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import geopandas as gpd
import pandas as pd
from pytorch_lightning import seed_everything
from shapely.geometry import Polygon
from srai.constants import REGIONS_INDEX
from srai.embedders import GTFS2VecEmbedder
import geopandas as gpd
import pandas as pd
from pytorch_lightning import seed_everything
from shapely.geometry import Polygon
from srai.constants import REGIONS_INDEX
from srai.embedders import GTFS2VecEmbedder
Example on artificial data¶
Define features and regions¶
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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]),
)
features_gdf
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]),
)
features_gdf
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| trip_count_at_6 | trip_count_at_7 | trip_count_at_8 | directions_at_6 | geometry | |
|---|---|---|---|---|---|
| stop_id | |||||
| 1 | 1 | 1 | 0 | {A1, A} | POINT (1.00000 1.00000) |
| 2 | 0 | 1 | 0 | {B, B1} | POINT (2.00000 2.00000) |
| 3 | 0 | 0 | 1 | {C} | POINT (5.00000 2.00000) |
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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"]),
)
regions_gdf
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"]),
)
regions_gdf
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| geometry | |
|---|---|
| region_id | |
| ff1 | POLYGON ((0.00000 0.00000, 0.00000 3.00000, 3.... |
| ff2 | POLYGON ((4.00000 0.00000, 4.00000 3.00000, 7.... |
| ff3 | POLYGON ((8.00000 0.00000, 8.00000 3.00000, 11... |
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ax = regions_gdf.plot()
features_gdf.plot(ax=ax, color="red")
ax = regions_gdf.plot()
features_gdf.plot(ax=ax, color="red")
Out[4]:
<Axes: >
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joint_gdf = gpd.GeoDataFrame()
joint_gdf.index = pd.MultiIndex.from_tuples(
[("ff1", 1), ("ff1", 2), ("ff2", 3)],
names=[REGIONS_INDEX, "stop_id"],
)
joint_gdf
joint_gdf = gpd.GeoDataFrame()
joint_gdf.index = pd.MultiIndex.from_tuples(
[("ff1", 1), ("ff1", 2), ("ff2", 3)],
names=[REGIONS_INDEX, "stop_id"],
)
joint_gdf
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| region_id | stop_id |
|---|---|
| ff1 | 1 |
| 2 | |
| ff2 | 3 |
Get features without embedding them¶
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embedder = GTFS2VecEmbedder(skip_autoencoder=True)
res = embedder.transform(regions_gdf, features_gdf, joint_gdf)
res
embedder = GTFS2VecEmbedder(skip_autoencoder=True)
res = embedder.transform(regions_gdf, features_gdf, joint_gdf)
res
Out[6]:
| directions_at_6 | |
|---|---|
| region_id | |
| ff1 | 1.00 |
| ff2 | 0.25 |
| ff3 | 0.00 |
Fit and train the embedder¶
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seed_everything(42)
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
seed_everything(42)
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
Seed set to 42
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/root/development/srai/.venv/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
Missing logger folder: /root/development/srai/examples/embedders/lightning_logs
| 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)
/root/development/srai/.venv/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:298: 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[7]:
| 0 | 1 | 2 | 3 | |
|---|---|---|---|---|
| region_id | ||||
| ff1 | 0.657301 | 0.599207 | -0.188990 | 0.438122 |
| ff2 | 0.663876 | 0.541362 | -0.220063 | 0.030094 |
| ff3 | 0.636288 | 0.457780 | -0.115227 | 0.004630 |