Highway2Vec Embedder¶

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from IPython.display import display

from srai.plotting import plot_numeric_data, plot_regions
from IPython.display import display

from srai.plotting import plot_numeric_data, plot_regions

Get an area to embed¶

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from srai.regionalizers import geocode_to_region_gdf

area_gdf = geocode_to_region_gdf("Wrocław, PL")
plot_regions(area_gdf, tiles_style="CartoDB positron")
from srai.regionalizers import geocode_to_region_gdf

area_gdf = geocode_to_region_gdf("Wrocław, PL")
plot_regions(area_gdf, tiles_style="CartoDB positron")

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Regionalize the area with a regionalizer¶

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from srai.regionalizers import H3Regionalizer

regionalizer = H3Regionalizer(9)
regions_gdf = regionalizer.transform(area_gdf)
print(len(regions_gdf))
display(regions_gdf.head(3))
plot_regions(regions_gdf, tiles_style="CartoDB positron")
from srai.regionalizers import H3Regionalizer

regionalizer = H3Regionalizer(9)
regions_gdf = regionalizer.transform(area_gdf)
print(len(regions_gdf))
display(regions_gdf.head(3))
plot_regions(regions_gdf, tiles_style="CartoDB positron")

	geometry
region_id
891e2040e43ffff	POLYGON ((16.98227 51.09526, 16.98231 51.09357...
891e204e413ffff	POLYGON ((17.02548 51.06877, 17.02552 51.06708...
891e2040d7bffff	POLYGON ((17.04212 51.11219, 17.04215 51.11051...

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Download a road infrastructure for the area¶

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from srai.loaders import OSMNetworkType, OSMWayLoader

loader = OSMWayLoader(OSMNetworkType.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 OSMNetworkType, OSMWayLoader

loader = OSMWayLoader(OSMNetworkType.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")

/root/development/srai/srai/loaders/osm_way_loader/osm_way_loader.py:229: FutureWarning: The clean_periphery argument has been deprecated and will be removed in the v2.0.0 release. Future behavior will be as though clean_periphery=True. See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123
  G_directed = ox.graph_from_polygon(

/root/development/srai/.venv/lib/python3.10/site-packages/osmnx/_overpass.py:350: FutureWarning: `settings.timeout` is deprecated and will be removed in the v2.0.0 release: use `settings.requests_timeout` instead. See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123
  overpass_settings = _make_overpass_settings()
/root/development/srai/.venv/lib/python3.10/site-packages/osmnx/_overpass.py:360: FutureWarning: `settings.timeout` is deprecated and will be removed in the v2.0.0 release: use `settings.requests_timeout` instead. See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123
  yield _overpass_request(data={"data": query_str})
/root/development/srai/.venv/lib/python3.10/site-packages/osmnx/_overpass.py:442: FutureWarning: `settings.timeout` is deprecated and will be removed in the v2.0.0 release: use `settings.requests_timeout` instead. See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123
  this_pause = _get_overpass_pause(overpass_endpoint)

/root/development/srai/srai/loaders/osm_way_loader/osm_way_loader.py:237: FutureWarning: The `get_undirected` function is deprecated and will be removed in the v2.0.0 release. Replace it with `convert.to_undirected` instead. See the OSMnx v2 migration guide: https://github.com/gboeing/osmnx/issues/1123
  G_undirected = ox.utils_graph.get_undirected(G_directed)

	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

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Find out which edges correspond to which regions¶

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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

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region_id	feature_id
891e2040e43ffff	1865
	1012
	1013
	8145
	8144
...	...
891e204087bffff	1261
	1262
	4294
	1299
	9014

15647 rows × 0 columns

Get the embeddings for regions based on the road infrastructure¶

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from pytorch_lightning import seed_everything

from srai.embedders import Highway2VecEmbedder

seed_everything(42)

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

from srai.embedders import Highway2VecEmbedder

seed_everything(42)

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

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

/root/development/srai/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: 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

  | 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)

/root/development/srai/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.

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

/root/development/srai/srai/embedders/highway2vec/embedder.py:75: FutureWarning: The behavior of array concatenation with empty entries is deprecated. In a future version, this will no longer exclude empty items when determining the result dtype. To retain the old behavior, exclude the empty entries before the concat operation.
  embeddings_joint = joint_gdf.join(embeddings_df)

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	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
region_id
891e2040007ffff	-0.504556	-0.155268	0.248344	-0.168650	0.372300	-0.108547	0.060736	-0.567865	-0.246932	0.704372	...	0.351554	-0.545886	0.100016	0.071949	0.036495	0.044149	0.138407	0.349743	0.143677	0.377119
891e2040013ffff	-0.063886	-0.108321	0.347798	-0.290935	0.602296	-0.059722	-0.044114	-0.559339	-0.445059	0.776166	...	0.484868	-0.795263	0.164593	-0.346911	0.031975	0.071917	0.416641	0.402075	0.191143	0.371276
891e2040017ffff	-0.228718	-0.275933	0.255260	-0.190627	0.432342	-0.215445	0.133701	-0.700056	-0.202570	0.596845	...	0.287095	-0.573274	0.138941	-0.006875	0.066737	0.056983	0.448730	0.247664	0.155226	0.399925
891e2040023ffff	-0.234212	-0.375628	0.367217	-0.050228	0.514135	-0.154354	0.117761	-0.352391	-0.092301	0.592975	...	0.164884	-0.505890	0.135076	0.015516	-0.047556	0.226682	0.383343	0.077571	-0.039865	0.448460
891e2040027ffff	-0.575319	-0.284612	0.151406	-0.326954	0.650756	0.059048	0.051050	-0.612110	-0.325108	0.762787	...	0.046838	-0.616633	0.154819	-0.128314	-0.016028	0.298513	0.157719	0.189196	0.009537	0.601834
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
891e2055bc3ffff	-0.454497	-0.025613	0.235932	-0.094423	0.557578	-0.266869	0.049226	-0.635413	-0.269236	0.767842	...	0.591636	-0.581389	-0.040448	-0.234895	-0.278276	0.281510	0.030581	0.357593	0.298373	0.764828
891e2055bc7ffff	-0.257726	0.119923	0.215199	-0.174138	0.857351	-0.446207	0.197454	-0.412605	-0.361281	0.772351	...	0.420934	-0.605371	0.024094	-0.226583	-0.369977	0.297929	-0.087533	0.319609	0.256416	0.522280
891e2055bcbffff	-0.635451	-0.077627	0.333176	-0.028319	0.522318	-0.090063	0.017219	-0.862788	-0.221086	0.758205	...	0.803460	-0.498680	-0.225858	-0.347445	-0.263835	0.403450	0.074003	0.436040	0.425636	1.040164
891e205a967ffff	-0.648195	0.104950	0.235534	-0.266269	0.352077	0.191819	-0.106896	-0.260897	-0.384404	0.531822	...	-0.268065	-0.331405	0.331181	-0.005222	0.203001	0.011407	0.175702	0.221664	0.043823	0.566290
891e205a9a7ffff	-1.458649	-1.413952	0.511791	-1.238288	0.946567	-0.871188	-0.053677	-0.542523	-0.548428	1.703901	...	0.655956	-0.768179	-0.194526	-0.075900	-0.438048	0.374782	0.549085	0.899736	0.075891	1.142536

2035 rows × 30 columns

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from sklearn.cluster import KMeans

clusterizer = KMeans(n_clusters=5, random_state=42)
clusterizer.fit(embeddings)

embeddings["cluster"] = clusterizer.labels_
embeddings
from sklearn.cluster import KMeans

clusterizer = KMeans(n_clusters=5, random_state=42)
clusterizer.fit(embeddings)

embeddings["cluster"] = clusterizer.labels_
embeddings

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	0	1	2	3	4	5	6	7	8	9	...	21	22	23	24	25	26	27	28	29	cluster
region_id
891e2040007ffff	-0.504556	-0.155268	0.248344	-0.168650	0.372300	-0.108547	0.060736	-0.567865	-0.246932	0.704372	...	-0.545886	0.100016	0.071949	0.036495	0.044149	0.138407	0.349743	0.143677	0.377119	3
891e2040013ffff	-0.063886	-0.108321	0.347798	-0.290935	0.602296	-0.059722	-0.044114	-0.559339	-0.445059	0.776166	...	-0.795263	0.164593	-0.346911	0.031975	0.071917	0.416641	0.402075	0.191143	0.371276	0
891e2040017ffff	-0.228718	-0.275933	0.255260	-0.190627	0.432342	-0.215445	0.133701	-0.700056	-0.202570	0.596845	...	-0.573274	0.138941	-0.006875	0.066737	0.056983	0.448730	0.247664	0.155226	0.399925	0
891e2040023ffff	-0.234212	-0.375628	0.367217	-0.050228	0.514135	-0.154354	0.117761	-0.352391	-0.092301	0.592975	...	-0.505890	0.135076	0.015516	-0.047556	0.226682	0.383343	0.077571	-0.039865	0.448460	4
891e2040027ffff	-0.575319	-0.284612	0.151406	-0.326954	0.650756	0.059048	0.051050	-0.612110	-0.325108	0.762787	...	-0.616633	0.154819	-0.128314	-0.016028	0.298513	0.157719	0.189196	0.009537	0.601834	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
891e2055bc3ffff	-0.454497	-0.025613	0.235932	-0.094423	0.557578	-0.266869	0.049226	-0.635413	-0.269236	0.767842	...	-0.581389	-0.040448	-0.234895	-0.278276	0.281510	0.030581	0.357593	0.298373	0.764828	2
891e2055bc7ffff	-0.257726	0.119923	0.215199	-0.174138	0.857351	-0.446207	0.197454	-0.412605	-0.361281	0.772351	...	-0.605371	0.024094	-0.226583	-0.369977	0.297929	-0.087533	0.319609	0.256416	0.522280	2
891e2055bcbffff	-0.635451	-0.077627	0.333176	-0.028319	0.522318	-0.090063	0.017219	-0.862788	-0.221086	0.758205	...	-0.498680	-0.225858	-0.347445	-0.263835	0.403450	0.074003	0.436040	0.425636	1.040164	1
891e205a967ffff	-0.648195	0.104950	0.235534	-0.266269	0.352077	0.191819	-0.106896	-0.260897	-0.384404	0.531822	...	-0.331405	0.331181	-0.005222	0.203001	0.011407	0.175702	0.221664	0.043823	0.566290	3
891e205a9a7ffff	-1.458649	-1.413952	0.511791	-1.238288	0.946567	-0.871188	-0.053677	-0.542523	-0.548428	1.703901	...	-0.768179	-0.194526	-0.075900	-0.438048	0.374782	0.549085	0.899736	0.075891	1.142536	1

2035 rows × 31 columns

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plot_numeric_data(regions_gdf, "cluster", embeddings)
plot_numeric_data(regions_gdf, "cluster", embeddings)

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