def predict_log_marginal_probabilities(self, X: np.ndarray) -> np.ndarray:
# TODO: Check against GPFlow.
# TODO: Is this really worth it? Could just use predict_y.
assert self.is_fit
X = self.scaler.transform(X)
# Run the prediction for each model
results = list()
for cur_model in self.models:
if self.use_cache:
# Load model
cur_model = load_saved_gpflow_model(cur_model)
# Predict f, the latent probability on the probit scale
f_mean, f_var = cur_model.predict_f(X)
f_std = np.sqrt(f_var)
if self.use_cache:
gpflow.reset_default_graph_and_session()
result = log_probability_via_sampling(
np.squeeze(f_mean), np.squeeze(f_std), self.n_draws_predict)
results.append(result)
results = np.stack(results, axis=1)
return results
def evalMCMCSamples(X, Y, x):
gpflow.reset_default_graph_and_session()
Y = np.atleast_2d(Y).T
traces, m = evalMCMC(X, Y)
f_samples = []
nn = 1
for i, s in traces.iloc[::10].iterrows():
f = m.predict_f_samples(x,
nn,
initialize=False,
feed_dict=m.sample_feed_dict(s))
f_samples.append(f)
f_samples = np.array(f_samples)
# print("f_samples.shape=", f_samples.shape)
# print("x.shape=", x.shape)
out = f_samples[:, 0, :, 0].reshape(f_samples.shape[0], f_samples.shape[2])
# print("out.shape=", out.shape)
m.clear()
return x, out
def evalMLESamples(X, Y, x):
gpflow.reset_default_graph_and_session()
Y = np.atleast_2d(Y).T
_, m = evalMLE(X, Y)
num_samples = 10
ff = m.predict_f_samples(x, num_samples, initialize=False)
# print("ff.shape=", ff.shape)
m.clear()
return x, ff[:, :, 0]
def calculate_log_likelihood(self, X, y):
assert self.is_fit
assert y.shape[1] == len(self.models)
X = self.scaler.transform(X)
means, sds = list(), list()
for cur_model in self.models:
if self.use_cache:
cur_model = load_saved_gpflow_model(cur_model)
cur_mean, cur_vars = cur_model.predict_f(X)
cur_sds = np.sqrt(cur_vars)
if self.use_cache:
gpflow.reset_default_graph_and_session()
means.append(np.squeeze(cur_mean))
sds.append(np.squeeze(cur_sds))
means = np.stack(means, axis=1)
sds = np.stack(sds, axis=1)
site_log_liks = np.zeros(means.shape[0])
# Estimate site by site
for i, (cur_y, cur_mean, cur_sd) in enumerate(zip(y, means, sds)):
draws = np.random.normal(
cur_mean, cur_sd, size=(self.n_draws_predict, means.shape[1]))
log_lik = calculate_log_joint_bernoulli_likelihood(draws, cur_y)
site_log_liks[i] = log_lik
return site_log_liks
def main():
X = np.loadtxt("../data/neur.X.txt")
Y = np.loadtxt("../data/neur.Y.txt")
gpflow.reset_default_graph_and_session()
name = 'test'
minibatch_size = 500
W1_init = normalize(np.random.random(size=(C, K1)))
W2_init = normalize(np.random.random(size=(G, K2)))
with gpflow.defer_build():
kernel = mk.SharedIndependentMok(
gpflow.kernels.RBF(1, active_dims=[0]), K1 * K2)
Z = np.linspace(0, 1, T)[:, None].astype(np.float64)
feature = gpflow.features.InducingPoints(Z)
feature = mf.SharedIndependentMof(feature)
model = SplitGPM(X,
Y,
np.log(W1_init + 1e-5),
np.log(W2_init + 1e-5),
kernel,
gpflow.likelihoods.Gaussian(),
feat=feature,
minibatch_size=minibatch_size,
name=name)
model.compile()
model.W1.set_trainable(True) # learn cell assignments
model.W2.set_trainable(True) # learn gene assignments
model.feature.set_trainable(True) # move inducing points
model.kern.set_trainable(True) # learn kernel parameters
model.likelihood.set_trainable(True) # lear likelihood parameters
adam = gpflow.train.AdamOptimizer(0.005)
adam.minimize(model, maxiter=10000)
save_model(model)
def fit(self, X, y):
self.scaler = StandardScaler()
X = self.scaler.fit_transform(X)
Z = find_starting_z(X, num_inducing=self.n_inducing,
use_minibatching=False)
self.models = list()
# We need to fit each species separately
for cur_output in tqdm(range(y.shape[1])):
cur_kernel = self.kernel_function()
cur_likelihood = gpflow.likelihoods.Bernoulli()
cur_y = y[:, [cur_output]]
cur_m = gpflow.models.SVGP(X, cur_y, kern=cur_kernel,
likelihood=cur_likelihood, Z=Z)
opt = gpflow.train.ScipyOptimizer(
options={'maxfun': self.maxiter})
opt.minimize(cur_m, maxiter=self.maxiter, disp=self.verbose_fit)
if self.use_cache:
# Store in cache dir
save_dir = join(self.cache_dir, f'model_{cur_output}')
save_gpflow_model(cur_m, save_dir)
self.models.append(save_dir)
# Reset graph
gpflow.reset_default_graph_and_session()
else:
# Append directly
self.models.append(cur_m)
self.is_fit = True
idx = [d[x] for x in zip(data.line, data.time)]
library_size = data.groupby(['line', 'time']).value.sum().values[idx]
gene_lengths = gene_attributes['size'].values[W2_idx]
gc_content = gene_attributes.percentage_gene_gc_content.values[W2_idx] / 100
# make feature
Zlibsize = np.quantile(library_size, np.linspace(0.01, .99, 200))
Zgenesize = np.quantile(gene_lengths, np.linspace(0.01, 0.99, 200))
Z = np.log(np.vstack([Zlibsize, Zgenesize]).T)
X_aug = np.log(np.stack([library_size.flatten(), gene_lengths.flatten()]).T)
# build model
gpflow.reset_default_graph_and_session()
with gpflow.defer_build():
kernel = gpflow.kernels.RBF(1, active_dims=[0]) + gpflow.kernels.RBF(1, active_dims=[1])
feature = gpflow.features.InducingPoints(Z)
model = gpflow.models.SVGP(
X_aug, Y, kernel, NegativeBinomial(),
feat=feature, minibatch_size=500, name=name)
model.compile()
# restore/create monitor session
lr = 0.01
monitor_tasks, session, global_step, file_writer = build_monitor(model, path)
optimiser = gpflow.train.AdamOptimizer(lr)
def fitGP(method, normalize=True):
assert method in ('VFE', 'FITC', 'GP')
if data_directory == 'year-prediction-MSD' and method == 'FITC':
return # big data, thus using only SVGP, no FITC
perf = np.nan * np.zeros((n_splits, 2))
np.random.seed(1)
for split in range(n_splits):
(X_train_normalized, y_train_normalized, y_train, X_test_normalized,
X_test, y_test, mean_X_train, std_X_train, mean_y_train,
std_y_train) = _split_data(split, normalize)
if data_directory != 'year-prediction-MSD':
if method == 'GP':
gp = GPy.models.GPRegression(
X_train_normalized, y_train_normalized[:, None],
GPy.kern.RBF(X_train_normalized.shape[1], ARD=True))
else:
gp = GPy.models.SparseGPRegression(
X_train_normalized,
y_train_normalized[:, None],
GPy.kern.RBF(X_train_normalized.shape[1], ARD=True),
num_inducing=n_hidden)
if method == 'FITC':
gp.inference_method = GPy.inference.latent_function_inference.FITC(
)
success = False
for _ in range(10):
try:
gp.optimize_restarts(robust=True)
success = True
break
except:
pass
if success:
gp.save('results/%s/%s_split%g.hdf5' %
(method, data_directory, split))
else:
continue
else:
gpflow.reset_default_graph_and_session()
Z = X_train_normalized[np.random.choice(np.arange(
len(X_train_normalized)),
n_hidden,
replace=False)].copy()
gp = gpflow.models.SVGP(X_train_normalized,
y_train_normalized[:, None],
gpflow.kernels.RBF(
X_train_normalized.shape[1], ARD=True),
gpflow.likelihoods.Gaussian(),
Z,
minibatch_size=1000)
adam = gpflow.train.AdamOptimizer().make_optimize_action(gp)
gpflow.actions.Loop(adam, stop=30000)()
gp.anchor(gp.enquire_session())
saver = gpflow.saver.Saver()
saver.save(
'results/%s/%s_split%g' % (method, data_directory, split), gp)
if data_directory != 'year-prediction-MSD':
m, v = np.squeeze(gp.predict(X_test_normalized))
else:
m, v = np.squeeze(gp.predict_y(X_test_normalized))
if normalize:
v *= std_y_train**2
m = m * std_y_train + mean_y_train
perf[split] = np.sqrt(np.mean(
(y_test - m)**2)), -logpdf(y_test - m, v).mean()
np.save('results/%s/%s' % (method, data_directory), perf)
def fitANN(normalize=True):
etas = np.array([1, 2, 5, 10, 20, 50, 100]) * {
'bostonHousing': 1e-7,
'concrete': 1e-7,
'energy': 1e-10,
'kin8nm': 1e-5,
'naval-propulsion-plant': 1e-9,
'power-plant': 1e-6,
'protein-tertiary-structure': 1e-5,
'wine-quality-red': 1e-6,
'yacht': 1e-9,
'year-prediction-MSD': 1e-4
}[data_directory]
perf = np.nan * np.zeros((n_splits, 8))
np.random.seed(1)
for split in range(n_splits):
(X_train_normalized, y_train_normalized, y_train, X_test_normalized,
X_test, y_test, mean_X_train, std_X_train, mean_y_train,
std_y_train) = _split_data(split, normalize)
if data_directory != 'year-prediction-MSD':
gp = GPy.models.SparseGPRegression(X_train_normalized,
y_train_normalized[:, None],
GPy.kern.RBF(
X_train_normalized.shape[1],
ARD=True),
num_inducing=n_hidden)
gp[:] = h5py.File(
'results/VFE/%s_split%g.hdf5' % (data_directory, split),
'r')['param_array']
var = gp.Gaussian_noise.variance
varK = gp.kern.variance
Kfu = gp.kern.K(X_train_normalized, gp.inducing_inputs)
Kfu_test = gp.kern.K(X_test_normalized, gp.inducing_inputs)
w = gp.posterior.woodbury_vector.ravel()
woodbury_inv = gp.posterior.woodbury_inv
else:
gpflow.reset_default_graph_and_session()
saver = gpflow.saver.Saver()
gp = saver.load('results/VFE/%s_split%g' % (data_directory, split))
var = gp.likelihood.variance.value
varK = gp.kern.variance.value
Kfu = gp.kern.compute_K(X_train_normalized, gp.feature.Z.value)
Kuu = gp.kern.compute_K(gp.feature.Z.value, gp.feature.Z.value)
Kfu_test = gp.kern.compute_K(X_test_normalized, gp.feature.Z.value)
Sigma = np.linalg.inv(Kfu.T.dot(Kfu) + var * Kuu)
w = Sigma.dot(Kfu.T.dot(y_train_normalized))
woodbury_inv = np.linalg.inv(Kuu) - var * Sigma
def custom_loss(): # neg loglikelihood
def loss(y_true, y_pred):
return tf.divide(tf.square(y_pred[..., 0] - y_true[..., 0]), y_pred[..., 1]) + \
tf.math.log(y_pred[..., 1])
return loss
def build_model(eta):
u, s, v = np.linalg.svd(woodbury_inv)
U = (u + v.T).dot(np.diag(np.sqrt(s))) / 2
inputs = layers.Input(shape=(n_hidden, ))
m = layers.Dense(1,
kernel_initializer=tf.constant_initializer(w),
trainable=False)(inputs)
x = layers.Dense(n_hidden,
kernel_initializer=tf.constant_initializer(U),
activation=tf.square)(inputs)
def act(a):
return tf.math.softplus(a / var / 2) * var * 2
v = layers.Dense(1,
kernel_initializer=tf.constant_initializer(
-np.ones((1, n_hidden))),
bias_initializer=tf.constant_initializer(var +
varK),
activation=act)(x)
outputs = layers.concatenate([m, v])
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss=custom_loss(),
optimizer=tf.keras.optimizers.Adam(eta))
return model
# find best learning rate using 5-fold cross validation
best_loss = np.inf
best_eta = etas[0]
for eta in etas:
loss = 0
for fold in range(5):
model = build_model(eta)
train_idx = np.ones(X_train_normalized.shape[0], dtype=bool)
train_idx[fold::5] = False
history = model.fit(
Kfu[train_idx],
y_train_normalized[train_idx],
epochs=n_epochs,
validation_data=(Kfu[~train_idx],
y_train_normalized[~train_idx]),
verbose=0)
loss += history.history['val_loss'][-1]
if loss < best_loss:
best_loss = loss
best_eta = eta
model = build_model(best_eta)
history = model.fit(Kfu,
y_train_normalized,
epochs=n_epochs,
verbose=0)
if data_directory != 'year-prediction-MSD':
m = np.squeeze(gp.predict(X_test_normalized))[0]
else:
m = np.squeeze(gp.predict_y(X_test_normalized))[0]
v = np.squeeze(model.predict(Kfu_test)).T[1]
if normalize:
m = m * std_y_train + mean_y_train
v = v * std_y_train**2
perf[split, :2] = np.sqrt(np.mean(
(y_test - m)**2)), -logpdf(y_test - m, v).mean()
perf[split, 2] = best_eta
# measure prediction time
if data_directory != 'year-prediction-MSD':
U, Ub, _, _, w, wb = model.get_weights()
m = gp.posterior.woodbury_vector
var = 2 * gp.Gaussian_noise.variance
def act(a):
return np.log(1 + np.exp(a / var)) * var
if normalize:
def predict(X_test):
X_test_normalized = (X_test - mean_X_train) / std_X_train
K = gp.kern.K(X_test_normalized, gp.inducing_inputs)
return np.concatenate([
K.dot(m) * std_y_train + mean_y_train,
act(((K.dot(U) + Ub)**2).dot(w) + wb) * std_y_train**2
], 1)
else:
def predict(X_test):
K = gp.kern.K(X_test, gp.inducing_inputs)
return np.concatenate(
[K.dot(m),
act(((K.dot(U) + Ub)**2).dot(w) + wb)], 1)
else:
if normalize:
def predict(X_test):
X_test_normalized = (X_test - mean_X_train) / std_X_train
K = gp.kern.compute_K(X_test_normalized,
gp.feature.Z.value)
m, v = np.squeeze(model.predict(K)).T
return np.array(
[m * std_y_train + mean_y_train, v * std_y_train**2])
else:
def predict(X_test):
K = gp.kern.compute_K(X_test, gp.feature.Z.value)
m, v = np.squeeze(model.predict(K)).T
return np.array([m, v])
for i in range(5):
t = -time()
_ = predict(X_test)
t += time()
perf[split, 3 + i] = t
np.save('results/ANN/' + data_directory, perf)
def fitBioNN(normalize=True):
perf = np.nan * np.zeros((n_splits, 8))
np.random.seed(1)
for split in range(n_splits):
(X_train_normalized, y_train_normalized, y_train, X_test_normalized,
X_test, y_test, mean_X_train, std_X_train, mean_y_train,
std_y_train) = _split_data(split, normalize)
if data_directory != 'year-prediction-MSD':
vfe = GPy.models.SparseGPRegression(
X_train_normalized,
y_train_normalized[:, None],
GPy.kern.RBF(X_train_normalized.shape[1], ARD=True),
num_inducing=n_hidden)
vfe[:] = h5py.File(
'results/VFE/%s_split%g.hdf5' % (data_directory, split),
'r')['param_array']
nn = BioNN(X_train_normalized, y_train_normalized[:, None],
vfe.inducing_inputs, vfe.kern.lengthscale)
else:
gpflow.reset_default_graph_and_session()
saver = gpflow.saver.Saver()
vfe = saver.load('results/VFE/%s_split%g' %
(data_directory, split))
nn = BioNN(X_train_normalized, y_train_normalized[:, None],
vfe.feature.Z.value, vfe.kern.lengthscales.value)
m, v = np.squeeze(nn.predict(X_test_normalized))
if normalize:
m = m * std_y_train + mean_y_train
v = v * std_y_train**2
perf[split, :2] = np.sqrt(np.mean(
(y_test - m)**2)), -logpdf(y_test - m, v).mean()
perf[split, 2] = v.var()
# measure prediction time
if normalize:
def predict(X_test):
X_test_normalized = (X_test - mean_X_train) / std_X_train
K = nn.kern.K(X_test_normalized, nn.inducing_inputs)
m = K.dot(nn.w_mean)
SNRinv = np.maximum(1 - np.sum(K**2, 1), 0)
v = np.vstack([SNRinv, np.ones(len(m))]).T.dot(nn.wb_var)
return np.concatenate(
[m * std_y_train + mean_y_train, v * std_y_train**2], 1)
else:
def predict(X_test):
K = nn.kern.K(X_test, nn.inducing_inputs)
m = K.dot(nn.w_mean)
SNRinv = np.maximum(1 - np.sum(K**2, 1), 0)
v = np.vstack([SNRinv, np.ones(len(m))]).T.dot(nn.wb_var)
return np.concatenate([m, v], 1)
for i in range(5):
t = -time()
_ = predict(X_test)
t += time()
perf[split, 3 + i] = t
np.save('results/BioNN/' + data_directory, perf)
def load_saved_gpflow_model(gpflow_model_path: str):
gpflow.reset_default_graph_and_session()
m = gpflow.saver.Saver().load(gpflow_model_path)
return m
def fit(self, X, y):
n_dims = X.shape[1]
n_outputs = y.shape[1]
kern_fun = partial(self.kernel_fun, n_dims=n_dims, n_outputs=n_outputs)
def get_model(w_prior, bias_var):
# We need to make a model creation function.
cur_kernel = kern_fun(w_prior=w_prior, bias_var=bias_var)
model_fun = partial(self.model_fun, kernel=cur_kernel)
return model_fun()
scores = list()
for cur_variance in self.variances_to_try:
# Compute the bias variance so that we have a variance of 0.4
# for that overall
bias_var = 0.4 / cur_variance
print(f'Fitting {cur_variance:.2f} with bias var {bias_var:.2f}')
model_fun = lambda: get_model(cur_variance, bias_var) # NOQA
cur_mean_score, cur_stderr = MultiOutputGP.cross_val_score(
X,
y,
model_fun,
save_dir=join(self.cv_save_dir, f'{cur_variance:.4f}'),
n_folds=self.n_folds)
gpf.reset_default_graph_and_session()
print(f'Mean likelihood is {cur_mean_score}')
scores.append({
'mean': cur_mean_score,
'stderr': cur_stderr,
'variance': cur_variance
})
scores = pd.DataFrame(scores)
# Sort by ascending complexity
scores = scores.sort_values('variance')
# Find best index; invert mean since error rule expects errors,
# where smaller is better, rather than likelihoods where higher is
# better.
best_idx = select_using_standard_error_rule(-scores['mean'].values,
scores['stderr'].values)
best_variance = scores.iloc[best_idx]['variance']
print(f'Selected model using one standard error rule has variance '
f' {best_variance:.2f}')
bias_var = 0.4 / best_variance
best_model = get_model(best_variance, bias_var)
best_model.fit(X, y)
self.is_fit = True
self.model = best_model
def del_graph(self):
gpflow.reset_default_graph_and_session()
return
def cross_val_score(X, y, model_creation_fun, save_dir, n_folds=4):
kfold = KFold(n_splits=n_folds)
fold_liks = np.empty(n_folds)
for i, (cur_train_ind,
cur_test_ind) in tqdm(enumerate(kfold.split(X, y))):
cur_X = X[cur_train_ind]
cur_y = y[cur_train_ind]
gpf.reset_default_graph_and_session()
model = model_creation_fun()
model.fit(cur_X, cur_y)
cur_save_dir = join(save_dir, f'fold_{i + 1}')
os.makedirs(cur_save_dir, exist_ok=True)
model.save_model(cur_save_dir)
cur_test_x = X[cur_test_ind]
cur_test_y = y[cur_test_ind]
log_liks = model.calculate_log_likelihood(cur_test_x, cur_test_y)
marg_pred = pd.DataFrame(
model.predict_marginal_probabilities(cur_test_x))
marg_pred.to_csv(join(cur_save_dir, 'marginal_probs.csv'))
pd.DataFrame(cur_test_y).to_csv(join(cur_save_dir, 'y_t.csv'))
# I am also interested in the log loss.
y_t_df = pd.DataFrame(cur_test_y)
neg_log_loss_results = multi_class_eval(marg_pred, y_t_df,
neg_log_loss_with_labels,
'log_lik')
neg_log_loss_results.to_csv(
join(cur_save_dir, 'marginal_species_log_lik.csv'))
pd.Series(neg_log_loss_results.mean()).to_csv(
join(cur_save_dir, 'neg_log_loss_mean.csv'))
fold_liks[i] = np.mean(log_liks)
np.savez(join(cur_save_dir, 'cv_results'),
site_log_liks=log_liks,
cur_train_X=cur_X,
cur_train_y=cur_y,
cur_test_X=cur_test_x,
cur_test_y=cur_test_y,
train_ind=cur_train_ind,
test_ind=cur_test_ind)
pd.Series({
'mean_lik': np.mean(fold_liks)
}).to_csv(join(save_dir, 'mean_lik.csv'))
pd.Series(fold_liks, index=[f'fold_{i+1}' for i in range(n_folds)
]).to_csv(join(save_dir, 'fold_liks.csv'))
return np.mean(fold_liks), np.std(fold_liks) / np.sqrt(len(fold_liks))
