Contextual count embedder
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from srai.embedders import ContextualCountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer
from srai.embedders import ContextualCountEmbedder
from srai.joiners import IntersectionJoiner
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.neighbourhoods import H3Neighbourhood
from srai.plotting.folium_wrapper import plot_numeric_data, plot_regions
from srai.regionalizers import H3Regionalizer
Data preparation¶
In order to use ContextualCountEmbedder we need to prepare some data.
Namely we need: regions_gdf, features_gdf, and joint_gdf.
These are the outputs of Regionalizers, Loaders and Joiners respectively.
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from srai.regionalizers import geocode_to_region_gdf
area_gdf = geocode_to_region_gdf("Lisboa, PT")
plot_regions(area_gdf)
from srai.regionalizers import geocode_to_region_gdf
area_gdf = geocode_to_region_gdf("Lisboa, PT")
plot_regions(area_gdf)
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Make this Notebook Trusted to load map: File -> Trust Notebook
Regionalize the area using an H3Regionalizer¶
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regionalizer = H3Regionalizer(resolution=9, buffer=True)
regions_gdf = regionalizer.transform(area_gdf)
regions_gdf
regionalizer = H3Regionalizer(resolution=9, buffer=True)
regions_gdf = regionalizer.transform(area_gdf)
regions_gdf
Out[3]:
| geometry | |
|---|---|
| region_id | |
| 89393375eafffff | POLYGON ((-9.15199 38.78201, -9.15370 38.78067... |
| 89393375bd7ffff | POLYGON ((-9.12230 38.77006, -9.12400 38.76872... |
| 89393367593ffff | POLYGON ((-9.09957 38.75969, -9.10128 38.75835... |
| 893933674c7ffff | POLYGON ((-9.12699 38.74520, -9.12869 38.74386... |
| 8939336291bffff | POLYGON ((-9.15222 38.73712, -9.15392 38.73578... |
| ... | ... |
| 893933670c7ffff | POLYGON ((-9.10645 38.72842, -9.10816 38.72708... |
| 89393375e77ffff | POLYGON ((-9.15526 38.77238, -9.15696 38.77104... |
| 8939336050fffff | POLYGON ((-9.20920 38.70168, -9.21090 38.70034... |
| 89393362953ffff | POLYGON ((-9.15733 38.73310, -9.15904 38.73176... |
| 89393362923ffff | POLYGON ((-9.13721 38.73716, -9.13892 38.73582... |
830 rows × 1 columns
Download some objects from OpenStreetMap¶
You can use both OsmTagsFilter and GroupedOsmTagsFilter filters. In this example, a predefined GroupedOsmTagsFilter filter BASE_OSM_GROUPS_FILTER is used.
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from srai.loaders.osm_loaders.filters import BASE_OSM_GROUPS_FILTER
loader = OSMPbfLoader()
features_gdf = loader.load(area_gdf, tags=BASE_OSM_GROUPS_FILTER)
features_gdf
from srai.loaders.osm_loaders.filters import BASE_OSM_GROUPS_FILTER
loader = OSMPbfLoader()
features_gdf = loader.load(area_gdf, tags=BASE_OSM_GROUPS_FILTER)
features_gdf
/root/development/srai/srai/loaders/osm_loaders/osm_pbf_loader.py:128: FutureWarning: Use `convert_geometry_to_geodataframe` instead. Deprecated since 0.8.1 version. features_gdf = pbf_reader.get_features_gdf_from_geometry(
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Finished operation in 0:02:32
Out[4]:
| geometry | aerialway | airports | buildings | culture_art_entertainment | education | emergency | finances | greenery | healthcare | historic | leisure | other | shops | sport | sustenance | tourism | transportation | water | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| feature_id | |||||||||||||||||||
| node/33082025 | POINT (-9.12436 38.72831) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| node/33082026 | POINT (-9.12458 38.72836) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| node/33084039 | POINT (-9.10711 38.72899) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| node/33360557 | POINT (-9.13908 38.71066) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | railway=subway_entrance | None |
| node/254753716 | POINT (-9.14165 38.72275) | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | public_transport=stop_position | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| way/1249257754 | POLYGON ((-9.14865 38.72863, -9.14881 38.72855... | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | tourism=hotel | None | None |
| way/1249344025 | POLYGON ((-9.10443 38.76544, -9.10439 38.76511... | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | amenity=parking | None |
| way/1249656110 | POLYGON ((-9.13161 38.71549, -9.13163 38.71548... | None | None | None | None | None | None | None | None | None | None | leisure=swimming_pool | None | None | None | None | None | None | None |
| way/1257228044 | POLYGON ((-9.11005 38.74418, -9.11002 38.74418... | None | None | None | None | None | None | None | landuse=meadow | None | None | None | None | None | None | None | None | None | None |
| way/1257228053 | POLYGON ((-9.10917 38.74387, -9.10909 38.74383... | None | None | None | None | None | None | None | landuse=grass | None | None | None | None | None | None | None | None | None | None |
29648 rows × 19 columns
Join the objects with the regions they belong to¶
In [5]:
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[5]:
| region_id | feature_id |
|---|---|
| 89393375eafffff | node/11287438896 |
| way/182889607 | |
| node/6467058270 | |
| node/6467058269 | |
| way/1103408947 | |
| ... | ... |
| 89393362923ffff | node/4364239902 |
| node/11918427761 | |
| node/5973556958 | |
| way/427210041 | |
| way/93763775 |
33141 rows × 0 columns
Embed using features existing in data¶
ContextualCountEmbedder extends capabilities of basic CountEmbedder by incorporating the neighbourhood of embedded region. In this example we will use the H3Neighbourhood.
In [6]:
Copied!
h3n = H3Neighbourhood()
h3n = H3Neighbourhood()
Squashed vector version (default)¶
Embedder will return vector of the same length as CountEmbedder, but will sum averaged values from the neighbourhoods diminished by the neighbour distance squared.
In [7]:
Copied!
cce = ContextualCountEmbedder(
neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=False
)
embeddings = cce.transform(regions_gdf, features_gdf, joint_gdf)
embeddings
cce = ContextualCountEmbedder(
neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=False
)
embeddings = cce.transform(regions_gdf, features_gdf, joint_gdf)
embeddings
Generating embeddings for neighbours: 0%| | 0/10 [00:00<?, ?it/s]
Generating embeddings for neighbours: 10%|█████████████▉ | 1/10 [00:00<00:04, 2.16it/s]
Generating embeddings for neighbours: 20%|███████████████████████████▊ | 2/10 [00:01<00:05, 1.42it/s]
Generating embeddings for neighbours: 30%|█████████████████████████████████████████▋ | 3/10 [00:02<00:05, 1.23it/s]
Generating embeddings for neighbours: 40%|███████████████████████████████████████████████████████▌ | 4/10 [00:03<00:05, 1.17it/s]
Generating embeddings for neighbours: 50%|█████████████████████████████████████████████████████████████████████▌ | 5/10 [00:04<00:04, 1.11it/s]
Generating embeddings for neighbours: 60%|███████████████████████████████████████████████████████████████████████████████████▍ | 6/10 [00:05<00:03, 1.12it/s]
Generating embeddings for neighbours: 70%|█████████████████████████████████████████████████████████████████████████████████████████████████▎ | 7/10 [00:05<00:02, 1.25it/s]
Generating embeddings for neighbours: 80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 8/10 [00:06<00:01, 1.38it/s]
Generating embeddings for neighbours: 90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 9/10 [00:06<00:00, 1.38it/s]
Generating embeddings for neighbours: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.27it/s]
Generating embeddings for neighbours: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:07<00:00, 1.27it/s]
Out[7]:
| aerialway | airports | buildings | culture_art_entertainment | education | emergency | finances | greenery | healthcare | historic | leisure | other | shops | sport | sustenance | tourism | transportation | water | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region_id | ||||||||||||||||||
| 89393375eafffff | 0.000000 | 0.072681 | 0.172622 | 0.061501 | 1.528754 | 0.006953 | 0.077612 | 7.063242 | 1.232352 | 0.102468 | 6.066067 | 0.085638 | 0.511175 | 2.184690 | 0.344730 | 0.359538 | 17.358640 | 3.179877 |
| 89393375bd7ffff | 0.002571 | 0.085539 | 0.342563 | 0.025650 | 1.318792 | 0.051086 | 0.175110 | 40.441995 | 0.314964 | 0.091541 | 6.361588 | 0.339900 | 1.471082 | 0.849505 | 0.648031 | 0.384639 | 10.448222 | 0.059617 |
| 89393367593ffff | 0.029869 | 0.005094 | 3.895792 | 0.047278 | 1.467486 | 0.059723 | 0.247737 | 6.789595 | 1.613498 | 0.121502 | 3.628074 | 0.183688 | 6.269584 | 1.581679 | 6.517240 | 0.863253 | 23.838351 | 0.622676 |
| 893933674c7ffff | 0.000000 | 0.004865 | 0.453913 | 0.079248 | 1.734145 | 0.004311 | 0.339349 | 7.434820 | 0.406112 | 0.190524 | 6.272197 | 1.324375 | 3.781918 | 3.942077 | 2.996424 | 2.912269 | 11.585708 | 0.111753 |
| 8939336291bffff | 0.002999 | 0.001107 | 7.677233 | 0.563714 | 3.179352 | 0.061792 | 1.373996 | 6.856475 | 5.665501 | 4.728557 | 7.547984 | 0.625591 | 21.008825 | 1.389960 | 20.018408 | 11.039550 | 39.739968 | 3.437012 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 893933670c7ffff | 0.000000 | 0.000000 | 0.449794 | 0.118885 | 0.203727 | 0.002279 | 0.267195 | 0.983572 | 0.242713 | 0.542425 | 0.768711 | 0.319655 | 1.130012 | 0.221153 | 1.261395 | 0.562957 | 6.168536 | 2.595571 |
| 89393375e77ffff | 0.001260 | 0.062867 | 1.575070 | 1.034028 | 0.670339 | 0.006059 | 0.453115 | 10.085802 | 0.648666 | 0.327498 | 6.117789 | 0.220976 | 2.393762 | 0.639035 | 3.040032 | 4.257465 | 8.345633 | 4.231709 |
| 8939336050fffff | 0.000000 | 0.000000 | 1.052120 | 1.145575 | 3.899146 | 0.002337 | 0.201507 | 5.341798 | 0.289971 | 1.672643 | 25.349041 | 1.434187 | 1.601335 | 7.882438 | 1.586429 | 1.700717 | 10.169676 | 0.724271 |
| 89393362953ffff | 0.003912 | 0.000140 | 2.691972 | 0.368362 | 1.050498 | 0.068171 | 1.030027 | 8.839801 | 1.391188 | 2.846826 | 12.063737 | 1.648895 | 8.512483 | 2.703981 | 8.078421 | 8.435443 | 24.200379 | 2.625915 |
| 89393362923ffff | 0.000721 | 0.001473 | 5.116668 | 0.216235 | 7.217274 | 0.010448 | 1.573282 | 5.838340 | 1.552192 | 1.702333 | 17.844794 | 2.837060 | 26.570471 | 5.375883 | 22.269152 | 4.335175 | 78.863438 | 0.166823 |
830 rows × 18 columns
Concatenated vector version¶
Embedder will return vector of length n * distance where n is number of features from the CountEmbedder and distance is number of neighbourhoods analysed.
Each feature will be postfixed with _n string, where n is the current distance. Values are averaged from all neighbours.
In [8]:
Copied!
wide_cce = ContextualCountEmbedder(
neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=True
)
wide_embeddings = wide_cce.transform(regions_gdf, features_gdf, joint_gdf)
wide_embeddings
wide_cce = ContextualCountEmbedder(
neighbourhood=h3n, neighbourhood_distance=10, concatenate_vectors=True
)
wide_embeddings = wide_cce.transform(regions_gdf, features_gdf, joint_gdf)
wide_embeddings
Generating embeddings for neighbours: 0%| | 0/10 [00:00<?, ?it/s]
Generating embeddings for neighbours: 10%|█████████████▉ | 1/10 [00:00<00:06, 1.46it/s]
Generating embeddings for neighbours: 20%|███████████████████████████▊ | 2/10 [00:01<00:05, 1.33it/s]
Generating embeddings for neighbours: 30%|█████████████████████████████████████████▋ | 3/10 [00:02<00:04, 1.44it/s]
Generating embeddings for neighbours: 40%|███████████████████████████████████████████████████████▌ | 4/10 [00:02<00:03, 1.63it/s]
Generating embeddings for neighbours: 50%|█████████████████████████████████████████████████████████████████████▌ | 5/10 [00:03<00:02, 1.75it/s]
Generating embeddings for neighbours: 60%|███████████████████████████████████████████████████████████████████████████████████▍ | 6/10 [00:03<00:02, 1.82it/s]
Generating embeddings for neighbours: 70%|█████████████████████████████████████████████████████████████████████████████████████████████████▎ | 7/10 [00:04<00:01, 1.87it/s]
Generating embeddings for neighbours: 80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 8/10 [00:04<00:01, 1.85it/s]
Generating embeddings for neighbours: 90%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 9/10 [00:05<00:00, 1.88it/s]
Generating embeddings for neighbours: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00, 1.90it/s]
Generating embeddings for neighbours: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:05<00:00, 1.76it/s]
Out[8]:
| aerialway_0 | airports_0 | buildings_0 | culture_art_entertainment_0 | education_0 | emergency_0 | finances_0 | greenery_0 | healthcare_0 | historic_0 | ... | healthcare_10 | historic_10 | leisure_10 | other_10 | shops_10 | sport_10 | sustenance_10 | tourism_10 | transportation_10 | water_10 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region_id | |||||||||||||||||||||
| 89393375eafffff | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.0 | 1.0 | 0.0 | ... | 1.103448 | 0.344828 | 5.862069 | 0.517241 | 2.655172 | 2.241379 | 2.586207 | 1.241379 | 15.275862 | 0.103448 |
| 89393375bd7ffff | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 33.0 | 0.0 | 0.0 | ... | 0.756757 | 0.729730 | 4.459459 | 0.702703 | 4.810811 | 1.918919 | 3.243243 | 2.054054 | 15.918919 | 0.945946 |
| 89393367593ffff | 0.0 | 0.0 | 2.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.0 | 1.0 | 0.0 | ... | 0.321429 | 0.285714 | 4.357143 | 0.464286 | 2.678571 | 2.214286 | 1.500000 | 1.107143 | 11.821429 | 0.785714 |
| 893933674c7ffff | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | ... | 1.545455 | 2.181818 | 4.045455 | 1.022727 | 11.704545 | 1.659091 | 11.454545 | 6.704545 | 19.181818 | 0.659091 |
| 8939336291bffff | 0.0 | 0.0 | 5.0 | 0.0 | 2.0 | 0.0 | 0.0 | 3.0 | 4.0 | 4.0 | ... | 0.533333 | 1.033333 | 3.366667 | 0.716667 | 3.583333 | 1.300000 | 5.950000 | 2.900000 | 12.300000 | 0.666667 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 893933670c7ffff | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 2.000000 | 2.222222 | 3.777778 | 1.444444 | 19.962963 | 0.592593 | 15.851852 | 7.555556 | 22.777778 | 0.703704 |
| 89393375e77ffff | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 7.0 | 0.0 | 0.0 | ... | 1.027778 | 0.277778 | 4.222222 | 0.527778 | 4.638889 | 1.222222 | 3.083333 | 2.500000 | 17.138889 | 0.666667 |
| 8939336050fffff | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | ... | 0.352941 | 0.529412 | 4.411765 | 0.235294 | 1.588235 | 2.000000 | 1.470588 | 1.529412 | 6.470588 | 0.411765 |
| 89393362953ffff | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 2.0 | ... | 0.559322 | 0.542373 | 2.983051 | 0.559322 | 2.338983 | 1.016949 | 2.627119 | 1.711864 | 10.322034 | 0.796610 |
| 89393362923ffff | 0.0 | 0.0 | 3.0 | 0.0 | 6.0 | 0.0 | 0.0 | 3.0 | 0.0 | 1.0 | ... | 0.812500 | 0.541667 | 3.250000 | 0.687500 | 5.020833 | 0.687500 | 4.395833 | 2.354167 | 10.062500 | 1.104167 |
830 rows × 198 columns
Plotting example features¶
In [9]:
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plot_numeric_data(regions_gdf, "leisure", embeddings)
plot_numeric_data(regions_gdf, "leisure", embeddings)
Out[9]:
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In [10]:
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plot_numeric_data(regions_gdf, "transportation", embeddings)
plot_numeric_data(regions_gdf, "transportation", embeddings)
Out[10]:
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