From ee53d0cead34e78c374cf89e42de21e0db0bd355 Mon Sep 17 00:00:00 2001
From: yangdsh <dongshen@andrew.cmu.edu>
Date: Mon, 4 Nov 2019 03:48:05 +0000
Subject: [PATCH] remove contextual gpr

---
 server/analysis/gpr/gpr_models.py | 128 ------------------------------
 1 file changed, 128 deletions(-)

diff --git a/server/analysis/gpr/gpr_models.py b/server/analysis/gpr/gpr_models.py
index 0fa3b77..6cb7065 100644
--- a/server/analysis/gpr/gpr_models.py
+++ b/server/analysis/gpr/gpr_models.py
@@ -126,136 +126,8 @@ class BasicGP(BaseModel):
         return k
 
 
-class ContextualGP(BaseModel):
-
-    _KERNEL_HP_KEYS = [
-        [
-            'GPRC/kern/kernels/0/kernels/0/variance',
-            'GPRC/kern/kernels/0/kernels/0/lengthscales',
-        ],
-        [
-            'GPRC/kern/kernels/0/kernels/1/variance',
-            'GPRC/kern/kernels/0/kernels/1/lengthscales',
-        ],
-        [
-            'GPRC/kern/kernels/1/variance',
-        ]
-    ]
-
-    def _get_kernel_kwargs(self, **kwargs):
-        k0_active_dims = kwargs.pop('k0_active_dims')
-        k1_active_dims = kwargs.pop('k1_active_dims')
-        return [
-            {
-                'input_dim': len(k0_active_dims),
-                'active_dims': k0_active_dims,
-                'ARD': True,
-            },
-            {
-                'input_dim': len(k1_active_dims),
-                'active_dims': k1_active_dims,
-                'ARD': True,
-            },
-            {
-                'input_dim': kwargs.pop('X_dim'),
-            }
-        ]
-
-    def _build_kernel(self, kernel_kwargs, **kwargs):
-        k0 = gpflow.kernels.Exponential(**kernel_kwargs[0])
-        k1 = gpflow.kernels.Exponential(**kernel_kwargs[1])
-        k2 = gpflow.kernels.White(**kernel_kwargs[2])
-        if kwargs['optimize_hyperparameters']:
-            k0.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-            k1.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-        k = k0 * k1 + k2
-        return k
-
-
-class ContextualGP_Alt0(ContextualGP):
-
-    def __init__(self, X, y, hyperparameters=None, optimize_hyperparameters=False,
-                 learning_rate=0.001, maxiter=5000, **kwargs):
-        self._context_lengthscale_const = kwargs.pop('context_lengthscale_const', 9.0)
-        super(ContextualGP_Alt0, self).__init__(
-            X, y, hyperparameters=hyperparameters,
-            optimize_hyperparameters=optimize_hyperparameters,
-            learning_rate=learning_rate, maxiter=maxiter, **kwargs)
-
-    def _build_kernel(self, kernel_kwargs, **kwargs):
-        kernel_kwargs[1]['lengthscales'] = np.ones((kernel_kwargs[1]['input_dim'],)) * \
-                                           self._context_lengthscale_const
-
-        k0 = gpflow.kernels.Exponential(**kernel_kwargs[0])
-        k1 = gpflow.kernels.Exponential(**kernel_kwargs[1])
-        k1.lengthscales.trainable = False
-        k2 = gpflow.kernels.White(**kernel_kwargs[2])
-        if kwargs['optimize_hyperparameters']:
-            k0.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-        k = k0 * k1 + k2
-        return k
-
-
-class ContextualGP_Alt1(ContextualGP):
-
-    def __init__(self, X, y, hyperparameters=None, optimize_hyperparameters=False,
-                 learning_rate=0.001, maxiter=5000, **kwargs):
-        self._hyperparams_path = kwargs.pop('hyperparameters_path')
-        self._hyperparams_idx = kwargs.pop('hyperparameters_idx', 0)
-        self._context_only = kwargs.pop('context_only', True)
-        super(ContextualGP_Alt1, self).__init__(
-            X, y, hyperparameters=hyperparameters,
-            optimize_hyperparameters=optimize_hyperparameters,
-            learning_rate=learning_rate, maxiter=maxiter, **kwargs)
-
-    def _build_kernel(self, kernel_kwargs, **kwargs):
-        hyperparams = self.load_hyperparameters(self._hyperparams_path,
-                                                self._hyperparams_idx)
-        if not self._context_only:
-            kernel_kwargs[0]['lengthscales'] = np.array(
-                hyperparams['GPRC/kern/kernels/0/kernels/0/lengthscales'])
-        kernel_kwargs[1]['lengthscales'] = np.array(
-            hyperparams['GPRC/kern/kernels/0/kernels/1/lengthscales'])
-
-        k0 = gpflow.kernels.Exponential(**kernel_kwargs[0])
-        k1 = gpflow.kernels.Exponential(**kernel_kwargs[1])
-        k2 = gpflow.kernels.White(**kernel_kwargs[2])
-
-        if not self._context_only:
-            k0.lengthscales.trainable = False
-        k1.lengthscales.trainable = False
-
-        if self._context_only and kwargs['optimize_hyperparameters']:
-            k0.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-        k = k0 * k1 + k2
-        return k
-
-
-class AdditiveContextualGP(ContextualGP):
-
-    def _build_kernel(self, kernel_kwargs, **kwargs):
-        k0 = gpflow.kernels.Exponential(**kernel_kwargs[0])
-        k1 = gpflow.kernels.Exponential(**kernel_kwargs[1])
-        k2 = gpflow.kernels.White(**kernel_kwargs[2])
-        if kwargs['optimize_hyperparameters']:
-            k0.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-            k1.lengthscales.transform = gpflow.transforms.Logistic(
-                *self._LENGTHSCALE_BOUNDS)
-        k = k0 + k1 + k2
-        return k
-
-
 _MODEL_MAP = {
     'BasicGP': BasicGP,
-    'ContextualGP': ContextualGP,
-    'ContextualGP_Alt0': ContextualGP_Alt0,
-    'ContextualGP_Alt1': ContextualGP_Alt1,
-    'AdditiveContextualGP': AdditiveContextualGP,
 }