use GPFlow in workload mapping
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389174302f
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25d1950e67
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@ -111,7 +111,13 @@ class BasicGP(BaseModel):
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]
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]
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def _build_kernel(self, kernel_kwargs, **kwargs):
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def _build_kernel(self, kernel_kwargs, **kwargs):
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k = gpflow.kernels.Matern12(lengthscales=2, **kernel_kwargs[0])
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ls = 2
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var = 1
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if kwargs.get('lengthscales') is not None:
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ls = kwargs['lengthscales']
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if kwargs.get('variance') is not None:
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var = kwargs['variance']
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k = gpflow.kernels.Matern12(variance=var, lengthscales=ls, **kernel_kwargs[0])
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if kwargs.pop('optimize_hyperparameters'):
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if kwargs.pop('optimize_hyperparameters'):
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k.lengthscales.transform = gpflow.transforms.Logistic(
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k.lengthscales.transform = gpflow.transforms.Logistic(
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*self._LENGTHSCALE_BOUNDS)
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*self._LENGTHSCALE_BOUNDS)
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@ -16,6 +16,13 @@ from analysis.util import get_analysis_logger
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LOG = get_analysis_logger(__name__)
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LOG = get_analysis_logger(__name__)
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class GPRResult():
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def __init__(self, ypreds=None, sigmas=None):
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self.ypreds = ypreds
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self.sigmas = sigmas
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class GPRGDResult():
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class GPRGDResult():
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def __init__(self, ypreds=None, sigmas=None, minl=None, minl_conf=None):
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def __init__(self, ypreds=None, sigmas=None, minl=None, minl_conf=None):
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@ -25,6 +32,20 @@ class GPRGDResult():
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self.minl_conf = minl_conf
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self.minl_conf = minl_conf
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def gpflow_predict(model, Xin):
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fmean, fvar, _, _, _ = model._build_predict(Xin) # pylint: disable=protected-access
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y_mean_var = model.likelihood.predict_mean_and_var(fmean, fvar)
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y_mean = y_mean_var[0]
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y_var = y_mean_var[1]
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y_std = tf.sqrt(y_var)
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session = model.enquire_session(session=None)
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with session.as_default():
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y_mean_value = session.run(y_mean)
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y_std_value = session.run(y_std)
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return GPRResult(y_mean_value, y_std_value)
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def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
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def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
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active_dims=None, bounds=None, debug=True):
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active_dims=None, bounds=None, debug=True):
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Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)
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Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)
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@ -14,6 +14,7 @@ from analysis.gp_tf import GPR
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from analysis.gp_tf import GPRGD
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from analysis.gp_tf import GPRGD
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from analysis.gpr import gpr_models
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from analysis.gpr import gpr_models
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from analysis.gpr.optimize import tf_optimize
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from analysis.gpr.optimize import tf_optimize
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from analysis.gpr.optimize import gpflow_predict
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# test numpy version GPR
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# test numpy version GPR
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class TestGPRNP(unittest.TestCase):
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class TestGPRNP(unittest.TestCase):
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@ -67,6 +68,37 @@ class TestGPRTF(unittest.TestCase):
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self.assertEqual(sigmas_round, expected_sigmas)
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self.assertEqual(sigmas_round, expected_sigmas)
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# test GPFlow version GPR
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class TestGPRGPF(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestGPRGPF, cls).setUpClass()
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boston = datasets.load_boston()
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data = boston['data']
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X_train = data[0:500]
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X_test = data[500:]
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y_train = boston['target'][0:500].reshape(500, 1)
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model_kwargs = {'lengthscales': 1, 'variance': 1, 'noise_variance': 1}
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tf.reset_default_graph()
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graph = tf.get_default_graph()
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gpflow.reset_default_session(graph=graph)
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cls.m = gpr_models.create_model('BasicGP', X=X_train, y=y_train,
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**model_kwargs)
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cls.gpr_result = gpflow_predict(cls.m.model, X_test)
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def test_gprnp_ypreds(self):
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ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
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expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
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self.assertEqual(ypreds_round, expected_ypreds)
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def test_gprnp_sigmas(self):
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sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
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expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
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self.assertEqual(sigmas_round, expected_sigmas)
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# test Tensorflow GPRGD model
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# test Tensorflow GPRGD model
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class TestGPRGD(unittest.TestCase):
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class TestGPRGD(unittest.TestCase):
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@ -96,11 +128,11 @@ class TestGPRGD(unittest.TestCase):
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# test Gradient Descent in GPFlow model
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# test Gradient Descent in GPFlow model
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class TestGPRGP(unittest.TestCase):
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class TestGPFGD(unittest.TestCase):
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@classmethod
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@classmethod
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def setUpClass(cls):
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def setUpClass(cls):
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super(TestGPRGP, cls).setUpClass()
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super(TestGPFGD, cls).setUpClass()
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boston = datasets.load_boston()
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boston = datasets.load_boston()
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data = boston['data']
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data = boston['data']
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X_train = data[0:500]
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X_train = data[0:500]
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@ -22,7 +22,7 @@ from analysis.gp_tf import GPRGD
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from analysis.nn_tf import NeuralNet
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from analysis.nn_tf import NeuralNet
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from analysis.gpr import gpr_models
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from analysis.gpr import gpr_models
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from analysis.gpr import ucb
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from analysis.gpr import ucb
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from analysis.gpr.optimize import tf_optimize
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from analysis.gpr.optimize import tf_optimize, gpflow_predict
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from analysis.preprocessing import Bin, DummyEncoder
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from analysis.preprocessing import Bin, DummyEncoder
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from analysis.constraints import ParamConstraintHelper
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from analysis.constraints import ParamConstraintHelper
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from website.models import PipelineData, PipelineRun, Result, Workload, SessionKnob, MetricCatalog
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from website.models import PipelineData, PipelineRun, Result, Workload, SessionKnob, MetricCatalog
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@ -932,12 +932,16 @@ def map_workload(map_workload_input):
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# and then predict the performance of each metric for each of
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# and then predict the performance of each metric for each of
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# the knob configurations attempted so far by the target.
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# the knob configurations attempted so far by the target.
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y_col = y_col.reshape(-1, 1)
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y_col = y_col.reshape(-1, 1)
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model = GPRNP(length_scale=params['GPR_LENGTH_SCALE'],
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model_kwargs = {'lengthscales': params['GPR_LENGTH_SCALE'],
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magnitude=params['GPR_MAGNITUDE'],
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'variance': params['GPR_MAGNITUDE'],
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max_train_size=params['GPR_MAX_TRAIN_SIZE'],
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'noise_variance': params['GPR_RIDGE']}
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batch_size=params['GPR_BATCH_SIZE'])
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tf.reset_default_graph()
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model.fit(X_scaled, y_col, ridge=params['GPR_RIDGE'])
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graph = tf.get_default_graph()
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predictions[:, j] = model.predict(X_target).ypreds.ravel()
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gpflow.reset_default_session(graph=graph)
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m = gpr_models.create_model(params['GPR_MODEL_NAME'], X=X_scaled, y=y_col,
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**model_kwargs)
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gpr_result = gpflow_predict(m.model, X_target)
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predictions[:, j] = gpr_result.ypreds.ravel()
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# Bin each of the predicted metric columns by deciles and then
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# Bin each of the predicted metric columns by deciles and then
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# compute the score (i.e., distance) between the target workload
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# compute the score (i.e., distance) between the target workload
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# and each of the known workloads
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# and each of the known workloads
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