-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathwrappers.py
136 lines (105 loc) · 3.98 KB
/
wrappers.py
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
import numpy as np
import openturns as ot
from sklearn.base import BaseEstimator
class GpOTtoSklearnStd(BaseEstimator):
"""
Wrapper for OpenTURNS Gaussian Process to be used in MAPIE.
"""
def __init__(
self, scale: int, amplitude: float,
nu: float, nugget: bool = False
) -> None:
self.scale = scale
self.amplitude = amplitude
self.nu = nu
self.trained_ = False
self.nugget = nugget
def fit(self, X_train, y_train):
input_dim = X_train.shape[1]
if isinstance(self.scale, int):
scale = input_dim * [self.scale]
else:
scale = self.scale
amplitude = [self.amplitude]
covarianceModel = ot.MaternModel(scale, amplitude, self.nu)
if self.nugget:
covarianceModel.activateNuggetFactor(True)
basis = ot.ConstantBasisFactory(input_dim).build()
self.gp = ot.KrigingAlgorithm(
ot.Sample(X_train),
ot.Sample(y_train.reshape(-1, 1)),
covarianceModel, basis
)
if self.nugget:
self.gp.setOptimizationAlgorithm(ot.NLopt("GN_DIRECT"))
self.gp.run()
self.trained_ = True
def predict(self, X_test, return_std=False):
metamodel = self.gp.getResult()(X_test)
y_pred = metamodel.getMean()
y_std = metamodel.getStandardDeviation()
if not return_std:
return np.array(y_pred)
else:
return np.array(y_pred), np.array(y_std)
def __sklearn_is_fitted__(self):
if self.trained_:
return True
else:
return False
class GpOTtoSklearnChooseKernel(BaseEstimator):
"""
Wrapper for OpenTURNS Gaussian Process to be used in MAPIE.
"""
def __init__(
self, trend: str, kernel: str,
dimension:int, noise: float = None
) -> None:
self.trend = trend
self.kernel = kernel
self.dimension = dimension
self.trained_ = False
self.noise = noise
def fit(self, X_train, y_train):
if self.trend not in ['Constant', 'Linear', 'Quad']:
raise ValueError(f"trend must be one of ['Constant', 'Linear', 'Quad']")
if self.kernel not in ['AbsExp', 'SqExp', 'M-1/2', 'M-3/2', 'M-5/2']:
raise ValueError(f"kernel must be one of ['AbsExp', 'SqExp', 'M-1/2', 'M-3/2', 'M-5/2']")
if self.trend == 'Constant':
basis = ot.ConstantBasisFactory(self.dimension).build()
elif self.trend == 'Linear':
basis = ot.LinearBasisFactory(self.dimension).build()
elif self.trend == 'Quad':
basis = ot.QuadraticBasisFactory(self.dimension).build()
if self.kernel == 'AbsExp':
covarianceModel = ot.AbsoluteExponential([1.0]*self.dimension)
elif self.kernel == 'SqExp':
covarianceModel = ot.SquaredExponential([1.0]*self.dimension)
elif self.kernel == 'M-1/2':
covarianceModel = ot.MaternModel([1.0]*self.dimension, [1.0], 0.5)
elif self.kernel == 'M-3/2':
covarianceModel = ot.MaternModel([1.0]*self.dimension, [1.0], 1.5)
elif self.kernel == 'M-5/2':
covarianceModel = ot.MaternModel([1.0]*self.dimension, [1.0], 2.5)
if self.noise:
covarianceModel.setNuggetFactor(self.noise)
self.gp = ot.KrigingAlgorithm(
ot.Sample(X_train),
ot.Sample(y_train.reshape(-1, 1)),
covarianceModel, basis
)
self.gp.run()
self.trained_ = True
def predict(self, X_test, return_std=False):
metamodel = self.gp.getResult()(X_test)
y_pred = metamodel.getMean()
y_std = metamodel.getStandardDeviation()
if not return_std:
return np.array(y_pred)
else:
return np.array(y_pred), np.array(y_std)
def __sklearn_is_fitted__(self):
if self.trained_:
return True
else:
return False