feat: DataProfiler - foundational data-discovery layer for AStats agent.#2
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feat: DataProfiler - foundational data-discovery layer for AStats agent.#2
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- Column-type detection (continuous, categorical, datetime) - Normality: Shapiro-Wilk (n<=5000) or D'Agostino-Pearson - Variance homogeneity: Levene's test (optional, via group_col) - IQR-based outlier detection per numeric column - Structured agent_hints JSON: parametric/nonparametric/welch routing - Ground-truth simulated dataset generator for eval harness - 8-test pytest suite covering shape, normality, outliers, hints, variance Foundation for Phase 2: LangGraph agent test-selection harness.
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What this does
Implements the data auto-discovery and summarization layer described in the project.
The profiler outputs a structured JSON with
agent_hints(parametric/nonparametric/welchrouting) that the LangGraph agent (Phase 2) will consume for assumption-checked test selection.
Modules added
astats/profiler/data_profiler.py- full profiler with normality, variance, outlier checksexamples/data/generate_sample.py- simulated dataset generator with known ground truthtests/test_profiler.py- 8 pytest casesNext step
Phase 2: LangGraph agent that reads this profile and performs
assumption-checked statistical test selection and execution.