feat: remove target_name parameter of predict (#70)

### Summary of Changes This can also be accomplished by renaming columns in the returned table. If the input table already contains the column we want to predict, it's an indicator for #9, so we shouldn't just let people change the name here. --------- Co-authored-by: lars-reimann <[email protected]>
Safe-DS · Mar 24, 2023 · b513454 · b513454
1 parent cfbcd64
commit b513454
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Showing 17 changed files with 32 additions and 98 deletions.
diff --git a/src/safeds/ml/classification/_ada_boost.py b/src/safeds/ml/classification/_ada_boost.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -37,16 +35,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
             self._classification, tagged_table
         )
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -61,5 +57,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/classification/_classifier.py b/src/safeds/ml/classification/_classifier.py
@@ -1,5 +1,4 @@
 from abc import ABC, abstractmethod
-from typing import Optional
 
 from safeds.data.tabular.containers import Table, TaggedTable
 
@@ -22,16 +21,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
 
     @abstractmethod
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------

diff --git a/src/safeds/ml/classification/_decision_tree.py b/src/safeds/ml/classification/_decision_tree.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -37,16 +35,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
             self._classification, tagged_table
         )
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -61,5 +57,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/classification/_gradient_boosting_classification.py b/src/safeds/ml/classification/_gradient_boosting_classification.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -38,16 +36,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         )
 
     # noinspection PyProtectedMember
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -62,5 +58,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/classification/_k_nearest_neighbors.py b/src/safeds/ml/classification/_k_nearest_neighbors.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -41,16 +39,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
             self._classification, tagged_table
         )
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -65,5 +61,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/classification/_logistic_regression.py b/src/safeds/ml/classification/_logistic_regression.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -37,16 +35,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
             self._classification, tagged_table
         )
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -61,5 +57,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/classification/_random_forest.py b/src/safeds/ml/classification/_random_forest.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -36,16 +34,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
             self._classification, tagged_table
         )
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -60,5 +56,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_ada_boost.py b/src/safeds/ml/regression/_ada_boost.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -35,16 +33,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -59,5 +55,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_decision_tree.py b/src/safeds/ml/regression/_decision_tree.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -35,16 +33,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name:  Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -59,5 +55,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_elastic_net_regression.py b/src/safeds/ml/regression/_elastic_net_regression.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -35,16 +33,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name: Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -59,5 +55,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_gradient_boosting_regression.py b/src/safeds/ml/regression/_gradient_boosting_regression.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -37,16 +35,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
     # noinspection PyProtectedMember
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name : Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -61,5 +57,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_k_nearest_neighbors.py b/src/safeds/ml/regression/_k_nearest_neighbors.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -39,16 +37,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name: Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -63,5 +59,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )
diff --git a/src/safeds/ml/regression/_lasso_regression.py b/src/safeds/ml/regression/_lasso_regression.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 # noinspection PyProtectedMember
 import safeds.ml._util_sklearn
 from safeds.data.tabular.containers import Table, TaggedTable
@@ -35,16 +33,14 @@ def fit(self, tagged_table: TaggedTable) -> None:
         """
         self.target_name = safeds.ml._util_sklearn.fit(self._regression, tagged_table)
 
-    def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
+    def predict(self, dataset: Table) -> Table:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
 
         Parameters
         ----------
         dataset : Table
             The dataset containing the feature vectors.
-        target_name: Optional[str]
-            The name of the target vector. The name of the target column inferred from fit is used by default.
 
         Returns
         -------
@@ -59,5 +55,5 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
-            target_name if target_name is not None else self.target_name,
+            self.target_name,
         )