| name | feature-calculator |
| description | Calculate derived columns and financial metrics from transaction data — revenue, profit margin, time-based features, moving averages, and growth rates. |
| version | 0.7.0 |
Feature Calculator
Skill Summary
Computes derived features and financial metrics from raw transaction data. Handles common calculations like revenue (quantity × price), profit margin (using cost lookup), time-based feature extraction (month, weekday, week number), rolling window aggregates (moving averages), and period-over-period growth rates. Designed for retail/e-commerce transaction pipelines.
Available Calculations
- revenue:
quantity * unit_price
- cost_total:
quantity * unit_cost (requires cost lookup)
- margin:
revenue - cost_total
- margin_pct:
margin / revenue * 100
- time_features: Extract
year, month, weekday, week_number from a date column
- moving_avg: Rolling N-day moving average of a numeric column
- growth_rate: Period-over-period percentage change
- cumulative_sum: Running cumulative total
- rank: Rank within a group (e.g., rank products by revenue)
Inputs
--input / -i: Path to transaction data (CSV or Parquet) (required)--features: Comma-separated list of features to calculate (default: all)--cost-lookup: Path to product catalog/cost lookup JSON--date-column: Column name containing dates (default: date)--window: Rolling window size in days for moving average (default: 7)--group-by: Column for group-level calculations--output / -o: Output path
Processing Steps
- Load transaction data
- Parse date column to datetime
- Calculate requested features:
- Arithmetic: revenue, cost, margin
- Temporal: extract date components
- Rolling: compute moving averages per group
- Growth: calculate period-over-period changes
- Join with cost lookup if margin calculations requested
- Save enriched dataset
Output
- Enriched dataset with new columns appended (CSV or Parquet)
feature_summary.json: Statistics for each computed feature (min, max, mean, null count)- Console: List of features calculated, row count
Implementation
- Command:
python3 ./skills/feature_calculator/calculate.py -i <file> -o <output> --features <list>
- Dependencies:
pandas, numpy
