def test_split_batchify_fetch(self):
data = np.arange(105) + 100
init, fetch = split_batchify_data((data, ), 10)
batchifier_state = jax.random.permutation(jax.random.PRNGKey(0),
jnp.arange(0, 105))
num_batches = 10
counts = np.zeros(105)
for i in range(num_batches):
batch = fetch(i, batchifier_state)
batch = batch[0]
unq_idxs, unq_counts = np.unique(batch, return_counts=True)
counts[unq_idxs - 100] = unq_counts
self.assertTrue(
np.alltrue(unq_counts <= 1)
) # ensure each item occurs at most once in the batch
self.assertTrue(
np.alltrue(batch >= 100) and np.alltrue(
batch < 205)) # ensure batch was plausibly drawn from data
self.assertTrue(np.alltrue(
counts <= 1)) # ensure each item occurs at most once in the epoch
self.assertEqual(
100, np.sum(counts)
) # ensure that amount of elements in batches cover an epoch worth of data
def test_split_batchify_fetch_correct_shape(self):
data = np.random.normal(size=(105, 3))
init, fetch = split_batchify_data((data, ), 10)
batchifier_state = jax.random.permutation(jax.random.PRNGKey(0),
jnp.arange(0, 105))
batch = fetch(6, batchifier_state)
batch = batch[0]
self.assertEqual((10, 3), jnp.shape(batch))
def test_split_batchify_batches_differ(self):
data = np.arange(105) + 100
init, fetch = split_batchify_data((data, ), 10)
num_batches, batchifier_state = init(jax.random.PRNGKey(10))
batch_0 = fetch(3, batchifier_state)
batch_1 = fetch(8, batchifier_state)
self.assertFalse(jnp.allclose(batch_0,
batch_1)) # ensure batches are different
def test_split_batchify_init_non_divisiable_size(self):
data = jnp.arange(0, 105)
init, fetch = split_batchify_data((data, ), 10)
rng_key = jax.random.PRNGKey(0)
num_batches, batchifier_state = init(rng_key)
self.assertEqual(10, num_batches)
self.assertTrue(
np.alltrue(np.unique(batchifier_state, return_counts=True)[1] < 2))
def test_split_batchify_init(self):
data = jnp.arange(0, 100)
init, fetch = split_batchify_data((data, ), 10)
rng_key = jax.random.PRNGKey(0)
num_batches, batchifier_state = init(rng_key)
self.assertEqual(10, num_batches)
self.assertEqual(jnp.size(data), jnp.size(batchifier_state))
self.assertTrue(np.allclose(np.unique(batchifier_state), data))
def main(args):
rng = PRNGKey(123)
rng, toy_data_rng = jax.random.split(rng)
train_data, test_data, true_params = create_toy_data(
toy_data_rng, args.num_samples, args.dimensions)
train_init, train_fetch = subsample_batchify_data(
train_data, batch_size=args.batch_size)
test_init, test_fetch = split_batchify_data(test_data,
batch_size=args.batch_size)
## Init optimizer and training algorithms
optimizer = optimizers.Adam(args.learning_rate)
svi = DPSVI(model,
guide,
optimizer,
ELBO(),
dp_scale=0.01,
clipping_threshold=20.,
num_obs_total=args.num_samples)
rng, svi_init_rng, data_fetch_rng = random.split(rng, 3)
_, batchifier_state = train_init(rng_key=data_fetch_rng)
sample_batch = train_fetch(0, batchifier_state)
svi_state = svi.init(svi_init_rng, *sample_batch)
@jit
def epoch_train(svi_state, batchifier_state, num_batch):
def body_fn(i, val):
svi_state, loss = val
batch = train_fetch(i, batchifier_state)
batch_X, batch_Y = batch
svi_state, batch_loss = svi.update(svi_state, batch_X, batch_Y)
loss += batch_loss / (args.num_samples * num_batch)
return svi_state, loss
return lax.fori_loop(0, num_batch, body_fn, (svi_state, 0.))
@jit
def eval_test(svi_state, batchifier_state, num_batch, rng):
params = svi.get_params(svi_state)
def body_fn(i, val):
loss_sum, acc_sum = val
batch = test_fetch(i, batchifier_state)
batch_X, batch_Y = batch
loss = svi.evaluate(svi_state, batch_X, batch_Y)
loss_sum += loss / (args.num_samples * num_batch)
acc_rng = jax.random.fold_in(rng, i)
acc = estimate_accuracy(batch_X, batch_Y, params, acc_rng, 1)
acc_sum += acc / num_batch
return loss_sum, acc_sum
return lax.fori_loop(0, num_batch, body_fn, (0., 0.))
## Train model
for i in range(args.num_epochs):
t_start = time.time()
rng, data_fetch_rng = random.split(rng, 2)
num_train_batches, train_batchifier_state = train_init(
rng_key=data_fetch_rng)
svi_state, train_loss = epoch_train(svi_state, train_batchifier_state,
num_train_batches)
train_loss.block_until_ready()
t_end = time.time()
if (i % (args.num_epochs // 10)) == 0:
rng, test_rng, test_fetch_rng = random.split(rng, 3)
num_test_batches, test_batchifier_state = test_init(
rng_key=test_fetch_rng)
test_loss, test_acc = eval_test(svi_state, test_batchifier_state,
num_test_batches, test_rng)
print(
"Epoch {}: loss = {}, acc = {} (loss on training set: {}) ({:.2f} s.)"
.format(i, test_loss, test_acc, train_loss, t_end - t_start))
# parameters for logistic regression may be scaled arbitrarily. normalize
# w (and scale intercept accordingly) for comparison
w_true = normalize(true_params[0])
scale_true = jnp.linalg.norm(true_params[0])
intercept_true = true_params[1] / scale_true
params = svi.get_params(svi_state)
w_post = normalize(params['w_loc'])
scale_post = jnp.linalg.norm(params['w_loc'])
intercept_post = params['intercept_loc'] / scale_post
print("w_loc: {}\nexpected: {}\nerror: {}".format(
w_post, w_true, jnp.linalg.norm(w_post - w_true)))
print("w_std: {}".format(jnp.exp(params['w_std_log'])))
print("")
print("intercept_loc: {}\nexpected: {}\nerror: {}".format(
intercept_post, intercept_true,
jnp.abs(intercept_post - intercept_true)))
print("intercept_std: {}".format(jnp.exp(params['intercept_std_log'])))
print("")
X_test, y_test = test_data
rng, rng_acc_true, rng_acc_post = jax.random.split(rng, 3)
# for evaluation accuracy with true parameters, we scale them to the same
# scale as the found posterior. (gives better results than normalized
# parameters (probably due to numerical instabilities))
acc_true = estimate_accuracy_fixed_params(X_test, y_test, w_true,
intercept_true, rng_acc_true, 10)
acc_post = estimate_accuracy(X_test, y_test, params, rng_acc_post, 10)
print(
"avg accuracy on test set: with true parameters: {} ; with found posterior: {}\n"
.format(acc_true, acc_post))
def main(args):
N = args.num_samples
k = args.num_components
d = args.dimensions
rng = PRNGKey(1234)
rng, toy_data_rng = jax.random.split(rng, 2)
X_train, X_test, latent_vals = create_toy_data(toy_data_rng, N, d)
train_init, train_fetch = subsample_batchify_data((X_train,), batch_size=args.batch_size)
test_init, test_fetch = split_batchify_data((X_test,), batch_size=args.batch_size)
## Init optimizer and training algorithms
optimizer = optimizers.Adam(args.learning_rate)
# note(lumip): fix the parameters in the models
def fix_params(model_fn, k):
def fixed_params_fn(obs, **kwargs):
return model_fn(k, obs, **kwargs)
return fixed_params_fn
model_fixed = fix_params(model, k)
guide_fixed = fix_params(guide, k)
svi = DPSVI(
model_fixed, guide_fixed, optimizer, ELBO(),
dp_scale=0.01, clipping_threshold=20., num_obs_total=args.num_samples
)
rng, svi_init_rng, fetch_rng = random.split(rng, 3)
_, batchifier_state = train_init(fetch_rng)
batch = train_fetch(0, batchifier_state)
svi_state = svi.init(svi_init_rng, *batch)
@jit
def epoch_train(svi_state, data_idx, num_batch):
def body_fn(i, val):
svi_state, loss = val
batch = train_fetch(i, batchifier_state)
svi_state, batch_loss = svi.update(
svi_state, *batch
)
loss += batch_loss / (args.num_samples * num_batch)
return svi_state, loss
return lax.fori_loop(0, num_batch, body_fn, (svi_state, 0.))
@jit
def eval_test(svi_state, batchifier_state, num_batch):
def body_fn(i, loss_sum):
batch = test_fetch(i, batchifier_state)
loss = svi.evaluate(svi_state, *batch)
loss_sum += loss / (args.num_samples * num_batch)
return loss_sum
return lax.fori_loop(0, num_batch, body_fn, 0.)
## Train model
for i in range(args.num_epochs):
t_start = time.time()
rng, data_fetch_rng = random.split(rng, 2)
num_train_batches, train_batchifier_state = train_init(rng_key=data_fetch_rng)
svi_state, train_loss = epoch_train(
svi_state, train_batchifier_state, num_train_batches
)
train_loss.block_until_ready()
t_end = time.time()
if i % 100 == 0:
rng, test_fetch_rng = random.split(rng, 2)
num_test_batches, test_batchifier_state = test_init(rng_key=test_fetch_rng)
test_loss = eval_test(
svi_state, test_batchifier_state, num_test_batches
)
print("Epoch {}: loss = {} (on training set = {}) ({:.2f} s.)".format(
i, test_loss, train_loss, t_end - t_start
))
params = svi.get_params(svi_state)
print(params)
posterior_modes = params['mus_loc']
posterior_pis = dist.Dirichlet(jnp.exp(params['alpha_log'])).mean
print("MAP estimate of mixture weights: {}".format(posterior_pis))
print("MAP estimate of mixture modes : {}".format(posterior_modes))
acc = compute_assignment_accuracy(
X_test, latent_vals[1], latent_vals[2], posterior_modes, posterior_pis
)
print("assignment accuracy: {}".format(acc))
def main(args):
rng = PRNGKey(1234)
rng, toy_data_rng = jax.random.split(rng, 2)
X_train, X_test, mu_true = create_toy_data(toy_data_rng, args.num_samples,
args.dimensions)
train_init, train_fetch = subsample_batchify_data(
(X_train, ), batch_size=args.batch_size)
test_init, test_fetch = split_batchify_data((X_test, ),
batch_size=args.batch_size)
## Init optimizer and training algorithms
optimizer = optimizers.Adam(args.learning_rate)
svi = DPSVI(model,
guide,
optimizer,
ELBO(),
dp_scale=args.sigma,
clipping_threshold=args.clip_threshold,
d=args.dimensions,
num_obs_total=args.num_samples)
rng, svi_init_rng, batchifier_rng = random.split(rng, 3)
_, batchifier_state = train_init(rng_key=batchifier_rng)
batch = train_fetch(0, batchifier_state)
svi_state = svi.init(svi_init_rng, *batch)
q = args.batch_size / args.num_samples
eps = svi.get_epsilon(args.delta, q, num_epochs=args.num_epochs)
print("Privacy epsilon {} (for sigma: {}, delta: {}, C: {}, q: {})".format(
eps, args.sigma, args.clip_threshold, args.delta, q))
@jit
def epoch_train(svi_state, batchifier_state, num_batch):
def body_fn(i, val):
svi_state, loss = val
batch = train_fetch(i, batchifier_state)
svi_state, batch_loss = svi.update(svi_state, *batch)
loss += batch_loss / (args.num_samples * num_batch)
return svi_state, loss
return lax.fori_loop(0, num_batch, body_fn, (svi_state, 0.))
@jit
def eval_test(svi_state, batchifier_state, num_batch):
def body_fn(i, loss_sum):
batch = test_fetch(i, batchifier_state)
loss = svi.evaluate(svi_state, *batch)
loss_sum += loss / (args.num_samples * num_batch)
return loss_sum
return lax.fori_loop(0, num_batch, body_fn, 0.)
## Train model
for i in range(args.num_epochs):
t_start = time.time()
rng, data_fetch_rng = random.split(rng, 2)
num_train_batches, train_batchifier_state = train_init(
rng_key=data_fetch_rng)
svi_state, train_loss = epoch_train(svi_state, train_batchifier_state,
num_train_batches)
train_loss.block_until_ready()
t_end = time.time()
if (i % (args.num_epochs // 10) == 0):
rng, test_fetch_rng = random.split(rng, 2)
num_test_batches, test_batchifier_state = test_init(
rng_key=test_fetch_rng)
test_loss = eval_test(svi_state, test_batchifier_state,
num_test_batches)
print(
"Epoch {}: loss = {} (on training set: {}) ({:.2f} s.)".format(
i, test_loss, train_loss, t_end - t_start))
params = svi.get_params(svi_state)
mu_loc = params['mu_loc']
mu_std = jnp.exp(params['mu_std_log'])
print("### expected: {}".format(mu_true))
print("### svi result\nmu_loc: {}\nerror: {}\nmu_std: {}".format(
mu_loc, jnp.linalg.norm(mu_loc - mu_true), mu_std))
mu_loc, mu_std = analytical_solution(X_train)
print("### analytical solution\nmu_loc: {}\nerror: {}\nmu_std: {}".format(
mu_loc, jnp.linalg.norm(mu_loc - mu_true), mu_std))
mu_loc, mu_std = ml_estimate(X_train)
print("### ml estimate\nmu_loc: {}\nerror: {}\nmu_std: {}".format(
mu_loc, jnp.linalg.norm(mu_loc - mu_true), mu_std))
