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Normalization MinMax

NormalizationMinMax

Bases: NormalizationBaseClass

Implements Min-Max normalization for specified columns in a PySpark DataFrame.

Example

from rtdip_sdk.pipelines.data_quality.data_manipulation.spark.normalization.normalization_minmax import NormalizationMinMax
from pyspark.sql import SparkSession
from pyspark.sql.dataframe import DataFrame

normalization = NormalizationMinMax(df, column_names=["value_column_1", "value_column_2"], in_place=False)
normalized_df = normalization.filter_data()

Parameters:

Name Type Description Default
df DataFrame

PySpark DataFrame to be normalized.

 required
column_names List[str]

List of columns in the DataFrame to be normalized.

 required
in_place bool

If true, then result of normalization is stored in the same column.

 False
Source code in src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/normalization/normalization_minmax.py 
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class NormalizationMinMax(NormalizationBaseClass):
    """
    Implements Min-Max normalization for specified columns in a PySpark DataFrame.

    Example
    --------
    ```python
    from rtdip_sdk.pipelines.data_quality.data_manipulation.spark.normalization.normalization_minmax import NormalizationMinMax
    from pyspark.sql import SparkSession
    from pyspark.sql.dataframe import DataFrame

    normalization = NormalizationMinMax(df, column_names=["value_column_1", "value_column_2"], in_place=False)
    normalized_df = normalization.filter_data()
    ```

    Parameters:
        df (DataFrame): PySpark DataFrame to be normalized.
        column_names (List[str]): List of columns in the DataFrame to be normalized.
        in_place (bool): If true, then result of normalization is stored in the same column.
    """

    NORMALIZED_COLUMN_NAME = "minmax"

    def _normalize_column(self, df: PySparkDataFrame, column: str) -> PySparkDataFrame:
        """
        Private method to revert Min-Max normalization to the specified column.
        Min-Max denormalization: normalized_value * (max - min) + min = value
        """
        min_val = df.select(F.min(F.col(column))).collect()[0][0]
        max_val = df.select(F.max(F.col(column))).collect()[0][0]

        divisor = max_val - min_val
        if math.isclose(divisor, 0.0, abs_tol=10e-8) or not math.isfinite(divisor):
            raise ZeroDivisionError("Division by Zero in MinMax")

        store_column = self._get_norm_column_name(column)
        self.reversal_value = [min_val, max_val]

        return df.withColumn(
            store_column,
            (F.col(column) - F.lit(min_val)) / (F.lit(max_val) - F.lit(min_val)),
        )

    def _denormalize_column(
        self, df: PySparkDataFrame, column: str
    ) -> PySparkDataFrame:
        """
        Private method to revert Z-Score normalization to the specified column.
        Z-Score denormalization: normalized_value * std_dev + mean = value
        """
        min_val = self.reversal_value[0]
        max_val = self.reversal_value[1]

        store_column = self._get_norm_column_name(column)

        return df.withColumn(
            store_column,
            (F.col(column) * (F.lit(max_val) - F.lit(min_val))) + F.lit(min_val),
        )