In [1]:

import pandas as pd
import geopandas as gpd
from shapely.geometry import Polygon
from pytorch_lightning import seed_everything

from srai.embedders import GTFS2VecEmbedder
from srai.constants import REGIONS_INDEX

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

Example on artificial data¶

Define features and regions¶

In [2]:

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

Out[2]:

	trip_count_at_6	trip_count_at_7	trip_count_at_8	directions_at_6	geometry
stop_id
1	1	1	0	{A, A1}	POINT (1.00000 1.00000)
2	0	1	0	{B1, B}	POINT (2.00000 2.00000)
3	0	0	1	{C}	POINT (5.00000 2.00000)

In [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"]),
)
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

Out[3]:

	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...

In [4]:

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: >

In [5]:

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

Out[5]:

region_id	stop_id
ff1	1
	2
ff2	3

Get features without embedding them¶

In [6]:

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¶

In [7]:

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

Global seed set to 42
/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: 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
/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: 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
  warning_cache.warn(

  | 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/3.10.12/x64/lib/python3.10/site-packages/torch/utils/data/dataloader.py:560: 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(
/opt/hostedtoolcache/Python/3.10.12/x64/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:280: PossibleUserWarning: 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.
  rank_zero_warn(

`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