feat(KDP): add the AdvancedNumericalEmbedding feature

Author: Gandalfdore

kdp/custom_layers.py

@@ -6,6 +6,9 @@
 import numpy as np
 import tensorflow as tf
 import tensorflow_probability as tfp
+from tensorflow.keras import layers
+
+from loguru import logger
 
 
 class TextPreprocessingLayer(tf.keras.layers.Layer):
@@ -1945,3 +1948,220 @@ def from_config(cls, config: dict) -> "VariableSelection":
             VariableSelection: A new instance of the layer.
         """
         return cls(**config)
+
+
+class AdvancedNumericalEmbedding(layers.Layer):
+    """Advanced numerical embedding layer for continuous features.
+
+    This layer embeds each continuous numerical feature into a higher-dimensional space by
+    combining two branches:
+
+      1. Continuous Branch: Each feature is processed via a small MLP (using TimeDistributed layers).
+      2. Discrete Branch: Each feature is discretized into bins using learnable min/max boundaries
+         and then an embedding is looked up for its bin.
+
+    A learnable gate (of shape (num_features, embedding_dim)) combines the two branch outputs
+    per feature and per embedding dimension. Additionally, the continuous branch uses a residual
+    connection and optional batch normalization to improve training stability.
+
+    The layer supports inputs of shape (batch, num_features) for any number of features and returns
+    outputs of shape (batch, num_features, embedding_dim).
+
+    Args:
+        embedding_dim (int): Output embedding dimension per feature.
+        mlp_hidden_units (int): Hidden units for the continuous branch MLP.
+        num_bins (int): Number of bins for discretization.
+        init_min (float or list): Initial minimum values for discretization boundaries. If a scalar is
+            provided, it is applied to all features.
+        init_max (float or list): Initial maximum values for discretization boundaries.
+        dropout_rate (float): Dropout rate applied to the continuous branch.
+        use_batch_norm (bool): Whether to apply batch normalization to the continuous branch.
+
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_hidden_units: int,
+        num_bins: int,
+        init_min,
+        init_max,
+        dropout_rate: float = 0.0,
+        use_batch_norm: bool = False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embedding_dim = embedding_dim
+        self.mlp_hidden_units = mlp_hidden_units
+        self.num_bins = num_bins
+        self.dropout_rate = dropout_rate
+        self.use_batch_norm = use_batch_norm
+        self.init_min = init_min
+        self.init_max = init_max
+
+        if self.num_bins is None:
+            raise ValueError(
+                "num_bins must be provided to activate the discrete branch."
+            )
+
+    def build(self, input_shape):
+        # input_shape: (batch, num_features)
+        self.num_features = input_shape[-1]
+        # Continuous branch: process each feature independently using TimeDistributed MLP.
+        self.cont_mlp = tf.keras.Sequential(
+            [
+                layers.TimeDistributed(
+                    layers.Dense(self.mlp_hidden_units, activation="relu")
+                ),
+                layers.TimeDistributed(layers.Dense(self.embedding_dim)),
+            ],
+            name="cont_mlp",
+        )
+        self.dropout = (
+            layers.Dropout(self.dropout_rate)
+            if self.dropout_rate > 0
+            else lambda x, training: x
+        )
+        if self.use_batch_norm:
+            self.batch_norm = layers.TimeDistributed(
+                layers.BatchNormalization(), name="cont_batch_norm"
+            )
+        # Residual projection to match embedding_dim.
+        self.residual_proj = layers.TimeDistributed(
+            layers.Dense(self.embedding_dim, activation=None), name="residual_proj"
+        )
+        # Discrete branch: Create one Embedding layer per feature.
+        self.bin_embeddings = []
+        for i in range(self.num_features):
+            embed_layer = layers.Embedding(
+                input_dim=self.num_bins,
+                output_dim=self.embedding_dim,
+                name=f"bin_embed_{i}",
+            )
+            self.bin_embeddings.append(embed_layer)
+        # Learned bin boundaries for each feature, shape: (num_features,)
+        init_min_tensor = tf.convert_to_tensor(self.init_min, dtype=tf.float32)
+        init_max_tensor = tf.convert_to_tensor(self.init_max, dtype=tf.float32)
+        if init_min_tensor.shape.ndims == 0:
+            init_min_tensor = tf.fill([self.num_features], init_min_tensor)
+        if init_max_tensor.shape.ndims == 0:
+            init_max_tensor = tf.fill([self.num_features], init_max_tensor)
+        # Convert tensors to numpy arrays, which are acceptable by tf.constant_initializer.
+        init_min_value = (
+            init_min_tensor.numpy()
+            if hasattr(init_min_tensor, "numpy")
+            else init_min_tensor
+        )
+        init_max_value = (
+            init_max_tensor.numpy()
+            if hasattr(init_max_tensor, "numpy")
+            else init_max_tensor
+        )
+
+        self.learned_min = self.add_weight(
+            name="learned_min",
+            shape=(self.num_features,),
+            initializer=tf.constant_initializer(init_min_value),
+            trainable=True,
+        )
+        self.learned_max = self.add_weight(
+            name="learned_max",
+            shape=(self.num_features,),
+            initializer=tf.constant_initializer(init_max_value),
+            trainable=True,
+        )
+        # Gate to combine continuous and discrete branches, shape: (num_features, embedding_dim)
+        self.gate = self.add_weight(
+            name="gate",
+            shape=(self.num_features, self.embedding_dim),
+            initializer="zeros",
+            trainable=True,
+        )
+        logger.debug(
+            "AdvancedNumericalEmbedding built for {} features with embedding_dim={}",
+            self.num_features,
+            self.embedding_dim,
+        )
+        super().build(input_shape)
+
+    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+        # Continuous branch.
+        inputs_expanded = tf.expand_dims(inputs, axis=-1)  # (batch, num_features, 1)
+        cont = self.cont_mlp(inputs_expanded)
+        cont = self.dropout(cont, training=training)
+        if self.use_batch_norm:
+            cont = self.batch_norm(cont, training=training)
+        # Residual connection.
+        cont_res = self.residual_proj(inputs_expanded)
+        cont = cont + cont_res  # (batch, num_features, embedding_dim)
+
+        # Discrete branch.
+        inputs_float = tf.cast(inputs, tf.float32)
+        # Use learned min and max for scaling.
+        scaled = (inputs_float - self.learned_min) / (
+            self.learned_max - self.learned_min + 1e-6
+        )
+        # Compute bin indices.
+        bin_indices = tf.floor(scaled * self.num_bins)
+        bin_indices = tf.cast(bin_indices, tf.int32)
+        bin_indices = tf.clip_by_value(bin_indices, 0, self.num_bins - 1)
+        disc_embeddings = []
+        for i in range(self.num_features):
+            feat_bins = bin_indices[:, i]  # (batch,)
+            feat_embed = self.bin_embeddings[i](
+                feat_bins
+            )  # i is a Python integer here.
+            disc_embeddings.append(feat_embed)
+        disc = tf.stack(disc_embeddings, axis=1)  # (batch, num_features, embedding_dim)
+
+        # Combine branches via a per-feature, per-dimension gate.
+        gate = tf.nn.sigmoid(self.gate)  # (num_features, embedding_dim)
+        output = gate * cont + (1 - gate) * disc  # (batch, num_features, embedding_dim)
+        return output
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "embedding_dim": self.embedding_dim,
+                "mlp_hidden_units": self.mlp_hidden_units,
+                "num_bins": self.num_bins,
+                "init_min": self.init_min,
+                "init_max": self.init_max,
+                "dropout_rate": self.dropout_rate,
+                "use_batch_norm": self.use_batch_norm,
+            }
+        )
+        return config
+
+
+if __name__ == "__main__":
+    tf.random.set_seed(42)
+    logger.info("Testing AdvancedNumericalEmbedding with multi-feature input.")
+    # Multi-feature test: 32 samples, 3 features.
+    x_multi = tf.random.normal((32, 3))
+    layer_multi = AdvancedNumericalEmbedding(
+        embedding_dim=8,
+        mlp_hidden_units=16,
+        num_bins=10,
+        init_min=[-3.0, -2.0, -4.0],
+        init_max=[3.0, 2.0, 4.0],
+        dropout_rate=0.1,
+        use_batch_norm=True,
+    )
+    y_multi = layer_multi(x_multi)
+    logger.info("Multi-feature output shape: {}", y_multi.shape)
+
+    # Single-feature test: 32 samples, 1 feature.
+    x_single = tf.random.normal((32, 1))
+    layer_single = AdvancedNumericalEmbedding(
+        embedding_dim=8,
+        mlp_hidden_units=16,
+        num_bins=10,
+        init_min=-3.0,
+        init_max=3.0,
+        dropout_rate=0.1,
+        use_batch_norm=True,
+    )
+    y_single = layer_single(x_single)
+    logger.info("Single-feature output shape: {}", y_single.shape)

kdp/features.py

@@ -117,26 +117,52 @@ def from_string(type_str: str) -> "FeatureType":
 
 
 class NumericalFeature(Feature):
-    """NumericalFeature with dynamic kwargs passing."""
+    """NumericalFeature with dynamic kwargs passing and embedding support."""
 
     def __init__(
         self,
         name: str,
         feature_type: FeatureType = FeatureType.FLOAT_NORMALIZED,
         preferred_distribution: DistributionType | None = None,
+        use_embedding: bool = False,
+        embedding_dim: int = 8,
+        num_bins: int = 10,
         **kwargs,
     ) -> None:
         """Initializes a NumericalFeature instance.
 
         Args:
             name (str): The name of the feature.
             feature_type (FeatureType): The type of the feature.
-            preferred_distribution (DistributionType | None): The preferred distribution type for the feature.
+            preferred_distribution (DistributionType | None): The preferred distribution type.
+            use_embedding (bool): Whether to use advanced numerical embedding.
+            embedding_dim (int): Dimension of the embedding space.
+            num_bins (int): Number of bins for discretization.
             **kwargs: Additional keyword arguments for the feature.
         """
         super().__init__(name, feature_type, **kwargs)
         self.dtype = tf.float32
         self.preferred_distribution = preferred_distribution
+        self.use_embedding = use_embedding
+        self.embedding_dim = embedding_dim
+        self.num_bins = num_bins
+
+    def get_embedding_layer(self, input_shape: tuple) -> tf.keras.layers.Layer:
+        """Creates and returns an AdvancedNumericalEmbedding layer configured for this feature."""
+        from kdp.custom_layers import (
+            AdvancedNumericalEmbedding,
+        )  # Avoid circular import
+
+        return AdvancedNumericalEmbedding(
+            embedding_dim=self.embedding_dim,
+            mlp_hidden_units=max(16, self.embedding_dim * 2),
+            num_bins=self.num_bins,
+            init_min=self.kwargs.get("init_min", -3.0),
+            init_max=self.kwargs.get("init_max", 3.0),
+            dropout_rate=self.kwargs.get("dropout_rate", 0.1),
+            use_batch_norm=self.kwargs.get("use_batch_norm", True),
+            name=f"{self.name}_embedding",
+        )
 
 
 class CategoricalFeature(Feature):

kdp/processor.py

@@ -192,6 +192,7 @@ def __init__(
         distribution_aware_bins: int = 1000,
         feature_selection_units: int = 32,
         feature_selection_dropout: float = 0.2,
+        use_advanced_numerical_embedding: bool = False,
     ) -> None:
         """Initialize a preprocessing model.
 
@@ -258,6 +259,9 @@ def __init__(
         self.distribution_aware_bins = distribution_aware_bins
         self.feature_selection_dropout = feature_selection_dropout
 
+        # advanced numerical embedding control
+        self.use_advanced_numerical_embedding = use_advanced_numerical_embedding
+
         # PLACEHOLDERS
         self.preprocessors = {}
         self.inputs = {}
@@ -576,13 +580,13 @@ def _add_pipeline_numeric(
             stats (dict): A dictionary containing the metadata of the feature, including
                 the mean and variance of the feature.
         """
-        # getting feature object
+        # Get the feature specifications
         _feature = self.features_specs[feature_name]
 
-        # initializing preprocessor
+        # Initialize preprocessor
         preprocessor = FeaturePreprocessor(name=feature_name)
 
-        # Add cast to float32 first for all numeric features
+        # First, cast to float32 is applied to all numeric features.
         preprocessor.add_processing_step(
             layer_creator=PreprocessorLayerFactory.cast_to_float32_layer,
             name=f"cast_to_float_{feature_name}",
@@ -676,10 +680,23 @@ def _add_pipeline_numeric(
                         name=f"norm_{feature_name}",
                     )
 
+        # Check for advanced numerical embedding.
+        if self.use_advanced_numerical_embedding:
+            logger.info(f"Using AdvancedNumericalEmbedding for {feature_name}")
+            # Obtain the embedding layer.
+            embedding_layer = _feature.get_embedding_layer(
+                input_shape=input_layer.shape
+            )
+            preprocessor.add_processing_step(
+                layer_creator=lambda **kwargs: embedding_layer,
+                layer_class="AdvancedNumericalEmbedding",
+                name=f"advanced_embedding_{feature_name}",
+            )
+
         # Process the feature
         _output_pipeline = preprocessor.chain(input_layer=input_layer)
 
-        # Apply feature selection if enabled for numeric features
+        # Optionally, apply feature selection for numeric features.
         if (
             self.feature_selection_placement == FeatureSelectionPlacementOptions.NUMERIC
             or self.feature_selection_placement