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
891e2047303ffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400
891e20404c7ffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400
891e204212bffff	-0.329779	0.040839	-0.070775	-0.314209	0.056109	0.149004	-0.064876	0.201306	0.015928	-0.140280
891e204563bffff	-0.291778	0.046439	-0.286547	-0.423768	0.014743	0.219305	-0.381652	0.303765	-0.121125	0.008270
891e204754bffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400
...	...	...	...	...	...	...	...	...	...	...
891e204550fffff	-0.291778	0.046439	-0.286547	-0.423768	0.014743	0.219305	-0.381652	0.303765	-0.121125	0.008270
891e2040b13ffff	0.173853	0.151073	0.402511	0.401253	-0.121004	-0.201290	0.662516	-0.408048	0.422258	-0.270984
891e2040da3ffff	0.408749	-0.309027	0.075073	0.203427	0.093649	-0.139810	0.031921	-0.251953	-0.077973	0.095363
891e2051857ffff	-0.444953	0.212055	-0.382394	-0.606828	-0.047674	0.245112	-0.314173	0.380123	-0.153552	-0.145722
891e2040007ffff	0.095493	0.123367	0.443478	0.595970	-0.123755	-0.044791	0.349595	-0.360595	0.407458	0.128345

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

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	0	1	2	3	4	5	6	7	8	9	cluster
region_id
891e2047303ffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400	0
891e20404c7ffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400	0
891e204212bffff	-0.329779	0.040839	-0.070775	-0.314209	0.056109	0.149004	-0.064876	0.201306	0.015928	-0.140280	2
891e204563bffff	-0.291778	0.046439	-0.286547	-0.423768	0.014743	0.219305	-0.381652	0.303765	-0.121125	0.008270	2
891e204754bffff	0.341400	-0.253870	-0.016749	0.323208	0.238173	-0.123129	-0.279072	-0.088379	-0.194273	0.370400	0
...	...	...	...	...	...	...	...	...	...	...	...
891e204550fffff	-0.291778	0.046439	-0.286547	-0.423768	0.014743	0.219305	-0.381652	0.303765	-0.121125	0.008270	2
891e2040b13ffff	0.173853	0.151073	0.402511	0.401253	-0.121004	-0.201290	0.662516	-0.408048	0.422258	-0.270984	1
891e2040da3ffff	0.408749	-0.309027	0.075073	0.203427	0.093649	-0.139810	0.031921	-0.251953	-0.077973	0.095363	0
891e2051857ffff	-0.444953	0.212055	-0.382394	-0.606828	-0.047674	0.245112	-0.314173	0.380123	-0.153552	-0.145722	2
891e2040007ffff	0.095493	0.123367	0.443478	0.595970	-0.123755	-0.044791	0.349595	-0.360595	0.407458	0.128345	1

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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