quivers.formulas.formula

Parsed-formula IR: Formula, FixedColumn, RandomTerm, plus the formula_from_data adapter over formulae.design_matrices.

formula

Parsed-formula IR: a typed didactic.api.Model wrapping the raw formulae.matrices.DesignMatrices so the rest of the formula frontend operates on typed values.

The Formula IR is the canonical source representation of the formula→QVR lens. Future versions can register it as a panproto protocol so the lens machinery applies; for now the compiler walks this IR directly.

Convention

Each fixed-effect term may produce one or more design-matrix columns (a single column for x, two for poly(x, 2), K for an unordered factor with K + 1 levels, etc.). R / brms assign one coefficient per column; this IR follows the same convention by exploding each term into a tuple of FixedColumn records. Multi-column terms thus produce multiple named scalar latents downstream, with deterministic naming {term}_1, {term}_2, ... that mirrors R's poly(x, 2)1 / poly(x, 2)2 display.

Polynomial default: formulae.design_matrices's poly transform is orthogonal by default (matches R's stats::poly). Raw monomials are available via I(x^2) / I(x**2). Transforms log, exp, sqrt, abs, sin, cos, tan, log10, log2, log1p, expm1 are wired through the formulae evaluation namespace so users coming from R get the expected base R behaviour.

FixedColumn

Bases: Model

One column of the fixed-effects design matrix.

ATTRIBUTE	DESCRIPTION
term	Originating term name (e.g. "poly(x, 2)" or "x"). TYPE: str
name	Per-column label, equal to term for single-column terms and f"{term}_{k+1}" (1-indexed, matching R's display) for multi-column terms like poly(x, 2). TYPE: str
qvr_name	QVR-legal identifier derived from name (alnum / _ only); used as the variable name in the emitted program. TYPE: str
is_intercept	True for the constant-1 column. TYPE: bool

RandomTerm

Bases: Model

One random-effect group, e.g. (1 | g) or (x | g).

ATTRIBUTE	DESCRIPTION
slope	"Intercept" for (1 | g); otherwise the slope variable name. TYPE: str
group	Grouping factor name. TYPE: str

Formula

Bases: Model

A parsed regression formula plus the data it was parsed against.

ATTRIBUTE	DESCRIPTION
formula	Original formula string. TYPE: str
response_name	Name of the response column. TYPE: str
fixed_columns	One entry per design-matrix column (matches R/brms's one-coefficient-per-column convention). TYPE: tuple[FixedColumn, ...]
random_terms	Random-effect group specifications. TYPE: tuple[RandomTerm, ...]
response_values	Response column values, shape (N,). TYPE: ndarray
group_levels	Canonical level ordering per grouping factor, used to derive deterministic plate-index tensors. TYPE: Mapping[str, tuple[str, ...]]
group_indices	Per-group integer index array, shape (N,). TYPE: Mapping[str, tuple[int, ...]]

FormulaData

Bases: Model

Store the nonstructural complement of a Formula lens mapping.

The emitted QVR module records formula structure under normalized identifiers. This complement retains the source formula, original names, presentation labels, per-row arrays, group levels, and group indices so backward(module, complement) can reconstruct the original Formula.

ATTRIBUTE	DESCRIPTION
formula	Original formula string. TYPE: str
response_name	Original response column name. TYPE: str
response_values	Response values with shape (N,). TYPE: ndarray
fixed_column_names	(term, name) pairs keyed by normalized fixed-column name. TYPE: Mapping[str, tuple[str, str]]
fixed_column_data	Predictor values keyed by normalized fixed-column name. TYPE: Mapping[str, ndarray]
group_original_names	Original group names keyed by normalized group name. TYPE: Mapping[str, str]
group_levels	Canonical level order for each group. TYPE: Mapping[str, tuple[str, ...]]
group_indices	Per-row integer codes for each group. TYPE: Mapping[str, tuple[int, ...]]

formula_from_data

formula_from_data(formula: str, data: IntoDataFrame, *, extra_namespace: Mapping[str, object] | None = None) -> Formula

Build a typed Formula IR by lifting formulae.design_matrices over a dataframe.

This is an adapter, not a parser: the brms-style formula syntax is parsed by the formulae library; we lift its formulae.matrices.DesignMatrices result into a typed didactic record, augmented with deterministic per-group level orderings and integer-code arrays derived from the dataframe.

The R-style numeric transforms (log, exp, sqrt, abs, sin, cos, tan, log10, log2, log1p, expm1, asin, acos, atan, sinh, cosh, tanh) are pre-loaded into the formulae evaluation namespace so users coming from R / brms get the expected base R behaviour without explicit registration. Polynomial terms via poly(x, k) are orthogonal by default, matching R's stats::poly.

PARAMETER	DESCRIPTION
formula	Formula string in brms / lme4 syntax. TYPE: str
data	Pandas, polars, or any other Narwhals-compatible dataframe. TYPE: IntoDataFrame
extra_namespace	Additional names visible inside the formula's expression evaluation, merged on top of the R-style transforms. TYPE: Mapping[str, object] DEFAULT: None

Source code in src/quivers/formulas/formula.py

def formula_from_data(
    formula: str,
    data: IntoDataFrame,
    *,
    extra_namespace: Mapping[str, object] | None = None,
) -> Formula:
    """Build a typed `Formula` IR by lifting
    `formulae.design_matrices` over a dataframe.

    This is an adapter, not a parser: the brms-style formula syntax
    is parsed by the [`formulae`](https://bambinos.github.io/formulae/)
    library; we lift its `formulae.matrices.DesignMatrices`
    result into a typed didactic record, augmented with deterministic
    per-group level orderings and integer-code arrays derived from
    the dataframe.

    The R-style numeric transforms (``log``, ``exp``, ``sqrt``,
    ``abs``, ``sin``, ``cos``, ``tan``, ``log10``, ``log2``,
    ``log1p``, ``expm1``, ``asin``, ``acos``, ``atan``, ``sinh``,
    ``cosh``, ``tanh``) are pre-loaded into the formulae evaluation
    namespace so users coming from R / brms get the expected base
    R behaviour without explicit registration.  Polynomial terms via
    ``poly(x, k)`` are orthogonal by default, matching R's
    ``stats::poly``.

    Parameters
    ----------
    formula : str
        Formula string in brms / lme4 syntax.
    data : IntoDataFrame
        Pandas, polars, or any other Narwhals-compatible dataframe.
    extra_namespace : Mapping[str, object], optional
        Additional names visible inside the formula's expression
        evaluation, merged on top of the R-style transforms.
    """
    nw_df = nw.from_native(data, eager_only=True)
    pandas_df = nw_df.to_pandas()
    namespace: dict[str, object] = dict(_R_TRANSFORMS)
    if extra_namespace:
        namespace.update(extra_namespace)
    dm = fo.design_matrices(formula, data=pandas_df, extra_namespace=namespace)
    if dm.response is None:
        raise ValueError(
            f"formula_from_data: formula {formula!r} has no response "
            f"variable on the left of `~`"
        )
    response_name = dm.response.name
    n_obs = int(pandas_df.shape[0])

    fixed_columns: list[FixedColumn] = []
    if dm.common is not None:
        for term_name, term in dm.common.terms.items():
            fixed_columns.extend(_explode_term(term_name, term, n_obs))

    random_terms: list[RandomTerm] = []
    group_levels: dict[str, tuple[str, ...]] = {}
    group_indices: dict[str, tuple[int, ...]] = {}
    if dm.group is not None:
        for term_name in dm.group.terms.keys():
            if "|" not in term_name:
                raise ValueError(
                    f"formula_from_data: unexpected random term name "
                    f"{term_name!r}; expected `(slope | group)` syntax"
                )
            slope, group = term_name.split("|", 1)
            slope = slope.strip()
            group = group.strip()
            if slope == "1":
                slope = "Intercept"
            random_terms.append(RandomTerm(slope=slope, group=group))
            if group not in group_levels:
                levels = tuple(
                    str(v) for v in nw_df[group].drop_nulls().unique().sort().to_list()
                )
                group_levels[group] = levels
                level_index = {v: i for i, v in enumerate(levels)}
                codes = tuple(level_index[str(v)] for v in nw_df[group].to_list())
                group_indices[group] = codes

    response_values = (
        np.asarray(dm.response.design_matrix).reshape(-1).astype(np.float64)
    )

    return Formula(
        formula=formula,
        response_name=response_name,
        fixed_columns=tuple(fixed_columns),
        random_terms=tuple(random_terms),
        response_values=response_values,
        group_levels=group_levels,
        group_indices=group_indices,
    )