Highway2Vec Embedder¶

In [1]:

from IPython.display import display

from IPython.display import display

Get an area to embed¶

In [2]:

from srai.utils import geocode_to_region_gdf

area_gdf = geocode_to_region_gdf("Wrocław, PL")
area_gdf.plot()

from srai.utils import geocode_to_region_gdf area_gdf = geocode_to_region_gdf("Wrocław, PL") area_gdf.plot()

Out[2]:

<Axes: >

Regionalize the area with a regionalizer¶

In [3]:

from srai.regionalizers import H3Regionalizer

regionalizer = H3Regionalizer(9)
regions_gdf = regionalizer.transform(area_gdf)
print(len(regions_gdf))
display(regions_gdf.head(3))
regions_gdf.plot()

from srai.regionalizers import H3Regionalizer regionalizer = H3Regionalizer(9) regions_gdf = regionalizer.transform(area_gdf) print(len(regions_gdf)) display(regions_gdf.head(3)) regions_gdf.plot()

3168

	geometry
region_id
891e20435c7ffff	POLYGON ((16.86311 51.14966, 16.86559 51.15058...
891e2042557ffff	POLYGON ((16.93008 51.18452, 16.93012 51.18284...
891e2041c27ffff	POLYGON ((16.95755 51.07936, 16.95758 51.07767...

Out[3]:

<Axes: >

Download a road infrastructure for the area¶

In [4]:

from srai.loaders import OSMWayLoader
from srai.loaders.osm_way_loader import NetworkType

loader = OSMWayLoader(NetworkType.DRIVE)
nodes_gdf, edges_gdf = loader.load(area_gdf)

display(nodes_gdf.head(3))
display(edges_gdf.head(3))

ax = edges_gdf.plot(linewidth=1, figsize=(12, 7))
nodes_gdf.plot(ax=ax, markersize=3, color="red")

from srai.loaders import OSMWayLoader from srai.loaders.osm_way_loader import NetworkType loader = OSMWayLoader(NetworkType.DRIVE) nodes_gdf, edges_gdf = loader.load(area_gdf) display(nodes_gdf.head(3)) display(edges_gdf.head(3)) ax = edges_gdf.plot(linewidth=1, figsize=(12, 7)) nodes_gdf.plot(ax=ax, markersize=3, color="red")

	y	x	street_count	highway	ref	geometry
osmid
95584835	51.083111	17.049513	4	NaN	NaN	POINT (17.04951 51.08311)
95584841	51.084699	17.064367	3	NaN	NaN	POINT (17.06437 51.08470)
95584850	51.083328	17.035057	4	NaN	NaN	POINT (17.03506 51.08333)

	oneway	lanes-1	lanes-2	lanes-3	lanes-4	lanes-5	lanes-6	lanes-7	lanes-8	lanes-9	...	bicycle-official	lit-yes	lit-no	lit-sunset-sunrise	lit-24/7	lit-automatic	lit-disused	lit-limited	lit-interval	geometry
feature_id
0	0	0	1	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	LINESTRING (17.04951 51.08311, 17.04947 51.083...
1	0	0	1	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	LINESTRING (17.04951 51.08311, 17.04933 51.083...
2	0	0	1	1	1	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	LINESTRING (17.05357 51.08301, 17.05335 51.082...

3 rows × 219 columns

Out[4]:

<Axes: >

Find out which edges correspond to which regions¶

In [5]:

from srai.joiners import IntersectionJoiner

joiner = IntersectionJoiner()
joint_gdf = joiner.transform(regions_gdf, edges_gdf)
joint_gdf

from srai.joiners import IntersectionJoiner joiner = IntersectionJoiner() joint_gdf = joiner.transform(regions_gdf, edges_gdf) joint_gdf

Out[5]:

region_id	feature_id
891e20435c7ffff	6980
891e204351bffff	6980
891e20435cfffff	6980
891e20435c7ffff	8305
891e204351bffff	8305
...	...
891e2042ba7ffff	6370
	8125
	6372
	6371
891e204759bffff	5381

15339 rows × 0 columns

Get the embeddings for regions based on the road infrastructure¶

In [6]:

from srai.embedders import Highway2VecEmbedder
from pytorch_lightning import seed_everything

seed_everything(42)

embedder = Highway2VecEmbedder()
embeddings = embedder.fit_transform(regions_gdf, edges_gdf, joint_gdf)
embeddings

from srai.embedders import Highway2VecEmbedder from pytorch_lightning import seed_everything seed_everything(42) embedder = Highway2VecEmbedder() embeddings = embedder.fit_transform(regions_gdf, edges_gdf, joint_gdf) embeddings

Global seed set to 42
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
  warning_cache.warn(

  | Name    | Type       | Params
---------------------------------------
0 | encoder | Sequential | 16.0 K
1 | decoder | Sequential | 16.2 K
---------------------------------------
32.1 K    Trainable params
0         Non-trainable params
32.1 K    Total params
0.128     Total estimated model params size (MB)

`Trainer.fit` stopped: `max_epochs=10` reached.

Out[6]:

	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
region_id
891e2040007ffff	-0.588394	-0.145310	0.329390	-0.199922	0.333583	-0.053468	-0.029244	-0.731217	-0.276920	0.579728	...	0.222044	-0.383879	0.100069	0.175837	0.086110	0.089029	0.182174	0.340044	0.301474	0.296530
891e2040013ffff	-0.136220	-0.105376	0.310452	-0.394829	0.550002	-0.056917	-0.016684	-0.762693	-0.447748	0.844249	...	0.426936	-0.819544	0.241111	-0.330227	0.048865	0.168811	0.544063	0.559132	0.266924	0.328178
891e2040017ffff	-0.257393	-0.171729	0.178272	-0.149288	0.176805	-0.130743	0.100364	-0.717141	-0.172072	0.529592	...	0.103032	-0.533517	0.181119	0.002176	-0.082720	0.124841	0.460044	0.334705	0.140837	0.298622
891e2040023ffff	-0.273172	-0.386001	0.398755	-0.082176	0.507767	-0.113934	0.082887	-0.447940	-0.095078	0.584399	...	0.136490	-0.520931	0.175278	0.002595	-0.069401	0.304857	0.444537	0.130316	-0.027166	0.442138
891e2040027ffff	-0.623637	-0.279568	0.180895	-0.373369	0.629895	0.111698	-0.021915	-0.717526	-0.295012	0.726249	...	-0.017105	-0.623780	0.175905	-0.149013	-0.041487	0.410061	0.236078	0.222020	0.010708	0.578343
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
891e2055bc3ffff	-0.577809	-0.089938	0.250177	-0.174679	0.512738	-0.190829	0.059606	-0.842934	-0.292998	0.720076	...	0.416265	-0.579532	0.033998	-0.237962	-0.288905	0.472219	0.131982	0.490269	0.309203	0.742934
891e2055bc7ffff	-0.409222	0.095479	0.269943	-0.261908	0.821063	-0.368225	0.143521	-0.581799	-0.377443	0.738584	...	0.310113	-0.636391	0.050789	-0.241962	-0.347940	0.489631	0.012203	0.435431	0.263317	0.519553
891e2055bcbffff	-0.771704	-0.195970	0.328046	-0.108660	0.493283	0.000060	0.057297	-1.090019	-0.258585	0.696927	...	0.554331	-0.470081	-0.088866	-0.341404	-0.304259	0.633277	0.199738	0.570181	0.447835	0.974461
891e205a967ffff	-0.671492	0.112009	0.211116	-0.277808	0.285113	0.217281	-0.094777	-0.439988	-0.376565	0.489636	...	-0.294597	-0.336413	0.384200	-0.012502	0.182029	0.069917	0.270887	0.259210	0.021868	0.548017
891e205a9a7ffff	-1.641847	-1.523713	0.689654	-1.028500	1.017919	-0.716355	-0.096624	-0.636827	-0.365205	1.873264	...	0.592826	-0.650813	-0.206562	-0.080894	-0.482702	0.543556	0.592136	1.087363	0.147414	1.048715

2027 rows × 30 columns