From 1f2287e7f975980fd44e4bbf8ffd5d1aae07152d Mon Sep 17 00:00:00 2001
From: Bogdan Cebere <bogdan.cebere@gmail.com>
Date: Mon, 28 Nov 2022 17:33:59 +0000
Subject: [PATCH] revert 1

---
 .../plugins/ensemble/classifiers.py           | 46 ++-----------------
 1 file changed, 3 insertions(+), 43 deletions(-)

diff --git a/src/autoprognosis/plugins/ensemble/classifiers.py b/src/autoprognosis/plugins/ensemble/classifiers.py
index 45261fd9..dd7f4730 100644
--- a/src/autoprognosis/plugins/ensemble/classifiers.py
+++ b/src/autoprognosis/plugins/ensemble/classifiers.py
@@ -91,22 +91,12 @@ def __init__(
         self.explanations_nepoch = explanations_nepoch
         self.explainers = explainers
 
-        self._fitted = True
-        for model in models:
-            self._fitted |= model.is_fitted()
-
         for idx, weight in enumerate(weights):
             if weight == 0:
                 continue
             self.models.append(models[idx])
             self.weights.append(weights[idx])
 
-    def is_fitted(self) -> bool:
-        try:
-            return self._fitted
-        except BaseException:
-            return True  # backwards compatible
-
     def fit(self, X: pd.DataFrame, Y: pd.DataFrame) -> "WeightedEnsemble":
         def fit_model(k: int) -> Any:
             return self.models[k].fit(X, Y)
@@ -131,13 +121,9 @@ def fit_model(k: int) -> Any:
             )
             self.explainers[exp] = exp_model
 
-        self._fitted = True
         return self
 
     def predict_proba(self, X: pd.DataFrame, *args: Any) -> pd.DataFrame:
-        if not self.is_fitted():
-            raise RuntimeError("Fit the model first")
-
         preds_ = []
         for k in range(len(self.models)):
             preds_.append(self.models[k].predict_proba(X, *args) * self.weights[k])
@@ -322,7 +308,7 @@ def __init__(
         self,
         models: List[PipelineMeta],
         meta_model: PipelineMeta = Pipeline(
-            ["imputer.default.ice", "prediction.classifier.logistic_regression"]
+            ["prediction.classifier.logistic_regression"]
         )(output="numpy"),
         clf: Union[None, Stacking] = None,
         explainer_plugins: list = [],
@@ -337,10 +323,6 @@ def __init__(
         self.explainers: Optional[dict]
         self.explanations_nepoch = explanations_nepoch
 
-        self._fitted = True
-        for model in models:
-            self._fitted |= model.is_fitted()
-
         for model in self.models:
             model.change_output("numpy")
 
@@ -353,12 +335,6 @@ def __init__(
                 use_proba=True,
             )
 
-    def is_fitted(self) -> bool:
-        try:
-            return self._fitted
-        except BaseException:
-            return True  # backwards compatible
-
     def fit(self, X: pd.DataFrame, Y: pd.DataFrame) -> "StackingEnsemble":
         self.clf.fit(X, Y)
 
@@ -373,13 +349,10 @@ def fit(self, X: pd.DataFrame, Y: pd.DataFrame) -> "StackingEnsemble":
                 n_epoch=self.explanations_nepoch,
                 prefit=True,
             )
-        self._fitted = True
+
         return self
 
     def predict_proba(self, X: pd.DataFrame, *args: Any) -> pd.DataFrame:
-        if not self.is_fitted():
-            raise RuntimeError("Fit the model first")
-
         return pd.DataFrame(self.clf.predict_proba(X))
 
     def explain(self, X: pd.DataFrame, *args: Any) -> pd.DataFrame:
@@ -455,21 +428,11 @@ def __init__(
         self.explainers: Optional[dict]
         self.explanations_nepoch = explanations_nepoch
 
-        self._fitted = True
-        for model in models:
-            self._fitted |= model.is_fitted()
-
         if clf:
             self.clf = clf
         else:
             self.clf = SimpleClassifierAggregator(models, method=method)
 
-    def is_fitted(self) -> bool:
-        try:
-            return self._fitted
-        except BaseException:
-            return True  # backwards compatible
-
     def fit(self, X: pd.DataFrame, Y: pd.DataFrame) -> "AggregatingEnsemble":
         Y = pd.DataFrame(Y).values.ravel()
 
@@ -486,13 +449,10 @@ def fit(self, X: pd.DataFrame, Y: pd.DataFrame) -> "AggregatingEnsemble":
                 n_epoch=self.explanations_nepoch,
                 prefit=True,
             )
-        self._fitted = True
+
         return self
 
     def predict_proba(self, X: pd.DataFrame, *args: Any) -> pd.DataFrame:
-        if not self.is_fitted():
-            raise RuntimeError("Fit the model first")
-
         return pd.DataFrame(self.clf.predict_proba(X))
 
     def explain(self, X: pd.DataFrame, *args: Any) -> pd.DataFrame: