Repository: Oloren-AI/olorenchemengine (language: python)

Issue:
How to save error model
When loading a saved model with error model, the error model is always getting rebuilt and refitted. How can one save the model along with the trained error model?

This is how I save and load the model:

```
model.create_error_model(
        error_model=oce.BootstrapEnsemble(), 
        n_ensembles=5,
        bootstrap_size=0.8,
        X_train=dataset.entire_dataset[0], 
        y_train=dataset.entire_dataset[1]
    )

oce.save(model, 'test_model.oce')

model = oce.load('test_model.oce')
```


Discussion / hints:
(Re)building and (re)fitting the error model is near-deterministic because the corresponding residuals and scores are saved, so it is basically just redoing the curve-fit.

Adding this to the TODO:

-  saving the curve-fit parameters for error_models.
It's not only about the curve-fitting. In the example above which uses BootstrapEnsemble as the scoring model, it appears that the ensemble of models are getting retrained during rebuilding, which takes a long time and doesn't seem necessary. 

Fix patch:
diff --git a/examples/0_Minimal_Example.ipynb b/examples/0_Minimal_Example.ipynb
index d827807a..c1328d81 100644
--- a/examples/0_Minimal_Example.ipynb
+++ b/examples/0_Minimal_Example.ipynb
@@ -17,6 +17,17 @@
             "execution_count": 1,
             "metadata": {},
             "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-12-29 03:58:25.997255: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+                        "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+                        "2022-12-29 03:58:26.767565: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+                        "2022-12-29 03:58:26.767653: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+                        "2022-12-29 03:58:26.767660: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+                    ]
+                },
                 {
                     "name": "stdout",
                     "output_type": "stream",
@@ -46,9 +57,9 @@
                     "name": "stderr",
                     "output_type": "stream",
                     "text": [
-                        "790it [00:02, 281.33it/s]\n",
-                        "790it [00:03, 262.17it/s]\n",
-                        "100%|██████████| 13/13 [00:00<00:00, 223.46it/s]\n"
+                        "790it [00:02, 289.55it/s]\n",
+                        "790it [00:02, 272.57it/s]\n",
+                        "100%|██████████| 13/13 [00:00<00:00, 238.13it/s]\n"
                     ]
                 }
             ],
@@ -71,20 +82,20 @@
                     "name": "stderr",
                     "output_type": "stream",
                     "text": [
-                        "100it [00:00, 288.72it/s]\n",
-                        "100%|██████████| 2/2 [00:00<00:00, 225.05it/s]\n"
+                        "100it [00:00, 301.36it/s]\n",
+                        "100%|██████████| 2/2 [00:00<00:00, 238.63it/s]\n"
                     ]
                 },
                 {
                     "data": {
                         "text/plain": [
-                            "{'r2': 0.5439019628176405,\n",
-                            " 'Spearman': 0.7803955418791196,\n",
-                            " 'Explained Variance': 0.5972074636942537,\n",
-                            " 'Max Error': 2.635779283747219,\n",
-                            " 'Mean Absolute Error': 0.4983299807077439,\n",
-                            " 'Mean Squared Error': 0.5554532659911895,\n",
-                            " 'Root Mean Squared Error': 0.7452873714153417}"
+                            "{'r2': 0.5452633756506161,\n",
+                            " 'Spearman': 0.7784959345185842,\n",
+                            " 'Explained Variance': 0.5949236200632468,\n",
+                            " 'Max Error': 2.5975678196477157,\n",
+                            " 'Mean Absolute Error': 0.5007694841205477,\n",
+                            " 'Mean Squared Error': 0.5537952864718954,\n",
+                            " 'Root Mean Squared Error': 0.7441742312603248}"
                         ]
                     },
                     "execution_count": 3,
@@ -105,83 +116,72 @@
                     "name": "stderr",
                     "output_type": "stream",
                     "text": [
-                        "790it [00:02, 279.69it/s]\n",
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                     ]
                 },
                 {
                     "data": {
                         "text/plain": [
-                            "[0.29389526646917274,\n",
-                            " 0.4778417740859384,\n",
-                            " 0.4091443567016304,\n",
-                            " 0.40356587767026075,\n",
-                            " 0.347584515398879]"
+                            "[0.46459183983941876,\n",
+                            " 0.4263721628711872,\n",
+                            " 0.40946986973384736,\n",
+                            " 0.4449925997238581,\n",
+                            " 0.4749022516025433]"
                         ]
                     },
                     "execution_count": 4,
                     "metadata": {},
                     "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
                 }
             ],
             "source": [
-                "model.fit_cv(*dataset.train_dataset, error_model = oce.TrainDistKNN())"
+                "model.fit_cv(*dataset.train_dataset, error_model = oce.KernelRegressionError(kernel = \"sdc\", ci=0.8))"
             ]
         },
         {
@@ -195,18 +195,18 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 7,
+            "execution_count": 6,
             "metadata": {},
             "outputs": [
                 {
                     "name": "stderr",
                     "output_type": "stream",
                     "text": [
-                        "100it [00:00, 287.36it/s]\n",
-                        "100%|██████████| 2/2 [00:00<00:00, 229.07it/s]\n",
-                        "100it [00:00, 287.17it/s]\n",
-                        "100%|██████████| 2/2 [00:00<00:00, 225.22it/s]\n",
-                        "100%|██████████| 100/100 [00:00<00:00, 608.78it/s]\n"
+                        "100it [00:00, 248.64it/s]\n",
+                        "100%|██████████| 2/2 [00:00<00:00, 168.18it/s]\n",
+                        "100it [00:00, 255.07it/s]\n",
+                        "100%|██████████| 2/2 [00:00<00:00, 236.63it/s]\n",
+                        "100it [00:00, 515.84it/s]\n"
                     ]
                 }
             ],
@@ -226,7 +226,7 @@
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                             "            width=\"800\"\n",
                             "            height=\"600\"\n",
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                             "            frameborder=\"0\"\n",
                             "            allowfullscreen\n",
                             "            \n",
@@ -234,7 +234,7 @@
                             "        "
                         ],
                         "text/plain": [
-                            "<IPython.lib.display.IFrame at 0x7fdc52b2f940>"
+                            "<IPython.lib.display.IFrame at 0x7f693e680190>"
                         ]
                     },
                     "execution_count": 7,
@@ -255,8 +255,8 @@
                     "name": "stderr",
                     "output_type": "stream",
                     "text": [
-                        "100it [00:00, 298.72it/s]\n",
-                        "100%|██████████| 2/2 [00:00<00:00, 22.79it/s]\n"
+                        "100it [00:00, 249.96it/s]\n",
+                        "100%|██████████| 2/2 [00:00<00:00, 195.20it/s]\n"
                     ]
                 },
                 {
@@ -266,7 +266,7 @@
                             "        <iframe\n",
                             "            width=\"800\"\n",
                             "            height=\"600\"\n",
-                            "            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                             "            frameborder=\"0\"\n",
                             "            allowfullscreen\n",
                             "            \n",
@@ -274,7 +274,7 @@
                             "        "
                         ],
                         "text/plain": [
-                            "<IPython.lib.display.IFrame at 0x7fdc52cbde80>"
+                            "<IPython.lib.display.IFrame at 0x7f69380ca5b0>"
                         ]
                     },
                     "execution_count": 8,
@@ -286,13 +286,20 @@
                 "vis = oce.VisualizeModelSim(dataset, model, eval_set=\"test\")\n",
                 "vis.render_ipynb()"
             ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": []
         }
     ],
     "metadata": {
         "kernelspec": {
-            "display_name": "base",
+            "display_name": "oce",
             "language": "python",
-            "name": "base"
+            "name": "python3"
         },
         "language_info": {
             "codemirror_mode": {
@@ -304,12 +311,12 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.8.13"
+            "version": "3.9.15"
         },
         "orig_nbformat": 4,
         "vscode": {
             "interpreter": {
-                "hash": "141e64549bfdb076333d9365c01d8c02f4950d2f36a9d80a48f71e1d8ed13112"
+                "hash": "106d027d5062398cac873161aed9d16750cafe87a273851ecd62758bbea0eb2e"
             }
         }
     },
diff --git a/src/olorenchemengine/base_class.py b/src/olorenchemengine/base_class.py
index 2e01f954..b34444b7 100644
--- a/src/olorenchemengine/base_class.py
+++ b/src/olorenchemengine/base_class.py
@@ -1076,8 +1076,8 @@ def _fit_regression(self, quantile, mode: str = None):
             elif self.method == "roll":
                 window = min(len(self.scores), self.window)
                 idxs = np.argsort(self.scores)
-                X = list(pd.Series(self.scores[idxs]).rolling(window).mean())[window-1:]
-                y = list(pd.Series(self.residuals[idxs]).rolling(window).quantile(quantile))[window-1:]
+                X = pd.Series(self.scores[idxs]).rolling(window).mean()[window-1:]
+                y = pd.Series(self.residuals[idxs]).rolling(window).quantile(quantile)[window-1:]
             else:
                 raise NameError("Method {} is not recognized. Valid inputs are \"bin\", \"qbin\", and \"roll\".".format(self.method))
         
@@ -1105,9 +1105,11 @@ def _fit_regression(self, quantile, mode: str = None):
                         opt_func = func
                         opt_params = params
                         min_mse = mse
+                        used_curvetype = reg
                 assert min_mse is not None, "Curve fit failed to find a suitable regression model."
             else:
                 opt_func = regression_functions[self.curvetype]
+                used_curvetype = self.curvetype
                 init = regression_init[self.curvetype](X, y)
                 try:
                     opt_params = curve_fit(opt_func, X, y, p0=init, maxfev = 500 * len(init))[0]
@@ -1116,13 +1118,13 @@ def _fit_regression(self, quantile, mode: str = None):
         
             reg = lambda x: np.maximum(np.minimum(opt_func(x, *opt_params), np.max(self.residuals)), np.min(self.residuals))
             
-            self._fit_regression_params[mode] = {"func": opt_func, "params": opt_params, "X": X, "y": y}
+            self._fit_regression_params[mode] = {"used_curvetype": used_curvetype, "params": opt_params, "X": X.tolist(), "y": y.tolist()}
             
             return reg, X, y
         else:
             params = self._fit_regression_params[mode]
             
-            return lambda x: np.maximum(np.minimum(params["func"](x, *params["params"]), np.max(self.residuals)), np.min(self.residuals)), params["X"], params["y"]
+            return lambda x: np.maximum(np.minimum(regression_functions[params["used_curvetype"]](x, *params["params"]), np.max(self.residuals)), np.min(self.residuals)), np.array(params["X"]), np.array(params["y"])
 
     @abstractmethod
     def calculate(
@@ -1184,6 +1186,8 @@ def _save(self) -> dict:
         if hasattr(self, "residuals"):
             d.update({"residuals": self.residuals})
             d.update({"scores": self.scores})
+            
+        d.update({"fit_regression_params": self._fit_regression_params})
         return d
 
     def _load(self, d) -> None:
@@ -1194,11 +1198,9 @@ def _load(self, d) -> None:
             self.y_train = d["y_train"]
             self.y_pred_train = d["y_pred_train"]
             if hasattr(self, "model"):
-                print("Rebuilding error model...")
                 self._build()
         if "residuals" in d.keys():
             self.residuals = d["residuals"]
             self.scores = d["scores"]
             if hasattr(self, "model"):
-                print("Refitting error model...")
                 self._fit(self.residuals, self.scores)
\ No newline at end of file
diff --git a/src/olorenchemengine/uncertainty.py b/src/olorenchemengine/uncertainty.py
index 5fd0613c..a78b97b4 100644
--- a/src/olorenchemengine/uncertainty.py
+++ b/src/olorenchemengine/uncertainty.py
@@ -257,13 +257,13 @@ class BaseEnsembleModel(BaseErrorModel):
     @log_arguments
     def __init__(self, ensemble_model = None, n_ensembles = 16, log=True, **kwargs):      
         self.ensemble_model = ensemble_model
+        self.ensembles = []
         self.n_ensembles = n_ensembles
         super().__init__(log=False, **kwargs)
 
     def _build(self, **kwargs):
         if self.ensemble_model is None:
             self.ensemble_model = self.model.copy()
-        self.ensembles = []
     
     def calculate(self, X, y_pred):
         predictions = np.stack([model.predict(X) for model in tqdm(self.ensembles)])
@@ -308,16 +308,19 @@ def __init__(self, ensemble_model = None, n_ensembles = 12, bootstrap_size = 0.2
 
     def _build(self, **kwargs):
         super()._build()
-
-        from sklearn.model_selection import train_test_split
         
-        for _ in tqdm(range(self.n_ensembles)):
-            X_train, X_test, y_train, y_test = train_test_split(
-                self.X_train, self.y_train, train_size=self.bootstrap_size
-            )
-            ensemble_model = self.ensemble_model.copy()
-            ensemble_model.fit(X_train, y_train)
-            self.ensembles.append(ensemble_model)
+        if len(self.ensembles) > 0:
+            return
+        else:
+            from sklearn.model_selection import train_test_split
+        
+            for _ in tqdm(range(self.n_ensembles)):
+                X_train, X_test, y_train, y_test = train_test_split(
+                    self.X_train, self.y_train, train_size=self.bootstrap_size
+                )
+                ensemble_model = self.ensemble_model.copy()
+                ensemble_model.fit(X_train, y_train)
+                self.ensembles.append(ensemble_model)
 
 class RandomForestEnsemble(BaseEnsembleModel):
     """Ensemble of random forests


Test patch:
diff --git a/tests/test_backward_compatibility.py b/tests/test_backward_compatibility.py
index 508ec5ce..e0d3a829 100644
--- a/tests/test_backward_compatibility.py
+++ b/tests/test_backward_compatibility.py
@@ -43,13 +43,6 @@ def test_reg_2():
     )
 
 
-def test_reg_3():
-    model = get_model_for_testing("reg model 3")
-    print(model.predict(test_molecules))
-    assert np.allclose(
-        model.predict(test_molecules), [0.5101875, 0.27005324], atol=1e-3
-    )
-
 def test_class_0():
     model = get_model_for_testing("class model 0")
     assert np.allclose(model.predict(test_molecules), [0.9907742, 0.9907742])
@@ -62,9 +55,4 @@ def test_class_1():
 
 def test_class_2():
     model = get_model_for_testing("class model 2")
-    assert np.allclose(model.predict(test_molecules), [1.0, 1.0])
-
-
-def test_class_3():
-    model = get_model_for_testing("class model 3")
-    assert np.allclose(model.predict(test_molecules), [0.580161, 0.56691015])
\ No newline at end of file
+    assert np.allclose(model.predict(test_molecules), [1.0, 1.0])
\ No newline at end of file
diff --git a/tests/test_tutorials.py b/tests/test_tutorials.py
index 713ec439..ec1a28a9 100644
--- a/tests/test_tutorials.py
+++ b/tests/test_tutorials.py
@@ -33,11 +33,19 @@ def test_0():
         + oce.CleanStructures()
         + oce.RandomSplit()
     )
-    model = oce.RandomForestModel(
-        oce.DescriptastorusDescriptor("morgan3counts"), n_estimators=1000
-    )
-    _ = model.fit_cv(*dataset.train_dataset, error_model=oce.TrainDistKNN())
-    oce.save(model, "vis-model.oce")
+    model = oce.BaseBoosting([
+        oce.RandomForestModel(oce.DescriptastorusDescriptor("morgan3counts"), n_estimators=1000),
+        oce.RandomForestModel(oce.OlorenCheckpoint("default"), n_estimators=1000),
+        oce.ChemPropModel(epochs=20, batch_size=64)
+    ])
+
+    model.fit(*dataset.train_dataset)
+    model.test(*dataset.test_dataset)
+    model.fit_cv(*dataset.train_dataset, error_model = oce.KernelRegressionError(kernel = "sdc", ci=0.8))
+    oce.save(model, "tmp.oce")
+    results = model.predict(dataset.test_dataset[0], return_ci = True, return_vis = True)
+    results["vis"].iloc[32].render_ipynb()
+
 
 @pytest.mark.timeout(300)
 def test_1d():