出し用コード
9 | # model.py DO実装コード model.builderで動作確認したもの
10 |
11 | import sys
12 |
13 | # Watson ML credentails
14 | apikey = 'xxxx'
15 | location = 'us-south'
16 |
17 | tarfile = 'diet-model.gz'
18 |
19 | # --------------------------------------------------------
20 | # メインルーチン
21 | # --------------------------------------------------------
22 | if __name__ == '__main__':
23 |
24 | # 引数の受け取り
25 | argv = sys.argv
26 | argc = len(argv)
27 |
28 |
29 | wml_credentials = {
30 | "apikey": apikey,
31 | "url": 'https://' + location + '.ml.cloud.ibm.com'
32 | }
33 |
34 | from ibm_watson_machine_learning import APIClient
35 | client = APIClient(wml_credentials)
36 |
37 | client.spaces.list()
38 | space_id = 'xxxx'
39 | client.set.default_space(space_id)
40 |
41 | software_spec_uid = client.software_specifications.get_uid_by_name("do_12.10")
42 | print(software_spec_uid)
43 |
44 | # 登録に必要な情報の設定
45 | mdl_metadata = {
46 | client.repository.ModelMetaNames.NAME: "Diet Python",
47 | client.repository.ModelMetaNames.DESCRIPTION: "Diet Python",
48 | client.repository.ModelMetaNames.TYPE: "do-docplex_12.10",
49 | client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: software_spec_uid
50 | }
51 |
52 | # モデルの登録
53 | model_details = client.repository.store_model(model=tarfile, meta_props=mdl_metadata)
54 |
55 | # モデルUIDの取得
56 | model_uid = client.repository.get_model_uid(model_details)
57 | print( model_uid )
58 |
59 | # Webサービス化に必要な情報
60 |
61 | meta_props = {
62 | client.deployments.ConfigurationMetaNames.NAME: "Diet Python Web",
63 | client.deployments.ConfigurationMetaNames.DESCRIPTION: "Diet Python Web",
64 | client.deployments.ConfigurationMetaNames.BATCH: {},
65 | client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {'name': 'S', 'nodes': 1} # S / M / XL
66 | }
67 |
68 | # Webサービス化
69 | deployment_details = client.deployments.create(model_uid, meta_props=meta_props)
70 |
71 | deployment_uid = client.deployments.get_uid(deployment_details)
72 | print( deployment_uid )
73 |
74 | # Webサービスの一覧表示
75 | client.deployments.list()
76 |
--------------------------------------------------------------------------------
/warehouse-do/warehouse_ml.mod:
--------------------------------------------------------------------------------
1 | // --------------------------------------------------------------------------
2 | // Licensed Materials - Property of IBM
3 | //
4 | // 5725-A06 5725-A29 5724-Y48 5724-Y49 5724-Y54 5724-Y55
5 | // Copyright IBM Corporation 1998, 2013. All Rights Reserved.
6 | //
7 | // Note to U.S. Government Users Restricted Rights:
8 | // Use, duplication or disclosure restricted by GSA ADP Schedule
9 | // Contract with IBM Corp.
10 | // --------------------------------------------------------------------------
11 |
12 | tuple TWarehouse {
13 | key string name;
14 | int capacity;
15 | float fixedCost;
16 | }
17 | {TWarehouse} warehouses = ...;
18 |
19 | tuple TSupplyCost {
20 | key string warehouseName;
21 | key int storeId;
22 | float cost;
23 | }
24 | {TSupplyCost} supplyCosts = ...;
25 |
26 | tuple TPlan {
27 | int nbStores;
28 | }
29 |
30 | // Excel化できないための一時対応
31 | TPlan plan = <10>;
32 |
33 | range stores = 1..plan.nbStores;
34 |
35 | dvar boolean Open[ warehouses ];
36 | dvar boolean Supply[ stores ][ warehouses ];
37 |
38 | // expression
39 | dexpr float totalOpeningCost = sum( w in warehouses ) w.fixedCost * Open[w];
40 | dexpr float totalSupplyCost = sum( w in warehouses , s in stores, k in supplyCosts : k.storeId == s && k.warehouseName == w.name )
41 | Supply[s][w] * k.cost;
42 |
43 | minimize
44 | totalOpeningCost + totalSupplyCost;
45 |
46 | subject to {
47 | forall( s in stores )
48 | ctEachStoreHasOneWarehouse:
49 | sum( w in warehouses ) Supply[s][w] == 1;
50 |
51 | forall( w in warehouses, s in stores )
52 | ctUseOpenWarehouses:
53 | Supply[s][w] <= Open[w];
54 |
55 | forall( w in warehouses )
56 | ctMaxUseOfWarehouse:
57 | sum( s in stores) Supply[s][w] <= w.capacity;
58 | }
59 |
60 |
61 | {int} StoresSupplied[w in warehouses] = { s | s in stores : Supply[s][w] == 1 };
62 | {string} OpenWarehouses = { w.name | w in warehouses : Open[w] == 1 };
63 | tuple TSuppliedStore {
64 | string warehouseName;
65 | int storeId;
66 | }
67 | {TSuppliedStore} network;
68 |
69 | execute DISPLAY_RESULTS{
70 | network.clear();
71 | writeln("* Open Warehouses=", OpenWarehouses);
72 | for ( var w in warehouses) {
73 | if ( Open[w] ==1) {
74 | writeln("* stores supplied by ", w.name, ": ", StoresSupplied[w]);
75 | for (var s in stores) {
76 | if (Supply[s][w] == 1) {
77 | network.addOnly(w.name, s);
78 | }
79 | }
80 | }
81 | }
82 | }
83 |
--------------------------------------------------------------------------------
/warehouse-excel/warehouse_ml.mod:
--------------------------------------------------------------------------------
1 | // --------------------------------------------------------------------------
2 | // Licensed Materials - Property of IBM
3 | //
4 | // 5725-A06 5725-A29 5724-Y48 5724-Y49 5724-Y54 5724-Y55
5 | // Copyright IBM Corporation 1998, 2013. All Rights Reserved.
6 | //
7 | // Note to U.S. Government Users Restricted Rights:
8 | // Use, duplication or disclosure restricted by GSA ADP Schedule
9 | // Contract with IBM Corp.
10 | // --------------------------------------------------------------------------
11 |
12 | tuple TWarehouse {
13 | key string name;
14 | int capacity;
15 | float fixedCost;
16 | }
17 | {TWarehouse} warehouses = ...;
18 |
19 | tuple TSupplyCost {
20 | key string warehouseName;
21 | key int storeId;
22 | float cost;
23 | }
24 | {TSupplyCost} supplyCosts = ...;
25 |
26 | tuple TPlan {
27 | int nbStores;
28 | }
29 |
30 | // Excel化できないための一時対応
31 | TPlan plan = <10>;
32 |
33 | range stores = 1..plan.nbStores;
34 |
35 | dvar boolean Open[ warehouses ];
36 | dvar boolean Supply[ stores ][ warehouses ];
37 |
38 | // expression
39 | dexpr float totalOpeningCost = sum( w in warehouses ) w.fixedCost * Open[w];
40 | dexpr float totalSupplyCost = sum( w in warehouses , s in stores, k in supplyCosts : k.storeId == s && k.warehouseName == w.name )
41 | Supply[s][w] * k.cost;
42 |
43 | minimize
44 | totalOpeningCost + totalSupplyCost;
45 |
46 | subject to {
47 | forall( s in stores )
48 | ctEachStoreHasOneWarehouse:
49 | sum( w in warehouses ) Supply[s][w] == 1;
50 |
51 | forall( w in warehouses, s in stores )
52 | ctUseOpenWarehouses:
53 | Supply[s][w] <= Open[w];
54 |
55 | forall( w in warehouses )
56 | ctMaxUseOfWarehouse:
57 | sum( s in stores) Supply[s][w] <= w.capacity;
58 | }
59 |
60 |
61 | {int} StoresSupplied[w in warehouses] = { s | s in stores : Supply[s][w] == 1 };
62 | {string} OpenWarehouses = { w.name | w in warehouses : Open[w] == 1 };
63 | tuple TSuppliedStore {
64 | string warehouseName;
65 | int storeId;
66 | }
67 | {TSuppliedStore} network;
68 |
69 | execute DISPLAY_RESULTS{
70 | network.clear();
71 | writeln("* Open Warehouses=", OpenWarehouses);
72 | for ( var w in warehouses) {
73 | if ( Open[w] ==1) {
74 | writeln("* stores supplied by ", w.name, ": ", StoresSupplied[w]);
75 | for (var s in stores) {
76 | if (Supply[s][w] == 1) {
77 | network.addOnly(w.name, s);
78 | }
79 | }
80 | }
81 | }
82 | }
83 |
--------------------------------------------------------------------------------
/warehouse-python-batch/warehouse_ml.mod:
--------------------------------------------------------------------------------
1 | // --------------------------------------------------------------------------
2 | // Licensed Materials - Property of IBM
3 | //
4 | // 5725-A06 5725-A29 5724-Y48 5724-Y49 5724-Y54 5724-Y55
5 | // Copyright IBM Corporation 1998, 2013. All Rights Reserved.
6 | //
7 | // Note to U.S. Government Users Restricted Rights:
8 | // Use, duplication or disclosure restricted by GSA ADP Schedule
9 | // Contract with IBM Corp.
10 | // --------------------------------------------------------------------------
11 |
12 | tuple TWarehouse {
13 | key string name;
14 | int capacity;
15 | float fixedCost;
16 | }
17 | {TWarehouse} warehouses = ...;
18 |
19 | tuple TSupplyCost {
20 | key string warehouseName;
21 | key int storeId;
22 | float cost;
23 | }
24 | {TSupplyCost} supplyCosts = ...;
25 |
26 | tuple TPlan {
27 | int nbStores;
28 | }
29 |
30 | // Excel化できないための一時対応
31 | TPlan plan = <10>;
32 |
33 | range stores = 1..plan.nbStores;
34 |
35 | dvar boolean Open[ warehouses ];
36 | dvar boolean Supply[ stores ][ warehouses ];
37 |
38 | // expression
39 | dexpr float totalOpeningCost = sum( w in warehouses ) w.fixedCost * Open[w];
40 | dexpr float totalSupplyCost = sum( w in warehouses , s in stores, k in supplyCosts : k.storeId == s && k.warehouseName == w.name )
41 | Supply[s][w] * k.cost;
42 |
43 | minimize
44 | totalOpeningCost + totalSupplyCost;
45 |
46 | subject to {
47 | forall( s in stores )
48 | ctEachStoreHasOneWarehouse:
49 | sum( w in warehouses ) Supply[s][w] == 1;
50 |
51 | forall( w in warehouses, s in stores )
52 | ctUseOpenWarehouses:
53 | Supply[s][w] <= Open[w];
54 |
55 | forall( w in warehouses )
56 | ctMaxUseOfWarehouse:
57 | sum( s in stores) Supply[s][w] <= w.capacity;
58 | }
59 |
60 |
61 | {int} StoresSupplied[w in warehouses] = { s | s in stores : Supply[s][w] == 1 };
62 | {string} OpenWarehouses = { w.name | w in warehouses : Open[w] == 1 };
63 | tuple TSuppliedStore {
64 | string warehouseName;
65 | int storeId;
66 | }
67 | {TSuppliedStore} network;
68 |
69 | execute DISPLAY_RESULTS{
70 | network.clear();
71 | writeln("* Open Warehouses=", OpenWarehouses);
72 | for ( var w in warehouses) {
73 | if ( Open[w] ==1) {
74 | writeln("* stores supplied by ", w.name, ": ", StoresSupplied[w]);
75 | for (var s in stores) {
76 | if (Supply[s][w] == 1) {
77 | network.addOnly(w.name, s);
78 | }
79 | }
80 | }
81 | }
82 | }
83 |
--------------------------------------------------------------------------------
/warehouse-python-batch/ml-submit.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | # コマンドによる事前準備
4 | # pip install watson-machine-learning-client-V4
5 |
6 | import sys
7 | import pandas as pd
8 |
9 | # Watson ML credentails
10 | apikey = 'xxxx'
11 | instance_id = 'xxxx'
12 |
13 | # DO Deployment ID
14 | deployment_uid = 'xxxx'
15 |
16 | # Input CSV File
17 | input_data1 = 'warehouses.csv'
18 | input_data2 = 'supplyCosts.csv'
19 |
20 | # --------------------------------------------------------
21 | # メインルーチン
22 | # --------------------------------------------------------
23 | if __name__ == '__main__':
24 |
25 | # 引数の受け取り
26 | argv = sys.argv
27 | argc = len(argv)
28 |
29 | wml_credentials = {
30 | "apikey": apikey,
31 | "instance_id": instance_id,
32 | "url": 'https://us-south.ml.cloud.ibm.com'
33 | }
34 |
35 | from watson_machine_learning_client import WatsonMachineLearningAPIClient
36 | client = WatsonMachineLearningAPIClient(wml_credentials)
37 |
38 | input_df1 = pd.read_csv(input_data1)
39 | input_df2 = pd.read_csv(input_data2)
40 |
41 | solve_payload = {
42 | client.deployments.DecisionOptimizationMetaNames.INPUT_DATA: [
43 | {
44 | "id": input_data1,
45 | "values" : input_df1
46 | },
47 | {
48 | "id": input_data2,
49 | "values" : input_df2
50 | }
51 | ],
52 | client.deployments.DecisionOptimizationMetaNames.OUTPUT_DATA: [
53 | {
54 | "id":".*\.csv"
55 | }
56 | ]
57 | }
58 |
59 | # DO Job 投入
60 | job_details = client.deployments.create_job(deployment_uid, solve_payload)
61 | job_uid = client.deployments.get_job_uid(job_details)
62 | print( job_uid )
63 |
64 |
65 | # status確認
66 | from time import sleep
67 | while job_details['entity']['decision_optimization']['status']['state'] not in ['completed', 'failed', 'canceled']:
68 | print(job_details['entity']['decision_optimization']['status']['state'] + '...')
69 | sleep(5)
70 | job_details=client.deployments.get_job_details(job_uid)
71 |
72 | detail = job_details['entity']['decision_optimization']['output_data']
73 |
74 | # 結果確認
75 | import json
76 | detail2 = job_details['entity']['decision_optimization']
77 |
78 | # 最終ステータス表示
79 | print(json.dumps(detail2['status'], indent=2))
80 |
81 | # 結果表示
82 | for item in detail:
83 | id = item['id']
84 | fields = item['fields']
85 | values = item['values']
86 | df_work = pd.DataFrame(values, columns=fields)
87 | print(df_work.head(10))
88 |
--------------------------------------------------------------------------------
/diet-ml/ml-submit.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 |
3 | # コマンドによる事前準備
4 | # $ pip install -U ibm-watson-machine-learning
5 |
6 | import sys
7 |
8 | # Watson ML credentails
9 | apikey = 'xxxx'
10 | location = 'us-south'
11 |
12 | import pandas as pd
13 |
14 | # Watson ML credentails
15 |
16 | # DO Deployment ID
17 | deployment_uid = 'xxxx'
18 |
19 | # Input CSV File
20 | input_data1 = 'diet_food.csv'
21 | input_data2 = 'diet_nutrients.csv'
22 | input_data3 = 'diet_food_nutrients.csv'
23 |
24 | # --------------------------------------------------------
25 | # メインルーチン
26 | # --------------------------------------------------------
27 | if __name__ == '__main__':
28 |
29 | # 引数の受け取り
30 | argv = sys.argv
31 | argc = len(argv)
32 |
33 | wml_credentials = {
34 | "apikey": apikey,
35 | "url": 'https://' + location + '.ml.cloud.ibm.com'
36 | }
37 |
38 | from ibm_watson_machine_learning import APIClient
39 | client = APIClient(wml_credentials)
40 |
41 | client.spaces.list()
42 | space_id = '20f3d4c5-1faa-4c80-a361-4da68d362b0f'
43 | client.set.default_space(space_id)
44 |
45 | input_df1 = pd.read_csv(input_data1)
46 | input_df2 = pd.read_csv(input_data2)
47 | input_df3 = pd.read_csv(input_data3)
48 |
49 | solve_payload = {
50 | client.deployments.DecisionOptimizationMetaNames.INPUT_DATA: [
51 | {
52 | "id": input_data1,
53 | "values" : input_df1
54 | },
55 | {
56 | "id": input_data2,
57 | "values" : input_df2
58 | },
59 | {
60 | "id": input_data3,
61 | "values" : input_df3
62 | }
63 | ],
64 | client.deployments.DecisionOptimizationMetaNames.OUTPUT_DATA: [
65 | {
66 | "id":".*\.csv"
67 | }
68 | ]
69 | }
70 |
71 | # DO Job 投入
72 | job_details = client.deployments.create_job(deployment_uid, solve_payload)
73 | job_uid = client.deployments.get_job_uid(job_details)
74 | print( job_uid )
75 |
76 |
77 | # status確認
78 | from time import sleep
79 | while job_details['entity']['decision_optimization']['status']['state'] not in ['completed', 'failed', 'canceled']:
80 | print(job_details['entity']['decision_optimization']['status']['state'] + '...')
81 | sleep(5)
82 | job_details=client.deployments.get_job_details(job_uid)
83 |
84 | detail = job_details['entity']['decision_optimization']['output_data']
85 |
86 | # 結果確認
87 | import json
88 | detail2 = job_details['entity']['decision_optimization']
89 |
90 | # 最終ステータス表示
91 | print(json.dumps(detail2['status'], indent=2))
92 |
93 | for item in detail:
94 | id = item['id']
95 | fields = item['fields']
96 | values = item['values']
97 | df_work = pd.DataFrame(values, columns=fields)
98 | name = id[:id.index('.csv')]
99 | print('name = ', name)
100 | print(df_work.head())
101 | df_work.to_csv(id, index=False)
102 |
--------------------------------------------------------------------------------
/house-building/house-building1.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# House Building1\n",
8 | "\n",
9 | "## 単純な問題\n",
10 | "各工程の期間、依存関係のみが制約"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "元リンク\n",
18 | "\n",
19 | "https://ibmdecisionoptimization.github.io/tutorials/html/Scheduling_Tutorial.html"
20 | ]
21 | },
22 | {
23 | "cell_type": "markdown",
24 | "metadata": {},
25 | "source": [
26 | "API reference\n",
27 | "\n",
28 | "https://ibmdecisionoptimization.github.io/docplex-doc/cp/docplex.cp.expression.py.html"
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": null,
34 | "metadata": {},
35 | "outputs": [],
36 | "source": [
37 | "# 工程名\n",
38 | "TaskNames = [\"masonry\", \"carpentry\", \"plumbing\", \n",
39 | " \"ceiling\", \"roofing\", \"painting\", \n",
40 | " \"windows\", \"facade\", \"garden\", \"moving\"]\n",
41 | "\n",
42 | "# 工程毎の期間\n",
43 | "Duration = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]\n",
44 | "\n",
45 | "# 工程間依存関係\n",
46 | "Precedences = [(\"masonry\", \"carpentry\"),(\"masonry\", \"plumbing\"),\n",
47 | " (\"masonry\", \"ceiling\"), (\"carpentry\", \"roofing\"),\n",
48 | " (\"ceiling\", \"painting\"), (\"roofing\", \"windows\"), \n",
49 | " (\"roofing\", \"facade\"), (\"plumbing\", \"facade\"),\n",
50 | " (\"roofing\", \"garden\"), (\"plumbing\", \"garden\"),\n",
51 | " (\"windows\", \"moving\"), (\"facade\", \"moving\"), \n",
52 | " (\"garden\", \"moving\"), (\"painting\", \"moving\")]"
53 | ]
54 | },
55 | {
56 | "cell_type": "code",
57 | "execution_count": null,
58 | "metadata": {},
59 | "outputs": [],
60 | "source": [
61 | "# ライブラリのimportとモデルのインスタンス作成\n",
62 | "\n",
63 | "import sys\n",
64 | "from docplex.cp.model import *\n",
65 | "mdl0 = CpoModel()"
66 | ]
67 | },
68 | {
69 | "cell_type": "code",
70 | "execution_count": null,
71 | "metadata": {},
72 | "outputs": [],
73 | "source": [
74 | "# 工程ごとの期間の定義\n",
75 | "\n",
76 | "# タスク名からInterval変数を引く辞書\n",
77 | "itvs = {}\n",
78 | "\n",
79 | "# Interval変数の定義\n",
80 | "# タスク名\n",
81 | "for i,taskName in enumerate(TaskNames):\n",
82 | " itvs[taskName] = mdl0.interval_var(size=Duration[i], name=taskName)"
83 | ]
84 | },
85 | {
86 | "cell_type": "code",
87 | "execution_count": null,
88 | "metadata": {},
89 | "outputs": [],
90 | "source": [
91 | "# タスク間の依存関係定義\n",
92 | "\n",
93 | "for p in Precedences:\n",
94 | " mdl0.add(mdl0.end_before_start(itvs[p[0]], itvs[p[1]]) )"
95 | ]
96 | },
97 | {
98 | "cell_type": "code",
99 | "execution_count": null,
100 | "metadata": {},
101 | "outputs": [],
102 | "source": [
103 | "# モデルを解く\n",
104 | "\n",
105 | "print(\"\\nSolving model....\")\n",
106 | "msol0 = mdl0.solve(TimeLimit=10)\n",
107 | "print(\"done\")"
108 | ]
109 | },
110 | {
111 | "cell_type": "code",
112 | "execution_count": null,
113 | "metadata": {},
114 | "outputs": [],
115 | "source": [
116 | "# 結果表示\n",
117 | "\n",
118 | "for taskName in TaskNames:\n",
119 | " var_sol = msol0.get_var_solution(taskName)\n",
120 | " print(f\"{taskName} : {var_sol.get_start()}..{var_sol.get_end()}\")"
121 | ]
122 | },
123 | {
124 | "cell_type": "code",
125 | "execution_count": null,
126 | "metadata": {},
127 | "outputs": [],
128 | "source": [
129 | "# グラフ化\n",
130 | "\n",
131 | "import docplex.cp.utils_visu as visu\n",
132 | "import matplotlib.pyplot as plt\n",
133 | "%matplotlib inline\n",
134 | "#Change the plot size\n",
135 | "from pylab import rcParams\n",
136 | "rcParams['figure.figsize'] = 15, 3\n",
137 | "\n",
138 | "for taskName in TaskNames:\n",
139 | " wt = msol0.get_var_solution(taskName)\n",
140 | " visu.interval(wt, 'lightblue', taskName) \n",
141 | "visu.show()"
142 | ]
143 | },
144 | {
145 | "cell_type": "code",
146 | "execution_count": null,
147 | "metadata": {},
148 | "outputs": [],
149 | "source": []
150 | }
151 | ],
152 | "metadata": {
153 | "kernelspec": {
154 | "display_name": "Python 3",
155 | "language": "python",
156 | "name": "python3"
157 | },
158 | "language_info": {
159 | "codemirror_mode": {
160 | "name": "ipython",
161 | "version": 3
162 | },
163 | "file_extension": ".py",
164 | "mimetype": "text/x-python",
165 | "name": "python",
166 | "nbconvert_exporter": "python",
167 | "pygments_lexer": "ipython3",
168 | "version": "3.7.3"
169 | }
170 | },
171 | "nbformat": 4,
172 | "nbformat_minor": 2
173 | }
174 |
--------------------------------------------------------------------------------
/diet-ml/main.py:
--------------------------------------------------------------------------------
1 | from functools import partial, wraps
2 | import os
3 | from os.path import splitext
4 | import time
5 | import threading
6 | import traceback
7 | import sys
8 | import ntpath
9 |
10 | import pandas
11 | from six import iteritems
12 |
13 | from docplex.util.environment import get_environment
14 |
15 | output_lock = threading.Lock()
16 |
17 |
18 | def set_stop_callback(cb):
19 | env = get_environment()
20 | env.abort_callbacks += [cb]
21 |
22 |
23 | def get_all_inputs():
24 | '''Utility method to read a list of files and return a tuple with all
25 | read data frames.
26 | Returns:
27 | a map { datasetname: data frame }
28 | '''
29 | result = {}
30 | env = get_environment()
31 | for iname in [f for f in os.listdir('.') if splitext(f)[1] == '.csv']:
32 | with env.get_input_stream(iname) as in_stream:
33 | df = pandas.read_csv(in_stream)
34 | datasetname, _ = splitext(iname)
35 | result[datasetname] = df
36 | return result
37 |
38 |
39 | def callonce(f):
40 | @wraps(f)
41 | def wrapper(*args, **kwargs):
42 | if not wrapper.called:
43 | wrapper.called = True
44 | return f(*args, **kwargs)
45 | wrapper.called = False
46 | return wrapper
47 |
48 |
49 | @callonce
50 | def write_all_outputs(outputs):
51 | '''Write all dataframes in ``outputs`` as .csv.
52 |
53 | Args:
54 | outputs: The map of outputs 'outputname' -> 'output df'
55 | '''
56 | global output_lock
57 | with output_lock:
58 | for (name, df) in iteritems(outputs):
59 | csv_file = '%s.csv' % name
60 | print(csv_file)
61 | with get_environment().get_output_stream(csv_file) as fp:
62 | if sys.version_info[0] < 3:
63 | fp.write(df.to_csv(index=False, encoding='utf8'))
64 | else:
65 | fp.write(df.to_csv(index=False).encode(encoding='utf8'))
66 | if len(outputs) == 0:
67 | print("Warning: no outputs written")
68 |
69 |
70 | def wait_and_save_all_cb(outputs):
71 | global output_lock
72 | # just wait for the output_lock to be available
73 | t = time.time()
74 | with output_lock:
75 | pass
76 | elapsed = time.time() - t
77 | # write outputs
78 | write_all_outputs(outputs)
79 |
80 |
81 | def get_line_of_model(n):
82 | env = get_environment()
83 | with env.get_input_stream('model.py') as m:
84 | lines = m.readlines()
85 | return lines[n - 1].decode("utf-8")
86 |
87 |
88 | class InterpreterError(Exception):
89 | pass
90 |
91 | if __name__ == '__main__':
92 | inputs = get_all_inputs()
93 | outputs = {}
94 | set_stop_callback(partial(wait_and_save_all_cb, outputs))
95 |
96 | env = get_environment()
97 | # The IS_DODS env must be True for model.py if running in DODS
98 | os.environ['IS_DODS'] = 'True'
99 | # This allows docplex.mp to behave the same (publish kpis.csv and solution.json
100 | # if this script is run locally
101 | os.environ['DOCPLEX_CONTEXT'] = 'solver.auto_publish=True'
102 | with env.get_input_stream('model.py') as m:
103 | try:
104 | exec(m.read().decode('utf-8'), globals())
105 | except SyntaxError as err:
106 | error_class = err.__class__.__name__
107 | detail = err.args[0]
108 | line_number = err.lineno
109 | fileName = ntpath.basename(err.filename)
110 | # When the error occurs in model.py, there is no err.filename
111 | if fileName == "":
112 | fileName = "model.py"
113 | imsg = '\nFile "' + fileName + '", line %s\n' % line_number
114 | if err.text != None:
115 | imsg += err.text.rstrip() + '\n'
116 | spaces = ' ' * (err.offset - 1) if err.offset > 1 else ''
117 | imsg += spaces + "^\n"
118 | imsg += '%s: %s\n' % (error_class, detail)
119 | sys.tracebacklimit = 0
120 | raise InterpreterError(imsg)
121 | except Exception as err:
122 | error_class = err.__class__.__name__
123 | detail = ""
124 | if(len(err.args) > 0):
125 | detail = err.args[0]
126 | cl, exc, tb = sys.exc_info()
127 | ttb = traceback.extract_tb(tb)
128 | if(len(ttb) > 1):
129 | for i in range(len(ttb)):
130 | fileName = ntpath.basename(ttb[i][0])
131 | line = ttb[i][3]
132 | if(fileName == ""):
133 | fileName = "model.py"
134 | line = get_line_of_model(ttb[i][1])
135 | ## need to use basename, otherwise we get the full path
136 | ttb[i] = (fileName, ttb[i][1], ttb[i][2], line)
137 | ttb = ttb[1:]
138 | s = traceback.format_list(ttb)
139 | imsg = '\n' + (''.join(s))
140 | imsg += '%s: %s\n' % (error_class, detail)
141 | sys.tracebacklimit = 0
142 | raise InterpreterError(imsg)
143 | else:
144 | write_all_outputs(outputs)
145 |
146 |
--------------------------------------------------------------------------------
/mp-sample/main.py:
--------------------------------------------------------------------------------
1 | from functools import partial, wraps
2 | import os
3 | from os.path import splitext
4 | import time
5 | import threading
6 | import traceback
7 | import sys
8 | import ntpath
9 |
10 | import pandas
11 | from six import iteritems
12 |
13 | from docplex.util.environment import get_environment
14 |
15 | output_lock = threading.Lock()
16 |
17 |
18 | def set_stop_callback(cb):
19 | env = get_environment()
20 | env.abort_callbacks += [cb]
21 |
22 |
23 | def get_all_inputs():
24 | '''Utility method to read a list of files and return a tuple with all
25 | read data frames.
26 | Returns:
27 | a map { datasetname: data frame }
28 | '''
29 | result = {}
30 | env = get_environment()
31 | for iname in [f for f in os.listdir('.') if splitext(f)[1] == '.csv']:
32 | with env.get_input_stream(iname) as in_stream:
33 | df = pandas.read_csv(in_stream)
34 | datasetname, _ = splitext(iname)
35 | result[datasetname] = df
36 | return result
37 |
38 |
39 | def callonce(f):
40 | @wraps(f)
41 | def wrapper(*args, **kwargs):
42 | if not wrapper.called:
43 | wrapper.called = True
44 | return f(*args, **kwargs)
45 | wrapper.called = False
46 | return wrapper
47 |
48 |
49 | @callonce
50 | def write_all_outputs(outputs):
51 | '''Write all dataframes in ``outputs`` as .csv.
52 |
53 | Args:
54 | outputs: The map of outputs 'outputname' -> 'output df'
55 | '''
56 | global output_lock
57 | with output_lock:
58 | for (name, df) in iteritems(outputs):
59 | csv_file = '%s.csv' % name
60 | print(csv_file)
61 | with get_environment().get_output_stream(csv_file) as fp:
62 | if sys.version_info[0] < 3:
63 | fp.write(df.to_csv(index=False, encoding='utf8'))
64 | else:
65 | fp.write(df.to_csv(index=False).encode(encoding='utf8'))
66 | if len(outputs) == 0:
67 | print("Warning: no outputs written")
68 |
69 |
70 | def wait_and_save_all_cb(outputs):
71 | global output_lock
72 | # just wait for the output_lock to be available
73 | t = time.time()
74 | with output_lock:
75 | pass
76 | elapsed = time.time() - t
77 | # write outputs
78 | write_all_outputs(outputs)
79 |
80 |
81 | def get_line_of_model(n):
82 | env = get_environment()
83 | with env.get_input_stream('model.py') as m:
84 | lines = m.readlines()
85 | return lines[n - 1].decode("utf-8")
86 |
87 |
88 | class InterpreterError(Exception):
89 | pass
90 |
91 | if __name__ == '__main__':
92 | inputs = get_all_inputs()
93 | outputs = {}
94 | set_stop_callback(partial(wait_and_save_all_cb, outputs))
95 |
96 | env = get_environment()
97 | # The IS_DODS env must be True for model.py if running in DODS
98 | os.environ['IS_DODS'] = 'True'
99 | # This allows docplex.mp to behave the same (publish kpis.csv and solution.json
100 | # if this script is run locally
101 | os.environ['DOCPLEX_CONTEXT'] = 'solver.auto_publish=True'
102 | with env.get_input_stream('model.py') as m:
103 | try:
104 | exec(m.read().decode('utf-8'), globals())
105 | except SyntaxError as err:
106 | error_class = err.__class__.__name__
107 | detail = err.args[0]
108 | line_number = err.lineno
109 | fileName = ntpath.basename(err.filename)
110 | # When the error occurs in model.py, there is no err.filename
111 | if fileName == "":
112 | fileName = "model.py"
113 | imsg = '\nFile "' + fileName + '", line %s\n' % line_number
114 | if err.text != None:
115 | imsg += err.text.rstrip() + '\n'
116 | spaces = ' ' * (err.offset - 1) if err.offset > 1 else ''
117 | imsg += spaces + "^\n"
118 | imsg += '%s: %s\n' % (error_class, detail)
119 | sys.tracebacklimit = 0
120 | raise InterpreterError(imsg)
121 | except Exception as err:
122 | error_class = err.__class__.__name__
123 | detail = ""
124 | if(len(err.args) > 0):
125 | detail = err.args[0]
126 | cl, exc, tb = sys.exc_info()
127 | ttb = traceback.extract_tb(tb)
128 | if(len(ttb) > 1):
129 | for i in range(len(ttb)):
130 | fileName = ntpath.basename(ttb[i][0])
131 | line = ttb[i][3]
132 | if(fileName == ""):
133 | fileName = "model.py"
134 | line = get_line_of_model(ttb[i][1])
135 | ## need to use basename, otherwise we get the full path
136 | ttb[i] = (fileName, ttb[i][1], ttb[i][2], line)
137 | ttb = ttb[1:]
138 | s = traceback.format_list(ttb)
139 | imsg = '\n' + (''.join(s))
140 | imsg += '%s: %s\n' % (error_class, detail)
141 | sys.tracebacklimit = 0
142 | raise InterpreterError(imsg)
143 | else:
144 | write_all_outputs(outputs)
145 |
146 |
--------------------------------------------------------------------------------
/diet-python/main.py:
--------------------------------------------------------------------------------
1 | from functools import partial, wraps
2 | import os
3 | from os.path import splitext
4 | import time
5 | import threading
6 | import traceback
7 | import sys
8 | import ntpath
9 |
10 | import pandas
11 | from six import iteritems
12 |
13 | from docplex.util.environment import get_environment
14 |
15 | output_lock = threading.Lock()
16 |
17 |
18 | def set_stop_callback(cb):
19 | env = get_environment()
20 | env.abort_callbacks += [cb]
21 |
22 |
23 | def get_all_inputs():
24 | '''Utility method to read a list of files and return a tuple with all
25 | read data frames.
26 | Returns:
27 | a map { datasetname: data frame }
28 | '''
29 | result = {}
30 | env = get_environment()
31 | for iname in [f for f in os.listdir('.') if splitext(f)[1] == '.csv']:
32 | with env.get_input_stream(iname) as in_stream:
33 | df = pandas.read_csv(in_stream)
34 | datasetname, _ = splitext(iname)
35 | result[datasetname] = df
36 | return result
37 |
38 |
39 | def callonce(f):
40 | @wraps(f)
41 | def wrapper(*args, **kwargs):
42 | if not wrapper.called:
43 | wrapper.called = True
44 | return f(*args, **kwargs)
45 | wrapper.called = False
46 | return wrapper
47 |
48 |
49 | @callonce
50 | def write_all_outputs(outputs):
51 | '''Write all dataframes in ``outputs`` as .csv.
52 |
53 | Args:
54 | outputs: The map of outputs 'outputname' -> 'output df'
55 | '''
56 | global output_lock
57 | with output_lock:
58 | for (name, df) in iteritems(outputs):
59 | csv_file = '%s.csv' % name
60 | print(csv_file)
61 | with get_environment().get_output_stream(csv_file) as fp:
62 | if sys.version_info[0] < 3:
63 | fp.write(df.to_csv(index=False, encoding='utf8'))
64 | else:
65 | fp.write(df.to_csv(index=False).encode(encoding='utf8'))
66 | if len(outputs) == 0:
67 | print("Warning: no outputs written")
68 |
69 |
70 | def wait_and_save_all_cb(outputs):
71 | global output_lock
72 | # just wait for the output_lock to be available
73 | t = time.time()
74 | with output_lock:
75 | pass
76 | elapsed = time.time() - t
77 | # write outputs
78 | write_all_outputs(outputs)
79 |
80 |
81 | def get_line_of_model(n):
82 | env = get_environment()
83 | with env.get_input_stream('model.py') as m:
84 | lines = m.readlines()
85 | return lines[n - 1].decode("utf-8")
86 |
87 |
88 | class InterpreterError(Exception):
89 | pass
90 |
91 | if __name__ == '__main__':
92 | inputs = get_all_inputs()
93 | outputs = {}
94 | set_stop_callback(partial(wait_and_save_all_cb, outputs))
95 |
96 | env = get_environment()
97 | # The IS_DODS env must be True for model.py if running in DODS
98 | os.environ['IS_DODS'] = 'True'
99 | # This allows docplex.mp to behave the same (publish kpis.csv and solution.json
100 | # if this script is run locally
101 | os.environ['DOCPLEX_CONTEXT'] = 'solver.auto_publish=True'
102 | with env.get_input_stream('model.py') as m:
103 | try:
104 | exec(m.read().decode('utf-8'), globals())
105 | except SyntaxError as err:
106 | error_class = err.__class__.__name__
107 | detail = err.args[0]
108 | line_number = err.lineno
109 | fileName = ntpath.basename(err.filename)
110 | # When the error occurs in model.py, there is no err.filename
111 | if fileName == "":
112 | fileName = "model.py"
113 | imsg = '\nFile "' + fileName + '", line %s\n' % line_number
114 | if err.text != None:
115 | imsg += err.text.rstrip() + '\n'
116 | spaces = ' ' * (err.offset - 1) if err.offset > 1 else ''
117 | imsg += spaces + "^\n"
118 | imsg += '%s: %s\n' % (error_class, detail)
119 | sys.tracebacklimit = 0
120 | raise InterpreterError(imsg)
121 | except Exception as err:
122 | error_class = err.__class__.__name__
123 | detail = ""
124 | if(len(err.args) > 0):
125 | detail = err.args[0]
126 | cl, exc, tb = sys.exc_info()
127 | ttb = traceback.extract_tb(tb)
128 | if(len(ttb) > 1):
129 | for i in range(len(ttb)):
130 | fileName = ntpath.basename(ttb[i][0])
131 | line = ttb[i][3]
132 | if(fileName == ""):
133 | fileName = "model.py"
134 | line = get_line_of_model(ttb[i][1])
135 | ## need to use basename, otherwise we get the full path
136 | ttb[i] = (fileName, ttb[i][1], ttb[i][2], line)
137 | ttb = ttb[1:]
138 | s = traceback.format_list(ttb)
139 | imsg = '\n' + (''.join(s))
140 | imsg += '%s: %s\n' % (error_class, detail)
141 | sys.tracebacklimit = 0
142 | raise InterpreterError(imsg)
143 | else:
144 | write_all_outputs(outputs)
145 |
146 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | Apache License
2 | Version 2.0, January 2004
3 | http://www.apache.org/licenses/
4 |
5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
6 |
7 | 1. Definitions.
8 |
9 | "License" shall mean the terms and conditions for use, reproduction,
10 | and distribution as defined by Sections 1 through 9 of this document.
11 |
12 | "Licensor" shall mean the copyright owner or entity authorized by
13 | the copyright owner that is granting the License.
14 |
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16 | other entities that control, are controlled by, or are under common
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19 | direction or management of such entity, whether by contract or
20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the
21 | outstanding shares, or (iii) beneficial ownership of such entity.
22 |
23 | "You" (or "Your") shall mean an individual or Legal Entity
24 | exercising permissions granted by this License.
25 |
26 | "Source" form shall mean the preferred form for making modifications,
27 | including but not limited to software source code, documentation
28 | source, and configuration files.
29 |
30 | "Object" form shall mean any form resulting from mechanical
31 | transformation or translation of a Source form, including but
32 | not limited to compiled object code, generated documentation,
33 | and conversions to other media types.
34 |
35 | "Work" shall mean the work of authorship, whether in Source or
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--------------------------------------------------------------------------------
/house-building/house-building3.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# House Building 3"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "## より複雑な問題"
15 | ]
16 | },
17 | {
18 | "cell_type": "markdown",
19 | "metadata": {},
20 | "source": [
21 | "* 5件の家を、JoeとJimの2人で建てる \n",
22 | "* 工程ごとに担当者が決まっている\n",
23 | "* 家ごとにReleaseDate以降に着手する必要がある\n",
24 | "* 家ごとにDueDateが決まっている。遅れてもいいが、Weightで規定されるコストが余分にかかる\n",
25 | "* 評価関数は「遅延によるコスト」+「家ごとの建築期間」でこれを最小化する"
26 | ]
27 | },
28 | {
29 | "cell_type": "markdown",
30 | "metadata": {},
31 | "source": [
32 | "元リンク\n",
33 | "\n",
34 | "https://ibmdecisionoptimization.github.io/tutorials/html/Scheduling_Tutorial.html"
35 | ]
36 | },
37 | {
38 | "cell_type": "markdown",
39 | "metadata": {},
40 | "source": [
41 | "API reference\n",
42 | "\n",
43 | "https://ibmdecisionoptimization.github.io/docplex-doc/cp/docplex.cp.expression.py.html"
44 | ]
45 | },
46 | {
47 | "cell_type": "code",
48 | "execution_count": 1,
49 | "metadata": {},
50 | "outputs": [],
51 | "source": [
52 | "# 工程名\n",
53 | "TaskNames = [\"masonry\", \"carpentry\", \"plumbing\", \n",
54 | " \"ceiling\", \"roofing\", \"painting\", \n",
55 | " \"windows\", \"facade\", \"garden\", \"moving\"]\n",
56 | "\n",
57 | "# 工程毎の期間\n",
58 | "Duration = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]\n",
59 | "\n",
60 | "# 工程間依存関係\n",
61 | "Precedences = [(\"masonry\", \"carpentry\"),(\"masonry\", \"plumbing\"),\n",
62 | " (\"masonry\", \"ceiling\"), (\"carpentry\", \"roofing\"),\n",
63 | " (\"ceiling\", \"painting\"), (\"roofing\", \"windows\"), \n",
64 | " (\"roofing\", \"facade\"), (\"plumbing\", \"facade\"),\n",
65 | " (\"roofing\", \"garden\"), (\"plumbing\", \"garden\"),\n",
66 | " (\"windows\", \"moving\"), (\"facade\", \"moving\"), \n",
67 | " (\"garden\", \"moving\"), (\"painting\", \"moving\")]\n",
68 | "\n",
69 | "# 家の件数\n",
70 | "NbHouses = 5\n",
71 | "\n",
72 | "# 作業者\n",
73 | "WorkerNames = [\"Joe\", \"Jim\"]\n",
74 | "\n",
75 | "# 工程毎の作業者分担\n",
76 | "Worker = {\"masonry\" : \"Joe\" , \n",
77 | " \"carpentry\": \"Joe\" , \n",
78 | " \"plumbing\" : \"Jim\" , \n",
79 | " \"ceiling\" : \"Jim\" , \n",
80 | " \"roofing\" : \"Joe\" , \n",
81 | " \"painting\" : \"Jim\" , \n",
82 | " \"windows\" : \"Jim\" , \n",
83 | " \"facade\" : \"Joe\" , \n",
84 | " \"garden\" : \"Joe\" , \n",
85 | " \"moving\" : \"Jim\"}\n",
86 | "\n",
87 | "# 着手日 (必ず守る必要あり)\n",
88 | "ReleaseDate = [ 0, 0, 151, 59, 243]\n",
89 | "\n",
90 | "# 終了日 (遅れていいがコストが発生)\n",
91 | "DueDate = [120, 212, 304, 181, 425]\n",
92 | "\n",
93 | "# 遅延コスト\n",
94 | "Weight = [100.0, 100.0, 100.0, 200.0, 100.0]\n",
95 | "\n",
96 | "# 家IDのリスト([0, 1, 2, 3, 4])を生成\n",
97 | "Houses = range(NbHouses)"
98 | ]
99 | },
100 | {
101 | "cell_type": "code",
102 | "execution_count": 2,
103 | "metadata": {},
104 | "outputs": [],
105 | "source": [
106 | "# ライブラリのインポートとモデルインスタンスの生成\n",
107 | "\n",
108 | "import sys\n",
109 | "from docplex.cp.model import *\n",
110 | "mdl2 = CpoModel()"
111 | ]
112 | },
113 | {
114 | "cell_type": "code",
115 | "execution_count": 3,
116 | "metadata": {},
117 | "outputs": [],
118 | "source": [
119 | "# housesは家毎のInterval変数の配列\n",
120 | "# 開始日はReleaseDateにより個別に指定\n",
121 | "\n",
122 | "houses = [mdl2.interval_var(start=(ReleaseDate[i], INTERVAL_MAX), name=\"house\"+str(i)) for i in Houses]"
123 | ]
124 | },
125 | {
126 | "cell_type": "code",
127 | "execution_count": 4,
128 | "metadata": {},
129 | "outputs": [],
130 | "source": [
131 | "# _<タスク名>でタスクごとのラベルを振る\n",
132 | "# TaskNames_ids: タスク毎のラベルからタスクIDを取得する辞書\n",
133 | "# itvs: (house_id, task_id)から該当するInterval変数を取得する辞書\n",
134 | "# タスク毎の期間(size)はDurationで決められている値を初期設定する\n",
135 | "\n",
136 | "TaskNames_ids = {}\n",
137 | "itvs = {}\n",
138 | "for h in Houses:\n",
139 | " for i,t in enumerate(TaskNames):\n",
140 | " _name = str(h)+\"_\"+str(t)\n",
141 | " itvs[(h,t)] = mdl2.interval_var(size=Duration[i], name=_name)\n",
142 | " TaskNames_ids[_name] = i"
143 | ]
144 | },
145 | {
146 | "cell_type": "code",
147 | "execution_count": 5,
148 | "metadata": {},
149 | "outputs": [
150 | {
151 | "name": "stdout",
152 | "output_type": "stream",
153 | "text": [
154 | "\"3_masonry\" = intervalVar(size=35)\n"
155 | ]
156 | }
157 | ],
158 | "source": [
159 | "# itvsの辞書の内容確認\n",
160 | "\n",
161 | "print(itvs[(3, 'masonry')])"
162 | ]
163 | },
164 | {
165 | "cell_type": "code",
166 | "execution_count": 6,
167 | "metadata": {},
168 | "outputs": [],
169 | "source": [
170 | "# タスク間の依存関係定義\n",
171 | "\n",
172 | "for h in Houses:\n",
173 | " for p in Precedences:\n",
174 | " mdl2.add(mdl2.end_before_start(itvs[(h,p[0])], itvs[(h,p[1])]) )"
175 | ]
176 | },
177 | {
178 | "cell_type": "code",
179 | "execution_count": 7,
180 | "metadata": {},
181 | "outputs": [],
182 | "source": [
183 | "# span制約の設定\n",
184 | "\n",
185 | "# 家のInterval変数は、個別タスクの集計で定まる。その関係をspan関数で設定\n",
186 | "for h in Houses:\n",
187 | " mdl2.add( mdl2.span(houses[h], [itvs[(h,t)] for t in TaskNames] ) )"
188 | ]
189 | },
190 | {
191 | "cell_type": "code",
192 | "execution_count": 8,
193 | "metadata": {},
194 | "outputs": [],
195 | "source": [
196 | "# workersの定義\n",
197 | "\n",
198 | "# 作業者(Joe, Jim)ごとのタスクシーケンス変数として定義\n",
199 | "# シーケンス変数は、次のno_overlap関数で条件の定義として用いられる\n",
200 | "workers = {w : mdl2.sequence_var([ itvs[(h,t)] for h in Houses for t in TaskNames if Worker[t]==w ], \n",
201 | " name=\"workers_\"+w) for w in WorkerNames}"
202 | ]
203 | },
204 | {
205 | "cell_type": "code",
206 | "execution_count": 9,
207 | "metadata": {},
208 | "outputs": [
209 | {
210 | "name": "stdout",
211 | "output_type": "stream",
212 | "text": [
213 | "workers_Jim = sequenceVar([\"0_plumbing\", \"0_ceiling\", \"0_painting\", \"0_windows\", \"0_moving\", \"1_plumbing\", \"1_ceiling\", \"1_painting\", \"1_windows\", \"1_moving\", \"2_plumbing\", \"2_ceiling\", \"2_painting\", \"2_windows\", \"2_moving\", \"3_plumbing\", \"3_ceiling\", \"3_painting\", \"3_windows\", \"3_moving\", \"4_plumbing\", \"4_ceiling\", \"4_painting\", \"4_windows\", \"4_moving\"])\n"
214 | ]
215 | }
216 | ],
217 | "source": [
218 | "# 結果の確認('Jim'に対する結果)\n",
219 | "\n",
220 | "print(workers['Jim'])"
221 | ]
222 | },
223 | {
224 | "cell_type": "code",
225 | "execution_count": 10,
226 | "metadata": {},
227 | "outputs": [],
228 | "source": [
229 | "# 作業者は同時に一つのタスクしかできないことをno_overlap関数で表現する\n",
230 | "# (オリジナルのチュートリアルにあった遷移時間の条件は簡略化のため削除)\n",
231 | "\n",
232 | "for w in WorkerNames:\n",
233 | " mdl2.add( mdl2.no_overlap(workers[w]) )"
234 | ]
235 | },
236 | {
237 | "cell_type": "code",
238 | "execution_count": 11,
239 | "metadata": {},
240 | "outputs": [],
241 | "source": [
242 | "# 目的関数の定義\n",
243 | "# 「遅延コスト」+「建築期間」を家ごとに集計した値を最小化する\n",
244 | "\n",
245 | "mdl2.add( \n",
246 | " mdl2.minimize( \n",
247 | " mdl2.sum(Weight[h] * mdl2.max([0, mdl2.end_of(houses[h])-DueDate[h]]) +\\\n",
248 | " mdl2.length_of(houses[h]) for h in Houses) \n",
249 | " ) \n",
250 | ")"
251 | ]
252 | },
253 | {
254 | "cell_type": "code",
255 | "execution_count": 12,
256 | "metadata": {},
257 | "outputs": [
258 | {
259 | "name": "stdout",
260 | "output_type": "stream",
261 | "text": [
262 | "\n",
263 | "Solving model....\n",
264 | "done\n"
265 | ]
266 | }
267 | ],
268 | "source": [
269 | "# モデルを解く\n",
270 | "\n",
271 | "print(\"\\nSolving model....\")\n",
272 | "msol2 = mdl2.solve(FailLimit=30000)\n",
273 | "print(\"done\")"
274 | ]
275 | },
276 | {
277 | "cell_type": "code",
278 | "execution_count": 13,
279 | "metadata": {},
280 | "outputs": [
281 | {
282 | "name": "stdout",
283 | "output_type": "stream",
284 | "text": [
285 | "Cost will be 9820\n"
286 | ]
287 | }
288 | ],
289 | "source": [
290 | "# 目的関数値の確認\n",
291 | "\n",
292 | "print(\"Cost will be \" + str(msol2.get_objective_values()[0]))"
293 | ]
294 | },
295 | {
296 | "cell_type": "code",
297 | "execution_count": 14,
298 | "metadata": {},
299 | "outputs": [
300 | {
301 | "name": "stdout",
302 | "output_type": "stream",
303 | "text": [
304 | "Joe 0_masonry 0 35\n",
305 | "Joe 0_carpentry 35 50\n",
306 | "Joe 0_roofing 50 55\n",
307 | "Joe 3_masonry 60 95\n",
308 | "Joe 0_facade 95 105\n",
309 | "Joe 0_garden 105 110\n",
310 | "Joe 3_carpentry 110 125\n",
311 | "Joe 3_roofing 125 130\n",
312 | "Joe 1_masonry 130 165\n",
313 | "Joe 3_facade 165 175\n",
314 | "Joe 3_garden 175 180\n",
315 | "Joe 1_carpentry 180 195\n",
316 | "Joe 1_roofing 195 200\n",
317 | "Joe 2_masonry 205 240\n",
318 | "Joe 1_garden 240 245\n",
319 | "Joe 1_facade 245 255\n",
320 | "Joe 2_carpentry 255 270\n",
321 | "Joe 2_roofing 270 275\n",
322 | "Joe 2_facade 300 310\n",
323 | "Joe 2_garden 310 315\n",
324 | "Joe 4_masonry 315 350\n",
325 | "Joe 4_carpentry 350 365\n",
326 | "Joe 4_roofing 365 370\n",
327 | "Joe 4_facade 390 400\n",
328 | "Joe 4_garden 400 405\n",
329 | "Jim 0_plumbing 35 75\n",
330 | "Jim 0_windows 75 80\n",
331 | "Jim 0_ceiling 80 95\n",
332 | "Jim 0_painting 95 105\n",
333 | "Jim 3_ceiling 105 120\n",
334 | "Jim 0_moving 120 125\n",
335 | "Jim 3_plumbing 125 165\n",
336 | "Jim 3_painting 165 175\n",
337 | "Jim 3_windows 175 180\n",
338 | "Jim 3_moving 180 185\n",
339 | "Jim 1_ceiling 185 200\n",
340 | "Jim 1_plumbing 200 240\n",
341 | "Jim 1_painting 240 250\n",
342 | "Jim 1_windows 250 255\n",
343 | "Jim 1_moving 255 260\n",
344 | "Jim 2_plumbing 260 300\n",
345 | "Jim 2_ceiling 300 315\n",
346 | "Jim 2_painting 315 325\n",
347 | "Jim 2_windows 325 330\n",
348 | "Jim 2_moving 330 335\n",
349 | "Jim 4_plumbing 350 390\n",
350 | "Jim 4_ceiling 390 405\n",
351 | "Jim 4_painting 405 415\n",
352 | "Jim 4_windows 415 420\n",
353 | "Jim 4_moving 420 425\n"
354 | ]
355 | }
356 | ],
357 | "source": [
358 | "# Workerごとの詳細スケジュール確認\n",
359 | "\n",
360 | "for worker in WorkerNames:\n",
361 | " w_name = 'workers_' + worker\n",
362 | " sequence = msol2.get_var_solution(w_name)\n",
363 | " intervals = sequence.get_interval_variables()\n",
364 | " for interval in intervals:\n",
365 | " name = interval.get_name()\n",
366 | " start = interval.get_start()\n",
367 | " end = interval.get_end()\n",
368 | " print(worker, name, start, end)"
369 | ]
370 | },
371 | {
372 | "cell_type": "code",
373 | "execution_count": 15,
374 | "metadata": {},
375 | "outputs": [],
376 | "source": [
377 | "# visu = reload(visu)"
378 | ]
379 | },
380 | {
381 | "cell_type": "code",
382 | "execution_count": 16,
383 | "metadata": {},
384 | "outputs": [],
385 | "source": [
386 | "# グラフ表示\n",
387 | "\n",
388 | "# グラフ表示の準備\n",
389 | "import docplex.cp.utils_visu as visu\n",
390 | "import matplotlib.pyplot as plt\n",
391 | "%matplotlib inline\n",
392 | "from pylab import rcParams"
393 | ]
394 | },
395 | {
396 | "cell_type": "code",
397 | "execution_count": 17,
398 | "metadata": {},
399 | "outputs": [
400 | {
401 | "data": {
402 | "image/png": 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id0JpNHvQ7EGzT8Rumj1o9qDZI2KzFLObZg+aPWiOq1YDjGHDhuVOKI1mD5o9aPaJ2E2zB80eNHtEbJZidtPsQbMHzXGxhQQAAAAAAFQGW0gkzZ8/P3dCaTR70OxBs0/Ebpo9aPag2SNisxSzm2YPmj1ojos7MAAAAAAAQGVwB4akDRs25E4ojWYPmj1o9onYTbMHzR40e0RslmJ20+xBswfNcdVqgLFp06bcCaXR7EGzB80+Ebtp9qDZg2aPiM1SzG6aPWj2oDkutpAAAAAAAIDKYAuJpLa2ttwJpdHsQbMHzT4Ru2n2oNmDZo+IzVLMbpo9aPagOa5aDTCGDx+eO6E0mj1o9qDZJ2I3zR40e9DsEbFZitlNswfNHjTHVasBxtChQ3MnlEazB80eNPtE7KbZg2YPmj0iNksxu2n2oNmD5rhqNcBYvHhx7oTSaPag2YNmn4jdNHvQ7EGzR8RmKWY3zR40e9AcFw/xBAAAAAAAlcFDPCWtW7cud0JpNHvQ7EGzT8Rumj1o9qDZI2KzFLObZg+aPWiOq1YDjM7OztwJpdHsQbMHzT4Ru2n2oNmDZo+IzVLMbpo9aPagOS62kAAAAAAAgMpgC4mk5cuX504ojWYPmj1o9onYTbMHzR40e0RslmJ20+xBswfNcdVqgDFy5MjcCaXR7EGzB80+Ebtp9qDZg2aPiM1SzG6aPWj2oDmuWg0wWltbcyeURrMHzR40+0TsptmDZg+aPSI2SzG7afag2YPmuGo1wFi2bFnuhNJo9qDZg2afiN00e9DsQbNHxGYpZjfNHjR70BwXD/EEAAAAAACV8UYP8RyYIyaXNWvWaMSIEbkzSqHZ4/YfzNbuBx+eOwNAExvR/pQmTJiQO6PUGs3aCKBqyqylEb8mpdmD5rhqtYWku7s7d0JpNHusXfib3AkAmtzDDz+cO0FSuTWatRFA1ZRZSyN+TUqzB81x1WqAMXr06NwJpdEMANiRWKMB1EXE9Y5mD5rjqtUAY+nSpbkTSqMZALAjsUYDqIuI6x3NHjTHVasBxpgxY3InlEYzsON1rFyhK8480f77fuay8/U/33eypk46Qbd/6mr19PTYGwDWaJSVY83s6nxRN/z1BfrEacdr6qQT9K2bbrD+/mgOEdc7mj1ojqtWA4wBA+KdLs1A85h2y+364qx7dcvd92vjurV65Cd3505CDbFGI4qzLp6i//PjOfrC9+/R4l//l3790M9zJyGYiOsdzR40x1WrP4W2trbcCaXRDPSP3p4effUfP6mpk07Q9Zecp66XOvX0wvm65gOTdOVZJ+nGj1+i323cIEm67oJztPSJ30qSXli/VlMmHi1Jevapxbp68umadvbJuvKsk/TcM8slSQ/eNXPL8duuu2rLnRY77zJMktTzyit6pftlpZTcpw2wRmO7uNfM1iE769BjjpMkDRo8WPsefKjWPr86w5kjsojrHc0eNMdVqwHGgQcemDuhNJqB/rG67Wmdev5H9KUfPqCd3/JWPXrPbN169VRdMO0fdPNd92nvA9+hO77yxTf9HPd875s648LLdNOd9+rzM36s3fYYrZXLntIvZs/SDd+ZpZvuvFcDWlo05+7vb/k111/6QV1y3J9ryNBddMx7J/X3aQJ/gDUa2yPXmilJm1/YqLn3/0yHHju+P08RTSjiekezB81x1WqA0d7enjuhNJqB/jFyz7217zvfJUl6+yGHqv3ZZ/Tipo065OhjJUknnv1XWjD30Tf9HAcddqRm3n6rfvB/v6z/fm6lWncaoscfmaPlTz6hqyefpmlnn6wnHpmj9hW/n5hf9/Xv6mtzHlP3yy9r/qPV+LaaqBfWaGyPXGtmzyuv6OZpl+uMCy7VHnvt038niKYUcb2j2YPmuAbmDgCAHAYNHrzlxwMGtGjzphfe8LUDBraoKHolSd1dXVuOH3/m+3XAu4/QvAfu1WcuO1+Xf/YLUlHohLMn68PTrn3Dzze4dScdNfEU/eq+n+rdx71nB5wNAPSvXGvmbdf9nUbvs68mXfTRHXQmAIDIanUHxqhRo3InlEYz4LHzLsM09C27asHcX0qSHpw1Q4cc1fc3iyPH7KVlTz4uSXrkpz/a8mueX9GmUXvtozMuvExHTTxFbYsX6tBjj9cj9/xIG9eukSRt2rBeHatWqnPzZq3v6Juc97zyin790H0as9/+zlMEJLFGY8fo7zVTkr5zy43avGmTLr72euepoYlEXO9o9qA5rlrdgbFkyZJwe4doBnw+8blbdPs/XaOuzk6N2mtvfXz6zZKksy6ZopuumKKHZs3Qu475/R7s/5x9lx68e6YGDhyoXUeM1OTLr9SwXYfr/KlX6fpLz1Nvb6GBAwfqo9dN1+DWVv3z5R9R98svq7e3R4f+xXF673kX5jpV1BhrNHaU/lwzW1oGaOZtX9KY/fbX373/FEnSaR+6WCdP/lCWc0VMEdc7mj1ojisVRbHNLx43blwxd+7cfszpX11dXWptbc2dUQrNHtOnT9c7zrk4dwaAJrZo5r/p2mvfeGuRS5k1mrURQNWUWUsjfk1KswfN1ZdSmlcUxbjXHq/VFpLe3t7cCaXRDADYkVijAdRFxPWOZg+a46rVAGPVqlW5E0qjGQCwI7FGA6iLiOsdzR40x1WrAcb++8d7YB7NAIAdiTUaQF1EXO9o9qA5rlo9xHP16tUaPXp07oxSaPY47LDDdPpBsZoj/jnT7BOxu9mbHxo//o+/yKBM8/jx4zWhAmtjs/9/oypo9onYXZXmMmtpVZrLoNmD5rhqdQfGoEGDcieURrPH0UcfnTuhtIh/zjT7ROxu9uYJEyb0Y8m2a/bmqqDZI2KzFLO7Ks1l1qWqNJdBswfNcdXqu5AAAAAAAIBq47uQSFq0aFHuhNJo9qDZg2afiN00e9DsQbNHxGYpZjfNHjR70BxXre7A6O7uDnfrDc0eNHvQ7BOxm2YPmj1o9ojYLMXsptmDZg+aq487MCR1dXXlTiiNZg+aPWj2idhNswfNHjR7RGyWYnbT7EGzB81x1WqA0dHRkTuhNJo9aPag2SdiN80eNHvQ7BGxWYrZTbMHzR40x1WrLSQAAAAAAKDa2EIiadWqVbkTSqPZg2YPmn0idtPsQbMHzR4Rm6WY3TR70OxBc1y1GmAMGTIkd0JpNHvQ7EGzT8Rumj1o9qDZI2KzFLObZg+aPWiOiy0kAAAAAACgMthCImnBggW5E0qj2YNmD5p9InbT7EGzB80eEZulmN00e9DsQXNctboDo6enRy0tLbkzSqHZg2YPmn0idtPsQbMHzR4Rm6WY3TR70OxBc/VxB4akzZs3504ojWYPmj1o9onYTbMHzR40e0RslmJ20+xBswfNcdVqgLF+/frcCaXR7EGzB80+Ebtp9qDZg2aPiM1SzG6aPWj2oDmuWm0hAQAAAAAA1cYWEkkrVqzInVAazR40e9DsE7GbZg+aPWj2iNgsxeym2YNmD5rjqtUAY9iwYbkTSqPZg2YPmn0idtPsQbMHzR4Rm6WY3TR70OxBc1xsIQEAAAAAAJXBFhJJ8+fPz51QGs0eNHvQ7BOxm2YPmj1o9ojYLMXsptmDZg+a4+IODAAAAAAAUBncgSFpw4YNuRNKo9mDZg+afSJ20+xBswfNHhGbpZjdNHvQ7EFzXLUaYGzatCl3Qmk0e9DsQbNPxG6aPWj2oNkjYrMUs5tmD5o9aI6LLSQAAAAAAKAy2EIiqa2tLXdCaTR70OxBs0/Ebpo9aPag2SNisxSzm2YPmj1ojqtWA4zhw4fnTiiNZg+aPWj2idhNswfNHjR7RGyWYnbT7EGzB81x1WqAMXTo0NwJpdHsQbMHzT4Ru2n2oNmDZo+IzVLMbpo9aPagOa5aDTAWL16cO6E0mj1o9qDZJ2I3zR40e9DsEbFZitlNswfNHjTHxUM8AQAAAABAZfAQT0nr1q3LnVAazR40e9DsE7GbZg+aPWj2iNgsxeym2YNmD5rjqtUAo7OzM3dCaTR70OxBs0/Ebpo9aPag2SNisxSzm2YPmj1ojostJAAAAAAAoDLYQiJp+fLluRNKo9mDZg+afSJ20+xBswfNHhGbpZjdNHvQ7EFzXLUaYIwcOTJ3Qmk0e9DsQbNPxG6aPWj2oNkjYrMUs5tmD5o9aI6rVgOM1tbW3Aml0exBswfNPhG7afag2YNmj4jNUsxumj1o9qA5rloNMJYtW5Y7oTSaPWj2oNknYjfNHjR70OwRsVmK2U2zB80eNMfFQzwBAAAAAEBl8BBPSWvWrMmdUBrNHjR70OwTsZtmD5o9aPaI2CzF7KbZg2YPmuOq1QCju7s7d0JpNHvQ7EGzT8Rumj1o9qDZI2KzFLObZg+aPWiOiy0kAAAAAACgMthCImnp0qW5E0qj2YNmD5p9InbT7EGzB80eEZulmN00e9DsQXNctboDo7OzU0OGDMmdUQrNHjR70OwTsZtmD5o9aPaI2CzF7KbZg2YPmquPOzAkDRgQ73Rp9qDZg2afiN00e9DsQbNHxGYpZjfNHjR70BxXrf4U2tracieURrMHzR40+0TsptmDZg+aPSI2SzG7afag2YPmuGq1hQQAAAAAAFQbW0gktbe3504ojWYPmj1o9onYTbMHzR40e0RslmJ20+xBswfNcdVqgAEAAAAAAGJiCwkAAAAAAKgMtpAAAAAAAICwGGAAAAAAAIDKY4ABAAAAAAAqjwEGAAAAAACoPAYYAAAAAACg8hhgAAAAAACAyiv1bVRTSv8tqa3/cvrdCElrckcANcY1COTHdQjkx3UI5MU1WH37FEWx+2sPlhpgRJdSmvt630sWgAfXIJAf1yGQH9chkBfXYFxsIQEAAAAAAJXHAAMAAAAAAFRe3QYY/5I7AKg5rkEgP65DID+uQyAvrsGgavUMDAAAAAAAEFPd7sAAAAAAAAABMcAAAAAAAACVV4sBRkrp1JTS4pTS0pTSNbl7gGaVUvrXlFJHSmn+VsfellL6WUrpqca/hzeOp5TSrY3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403 | "text/plain": [
404 | ""
405 | ]
406 | },
407 | "metadata": {
408 | "needs_background": "light"
409 | },
410 | "output_type": "display_data"
411 | }
412 | ],
413 | "source": [
414 | "# 家単位の建築スケジュール\n",
415 | "\n",
416 | "rcParams['figure.figsize'] = 15, 3\n",
417 | "\n",
418 | "for h in Houses:\n",
419 | " hkey = 'house' + str(h)\n",
420 | " wt = msol2.get_var_solution(hkey)\n",
421 | " visu.interval(wt, 'lightblue', hkey)\n",
422 | "visu.show()"
423 | ]
424 | },
425 | {
426 | "cell_type": "code",
427 | "execution_count": 18,
428 | "metadata": {},
429 | "outputs": [],
430 | "source": [
431 | "visu = reload(visu)"
432 | ]
433 | },
434 | {
435 | "cell_type": "code",
436 | "execution_count": 19,
437 | "metadata": {},
438 | "outputs": [
439 | {
440 | "data": {
441 | "image/png": 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442 | "text/plain": [
443 | ""
444 | ]
445 | },
446 | "metadata": {
447 | "needs_background": "light"
448 | },
449 | "output_type": "display_data"
450 | }
451 | ],
452 | "source": [
453 | "# 作業者単位の建築スケジュール\n",
454 | "# ダブルクリックで拡大表示されます\n",
455 | "\n",
456 | "rcParams['figure.figsize'] = 80, 3\n",
457 | "\n",
458 | "for worker in WorkerNames:\n",
459 | " name = 'workers_' + worker\n",
460 | " seq = msol2.get_var_solution(name)\n",
461 | " visu.sequence(name=name, intervals=seq)\n",
462 | "visu.show()"
463 | ]
464 | },
465 | {
466 | "cell_type": "code",
467 | "execution_count": null,
468 | "metadata": {},
469 | "outputs": [],
470 | "source": []
471 | },
472 | {
473 | "cell_type": "code",
474 | "execution_count": null,
475 | "metadata": {},
476 | "outputs": [],
477 | "source": []
478 | }
479 | ],
480 | "metadata": {
481 | "kernelspec": {
482 | "display_name": "Python 3",
483 | "language": "python",
484 | "name": "python3"
485 | },
486 | "language_info": {
487 | "codemirror_mode": {
488 | "name": "ipython",
489 | "version": 3
490 | },
491 | "file_extension": ".py",
492 | "mimetype": "text/x-python",
493 | "name": "python",
494 | "nbconvert_exporter": "python",
495 | "pygments_lexer": "ipython3",
496 | "version": "3.7.3"
497 | }
498 | },
499 | "nbformat": 4,
500 | "nbformat_minor": 2
501 | }
502 |
--------------------------------------------------------------------------------