Hex2vec embedder

Run this notebook in Google Colab:

Remember to install the srai library before running the notebook:

%pip install srai[all]

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from pytorch_lightning import seed_everything

from srai.embedders import Hex2VecEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders import OSMOnlineLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer, geocode_to_region_gdf
from pytorch_lightning import seed_everything

from srai.embedders import Hex2VecEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders import OSMOnlineLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer, geocode_to_region_gdf

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SEED = 71
seed_everything(SEED)
SEED = 71
seed_everything(SEED)

Seed set to 71

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Load data from OSM¶

First use geocoding to get the area

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

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Next, download the data for the selected region and the specified tags. We're using OSMOnlineLoader here, as it's faster for low numbers of tags. In a real life scenario with more tags, you would likely want to use the OSMPbfLoader.

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tags = {
    "leisure": "park",
    "landuse": "forest",
    "amenity": ["bar", "restaurant", "cafe"],
    "water": "river",
    "sport": "soccer",
}
loader = OSMOnlineLoader()

features_gdf = loader.load(area_gdf, tags)

folium_map = plot_regions(area_gdf, colormap=["rgba(0,0,0,0)"], tiles_style="CartoDB positron")
features_gdf.explore(m=folium_map)
tags = {
    "leisure": "park",
    "landuse": "forest",
    "amenity": ["bar", "restaurant", "cafe"],
    "water": "river",
    "sport": "soccer",
}
loader = OSMOnlineLoader()

features_gdf = loader.load(area_gdf, tags)

folium_map = plot_regions(area_gdf, colormap=["rgba(0,0,0,0)"], tiles_style="CartoDB positron")
features_gdf.explore(m=folium_map)

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Prepare the data for embedding¶

After downloading the data, we need to prepare it for embedding. Namely - we need to regionalize the selected area, and join the features with regions.

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regionalizer = H3Regionalizer(resolution=9)
regions_gdf = regionalizer.transform(area_gdf)
plot_regions(regions_gdf, tiles_style="CartoDB positron")
regionalizer = H3Regionalizer(resolution=9)
regions_gdf = regionalizer.transform(area_gdf)
plot_regions(regions_gdf, tiles_style="CartoDB positron")

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

Out[6]:


region_id	feature_id
891e2040897ffff	node/280727473
891e2040d4bffff	node/300461026
891e2040d4bffff	node/300461036
891e2040d5bffff	node/300461042
891e2040887ffff	node/300461045
	...
	way/1422748565
891e2040d4bffff	way/1422748565
891e20454c3ffff	way/1422964443
891e20454c7ffff	way/1423396175
891e204055bffff	way/1426281734

4184 rows × 0 columns

Embedding¶

After preparing the data we can proceed with generating embeddings for the regions.

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import warnings

neighbourhood = H3Neighbourhood(regions_gdf)
embedder = Hex2VecEmbedder([15, 10])

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    embeddings = embedder.fit_transform(
        regions_gdf,
        features_gdf,
        joint_gdf,
        neighbourhood,
        trainer_kwargs={"max_epochs": 5, "accelerator": "cpu"},
        batch_size=100,
    )
embeddings
import warnings

neighbourhood = H3Neighbourhood(regions_gdf)
embedder = Hex2VecEmbedder([15, 10])

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    embeddings = embedder.fit_transform(
        regions_gdf,
        features_gdf,
        joint_gdf,
        neighbourhood,
        trainer_kwargs={"max_epochs": 5, "accelerator": "cpu"},
        batch_size=100,
    )
embeddings

GPU available: False, used: False

TPU available: False, using: 0 TPU cores

HPU available: False, using: 0 HPUs

  | Name    | Type       | Params | Mode 
-----------------------------------------------
0 | encoder | Sequential | 280    | train
-----------------------------------------------
280       Trainable params
0         Non-trainable params
280       Total params
0.001     Total estimated model params size (MB)
4         Modules in train mode
0         Modules in eval mode

`Trainer.fit` stopped: `max_epochs=5` reached.

Out[7]:

	0	1	2	3	4	5	6	7	8	9
region_id
891e20420a3ffff	-0.174341	-0.056702	-0.141757	-0.667297	-0.202505	0.206961	-0.021806	0.321687	-0.127026	-0.436821
891e204464bffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786
891e20451d7ffff	-0.640497	0.170387	-0.494039	-1.028213	-0.137231	0.290711	-0.081518	0.542585	-0.223446	-0.550366
891e2043003ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786
891e204e58bffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786
...	...	...	...	...	...	...	...	...	...	...
891e2042047ffff	-0.556559	0.221652	-0.474314	-0.786877	-0.063241	0.234308	-0.228298	0.460485	-0.162664	-0.378898
891e2042b5bffff	0.190548	-0.161601	0.279989	-0.584597	-0.442690	0.057572	0.527642	0.084223	-0.035751	-0.852826
891e2040c6fffff	-0.082226	-0.234706	-0.108623	-0.060088	0.117618	0.030050	-0.293866	0.166905	-0.140017	0.083183
891e2042937ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786
891e2040613ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786

3168 rows × 10 columns

Visualizing the embeddings' similarity¶

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from sklearn.cluster import KMeans

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

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

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

embeddings["cluster"] = clusterizer.labels_
embeddings

Out[8]:

	0	1	2	3	4	5	6	7	8	9	cluster
region_id
891e20420a3ffff	-0.174341	-0.056702	-0.141757	-0.667297	-0.202505	0.206961	-0.021806	0.321687	-0.127026	-0.436821	1
891e204464bffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786	0
891e20451d7ffff	-0.640497	0.170387	-0.494039	-1.028213	-0.137231	0.290711	-0.081518	0.542585	-0.223446	-0.550366	4
891e2043003ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786	0
891e204e58bffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786	0
...	...	...	...	...	...	...	...	...	...	...	...
891e2042047ffff	-0.556559	0.221652	-0.474314	-0.786877	-0.063241	0.234308	-0.228298	0.460485	-0.162664	-0.378898	4
891e2042b5bffff	0.190548	-0.161601	0.279989	-0.584597	-0.442690	0.057572	0.527642	0.084223	-0.035751	-0.852826	3
891e2040c6fffff	-0.082226	-0.234706	-0.108623	-0.060088	0.117618	0.030050	-0.293866	0.166905	-0.140017	0.083183	0
891e2042937ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786	0
891e2040613ffff	0.343515	-0.252565	-0.024092	0.285924	0.227262	-0.109314	-0.294415	-0.081705	-0.220079	0.355786	0

3168 rows × 11 columns

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

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