def test_fit_transform():
feats = [0, 1, 2, np.nan, 5, np.nan]
labels = [0, 1, 0, 1, 1, 0]
binner = tfb.DecisionTreeBinner('test', max_leaf_nodes=3)
trans = binner.fit_transform(feats, labels, class_index=1)
np.testing.assert_equal(trans, [0.0, 1.0, 0.5, 0.5, 0.5, 0.5])
def test_fit():
feats = [0, 1, 2, np.nan, 5, np.nan]
labels = [0, 1, 0, 1, 1, 0]
binner = tfb.DecisionTreeBinner('test', max_leaf_nodes=3)
binner.fit(feats, labels)
np.testing.assert_equal(binner.splits, [0.5, 1.5, np.PINF, np.NaN])
np.testing.assert_equal(binner.values,
[[1.0, 0.0], [0.0, 1.0], [0.5, 0.5], [0.5, 0.5]])
def test_str_repr_with_mdlp():
assert_str = \
"""<= 13.0949993134: [ 0.0490566 0.9509434]
<= 15.0450000763: [ 0.28787879 0.71212121]
<= 17.8800010681: [ 0.85333333 0.14666667]
<= inf: [ 1. 0.]
NaN: [ 0.37258348 0.62741652]"""
binner = tfb.DecisionTreeBinner('test', mdlp=True)
binner.fit(data[:, 0], target)
assert str(binner) == assert_str
def test_recursion_with_mdlp():
binner = tfb.DecisionTreeBinner('test', mdlp=True)
binner.fit(data[:, 0], target)
np.testing.assert_equal(binner.splits, [
13.094999313354492, 15.045000076293945, 17.880001068115234, np.PINF,
np.NaN
])
np.testing.assert_equal(
binner.values, [[0.04905660377358491, 0.9509433962264151],
[0.2878787878787879, 0.7121212121212122],
[0.8533333333333334, 0.14666666666666667], [1.0, 0.0],
[0.37258347978910367, 0.62741652021089633]])
def test_recursion():
binner = tfb.DecisionTreeBinner('test', max_leaf_nodes=4)
binner.fit(data[:, 0], target)
np.testing.assert_equal(binner.splits, [
13.094999313354492, 15.045000076293945, 16.924999237060547, np.PINF,
np.NaN
])
np.testing.assert_equal(binner.values,
[[0.04905660377358491, 0.9509433962264151],
[0.2878787878787879, 0.7121212121212122],
[0.8148148148148148, 0.18518518518518517],
[0.9915254237288136, 0.00847457627118644],
[0.37258347978910367, 0.62741652021089633]])