Bases: InvertibleTableTransformer
The StandardScaler transforms column values to a range by removing the mean and scaling to unit variance.
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| class StandardScaler(InvertibleTableTransformer):
"""The StandardScaler transforms column values to a range by removing the mean and scaling to unit variance."""
def __init__(self) -> None:
self._column_names: list[str] | None = None
self._wrapped_transformer: sk_StandardScaler | None = None
def fit(self, table: Table, column_names: list[str] | None) -> StandardScaler:
"""
Learn a transformation for a set of columns in a table.
This transformer is not modified.
Parameters
----------
table : Table
The table used to fit the transformer.
column_names : list[str] | None
The list of columns from the table used to fit the transformer. If `None`, all columns are used.
Returns
-------
fitted_transformer : TableTransformer
The fitted transformer.
Raises
------
UnknownColumnNameError
If column_names contain a column name that is missing in the table.
NonNumericColumnError
If at least one of the specified columns in the table contains non-numerical data.
ValueError
If the table contains 0 rows.
"""
if column_names is None:
column_names = table.column_names
else:
missing_columns = sorted(set(column_names) - set(table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot be fitted because the table contains 0 rows")
if (
table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns
< table.keep_only_columns(column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(table.keep_only_columns(column_names).column_names)
- set(
table.keep_only_columns(column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
wrapped_transformer = sk_StandardScaler()
wrapped_transformer.fit(table._data[column_names])
result = StandardScaler()
result._wrapped_transformer = wrapped_transformer
result._column_names = column_names
return result
def transform(self, table: Table) -> Table:
"""
Apply the learned transformation to a table.
The table is not modified.
Parameters
----------
table : Table
The table to which the learned transformation is applied.
Returns
-------
transformed_table : Table
The transformed table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
UnknownColumnNameError
If the input table does not contain all columns used to fit the transformer.
NonNumericColumnError
If at least one of the columns in the input table that is used to fit contains non-numerical data.
ValueError
If the table contains 0 rows.
"""
# Transformer has not been fitted yet
if self._wrapped_transformer is None or self._column_names is None:
raise TransformerNotFittedError
# Input table does not contain all columns used to fit the transformer
missing_columns = sorted(set(self._column_names) - set(table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows")
if (
table.keep_only_columns(self._column_names).remove_columns_with_non_numerical_values().number_of_columns
< table.keep_only_columns(self._column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(table.keep_only_columns(self._column_names).column_names)
- set(
table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
data = table._data.reset_index(drop=True)
data.columns = table.column_names
data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
return Table._from_pandas_dataframe(data)
def inverse_transform(self, transformed_table: Table) -> Table:
"""
Undo the learned transformation.
The table is not modified.
Parameters
----------
transformed_table : Table
The table to be transformed back to the original version.
Returns
-------
table : Table
The original table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
UnknownColumnNameError
If the input table does not contain all columns used to fit the transformer.
NonNumericColumnError
If the transformed columns of the input table contain non-numerical data.
ValueError
If the table contains 0 rows.
"""
# Transformer has not been fitted yet
if self._wrapped_transformer is None or self._column_names is None:
raise TransformerNotFittedError
missing_columns = sorted(set(self._column_names) - set(transformed_table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if transformed_table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows")
if (
transformed_table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.number_of_columns
< transformed_table.keep_only_columns(self._column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(transformed_table.keep_only_columns(self._column_names).column_names)
- set(
transformed_table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
data = transformed_table._data.reset_index(drop=True)
data.columns = transformed_table.column_names
data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
return Table._from_pandas_dataframe(data)
def is_fitted(self) -> bool:
"""
Check if the transformer is fitted.
Returns
-------
is_fitted : bool
Whether the transformer is fitted.
"""
return self._wrapped_transformer is not None
def get_names_of_added_columns(self) -> list[str]:
"""
Get the names of all new columns that have been added by the StandardScaler.
Returns
-------
added_columns : list[str]
A list of names of the added columns, ordered as they will appear in the table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if not self.is_fitted():
raise TransformerNotFittedError
return []
# (Must implement abstract method, cannot instantiate class otherwise.)
def get_names_of_changed_columns(self) -> list[str]:
"""
Get the names of all columns that may have been changed by the StandardScaler.
Returns
-------
changed_columns : list[str]
The list of (potentially) changed column names, as passed to fit.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if self._column_names is None:
raise TransformerNotFittedError
return self._column_names
def get_names_of_removed_columns(self) -> list[str]:
"""
Get the names of all columns that have been removed by the StandardScaler.
Returns
-------
removed_columns : list[str]
A list of names of the removed columns, ordered as they appear in the table the StandardScaler was fitted on.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if not self.is_fitted():
raise TransformerNotFittedError
return []
|
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def __init__(self) -> None:
self._column_names: list[str] | None = None
self._wrapped_transformer: sk_StandardScaler | None = None
|
Learn a transformation for a set of columns in a table.
This transformer is not modified.
Parameters:
| Name |
Type |
Description |
Default |
table |
Table
|
The table used to fit the transformer.
|
required
|
column_names |
list[str] | None
|
The list of columns from the table used to fit the transformer. If None, all columns are used.
|
required
|
Returns:
Raises:
| Type |
Description |
UnknownColumnNameError
|
If column_names contain a column name that is missing in the table.
|
NonNumericColumnError
|
If at least one of the specified columns in the table contains non-numerical data.
|
ValueError
|
If the table contains 0 rows.
|
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def fit(self, table: Table, column_names: list[str] | None) -> StandardScaler:
"""
Learn a transformation for a set of columns in a table.
This transformer is not modified.
Parameters
----------
table : Table
The table used to fit the transformer.
column_names : list[str] | None
The list of columns from the table used to fit the transformer. If `None`, all columns are used.
Returns
-------
fitted_transformer : TableTransformer
The fitted transformer.
Raises
------
UnknownColumnNameError
If column_names contain a column name that is missing in the table.
NonNumericColumnError
If at least one of the specified columns in the table contains non-numerical data.
ValueError
If the table contains 0 rows.
"""
if column_names is None:
column_names = table.column_names
else:
missing_columns = sorted(set(column_names) - set(table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot be fitted because the table contains 0 rows")
if (
table.keep_only_columns(column_names).remove_columns_with_non_numerical_values().number_of_columns
< table.keep_only_columns(column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(table.keep_only_columns(column_names).column_names)
- set(
table.keep_only_columns(column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
wrapped_transformer = sk_StandardScaler()
wrapped_transformer.fit(table._data[column_names])
result = StandardScaler()
result._wrapped_transformer = wrapped_transformer
result._column_names = column_names
return result
|
Get the names of all new columns that have been added by the StandardScaler.
Returns:
| Name | Type |
Description |
added_columns |
list[str]
|
A list of names of the added columns, ordered as they will appear in the table.
|
Raises:
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def get_names_of_added_columns(self) -> list[str]:
"""
Get the names of all new columns that have been added by the StandardScaler.
Returns
-------
added_columns : list[str]
A list of names of the added columns, ordered as they will appear in the table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if not self.is_fitted():
raise TransformerNotFittedError
return []
|
Get the names of all columns that may have been changed by the StandardScaler.
Returns:
| Name | Type |
Description |
changed_columns |
list[str]
|
The list of (potentially) changed column names, as passed to fit.
|
Raises:
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def get_names_of_changed_columns(self) -> list[str]:
"""
Get the names of all columns that may have been changed by the StandardScaler.
Returns
-------
changed_columns : list[str]
The list of (potentially) changed column names, as passed to fit.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if self._column_names is None:
raise TransformerNotFittedError
return self._column_names
|
Get the names of all columns that have been removed by the StandardScaler.
Returns:
| Name | Type |
Description |
removed_columns |
list[str]
|
A list of names of the removed columns, ordered as they appear in the table the StandardScaler was fitted on.
|
Raises:
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def get_names_of_removed_columns(self) -> list[str]:
"""
Get the names of all columns that have been removed by the StandardScaler.
Returns
-------
removed_columns : list[str]
A list of names of the removed columns, ordered as they appear in the table the StandardScaler was fitted on.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
"""
if not self.is_fitted():
raise TransformerNotFittedError
return []
|
Undo the learned transformation.
The table is not modified.
Parameters:
| Name |
Type |
Description |
Default |
transformed_table |
Table
|
The table to be transformed back to the original version.
|
required
|
Returns:
| Name | Type |
Description |
table |
Table
|
|
Raises:
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def inverse_transform(self, transformed_table: Table) -> Table:
"""
Undo the learned transformation.
The table is not modified.
Parameters
----------
transformed_table : Table
The table to be transformed back to the original version.
Returns
-------
table : Table
The original table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
UnknownColumnNameError
If the input table does not contain all columns used to fit the transformer.
NonNumericColumnError
If the transformed columns of the input table contain non-numerical data.
ValueError
If the table contains 0 rows.
"""
# Transformer has not been fitted yet
if self._wrapped_transformer is None or self._column_names is None:
raise TransformerNotFittedError
missing_columns = sorted(set(self._column_names) - set(transformed_table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if transformed_table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows")
if (
transformed_table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.number_of_columns
< transformed_table.keep_only_columns(self._column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(transformed_table.keep_only_columns(self._column_names).column_names)
- set(
transformed_table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
data = transformed_table._data.reset_index(drop=True)
data.columns = transformed_table.column_names
data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
return Table._from_pandas_dataframe(data)
|
Check if the transformer is fitted.
Returns:
| Name | Type |
Description |
is_fitted |
bool
|
Whether the transformer is fitted.
|
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def is_fitted(self) -> bool:
"""
Check if the transformer is fitted.
Returns
-------
is_fitted : bool
Whether the transformer is fitted.
"""
return self._wrapped_transformer is not None
|
Apply the learned transformation to a table.
The table is not modified.
Parameters:
| Name |
Type |
Description |
Default |
table |
Table
|
The table to which the learned transformation is applied.
|
required
|
Returns:
| Name | Type |
Description |
transformed_table |
Table
|
|
Raises:
| Type |
Description |
TransformerNotFittedError
|
If the transformer has not been fitted yet.
|
UnknownColumnNameError
|
If the input table does not contain all columns used to fit the transformer.
|
NonNumericColumnError
|
If at least one of the columns in the input table that is used to fit contains non-numerical data.
|
ValueError
|
If the table contains 0 rows.
|
Source code in src/safeds/data/tabular/transformation/_standard_scaler.py
| def transform(self, table: Table) -> Table:
"""
Apply the learned transformation to a table.
The table is not modified.
Parameters
----------
table : Table
The table to which the learned transformation is applied.
Returns
-------
transformed_table : Table
The transformed table.
Raises
------
TransformerNotFittedError
If the transformer has not been fitted yet.
UnknownColumnNameError
If the input table does not contain all columns used to fit the transformer.
NonNumericColumnError
If at least one of the columns in the input table that is used to fit contains non-numerical data.
ValueError
If the table contains 0 rows.
"""
# Transformer has not been fitted yet
if self._wrapped_transformer is None or self._column_names is None:
raise TransformerNotFittedError
# Input table does not contain all columns used to fit the transformer
missing_columns = sorted(set(self._column_names) - set(table.column_names))
if len(missing_columns) > 0:
raise UnknownColumnNameError(missing_columns)
if table.number_of_rows == 0:
raise ValueError("The StandardScaler cannot transform the table because it contains 0 rows")
if (
table.keep_only_columns(self._column_names).remove_columns_with_non_numerical_values().number_of_columns
< table.keep_only_columns(self._column_names).number_of_columns
):
raise NonNumericColumnError(
str(
sorted(
set(table.keep_only_columns(self._column_names).column_names)
- set(
table.keep_only_columns(self._column_names)
.remove_columns_with_non_numerical_values()
.column_names,
),
),
),
)
data = table._data.reset_index(drop=True)
data.columns = table.column_names
data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
return Table._from_pandas_dataframe(data)
|