Hex2vec embedder
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import warnings
import matplotlib.pyplot as plt
import pandas as pd
from pytorch_lightning import seed_everything
from pytorch_lightning.loggers import CSVLogger
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
import warnings
import matplotlib.pyplot as plt
import pandas as pd
from pytorch_lightning import seed_everything
from pytorch_lightning.loggers import CSVLogger
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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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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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
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region_id | feature_id |
---|---|
891e2040897ffff | node/280727473 |
891e2040d4bffff | node/300461026 |
node/300461036 | |
891e2040d5bffff | node/300461042 |
891e2040887ffff | node/300461045 |
... | ... |
891e2042e73ffff | way/1427496434 |
891e2040a8fffff | way/1428809179 |
891e2045203ffff | way/1429016156 |
891e2045217ffff | way/1429016156 |
891e2040e43ffff | way/1429586876 |
4192 rows × 0 columns
Embedding¶
After preparing the data we can proceed with generating embeddings for the regions.
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neighbourhood = H3Neighbourhood(regions_gdf)
embedder = Hex2VecEmbedder([15, 10])
csv_logger = CSVLogger(save_dir="hex2vec_logs")
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", "logger": csv_logger},
batch_size=100,
)
embeddings
neighbourhood = H3Neighbourhood(regions_gdf)
embedder = Hex2VecEmbedder([15, 10])
csv_logger = CSVLogger(save_dir="hex2vec_logs")
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", "logger": csv_logger},
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.
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0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
---|---|---|---|---|---|---|---|---|---|---|
region_id | ||||||||||
891e2041943ffff | -0.540500 | 0.368682 | -0.502314 | -0.796841 | -0.066350 | 0.231252 | -0.294797 | 0.403219 | -0.200558 | -0.282214 |
891e2041973ffff | 0.041568 | 0.127272 | 0.433192 | 0.560574 | -0.128483 | 0.006710 | 0.322844 | -0.352005 | 0.398866 | 0.111660 |
891e204e633ffff | 0.312323 | -0.231362 | -0.038064 | 0.301079 | 0.230049 | -0.098567 | -0.285888 | -0.072734 | -0.201323 | 0.377655 |
891e2051b07ffff | -0.540500 | 0.368682 | -0.502314 | -0.796841 | -0.066350 | 0.231252 | -0.294797 | 0.403219 | -0.200558 | -0.282214 |
891e2041873ffff | -0.275702 | 0.036633 | -0.284333 | -0.419740 | 0.028889 | 0.215045 | -0.426803 | 0.316230 | -0.118924 | 0.039350 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
891e2042913ffff | 0.312323 | -0.231362 | -0.038064 | 0.301079 | 0.230049 | -0.098567 | -0.285888 | -0.072734 | -0.201323 | 0.377655 |
891e2042dbbffff | 0.095703 | -0.365364 | 0.175965 | -0.416457 | -0.229633 | 0.114342 | 0.170426 | 0.129182 | -0.026517 | -0.460903 |
891e20426dbffff | -0.275702 | 0.036633 | -0.284333 | -0.419740 | 0.028889 | 0.215045 | -0.426803 | 0.316230 | -0.118924 | 0.039350 |
891e204556fffff | -0.414475 | 0.007036 | -0.279861 | -0.851557 | -0.149023 | 0.274329 | -0.127790 | 0.443625 | -0.177185 | -0.404225 |
891e2045413ffff | 0.041568 | 0.127272 | 0.433192 | 0.560574 | -0.128483 | 0.006710 | 0.322844 | -0.352005 | 0.398866 | 0.111660 |
3168 rows × 10 columns
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metrics_df = pd.read_csv(csv_logger.log_dir + "/metrics.csv").dropna(
subset="train_f1_epoch"
)
fig, ax1 = plt.subplots(1, 1, figsize=(10, 5))
ax2 = ax1.twinx()
line1 = ax1.plot(metrics_df["epoch"], metrics_df["train_f1_epoch"])
line2 = ax2.plot(metrics_df["epoch"], metrics_df["train_loss_epoch"], color="orange")
ax1.legend(line1 + line2, ["F1", "Loss"], loc=7)
ax1.set_title("Training metrics")
ax1.set_ylabel("F1")
ax2.set_ylabel("Loss")
ax1.set_xlabel("Training epoch")
plt.show()
metrics_df = pd.read_csv(csv_logger.log_dir + "/metrics.csv").dropna(
subset="train_f1_epoch"
)
fig, ax1 = plt.subplots(1, 1, figsize=(10, 5))
ax2 = ax1.twinx()
line1 = ax1.plot(metrics_df["epoch"], metrics_df["train_f1_epoch"])
line2 = ax2.plot(metrics_df["epoch"], metrics_df["train_loss_epoch"], color="orange")
ax1.legend(line1 + line2, ["F1", "Loss"], loc=7)
ax1.set_title("Training metrics")
ax1.set_ylabel("F1")
ax2.set_ylabel("Loss")
ax1.set_xlabel("Training epoch")
plt.show()
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 | |||||||||||
891e2041943ffff | -0.540500 | 0.368682 | -0.502314 | -0.796841 | -0.066350 | 0.231252 | -0.294797 | 0.403219 | -0.200558 | -0.282214 | 4 |
891e2041973ffff | 0.041568 | 0.127272 | 0.433192 | 0.560574 | -0.128483 | 0.006710 | 0.322844 | -0.352005 | 0.398866 | 0.111660 | 3 |
891e204e633ffff | 0.312323 | -0.231362 | -0.038064 | 0.301079 | 0.230049 | -0.098567 | -0.285888 | -0.072734 | -0.201323 | 0.377655 | 0 |
891e2051b07ffff | -0.540500 | 0.368682 | -0.502314 | -0.796841 | -0.066350 | 0.231252 | -0.294797 | 0.403219 | -0.200558 | -0.282214 | 4 |
891e2041873ffff | -0.275702 | 0.036633 | -0.284333 | -0.419740 | 0.028889 | 0.215045 | -0.426803 | 0.316230 | -0.118924 | 0.039350 | 4 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
891e2042913ffff | 0.312323 | -0.231362 | -0.038064 | 0.301079 | 0.230049 | -0.098567 | -0.285888 | -0.072734 | -0.201323 | 0.377655 | 0 |
891e2042dbbffff | 0.095703 | -0.365364 | 0.175965 | -0.416457 | -0.229633 | 0.114342 | 0.170426 | 0.129182 | -0.026517 | -0.460903 | 1 |
891e20426dbffff | -0.275702 | 0.036633 | -0.284333 | -0.419740 | 0.028889 | 0.215045 | -0.426803 | 0.316230 | -0.118924 | 0.039350 | 4 |
891e204556fffff | -0.414475 | 0.007036 | -0.279861 | -0.851557 | -0.149023 | 0.274329 | -0.127790 | 0.443625 | -0.177185 | -0.404225 | 4 |
891e2045413ffff | 0.041568 | 0.127272 | 0.433192 | 0.560574 | -0.128483 | 0.006710 | 0.322844 | -0.352005 | 0.398866 | 0.111660 | 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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