<gh_stars>0
import matplotlib.pyplot as plt
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer


def _generate_vector(shift=0.5, noise=15):
    return np.arange(1000) + (np.random.rand(1000) - shift) * noise


def generate_dataset():
    return np.vstack((
        np.vstack((
            _generate_vector(),
            _generate_vector() + 100,
        )).T,
        np.vstack((
            _generate_vector(),
            _generate_vector(),
        )).T,
    )), np.hstack((np.zeros(1000), np.ones(1000)))


def all_but_first_column(X):
    return X[:, 1:]


def drop_first_component(X, y):
    pipeline = make_pipeline(PCA(), FunctionTransformer(all_but_first_column))
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    pipeline.fit(X_train, y_train)
    return pipeline.transform(X_test), y_test


if __name__ == '__main__':
    X, y = generate_dataset()
    lw = 0
    plt.figure()
    plt.scatter(X[:, 0], X[:, 1], c=y, lw=lw)
    plt.figure()
    X_transformed, y_transformed = drop_first_component(*generate_dataset())
    plt.scatter(X_transformed[:, 0], np.zeros(len(X_transformed)), c=y_transformed, lw=lw, s=60)
    plt.show()

import matplotlib.pyplot as plt
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer


def _generate_vector(shift=0.5, noise=15):
    return np.arange(1000) + (np.random.rand(1000) - shift) * noise


def generate_dataset():
    return np.vstack((
        np.vstack((
            _generate_vector(),
            _generate_vector() + 100,
        )).T,
        np.vstack((
            _generate_vector(),
            _generate_vector(),
        )).T,
    )), np.hstack((np.zeros(1000), np.ones(1000)))


def all_but_first_column(X):
    return X[:, 1:]


def drop_first_component(X, y):
    pipeline = make_pipeline(PCA(), FunctionTransformer(all_but_first_column))
    X_train, X_test, y_train, y_test = train_test_split(X, y)
    pipeline.fit(X_train, y_train)
    return pipeline.transform(X_test), y_test


if __name__ == '__main__':
    X, y = generate_dataset()
    lw = 0
    plt.figure()
    plt.scatter(X[:, 0], X[:, 1], c=y, lw=lw)
    plt.figure()
    X_transformed, y_transformed = drop_first_component(*generate_dataset())
    plt.scatter(X_transformed[:, 0], np.zeros(len(X_transformed)), c=y_transformed, lw=lw, s=60)
    plt.show()