src/router.py

import numpy as np
from sklearn.cluster import KMeans
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
import lightgbm as lgb
from typing import Tuple, Any
import joblib
import os

class LearnedRouter:
    def __init__(self, model_type: str = "lightgbm", n_clusters: int = 32, mrl_dims: int = 64):
        self.model_type = model_type
        self.n_clusters = n_clusters
        self.mrl_dims = mrl_dims
        self.kmeans = None
        self.classifier = None
        
    def train(self, X_full: np.ndarray):
        """
        Trains the router:
        1. Cluster X_full using K-Means to generate ground-truth labels.
        2. Slice X_full to MRL_DIMS.
        3. Train the specified classifier on sliced vectors to predict cluster labels.
        """
        print(f"Training Router ({self.model_type})...")
        
        # 1. Generate Ground Truth Labels with K-Means on FULL vectors
        # (We want the clusters to be based on the high-fidelity data)
        print("  - Running K-Means for ground truth labels...")
        self.kmeans = KMeans(n_clusters=self.n_clusters, random_state=42, n_init=10)
        y_labels = self.kmeans.fit_predict(X_full)
        
        # 2. Slice Input Data for the Router
        # The router only sees the low-dim MRL vector
        print(f"  - Slicing vectors to {self.mrl_dims} dimensions...")
        # Note: We assume X_full is already normalized if needed, 
        # but for MRL slicing we should re-normalize the slice.
        # We'll do a quick slice and normalize here locally or assume caller handles it.
        # Ideally, we use the mrl_slice function from data_pipeline, but to avoid circular imports
        # or dependency issues, let's implement the logic here or import it.
        # Let's do the math here to be self-contained in the class logic.
        X_sliced = X_full[:, :self.mrl_dims]
        norms = np.linalg.norm(X_sliced, axis=1, keepdims=True)
        norms[norms == 0] = 1e-10
        X_train = X_sliced / norms
        
        # 3. Train Classifier
        print(f"  - Training classifier: {self.model_type}...")
        if self.model_type == "lightgbm":
            # LightGBM
            train_data = lgb.Dataset(X_train, label=y_labels)
            params = {
                'objective': 'multiclass',
                'num_class': self.n_clusters,
                'metric': 'multi_logloss',
                'verbosity': -1,
                'seed': 42
            }
            self.classifier = lgb.train(params, train_data, num_boost_round=100)
            
        elif self.model_type == "logistic":
            # Logistic Regression
            self.classifier = LogisticRegression(max_iter=1000, multi_class='multinomial', random_state=42)
            self.classifier.fit(X_train, y_labels)
            
        elif self.model_type == "mlp":
            # MLP Classifier
            self.classifier = MLPClassifier(hidden_layer_sizes=(128, 64), max_iter=500, random_state=42)
            self.classifier.fit(X_train, y_labels)
            
        else:
            raise ValueError(f"Unknown router model type: {self.model_type}")
            
        print("Router training complete.")

    def predict(self, vector_full: np.ndarray) -> Tuple[int, float]:
        """
        Predicts the target cluster for a query vector.
        1. Slice input to MRL dims.
        2. Predict probabilities.
        3. Return (best_cluster, confidence_score).
        """
        # Ensure input is 2D
        if vector_full.ndim == 1:
            vector_full = vector_full.reshape(1, -1)
            
        # 1. Slice and Normalize
        X_sliced = vector_full[:, :self.mrl_dims]
        norms = np.linalg.norm(X_sliced, axis=1, keepdims=True)
        norms[norms == 0] = 1e-10
        X_input = X_sliced / norms
        
        # 2. Predict
        if self.model_type == "lightgbm":
            probs = self.classifier.predict(X_input) # Returns (n_samples, n_classes)
        elif self.model_type in ["logistic", "mlp"]:
            probs = self.classifier.predict_proba(X_input)
        else:
            raise ValueError("Model not trained or unknown type")
            
        # 3. Get best cluster and confidence
        best_cluster = np.argmax(probs, axis=1)[0]
        confidence = np.max(probs, axis=1)[0]
        
        return int(best_cluster), float(confidence)

    def save(self, path: str):
        """Saves the router (KMeans + Classifier) to disk."""
        print(f"Saving router to {path}...")
        joblib.dump({
            'model_type': self.model_type,
            'n_clusters': self.n_clusters,
            'mrl_dims': self.mrl_dims,
            'kmeans': self.kmeans,
            'classifier': self.classifier
        }, path)
        print("Router saved.")

    @classmethod
    def load(cls, path: str):
        """Loads the router from disk."""
        print(f"Loading router from {path}...")
        data = joblib.load(path)
        router = cls(
            model_type=data['model_type'],
            n_clusters=data['n_clusters'],
            mrl_dims=data['mrl_dims']
        )
        router.kmeans = data['kmeans']
        router.classifier = data['classifier']
        print("Router loaded.")
        return router