work started on new MCMC, hps_bounds no longer required at initializa…

…tion
lbl-camera · Nov 10, 2023 · 92beba5 · 92beba5
1 parent facf510
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diff --git a/docs/source/examples/MultiTaskTest.ipynb b/docs/source/examples/MultiTaskTest.ipynb
diff --git a/docs/source/examples/NonEuclideanInputSpaces.ipynb b/docs/source/examples/NonEuclideanInputSpaces.ipynb
@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "#install the newest version of fvgp\n",
-    "#!pip install fvgp==4.0.9"
+    "#!pip install fvgp==4.0.11"
    ]
   },
   {
@@ -56,40 +56,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "differential_evolution step 1: f(x)= 8.78181\n",
-      "differential_evolution step 2: f(x)= 8.78181\n",
-      "differential_evolution step 3: f(x)= 8.78181\n",
-      "differential_evolution step 4: f(x)= 8.78181\n",
-      "differential_evolution step 5: f(x)= 8.78181\n",
-      "differential_evolution step 6: f(x)= 8.78181\n",
-      "differential_evolution step 7: f(x)= 8.78181\n",
-      "differential_evolution step 8: f(x)= 8.70813\n",
-      "differential_evolution step 9: f(x)= 8.55749\n",
-      "differential_evolution step 10: f(x)= 8.45097\n",
-      "differential_evolution step 11: f(x)= 8.22163\n",
-      "differential_evolution step 12: f(x)= 8.06583\n",
-      "differential_evolution step 13: f(x)= 8.06583\n",
-      "differential_evolution step 14: f(x)= 8.06583\n",
-      "differential_evolution step 15: f(x)= 8.06583\n",
-      "differential_evolution step 16: f(x)= 8.06543\n",
-      "differential_evolution step 17: f(x)= 8.06543\n",
-      "differential_evolution step 18: f(x)= 8.0514\n",
-      "differential_evolution step 19: f(x)= 8.04614\n",
-      "differential_evolution step 20: f(x)= 8.04614\n",
-      "differential_evolution step 21: f(x)= 8.0449\n",
-      "differential_evolution step 22: f(x)= 8.0449\n",
-      "differential_evolution step 23: f(x)= 8.0449\n",
-      "differential_evolution step 24: f(x)= 8.0449\n",
-      "differential_evolution step 25: f(x)= 8.0449\n",
-      "differential_evolution step 26: f(x)= 8.0449\n",
+      "differential_evolution step 1: f(x)= 9.91727\n",
+      "differential_evolution step 2: f(x)= 9.06802\n",
+      "differential_evolution step 3: f(x)= 9.06802\n",
+      "differential_evolution step 4: f(x)= 9.06802\n",
+      "differential_evolution step 5: f(x)= 8.4285\n",
+      "differential_evolution step 6: f(x)= 8.05457\n",
+      "differential_evolution step 7: f(x)= 8.05457\n",
+      "differential_evolution step 8: f(x)= 8.05457\n",
+      "differential_evolution step 9: f(x)= 8.05457\n",
+      "differential_evolution step 10: f(x)= 8.05457\n",
+      "differential_evolution step 11: f(x)= 8.05457\n",
+      "differential_evolution step 12: f(x)= 8.05457\n",
+      "differential_evolution step 13: f(x)= 8.05457\n",
+      "differential_evolution step 14: f(x)= 8.05457\n",
+      "differential_evolution step 15: f(x)= 8.05171\n",
+      "differential_evolution step 16: f(x)= 8.05171\n",
+      "differential_evolution step 17: f(x)= 8.04794\n",
+      "differential_evolution step 18: f(x)= 8.04555\n",
+      "differential_evolution step 19: f(x)= 8.04489\n",
+      "differential_evolution step 20: f(x)= 8.04489\n",
+      "differential_evolution step 21: f(x)= 8.04489\n",
+      "differential_evolution step 22: f(x)= 8.04489\n",
+      "differential_evolution step 23: f(x)= 8.04489\n",
+      "differential_evolution step 24: f(x)= 8.04489\n",
+      "differential_evolution step 25: f(x)= 8.04489\n",
+      "differential_evolution step 26: f(x)= 8.04489\n",
       "differential_evolution step 27: f(x)= 8.04489\n",
-      "differential_evolution step 28: f(x)= 8.04489\n",
-      "differential_evolution step 29: f(x)= 8.04489\n",
-      "differential_evolution step 30: f(x)= 8.04489\n",
-      "differential_evolution step 31: f(x)= 8.04489\n",
-      "hyperparameters:  [1.43486911 0.11327405]\n",
+      "hyperparameters:  [1.4350853  0.12085441]\n",
       "prediction :  [2.74]\n",
-      "uncertainty:  [1.19786022]\n"
+      "uncertainty:  [1.19795046]\n"
      ]
     }
    ],
@@ -124,24 +120,17 @@
     "    return hps[0] * obj.matern_kernel_diff1(d,hps[1])\n",
     "    \n",
     "\n",
-    "bounds = np.array([[0.001,100.],[0.001,100]])\n",
     "\n",
-    "my_gp = GP(1, x_data,y_data,init_hyperparameters=np.ones((2)), \n",
-    "           hyperparameter_bounds=bounds, gp_kernel_function=kernel, info = True)\n",
-    "my_gp.train()\n",
+    "\n",
+    "my_gp = GP(1, x_data,y_data,init_hyperparameters=np.ones((2)), gp_kernel_function=kernel, info = True)\n",
+    "\n",
+    "bounds = np.array([[0.001,100.],[0.001,100]])\n",
+    "my_gp.train(hyperparameter_bounds=bounds)\n",
     "\n",
     "print(\"hyperparameters: \", my_gp.hyperparameters)\n",
     "print(\"prediction : \",my_gp.posterior_mean({'full'})[\"f(x)\"])\n",
     "print(\"uncertainty: \",np.sqrt(my_gp.posterior_covariance({'full'})[\"v(x)\"]))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e409d084",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/docs/source/examples/SingleTaskTest.ipynb b/docs/source/examples/SingleTaskTest.ipynb
diff --git a/examples/MultiTaskTest.ipynb b/examples/MultiTaskTest.ipynb
diff --git a/examples/NonEuclideanInputSpaces.ipynb b/examples/NonEuclideanInputSpaces.ipynb
@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "#install the newest version of fvgp\n",
-    "#!pip install fvgp==4.0.9"
+    "#!pip install fvgp==4.0.11"
    ]
   },
   {
@@ -56,40 +56,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "differential_evolution step 1: f(x)= 8.78181\n",
-      "differential_evolution step 2: f(x)= 8.78181\n",
-      "differential_evolution step 3: f(x)= 8.78181\n",
-      "differential_evolution step 4: f(x)= 8.78181\n",
-      "differential_evolution step 5: f(x)= 8.78181\n",
-      "differential_evolution step 6: f(x)= 8.78181\n",
-      "differential_evolution step 7: f(x)= 8.78181\n",
-      "differential_evolution step 8: f(x)= 8.70813\n",
-      "differential_evolution step 9: f(x)= 8.55749\n",
-      "differential_evolution step 10: f(x)= 8.45097\n",
-      "differential_evolution step 11: f(x)= 8.22163\n",
-      "differential_evolution step 12: f(x)= 8.06583\n",
-      "differential_evolution step 13: f(x)= 8.06583\n",
-      "differential_evolution step 14: f(x)= 8.06583\n",
-      "differential_evolution step 15: f(x)= 8.06583\n",
-      "differential_evolution step 16: f(x)= 8.06543\n",
-      "differential_evolution step 17: f(x)= 8.06543\n",
-      "differential_evolution step 18: f(x)= 8.0514\n",
-      "differential_evolution step 19: f(x)= 8.04614\n",
-      "differential_evolution step 20: f(x)= 8.04614\n",
-      "differential_evolution step 21: f(x)= 8.0449\n",
-      "differential_evolution step 22: f(x)= 8.0449\n",
-      "differential_evolution step 23: f(x)= 8.0449\n",
-      "differential_evolution step 24: f(x)= 8.0449\n",
-      "differential_evolution step 25: f(x)= 8.0449\n",
-      "differential_evolution step 26: f(x)= 8.0449\n",
+      "differential_evolution step 1: f(x)= 9.91727\n",
+      "differential_evolution step 2: f(x)= 9.06802\n",
+      "differential_evolution step 3: f(x)= 9.06802\n",
+      "differential_evolution step 4: f(x)= 9.06802\n",
+      "differential_evolution step 5: f(x)= 8.4285\n",
+      "differential_evolution step 6: f(x)= 8.05457\n",
+      "differential_evolution step 7: f(x)= 8.05457\n",
+      "differential_evolution step 8: f(x)= 8.05457\n",
+      "differential_evolution step 9: f(x)= 8.05457\n",
+      "differential_evolution step 10: f(x)= 8.05457\n",
+      "differential_evolution step 11: f(x)= 8.05457\n",
+      "differential_evolution step 12: f(x)= 8.05457\n",
+      "differential_evolution step 13: f(x)= 8.05457\n",
+      "differential_evolution step 14: f(x)= 8.05457\n",
+      "differential_evolution step 15: f(x)= 8.05171\n",
+      "differential_evolution step 16: f(x)= 8.05171\n",
+      "differential_evolution step 17: f(x)= 8.04794\n",
+      "differential_evolution step 18: f(x)= 8.04555\n",
+      "differential_evolution step 19: f(x)= 8.04489\n",
+      "differential_evolution step 20: f(x)= 8.04489\n",
+      "differential_evolution step 21: f(x)= 8.04489\n",
+      "differential_evolution step 22: f(x)= 8.04489\n",
+      "differential_evolution step 23: f(x)= 8.04489\n",
+      "differential_evolution step 24: f(x)= 8.04489\n",
+      "differential_evolution step 25: f(x)= 8.04489\n",
+      "differential_evolution step 26: f(x)= 8.04489\n",
       "differential_evolution step 27: f(x)= 8.04489\n",
-      "differential_evolution step 28: f(x)= 8.04489\n",
-      "differential_evolution step 29: f(x)= 8.04489\n",
-      "differential_evolution step 30: f(x)= 8.04489\n",
-      "differential_evolution step 31: f(x)= 8.04489\n",
-      "hyperparameters:  [1.43486911 0.11327405]\n",
+      "hyperparameters:  [1.4350853  0.12085441]\n",
       "prediction :  [2.74]\n",
-      "uncertainty:  [1.19786022]\n"
+      "uncertainty:  [1.19795046]\n"
      ]
     }
    ],
@@ -124,24 +120,17 @@
     "    return hps[0] * obj.matern_kernel_diff1(d,hps[1])\n",
     "    \n",
     "\n",
-    "bounds = np.array([[0.001,100.],[0.001,100]])\n",
     "\n",
-    "my_gp = GP(1, x_data,y_data,init_hyperparameters=np.ones((2)), \n",
-    "           hyperparameter_bounds=bounds, gp_kernel_function=kernel, info = True)\n",
-    "my_gp.train()\n",
+    "\n",
+    "my_gp = GP(1, x_data,y_data,init_hyperparameters=np.ones((2)), gp_kernel_function=kernel, info = True)\n",
+    "\n",
+    "bounds = np.array([[0.001,100.],[0.001,100]])\n",
+    "my_gp.train(hyperparameter_bounds=bounds)\n",
     "\n",
     "print(\"hyperparameters: \", my_gp.hyperparameters)\n",
     "print(\"prediction : \",my_gp.posterior_mean({'full'})[\"f(x)\"])\n",
     "print(\"uncertainty: \",np.sqrt(my_gp.posterior_covariance({'full'})[\"v(x)\"]))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e409d084",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/SingleTaskTest.ipynb b/examples/SingleTaskTest.ipynb
diff --git a/examples/gp2ScaleTest.ipynb b/examples/gp2ScaleTest.ipynb
diff --git a/fvgp/gp.py b/fvgp/gp.py
@@ -39,8 +39,9 @@ class GP():
     ----------
     input_space_dim : int
         Dimensionality of the input space (D). If the input is non-Euclidean, the input dimensionality will be ignored.
-    x_data : np.ndarray
-        The input point positions. Shape (V x D), where D is the `input_space_dim`.
+    x_data : np.ndarray or set or list
+        The input point positions. Shape (V x D), where D is the `input_space_dim`. If dealing with non-Euclidean inputs
+        x_data should be a set or iterable, not a numpy array.
     y_data : np.ndarray
         The values of the data points. Shape (V,1) or (V).
     init_hyperparameters : np.ndarray, optional
@@ -239,20 +240,21 @@ def __init__(
         else:
             if not callable(gp_kernel_function):
                 raise Exception(
-                    "For GPs on non-Eucledian input spaces you need a user-defined kernel, hyperparameters, and hyperparameter_bounds.")
+                    "For GPs on non-Eucledian input spaces you need a user-defined kernel, hyperparameters, \
+                    and hyperparameter_bounds.")
             input_space_dim = 1
             self.non_Euclidean = True
-
-        if self.non_Euclidean and (init_hyperparameters is None or hyperparameter_bounds is None):
+        if self.non_Euclidean and init_hyperparameters is None:
             raise Exception(
-                "You are running fvGP on non-Euclidean inputs. Please provide initial hyperparameters and hyperparameter bounds.")
+                "You are running fvGP on non-Euclidean inputs. Please provide initial hyperparameters.")
         if np.ndim(y_data) == 2: y_data = y_data[:, 0]
         if compute_device == 'gpu':
             try:
                 import torch
             except:
                 raise Exception(
-                    "You have specified the 'gpu' as your compute device. You need to install pytorch manually for this to work.")
+                    "You have specified the 'gpu' as your compute device. You need to install pytorch\
+                     manually for this to work.")
         self.normalize_y = normalize_y
         self.input_space_dim = input_space_dim
         self.x_data = x_data
@@ -267,29 +269,34 @@ def __init__(
         self.KVinv = None
         self.mcmc_info = None
         self.gp2Scale = gp2Scale
+        self.hyperparameter_bounds = hyperparameter_bounds
 
         if (callable(gp_kernel_function) or callable(gp_mean_function) or callable(
             gp_noise_function)) and init_hyperparameters is None:
             warnings.warn(
-                "You have provided callables for kernel, mean, or noise functions but no initial hyperparameters.",
+                "You have provided callables for kernel, mean, or noise \
+                functions but no initial hyperparameters.",
                 stacklevel=2)
-            warnings.warn("It is likley they have to be defined for a success initialization.", stacklevel=2)
+            warnings.warn("It is likely they have to be defined for a success initialization.", stacklevel=2)
 
         ##########################################
         #######prepare hyper parameters###########
         ##########################################
-        if hyperparameter_bounds is None and not self.non_Euclidean:
-            if callable(gp_kernel_function):
-                raise Exception(
-                    "You have not provided hyperparameter bounds but a kernel function. Your kernel needs user defined hyperparameters and bounds.")
-            hyperparameter_bounds = np.zeros((input_space_dim + 1, 2))
-            hyperparameter_bounds[0] = np.array([np.var(y_data) / 100., np.var(y_data) * 10.])
-            for i in range(input_space_dim):
-                range_xi = np.max(x_data[:, i]) - np.min(x_data[:, i])
-                hyperparameter_bounds[i + 1] = np.array([range_xi / 100., range_xi * 10.])
-        self.hyperparameter_bounds = hyperparameter_bounds
+        if self.hyperparameter_bounds is None:
+            if self.non_Euclidean:
+                if callable(gp_kernel_function):
+                    warnings.warn("You have not provided hyperparameter bounds but a kernel function. \n \
+                    Make sure you provide them tot he training.")
+            else:
+                hyperparameter_bounds = np.zeros((input_space_dim + 1, 2))
+                hyperparameter_bounds[0] = np.array([np.var(y_data) / 100., np.var(y_data) * 10.])
+                for i in range(input_space_dim):
+                    range_xi = np.max(x_data[:, i]) - np.min(x_data[:, i])
+                    hyperparameter_bounds[i + 1] = np.array([range_xi / 100., range_xi * 10.])
+                self.hyperparameter_bounds = hyperparameter_bounds
 
         if init_hyperparameters is None:
+            if self.hyperparameter_bounds is None: raise Exception("hyperparameter_bounds non available.")
             init_hyperparameters = np.random.uniform(low=self.hyperparameter_bounds[:, 0],
                                                      high=self.hyperparameter_bounds[:, 1],
                                                      size=len(self.hyperparameter_bounds))
@@ -299,12 +306,14 @@ def __init__(
         ##########################################
         if self.sparse_mode and self.store_inv:
             warnings.warn(
-                "sparse_mode and store_inv enabled but they should not be used together. I'll set store_inv = False.",
+                "sparse_mode and store_inv enabled but they should not be used together. \
+                I'll set store_inv = False.",
                 stacklevel=2)
             self.store_inv = False
         if self.sparse_mode and not callable(gp_kernel_function):
             warnings.warn("You have chosen to activate sparse mode. Great! \n \
-                        But you have not supplied a kernel that is compactly supported. \n I will use an anisotropic Wendland kernel for now.",
+                        But you have not supplied a kernel that is compactly supported. \n I will use an \
+                        anisotropic Wendland kernel for now.",
                           stacklevel=2)
             gp_kernel_function = self.wendland_anisotropic
 
@@ -314,7 +323,8 @@ def __init__(
         if gp2Scale:
             if gp2Scale_dask_client is None:
                 gp2Scale_dask_client = Client()
-                warnings.warn("gp2Scale needs a 'gp2Scale_dask_client'. Set to distributed.Client().", stacklevel=2)
+                warnings.warn("gp2Scale needs a 'gp2Scale_dask_client'. \
+                Set to distributed.Client().", stacklevel=2)
             self.gp2Scale_dask_client = gp2Scale_dask_client
 
             if not callable(gp_kernel_function):