Load and save

In [1]:

from srai.embedders import Hex2VecEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders import OSMOnlineLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.regionalizers import H3Regionalizer, geocode_to_region_gdf
from srai.plotting import plot_regions
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.regionalizers import H3Regionalizer, geocode_to_region_gdf from srai.plotting import plot_regions from pytorch_lightning import seed_everything

In [2]:

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

In [3]:

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]:

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)

Downloading sport: soccer      : 100%|██████████| 7/7 [00:03<00:00,  1.87it/s]

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]:

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]:

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
891e204235bffff	way/264158272
891e204222fffff	way/264158272
891e2040497ffff	way/264158272
891e204235bffff	way/264158278
891e204234bffff	way/264158278
...	...
891e204736bffff	node/7005046421
	node/8072227589
	node/6325708012
	way/277443381
891e2040a37ffff	node/9790270888

3797 rows × 0 columns

Embedding¶

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

In [7]:

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

100%|██████████| 3168/3168 [00:00<00:00, 17002.26it/s]
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name    | Type       | Params
---------------------------------------
0 | encoder | Sequential | 280   
---------------------------------------
280       Trainable params
0         Non-trainable params
280       Total params
0.001     Total estimated model params size (MB)

`Trainer.fit` stopped: `max_epochs=5` reached.

Out[7]:

	0	1	2	3	4	5	6	7	8	9
region_id
891e20476afffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222
891e204235bffff	-1.392891	1.015850	-1.121933	-1.819386	-0.346681	0.248776	0.324248	0.620070	-0.442757	-1.206173
891e2045023ffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222
891e2055353ffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222
891e2040dafffff	0.066636	0.194059	0.444324	0.564749	-0.161926	-0.018806	0.314564	-0.368049	0.375659	0.065593
...	...	...	...	...	...	...	...	...	...	...
891e2055b3bffff	-0.334951	-0.023913	-0.328034	-0.504773	0.004567	0.265352	-0.387918	0.394704	-0.115494	-0.007598
891e20460cfffff	-0.334951	-0.023913	-0.328034	-0.504773	0.004567	0.265352	-0.387918	0.394704	-0.115494	-0.007598
891e2047123ffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222
891e204e1b3ffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222
891e20415abffff	0.328070	-0.216528	-0.029245	0.251093	0.216896	-0.105440	-0.269402	-0.091342	-0.206274	0.338222

3168 rows × 10 columns

Visualizing the embeddings' similarity¶

In [8]:

embedder.save("./modello")

embedder.save("./modello")

In [9]:

embedder_loaded = Hex2VecEmbedder.load("./modello")
embedder_loaded

embedder_loaded = Hex2VecEmbedder.load("./modello") embedder_loaded

Out[9]:

<srai.embedders.hex2vec.embedder.Hex2VecEmbedder at 0x7f49bbc87c40>

In [10]:

from srai.embedders import Highway2VecEmbedder
from srai.loaders import OSMNetworkType, OSMWayLoader

d = OSMWayLoader(OSMNetworkType.DRIVE).load(area_gdf)

from srai.embedders import Highway2VecEmbedder from srai.loaders import OSMNetworkType, OSMWayLoader d = OSMWayLoader(OSMNetworkType.DRIVE).load(area_gdf)

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/srai/loaders/osm_way_loader/osm_way_loader.py:219: UserWarning: The clean_periphery argument has been deprecated and will be removed in a future release. Future behavior will be as though clean_periphery=True.
  G_directed = ox.graph_from_polygon(

In [11]:

joint = joiner.transform(regions_gdf, d[1])

joint = joiner.transform(regions_gdf, d[1])

In [12]:

highway2vec = Highway2VecEmbedder()
highway2vec.fit(regions_gdf, d[1], joint)

highway2vec = Highway2VecEmbedder() highway2vec.fit(regions_gdf, d[1], joint)

GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name    | Type       | Params
---------------------------------------
0 | encoder | Sequential | 16.0 K
1 | decoder | Sequential | 16.2 K
---------------------------------------
32.1 K    Trainable params
0         Non-trainable params
32.1 K    Total params
0.128     Total estimated model params size (MB)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.

`Trainer.fit` stopped: `max_epochs=10` reached.

In [13]:

highway2vec.save("highway2vec")

highway2vec.save("highway2vec")

In [14]:

vars(highway2vec)

vars(highway2vec)

Out[14]:

{'_model': Highway2VecModel(
   (encoder): Sequential(
     (0): Linear(in_features=218, out_features=64, bias=True)
     (1): ReLU()
     (2): Linear(in_features=64, out_features=30, bias=True)
   )
   (decoder): Sequential(
     (0): Linear(in_features=30, out_features=64, bias=True)
     (1): ReLU()
     (2): Linear(in_features=64, out_features=218, bias=True)
   )
 ),
 '_hidden_size': 64,
 '_embedding_size': 30,
 '_is_fitted': True}

In [15]:

Highway2VecEmbedder.load("highway2vec")

Highway2VecEmbedder.load("highway2vec")

Out[15]:

<srai.embedders.highway2vec.embedder.Highway2VecEmbedder at 0x7f49d14d6e30>

In [16]:

import geopandas as gpd
import pandas as pd
from srai.embedders import GTFS2VecEmbedder
from srai.constants import REGIONS_INDEX
from shapely.geometry import Polygon

features_gdf = gpd.GeoDataFrame(
    {
        "trip_count_at_6": [1, 0, 0],
        "trip_count_at_7": [1, 1, 0],
        "trip_count_at_8": [0, 0, 1],
        "directions_at_6": [
            {"A", "A1"},
            {"B", "B1"},
            {"C"},
        ],
    },
    geometry=gpd.points_from_xy([1, 2, 5], [1, 2, 2]),
    index=pd.Index(name="stop_id", data=[1, 2, 3]),
)
regions_gdf = gpd.GeoDataFrame(
    geometry=[
        Polygon([(0, 0), (0, 3), (3, 3), (3, 0)]),
        Polygon([(4, 0), (4, 3), (7, 3), (7, 0)]),
        Polygon([(8, 0), (8, 3), (11, 3), (11, 0)]),
    ],
    index=pd.Index(name=REGIONS_INDEX, data=["ff1", "ff2", "ff3"]),
)

joint_gdf = gpd.GeoDataFrame()
joint_gdf.index = pd.MultiIndex.from_tuples(
    [("ff1", 1), ("ff1", 2), ("ff2", 3)],
    names=[REGIONS_INDEX, "stop_id"],
)

embedder = GTFS2VecEmbedder(hidden_size=2, embedding_size=4)
embedder.fit(regions_gdf, features_gdf, joint_gdf)
res = embedder.transform(regions_gdf, features_gdf, joint_gdf)
res

import geopandas as gpd import pandas as pd from srai.embedders import GTFS2VecEmbedder from srai.constants import REGIONS_INDEX from shapely.geometry import Polygon features_gdf = gpd.GeoDataFrame( { "trip_count_at_6": [1, 0, 0], "trip_count_at_7": [1, 1, 0], "trip_count_at_8": [0, 0, 1], "directions_at_6": [ {"A", "A1"}, {"B", "B1"}, {"C"}, ], }, geometry=gpd.points_from_xy([1, 2, 5], [1, 2, 2]), index=pd.Index(name="stop_id", data=[1, 2, 3]), ) regions_gdf = gpd.GeoDataFrame( geometry=[ Polygon([(0, 0), (0, 3), (3, 3), (3, 0)]), Polygon([(4, 0), (4, 3), (7, 3), (7, 0)]), Polygon([(8, 0), (8, 3), (11, 3), (11, 0)]), ], index=pd.Index(name=REGIONS_INDEX, data=["ff1", "ff2", "ff3"]), ) joint_gdf = gpd.GeoDataFrame() joint_gdf.index = pd.MultiIndex.from_tuples( [("ff1", 1), ("ff1", 2), ("ff2", 3)], names=[REGIONS_INDEX, "stop_id"], ) embedder = GTFS2VecEmbedder(hidden_size=2, embedding_size=4) embedder.fit(regions_gdf, features_gdf, joint_gdf) res = embedder.transform(regions_gdf, features_gdf, joint_gdf) res

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
  warnings.warn(_create_warning_msg(
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs

  | Name    | Type       | Params
---------------------------------------
0 | encoder | Sequential | 16    
1 | decoder | Sequential | 13    
---------------------------------------
29        Trainable params
0         Non-trainable params
29        Total params
0.000     Total estimated model params size (MB)
/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:293: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.

`Trainer.fit` stopped: `max_epochs=10` reached.

Out[16]:

	0	1	2	3
region_id
ff1	0.687915	0.153115	-0.648070	-0.417944
ff2	0.849668	0.173882	-0.952428	-0.055791
ff3	0.913412	0.186434	-1.057311	0.080431

In [17]:

embedder.save("gtfs2vec")

embedder.save("gtfs2vec")

In [18]:

a = embedder.load("gtfs2vec")

a = embedder.load("gtfs2vec")

In [19]:

a = embedder.transform(regions_gdf, features_gdf, joint_gdf)

a = embedder.transform(regions_gdf, features_gdf, joint_gdf)

In [20]:

a

a

Out[20]:

	0	1	2	3
region_id
ff1	0.687915	0.153115	-0.648070	-0.417944
ff2	0.849668	0.173882	-0.952428	-0.055791
ff3	0.913412	0.186434	-1.057311	0.080431