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Aggregation and Interaction Features

Reference for feature-engineering-idea-generator · 111 lines

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20264 references2 scripts

Two dataset shapes

  1. Event-level rows (each transaction is scored): aggregates over the entity's previous events.
  2. Snapshot rows (each customer scored at a date): aggregates over events before the snapshot date.

Both obey the same rule: no data from the prediction moment or later.

Expanding (all prior history) per entity

python
df = df.sort_values(["customer_id", "ts"])
g = df.groupby("customer_id")
df["n_prior"] = g.cumcount()
df["amount_mean_prior"] = g["amount"].transform(lambda s: s.shift().expanding().mean())
df["amount_max_prior"] = g["amount"].transform(lambda s: s.shift().expanding().max())
df["secs_since_prev"] = g["ts"].diff().dt.total_seconds()

Time windows (last 1 h, 24 h, 7 d, 30 d)

python
df = df.sort_values("ts").set_index("ts")
roll = df.groupby("card_id")["amount"].rolling("24h", closed="left")   # closed="left" excludes the current row
feat = pd.DataFrame({"n_24h": roll.count(), "sum_24h": roll.sum()}).reset_index()
df = df.reset_index().merge(feat, on=["card_id", "ts"], how="left")
df[["n_24h", "sum_24h"]] = df[["n_24h", "sum_24h"]].fillna(0)        # NaN = no prior events in the window

With duplicate timestamps per entity, aggregate on a unique key (transaction ID) instead of merging on time. For large data, compute windows in SQL with RANGE BETWEEN INTERVAL '24 hours' PRECEDING AND CURRENT ROW minus the current row, or in polars rolling / group_by_dynamic.

Snapshot aggregates (one row per entity per scoring date)

python
def snapshot_features(events, entities, score_date, windows=(7, 30, 90)):
    past = events[events.ts < score_date]
    out = entities[["customer_id"]].copy()
    for w in windows:
        win = past[past.ts >= score_date - pd.Timedelta(days=w)]
        agg = win.groupby("customer_id").agg(**{f"n_{w}d": ("ts", "size"), f"spend_{w}d": ("amount", "sum")})
        out = out.merge(agg, on="customer_id", how="left")
    last = past.groupby("customer_id").ts.max()
    out["days_since_last"] = (score_date - out.customer_id.map(last)).dt.days
    return out.fillna({c: 0 for c in out.columns if c.startswith(("n_", "spend_"))})

RFM and engagement

FeatureDefinition
Recencydays since last purchase / login
Frequencyevents in the last 30 / 90 days
Monetaryspend in the last 30 / 90 days
Tenuredays since first event
Breadthdistinct products / categories / devices
Regularitystd of inter-event gaps; coefficient of variation
python
df["spend_trend"] = df["spend_30d"] / (df["spend_prev_30d"] + 1)          # < 1 declining
df["logins_trend"] = df["logins_7d"] / (df["logins_30d"] / 4 + 1e-3)
df["amount_vs_own_mean"] = df["amount"] / (df["amount_mean_prior"] + 1e-3)
df["zscore_vs_own"] = (df["amount"] - df["amount_mean_prior"]) / (df["amount_std_prior"] + 1e-3)

Group context

python
med = train.groupby("neighborhood")["price_per_sqft"].median()
df["ppsf_vs_hood"] = df["price_per_sqft"] / df["neighborhood"].map(med)

Group statistics of the target are target encoding: fit out-of-fold.

Velocity features (fraud, abuse)

FeatureExample
Count in windowtransactions per card in the last 10 min / 1 h / 24 h
Distinct in windowdistinct merchants, countries, devices per card in 24 h
Sum in windowamount per card in 24 h vs its 30-day daily average
Shared entitiescards per device in 7 d; accounts per IP in 24 h
Impossible traveldistance between consecutive transactions / time between them

Interactions

ModelApproach
Linearexplicit products (plan_basic * support_calls), PolynomialFeatures(degree=2, interaction_only=True) on a few features, splines (SplineTransformer) for non-linear effects
GBMusually finds interactions; add explicit ratios/differences it cannot express easily
Anyconcatenated categoricals (plan + "_" + region) when the combination has meaning

Find candidate interactions with SHAP interaction values or by comparing a GBM with a linear model per feature pair; keep only those that validate.

Splines and binning

python
from sklearn.preprocessing import SplineTransformer, KBinsDiscretizer
spline = SplineTransformer(n_knots=5, degree=3)              # smooth non-linear effect for linear models
bins = KBinsDiscretizer(n_bins=10, encode="onehot", strategy="quantile")

Fit both on training data only (inside the pipeline).

Cost of aggregates in production

Real-time aggregates need a stream processor or an online feature store; batch aggregates are cheap. Decide the freshness each feature needs (feature-store-schema-designer) before building it.

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