pyGPGO/surrogates/GaussianProcessMCMC.py
Killed 29 out of 59 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 64
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -6,7 +6,7 @@
covariance_equivalence = {'squaredExponential': pm.gp.cov.ExpQuad,
- 'matern52': pm.gp.cov.Matern52,
+ 'XXmatern52XX': pm.gp.cov.Matern52,
'matern32': pm.gp.cov.Matern32}
Mutant 65
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -7,7 +7,7 @@
covariance_equivalence = {'squaredExponential': pm.gp.cov.ExpQuad,
'matern52': pm.gp.cov.Matern52,
- 'matern32': pm.gp.cov.Matern32}
+ 'XXmatern32XX': pm.gp.cov.Matern32}
class GaussianProcessMCMC:
Mutant 67
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -11,7 +11,7 @@
class GaussianProcessMCMC:
- def __init__(self, covfunc, niter=2000, burnin=1000, init='ADVI', step=None):
+ def __init__(self, covfunc, niter=2001, burnin=1000, init='ADVI', step=None):
"""
Gaussian Process class using MCMC sampling of covariance function hyperparameters.
Mutant 68
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -11,7 +11,7 @@
class GaussianProcessMCMC:
- def __init__(self, covfunc, niter=2000, burnin=1000, init='ADVI', step=None):
+ def __init__(self, covfunc, niter=2000, burnin=1001, init='ADVI', step=None):
"""
Gaussian Process class using MCMC sampling of covariance function hyperparameters.
Mutant 69
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -11,7 +11,7 @@
class GaussianProcessMCMC:
- def __init__(self, covfunc, niter=2000, burnin=1000, init='ADVI', step=None):
+ def __init__(self, covfunc, niter=2000, burnin=1000, init='XXADVIXX', step=None):
"""
Gaussian Process class using MCMC sampling of covariance function hyperparameters.
Mutant 72
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -31,7 +31,7 @@
"""
self.covfunc = covfunc
self.niter = niter
- self.burnin = burnin
+ self.burnin = None
self.init = init
self.step = step
Mutant 73
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -32,7 +32,7 @@
self.covfunc = covfunc
self.niter = niter
self.burnin = burnin
- self.init = init
+ self.init = None
self.step = step
def _extractParam(self, unittrace, covparams):
Mutant 74
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -33,7 +33,7 @@
self.niter = niter
self.burnin = burnin
self.init = init
- self.step = step
+ self.step = None
def _extractParam(self, unittrace, covparams):
d = {}
Mutant 80
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -62,7 +62,7 @@
self.model = pm.Model()
with self.model as model:
- l = pm.Uniform('l', 0, 10)
+ l = pm.Uniform('XXlXX', 0, 10)
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
Mutant 81
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -62,7 +62,7 @@
self.model = pm.Model()
with self.model as model:
- l = pm.Uniform('l', 0, 10)
+ l = pm.Uniform('l', 1, 10)
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
Mutant 82
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -62,7 +62,7 @@
self.model = pm.Model()
with self.model as model:
- l = pm.Uniform('l', 0, 10)
+ l = pm.Uniform('l', 0, 11)
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
Mutant 84
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -64,7 +64,7 @@
with self.model as model:
l = pm.Uniform('l', 0, 10)
- log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
+ log_s2_f = pm.Uniform('XXlog_s2_fXX', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
Mutant 86
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -64,7 +64,7 @@
with self.model as model:
l = pm.Uniform('l', 0, 10)
- log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
+ log_s2_f = pm.Uniform('log_s2_f', lower=-8, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
Mutant 87
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -64,7 +64,7 @@
with self.model as model:
l = pm.Uniform('l', 0, 10)
- log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
+ log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=6)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
Mutant 89
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -65,7 +65,7 @@
l = pm.Uniform('l', 0, 10)
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
- s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
+ s2_f = pm.Deterministic('XXsigmafXX', tt.exp(log_s2_f))
log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
Mutant 91
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -67,7 +67,7 @@
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
- log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
+ log_s2_n = pm.Uniform('XXlog_s2_nXX', lower=-7, upper=5)
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Mutant 93
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -67,7 +67,7 @@
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
- log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
+ log_s2_n = pm.Uniform('log_s2_n', lower=-8, upper=5)
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Mutant 94
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -67,7 +67,7 @@
log_s2_f = pm.Uniform('log_s2_f', lower=-7, upper=5)
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
- log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
+ log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=6)
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Mutant 96
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -68,7 +68,7 @@
s2_f = pm.Deterministic('sigmaf', tt.exp(log_s2_f))
log_s2_n = pm.Uniform('log_s2_n', lower=-7, upper=5)
- s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
+ s2_n = pm.Deterministic('XXsigmanXX', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
Mutant 102
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -71,7 +71,7 @@
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
- Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
+ Sigma = f_cov(self.X) + tt.eye(self.n) / s2_n ** 2
y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
if self.step is not None:
Mutant 103
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -71,7 +71,7 @@
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
- Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
+ Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n * 2
y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
if self.step is not None:
Mutant 104
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -71,7 +71,7 @@
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
- Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
+ Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 3
y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
if self.step is not None:
Mutant 106
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -72,7 +72,7 @@
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
- y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
+ y_obs = pm.MvNormal('XXy_obsXX', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
if self.step is not None:
self.trace = pm.sample(self.niter, step=self.step())[self.burnin:]
Mutant 107
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -72,7 +72,7 @@
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
- y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
+ y_obs = None
with self.model as model:
if self.step is not None:
self.trace = pm.sample(self.niter, step=self.step())[self.burnin:]
Mutant 108
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -74,7 +74,7 @@
Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
- if self.step is not None:
+ if self.step is None:
self.trace = pm.sample(self.niter, step=self.step())[self.burnin:]
else:
self.trace = pm.sample(self.niter, init=self.init)[self.burnin:]
Mutant 110
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -88,7 +88,7 @@
plt.tight_layout()
plt.show()
- def predict(self, Xstar, return_std=False, nsamples=10):
+ def predict(self, Xstar, return_std=True, nsamples=10):
"""
Returns mean and covariances for each posterior sampled Gaussian Process.
Mutant 111
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -88,7 +88,7 @@
plt.tight_layout()
plt.show()
- def predict(self, Xstar, return_std=False, nsamples=10):
+ def predict(self, Xstar, return_std=False, nsamples=11):
"""
Returns mean and covariances for each posterior sampled Gaussian Process.
Mutant 113
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -110,7 +110,7 @@
Covariance posterior process for each MCMC sample and `Xstar`.
"""
chunk = list(self.trace)
- chunk = chunk[::-1][:nsamples]
+ chunk = chunk[::+1][:nsamples]
post_mean = []
post_var = []
for posterior_sample in chunk:
Mutant 114
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -110,7 +110,7 @@
Covariance posterior process for each MCMC sample and `Xstar`.
"""
chunk = list(self.trace)
- chunk = chunk[::-1][:nsamples]
+ chunk = chunk[::-2][:nsamples]
post_mean = []
post_var = []
for posterior_sample in chunk:
Suspicious
Mutants that made the test suite take longer, but otherwise seemed okMutant 101
--- pyGPGO/surrogates/GaussianProcessMCMC.py
+++ pyGPGO/surrogates/GaussianProcessMCMC.py
@@ -71,7 +71,7 @@
s2_n = pm.Deterministic('sigman', tt.exp(log_s2_n))
f_cov = s2_f * covariance_equivalence[type(self.covfunc).__name__](1, l)
- Sigma = f_cov(self.X) + tt.eye(self.n) * s2_n ** 2
+ Sigma = f_cov(self.X) - tt.eye(self.n) * s2_n ** 2
y_obs = pm.MvNormal('y_obs', mu=np.zeros(self.n), cov=Sigma, observed=self.y)
with self.model as model:
if self.step is not None: