Basic Components API Reference

This document provides detailed documentation for basic components in Torch-RecHub, including feature processing, data transformation, and other fundamental functionalities.

Feature Processing

Feature Columns

DenseFeature

• Introduction: Process continuous numerical features.
• Parameters:
  • name (str): Feature name
  • dimension (int): Feature dimension
  • dtype (str): Data type, default 'float32'

SparseFeature

• Introduction: Process discrete categorical features.
• Parameters:
  • name (str): Feature name
  • vocabulary_size (int): Size of category vocabulary
  • embedding_dim (int): Embedding vector dimension
  • dtype (str): Data type, default 'int32'
  • embedding_name (str): Embedding layer name, default None

VarLenSparseFeature

• Introduction: Process variable-length discrete features.
• Parameters:
  • name (str): Feature name
  • vocabulary_size (int): Size of category vocabulary
  • embedding_dim (int): Embedding vector dimension
  • maxlen (int): Maximum sequence length
  • dtype (str): Data type, default 'int32'
  • embedding_name (str): Embedding layer name, default None
  • combiner (str): Sequence pooling method, options: 'sum', 'mean', 'max', default 'mean'

Data Transformation

Data Preprocessing

MinMaxScaler

• Introduction: Normalize numerical features.
• Parameters:
  • feature_range (tuple): Normalization range, default (0, 1)

StandardScaler

• Introduction: Standardize numerical features.
• Parameters:
  • with_mean (bool): Whether to remove mean, default True
  • with_std (bool): Whether to scale by standard deviation, default True

LabelEncoder

• Introduction: Encode categorical features.
• Methods:
  • fit(values): Fit the encoder
  • transform(values): Transform data
  • fit_transform(values): Fit and transform

Data Format Conversion

pandas_to_torch

• Introduction: Convert Pandas data to PyTorch tensors.
• Parameters:
  • df (pd.DataFrame): Input DataFrame
  • dense_cols (list): List of continuous feature column names
  • sparse_cols (list): List of discrete feature column names
  • device (str): Device type, 'cpu' or 'cuda'

numpy_to_torch

• Introduction: Convert NumPy arrays to PyTorch tensors.
• Parameters:
  • arrays (list): List of NumPy arrays
  • device (str): Device type, 'cpu' or 'cuda'

Model Components

Activation Functions

Dice

• Introduction: Dice activation function, proposed in Deep Interest Network (DIN).
• Parameters:
  • epsilon (float): Smoothing parameter, default 1e-3
  • device (str): Device type, default 'cpu'

Attention Mechanisms

ScaledDotProductAttention

• Introduction: Scaled dot-product attention mechanism.
• Parameters:
  • temperature (float): Temperature parameter for scaling
  • attn_dropout (float): Attention dropout rate

MultiHeadAttention

• Introduction: Multi-head attention mechanism.
• Parameters:
  • d_model (int): Model dimension
  • n_heads (int): Number of attention heads
  • d_k (int): Key vector dimension
  • d_v (int): Value vector dimension
  • dropout (float): Dropout rate