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
...	...
891e2040e0fffff	way/1381322928
891e2040e07ffff	way/1381322928
891e2040e0fffff	way/1381322931
891e2045423ffff	way/1381875210
891e2045433ffff	way/1381875215

4080 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
891e20451dbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276
891e2040303ffff	0.161674	-0.324404	0.170621	-0.468899	-0.172985	0.014379	0.271801	0.007779	-0.035657	-0.579238
891e2042e87ffff	-0.335875	0.034417	-0.317022	-0.457310	0.010872	0.237203	-0.391713	0.332970	-0.116267	0.014791
891e2040047ffff	0.075466	0.231667	0.426737	0.639714	-0.117938	-0.045049	0.318600	-0.399800	0.392185	0.144864
891e20456abffff	-0.590075	0.408845	0.171205	-0.345272	-0.436478	0.255101	0.442141	0.022474	0.324550	-0.485723
...	...	...	...	...	...	...	...	...	...	...
891e2042a37ffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276
891e204192bffff	0.326246	0.224254	0.317510	0.535413	-0.143091	-0.390972	0.803022	-0.365947	0.583426	-0.236704
891e20422bbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276
891e20403dbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276
891e2042407ffff	-0.335875	0.034417	-0.317022	-0.457310	0.010872	0.237203	-0.391713	0.332970	-0.116267	0.014791

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
891e20451dbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276	0
891e2040303ffff	0.161674	-0.324404	0.170621	-0.468899	-0.172985	0.014379	0.271801	0.007779	-0.035657	-0.579238	1
891e2042e87ffff	-0.335875	0.034417	-0.317022	-0.457310	0.010872	0.237203	-0.391713	0.332970	-0.116267	0.014791	3
891e2040047ffff	0.075466	0.231667	0.426737	0.639714	-0.117938	-0.045049	0.318600	-0.399800	0.392185	0.144864	2
891e20456abffff	-0.590075	0.408845	0.171205	-0.345272	-0.436478	0.255101	0.442141	0.022474	0.324550	-0.485723	1
...	...	...	...	...	...	...	...	...	...	...	...
891e2042a37ffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276	0
891e204192bffff	0.326246	0.224254	0.317510	0.535413	-0.143091	-0.390972	0.803022	-0.365947	0.583426	-0.236704	2
891e20422bbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276	0
891e20403dbffff	0.341121	-0.233200	-0.032582	0.283789	0.225126	-0.121932	-0.268539	-0.101162	-0.202295	0.359276	0
891e2042407ffff	-0.335875	0.034417	-0.317022	-0.457310	0.010872	0.237203	-0.391713	0.332970	-0.116267	0.014791	3

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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