Hex2vec embedder
In [1]:
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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
Out[2]:
71
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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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
.
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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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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.
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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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Make this Notebook Trusted to load map: File -> Trust Notebook
In [6]:
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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 |
node/300461036 | |
891e2040d5bffff | node/300461042 |
891e2040887ffff | node/300461045 |
... | ... |
891e2042053ffff | way/1360073315 |
891e2042637ffff | way/1360073315 |
891e20420cbffff | way/1360073315 |
891e20420dbffff | way/1360073315 |
891e20420c3ffff | way/1360073315 |
4059 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 | ||||||||||
891e204051bffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
891e204086fffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
891e204e1a7ffff | 0.357209 | -0.308170 | 0.243200 | 0.477100 | 0.187059 | -0.170375 | 0.070069 | -0.102242 | -0.006278 | 0.197301 |
891e20421afffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
891e2041bd3ffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
891e2050a03ffff | -0.420077 | 0.197180 | -0.399123 | -0.607226 | -0.038572 | 0.246621 | -0.366357 | 0.383716 | -0.144264 | -0.115964 |
891e2042e53ffff | -0.535820 | 0.329670 | -0.470405 | -0.738800 | -0.111362 | 0.272061 | -0.339019 | 0.455612 | -0.176159 | -0.231504 |
891e204065bffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
891e2042b17ffff | 0.326123 | -0.271249 | 0.380848 | 0.620683 | 0.176729 | -0.240592 | 0.325408 | -0.150165 | 0.197455 | 0.181827 |
891e2042853ffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 |
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 | |||||||||||
891e204051bffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
891e204086fffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
891e204e1a7ffff | 0.357209 | -0.308170 | 0.243200 | 0.477100 | 0.187059 | -0.170375 | 0.070069 | -0.102242 | -0.006278 | 0.197301 | 2 |
891e20421afffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
891e2041bd3ffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
891e2050a03ffff | -0.420077 | 0.197180 | -0.399123 | -0.607226 | -0.038572 | 0.246621 | -0.366357 | 0.383716 | -0.144264 | -0.115964 | 0 |
891e2042e53ffff | -0.535820 | 0.329670 | -0.470405 | -0.738800 | -0.111362 | 0.272061 | -0.339019 | 0.455612 | -0.176159 | -0.231504 | 0 |
891e204065bffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
891e2042b17ffff | 0.326123 | -0.271249 | 0.380848 | 0.620683 | 0.176729 | -0.240592 | 0.325408 | -0.150165 | 0.197455 | 0.181827 | 1 |
891e2042853ffff | 0.340419 | -0.282504 | -0.023970 | 0.287975 | 0.239807 | -0.112942 | -0.283511 | -0.076848 | -0.224619 | 0.362411 | 2 |
3168 rows × 11 columns