| import numpy as np |
| from typing import Tuple, List, Optional |
| from sklearn.decomposition import PCA |
| from sklearn.linear_model import LogisticRegression |
|
|
| class LatentDirectionFinder: |
| """ |
| Provides methods to discover semantic directions in latent space using PCA or classifier-based approaches. |
| """ |
| def __init__(self, latent_vectors: np.ndarray): |
| """ |
| :param latent_vectors: Array of shape (N, D) where N is the number of samples and D is the latent dimension. |
| """ |
| self.latent_vectors = latent_vectors |
|
|
| def pca_direction(self, n_components: int = 10) -> Tuple[np.ndarray, np.ndarray]: |
| """ |
| Perform PCA on the latent vectors to find principal directions. |
| :return: (components, explained_variance) |
| """ |
| pca = PCA(n_components=n_components) |
| pca.fit(self.latent_vectors) |
| return pca.components_, pca.explained_variance_ratio_ |
|
|
| def classifier_direction(self, labels: List[int]) -> np.ndarray: |
| """ |
| Fit a linear classifier to find a direction separating two classes in latent space. |
| :param labels: List of 0/1 labels for each latent vector. |
| :return: Normalized direction vector (D,) |
| """ |
| clf = LogisticRegression() |
| clf.fit(self.latent_vectors, labels) |
| direction = clf.coef_[0] |
| direction = direction / np.linalg.norm(direction) |
| return direction |
