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update old gpr model

This commit is contained in:
bohanjason 2019-11-30 03:30:35 -05:00 committed by Dana Van Aken
parent fd7f111c26
commit 7ebb2ea473
5 changed files with 84 additions and 38 deletions
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88
server/analysis/gp_tf.py
View File

@ -36,18 +36,21 @@ class GPRGDResult(GPRResult):
class GPR(object):
def __init__(self, length_scale=1.0, magnitude=1.0, max_train_size=7000,
batch_size=3000, num_threads=4, check_numerics=True, debug=False):
def __init__(self, length_scale=1.0, magnitude=1.0, ridge=1.0, max_train_size=7000,
batch_size=3000, num_threads=4, check_numerics=True, debug=False,
hyperparameter_trainable=False):
assert np.isscalar(length_scale)
assert np.isscalar(magnitude)
assert length_scale > 0 and magnitude > 0
self.length_scale = length_scale
self.magnitude = magnitude
self.ridge = ridge
self.max_train_size_ = max_train_size
self.batch_size_ = batch_size
self.num_threads_ = num_threads
self.check_numerics = check_numerics
self.debug = debug
self.hyperparameter_trainable = hyperparameter_trainable
self.X_train = None
self.y_train = None
self.xy_ = None
@ -56,20 +59,36 @@ class GPR(object):
self.graph = None
self.vars = None
self.ops = None
self.ridge = None
def build_graph(self):
self.vars = {}
self.ops = {}
self.graph = tf.Graph()
with self.graph.as_default():
mag_const = tf.constant(self.magnitude,
if self.hyperparameter_trainable:
mag_ = np.log(np.exp(self.magnitude) - 1)
ls_ = np.log(np.exp(self.length_scale) - 1)
noise_ = np.log(np.exp(self.ridge) - 1)
mag_var = tf.nn.softplus(tf.Variable(mag_,
dtype=np.float32,
name='magnitude'))
ls_var = tf.nn.softplus(tf.Variable(ls_,
dtype=np.float32,
name='length_scale'))
noise_var = tf.nn.softplus(tf.Variable(noise_,
dtype=np.float32,
name='noise_scale'))
else:
mag_var = tf.constant(self.magnitude,
dtype=np.float32,
name='magnitude')
ls_const = tf.constant(self.length_scale,
ls_var = tf.constant(self.length_scale,
dtype=np.float32,
name='length_scale')
noise_var = tf.constant(self.ridge,
dtype=np.float32,
name='noise_scale')
# Nodes for distance computation
v1 = tf.placeholder(tf.float32, name="v1")
v2 = tf.placeholder(tf.float32, name="v2")
@ -80,11 +99,14 @@ class GPR(object):
self.vars['v1_h'] = v1
self.vars['v2_h'] = v2
self.ops['dist_op'] = dist_op
self.vars['mag_v'] = mag_var
self.vars['ls_v'] = ls_var
self.vars['noise_v'] = noise_var
# Nodes for kernel computation
X_dists = tf.placeholder(tf.float32, name='X_dists')
ridge_ph = tf.placeholder(tf.float32, name='ridge')
K_op = mag_const * tf.exp(-X_dists / ls_const)
K_op = mag_var * tf.exp(-X_dists / ls_var) # pylint: disable=invalid-name
if self.check_numerics:
K_op = tf.check_numerics(K_op, "K_op: ")
K_ridge_op = K_op + tf.diag(ridge_ph)
@ -184,23 +206,27 @@ class GPR(object):
raise Exception("Input contains non-finite values: {}"
.format(X[~finite_els]))
def fit(self, X_train, y_train, ridge=1.0):
def fit(self, X_train, y_train):
self._reset()
X_train, y_train = self.check_X_y(X_train, y_train)
self.X_train = np.float32(X_train)
self.y_train = np.float32(y_train)
sample_size = self.X_train.shape[0]
self.ridge = ridge
if np.isscalar(ridge):
ridge = np.ones(sample_size) * ridge
assert isinstance(ridge, np.ndarray)
assert ridge.ndim == 1
ridge = self.ridge
X_dists = np.zeros((sample_size, sample_size), dtype=np.float32)
with tf.Session(graph=self.graph,
config=tf.ConfigProto(
intra_op_parallelism_threads=self.num_threads_)) as sess:
init = tf.global_variables_initializer()
sess.run(init)
noise_var = self.vars['noise_v']
if np.isscalar(ridge):
ridge = np.ones(sample_size) * sess.run(noise_var)
assert isinstance(ridge, np.ndarray)
assert ridge.ndim == 1
dist_op = self.ops['dist_op']
v1, v2 = self.vars['v1_h'], self.vars['v2_h']
for i in range(sample_size):
@ -230,14 +256,18 @@ class GPR(object):
test_size = X_test.shape[0]
sample_size = self.X_train.shape[0]
ridge = self.ridge
if np.isscalar(ridge):
ridge_test = np.ones(test_size) * ridge
arr_offset = 0
yhats = np.zeros([test_size, 1])
sigmas = np.zeros([test_size, 1])
with tf.Session(graph=self.graph,
config=tf.ConfigProto(
intra_op_parallelism_threads=self.num_threads_)) as sess:
init = tf.global_variables_initializer()
sess.run(init)
noise_var = self.vars['noise_v']
if np.isscalar(ridge):
ridge_test = np.ones(test_size) * sess.run(noise_var)
# Nodes for distance operation
dist_op = self.ops['dist_op']
v1 = self.vars['v1_h']
@ -320,6 +350,7 @@ class GPRGD(GPR):
def __init__(self,
length_scale=1.0,
magnitude=1.0,
ridge=1.0,
max_train_size=7000,
batch_size=3000,
num_threads=4,
@ -327,12 +358,19 @@ class GPRGD(GPR):
epsilon=1e-6,
max_iter=100,
sigma_multiplier=3.0,
mu_multiplier=1.0):
mu_multiplier=1.0,
check_numerics=True,
debug=False,
hyperparameter_trainable=False):
super(GPRGD, self).__init__(length_scale=length_scale,
magnitude=magnitude,
ridge=ridge,
max_train_size=max_train_size,
batch_size=batch_size,
num_threads=num_threads)
num_threads=num_threads,
check_numerics=check_numerics,
debug=debug,
hyperparameter_trainable=hyperparameter_trainable)
self.learning_rate = learning_rate
self.epsilon = epsilon
self.max_iter = max_iter
@ -341,8 +379,8 @@ class GPRGD(GPR):
self.X_min = None
self.X_max = None
def fit(self, X_train, y_train, X_min, X_max, ridge): # pylint: disable=arguments-differ
super(GPRGD, self).fit(X_train, y_train, ridge)
def fit(self, X_train, y_train, X_min, X_max): # pylint: disable=arguments-differ
super(GPRGD, self).fit(X_train, y_train)
self.X_min = X_min
self.X_max = X_max
@ -354,17 +392,21 @@ class GPRGD(GPR):
xt_assign_op = xt_.assign(xt_ph)
init = tf.global_variables_initializer()
sess.run(init)
mag_var = self.vars['mag_v']
ls_var = self.vars['ls_v']
noise_var = self.vars['noise_v']
K2_mat = tf.transpose(tf.expand_dims(tf.sqrt(tf.reduce_sum(tf.pow(
tf.subtract(xt_, self.X_train), 2), 1)), 0))
if self.check_numerics is True:
K2_mat = tf.check_numerics(K2_mat, "K2_mat: ")
K2__ = tf.cast(self.magnitude * tf.exp(-K2_mat / self.length_scale), tf.float32)
K2__ = tf.cast(mag_var * tf.exp(-K2_mat / ls_var), tf.float32) # pylint: disable=invalid-name
if self.check_numerics is True:
K2__ = tf.check_numerics(K2__, "K2__: ")
yhat_gd = tf.cast(tf.matmul(tf.transpose(K2__), self.xy_), tf.float32)
if self.check_numerics is True:
yhat_gd = tf.check_numerics(yhat_gd, message="yhat: ")
sig_val = tf.cast((tf.sqrt(self.magnitude + ridge - tf.matmul(
sig_val = tf.cast((tf.sqrt(mag_var + noise_var - tf.matmul(
tf.transpose(K2__), tf.matmul(self.K_inv, K2__)))), tf.float32)
if self.check_numerics is True:
sig_val = tf.check_numerics(sig_val, message="sigma: ")
@ -438,7 +480,7 @@ class GPRGD(GPR):
sigmas_it = np.empty((self.max_iter + 1,)) * np.nan
losses_it = np.empty((self.max_iter + 1,)) * np.nan
confs_it = np.empty((self.max_iter + 1, nfeats)) * np.nan
sess.run(init)
sess.run(assign_op, feed_dict={xt_ph: X_test_batch[i]})
step = 0
for step in range(self.max_iter):

2
server/analysis/gpr/gpr_models.py
View File

@ -111,7 +111,7 @@ class BasicGP(BaseModel):
]
def _build_kernel(self, kernel_kwargs, **kwargs):
k = gpflow.kernels.Exponential(**kernel_kwargs[0])
k = gpflow.kernels.Matern12(lengthscales=2, **kernel_kwargs[0])
if kwargs.pop('optimize_hyperparameters'):
k.lengthscales.transform = gpflow.transforms.Logistic(
*self._LENGTHSCALE_BOUNDS)

11
server/analysis/simulation.py
View File

@ -228,7 +228,7 @@ def gpr(env, config, n_loops=100):
# Tensorflow get broken if we use the training data points as
# starting points for GPRGD.
X_samples = np.vstack((X_samples, np.array(entry[0]) * 0.97 + 0.01))
model = GPRGD(length_scale=1.0,
model = GPRGD(length_scale=2.0,
magnitude=1.0,
max_train_size=2000,
batch_size=100,
@ -236,11 +236,14 @@ def gpr(env, config, n_loops=100):
learning_rate=0.01,
epsilon=1e-6,
max_iter=500,
sigma_multiplier=3.0,
mu_multiplier=1.0)
sigma_multiplier=1.0,
mu_multiplier=1.0,
ridge=1.0,
debug=False,
hyperparameter_trainable=True)
actions, rewards = memory.get_all()
model.fit(np.array(actions), -np.array(rewards), X_min, X_max, ridge=0.01)
model.fit(np.array(actions), -np.array(rewards), X_min, X_max)
res = model.predict(X_samples)
best_config_idx = np.argmin(res.minl.ravel())
best_config = res.minl_conf[best_config_idx, :]

8
server/analysis/tests/test_gpr.py
View File

@ -47,8 +47,8 @@ class TestGPRTF(unittest.TestCase):
X_train = data[0:500]
X_test = data[500:]
y_train = boston['target'][0:500].reshape(500, 1)
cls.model = GPR(length_scale=1.0, magnitude=1.0)
cls.model.fit(X_train, y_train, ridge=1.0)
cls.model = GPR(length_scale=1.0, magnitude=1.0, ridge=1.0)
cls.model.fit(X_train, y_train)
cls.gpr_result = cls.model.predict(X_test)
def test_gprnp_ypreds(self):
@ -75,8 +75,8 @@ class TestGPRGD(unittest.TestCase):
y_train = boston['target'][0:500].reshape(500, 1)
Xmin = np.min(X_train, 0)
Xmax = np.max(X_train, 0)
cls.model = GPRGD(length_scale=1.0, magnitude=1.0, max_iter=1, learning_rate=0)
cls.model.fit(X_train, y_train, Xmin, Xmax, ridge=1.0)
cls.model = GPRGD(length_scale=1.0, magnitude=1.0, max_iter=1, learning_rate=0, ridge=1.0)
cls.model.fit(X_train, y_train, Xmin, Xmax)
cls.gpr_result = cls.model.predict(X_test)
def test_gprnp_ypreds(self):

5
server/website/website/tasks/async_tasks.py
View File

@ -645,8 +645,9 @@ def configuration_recommendation(recommendation_input):
epsilon=DEFAULT_EPSILON,
max_iter=MAX_ITER,
sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
mu_multiplier=DEFAULT_MU_MULTIPLIER)
model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
mu_multiplier=DEFAULT_MU_MULTIPLIER,
ridge=DEFAULT_RIDGE)
model.fit(X_scaled, y_scaled, X_min, X_max)
res = model.predict(X_samples, constraint_helper=constraint_helper)
best_config_idx = np.argmin(res.minl.ravel())