Hex2vec embedder

Run this notebook in Google Colab:

Remember to install the srai library before running the notebook:

%pip install srai[all]

In [1]:

Copied!





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

In [2]:

Copied!

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

Seed set to 71

Out[2]:

Load data from OSM¶

First use geocoding to get the area

In [3]:

Copied!

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]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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]:

Copied!





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)

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

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]:

Copied!

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]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [6]:

Copied!

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
...	...
891e2042053ffff	way/1360073315
891e2042637ffff	way/1360073315
891e20420cbffff	way/1360073315
891e20420dbffff	way/1360073315
891e20420c3ffff	way/1360073315

4065 rows × 0 columns

Embedding¶

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

In [7]:

Copied!





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
891e2040dc3ffff	0.656268	0.134417	0.163386	0.327215	0.097115	-0.663489	1.178271	-0.628505	0.300285	-0.507696
891e2051b2fffff	-0.317609	0.008068	-0.308102	-0.471363	0.017219	0.221449	-0.371562	0.344145	-0.085226	-0.019029
891e2047533ffff	-0.610329	0.481623	0.088298	-0.091139	-0.337330	0.287571	0.079169	0.091325	0.388366	-0.183664
891e2055b5bffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357
891e2042b77ffff	0.172878	-0.418366	0.489317	-0.486085	-0.418959	0.046527	0.769972	-0.060110	0.081965	-0.888421
...	...	...	...	...	...	...	...	...	...	...
891e204045bffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357
891e20454a3ffff	0.087222	0.140724	0.454357	0.591962	-0.135180	0.023367	0.258327	-0.415607	0.333281	0.162284
891e20474a3ffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357
891e204e517ffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357
891e2040497ffff	-0.442270	0.109991	-0.376834	-0.603973	-0.056354	0.253124	-0.335904	0.426172	-0.112183	-0.158308

3168 rows × 10 columns

Visualizing the embeddings' similarity¶

In [8]:

Copied!

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
891e2040dc3ffff	0.656268	0.134417	0.163386	0.327215	0.097115	-0.663489	1.178271	-0.628505	0.300285	-0.507696	4
891e2051b2fffff	-0.317609	0.008068	-0.308102	-0.471363	0.017219	0.221449	-0.371562	0.344145	-0.085226	-0.019029	2
891e2047533ffff	-0.610329	0.481623	0.088298	-0.091139	-0.337330	0.287571	0.079169	0.091325	0.388366	-0.183664	2
891e2055b5bffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357	1
891e2042b77ffff	0.172878	-0.418366	0.489317	-0.486085	-0.418959	0.046527	0.769972	-0.060110	0.081965	-0.888421	0
...	...	...	...	...	...	...	...	...	...	...	...
891e204045bffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357	1
891e20454a3ffff	0.087222	0.140724	0.454357	0.591962	-0.135180	0.023367	0.258327	-0.415607	0.333281	0.162284	4
891e20474a3ffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357	1
891e204e517ffff	0.339083	-0.240901	-0.030583	0.259178	0.246003	-0.122454	-0.278094	-0.106361	-0.241188	0.372357	1
891e2040497ffff	-0.442270	0.109991	-0.376834	-0.603973	-0.056354	0.253124	-0.335904	0.426172	-0.112183	-0.158308	2

3168 rows × 11 columns