refactor: moved prediction into base class

chemfusekit/__base.py

@@ -41,3 +41,12 @@ def export_model(self, export_path: str):
         else:
             raise RuntimeError("You haven't trained the model yet! You cannot export it now.")
 
+    def predict(self, x_data: pd.DataFrame):
+        '''Performs prediction once the model is trained.'''
+        if x_data is None:
+            raise TypeError(f"X data for {self.__class__.__name__} prediction must be non-empty.")
+        if self.model is None:
+            raise RuntimeError(f"The {self.__class__.__name__} model is not trained yet!")
+
+        y_pred = self.model.predict(x_data)
+        return y_pred

chemfusekit/knn.py

@@ -79,13 +79,3 @@ def knn(self):
                 algorithm=self.settings.algorithm
             )
             run_split_test(self.data.x_data, self.data.y, knn_split)
-
-    def predict(self, x_data: pd.DataFrame):
-        '''Performs kNN prediction once the model is trained.'''
-        if x_data is None:
-            raise TypeError("X data for kNN prediction must be non-empty.")
-        if self.model is None:
-            raise RuntimeError("The kNN model is not trained yet!")
-
-        y_pred = self.model.predict(x_data)
-        return y_pred

chemfusekit/lda.py

@@ -101,18 +101,8 @@ def lda(self):
         # Run split tests if required by the user
         if self.settings.test_split:
             run_split_test(
-                (scores.drop('Substance', axis=1).values),
+                scores.drop('Substance', axis=1).values,
                 self.y,
                 LD(n_components=self.settings.components),
                 mode=self.settings.output
             )
-
-    def predict(self, x_data: pd.DataFrame):
-        '''Performs LDA prediction once the model is trained.'''
-        if x_data is None:
-            raise TypeError("X data for LDA prediction must be non-empty.")
-        if self.model is None:
-            raise RuntimeError("The LDA model is not trained yet!")
-
-        y_pred = self.model.predict(x_data)
-        return y_pred

chemfusekit/lr.py

@@ -115,10 +115,7 @@ def lr(self):
 
     def predict(self, x_sample: pd.DataFrame):
         '''Performs LR prediction once the model is trained.'''
-        if self.model is None:
-            raise RuntimeError("The LR model is not trained yet!")
-
-        prediction = self.model.predict(x_sample)
+        prediction = super().predict(x_sample)
         probabilities = self.model.predict_proba(x_sample)
 
         classes = self.model.classes_.reshape((self.model.classes_.shape[0], ))

chemfusekit/plsda.py

@@ -133,13 +133,3 @@ def plsda(self):
             x = self.data.x_data
             y = self.data.x_train.Substance.astype('category').cat.codes
             run_split_test(x, y, PLSR(self.settings.n_components), mode=self.settings.output)
-
-    def predict(self, x_data: pd.DataFrame):
-        '''Performs PLSDA prediction once the model is trained.'''
-        if x_data is None:
-            raise TypeError("X data for PLSDA prediction must be non-empty.")
-        if self.model is None:
-            raise RuntimeError("The PLSDA model is not trained yet!")
-
-        y_pred = self.model.predict(x_data)
-        return y_pred

chemfusekit/svm.py

@@ -60,12 +60,3 @@ def svm(self):
                 model=SVC(kernel=self.settings.kernel),
                 mode=self.settings.output
             )
-
-    def predict(self, x_data: pd.DataFrame):
-        '''Performs SVM prediction once the model is trained'''
-        if self.model is None:
-            raise RuntimeError("The model hasn't been trained yet!")
-        if x_data is None:
-            raise TypeError("X data for prediction cannot be empty.")
-
-        return self.model.predict(x_data)

docs/cookbook/structure.md

@@ -98,6 +98,7 @@ classDiagram
         __init__(settings, data)
         import_model(import_path: str)
         export_model(export_path: str)
+        predict(x_data: pd.DataFrame)
     }
 
     class KNN {

docs/cookbook_versioned_docs/version-2.0.0/structure.md

@@ -98,6 +98,7 @@ classDiagram
         __init__(settings, data)
         import_model(import_path: str)
         export_model(export_path: str)
+        predict(x_data: pd.DataFrame)
     }
 
     class KNN {

docs/docs/base/baseclassifier.md

@@ -31,3 +31,7 @@ BaseClassifier(settings: BaseSettings, data: BaseDataModel)
 - `export_model(export_path: str)`: exports a model to file
   - *raises*:
     - `RuntimeError("You haven't trained the model yet! You cannot export it now.")` when trying to export an untrained model
+- `predict(x_data: pd.DataFrame)`: performs prediction through the `model`
+  - *raises*:
+    - `TypeError("X data for prediction must be non-empty.")` on empty `x_data`
+    - `RuntimeError("The model is not trained yet!")` when run with an untrained `model`

docs/versioned_docs/version-2.0.0/base/baseclassifier.md

@@ -31,3 +31,7 @@ BaseClassifier(settings: BaseSettings, data: BaseDataModel)
 - `export_model(export_path: str)`: exports a model to file
   - *raises*:
     - `RuntimeError("You haven't trained the model yet! You cannot export it now.")` when trying to export an untrained model
+- `predict(x_data: pd.DataFrame)`: performs prediction through the `model`
+  - *raises*:
+    - `TypeError("X data for prediction must be non-empty.")` on empty `x_data`
+    - `RuntimeError("The model is not trained yet!")` when run with an untrained `model`