brain2vec_PCA.py

#!/usr/bin/env python3

"""
pca_autoencoder.py

This script demonstrates how to:
  1) Load a dataset of MRI volumes using MONAI transforms (as in brain2vec_linearAE.py).
  2) Flatten each 3D volume into a 1D vector (614,400 features if 80x96x80).
  3) Perform IncrementalPCA to reduce dimensionality to 1200 components.
  4) Provide a 'forward()' method that returns (reconstruction, embedding),
     mimicking the interface of a linear autoencoder.
"""

import os
import argparse
import numpy as np
import pandas as pd

import torch
from torch.utils.data import DataLoader

from monai import transforms
from monai.data import Dataset, PersistentDataset

from sklearn.decomposition import IncrementalPCA

###################################################################
# Constants for your typical config
###################################################################
RESOLUTION = 2
INPUT_SHAPE_AE = (80, 96, 80)
N_COMPONENTS = 1200

###################################################################
# Helper classes/functions
###################################################################
def get_dataset_from_pd(df: pd.DataFrame, transforms_fn, cache_dir: str):
    """
    Returns a monai.data.Dataset or monai.data.PersistentDataset
    if `cache_dir` is defined, to speed up loading.
    """
    if cache_dir and cache_dir.strip():
        os.makedirs(cache_dir, exist_ok=True)
        dataset = PersistentDataset(data=df.to_dict(orient='records'),
                                    transform=transforms_fn,
                                    cache_dir=cache_dir)
    else:
        dataset = Dataset(data=df.to_dict(orient='records'),
                          transform=transforms_fn)
    return dataset


class PCAAutoencoder:
    """
    A PCA 'autoencoder' using IncrementalPCA for memory efficiency, 
    providing:
      - fit(X): partial fit on batches
      - transform(X): get embeddings
      - inverse_transform(Z): reconstruct from embeddings
      - forward(X): returns (X_recon, Z) for a direct API 
                    similar to a shallow linear AE.
    """
    def __init__(self, n_components=N_COMPONENTS, batch_size=128):
        self.n_components = n_components
        self.batch_size = batch_size
        self.ipca = IncrementalPCA(n_components=self.n_components)

    def fit(self, X: np.ndarray):
        """
        Incrementally fit the PCA model on batches of data.
        X: shape (n_samples, n_features).
        """
        n_samples = X.shape[0]
        for start_idx in range(0, n_samples, self.batch_size):
            end_idx = min(start_idx + self.batch_size, n_samples)
            self.ipca.partial_fit(X[start_idx:end_idx])

    def transform(self, X: np.ndarray) -> np.ndarray:
        """
        Projects data into the PCA latent space in batches.
        Returns Z: shape (n_samples, n_components).
        """
        results = []
        n_samples = X.shape[0]
        for start_idx in range(0, n_samples, self.batch_size):
            end_idx = min(start_idx + self.batch_size, n_samples)
            Z_chunk = self.ipca.transform(X[start_idx:end_idx])
            results.append(Z_chunk)
        return np.vstack(results)

    def inverse_transform(self, Z: np.ndarray) -> np.ndarray:
        """
        Reconstruct data from PCA latent space in batches.
        Returns X_recon: shape (n_samples, n_features).
        """
        results = []
        n_samples = Z.shape[0]
        for start_idx in range(0, n_samples, self.batch_size):
            end_idx = min(start_idx + self.batch_size, n_samples)
            X_chunk = self.ipca.inverse_transform(Z[start_idx:end_idx])
            results.append(X_chunk)
        return np.vstack(results)

    def forward(self, X: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
        """
        Mimics a linear AE's forward() returning (X_recon, Z).
        """
        Z = self.transform(X)
        X_recon = self.inverse_transform(Z)
        return X_recon, Z


def load_and_flatten_dataset(csv_path: str, cache_dir: str, transforms_fn) -> np.ndarray:
    """
    Loads the dataset from csv_path, applies the monai transforms, 
    and flattens each 3D MRI into a 1D vector of shape (80*96*80).
    Returns a numpy array X with shape (n_samples, 614400).
    """
    df = pd.read_csv(csv_path)
    dataset = get_dataset_from_pd(df, transforms_fn, cache_dir)

    # We'll put the flattened data into this list, then stack.
    X_list = []

    # If memory allows, you can simply do a single-threaded loop 
    # or multi-worker DataLoader for speed.  
    # We'll demonstrate a simple single-worker here for clarity.
    loader = DataLoader(dataset, batch_size=1, num_workers=0)

    for batch in loader:
        # batch["image"] shape: (1, 1, 80, 96, 80)
        img = batch["image"].squeeze(0)  # shape: (1, 80, 96, 80)
        img_np = img.numpy()  # convert to np array, shape: (1, D, H, W)
        flattened = img_np.flatten()     # shape: (614400,)
        X_list.append(flattened)

    X = np.vstack(X_list)  # shape: (n_samples, 614400)
    return X


def main():
    parser = argparse.ArgumentParser(description="PCA Autoencoder with MONAI transforms example.")
    parser.add_argument("--inputs_csv", type=str, required=True, help="CSV with 'image_path' column.")
    parser.add_argument("--cache_dir", type=str, default="", help="Cache directory for MONAI PersistentDataset.")
    parser.add_argument("--output_dir", type=str, default="./pca_outputs", help="Where to save PCA model and embeddings.")
    parser.add_argument("--batch_size_ipca", type=int, default=128, help="Batch size for IncrementalPCA partial_fit().")
    parser.add_argument("--n_components", type=int, default=1200, help="Number of PCA components.")
    args = parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    # Same transforms as in brain2vec_linearAE.py
    transforms_fn = transforms.Compose([
        transforms.CopyItemsD(keys={'image_path'}, names=['image']),
        transforms.LoadImageD(image_only=True, keys=['image']),
        transforms.EnsureChannelFirstD(keys=['image']),
        transforms.SpacingD(pixdim=RESOLUTION, keys=['image']),
        transforms.ResizeWithPadOrCropD(spatial_size=INPUT_SHAPE_AE, mode='minimum', keys=['image']),
        transforms.ScaleIntensityD(minv=0, maxv=1, keys=['image']),
    ])

    print("Loading and flattening dataset from:", args.inputs_csv)
    X = load_and_flatten_dataset(args.inputs_csv, args.cache_dir, transforms_fn)
    print(f"Dataset shape after flattening: {X.shape}")

    # Build PCAAutoencoder
    model = PCAAutoencoder(n_components=args.n_components, batch_size=args.batch_size_ipca)

    # Fit the PCA model
    print("Fitting IncrementalPCA in batches...")
    model.fit(X)
    print("Done fitting PCA. Transforming data to embeddings...")

    # Get embeddings & reconstruction
    X_recon, Z = model.forward(X)
    print("Embeddings shape:", Z.shape)
    print("Reconstruction shape:", X_recon.shape)

    # Optional: Save
    embeddings_path = os.path.join(args.output_dir, "pca_embeddings.npy")
    recons_path = os.path.join(args.output_dir, "pca_reconstructions.npy")
    np.save(embeddings_path, Z)
    np.save(recons_path, X_recon)
    print(f"Saved embeddings to {embeddings_path} and reconstructions to {recons_path}")

    # If you want to store the actual PCA components for future usage:
    # from joblib import dump
    # ipca_model_path = os.path.join(args.output_dir, "pca_model.joblib")
    # dump(model.ipca, ipca_model_path)
    # print(f"Saved PCA model to {ipca_model_path}")


if __name__ == "__main__":
    main()