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test(sdk): canonical pipeline integration fixtures #62

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@petrpan26

Subtask of #56. Lock down the end-to-end behavior with realistic pipelines as test fixtures. These also double as the canonical examples in docs/python/expressions.mdx.

Four pipelines, each in examples/pipelines/<name>.py and exercised by python/tests/v0/test_pipeline_<name>.py:

  1. txn_fraud.py — Pipeline 1 from the capstone discussion (~22 features). Exercises Core / Sketch / Velocity / Recency / Decay / Geo families with @bv.expr-derived predicates.
  2. pageview_behavior.py — Pipeline 2 (~20 features). Exercises source-level filter, with_columns, the full operator-family span.
  3. sensor_anomaly.py — IoT telemetry: trend, trend_residual, z_score, outlier_count, seasonal_deviation, streak.
  4. playback_behavior.py — Content/video: event_type filtering, burst_count over filtered events, decayed_count, entropy.

Each test:

  • Registers the pipeline against a real embedded server.
  • Pushes a fixed sequence of N events.
  • Calls app.get(...) for each derived feature.
  • Asserts exact computed values against a golden file.

Done when all four pipelines register, push, and read back the expected feature values. ~400 LOC tests + ~300 LOC pipeline definitions.

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    area: sdk-pythonPython SDK under python/beava/domain: mlML feature engineering relevance (scalar transforms, hashing, encodings)

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