Hex2vec embedder

In [1]:

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

In [2]:

SEED = 71
seed_everything(SEED)

SEED = 71 seed_everything(SEED)

Global seed set to 71

Out[2]:

71

Load data from OSM¶

First use geocoding to get the area

In [3]:

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")

Out[3]:

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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.

In [4]:

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)

Downloading sport: soccer      : 100%|██████████| 7/7 [00:02<00:00,  2.84it/s]

Out[4]:

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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.

In [5]:

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")

Out[5]:

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In [6]:

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
891e204304bffff	relation/8833861
891e204304fffff	relation/8833861
891e2043047ffff	relation/8833861
891e204304bffff	way/346555876
891e2043043ffff	way/346555876
...	...
891e2047547ffff	way/772238372
	way/772238371
	way/772238373
891e2045537ffff	node/6780053133
	way/236429476

3778 rows × 0 columns

Embedding¶

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

In [7]:

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

100%|██████████| 3168/3168 [00:00<00:00, 22537.17it/s]
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Missing logger folder: /home/runner/work/srai/srai/examples/embedders/lightning_logs

  | Name    | Type       | Params
---------------------------------------
0 | encoder | Sequential | 280   
---------------------------------------
280       Trainable params
0         Non-trainable params
280       Total params
0.001     Total estimated model params size (MB)

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

Out[7]:

	0	1	2	3	4	5	6	7	8	9
region_id
891e2040ec3ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e204769bffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e2045017ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e204304bffff	-0.665747	0.135241	-0.414056	-1.090132	-0.105327	0.393818	0.001832	0.582357	-0.185385	-0.446995
891e204082fffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
...	...	...	...	...	...	...	...	...	...	...
891e20452cbffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e20402c7ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e2042ebbffff	-0.316712	0.105574	-0.301756	-0.492271	0.036685	0.233540	-0.301246	0.326174	-0.085026	0.006382
891e204262fffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638
891e2042357ffff	-0.618911	0.607905	0.091841	-0.111167	-0.364905	0.245106	0.198321	0.026477	0.398948	-0.180863

3168 rows × 10 columns

Visualizing the embeddings' similarity¶

In [8]:

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

/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

Out[8]:

	0	1	2	3	4	5	6	7	8	9	cluster
region_id
891e2040ec3ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e204769bffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e2045017ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e204304bffff	-0.665747	0.135241	-0.414056	-1.090132	-0.105327	0.393818	0.001832	0.582357	-0.185385	-0.446995	2
891e204082fffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
...	...	...	...	...	...	...	...	...	...	...	...
891e20452cbffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e20402c7ffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e2042ebbffff	-0.316712	0.105574	-0.301756	-0.492271	0.036685	0.233540	-0.301246	0.326174	-0.085026	0.006382	2
891e204262fffff	0.334089	-0.291687	-0.000071	0.276342	0.182729	-0.121621	-0.276749	-0.103540	-0.204520	0.302638	1
891e2042357ffff	-0.618911	0.607905	0.091841	-0.111167	-0.364905	0.245106	0.198321	0.026477	0.398948	-0.180863	2

3168 rows × 11 columns

In [9]:

plot_numeric_data(regions_gdf, embeddings, "cluster", tiles_style="CartoDB positron")

plot_numeric_data(regions_gdf, embeddings, "cluster", tiles_style="CartoDB positron")

Out[9]:

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