class_docstr: JoinAEEmbedding is a supervised auto-encoder architecture. It expects
  a class label with each input data. It jointly trains an auto-encoder and
  a classifier model. The classifier model classifies the obtained embedding
  by the provided class label.
class_docstr_tok: ["JoinAEEmbedding","is","a","supervised","auto-encoder","architecture",".","It","expects","a","class","label","with","each","input","data",".","It","jointly","trains","an","auto-encoder","and","a","classifier","model",".","The","classifier","model","classifies","the","obtained","embedding","by","the","provided","class","label","."]
code: def __init__(
      self,
      data: pd.DataFrame,
      features: List[str] = None,
      categorical_features: List[str] = [],
      categorical_values: dict = {},
      max_train_records: int = 10000,
      embedding_dim: int = 8,
      category_enc_dim: int = 3,
      category_encoding: str = "ohe",  # supported values ohe|label
      numeric_scaling: str = None,  # supported values minmax|standard|quantile
      layers_config: List[int] = [16, 16],
      encoder_activation: str = "relu",
      decoder_activation: str = "relu",
      embedding_activation: str = "tanh",
      encoder_kernel_regularizer: str = "l1",
      encoder_kernel_initializer: str = "glorot_uniform",
      encoder_bias_initializer: str = "zeros",
      encoder_activity_regularizer: str = None,
      decoder_kernel_regularizer: str = "l1",
      decoder_kernel_initializer: str = "glorot_uniform",
      decoder_bias_initializer: str = "zeros",
      decoder_activity_regularizer: str = None,
      decoder_last_layer_activation: str = "linear",
      embedding_activity_regularizer: str = None,
      classifier_layers: List[int] = [16],
      classifier_activation: str = "relu",
      classifier_kernel_regularizer: str = None,
      classifier_kernel_initilizer: str = "glorot_uniform",
      classifier_bias_initializer: str = "zeros",
  ):
      super(JointAEEmbedding, self).__init__(
          data=data,
          features=features,
          categorical_features=categorical_features,
          categorical_values=categorical_values,
          embedding_dim=embedding_dim,
          category_enc_dim=category_enc_dim,
          category_encoding=category_encoding,
          max_train_records=max_train_records,
          numeric_scaling=numeric_scaling,
          layers_config=layers_config,
          encoder_activation=encoder_activation,
          decoder_activation=decoder_activation,
          embedding_activation=embedding_activation,
          encoder_kernel_regularizer=encoder_kernel_regularizer,
          encoder_kernel_initializer=encoder_kernel_initializer,
          encoder_bias_initializer=encoder_bias_initializer,
          encoder_activity_regularizer=encoder_activity_regularizer,
          decoder_kernel_regularizer=decoder_kernel_regularizer,
          decoder_kernel_initializer=decoder_kernel_initializer,
          decoder_bias_initializer=decoder_bias_initializer,
          decoder_activity_regularizer=decoder_activity_regularizer,
          decoder_last_layer_activation=decoder_last_layer_activation,
          embedding_activity_regularizer=embedding_activity_regularizer,
      )
      self._config.update(
          dict(
              classifier_layers=classifier_layers,
              classifier_activation=classifier_activation,
              classifier_kernel_initilizer=classifier_kernel_initilizer,
              classifier_kernel_regularizer=classifier_kernel_regularizer,
              classifier_bias_initializer=classifier_bias_initializer,
          )
      )
      self._classifier = None
  
imports: import numpy
  import pandas
  from abc import ABC, abstractmethod
  from typing import Union, List, Any
  from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, QuantileTransformer, StandardScaler, MinMaxScaler
label_desc: Use image node_id 1 to create a new JointAEEmbedding object from inherited base classes: AEEmbedding with example: obj = JointAEEmbedding(data, features, categorical_features, categorical_values, max_train_records, embedding_dim, category_enc_dim, category_encoding, numeric_scaling, layers_config, encoder_activation, decoder_activation, embedding_activation, encoder_kernel_regularizer, encoder_kernel_initializer, encoder_bias_initializer, encoder_activity_regularizer, decoder_kernel_regularizer, decoder_kernel_initializer, decoder_bias_initializer, decoder_activity_regularizer, decoder_last_layer_activation, embedding_activity_regularizer, classifier_layers, classifier_activation, classifier_kernel_regularizer, classifier_kernel_initilizer, classifier_bias_initializer)