Forgottend commit of refernce test and bench log

Author: miranov25

UTILS/dfextensions/quantile_fit_nd/utils.py

@@ -0,0 +1,81 @@
+# dfextensions/quantile_fit_nd/utils.py
+import numpy as np
+from typing import Optional
+
+def discrete_to_uniform_rank_poisson(
+        k: np.ndarray,
+        lam: float,
+        mode: str = "randomized",
+        rng: Optional[np.random.Generator] = None,
+) -> np.ndarray:
+    """
+    Map Poisson counts k ~ Poisson(lam) to U ~ Uniform(0,1).
+
+    mode="randomized" (preferred, exact PIT):
+        U = F(k-1) + V * (F(k) - F(k-1)),  V ~ Unif(0,1)
+    mode="midrank" (deterministic):
+        U = 0.5 * (F(k-1) + F(k))
+
+    Returns U in [0,1].
+    """
+    k = np.asarray(k, dtype=np.int64)
+    if rng is None:
+        rng = np.random.default_rng()
+
+    k_max = int(np.max(k)) if k.size else 0
+    pmf = np.empty(k_max + 1, dtype=np.float64)
+    pmf[0] = np.exp(-lam)
+    for j in range(k_max):
+        pmf[j + 1] = pmf[j] * lam / (j + 1)
+
+    cdf = np.cumsum(pmf)
+    cdf = np.clip(cdf, 0.0, 1.0)
+
+    Fk   = cdf[k]
+    Fkm1 = np.where(k > 0, cdf[k - 1], 0.0)
+
+    if mode == "randomized":
+        u = rng.random(size=k.size)
+        U = Fkm1 + u * (Fk - Fkm1)
+    elif mode == "midrank":
+        U = 0.5 * (Fkm1 + Fk)
+    else:
+        raise ValueError(f"unknown mode {mode!r}")
+
+    return np.clip(U, 0.0, 1.0)
+
+
+def discrete_to_uniform_rank_empirical(
+        x: np.ndarray,
+        mode: str = "randomized",
+        rng: Optional[np.random.Generator] = None,
+) -> np.ndarray:
+    """
+    Generic discrete -> Uniform(0,1) using the empirical CDF of x.
+
+    For unique value v with mass p_v and cumulative F(v):
+      randomized: U ~ Uniform(F(v-), F(v))
+      midrank:    U = 0.5 * (F(v-) + F(v))
+    """
+    x = np.asarray(x)
+    n = x.size
+    if rng is None:
+        rng = np.random.default_rng()
+    if n == 0:
+        return np.array([], dtype=np.float64)
+
+    uniq, inv = np.unique(x, return_inverse=True)
+    counts = np.bincount(inv, minlength=uniq.size)
+    cum = np.cumsum(counts)
+    F_curr = cum / float(n)
+    F_prev = (cum - counts) / float(n)
+
+    if mode == "randomized":
+        u = rng.random(size=n)
+        U = F_prev[inv] + u * (F_curr[inv] - F_prev[inv])
+    elif mode == "midrank":
+        U = 0.5 * (F_prev[inv] + F_curr[inv])
+    else:
+        raise ValueError(f"unknown mode {mode!r}")
+
+    return np.clip(U, 0.0, 1.0)