anofox-regression 0.5.6

A robust statistics library for regression analysis
Documentation 
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

#!/usr/bin/env python3
"""Generate validation constants for PassiveAggressiveRegressor.

Oracle: sklearn.linear_model.PassiveAggressiveRegressor.

We disable shuffling (shuffle=False) so the per-epoch update order is
deterministic and does not depend on numpy's Mersenne-Twister sequence.
This lets us compare final weights directly without re-implementing
sklearn's RNG bit-for-bit.
"""
from __future__ import annotations

import datetime as _dt
import numpy as np
import sklearn
from sklearn.linear_model import PassiveAggressiveRegressor


def emit_header() -> None:
    print(
        "// =============================================================================\n"
        "// Passive-Aggressive Validation Data Generated from sklearn\n"
        f"// Generated on: {_dt.datetime.now().isoformat(timespec='seconds')}\n"
        f"// scikit-learn: {sklearn.__version__}\n"
        f"// numpy: {np.__version__}\n"
        "// =============================================================================\n"
    )


def emit_const_f64(name: str, value: float) -> None:
    print(f"const {name}: f64 = {value:.15e};")


def emit_array_f64(name: str, values: np.ndarray) -> None:
    body = ", ".join(f"{v:.15e}" for v in values)
    print(f"const {name}: [f64; {len(values)}] = [{body}];")


def emit_const_usize(name: str, value: int) -> None:
    print(f"const {name}: usize = {value};")


def case_pa1_univariate() -> None:
    print(
        "// --------------------------------------------------------------------------\n"
        "// Test 1: PA-I univariate, shuffle=False, max_iter=50\n"
        "// --------------------------------------------------------------------------"
    )
    rng = np.random.default_rng(42)
    n = 100
    x = np.linspace(-3.0, 3.0, n)
    y = 1.0 + 0.8 * x + rng.normal(0.0, 0.1, n)

    model = PassiveAggressiveRegressor(
        C=1.0,
        epsilon=0.1,
        fit_intercept=True,
        max_iter=50,
        tol=1e-6,
        shuffle=False,
        loss="epsilon_insensitive",
        random_state=42,
        average=False,
    ).fit(x.reshape(-1, 1), y)

    emit_const_usize("N_PA1_UNI", n)
    emit_array_f64("X_PA1_UNI", x)
    emit_array_f64("Y_PA1_UNI", y)
    emit_const_f64("EXPECTED_INTERCEPT_PA1_UNI", float(model.intercept_[0]))
    emit_const_f64("EXPECTED_COEF_PA1_UNI", float(model.coef_[0]))
    print()


def case_pa1_multi() -> None:
    print(
        "// --------------------------------------------------------------------------\n"
        "// Test 2: PA-I multivariate, shuffle=False, max_iter=100\n"
        "// --------------------------------------------------------------------------"
    )
    rng = np.random.default_rng(7)
    n, p = 80, 3
    X = rng.normal(size=(n, p))
    y = -0.5 + 1.5 * X[:, 0] - 0.7 * X[:, 1] + 0.3 * X[:, 2] + rng.normal(0.0, 0.05, n)

    model = PassiveAggressiveRegressor(
        C=1.0,
        epsilon=0.1,
        fit_intercept=True,
        max_iter=100,
        tol=1e-6,
        shuffle=False,
        loss="epsilon_insensitive",
        random_state=42,
        average=False,
    ).fit(X, y)

    emit_const_usize("N_PA1_MULTI", n)
    emit_const_usize("P_PA1_MULTI", p)
    emit_array_f64("X_PA1_MULTI_FLAT", X.flatten())
    emit_array_f64("Y_PA1_MULTI", y)
    emit_const_f64("EXPECTED_INTERCEPT_PA1_MULTI", float(model.intercept_[0]))
    emit_array_f64("EXPECTED_COEFS_PA1_MULTI", model.coef_)
    print()


def case_pa2_multi() -> None:
    print(
        "// --------------------------------------------------------------------------\n"
        "// Test 3: PA-II (squared epsilon-insensitive) multivariate\n"
        "// --------------------------------------------------------------------------"
    )
    rng = np.random.default_rng(11)
    n, p = 100, 2
    X = rng.normal(size=(n, p))
    y = 2.0 - 0.5 * X[:, 0] + 1.2 * X[:, 1] + rng.normal(0.0, 0.1, n)

    model = PassiveAggressiveRegressor(
        C=0.5,
        epsilon=0.05,
        fit_intercept=True,
        max_iter=80,
        tol=1e-6,
        shuffle=False,
        loss="squared_epsilon_insensitive",
        random_state=42,
        average=False,
    ).fit(X, y)

    emit_const_usize("N_PA2_MULTI", n)
    emit_const_usize("P_PA2_MULTI", p)
    emit_array_f64("X_PA2_MULTI_FLAT", X.flatten())
    emit_array_f64("Y_PA2_MULTI", y)
    emit_const_f64("EXPECTED_INTERCEPT_PA2_MULTI", float(model.intercept_[0]))
    emit_array_f64("EXPECTED_COEFS_PA2_MULTI", model.coef_)
    print()


def main() -> None:
    emit_header()
    case_pa1_univariate()
    case_pa1_multi()
    case_pa2_multi()


if __name__ == "__main__":
    main()