dred

dred = dimension reducing (DR) for ML (suit to sklearn)

Currently, only for regression.

Framework

X -DR-> X' -Regression-> Y' <-DR-> Y

train data X, Y test(predict) data Xtest, Ytest

1. X and Y will be dred-ed
2. a model will fit X' and Y'
3. After Xtest is dred-ed, predict the output of Xtest'
4. Get Y', reconstruct Y from Y'
5. get error of Y to Ytest

Requirements

scikit-learn

Usage

Basic usage

# X -> R^p, Y -> R^q
@DimReduce(p, q)
class cls(RegressorMixin):
    # Definition of cls, in sklearn form

Create yourself dim reduction method

I've defined two DR methods in the module

class SVDTransformer(FunctionTransformer):
    '''SVD DR transformer
    
    make sure it has transform and inverse_transform method
    '''
    def __init__(self, p=None, *args, **kwargs):
        super(SVDTransformer, self).__init__(*args, **kwargs)
        self.p = p


    def fit(self, X):
        def svd(X, p):
            V, s, Vh = LA.svd(X.T @ X)
            Vp = V[:, :p]
            Cp = X @ Vp
            return Cp, Vp
        if self.p:
            X, V = svd(X, self.p)
            self.func = lambda X: X @ V
            self.inverse_func = lambda X: X @ V.T


class SVDDimReduce(DimReduce):
    # SVD for X and y
    def __init__(self, p=3, q=None):
        dr1 = SVDTransformer(p)
        dr2 = SVDTransformer(q)
        super(SVDDimReduce, self).__init__(dr1, dr2)


class PCADimReduce(DimReduce):
    # PCA for X and y
    def __init__(self, p=3, q=None):
        dr1 = PCA(n_components=p)
        dr2 = PCA(n_components=q)
        super(PCADimReduce, self).__init__(dr1, dr2)

Make sure dr1 and dr2 has transform and inverse_transform method. :caution:

Example

an example regressor supplied in dred.py

run lineqx.py (demo program)

TODO

• More methods for DR
• make Classifier