dumb scikit-learn

Work in progress. Based on the popular scikit-learn python package...just a little dumber.

See: Dumb Starbucks.

Refreshing on the basics.

Installation

$ git clone git@github.com:samryan18/dumb_sklearn.git
$ pip install dumb_sklearn

Models

1. Neural Networks

Implemented in matlab. Sorry.

2. Principal Component Analysis (dumb_sklearn.PCA)

The API for this model is very similar to the one in sklearn.

Basic usage:

from dumb_sklearn import PCA

X = get_some_data()
pca = PCA(n_components=n_components, standardize=True)
X_transform = pca.fit_transform(X)
X_recon = pca.inverse_transform(X_transform)

Example 1: Eigenfaces

In

from sklearn.datasets import fetch_lfw_people

from dumb_sklearn import PCA
from dumb_sklearn.utils import plot_gallery

faces = fetch_lfw_people(min_faces_per_person=60)

_, h, w = faces.images.shape
X, y = faces.data, faces.target
target_names = faces.target_names
n_components = 200

pca = PCA(n_components=n_components, standardize=True)
X_transform = pca.fit_transform(X)
eigenfaces = pca.components.reshape((n_components, h, w))

print("ORIGINAL FACES:")
real_titles = [f"original {i}" for i in range(eigenfaces.shape[0])]
real_faces = faces.images
plot_gallery(real_faces, real_titles, h, w, n_row=2, n_col=7)

print("EIGENFACES:")
eigenface_titles = [f"eigenface {i}" for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w, n_row=2, n_col=7)

print("AVERAGE FACE:")
plot_gallery(pca._mean.reshape(1, h, w), ["mean"], h, w, n_row=1, n_col=1)

print("STANDARD DEV FACE:")
plot_gallery(pca._std.reshape(1, h, w), ["stddev"], h, w, n_row=1, n_col=1)

print("RECON FACES:")
recon = pca.inverse_transform(X_transform)
recon = recon.reshape((faces.images.shape[0], h, w))
recon_titles = [f"recon {i}" for i in range(eigenfaces.shape[0])]
plot_gallery(recon, recon_titles, h, w, n_row=2, n_col=7)

Out

ORIGINAL FACES

EIGENFACES

AVERAGE FACE

STANDARD DEV FACE

RECON FACES