use GPFlow in workload mapping

server/analysis/gpr/gpr_models.py

@@ -111,7 +111,13 @@ class BasicGP(BaseModel):
         ]
 
     def _build_kernel(self, kernel_kwargs, **kwargs):
-        k = gpflow.kernels.Matern12(lengthscales=2, **kernel_kwargs[0])
+        ls = 2
+        var = 1
+        if kwargs.get('lengthscales') is not None:
+            ls = kwargs['lengthscales']
+        if kwargs.get('variance') is not None:
+            var = kwargs['variance']
+        k = gpflow.kernels.Matern12(variance=var, lengthscales=ls, **kernel_kwargs[0])
         if kwargs.pop('optimize_hyperparameters'):
             k.lengthscales.transform = gpflow.transforms.Logistic(
                 *self._LENGTHSCALE_BOUNDS)

server/analysis/gpr/optimize.py

@@ -16,6 +16,13 @@ from analysis.util import get_analysis_logger
 LOG = get_analysis_logger(__name__)
 
 
+class GPRResult():
+
+    def __init__(self, ypreds=None, sigmas=None):
+        self.ypreds = ypreds
+        self.sigmas = sigmas
+
+
 class GPRGDResult():
 
     def __init__(self, ypreds=None, sigmas=None, minl=None, minl_conf=None):
@@ -25,6 +32,20 @@ class GPRGDResult():
         self.minl_conf = minl_conf
 
 
+def gpflow_predict(model, Xin):
+    fmean, fvar, _, _, _ = model._build_predict(Xin)  # pylint: disable=protected-access
+    y_mean_var = model.likelihood.predict_mean_and_var(fmean, fvar)
+    y_mean = y_mean_var[0]
+    y_var = y_mean_var[1]
+    y_std = tf.sqrt(y_var)
+
+    session = model.enquire_session(session=None)
+    with session.as_default():
+        y_mean_value = session.run(y_mean)
+        y_std_value = session.run(y_std)
+        return GPRResult(y_mean_value, y_std_value)
+
+
 def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
                 active_dims=None, bounds=None, debug=True):
     Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)