Source code for gators.scalers.standard_scaler
from typing import Dict, List, Optional
import polars as pl
from ..transformer._base_transformer import _BaseTransformer
[docs]
class StandardScaler(_BaseTransformer):
"""
Standardizes numeric features by removing the mean and scaling to unit variance.
Transforms features by centering them around zero and scaling by the standard
deviation. The transformation is given by: X_scaled = (X - mean) / std.
This is also known as z-score normalization.
Parameters
----------
subset : Optional[List[str]], default=None
List of numeric column names to standardize. If None, all numeric columns
(Float64, Int64, Float32, Int32) are automatically selected.
drop_columns : bool, default=True
If True, drop the original columns after scaling.
If False, keep both original and scaled columns.
Examples
--------
Create an instance of the StandardScaler class:
>>> import polars as pl
>>> from gators.scalers import StandardScaler
>>> scaler = StandardScaler(subset=["age", "income"])
Fit the transformer:
>>> X = pl.DataFrame({"age": [20, 30, 40, 50],
... "income": [20000, 40000, 60000, 80000]})
>>> scaler.fit(X)
Transform the DataFrame:
>>> transformed_X = scaler.transform(X)
>>> print(transformed_X)
shape: (4, 2)
┌────────────────────┬──────────────────────┐
│ age__standard_scale ┆ income__standard_scale│
│ --- ┆ --- │
│ f64 ┆ f64 │
├────────────────────┼──────────────────────┤
│ -1.161 ┆ -1.161 │
│ -0.387 ┆ -0.387 │
│ 0.387 ┆ 0.387 │
│ 1.161 ┆ 1.161 │
└────────────────────┴──────────────────────┘
"""
subset: Optional[List[str]] = None
_offset: Dict[str, float]
_scale: Dict[str, float]
_column_mapping: Dict[str, str]
drop_columns: bool = True
[docs]
def fit(self, X: pl.DataFrame, y: Optional[pl.Series] = None) -> "StandardScaler":
"""Fit the transformer by computing mean and standard deviation.
Parameters
----------
X : pl.DataFrame
Input DataFrame to fit.
y : Optional[pl.Series], default=None
Target series (not used, present for sklearn compatibility).
Returns
-------
StandardScaler
The fitted transformer instance.
"""
if not self.subset:
self.subset = [
col
for col, dtype in zip(X.columns, X.dtypes)
if dtype in [pl.Float64, pl.Int64, pl.Float32, pl.Int32]
]
self._column_mapping = {col: f"{col}__standard_scale" for col in self.subset}
mean_std_exprs = []
for col in self.subset:
mean_std_exprs.append(pl.col(col).mean().alias(f"{col}__mean"))
mean_std_exprs.append(pl.col(col).std().alias(f"{col}__std"))
stats = X.select(mean_std_exprs).row(0)
self._offset = {}
self._scale = {}
for i, col in enumerate(self.subset):
mean_val = stats[i * 2]
std_val = stats[i * 2 + 1]
self._offset[col] = mean_val if mean_val is not None else 0.0
self._scale[col] = 1.0 / std_val if std_val else 0.0
return self
