class_docstr: The RidgeReduction class is intended to reduce the feature space of an N by M block matrix X to an N by P<<M block
  matrix.  This is done by fitting K ridge models within each block of X on one or more target labels, such that a
  block with L columns to begin with will be reduced to a block with K columns, where each column is the prediction
  of one ridge model for one target label.
class_docstr_tok: ["The","RidgeReduction","class","is","intended","to","reduce","the","feature","space","of","an","N","by","M","block","matrix","X","to","an","N","by","P","<","<","M","block","matrix",".","This","is","done","by","fitting","K","ridge","models","within","each","block","of","X","on","one","or","more","target","labels",",","such","that","a","block","with","L","columns","to","begin","with","will","be","reduced","to","a","block","with","K","columns",",","where","each","column","is","the","prediction","of","one","ridge","model","for","one","target","label","."]
code: def set_label_df(self, label_df: pd.DataFrame) -> None:
      self._is_binary = _is_binary(label_df)
      self._std_label_df = _prepare_labels_and_warn(
          label_df, self._is_binary, self._label_type
      )
      self._label_df = label_df
  
imports: from .ridge_udfs import *
  from .model_functions import _is_binary, _prepare_covariates, _prepare_labels_and_warn, _check_model
  from nptyping import Float, NDArray
  import pandas
  from pyspark.sql import DataFrame, Row
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  import pyspark.sql.functions
  from typeguard import typechecked
  from typing import Any, Dict, List, Union
  from glow.logging import record_hls_event
  import warnings
label_desc: Use image node_id 3 for calling the RidgeReduction obj's underlying member method code with example usage: obj.set_label_df(label_df) without return types