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81e15fe | 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 | import json
import numpy as np
from pathlib import Path
class QuantileGridFromCoeffs:
def __init__(self, export_dir):
self.export_dir = Path(export_dir)
meta = json.loads((self.export_dir / "meta.json").read_text())
self.features = meta["features"]
self.taus = np.array(meta["taus"], dtype=float)
self.has_intercept = meta.get("has_intercept", False)
# load coefficients
coeffs = np.load(self.export_dir / "coeffs.npz")
# build a matrix shape (n_taus, n_coef)
coefs = []
for t in self.taus:
key = f"tau_{t}"
if key not in coeffs:
# try rounding formatting
found = [k for k in coeffs.files if k.startswith("tau_") and abs(float(k.split("_")[1]) - t) < 1e-12]
if not found:
raise KeyError(f"Coefficient for tau={t} not found in {coeffs.files}")
key = found[0]
coefs.append(coeffs[key])
self.coef_matrix = np.vstack(coefs) # shape (m_taus, n_coef)
def _create_polynomial_features(self, X):
"""
Create polynomial features for interaction terms.
Parameters:
X (array): Array with columns [x, y]
Returns:
Design matrix with polynomial features
"""
x = X[:, 0]
y = X[:, 1]
A = 2
# Create design matrix with polynomial features up to the specified degree
features = []
# Constant term (intercept)
if 'c' in self.features:
features.append(np.ones_like(x))
# Linear terms
if 'x' in self.features:
features.append(x)
if 'y' in self.features:
features.append(y)
if 'y_m' in self.features:
features.append(y-A)
if 'y_p' in self.features:
features.append(y+A)
# Interaction terms
if 'xy' in self.features:
features.append(x * y)
if 'xy_m' in self.features:
features.append(x * (y-A))
if 'xy_p' in self.features:
features.append(x * (y+A))
if 'xy2' in self.features:
features.append(x * y**2)
if 'xy2_m' in self.features:
features.append(x * (y-A)**2)
if 'xy2_p' in self.features:
features.append(x * (y+A)**2)
if 'x2y' in self.features:
features.append(x**2 * y)
if 'xy3' in self.features:
features.append(x * y**3)
if 'xy4' in self.features:
features.append(x * y**4)
if 'xy3_m' in self.features:
features.append(x * (y-A)**3)
if 'xy3_p' in self.features:
features.append(x * (y+A)**3)
if 'x3y' in self.features:
features.append(x**3 * y)
# Higher order terms
if 'x2' in self.features:
features.append(x**2)
if 'x3' in self.features:
features.append(x**3)
if 'y2' in self.features:
features.append(y**2)
if 'y3' in self.features:
features.append(y**3)
if 'y4' in self.features:
features.append(y**4)
if 'y2_m' in self.features:
features.append((y-A)**2)
if 'y3_m' in self.features:
features.append((y-A)**3)
if 'y4_m' in self.features:
features.append((y-A)**4)
if 'y2_p' in self.features:
features.append((y+A)**2)
if 'y3_p' in self.features:
features.append((y+A)**3)
if 'y4_p' in self.features:
features.append((y+A)**4)
return np.column_stack(features)
def predict_quantiles(self, X_new):
"""
Return Q (n_points, m_taus) predicted quantiles.
"""
X_new = np.asarray(X_new, dtype=float)
Xd = self._create_polynomial_features(X_new) # shape (n, p)
# matrix multiply: (m_taus, p) @ (p, n) -> (m_taus, n) then transpose
Q = (self.coef_matrix @ Xd.T).T
# optionally enforce monotonicity in tau
Q_sorted = np.sort(Q, axis=1)
return self.taus, Q_sorted
def predict_tau(self, X_new, tau_star):
taus, Q = self.predict_quantiles(X_new)
# vectorized interpolation (same approach as earlier)
# implement interpolation between nearest taus
import numpy as np
t0_idx = np.searchsorted(taus, tau_star, side='right') - 1
# for simplicity assume scalar tau_star
j = int(np.clip(t0_idx, 0, len(taus)-2))
t0, t1 = taus[j], taus[j+1]
q0, q1 = Q[:, j], Q[:, j+1]
w = (tau_star - t0) / (t1 - t0)
return q0 + w * (q1 - q0)
def sample(self, X_new, n_samples=1, rng=None):
"""
Draw samples from the approximate conditional distribution at X_new
using inverse-CDF sampling based on the saved quantile grid.
Parameters
----------
X_new : array-like, shape (n_points, 2)
New points [x, y] in your domain (e.g., LogP, PolarityIndex).
n_samples : int
Number of samples per point.
rng : None, int, or np.random.Generator
Random seed or Generator for reproducibility.
Returns
-------
samples : ndarray, shape (n_points, n_samples)
Samples drawn from the interpolated quantile function.
"""
if n_samples < 1:
raise ValueError("n_samples must be >= 1")
# Setup RNG
if isinstance(rng, np.random.Generator):
gen = rng
else:
gen = np.random.default_rng(rng)
X_new = np.asarray(X_new, dtype=float)
if X_new.ndim != 2 or X_new.shape[1] != 2:
raise ValueError("X_new must be a 2D array with exactly two columns [x, y]")
# Get quantile grid predictions: Q has shape (n_points, n_taus)
taus, Q = self.predict_quantiles(X_new)
taus = np.asarray(taus, dtype=float)
Q = np.asarray(Q, dtype=float)
n_points, m = Q.shape
if m < 2:
raise RuntimeError("Need at least two taus to sample with interpolation.")
# Sample u in the supported tau range of the grid
u = gen.uniform(taus[0], taus[-1], size=(n_points, n_samples))
# For each u, find interval [taus[j], taus[j+1]]
j = np.searchsorted(taus, u, side="right") - 1
j = np.clip(j, 0, m - 2)
# Gather endpoints
t0 = taus[j]
t1 = taus[j + 1]
row_idx = np.arange(n_points)[:, None]
q0 = Q[row_idx, j]
q1 = Q[row_idx, j + 1]
# Linear interpolation
w = (u - t0) / (t1 - t0)
samples = q0 + w * (q1 - q0)
return samples
## read saved model
#model = QuantileGridFromCoeffs(export_dir='Kps_model')
## example points: [(LogP, Polarity_Index), ...]
#X_new = np.array([[2.34665198, 10.2], ])
## sample the distribution at each X
#samples = model.sample(X_new, n_samples=50, rng=0)
#print(samples[0])
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