Util functions for computing and removing principal components

pc_utils.py

from sklearn.decomposition import TruncatedSVD


def compute_pc(X,npc=1):
    """
    Compute the principal components. 
    X: numpy array [data, features]
    npc: num principal components
    """
    svd = TruncatedSVD(n_components=npc, n_iter=7, random_state=0)
    svd.fit(X)
    return svd.components_

def remove_pc(X, npc=1, pc=None):
    """
    Remove the projection on the principal components
    X: numpy array [data, features]
    npc: num principal components
    pc (optional): direction to project and flatten on
    returns: out[i, :] is the data point after removing its projection
    """
    if pc is None:
        pc = compute_pc(X, npc)

    if npc==1:
        out = X - X.dot(pc.transpose()) * pc
    else:
        out = X - X.dot(pc.transpose()).dot(pc)

    return out