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- from ds4ml.metrics import (pairwise_mutual_information,
- relative_error, error_rate)
- def test_pairwise_mutual_information():
- from pandas import DataFrame
- from .testdata import adults01
- frame = DataFrame(adults01)
- mi = pairwise_mutual_information(frame)
- from numpy import allclose, alltrue
- assert allclose(mi, mi.T)
- assert alltrue(mi >= 0.0)
- assert alltrue(mi <= 1.0)
- def test_error_rate():
- from sklearn.metrics import accuracy_score
- from numpy.random import randint
- from numpy import isclose
- y_true = [2, 0, 2, 2, 0, 1]
- y_pred = [0, 0, 2, 2, 0, 2]
- # its confusion matrix:
- # 2 0 0
- # 0 0 1
- # 1 0 2
- assert isclose(error_rate(y_true, y_pred), 0.3333)
- for _ in range(10):
- y_true = randint(0, 20, 100)
- y_pred = randint(0, 20, 100)
- assert isclose(error_rate(y_true, y_pred),
- 1 - accuracy_score(y_true, y_pred))
- def test_relative_error():
- from numpy import array, random
- a = array([0, 1, 0])
- b = array([1, 1, 0])
- assert relative_error(a, b) == 0.3333
- for _ in range(10):
- size = random.randint(5, 100)
- x = random.choice(20, size)
- y = random.choice(20, size)
- assert 0.0 <= relative_error(x, y) <= 1.0
- x = random.rand(size)
- y = random.rand(size)
- assert 0.0 <= relative_error(x, y) <= 1.0
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