Services planning

import pandas as pd

blocks = pd.read_pickle('./blocks.pickle')
columns = [c for c in blocks.columns if 'capacity_' in c]
blocks = blocks[['geometry', 'population', 'site_area', 'land_use', 'is_project', *columns]].copy()
blocks.head(3)

	geometry	population	site_area	land_use	is_project	capacity_bakery	capacity_bank	capacity_bar	capacity_cafe	capacity_dog_park	...	capacity_polyclinic	capacity_religion	capacity_restaurant	capacity_school	capacity_stadium	capacity_supermarket	capacity_swimming_pool	capacity_train_station	capacity_university	capacity_veterinary
0	POLYGON ((388726.347 6637020.627, 388713.674 6...	2.0	2.127981e+07	None	NaN	0.0	0.0	0.0	0.0	0.0	...	0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0
1	POLYGON ((388713.674 6637030.346, 388726.347 6...	0.0	5.171425e+01	None	NaN	0.0	0.0	0.0	0.0	0.0	...	0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0
2	POLYGON ((374726.595 6642638.322, 374534.487 6...	491.0	7.593088e+07	None	NaN	0.0	0.0	0.0	500.0	0.0	...	0	0.0	0.0	0	0.0	0.0	0.0	0.0	120.0	0.0

3 rows × 33 columns

accessibility_matrix = pd.read_pickle('./accessibility_matrix.pickle')
accessibility_matrix.head()

/home/vasilstar/masterplanning/.venv/lib/python3.10/site-packages/pandas/io/formats/format.py:1458: RuntimeWarning: overflow encountered in cast
  has_large_values = (abs_vals > 1e6).any()

	0	1	2	3	4	5	6	7	8	9	...	2643	2644	2645	2646	2647	2648	2649	2650	2651	2652
0	0.00000	35.375	188.0000	168.500	116.6250	140.50000	114.2500	162.1250	107.750000	110.687500	...	91.75000	91.5000	91.31250	91.25000	90.8125	90.43750	90.93750	91.75000	68.12500	86.5625
1	34.65625	0.000	155.8750	136.375	84.5625	108.50000	82.1875	130.0000	75.625000	78.562500	...	59.71875	59.4375	59.28125	59.21875	58.8125	58.40625	58.90625	59.71875	36.09375	54.5000
2	191.87500	159.125	0.0000	105.875	113.9375	154.50000	128.2500	176.2500	121.812500	124.750000	...	159.75000	159.5000	159.25000	159.25000	158.8750	158.50000	159.00000	158.12500	136.12500	152.8750
3	171.12500	138.250	105.8750	0.000	171.6250	209.50000	186.0000	233.7500	179.375000	182.375000	...	139.00000	138.6250	138.50000	138.50000	138.0000	137.62500	138.12500	137.25000	115.31250	132.1250
4	118.75000	85.875	113.3125	175.250	0.0000	47.90625	21.6875	69.5625	15.148438	18.109375	...	117.31250	117.0625	116.87500	116.87500	116.4375	116.06250	116.50000	116.87500	93.75000	111.6875

5 rows × 2653 columns

blocks[blocks.is_project == True].plot('land_use', legend=True, edgecolor='black')
len(blocks[blocks.is_project == True])

176

Service distribution

basic_services = {
    "kindergarten": 0.2,
    "school": 0.2,
    "pharmacy": 0.2,
    "polyclinics": 0.3,
    "convenience": 0.2,
    "cafe": 0.15,
    "playground": 0.2,
    "post": 0.1,
    "hairdresser": 0.1
}
advanced_services = {
    "hospital": 0.5,
    "supermarket": 0.4,
    "restaurant": 0.3,
    "park": 0.8,
    "bank": 0.3,
    "fuel": 0.25,
    "police": 0.2,
    "cinema": 0.2,
    "market": 0.2,
    "religion": 0.5,
    "multifunctional_center": 0.1,
    "train_building": 0.1,
    "cemetery": 0.1,
    "notary": 0.1,
    "pitch": 0.2
}
comfort_services = {
    "mall": 0.3,
    "swimming_pool": 0.4,
    "theatre": 0.2,
    "museum": 0.2,
    "university": 0.2,
    "sanatorium": 0.2,
    "zoo": 0.1,
    "reserve": 0.1,
    "animal_shelter": 0.1,
    "aeroway_terminal": 0.2,
    "crematorium": 0.1,
    "prison": 0.1,
    "landfill": 0.1,
    "bar": 0.3,
    "hotel": 0.3,
}


def get_service_weight(service_name):
    if service_name in basic_services:
        return 0.5714 * basic_services[service_name]
    elif service_name in advanced_services:
        return 0.2857 * advanced_services[service_name]
    elif service_name in comfort_services:
        return 0.1429 * comfort_services[service_name]
    return 0

Service types and weights

chosen_service_types = set(basic_services) | set(advanced_services) | set(comfort_services)
service_weights = {service_type: get_service_weight(service_type) for service_type in chosen_service_types if f'capacity_{service_type}' in blocks.columns}

Select ID of optimized blocks

from blocksnet.enums import LandUse
from blocksnet.config import service_types_config


def get_land_use_min_site_area(lu : LandUse):
    land_use_df = service_types_config.land_use
    units_df = service_types_config.units

    land_use_s = land_use_df[lu]
    lu_service_types = land_use_s[land_use_s].index.to_list()

    lu_units = units_df[units_df.service_type.isin(lu_service_types) & units_df.service_type.isin(chosen_service_types)]
    return lu_units[lu_units.site_area > 0].site_area.min()

min_site_areas = {lu:get_land_use_min_site_area(lu) for lu in LandUse}

blocks_lu = {}
for block_id,row in blocks.iterrows():
    if row.is_project == True and row.land_use is not None:
        min_site_area = min_site_areas[row.land_use]
        if min_site_area>row.site_area*0.8:
            continue
        blocks_lu[block_id] = row.land_use
len(blocks_lu)

130

Initialize and run optimizer

from blocksnet.optimization.services import (
    TPEOptimizer,
    WeightedObjective,
    WeightedConstraints,
    Facade,
    GradientChooser,
    RandomOrder,
    AreaSolution
)

MAX_TIME_SEC = 60*5
MAX_PROV_EVALS = 100
MAX_OPT_RUNS = 1000

var_adapter = AreaSolution(blocks_lu)

facade = Facade(
    blocks_lu=blocks_lu,
    blocks_df=blocks,
    accessibility_matrix=accessibility_matrix,
    var_adapter=var_adapter,
)

from tqdm import tqdm

for service_type, weight in tqdm(service_weights.items()):
    facade.add_service_type(service_type, weight, blocks.rename(columns={f'capacity_{service_type}': 'capacity'})[['capacity']].fillna(0))

objective = WeightedObjective(num_params=facade.num_params, facade=facade, weights=service_weights, max_evals=MAX_PROV_EVALS)

constraints = WeightedConstraints(num_params=facade.num_params, facade=facade, priority=service_weights)

100%|██████████| 21/21 [00:07<00:00,  2.89it/s]

tpe_optimizer = TPEOptimizer(
    objective=objective, constraints=constraints, vars_order=RandomOrder(), vars_chooser=GradientChooser(facade, num_params=facade.num_params, num_top=5)
)

best_x, best_val, perc, func_evals = tpe_optimizer.run(max_runs=MAX_OPT_RUNS, timeout=MAX_TIME_SEC, initial_runs_num=1)

[I 2025-09-25 19:49:21,212] A new study created in memory with name: no-name-adea122e-ad09-4c4c-b258-587d9cbc3883

[I 2025-09-25 19:50:04,439] Trial 0 finished with value: 1.70666108849601 and parameters: {'x_499': 0, 'x_999': 0, 'x_95': 60, 'x_1771': 0, 'x_2140': 0, 'x_1057': 150, 'x_641': 270, 'x_433': 0, 'x_928': 450, 'x_1697': 0, 'x_1173': 0, 'x_1540': 0, 'x_1995': 0, 'x_966': 450, 'x_673': 200, 'x_701': 0, 'x_603': 270, 'x_596': 200, 'x_2249': 0, 'x_2171': 0, 'x_2228': 0, 'x_1026': 150, 'x_18': 60, 'x_765': 0, 'x_412': 0, 'x_690': 0, 'x_1876': 0, 'x_1909': 0, 'x_1474': 0, 'x_2033': 0, 'x_1793': 0, 'x_1213': 0, 'x_1836': 700, 'x_1565': 0, 'x_1361': 0, 'x_134': 100, 'x_1000': 0, 'x_2057': 0, 'x_576': 200, 'x_1624': 0, 'x_511': 0, 'x_944': 450, 'x_456': 0, 'x_526': 0, 'x_1638': 0, 'x_1597': 0, 'x_496': 0, 'x_1904': 700, 'x_1612': 0, 'x_141': 100, 'x_1905': 700, 'x_1206': 0, 'x_1014': 0, 'x_70': 0, 'x_1469': 0, 'x_489': 30000, 'x_1585': 0, 'x_275': 0, 'x_894': 0, 'x_372': 0, 'x_572': 270, 'x_2191': 0, 'x_785': 0, 'x_135': 100, 'x_1606': 0, 'x_1754': 0, 'x_235': 2000, 'x_442': 0, 'x_1023': 150, 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Best is trial 0 with value: 1.70666108849601.
[I 2025-09-25 19:50:59,149] Trial 1 finished with value: 1.7066610884960103 and parameters: {'x_1776': 1, 'x_1294': 50, 'x_127': 100, 'x_915': 450, 'x_507': 0, 'x_1614': 0, 'x_279': 2, 'x_357': 1, 'x_326': 0, 'x_1764': 1, 'x_144': 100, 'x_766': 0, 'x_716': 80, 'x_2148': 0, 'x_1528': 1, 'x_825': 0, 'x_397': 0, 'x_1711': 2, 'x_928': 450, 'x_1635': 1, 'x_380': 9, 'x_2080': 0, 'x_875': 0, 'x_1455': 0, 'x_426': 5, 'x_282': 2001, 'x_463': 3, 'x_292': 13, 'x_241': 2000, 'x_548': 0, 'x_81': 60, 'x_2071': 10, 'x_18': 60, 'x_56': 61, 'x_322': 0, 'x_1212': 0, 'x_1698': 18500, 'x_264': 0, 'x_1127': 150, 'x_1729': 1, 'x_677': 0, 'x_1898': 2, 'x_251': 2000, 'x_570': 0, 'x_383': 0, 'x_1536': 2, 'x_968': 450, 'x_1131': 0, 'x_771': 1, 'x_1352': 1, 'x_443': 1, 'x_38': 60, 'x_1545': 9, 'x_860': 1, 'x_557': 0, 'x_286': 1, 'x_1646': 0, 'x_482': 20000, 'x_2082': 0, 'x_472': 0, 'x_1897': 700, 'x_445': 6, 'x_177': 100, 'x_138': 100, 'x_1495': 0, 'x_2034': 0, 'x_294': 0, 'x_836': 0, 'x_1405': 14, 'x_2063': 0, 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Best is trial 1 with value: 1.7066610884960103.
[I 2025-09-25 19:52:38,362] Trial 2 finished with value: 1.6922742800520252 and parameters: {'x_2247': 400, 'x_963': 290, 'x_431': 114, 'x_382': 225, 'x_41': 24, 'x_940': 255, 'x_1956': 3396, 'x_1407': 0, 'x_2034': 22, 'x_1201': 0, 'x_1014': 2, 'x_275': 0, 'x_1581': 0, 'x_869': 0, 'x_227': 0, 'x_992': 117, 'x_1610': 0, 'x_108': 29, 'x_1275': 0, 'x_567': 0, 'x_1296': 50, 'x_257': 0, 'x_1994': 5388, 'x_2090': 0, 'x_1290': 50, 'x_1159': 3000, 'x_1567': 0, 'x_1297': 50, 'x_1375': 0, 'x_588': 0, 'x_2156': 0, 'x_90': 43, 'x_1509': 0, 'x_35': 58, 'x_1512': 12000, 'x_1831': 0, 'x_705': 0, 'x_1982': 2296, 'x_481': 20000, 'x_141': 0, 'x_170': 0, 'x_491': 0, 'x_535': 0, 'x_908': 225, 'x_391': 180, 'x_1566': 0, 'x_136': 0, 'x_1148': 0, 'x_985': 54, 'x_1178': 3000, 'x_1036': 0, 'x_1272': 50, 'x_1700': 0, 'x_1619': 0, 'x_75': 72, 'x_2186': 2069, 'x_898': 0, 'x_463': 0, 'x_1759': 0, 'x_1758': 0, 'x_2203': 2171, 'x_1062': 0, 'x_613': 270, 'x_2122': 0, 'x_27': 28, 'x_248': 2000, 'x_541': 0, 'x_1946': 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Best is trial 1 with value: 1.7066610884960103.
[I 2025-09-25 19:53:15,218] Trial 3 finished with value: 1.692274280052025 and parameters: {'x_136': 16, 'x_825': 803, 'x_1182': 3165, 'x_424': 873, 'x_1205': 720, 'x_1578': 200, 'x_657': 200, 'x_2232': 5, 'x_353': 787, 'x_175': 99, 'x_889': 35, 'x_93': 100, 'x_1065': 394, 'x_376': 272, 'x_2002': 1589, 'x_105': 38, 'x_980': 57, 'x_1013': 0, 'x_582': 902, 'x_735': 1454, 'x_357': 828, 'x_1288': 50, 'x_1947': 8084, 'x_1340': 50, 'x_2117': 706, 'x_1623': 1812, 'x_51': 603, 'x_1795': 237, 'x_1529': 112, 'x_545': 150, 'x_130': 288, 'x_2256': 50, 'x_86': 102, 'x_602': 718, 'x_2196': 1, 'x_241': 2211, 'x_34': 688, 'x_519': 710, 'x_1977': 2008, 'x_1218': 936, 'x_1316': 50, 'x_553': 0, 'x_1513': 11222, 'x_1796': 403, 'x_836': 704, 'x_1357': 839, 'x_276': 2758, 'x_285': 0, 'x_240': 2306, 'x_363': 9, 'x_1818': 769, 'x_1723': 0, 'x_1276': 60, 'x_710': 20162, 'x_1616': 895, 'x_1759': 8, 'x_560': 417, 'x_246': 2000, 'x_2': 305, 'x_1206': 1186, 'x_1267': 50, 'x_613': 595, 'x_79': 60, 'x_1178': 3030, 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60, 'x_606': 270, 'x_17': 0, 'x_292': 0, 'x_1662': 0, 'x_711': 0, 'x_394': 0, 'x_922': 0, 'x_515': 0, 'x_203': 0, 'x_784': 0, 'x_1856': 700, 'x_783': 0, 'x_247': 2000, 'x_1708': 0, 'x_716': 0, 'x_1226': 0, 'x_1569': 0, 'x_2114': 0, 'x_266': 2000, 'x_1456': 0, 'x_1330': 50, 'x_352': 0, 'x_1366': 0, 'x_1528': 0, 'x_1712': 0, 'x_1331': 50, 'x_971': 0, 'x_3': 0, 'x_1275': 0, 'x_1157': 3000, 'x_1051': 0, 'x_886': 0, 'x_862': 0, 'x_1035': 0, 'x_1016': 0, 'x_1862': 700, 'x_1403': 0, 'x_1435': 0, 'x_287': 0, 'x_1375': 0, 'x_1774': 0, 'x_1116': 0, 'x_555': 0, 'x_1967': 0, 'x_2243': 0, 'x_670': 0, 'x_766': 0, 'x_827': 3520, 'x_1195': 0, 'x_456': 0, 'x_1241': 3000, 'x_148': 0, 'x_1421': 0, 'x_452': 0, 'x_1378': 0, 'x_360': 0, 'x_591': 270, 'x_531': 0, 'x_1281': 50, 'x_41': 0, 'x_2087': 0, 'x_2005': 0, 'x_2124': 0, 'x_422': 0, 'x_1094': 0, 'x_1376': 0, 'x_304': 2000, 'x_1568': 0, 'x_571': 0, 'x_1959': 0, 'x_808': 0, 'x_372': 0, 'x_1758': 0, 'x_1119': 0, 'x_2069': 0, 'x_143': 0, 'x_709': 0, 'x_1608': 0, 'x_627': 270, 'x_2043': 0, 'x_29': 0, 'x_1724': 0, 'x_1741': 0, 'x_1064': 0, 'x_1965': 0, 'x_2107': 0, 'x_1945': 0, 'x_534': 0, 'x_324': 0, 'x_2007': 0, 'x_101': 0, 'x_0': 0, 'x_261': 2000, 'x_1144': 0, 'x_1845': 700, 'x_167': 0, 'x_345': 0, 'x_398': 0, 'x_1325': 50, 'x_525': 0, 'x_1491': 0, 'x_1349': 0, 'x_1486': 0, 'x_2126': 0, 'x_20': 0, 'x_1761': 0, 'x_822': 0, 'x_2105': 0, 'x_1128': 0, 'x_1131': 0, 'x_1036': 0, 'x_1141': 3000, 'x_1534': 0, 'x_1864': 700, 'x_1757': 0, 'x_59': 0, 'x_2082': 0, 'x_864': 0, 'x_966': 0, 'x_172': 0, 'x_1738': 0, 'x_780': 0, 'x_1787': 0, 'x_551': 0, 'x_4': 60, 'x_535': 0, 'x_1515': 0, 'x_1506': 0, 'x_450': 0, 'x_5': 0, 'x_183': 0, 'x_390': 0, 'x_628': 270, 'x_178': 0, 'x_523': 0, 'x_1093': 0, 'x_1775': 0, 'x_831': 0, 'x_1098': 0, 'x_1886': 534, 'x_957': 0, 'x_1880': 0, 'x_2236': 0, 'x_2053': 0, 'x_1228': 0, 'x_284': 0, 'x_189': 0, 'x_501': 0, 'x_1603': 0, 'x_1571': 0, 'x_462': 20000, 'x_2158': 0, 'x_1887': 700, 'x_2200': 0, 'x_906': 0, 'x_469': 0, 'x_68': 0, 'x_2129': 0, 'x_1526': 0, 'x_956': 0, 'x_1909': 0, 'x_1743': 0, 'x_1540': 0, 'x_1637': 0, 'x_810': 3520, 'x_920': 0, 'x_1527': 0, 'x_1976': 0, 'x_283': 0, 'x_1953': 0, 'x_1199': 0, 'x_883': 0, 'x_739': 0, 'x_1989': 0, 'x_1121': 0, 'x_611': 270, 'x_561': 0, 'x_1697': 0, 'x_954': 0, 'x_2269': 0, 'x_1567': 0, 'x_1846': 700, 'x_1450': 0, 'x_124': 0, 'x_1908': 4, 'x_1443': 0, 'x_1896': 0, 'x_1465': 0, 'x_985': 0, 'x_1483': 0, 'x_607': 270, 'x_1203': 0, 'x_537': 0, 'x_764': 0, 'x_1152': 0, 'x_1028': 0, 'x_1407': 0, 'x_1671': 0, 'x_1875': 0, 'x_2063': 0, 'x_1822': 700, 'x_794': 0, 'x_1006': 0, 'x_237': 0, 'x_1728': 0, 'x_1084': 0, 'x_963': 0, 'x_337': 0, 'x_1584': 0, 'x_1402': 0, 'x_106': 0, 'x_2260': 0, 'x_243': 2000, 'x_998': 0, 'x_2115': 0, 'x_932': 0, 'x_1129': 0, 'x_961': 0, 'x_1681': 0, 'x_1848': 0, 'x_1717': 0, 'x_1694': 0, 'x_1870': 700, 'x_1089': 0, 'x_211': 0, 'x_39': 0, 'x_533': 0, 'x_2050': 0, 'x_207': 0, 'x_1955': 0, 'x_943': 0, 'x_2135': 0, 'x_631': 270, 'x_536': 0, 'x_430': 0, 'x_1710': 0, 'x_1586': 0, 'x_1854': 700, 'x_1472': 0, 'x_1978': 0, 'x_1075': 0, 'x_2147': 0, 'x_1606': 0, 'x_2025': 0, 'x_977': 0, 'x_1269': 50, 'x_1872': 700, 'x_565': 0, 'x_2173': 0, 'x_2060': 0, 'x_461': 0, 'x_593': 200, 'x_603': 270, 'x_245': 2000, 'x_157': 0, 'x_1072': 0, 'x_1746': 0, 'x_1209': 0, 'x_1921': 0, 'x_578': 200, 'x_1777': 0, 'x_1487': 0, 'x_1419': 0, 'x_890': 0, 'x_1248': 0, 'x_1693': 0, 'x_1002': 0, 'x_2038': 0, 'x_14': 0, 'x_1832': 700, 'x_532': 0, 'x_2024': 0, 'x_260': 0, 'x_2149': 0, 'x_1771': 0, 'x_1319': 50, 'x_689': 0, 'x_361': 0, 'x_1581': 0, 'x_66': 60, 'x_1559': 20, 'x_1944': 0, 'x_768': 0, 'x_89': 0, 'x_512': 30000, 'x_1204': 0, 'x_1703': 0, 'x_654': 0, 'x_896': 0, 'x_95': 0, 'x_850': 3520, 'x_1113': 0, 'x_859': 0, 'x_1661': 0, 'x_499': 0, 'x_36': 0, 'x_156': 0, 'x_1620': 0, 'x_2021': 0, 'x_1531': 0, 'x_832': 0, 'x_2017': 0, 'x_548': 0, 'x_1406': 0, 'x_1397': 0, 'x_267': 2000, 'x_748': 0, 'x_1726': 0, 'x_1250': 0, 'x_427': 0, 'x_641': 270, 'x_1941': 0, 'x_1181': 0, 'x_1106': 0, 'x_2139': 0, 'x_1558': 0, 'x_170': 0, 'x_1963': 0, 'x_1633': 0, 'x_1727': 0, 'x_1763': 0, 'x_453': 0, 'x_529': 0, 'x_2065': 0, 'x_926': 0, 'x_1476': 0, 'x_1626': 0, 'x_1183': 0, 'x_2254': 0, 'x_1021': 0, 'x_736': 0, 'x_1772': 0, 'x_817': 0, 'x_775': 0, 'x_1324': 50, 'x_94': 0, 'x_1767': 0, 'x_1935': 0, 'x_27': 0, 'x_1474': 0, 'x_1855': 0, 'x_1426': 0, 'x_1825': 0, 'x_1666': 0, 'x_1938': 0, 'x_929': 0, 'x_1168': 0, 'x_496': 0, 'x_2179': 0, 'x_844': 3520, 'x_2191': 0, 'x_761': 0, 'x_37': 60, 'x_281': 2000, 'x_585': 0, 'x_313': 0, 'x_712': 0, 'x_549': 0, 'x_1490': 0, 'x_1514': 0, 'x_1236': 0, 'x_43': 0, 'x_1007': 0, 'x_1164': 0, 'x_1858': 700, 'x_212': 0, 'x_132': 0, 'x_1907': 730, 'x_367': 0, 'x_308': 0, 'x_1314': 50, 'x_2119': 0, 'x_1063': 0, 'x_319': 0, 'x_1455': 0, 'x_200': 0, 'x_1646': 0, 'x_1108': 0, 'x_168': 0, 'x_1462': 0, 'x_185': 0, 'x_1125': 0}. Best is trial 1 with value: 1.7066610884960103.
[I 2025-09-25 19:55:06,370] Trial 4 pruned.

Convert to services dataframe

solution_df = facade.solution_to_services_df(best_x)
solution_df

	block_id	service_type	site_area	build_floor_area	capacity	count
12	2481	market	3000.0	6000.0	3000	1
28	2485	market	3000.0	6000.0	3000	1
40	2488	market	3000.0	6000.0	3000	1
46	2489	market	6000.0	12000.0	8000	1
80	2500	market	3000.0	6000.0	3000	1
...	...	...	...	...	...	...
10739	2619	playground	50.0	0.0	20	1
10742	2620	playground	50.0	0.0	20	1
10745	2622	playground	50.0	0.0	20	1
10748	2623	playground	50.0	0.0	20	1
10754	2625	playground	50.0	0.0	20	1

1149 rows × 6 columns

Extract area dataframe from solution

solution_area_df = facade.get_solution_area_df(best_x)
solution_area_df.to_csv(f'solution_area.csv')
solution_area_df.loc[blocks_lu.keys()]

	population	build_floor_area	living_area	footprint_area
2477	0.0	515564.859674	0.000000	103112.971935
2478	0.0	54367.675735	0.000000	0.000000
2479	27.0	5554.606210	294.606210	987.485548
2480	1541.0	31312.721131	16512.721131	5566.705979
2481	3759.0	61444.753533	40284.753533	10923.511739
...	...	...	...	...
2648	0.0	43.745961	3.745961	7.777060
2649	3.0	104.006643	34.006643	18.490070
2650	7.0	225.528632	80.528632	40.093979
2651	0.0	888.350229	0.000000	94.757358
2652	0.0	1792.659911	0.000000	0.000000

130 rows × 4 columns

solution_area_df.population.sum()

np.float64(57202.0)

Visualization

import matplotlib.pyplot as plt


def plot_df(df):
    df_called_obj = df.loc[df["called_obj"] == True]

    plt.scatter(df_called_obj["func_evals"], df_called_obj["best_val"], c="red", s=5)

    df_called_obj = pd.concat(
        [pd.DataFrame([[-1, None, 0, 0, None, 0, False, 0]], columns=df_called_obj.columns), df_called_obj]
    )
    plt.plot(
        df_called_obj["func_evals"], df_called_obj["best_val"], label="Best Value (TPE)", color="blue", linewidth=1
    )
    # Добавление точек с цветом в зависимости от 'called_obj'
    # Настройка осей и заголовка
    plt.xlabel("Number of objective evaluation")
    plt.ylabel("Best Value")
    plt.title("Best Value Across Trials")
    plt.legend()
    plt.grid(True)

    # Отображение графика
    plt.show()

tpe_df

	best_val	provisions
0	1.709001	{'market': 1.0, 'hotel': 1.0, 'bar': 1.0, 'pit...
1	1.709001	{'bar': 1.0, 'hotel': 1.0, 'park': 1.0, 'super...

tpe_df

	best_val	provisions
0	1.709001	{'market': 1.0, 'hotel': 1.0, 'bar': 1.0, 'pit...
1	1.709001	{'bar': 1.0, 'hotel': 1.0, 'park': 1.0, 'super...

import pandas as pd

tpe_df = pd.read_csv("provisions.csv")
plot_df(tpe_df)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/masterplanning/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
   3804 try:
-> 3805     return self._engine.get_loc(casted_key)
   3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'called_obj'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[21], line 4
      1 import pandas as pd
      3 tpe_df = pd.read_csv("provisions.csv")
----> 4 plot_df(tpe_df)

Cell In[10], line 5, in plot_df(df)
      4 def plot_df(df):
----> 5     df_called_obj = df.loc[df["called_obj"] == True]
      7     plt.scatter(df_called_obj["func_evals"], df_called_obj["best_val"], c="red", s=5)
      9     df_called_obj = pd.concat(
     10         [pd.DataFrame([[-1, None, 0, 0, None, 0, False, 0]], columns=df_called_obj.columns), df_called_obj]
     11     )

File ~/masterplanning/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102, in DataFrame.__getitem__(self, key)
   4100 if self.columns.nlevels > 1:
   4101     return self._getitem_multilevel(key)
-> 4102 indexer = self.columns.get_loc(key)
   4103 if is_integer(indexer):
   4104     indexer = [indexer]

File ~/masterplanning/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3807     if isinstance(casted_key, slice) or (
   3808         isinstance(casted_key, abc.Iterable)
   3809         and any(isinstance(x, slice) for x in casted_key)
   3810     ):
   3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
   3813 except TypeError:
   3814     # If we have a listlike key, _check_indexing_error will raise
   3815     #  InvalidIndexError. Otherwise we fall through and re-raise
   3816     #  the TypeError.
   3817     self._check_indexing_error(key)

KeyError: 'called_obj'

facade.save_delta_demand(best_x)
population_df = pd.read_csv("demand_services.csv")
population_df.head(20)

	Block id	Service type	Demand
0	39	swimming_pool	0
1	39	park	0
2	39	university	0
3	39	police	0
4	39	multifunctional_center	0
5	39	museum	0
6	39	hotel	0
7	39	pitch	0
8	61	swimming_pool	0
9	61	park	0
10	61	university	0
11	61	police	0
12	61	multifunctional_center	0
13	61	museum	0
14	61	hotel	0
15	61	pitch	0
16	194	mall	262145
17	194	police	163840
18	194	museum	98304
19	194	cafe	819203