get mysql stored params first in cross validation

experimentation/Experiments.py

@@ -6,7 +6,7 @@ import warnings
 from sklearn.model_selection import GridSearchCV, cross_validate
 
 from . import Models
-from .Database import Hyperparameters, Outcomes
+from .Database import Hyperparameters, Outcomes, MySQL
 from .Sets import Datasets
 
 
@@ -20,12 +20,8 @@ class Experiment:
         kernel: str,
     ) -> None:
         self._random_state = random_state
-        self._model_name = model
+        self._set_model(model)
         self._set_of_files = set_of_files
-        self._type = getattr(
-            Models,
-            f"Model{model[0].upper() + model[1:]}",
-        )
         self._clf = self._type(random_state=self._random_state)
         self._host = host
         # used in gridsearch with ensembles to take best hyperparams of
@@ -36,15 +32,35 @@ class Experiment:
     def set_base_params(self, base_params: str) -> None:
         self._base_params = base_params
 
+    def _set_model(self, model_name: str) -> None:
+        self._model_name = model_name
+        self._type = getattr(
+            Models,
+            f"Model{model_name[0].upper() + model_name[1:]}",
+        )
+
     def cross_validation(self, dataset: str) -> None:
-        hyperparams = Hyperparameters(host=self._host, model=self._model_name)
-        try:
-            parameters, normalize, standardize = hyperparams.get_params(
-                dataset
-            )
-        except ValueError:
-            print(f"*** {dataset} not trained")
-            return
+        self._clf = self._type(random_state=self._random_state)
+        model = self._clf.get_model()
+        hyperparams = MySQL()
+        hyperparams.get_connection()
+        record = hyperparams.find_best(dataset, self._model_name)
+        hyperparams.close()
+        if record is None:
+            try:
+                hyperparams = Hyperparameters(
+                    host=self._host, model=self._model_name
+                )
+                parameters, normalize, standardize = hyperparams.get_params(
+                    dataset
+                )
+            except ValueError:
+                print(f"*** {dataset} not trained")
+                return
+        else:
+            normalize = record[6]
+            standardize = record[7]
+            parameters = record[8]
         datasets = Datasets(
             normalize=normalize,
             standardize=standardize,
@@ -54,7 +70,7 @@ class Experiment:
         X, y = datasets.load(dataset)
         # init cross validation object just in case consecutive experiments
         self._clf = self._type(random_state=self._random_state)
-        model = self._clf.get_model().set_params(**parameters)
+        model.set_params(**parameters)
         self._num_warnings = 0
         warnings.warn = self._warn
         with warnings.catch_warnings():