text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  import typing
  
  import demos
  import numpy as np
  import pandas as pd
  
  from forml import flow
  from forml.lib.pipeline import topology
  
  
  @topology.Mapper.operator
  class NaNImputer(flow.Actor):
      """Custom NaN imputation logic."""
  
      def train(self, features: pd.DataFrame, label: pd.DataFrame):
          """Impute missing values using the median for numeric columns and the most common value for string columns."""
          self._fill = pd.Series(
              [
                  features[f].value_counts().index[0] if features[f].dtype == np.dtype('O') else features[f].median()
                  for f in features
              ],
              index=features.columns,
          )
          return self
  
      def apply(self, df: pd.DataFrame) -> pd.DataFrame:
          """Filling the NaNs."""
          return df.fillna(self._fill)
  
      def get_params(self) -> dict[str, typing.Any]:
          """Mandatory get params."""
          return {}
  
      def set_params(self, params: dict[str, typing.Any]) -> None:
          """Mandatory set params."""
  
  
  PIPELINE = NaNImputer() >> demos.LR(
      max_iter=3, solver='lbfgs'
  )  # pylint: disable=unexpected-keyword-arg, no-value-for-parameter
  
  PROJECT = demos.SOURCE.bind(PIPELINE)
  
  if __name__ == '__main__':
      PROJECT.launcher('graphviz', [demos.FEED]).train()
  