add GPFLOW to website

This commit is contained in:
yangdsh 2019-11-23 19:52:28 +00:00 committed by Dana Van Aken
parent dceee6e0ba
commit b149684d77
3 changed files with 55 additions and 14 deletions

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@ -16,6 +16,15 @@ from analysis.util import get_analysis_logger
LOG = get_analysis_logger(__name__) LOG = get_analysis_logger(__name__)
class GPRGDResult():
def __init__(self, ypreds=None, sigmas=None, minl=None, minl_conf=None):
self.ypreds = ypreds
self.sigmas = sigmas
self.minl = minl
self.minl_conf = minl_conf
def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3., def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
active_dims=None, bounds=None): active_dims=None, bounds=None):
Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES) Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)
@ -61,4 +70,4 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
assert_all_finite(y_mean_value) assert_all_finite(y_mean_value)
assert_all_finite(y_var_value) assert_all_finite(y_var_value)
assert_all_finite(loss_value) assert_all_finite(loss_value)
return Xnew_value, y_mean_value, y_var_value, loss_value return GPRGDResult(y_mean_value, y_var_value, loss_value, Xnew_value)

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@ -25,6 +25,8 @@ INIT_FLIP_PROB = 0.3
FLIP_PROB_DECAY = 0.5 FLIP_PROB_DECAY = 0.5
# ---GPR CONSTANTS--- # ---GPR CONSTANTS---
USE_GPFLOW = True
DEFAULT_LENGTH_SCALE = 1.0 DEFAULT_LENGTH_SCALE = 1.0
DEFAULT_MAGNITUDE = 1.0 DEFAULT_MAGNITUDE = 1.0
@ -56,6 +58,13 @@ DEFAULT_SIGMA_MULTIPLIER = 3.0
DEFAULT_MU_MULTIPLIER = 1.0 DEFAULT_MU_MULTIPLIER = 1.0
DEFAULT_UCB_SCALE = 0.2
# ---HYPERPARAMETER TUNING FOR GPR---
HP_MAX_ITER = 5000
HP_DEFAULT_LEARNING_RATE = 0.001
# ---GRADIENT DESCENT FOR DNN--- # ---GRADIENT DESCENT FOR DNN---
DNN_TRAIN_ITER = 500 DNN_TRAIN_ITER = 500

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@ -6,6 +6,8 @@
import random import random
import queue import queue
import numpy as np import numpy as np
import tensorflow as tf
import gpflow
from pyDOE import lhs from pyDOE import lhs
from scipy.stats import uniform from scipy.stats import uniform
@ -18,6 +20,9 @@ from analysis.ddpg.ddpg import DDPG
from analysis.gp import GPRNP from analysis.gp import GPRNP
from analysis.gp_tf import GPRGD from analysis.gp_tf import GPRGD
from analysis.nn_tf import NeuralNet from analysis.nn_tf import NeuralNet
from analysis.gpr import gpr_models
from analysis.gpr import ucb
from analysis.gpr.optimize import tf_optimize
from analysis.preprocessing import Bin, DummyEncoder from analysis.preprocessing import Bin, DummyEncoder
from analysis.constraints import ParamConstraintHelper from analysis.constraints import ParamConstraintHelper
from website.models import PipelineData, PipelineRun, Result, Workload, KnobCatalog, SessionKnob from website.models import PipelineData, PipelineRun, Result, Workload, KnobCatalog, SessionKnob
@ -25,11 +30,12 @@ from website import db
from website.types import PipelineTaskType, AlgorithmType from website.types import PipelineTaskType, AlgorithmType
from website.utils import DataUtil, JSONUtil from website.utils import DataUtil, JSONUtil
from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG # pylint: disable=no-name-in-module from website.settings import IMPORTANT_KNOB_NUMBER, NUM_SAMPLES, TOP_NUM_CONFIG # pylint: disable=no-name-in-module
from website.settings import (DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE, from website.settings import (USE_GPFLOW, DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUDE,
MAX_TRAIN_SIZE, BATCH_SIZE, NUM_THREADS, MAX_TRAIN_SIZE, BATCH_SIZE, NUM_THREADS,
DEFAULT_RIDGE, DEFAULT_LEARNING_RATE, DEFAULT_RIDGE, DEFAULT_LEARNING_RATE,
DEFAULT_EPSILON, MAX_ITER, GPR_EPS, DEFAULT_EPSILON, MAX_ITER, GPR_EPS,
DEFAULT_SIGMA_MULTIPLIER, DEFAULT_MU_MULTIPLIER, DEFAULT_SIGMA_MULTIPLIER, DEFAULT_MU_MULTIPLIER,
DEFAULT_UCB_SCALE, HP_LEARNING_RATE, HP_MAX_ITER,
DDPG_BATCH_SIZE, ACTOR_LEARNING_RATE, DDPG_BATCH_SIZE, ACTOR_LEARNING_RATE,
CRITIC_LEARNING_RATE, UPDATE_EPOCHS, CRITIC_LEARNING_RATE, UPDATE_EPOCHS,
ACTOR_HIDDEN_SIZES, CRITIC_HIDDEN_SIZES, ACTOR_HIDDEN_SIZES, CRITIC_HIDDEN_SIZES,
@ -613,6 +619,23 @@ def configuration_recommendation(recommendation_input):
elif algorithm == AlgorithmType.GPR: elif algorithm == AlgorithmType.GPR:
# default gpr model # default gpr model
if USE_GPFLOW:
model_kwargs = {}
model_kwargs['model_learning_rate'] = HP_LEARNING_RATE
model_kwargs['model_maxiter'] = HP_MAX_ITER
opt_kwargs = {}
opt_kwargs['learning_rate'] = DEFAULT_LEARNING_RATE
opt_kwargs['maxiter'] = MAX_ITER
opt_kwargs['bounds'] = [X_min, X_max]
ucb_beta = 'get_beta_td'
opt_kwargs['ucb_beta'] = ucb.get_ucb_beta(ucb_beta, scale=DEFAULT_UCB_SCALE,
t=i + 1., ndim=X_scaled.shape[1])
tf.reset_default_graph()
graph = tf.get_default_graph()
gpflow.reset_default_session(graph=graph)
m = gpr_models.create_model('BasicGP', X=X_scaled, y=y_scaled, **model_kwargs)
res = tf_optimize(m.model, X_samples, **opt_kwargs)
else:
model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE, model = GPRGD(length_scale=DEFAULT_LENGTH_SCALE,
magnitude=DEFAULT_MAGNITUDE, magnitude=DEFAULT_MAGNITUDE,
max_train_size=MAX_TRAIN_SIZE, max_train_size=MAX_TRAIN_SIZE,