wayne-trade 0.1.0

Formula to model matrix converter

wayne

---

a formula to model matrix

Formula to Model Matrix in One Function Call

wyane is a Python package that converts statistical formulas into model matrices using Polars DataFrames. It does one thing: takes a formula and a DataFrame, returns a model matrix.

Usage

import wayne
import polars as pl

# Load your data
df = pl.read_csv("data/mtcars.csv")

# Define your formula
formula = 'mpg ~ cyl + wt*hp + poly(disp, 4) - 1'

# Get the model matrix
model_matrix = wayne.trade_formula_for_matrix(df, formula)
print(model_matrix)

Features

• Single Function: trade_formula_for_matrix(df, formula)
• Polars Integration: Works with Polars DataFrames
• R-Style Formulas: Supports R/Wilkinson notation
• Fast Parsing: Rust + fiasto for formula parsing
• Orthogonal Polynomials: NumPy-based polynomial generation
• Interactions: Handles interaction terms (e.g., x*z)
• Intercept Control: Add/remove intercept with +1/-1

Formula Syntax

Wayne supports R-style statistical formulas:

• Basic terms: y ~ x + z
• Interactions: y ~ x*z (expands to x + z + x:z)
• Polynomial terms: y ~ poly(x, 3) (generates 3 orthogonal polynomial columns)
• Intercept control: y ~ x - 1 (removes intercept)
• Complex formulas: y ~ x + z + poly(w, 3) + x:z - 1

Examples

See the examples/ directory for complete examples:

# Main example
uv run python examples/final_example.py

# Single function demo
uv run python examples/single_function_example.py

What You Get

The model matrix contains:

• Main effects: Original variables from your formula
• Interaction terms: Generated interaction columns (e.g., wt_x_hp for wt*hp)
• Polynomial terms: Orthogonal polynomial columns (e.g., poly_disp_1, poly_disp_2, etc.)
• Intercept: Optional intercept column

Perfect for Statistical Modeling

Wayne creates model matrices that are ready for:

• Linear regression
• Generalized linear models
• Mixed effects models
• Any statistical modeling that needs a design matrix

License

MIT