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test_random_under_sampler.py
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test_random_under_sampler.py
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"""Test the module random under sampler."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from collections import Counter
from datetime import datetime
import numpy as np
import pytest
from sklearn.datasets import make_classification
from sklearn.utils._testing import assert_array_equal
from imblearn.under_sampling import RandomUnderSampler
RND_SEED = 0
X = np.array(
[
[0.04352327, -0.20515826],
[0.92923648, 0.76103773],
[0.20792588, 1.49407907],
[0.47104475, 0.44386323],
[0.22950086, 0.33367433],
[0.15490546, 0.3130677],
[0.09125309, -0.85409574],
[0.12372842, 0.6536186],
[0.13347175, 0.12167502],
[0.094035, -2.55298982],
]
)
Y = np.array([1, 0, 1, 0, 1, 1, 1, 1, 0, 1])
@pytest.mark.parametrize("as_frame", [True, False], ids=["dataframe", "array"])
def test_rus_fit_resample(as_frame):
if as_frame:
pd = pytest.importorskip("pandas")
X_ = pd.DataFrame(X)
else:
X_ = X
rus = RandomUnderSampler(random_state=RND_SEED, replacement=True)
X_resampled, y_resampled = rus.fit_resample(X_, Y)
X_gt = np.array(
[
[0.92923648, 0.76103773],
[0.47104475, 0.44386323],
[0.13347175, 0.12167502],
[0.09125309, -0.85409574],
[0.12372842, 0.6536186],
[0.04352327, -0.20515826],
]
)
y_gt = np.array([0, 0, 0, 1, 1, 1])
if as_frame:
assert hasattr(X_resampled, "loc")
# FIXME: we should use to_numpy with pandas >= 0.25
X_resampled = X_resampled.values
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_rus_fit_resample_half():
sampling_strategy = {0: 3, 1: 6}
rus = RandomUnderSampler(
sampling_strategy=sampling_strategy,
random_state=RND_SEED,
replacement=True,
)
X_resampled, y_resampled = rus.fit_resample(X, Y)
X_gt = np.array(
[
[0.92923648, 0.76103773],
[0.47104475, 0.44386323],
[0.92923648, 0.76103773],
[0.15490546, 0.3130677],
[0.15490546, 0.3130677],
[0.15490546, 0.3130677],
[0.20792588, 1.49407907],
[0.15490546, 0.3130677],
[0.12372842, 0.6536186],
]
)
y_gt = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
def test_multiclass_fit_resample():
y = Y.copy()
y[5] = 2
y[6] = 2
rus = RandomUnderSampler(random_state=RND_SEED)
X_resampled, y_resampled = rus.fit_resample(X, y)
count_y_res = Counter(y_resampled)
assert count_y_res[0] == 2
assert count_y_res[1] == 2
assert count_y_res[2] == 2
def test_random_under_sampling_heterogeneous_data():
X_hetero = np.array(
[["xxx", 1, 1.0], ["yyy", 2, 2.0], ["zzz", 3, 3.0]], dtype=object
)
y = np.array([0, 0, 1])
rus = RandomUnderSampler(random_state=RND_SEED)
X_res, y_res = rus.fit_resample(X_hetero, y)
assert X_res.shape[0] == 2
assert y_res.shape[0] == 2
assert X_res.dtype == object
def test_random_under_sampling_nan_inf():
# check that we can undersample even with missing or infinite data
# regression tests for #605
rng = np.random.RandomState(42)
n_not_finite = X.shape[0] // 3
row_indices = rng.choice(np.arange(X.shape[0]), size=n_not_finite)
col_indices = rng.randint(0, X.shape[1], size=n_not_finite)
not_finite_values = rng.choice([np.nan, np.inf], size=n_not_finite)
X_ = X.copy()
X_[row_indices, col_indices] = not_finite_values
rus = RandomUnderSampler(random_state=0)
X_res, y_res = rus.fit_resample(X_, Y)
assert y_res.shape == (6,)
assert X_res.shape == (6, 2)
assert np.any(~np.isfinite(X_res))
@pytest.mark.parametrize(
"sampling_strategy", ["auto", "majority", "not minority", "not majority", "all"]
)
def test_random_under_sampler_strings(sampling_strategy):
"""Check that we support all supposed strings as `sampling_strategy` in
a sampler inheriting from `BaseUnderSampler`."""
X, y = make_classification(
n_samples=100,
n_clusters_per_class=1,
n_classes=3,
weights=[0.1, 0.3, 0.6],
random_state=0,
)
RandomUnderSampler(sampling_strategy=sampling_strategy).fit_resample(X, y)
def test_random_under_sampling_datetime():
"""Check that we don't convert input data and only sample from it."""
pd = pytest.importorskip("pandas")
X = pd.DataFrame({"label": [0, 0, 0, 1], "td": [datetime.now()] * 4})
y = X["label"]
rus = RandomUnderSampler(random_state=0)
X_res, y_res = rus.fit_resample(X, y)
pd.testing.assert_series_equal(X_res.dtypes, X.dtypes)
pd.testing.assert_index_equal(X_res.index, y_res.index)
assert_array_equal(y_res.to_numpy(), np.array([0, 1]))