68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import numpy as np
|
|
|
|
from numpy.testing import assert_allclose
|
|
from scalers import StandardScaler, MinMaxScaler
|
|
|
|
|
|
def test_scalers_0():
|
|
with open('scalers.py', 'r') as file:
|
|
lines = ' '.join(file.readlines())
|
|
assert 'import numpy' in lines
|
|
assert 'import typing' in lines
|
|
assert lines.count('import') == 2
|
|
assert 'sklearn' not in lines
|
|
|
|
def test_scalers_1():
|
|
X_1 = np.random.uniform(-10, 20, (10, 20))
|
|
|
|
scaler = StandardScaler()
|
|
scaler.fit(X_1)
|
|
X_2 = scaler.transform(X_1)
|
|
|
|
assert type(X_2) == np.ndarray
|
|
assert_allclose(np.mean(X_2, axis=0), np.zeros(20), rtol=1e-05, atol=1e-08)
|
|
assert_allclose(np.std(X_2, axis=0), np.ones(20), rtol=1e-05, atol=1e-08)
|
|
|
|
def test_scalers_2():
|
|
X_1 = np.random.uniform(-10, 20, (10, 20))
|
|
|
|
scaler = MinMaxScaler()
|
|
scaler.fit(X_1)
|
|
X_2 = scaler.transform(X_1)
|
|
|
|
assert type(X_2) == np.ndarray
|
|
assert_allclose(np.min(X_2, axis=0), np.zeros(20), rtol=1e-05, atol=1e-08)
|
|
assert_allclose(np.max(X_2, axis=0), np.ones(20), rtol=1e-05, atol=1e-08)
|
|
|
|
def test_scalers_3():
|
|
X_1 = np.array([[0, 1, 0], [1, 1, 1], [0.3, 0.25, 0.5], [-0.5, -1, 4]])
|
|
X_2 = np.array([[0, 1, 0], [1, 1, 1], [0.3, 0.25, 0.5], [-0.5, -1, 4], [0, 0, 0], [2, -1, 0.5]])
|
|
|
|
scaler = StandardScaler()
|
|
scaler.fit(X_1)
|
|
X_3 = scaler.transform(X_2)
|
|
answer = np.array([[-0.36822985, 0.84119102, -0.88354126],
|
|
[ 1.47291939, 0.84119102, -0.2409658 ],
|
|
[ 0.18411492, -0.07647191, -0.56225353],
|
|
[-1.28880447, -1.60591014, 1.68676059],
|
|
[-0.36822985, -0.38235956, -0.88354126],
|
|
[ 3.31406862, -1.60591014, -0.56225353]])
|
|
assert type(X_3) == np.ndarray
|
|
assert_allclose(X_3, answer, rtol=1e-05, atol=1e-08)
|
|
|
|
def test_scalers_4():
|
|
X_1 = np.array([[0, 1, 0], [1, 1, 1], [0.3, 0.25, 0.5], [-0.5, -1, 4]])
|
|
X_2 = np.array([[0, 1, 0], [1, 1, 1], [0.3, 0.25, 0.5], [-0.5, -1, 4], [0, 0, 0], [2, -1, 0.5]])
|
|
|
|
scaler = MinMaxScaler()
|
|
scaler.fit(X_1)
|
|
X_3 = scaler.transform(X_2)
|
|
answer = np.array([[0.33333333, 1. , 0. ],
|
|
[1. , 1. , 0.25 ],
|
|
[0.53333333, 0.625 , 0.125 ],
|
|
[0. , 0. , 1. ],
|
|
[0.33333333, 0.5 , 0. ],
|
|
[1.66666667, 0. , 0.125 ]])
|
|
assert type(X_3) == np.ndarray
|
|
assert_allclose(X_3, answer, rtol=1e-05, atol=1e-08)
|