# corerec/engines/unionizedFilterEngine/nn_base/neural_cf_base.py

from abc import ABC
from typing import List
from scipy.sparse import csr_matrix
from ..base_recommender import BaseRecommender

class NeuralCollaborativeFilteringBase(BaseRecommender, ABC):
    """
    Neural Collaborative Filtering (NCF) Base Implementation.

    A generalized framework for neural network-based collaborative filtering that can learn
    arbitrary function from data by leveraging neural networks' capacity of non-linear 
    transformation and deep representation learning.

    Attributes:
        embedding_size (int): Dimension of embedding vectors
        learning_rate (float): Learning rate for optimization
        epochs (int): Number of training epochs
        layers (List[int]): Architecture of neural layers
        batch_size (int): Size of mini-batches
        optimizer (str): Optimization algorithm

    Features:
        - Flexible neural architectures
        - Multi-layer perceptron for feature learning
        - Customizable embedding dimensions
        - Mini-batch training support
        - Multiple loss function options
        - Regularization techniques

    Training Process:
        1. Embedding Layer: Maps sparse features to dense vectors
        2. Feature Learning: Multiple neural layers for interaction
        3. Prediction Layer: Final layer for recommendation scores
        4. Optimization: Minimizes loss function through backpropagation

    Mathematical Formulation:
        For a user-item pair (u,i):
        1. User embedding: p_u = P * v_u
        2. Item embedding: q_i = Q * v_i
        3. Neural CF layers: φ_out = φ_L(...φ_2(φ_1([p_u, q_i]))...)
        4. Prediction: ŷ_ui = σ(h^T * φ_out)

    References:
        He, X., et al. "Neural Collaborative Filtering." WWW 2017.
    """

    def __init__(self, embedding_size: int = 50, learning_rate: float = 0.001, epochs: int = 10):
        """
        Initialize the Neural Collaborative Filtering model.

        Args:
            embedding_size (int): Dimension of the embedding vectors
            learning_rate (float): Learning rate for optimization
            epochs (int): Number of training epochs

        Note:
            The embedding size should be chosen based on the dataset size
            and complexity of the recommendation task.
        """
        self.embedding_size = embedding_size
        self.learning_rate = learning_rate
        self.epochs = epochs
        # Add additional initialization as needed

    def preprocess_data(self, interaction_matrix: csr_matrix, user_ids: List[int], item_ids: List[int]):
        # Implement any preprocessing steps specific to neural CF
        pass