GPR models saved and pickled

Author: shmouses

2_Paper_Reproduction.ipynb

@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   },
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-   "execution_count": 214,
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    "outputs": [
     {
@@ -206,7 +206,7 @@
        "4  CS-NRCan-014_A6     1.5     6.0      1.0  66  0.655983  0.344017        EXP"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,7 +218,7 @@
   },
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-   "execution_count": 90,
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@@ -328,7 +328,7 @@
        "4         HY  "
       ]
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@@ -339,7 +339,7 @@
   },
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-   "execution_count": 109,
+   "execution_count": 6,
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    "outputs": [
     {
@@ -683,7 +683,7 @@
        "22  1.666667e-01  0.000000        RND  "
       ]
      },
-     "execution_count": 109,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -701,7 +701,7 @@
   },
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-   "execution_count": 216,
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@@ -1289,7 +1289,7 @@
        "23        RND  "
       ]
      },
-     "execution_count": 216,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
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   },
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-   "execution_count": 217,
+   "execution_count": 9,
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    "outputs": [
     {
@@ -1491,7 +1491,7 @@
        "[64 rows x 8 columns]"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
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   },
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   },
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   },
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@@ -1672,22 +1672,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "# define 3 models with with kernels with 3 different lengthscales\n",
-    "gpr_model_0 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=5)\n",
-    "gpr_model_1 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=5)\n",
-    "gpr_model_2 = GaussianProcessRegressor(kernel=Matern(length_scale= [1, 1,1], nu=1.5), alpha = 1e-10, n_restarts_optimizer=5)\n",
+    "gpr_model_0 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=10)\n",
+    "gpr_model_1 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=10)\n",
+    "gpr_model_2 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-10, n_restarts_optimizer=10)\n",
     "\n",
-    "gpr_model_0_1 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=5)\n",
-    "gpr_model_0_2 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=5)"
+    "gpr_model_0_1 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=10)\n",
+    "gpr_model_0_2 = GaussianProcessRegressor(kernel=Matern(length_scale= [1,1,1], nu=1.5), alpha = 1e-5, n_restarts_optimizer=10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1716,12 +1716,37 @@
     "print(gpr_model_0_2.kernel_)    "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving GPR models for each iteration of the active learning"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Pickle scalers\n",
+    "with open('models/feature_standard_scaler_AL_iteration_0.pkl', 'wb') as f:\n",
+    "    pickle.dump(scale_0, f)\n",
+    "with open('models/feature_standard_scaler_AL_iteration_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(scale_0_1, f)\n",
+    "with open('models/feature_standard_scaler_AL_iteration_2.pkl', 'wb') as f:\n",
+    "    pickle.dump(scale_0_2, f)\n",
+    "\n",
+    "# Pickle models\n",
+    "with open('models/GPR_model_AL_iteration_0.pkl', 'wb') as f:\n",
+    "    pickle.dump(gpr_model_0, f)\n",
+    "with open('models/GPR_model_AL_iteration_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(gpr_model_0_1, f)\n",
+    "with open('models/GPR_model_AL_iteration_2.pkl', 'wb') as f:\n",
+    "    pickle.dump(gpr_model_0_2, f)"
+   ]
   },
   {
    "cell_type": "markdown",