impulse-ml

name

impulse-ml

description

Turn Impulse measurement recordings into a machine-learning feature matrix. Use when the user wants to "make ML features from measurement data", "train a model on containers/recordings", build a per-drive / per-recording feature vector from sensor statistics, or feed Impulse output to MLflow or AutoML. Covers computing event-scoped statistics with a sinkless Report, reading them from `report.aggregation_dfs`, and pivoting to one row per container.

Impulse — ML feature engineering

ML mode extracts event-scoped statistics as a flat feature matrix (one row per container, one column per feature) that you pass to MLflow, AutoML, or scikit-learn. It reuses the reporting machinery but does not persist to gold — you read the computed statistics straight off the report object.

The flow: compute a StatsAggregator over an event (usually a ContainerEvent) → pull the stats DataFrame from report.aggregation_dfs → pivot to one row per container → train.

1. Compute per-recording statistics (sinkless)

Build a report without unity_sink so nothing is written (see impulse-config), select the channels that characterize your recordings (see impulse-tsal), and aggregate over a ContainerEvent so every sample is in scope (see impulse-events / impulse-aggregations).

from databricks.sdk import WorkspaceClient
from impulse_reporting.core.report import Report
from impulse_reporting.core.page import Page
from impulse_reporting.events.container_event import ContainerEvent
from impulse_reporting.aggregations.stats_aggregator import StatsAggregator

ws = WorkspaceClient()
config = {
    "source": {
        "container_metrics_table": "my_catalog.silver.container_metrics",
        "channel_metrics_table": "my_catalog.silver.channel_metrics",
        "channels_uri": "my_catalog.silver.channels",
        "container_tags_table": "my_catalog.silver.container_tags",
        "channel_tags_table": "my_catalog.silver.channel_tags",
    },
    # no unity_sink -> sinkless: determine_report() computes, persist is a no-op
    "query_engine": {"solver": "DefaultSolver", "data_type": "RLE"},
    "measurement_dimensions": ["container_id", "vehicle_key", "start_ts", "stop_ts"],
}

report = Report(name="feature_engineering", spark=spark, workspace_client=ws, config=config)
db = report.get_db()

eng_rpm      = db.query.channel(channel_name="Engine RPM", brand="Seat", model="Leon")
coolant_temp = db.query.channel(channel_name="Engine Coolant Temperature", brand="Seat", model="Leon")
veh_spd      = db.query.channel(channel_name="Vehicle Speed Sensor", brand="Seat", model="Leon")

container_event = ContainerEvent(name="container_event", desc="Full measurement recording")
report.add_event(container_event)

page = Page(page_number=1)
report.add_page(page)
page.add_aggregation(StatsAggregator(
    name="drive_stats",
    input_expressions=[eng_rpm, coolant_temp, veh_spd],
    channel_names=["Engine RPM", "Coolant Temp", "Vehicle Speed"],
    statistics=["min", "mean", "max"],
    event=container_event,
    desc="Per-recording statistics",
))

report.determine_report()   # computes in parallel across containers; no gold write needed

2. Read the computed statistics

After determine_report(), the results live on the report under aggregation_dfs, keyed by aggregation type. StatsAggregator results are under "STATS_AGGREGATOR", returned as a bundle keyed by whether the rows were freshly computed ("changed") or reused from a prior incremental run ("unchanged"). In a fresh sinkless run take "changed":

stats_bundle = report.aggregation_dfs["STATS_AGGREGATOR"]
stats_df = stats_bundle.get("changed") or stats_bundle.get("unchanged")

stats_df has one row per (container_id, channel_name, aggregation_label) with a statistic_value column (the stats_aggregator_fact shape — see impulse-aggregations).

3. Pivot to a feature matrix

Combine channel_name + aggregation_label into a feature name and pivot to one row per container:

import pyspark.sql.functions as F

features_df = (
    stats_df
    .withColumn("feature", F.concat(F.col("channel_name"), F.lit("_"), F.col("aggregation_label")))
    .groupBy("container_id")
    .pivot("feature")
    .agg(F.first("statistic_value"))
)

That yields columns like Engine RPM_mean, Vehicle Speed_max, one row per recording — a feature vector ready for training.

4. Add labels and train

Labels typically come from container tags. Join them on container_id, then hand the columns to any trainer. With MLflow:

import mlflow
from sklearn.ensemble import RandomForestClassifier

mlflow.set_registry_uri("databricks-uc")
mlflow.sklearn.autolog()

feature_cols = sorted(c for c in labeled_df.columns if c not in ("container_id", "label"))
pdf = labeled_df.toPandas().dropna(subset=feature_cols)

with mlflow.start_run(run_name="my_model"):
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(pdf[feature_cols], pdf["label"])

Register and deploy with the standard MLflow / Databricks Model Serving APIs from here — Impulse's role ends once the feature matrix is built.

Notes

• Use a ContainerEvent for per-recording features (whole recording in scope). Use a BasicEvent or PointsInTimeEvent when you want per-interval or per-instant features instead — the stats then carry an event_instance_id you can aggregate over (see impulse-events).
• StatsAggregator inputs must be SampleSeries (channels or arithmetic over them), not comparisons.
• To materialize the same statistics to gold instead of reading them inline (e.g. for a shared feature store), add a unity_sink and call persist_results() — that is impulse-reporting.