def test_min_change_greater_than_zero_forward():
"""
Check that `min_change` is required to be > 0.
"""
msg = "`min_change` must be greater than zero."
with pytest.raises(ValueError, match=msg):
forward(min_change=-0.5, n_features=None)
def test_min_change_fails_on_string_forward():
"""
Check that forward raises when passed a string
for `min_change`.
"""
msg = "`min_change` must be of type int or float."
with pytest.raises(TypeError, match=msg):
forward(min_change='-0.75', n_features=None)
def test_selection_class_use_of_criterion():
"""Test Criterion through `forward()` and `backward()."""
msg = "`criterion` must be one of: None, 'aic', 'bic'."
with pytest.raises(ValueError, match=msg):
forward(min_change=0.5, criterion='acc')
with pytest.raises(ValueError, match=msg):
backward(n_features=0.5, criterion='Santa')
def test_both_non_none_forward():
"""
Check `forward()` raise when at least one
of `min_change` or `n_features` are not None.
"""
# Note: items in backticks (``) will be in alphabetical order.
msg = "At least one of `min_change` and `n_features` must be None."
with pytest.raises(TypeError, match=msg):
forward(min_change=0.5, n_features=0.3)
def test_too_few_features():
"""
Check that there are enough features for
for selection to be a coherent goal (i.e., >= 2).
"""
X_train = DEFAULT_SELECTION_PARAMS['X_train']
X_train = X_train[:, 0:1]
with pytest.raises(IndexError):
forward(n_features=1, X_train=X_train)
with pytest.raises(IndexError):
backward(n_features=1, X_train=X_train)
def test_fsel_verbose_output():
forward_output = forward(n_features=None, min_change=1, verbose=True)
assert len(forward_output) >= 1
def test_fsel_min_change_output():
forward_output = forward(n_features=None, min_change=1, criterion=None)
assert len(forward_output) >= 1
def test_fsel_bic_output():
forward_output = forward(n_features=2, min_change=None, criterion='bic')
assert len(forward_output) > 0
# -----------------------------------------------------------------------------
def test_passing_significant_change():
"""
Test cases where there is a significant `min_change`
during backward selection.
"""
backward(n_features=None, min_change=1, _last_score_punt=True)
# -----------------------------------------------------------------------------
# Outputs: Run the Forward and Backward Selection Algorithms
# -----------------------------------------------------------------------------
forward_output = forward()
# Run using the other parameter option
forward_output += forward(n_features=1, min_change=None)
# Force the backward selection algorithm to
# select the single feature it thinks is most predictive.
# If implemented correctly, `backward()` should be able to
# identify `true_best_features` as predictive.
backward_output = backward(n_features=1)
# Run using the other parameter option
backward_output += backward(min_change=0.0001, n_features=None)
# -----------------------------------------------------------------------------
# Test outputs: Type
def test_loop_exhaust():
"""Text Exhausting forwards()'s loop."""
# Should not raise.
forward(n_features=X_train.shape[-1], min_change=None, _do_not_skip=False)