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feat: remove target_name parameter of predict #70

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merged 3 commits into from
Mar 24, 2023
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feat: remove target_name parameter of predict #70

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5191dd9
feat: remove `target_name` parameter of `predict`
lars-reimann Mar 24, 2023
7659ae7
style: fix linter errors
lars-reimann Mar 24, 2023
20cf977
style: apply automated linter fixes
lars-reimann Mar 24, 2023
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8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_ada_boost.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
5 changes: 1 addition & 4 deletions src/safeds/ml/classification/_classifier.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
from abc import ABC, abstractmethod
from typing import Optional

from safeds.data.tabular.containers import Table, TaggedTable

Expand All @@ -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
-------
Expand Down
8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_decision_tree.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_gradient_boosting_classification.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_k_nearest_neighbors.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_logistic_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/classification/_random_forest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_ada_boost.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_decision_tree.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_elastic_net_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_gradient_boosting_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_k_nearest_neighbors.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
8 changes: 2 additions & 6 deletions src/safeds/ml/regression/_lasso_regression.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from typing import Optional

# noinspection PyProtectedMember
import safeds.ml._util_sklearn
from safeds.data.tabular.containers import Table, TaggedTable
Expand Down Expand Up @@ -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
-------
Expand All @@ -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,
)
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