def test_drop_low_std(self):
""" Test dropping features with low std. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_low_std(threshold=.05)
after = feat._x.shape[1]
assert before > after
def test_ignore(self):
""" Test if ignores a feature. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_low_std(ignore="single")
after = feat._x.shape[1]
assert before == after
def test_ignore(self):
""" Test if ignores a feature. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_na(threshold=self.na_ratio - 0.01, ignore="fake")
after = feat._x.shape[1]
assert before == after
def test_equals_threshold(self):
""" Test if keep feature with value equals the threshold. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_na(threshold=self.na_ratio, ignore="fake")
after = feat._x.shape[1]
assert before == after
def test_drop_dependent(self):
""" Test dropping columns. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_multiple_dependence(threshold=0.95)
after = feat._x.shape[1]
assert before > after
def test_drop(self):
""" Test if drop class with NA ratio above the threshold."""
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_na(threshold=self.na_ratio - 0.01, ignore=None)
after = feat._x.shape[1]
assert before > after
def test_drop(self):
""" Test dropping features. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_low_importance(threshold=0.95, n_times=1)
after = feat._x.shape[1]
assert before > after
def test_drop(self):
""" Test dropping features. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
feat.drop_correlated(threshold=0.95)
after = feat._x.shape[1]
assert before > after
def test_ignore(self):
""" Test if ignores a feature. """
feat = feature.Selector(self.data, target)
before = feat._x.shape[1]
# Ignore the the feature that would be dropped.
feat.drop_correlated(threshold=0.95, ignore="copy")
after = feat._x.shape[1]
assert before == after
def setup_class(cls):
cls.feat = feature.Selector(data, target, features, task)
def test_split(self):
""" Test split params method. """
feat = feature.Selector(data, target, features)
datasets = autolearn.split(feat._x, feat._y, 5000, 0.25)
test_len = 0.25 * feat._x.shape[0]
assert abs(datasets[1].shape[0] - test_len) < 1
