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!





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

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
...	...
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.

In [7]:

Copied!





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.

Out[7]:

	0	1	2	3	4	5	6	7	8	9
region_id
891e204e08bffff	0.337409	-0.290802	-0.029108	0.267048	0.231479	-0.110547	-0.289695	-0.081055	-0.223311	0.364989
891e20421d3ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961
891e2043693ffff	0.337409	-0.290802	-0.029108	0.267048	0.231479	-0.110547	-0.289695	-0.081055	-0.223311	0.364989
891e20430dbffff	-0.428209	0.105053	-0.403714	-0.598958	0.009985	0.227246	-0.296134	0.408191	-0.126779	-0.204594
891e2050b2bffff	-0.424239	0.030140	-0.124075	-0.893742	-0.347757	0.294886	0.226868	0.485079	-0.109166	-0.795256
...	...	...	...	...	...	...	...	...	...	...
891e2040113ffff	0.112514	0.150071	0.424759	0.565989	-0.135653	-0.035017	0.278824	-0.381695	0.331679	0.204914
891e20424c3ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961
891e2050a27ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961
891e2041b97ffff	-0.399236	0.389605	0.168283	0.095617	-0.239077	0.196846	0.084885	-0.030419	0.390366	-0.007613
891e2043597ffff	-0.428209	0.105053	-0.403714	-0.598958	0.009985	0.227246	-0.296134	0.408191	-0.126779	-0.204594

3168 rows × 10 columns

In [8]:

Copied!





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()

No description has been provided for this image

Visualizing the embeddings' similarity¶

In [9]:

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[9]:

	0	1	2	3	4	5	6	7	8	9	cluster
region_id
891e204e08bffff	0.337409	-0.290802	-0.029108	0.267048	0.231479	-0.110547	-0.289695	-0.081055	-0.223311	0.364989	1
891e20421d3ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961	2
891e2043693ffff	0.337409	-0.290802	-0.029108	0.267048	0.231479	-0.110547	-0.289695	-0.081055	-0.223311	0.364989	1
891e20430dbffff	-0.428209	0.105053	-0.403714	-0.598958	0.009985	0.227246	-0.296134	0.408191	-0.126779	-0.204594	2
891e2050b2bffff	-0.424239	0.030140	-0.124075	-0.893742	-0.347757	0.294886	0.226868	0.485079	-0.109166	-0.795256	2
...	...	...	...	...	...	...	...	...	...	...	...
891e2040113ffff	0.112514	0.150071	0.424759	0.565989	-0.135653	-0.035017	0.278824	-0.381695	0.331679	0.204914	3
891e20424c3ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961	2
891e2050a27ffff	-0.281017	-0.040098	-0.308773	-0.411098	0.060625	0.210412	-0.395469	0.352511	-0.110494	-0.019961	2
891e2041b97ffff	-0.399236	0.389605	0.168283	0.095617	-0.239077	0.196846	0.084885	-0.030419	0.390366	-0.007613	0
891e2043597ffff	-0.428209	0.105053	-0.403714	-0.598958	0.009985	0.227246	-0.296134	0.408191	-0.126779	-0.204594	2

3168 rows × 11 columns

In [10]:

Copied!

plot_numeric_data(regions_gdf, "cluster", embeddings)
plot_numeric_data(regions_gdf, "cluster", embeddings)

Out[10]:

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