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End-to-End Case Study

Real dataset, real numbers, one click. This is the fastest way to watch Seahorse do something real: it installs from PyPI, loads the Citibike spatio-temporal dataset, explores it on a map, trains a baseline and a neural model, scores both under one metric, and visualizes the predictions — all on CPU, in a few minutes.

Open the case study in ColabCitibike · CPU-only · no local setup

What the notebook walks through

  1. 1
    Install and load real data

    pip install seahorse-stpp, then load_dataset("citibike") pulls the Citibike splits straight from the Hugging Face hub — no manual download.

  2. 2
    Explore it like a practitioner

    Inspect events per sequence, the daily ride rhythm, and a map of ride density across New York City — the spatial pattern a model has to capture.

  3. 3
    Train a baseline and a neural model

    Fit PoissonGMM as a fast parametric baseline and DeepSTPP as the neural model — the same fit() call for both.

  4. 4
    Evaluate head-to-head

    Score both on held-out test sequences and compare test_nll under one shared metric definition — comparable by construction.

  5. 5
    Visualize on the map

    Sample next events with predict_next and plot the model's predicted ride locations against the true next event, over the NYC map.

Why this dataset

  • Real, not synthetic — Citibike is the lightest real public dataset in the catalog, so it runs on a free CPU runtime.
  • Comparable by construction — the baseline and the neural model share the same splits, normalization, and metric, so the numbers actually mean something.
  • One click — the notebook installs the published package; nothing to clone or configure.

Then go deeper

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