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eed1cab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | """PC-based causal DAG discovery with functional-dependency priors."""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency # type: ignore[import-untyped]
from dataforge.causal.dag import CausalDAG
from dataforge.verifier.schema import Schema
__all__ = ["CausalDiscoveryResult", "discover_causal_dag"]
@dataclass(frozen=True)
class CausalDiscoveryResult:
"""Result of causal discovery.
Args:
dag: Directed acyclic graph over columns.
confidence_report: Column-pair confidence or diagnostic metadata.
warnings: Non-fatal discovery warnings.
"""
dag: CausalDAG
confidence_report: dict[str, float] = field(default_factory=dict)
warnings: tuple[str, ...] = ()
def discover_causal_dag(
df: pd.DataFrame,
schema: Schema | None = None,
*,
alpha: float = 0.05,
) -> CausalDiscoveryResult:
"""Infer a deterministic causal DAG from tabular data and FD priors.
Args:
df: Input DataFrame.
schema: Optional declared schema with functional dependencies.
alpha: Significance threshold for independence checks.
Returns:
CausalDiscoveryResult. A DAG is returned even if PC orientation is
underdetermined; low-confidence edges are tagged as such.
"""
columns = [str(column) for column in df.columns]
dag = CausalDAG(columns)
report: dict[str, float] = {}
warnings: list[str] = []
if schema is not None:
for fd in schema.functional_dependencies:
for determinant in fd.determinant:
_try_add_edge(
dag,
determinant,
fd.dependent,
confidence=0.95,
provenance="functional_dependency_prior",
warnings=warnings,
)
report[f"{determinant}->{fd.dependent}"] = 0.95
cleaned = _prepare_for_pc(df)
pc_edges, pc_warning = _run_causal_learn_pc(cleaned.to_numpy(), columns, alpha)
if pc_warning:
warnings.append(pc_warning)
for source, target in pc_edges:
_try_add_edge(
dag,
source,
target,
confidence=0.55,
provenance="causal_learn_pc",
warnings=warnings,
)
report.setdefault(f"{source}->{target}", 0.55)
for source, target, confidence in _pairwise_dependency_edges(df, alpha):
_try_add_edge(
dag,
source,
target,
confidence=confidence,
provenance="pairwise_ci_fallback",
warnings=warnings,
)
report.setdefault(f"{source}->{target}", confidence)
return CausalDiscoveryResult(dag=dag, confidence_report=report, warnings=tuple(warnings))
def _prepare_for_pc(df: pd.DataFrame) -> pd.DataFrame:
"""Return numeric data with no NaN values for causal-learn PC."""
prepared = pd.DataFrame(index=df.index)
for column in df.columns:
numeric = pd.to_numeric(df[column], errors="coerce")
if numeric.notna().sum() >= max(2, int(0.5 * len(df))):
fill = float(numeric.median()) if numeric.notna().any() else 0.0
prepared[str(column)] = numeric.fillna(fill)
else:
codes, _ = pd.factorize(df[column].astype("string").fillna("<missing>"), sort=True)
prepared[str(column)] = codes.astype(float)
return prepared.fillna(0.0)
def _run_causal_learn_pc(
data: np.ndarray[Any, Any], columns: list[str], alpha: float
) -> tuple[list[tuple[str, str]], str | None]:
"""Run causal-learn PC and return deterministic directed edges."""
try:
from causallearn.search.ConstraintBased.PC import pc # type: ignore[import-untyped]
result = pc(data, alpha=alpha, indep_test="fisherz", stable=True, show_progress=False)
except Exception as exc:
return [], f"causal-learn PC unavailable or failed: {exc}"
matrix = getattr(getattr(result, "G", None), "graph", None)
if matrix is None:
return [], "causal-learn PC returned no adjacency matrix"
edges: list[tuple[str, str]] = []
arr = np.asarray(matrix)
for i, source in enumerate(columns):
for j, target in enumerate(columns):
if i >= j or i >= arr.shape[0] or j >= arr.shape[1]:
continue
if arr[i, j] != 0 or arr[j, i] != 0:
edges.append((source, target))
return edges, None
def _pairwise_dependency_edges(df: pd.DataFrame, alpha: float) -> list[tuple[str, str, float]]:
"""Return deterministic low-confidence edges for dependent column pairs."""
columns = [str(column) for column in df.columns]
edges: list[tuple[str, str, float]] = []
for i, source in enumerate(columns):
for target in columns[i + 1 :]:
p_value = _pairwise_p_value(df[source], df[target])
if p_value < alpha:
confidence = max(0.25, min(0.75, 1.0 - p_value))
edges.append((source, target, round(confidence, 4)))
return edges
def _pairwise_p_value(left: pd.Series[Any], right: pd.Series[Any]) -> float:
"""Return a p-value using categorical, continuous, or mixed tests."""
left_numeric = pd.to_numeric(left, errors="coerce")
right_numeric = pd.to_numeric(right, errors="coerce")
left_cont = left_numeric.notna().sum() >= max(5, int(0.8 * len(left)))
right_cont = right_numeric.notna().sum() >= max(5, int(0.8 * len(right)))
if left_cont and right_cont:
return _hsic_p_value(
left_numeric.fillna(left_numeric.median()), right_numeric.fillna(right_numeric.median())
)
if not left_cont and not right_cont:
return _chi_squared_p_value(left, right)
return _mutual_information_p_value(left, right)
def _chi_squared_p_value(left: pd.Series[Any], right: pd.Series[Any]) -> float:
"""Return chi-squared independence p-value for categorical pairs."""
table = pd.crosstab(
left.astype("string").fillna("<missing>"), right.astype("string").fillna("<missing>")
)
if table.shape[0] < 2 or table.shape[1] < 2:
return 1.0
_, p_value, _, _ = chi2_contingency(table)
return float(p_value)
def _hsic_p_value(left: pd.Series[Any], right: pd.Series[Any]) -> float:
"""Return HSIC p-value for continuous pairs, with correlation fallback."""
x = left.to_numpy(dtype=float).reshape(-1, 1)
y = right.to_numpy(dtype=float).reshape(-1, 1)
try:
from hyppo.independence import Hsic # type: ignore[import-untyped]
_, p_value = Hsic().test(x, y, reps=100, auto=True)
return float(p_value)
except Exception:
corr = abs(float(np.corrcoef(x[:, 0], y[:, 0])[0, 1]))
return 0.0 if corr > 0.75 else 1.0
def _mutual_information_p_value(left: pd.Series[Any], right: pd.Series[Any]) -> float:
"""Return a bounded pseudo p-value from binned mutual information."""
left_codes = _codes(left)
right_codes = _codes(right)
table = pd.crosstab(left_codes, right_codes)
total = float(table.to_numpy().sum())
if total == 0.0 or table.shape[0] < 2 or table.shape[1] < 2:
return 1.0
joint = table.to_numpy(dtype=float) / total
px = joint.sum(axis=1, keepdims=True)
py = joint.sum(axis=0, keepdims=True)
expected = px @ py
mask = joint > 0
mi = float((joint[mask] * np.log(joint[mask] / expected[mask])).sum())
return float(np.exp(-mi))
def _codes(series: pd.Series[Any]) -> np.ndarray[Any, Any]:
"""Return stable integer codes for a mixed-type series."""
numeric = pd.to_numeric(series, errors="coerce")
if numeric.notna().sum() >= max(5, int(0.8 * len(series))):
return pd.qcut(
numeric.fillna(numeric.median()), q=4, duplicates="drop"
).cat.codes.to_numpy()
codes, _ = pd.factorize(series.astype("string").fillna("<missing>"), sort=True)
return codes
def _try_add_edge(
dag: CausalDAG,
source: str,
target: str,
*,
confidence: float,
provenance: str,
warnings: list[str],
) -> None:
"""Add an edge or record the cycle warning."""
try:
dag.add_edge(source, target, confidence=confidence, provenance=provenance)
except ValueError as exc:
warnings.append(str(exc))
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