marker='s', alpha=0.1, cmap="coolwarm_r")
ax.set_yticks([0, 1])
ax.set_yticklabels([r"$x_q \sim q(x)$", r"$x_p \sim p(x)$"])
ax.set_xlabel('$x$')
ax.legend()
plt.show()
# %%
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features,
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(X_train, seed=seed),
kernel_cls=kernel_cls, vgp_cls=SVGP, jitter=jitter, seed=seed)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_p, X_q, epochs=num_epochs, batch_size=batch_size,
buffer_size=shuffle_buffer_size)
# %%
log_ratio_mean = gpdre.logit(X_grid, convert_to_tensor_fn=tfd.Distribution.mean)
log_ratio_stddev = gpdre.logit(X_grid, convert_to_tensor_fn=tfd.Distribution.stddev)
# %%
fig, ax = plt.subplots()
ax.plot(X_grid, r.logit(X_grid), c='k',
label=r"$f(x) = \log p(x) - \log q(x)$")
ax.plot(X_grid, log_ratio_mean.numpy().T,
def main(name, summary_dir, seed):
summary_path = Path(summary_dir) # .joinpath(name)
summary_path.mkdir(parents=True, exist_ok=True)
r = SugiyamaKrauledatMuellerDensityRatioMarginals()
rows = []
for seed in range(num_seeds):
# (X_train, y_train), (X_test, y_test) = r.train_test_split(X, y, seed=seed)
(X_train, y_train), (X_test, y_test) = r.make_covariate_shift_dataset(
num_test, num_train, class_posterior_fn=class_posterior, threshold=0.5,
seed=seed)
X, s = make_classification_dataset(X_test, X_train)
# # Uniform
# acc = metric(X_train, y_train, X_test, y_test, random_state=seed)
# rows.append(dict(weight="uniform", acc=acc, seed=seed))
# # Exact
# acc = metric(X_train, y_train, X_test, y_test,
# sample_weight=r.ratio(X_train).numpy(), random_state=seed)
# rows.append(dict(weight="exact", acc=acc, seed=seed))
for epochs in [500, 1000, 1500, 2000]:
# for sparsity_factor in sparsity_factors:
num_inducing_points = int(len(X) * sparsity_factor)
# Gaussian Processes
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features,
kernel_cls=Matern52,
use_ard=use_ard,
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(X, seed=seed),
vgp_cls=SVGP,
whiten=True,
jitter=jitter,
seed=seed)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test, X_train, epochs=epochs, batch_size=batch_size,
buffer_size=buffer_size)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train, convert_to_tensor_fn=prop)
acc = metric(X_train, y_train, X_test, y_test,
sample_weight=r_prop.numpy(), random_state=seed)
rows.append(dict(weight=prop_name, acc=acc, seed=seed,
sparsity_factor=sparsity_factor,
use_ard=use_ard, epochs=epochs))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{name}.csv")))
return 0
def main(name, summary_dir, seed):
summary_path = Path(summary_dir).joinpath("sugiyama")
summary_path.mkdir(parents=True, exist_ok=True)
r = SugiyamaKrauledatMuellerDensityRatioMarginals()
rows = []
for seed in range(num_seeds):
# (X_train, y_train), (X_test, y_test) = r.train_test_split(X, y, seed=seed)
(X_train, y_train), (X_test, y_test) = r.make_covariate_shift_dataset(
num_test,
num_train,
class_posterior_fn=class_posterior,
threshold=0.5,
seed=seed)
X, s = make_classification_dataset(X_test, X_train)
# Uniform
acc = metric(X_train, y_train, X_test, y_test, random_state=seed)
rows.append(
dict(weight="uniform", acc=acc, seed=seed, dataset_seed=seed))
# Exact
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r.ratio(X_train).numpy(),
random_state=seed)
rows.append(dict(weight="exact", acc=acc, seed=seed,
dataset_seed=seed))
# RuLSIF
r_rulsif = RuLSIFDensityRatioEstimator(alpha=1e-6)
r_rulsif.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_rulsif.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="rulsif", acc=acc, seed=seed, dataset_seed=seed))
# KLIEP
# sigmas = [0.1, 0.25, 0.5, 0.75, 1.0]
sigmas = list(np.maximum(0.25 * np.arange(5), 0.1))
r_kliep = KLIEPDensityRatioEstimator(sigmas=sigmas, seed=seed)
r_kliep.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kliep.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="kliep", acc=acc, seed=seed,
dataset_seed=seed))
# KMM
r_kmm = KMMDensityRatioEstimator(B=1000.0)
r_kmm.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kmm.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="kmm", acc=acc, seed=seed, dataset_seed=seed))
# Logistic Regression (Linear)
r_logreg = LogisticRegressionDensityRatioEstimator(seed=seed)
r_logreg.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_logreg.ratio(X_train).numpy())
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="logreg", acc=acc, seed=seed, dataset_seed=seed))
# Logistic Regression (MLP)
r_mlp = MLPDensityRatioEstimator(num_layers=1,
num_units=8,
activation="tanh",
seed=seed)
r_mlp.compile(optimizer=optimizer, metrics=["accuracy"])
r_mlp.fit(X_test, X_train, epochs=epochs, batch_size=batch_size)
sample_weight = np.maximum(1e-6, r_mlp.ratio(X_train).numpy())
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="mlp", acc=acc, seed=seed, dataset_seed=seed))
# Gaussian Processes
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features,
kernel_cls=Matern52,
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(X, seed=seed),
vgp_cls=SVGP,
whiten=True,
jitter=jitter,
seed=seed)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test,
X_train,
epochs=epochs,
batch_size=batch_size,
buffer_size=buffer_size)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train, convert_to_tensor_fn=prop)
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r_prop.numpy(),
random_state=seed)
rows.append(
dict(weight=prop_name, acc=acc, seed=seed, dataset_seed=seed))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{name}.csv")))
return 0
def main(name, summary_dir, seed):
summary_path = Path(summary_dir).joinpath("two_moons")
summary_path.mkdir(parents=True, exist_ok=True)
X, y = make_moons(num_samples, noise=0.05, random_state=dataset_seed)
test = tfd.MultivariateNormalDiag(loc=[0.5, 0.25], scale_diag=[0.5, 0.5])
train = tfd.MixtureSameFamily(
mixture_distribution=tfd.Categorical(probs=[0.5, 0.5]),
components_distribution=tfd.MultivariateNormalDiag(
loc=[[-1., -0.5], [2., 1.0]], scale_diag=[0.5, 1.5]))
r = DensityRatioMarginals(top=test, bot=train)
rows = []
for seed in trange(num_seeds):
(X_train, y_train), (X_test, y_test) = r.train_test_split(X,
y,
seed=seed)
# Uniform
acc = metric(X_train, y_train, X_test, y_test, random_state=seed)
rows.append(dict(weight="uniform", acc=acc, seed=seed))
# Exact
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r.ratio(X_train).numpy(),
random_state=seed)
rows.append(dict(weight="exact", acc=acc, seed=seed))
# RuLSIF
r_rulsif = RuLSIFDensityRatioEstimator(alpha=1e-6)
r_rulsif.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_rulsif.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="rulsif", acc=acc, seed=seed))
# KLIEP
# sigmas = [0.1, 0.25, 0.5, 0.75, 1.0]
sigmas = list(np.maximum(0.25 * np.arange(5), 0.1))
r_kliep = KLIEPDensityRatioEstimator(sigmas=sigmas, seed=seed)
r_kliep.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kliep.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="kliep", acc=acc, seed=seed))
# KMM
r_kmm = KMMDensityRatioEstimator(B=1000.0)
r_kmm.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kmm.ratio(X_train))
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="kmm", acc=acc, seed=seed, dataset_seed=seed))
# Logistic Regression (Linear)
r_logreg = LogisticRegressionDensityRatioEstimator(seed=seed)
r_logreg.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_logreg.ratio(X_train).numpy())
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="logreg", acc=acc, seed=seed))
# Logistic Regression (MLP)
r_mlp = MLPDensityRatioEstimator(num_layers=1,
num_units=8,
activation="tanh",
seed=seed)
r_mlp.compile(optimizer=optimizer, metrics=["accuracy"])
r_mlp.fit(X_test, X_train, epochs=epochs, batch_size=batch_size)
sample_weight = np.maximum(1e-6, r_mlp.ratio(X_train).numpy())
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(dict(weight="mlp", acc=acc, seed=seed))
# Gaussian Processes
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features,
kernel_cls=Matern52,
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(X, seed=seed),
vgp_cls=SVGP,
whiten=True,
jitter=jitter,
seed=seed)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test,
X_train,
epochs=epochs,
batch_size=batch_size,
buffer_size=buffer_size)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train, convert_to_tensor_fn=prop)
acc = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r_prop.numpy(),
random_state=seed)
rows.append(dict(weight=prop_name, acc=acc, seed=seed))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{name}.csv")))
return 0
def main(name, summary_dir, seed):
summary_path = Path(summary_dir).joinpath("shimodaira")
summary_path.mkdir(parents=True, exist_ok=True)
test = tfd.Normal(loc=0.0, scale=0.3)
train = tfd.Normal(loc=0.5, scale=0.5)
r = DensityRatioMarginals(top=test, bot=train)
rows = []
# for dataset_seed in range(9):
for seed in range(num_seeds):
(X_train,
y_train), (X_test,
y_test) = r.make_regression_dataset(num_test,
num_train,
latent_fn=poly,
noise_scale=0.3,
seed=seed)
# Uniform
error = metric(X_train, y_train, X_test, y_test, random_state=seed)
rows.append(
dict(weight="uniform",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# Exact
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r.ratio(X_train).numpy().squeeze(),
random_state=seed)
rows.append(
dict(weight="exact",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# RuLSIF
r_rulsif = RuLSIFDensityRatioEstimator(alpha=1e-6)
r_rulsif.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_rulsif.ratio(X_train))
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="rulsif",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# KLIEP
# sigmas = [0.1, 0.25, 0.5, 0.75, 1.0]
sigmas = list(np.maximum(0.25 * np.arange(5), 0.1))
r_kliep = KLIEPDensityRatioEstimator(sigmas=sigmas, seed=seed)
r_kliep.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kliep.ratio(X_train))
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="kliep",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# KMM
r_kmm = KMMDensityRatioEstimator(B=1000.0)
r_kmm.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_kmm.ratio(X_train))
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="kmm",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# Logistic Regression (Linear)
r_logreg = LogisticRegressionDensityRatioEstimator(seed=seed)
r_logreg.fit(X_test, X_train)
sample_weight = np.maximum(1e-6, r_logreg.ratio(X_train).numpy())
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="logreg",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# print("Found optimal C={}".format(r_logreg.model.C_))
# Logistic Regression (MLP)
r_mlp = MLPDensityRatioEstimator(num_layers=2,
num_units=16,
activation="relu",
seed=seed)
r_mlp.compile(optimizer="adam", metrics=["accuracy"])
r_mlp.fit(X_test, X_train, epochs=200, batch_size=64)
sample_weight = np.maximum(1e-6, r_mlp.ratio(X_train).numpy())
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=sample_weight,
random_state=seed)
rows.append(
dict(weight="mlp",
error=error,
dataset_seed=dataset_seed,
seed=seed))
# Gaussian Processes
gpdre = GaussianProcessDensityRatioEstimator(input_dim=num_features,
kernel_cls=kernel_cls,
vgp_cls=VGP,
jitter=jitter,
seed=seed)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test, X_train)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train, convert_to_tensor_fn=prop)
error = metric(X_train,
y_train,
X_test,
y_test,
sample_weight=r_prop.numpy(),
random_state=seed)
rows.append(
dict(weight=prop_name,
error=error,
dataset_seed=dataset_seed,
seed=seed))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{name}.csv")))
return 0
def main(myname, summary_dir, seed):
summary_path = Path(summary_dir).joinpath("liacc")
summary_path.mkdir(parents=True, exist_ok=True)
names = [
"abalone", "ailerons", "bank32", "bank8", "cali", "cpuact",
"elevators", "puma8"
][4:]
rows = []
for name in names:
output_path = summary_path.joinpath(f"{name}")
output_path.mkdir(parents=True, exist_ok=True)
data_path = get_data_path(name)
data_mat = loadmat(data_path)
results_path = get_results_path(name)
results_mat = loadmat(results_path)
X_trains = get_splits(data_mat, key="X")
y_trains = get_splits(data_mat, key="Y")
X_tests = get_splits(data_mat, key="Xtest")
y_tests = get_splits(data_mat, key="Ytest")
weights = get_splits(data_mat, key="ideal")
# X_train = X_trains[-1]
# Y_train = y_trains[-1]
# X_test = X_tests[-1]
# Y_test = y_tests[-1]
# weight = weights[-1]
# proj = results_mat["W"]
# X_train_low = X_train.dot(proj)
# X_test_low = X_test.dot(proj)
for split, (X_train, Y_train, X_test, Y_test, weight) in \
enumerate(zip(X_trains, y_trains, X_tests, y_tests, weights)):
X, _ = make_classification_dataset(X_test, X_train)
# X_low, _ = make_classification_dataset(X_test_low, X_train_low)
num_features = X.shape[-1]
# num_features_low = X_low.shape[-1]
y_train = Y_train.squeeze(axis=-1)
y_test = Y_test.squeeze(axis=-1)
# sample_weight = weight.squeeze(axis=-1)
# error = regression_metric(X_train, y_train, X_test, y_test)
# rows.append(dict(weight="uniform", name=name, error=error, projection="none"))
# # rows.append(dict(weight="uniform", name=name, split=split, error=error))
# error = regression_metric(X_train, y_train, X_test, y_test,
# sample_weight=sample_weight)
# rows.append(dict(weight="exact", name=name, error=error, projection="none"))
# rows.append(dict(weight="exact", name=name, split=split, error=error))
# # # RuLSIF
# # r_rulsif = RuLSIFDensityRatioEstimator(alpha=1e-6)
# # r_rulsif.fit(X_test, X_train)
# # sample_weight = np.maximum(1e-6, r_rulsif.ratio(X_train))
# # error = regression_metric(X_train, y_train, X_test, y_test,
# # sample_weight=sample_weight)
# # rows.append(dict(weight="rulsif", name=name, split=split, error=error))
# # # # KLIEP
# # # r_kliep = KLIEPDensityRatioEstimator(seed=seed)
# # # r_kliep.fit(X_test, X_train)
# # # sample_weight = np.maximum(1e-6, r_kliep.ratio(X_train))
# # # error = regression_metric(X_train, y_train, X_test, y_test,
# # # sample_weight=sample_weight)
# # # rows.append(dict(weight="kliep", name=name, split=split, error=error))
# # # KMM
# # r_kmm = KMMDensityRatioEstimator(B=1000.0)
# # r_kmm.fit(X_test, X_train)
# # sample_weight = np.maximum(1e-6, r_kmm.ratio(X_train))
# # error = regression_metric(X_train, y_train, X_test, y_test,
# # sample_weight=sample_weight)
# # rows.append(dict(weight="kmm", name=name, split=split, error=error))
# # # Logistic Regression (Linear)
# # r_logreg = LogisticRegressionDensityRatioEstimator(C=1.0, seed=seed)
# # r_logreg.fit(X_test, X_train)
# # sample_weight = np.maximum(1e-6, r_logreg.ratio(X_train).numpy())
# # error = regression_metric(X_train, y_train, X_test, y_test,
# # sample_weight=sample_weight)
# # rows.append(dict(weight="logreg", name=name, split=split, error=error))
# # # Logistic Regression (MLP)
# # r_mlp = MLPDensityRatioEstimator(num_layers=2, num_units=32,
# # activation="relu", seed=seed)
# # r_mlp.compile(optimizer=optimizer, metrics=["accuracy"])
# # r_mlp.fit(X_test, X_train, epochs=epochs, batch_size=batch_size)
# # sample_weight = np.maximum(1e-6, r_mlp.ratio(X_train).numpy())
# # error = regression_metric(X_train, y_train, X_test, y_test,
# # sample_weight=sample_weight)
# # rows.append(dict(weight="mlp", name=name, split=split, error=error))
# for whiten in [True]:
for kernel_name, kernel_cls in kernels.items():
for num_inducing_points in [100, 300, 500]:
for use_ard in [False, True]:
# # Gaussian Processes (low-dimensional)
# gpdre = GaussianProcessDensityRatioEstimator(
# input_dim=num_features_low,
# kernel_cls=kernel_cls,
# num_inducing_points=num_inducing_points,
# inducing_index_points_initializer=KMeans(X_low, seed=9),
# vgp_cls=SVGP,
# whiten=True,
# jitter=jitter,
# seed=9)
# gpdre.compile(optimizer=optimizer)
# gpdre.fit(X_test_low, X_train_low,
# epochs=epochs,
# batch_size=batch_size,
# buffer_size=buffer_size)
# for prop_name, prop in props.items():
# r_prop = gpdre.ratio(X_train_low, convert_to_tensor_fn=prop)
# error = regression_metric(X_train, y_train, X_test, y_test,
# sample_weight=r_prop.numpy())
# rows.append(dict(name=name, error=error, projection="low",
# kernel_name=kernel_name,
# # whiten=whiten,
# weight=prop_name))
# Gaussian Processes
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features,
kernel_cls=kernel_cls,
use_ard=use_ard,
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(
X, seed=split),
vgp_cls=SVGP,
whiten=True,
jitter=jitter,
seed=split)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test,
X_train,
epochs=epochs,
batch_size=batch_size,
buffer_size=buffer_size)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train,
convert_to_tensor_fn=prop)
error = regression_metric(
X_train,
y_train,
X_test,
y_test,
sample_weight=r_prop.numpy())
rows.append(
dict(
name=name,
error=error,
projection="none",
kernel_name=kernel_name,
split=split,
# whiten=whiten,
use_ard=use_ard,
epochs=epochs,
num_inducing_points=num_inducing_points,
weight=prop_name))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{myname}.csv")))
# data = pd.DataFrame(rows)
# data.to_csv(str(summary_path.joinpath(f"{myname}.csv")))
return 0
def main(myname, summary_dir, seed):
summary_path = Path(summary_dir).joinpath("liacc")
summary_path.mkdir(parents=True, exist_ok=True)
names = [
"abalone", "ailerons", "bank32", "bank8", "cali", "cpuact",
"elevators", "puma8"
]
rows = []
for name in names:
output_path = summary_path.joinpath(f"{name}")
output_path.mkdir(parents=True, exist_ok=True)
data_path = get_path(name, kind="data", data_home="results/")
data_mat = loadmat(data_path)
X_trains = get_splits(data_mat, key="X")
Y_trains = get_splits(data_mat, key="Y")
X_tests = get_splits(data_mat, key="Xtest")
Y_tests = get_splits(data_mat, key="Ytest")
sample_weights = get_splits(data_mat, key="ideal")
result_path = get_path(name,
kind="results_CV",
data_home="results/20200530/")
results_mat = loadmat(result_path)
projs = get_splits(results_mat, key="all_W")
for split, (X_train, Y_train, X_test, Y_test, proj, sample_weight) in \
enumerate(zip(X_trains, Y_trains, X_tests, Y_tests, projs, sample_weights)):
y_train = Y_train.squeeze(axis=-1)
y_test = Y_test.squeeze(axis=-1)
# X, _ = make_classification_dataset(X_test, X_train)
# num_features = X.shape[-1]
X_train_low = X_train.dot(proj)
X_test_low = X_test.dot(proj)
X_low, _ = make_classification_dataset(X_test_low, X_train_low)
num_features_low = X_low.shape[-1]
error = regression_metric(X_train_low, y_train, X_test_low, y_test)
rows.append(
dict(weight="uniform",
name=name,
split=split,
error=error,
projection="low"))
error = regression_metric(
X_train_low,
y_train,
X_test_low,
y_test,
sample_weight=sample_weight.squeeze(axis=-1))
rows.append(
dict(weight="exact",
name=name,
split=split,
error=error,
projection="low"))
# # Gaussian Processes (full-dimensional)
# gpdre = GaussianProcessDensityRatioEstimator(
# input_dim=num_features, kernel_cls=kernels["sqr_exp"],
# num_inducing_points=num_inducing_points,
# inducing_index_points_initializer=KMeans(X, seed=split),
# vgp_cls=SVGP, whiten=True, jitter=jitter, seed=split)
# gpdre.compile(optimizer=optimizer)
# gpdre.fit(X_test, X_train,
# epochs=epochs,
# batch_size=batch_size,
# buffer_size=buffer_size)
# for prop_name, prop in props.items():
# r_prop = gpdre.ratio(X_train, convert_to_tensor_fn=prop)
# error = regression_metric(X_train, y_train, X_test, y_test,
# sample_weight=r_prop.numpy())
# rows.append(dict(name=name, split=split, error=error,
# projection="none", kernel_name="sqr_exp",
# use_ard=True, epochs=epochs, weight=prop_name,
# num_inducing_points=num_inducing_points,
# num_features=num_features))
# Gaussian Processes (low-dimensional)
gpdre = GaussianProcessDensityRatioEstimator(
input_dim=num_features_low,
kernel_cls=kernels["sqr_exp"],
num_inducing_points=num_inducing_points,
inducing_index_points_initializer=KMeans(X_low, seed=split),
vgp_cls=SVGP,
whiten=True,
jitter=jitter,
seed=split)
gpdre.compile(optimizer=optimizer)
gpdre.fit(X_test_low,
X_train_low,
epochs=epochs,
batch_size=batch_size,
buffer_size=buffer_size)
for prop_name, prop in props.items():
r_prop = gpdre.ratio(X_train_low, convert_to_tensor_fn=prop)
error = regression_metric(X_train_low,
y_train,
X_test_low,
y_test,
sample_weight=r_prop.numpy())
rows.append(
dict(name=name,
split=split,
error=error,
projection="low",
kernel_name="sqr_exp",
use_ard=True,
epochs=epochs,
weight=prop_name,
num_inducing_points=num_inducing_points,
num_features=num_features_low))
data = pd.DataFrame(rows)
data.to_csv(str(summary_path.joinpath(f"{myname}.csv")))
# data = pd.DataFrame(rows)
# data.to_csv(str(summary_path.joinpath(f"{myname}.csv")))
return 0