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
891e204296bffff	POLYGON ((17.04393 51.14506, 17.04397 51.14338...
891e204022bffff	POLYGON ((16.93214 51.09373, 16.93218 51.09205...
891e20402b7ffff	POLYGON ((16.93436 51.10643, 16.9344 51.10475,...

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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")

	y	x	street_count	highway	railway	ref	geometry
osmid
95584835	51.083111	17.049513	4	NaN	NaN	NaN	POINT (17.04951 51.08311)
95584841	51.084699	17.064367	3	NaN	NaN	NaN	POINT (17.06437 51.0847)
95584850	51.083328	17.035057	4	NaN	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
891e204296bffff	5792
	8189
	8188
	8203
	8204
...	...
891e204227bffff	7663
	7666
	7665
	7667
	9476

15722 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

HPU available: False, using: 0 HPUs

/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: 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 | Mode 
-----------------------------------------------
0 | encoder | Sequential | 16.0 K | train
1 | decoder | Sequential | 16.2 K | train
-----------------------------------------------
32.1 K    Trainable params
0         Non-trainable params
32.1 K    Total params
0.128     Total estimated model params size (MB)
8         Modules in train mode
0         Modules in eval mode

/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: 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.

/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/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	-0.479510	-0.170161	0.141499	-0.131385	0.356130	-0.170253	0.082514	-0.594185	-0.235572	0.742436	...	0.336453	-0.519993	0.133136	0.112709	0.013159	-0.010626	0.178799	0.334447	0.163934	0.368286
891e2040013ffff	-0.053364	-0.095388	0.259534	-0.273419	0.601486	-0.127698	0.008694	-0.672795	-0.470622	0.827055	...	0.405657	-0.760753	0.165349	-0.286263	0.014819	0.039674	0.456278	0.353700	0.205730	0.390251
891e2040017ffff	-0.223151	-0.260382	0.203482	-0.188966	0.428696	-0.247260	0.226724	-0.783058	-0.208041	0.601338	...	0.224511	-0.559403	0.128883	0.047662	0.077394	0.010018	0.484214	0.256647	0.183641	0.395048
891e2040023ffff	-0.230657	-0.363989	0.309760	-0.017237	0.507161	-0.186252	0.154596	-0.425472	-0.097725	0.611651	...	0.151504	-0.503910	0.142945	0.062385	-0.037322	0.164119	0.409733	0.088270	-0.011714	0.466407
891e2040027ffff	-0.540541	-0.267364	0.084434	-0.310432	0.636206	0.008829	0.107817	-0.712202	-0.339696	0.760957	...	0.013064	-0.608031	0.147565	-0.054307	0.001965	0.230121	0.214177	0.215097	0.073425	0.621710
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
891e2055bc3ffff	-0.458865	-0.024982	0.189483	-0.148232	0.570965	-0.339826	0.255847	-0.743790	-0.316874	0.777996	...	0.528354	-0.560291	-0.137184	-0.185775	-0.229802	0.218840	0.220230	0.379667	0.356566	0.749199
891e2055bc7ffff	-0.301984	0.067865	0.129250	-0.229516	0.841595	-0.542149	0.344944	-0.466103	-0.370153	0.782236	...	0.305966	-0.580967	-0.050131	-0.214895	-0.340888	0.217396	0.066982	0.312978	0.256146	0.464525
891e2055bcbffff	-0.630770	-0.058533	0.289752	-0.102037	0.559412	-0.160441	0.306311	-0.977856	-0.258928	0.759188	...	0.734534	-0.482689	-0.345045	-0.264224	-0.224084	0.330036	0.322070	0.444812	0.515427	1.013915
891e205a967ffff	-0.616801	0.123673	0.193281	-0.259642	0.319446	0.160594	-0.038908	-0.363509	-0.401363	0.544136	...	-0.279612	-0.326650	0.313171	0.047732	0.233037	-0.036650	0.232588	0.292018	0.118049	0.574624
891e205a9a7ffff	-1.412213	-1.500952	0.435123	-1.049657	1.131710	-1.103062	0.149565	-0.713480	-0.489774	1.805687	...	0.713913	-0.570885	-0.016611	0.096510	-0.397899	0.092768	0.679317	0.989048	0.128019	0.926884

2034 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

Out[7]:

	0	1	2	3	4	5	6	7	8	9	...	21	22	23	24	25	26	27	28	29	cluster
region_id
891e2040007ffff	-0.479510	-0.170161	0.141499	-0.131385	0.356130	-0.170253	0.082514	-0.594185	-0.235572	0.742436	...	-0.519993	0.133136	0.112709	0.013159	-0.010626	0.178799	0.334447	0.163934	0.368286	1
891e2040013ffff	-0.053364	-0.095388	0.259534	-0.273419	0.601486	-0.127698	0.008694	-0.672795	-0.470622	0.827055	...	-0.760753	0.165349	-0.286263	0.014819	0.039674	0.456278	0.353700	0.205730	0.390251	0
891e2040017ffff	-0.223151	-0.260382	0.203482	-0.188966	0.428696	-0.247260	0.226724	-0.783058	-0.208041	0.601338	...	-0.559403	0.128883	0.047662	0.077394	0.010018	0.484214	0.256647	0.183641	0.395048	4
891e2040023ffff	-0.230657	-0.363989	0.309760	-0.017237	0.507161	-0.186252	0.154596	-0.425472	-0.097725	0.611651	...	-0.503910	0.142945	0.062385	-0.037322	0.164119	0.409733	0.088270	-0.011714	0.466407	3
891e2040027ffff	-0.540541	-0.267364	0.084434	-0.310432	0.636206	0.008829	0.107817	-0.712202	-0.339696	0.760957	...	-0.608031	0.147565	-0.054307	0.001965	0.230121	0.214177	0.215097	0.073425	0.621710	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
891e2055bc3ffff	-0.458865	-0.024982	0.189483	-0.148232	0.570965	-0.339826	0.255847	-0.743790	-0.316874	0.777996	...	-0.560291	-0.137184	-0.185775	-0.229802	0.218840	0.220230	0.379667	0.356566	0.749199	2
891e2055bc7ffff	-0.301984	0.067865	0.129250	-0.229516	0.841595	-0.542149	0.344944	-0.466103	-0.370153	0.782236	...	-0.580967	-0.050131	-0.214895	-0.340888	0.217396	0.066982	0.312978	0.256146	0.464525	1
891e2055bcbffff	-0.630770	-0.058533	0.289752	-0.102037	0.559412	-0.160441	0.306311	-0.977856	-0.258928	0.759188	...	-0.482689	-0.345045	-0.264224	-0.224084	0.330036	0.322070	0.444812	0.515427	1.013915	2
891e205a967ffff	-0.616801	0.123673	0.193281	-0.259642	0.319446	0.160594	-0.038908	-0.363509	-0.401363	0.544136	...	-0.326650	0.313171	0.047732	0.233037	-0.036650	0.232588	0.292018	0.118049	0.574624	1
891e205a9a7ffff	-1.412213	-1.500952	0.435123	-1.049657	1.131710	-1.103062	0.149565	-0.713480	-0.489774	1.805687	...	-0.570885	-0.016611	0.096510	-0.397899	0.092768	0.679317	0.989048	0.128019	0.926884	2

2034 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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