def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
m2.kern.rbf.lengthscale[:] = 1e6
m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
mu, var = m.predict(m.X)
np.testing.assert_allclose(mul, mu)
np.testing.assert_allclose(varl, var)
q50 = m.predict_quantiles(m.X, (50,))
np.testing.assert_allclose(mul, q50[0])
def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(
bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q,
np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing,
Q,
init="random",
num_inducing=num_inducing,
kernel=k,
missing_data=True)
assert (m.checkgrad())
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing,
Q,
init="random",
num_inducing=num_inducing,
kernel=k,
missing_data=True)
assert (m.checkgrad())
def mrd_simulation_missing_data(optimize=True,
verbose=True,
plot=True,
plot_sim=True,
**kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
k = kern.Linear(Q, ARD=True) + kern.White(Q, variance=1e-4)
inanlist = []
for Y in Ylist:
inan = _np.random.binomial(1, .6, size=Y.shape).astype(bool)
inanlist.append(inan)
Y[inan] = _np.nan
m = MRD(Ylist,
input_dim=Q,
num_inducing=num_inducing,
kernel=k,
inference_method=None,
initx="random",
initz='permute',
**kw)
if optimize:
print("Optimizing Model:")
m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales()
return m
def mrd_simulation(optimize=True,
verbose=True,
plot=True,
plot_sim=True,
**kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim)
k = kern.Linear(Q, ARD=True) + kern.White(Q, variance=1e-4)
m = MRD(Ylist,
input_dim=Q,
num_inducing=num_inducing,
kernel=k,
initx="PCA_concat",
initz='permute',
**kw)
m['.*noise'] = [Y.var() / 40. for Y in Ylist]
if optimize:
print("Optimizing Model:")
m.optimize(messages=verbose, max_iters=8e3)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales()
return m
def optimize(self,
views,
latent_dims=7,
messages=True,
max_iters=8e3,
save_model=False):
if (self.kernel):
if (self.kernel == 'rbf'):
print("Chosen kernel: RBF")
print("Chosen lengthscale: " + self.lengthscale)
k = kern.RBF(latent_dims,
ARD=True,
lengthscale=self.lengthscale) + kern.White(
latent_dims,
variance=1e-4) + GPy.kern.Bias(latent_dims)
elif (self.kernel == 'linear'):
print("Chosen kernel: Linear")
k = kern.Linear(latent_dims, ARD=True) + kern.White(
latent_dims, variance=1e-4) + GPy.kern.Bias(latent_dims)
else:
print("No kernel or chosen - using RBF with lengthscale 10...")
k = kern.RBF(latent_dims, ARD=True, lengthscale=10) + kern.White(
latent_dims, variance=1e-4) + GPy.kern.Bias(latent_dims)
print("Number of inducing inputs: " + str(self.num_inducing))
m = MRD(views,
input_dim=latent_dims,
num_inducing=self.num_inducing,
kernel=k,
normalizer=False)
print("Optimizing Model...")
m.optimize(messages=True, max_iters=8e3)
if (save_model):
pickle.dump(m, open(save_model, "wb"), protocol=2)
self.model = m
def latent_functions_prior(Q, lenghtscale=None, variance=None, input_dim=None):
if lenghtscale is None:
lenghtscale = np.random.rand(Q)
else:
lenghtscale = lenghtscale
if variance is None:
variance = np.random.rand(Q)
else:
variance = variance
kern_list = []
for q in range(Q):
kern_q = kern.RBF(input_dim=input_dim,
lengthscale=lenghtscale[q],
variance=variance[q],
name='rbf') + kern.White(input_dim,
variance=1e-8) # \
kern_q.name = 'kern_q' + str(q)
kern_list.append(kern_q)
return kern_list
def main():
sample_info = pd.read_csv('MOB_sample_info.csv', index_col=0)
df = pd.read_csv('data/Rep11_MOB_0.csv', index_col=0)
df = df.loc[sample_info.index]
df = df.T[df.sum(0) >= 3].T # Filter practically unobserved genes
dfm = NaiveDE.stabilize(df.T).T
res = NaiveDE.regress_out(sample_info, dfm.T, 'np.log(total_counts)').T
X = sample_info[['x', 'y']].values
times = pd.DataFrame(columns=['N', 'time'])
Ns = [50, 100, 200, 300, 500, 750, 1000, 2000]
j = 0
for N in Ns:
for i in range(5):
Y = res.sample(N, axis=1).values.T
t0 = time()
m = GPclust.MOHGP(X=X,
Y=Y,
kernF=kern.RBF(2) + kern.Bias(2),
kernY=kern.RBF(1) + kern.White(1),
K=5,
prior_Z='DP')
m.hyperparam_opt_args['messages'] = False
m.optimize(step_length=0.1, verbose=False, maxiter=2000)
times.loc[j] = [N, time() - t0]
print(times.loc[j])
j += 1
times.to_csv('AEH_times.csv')
def WN(): return _Gk.White(1) def C(): return _Gk.Bias(1)
