def test_numerical_default():
optb = MulticlassOptimalBinning()
optb.fit(x, y)
assert optb.status == "OPTIMAL"
assert optb.splits == approx(
[2.1450001, 2.245, 2.31499994, 2.6049999, 2.6450001], rel=1e-6)
def test_numerical_default_transform():
optb = MulticlassOptimalBinning()
with raises(NotFittedError):
x_transform = optb.transform(x)
optb.fit(x, y)
x_transform = optb.transform([0.3, 2.1, 2.5, 3], metric="mean_woe")
assert x_transform == approx([0.48973998, 0.48973998, -0.00074357,
0.02189459], rel=1e-5)
def test_numerical_default_solvers():
optb_mip_bop = MulticlassOptimalBinning(solver="mip", mip_solver="bop")
optb_mip_bop.fit(x, y)
optb_cp = MulticlassOptimalBinning(solver="cp")
optb_cp.fit(x, y)
for optb in [optb_mip_bop, optb_cp]:
assert optb.status == "OPTIMAL"
assert optb.splits == approx([2.1450001, 2.245, 2.31499994, 2.6049999,
2.6450001], rel=1e-6)
def test_numerical_default():
optb = MulticlassOptimalBinning()
optb.fit(x, y)
assert optb.status == "OPTIMAL"
assert optb.splits == approx([2.1450001, 2.245, 2.31499994, 2.6049999,
2.6450001], rel=1e-6)
optb.binning_table.build()
optb.binning_table.analysis()
assert optb.binning_table.js == approx(0.10989515, rel=1e-6)
assert optb.binning_table.quality_score == approx(0.05279822, rel=1e-6)
def test_numerical_default():
optb = MulticlassOptimalBinning()
optb.fit(x, y)
assert optb.status == "OPTIMAL"
assert optb.splits == approx(
[2.1450001, 2.245, 2.31499994, 2.6049999, 2.6450001], rel=1e-6)
optb.binning_table.build()
optb.binning_table.analysis()
assert optb.binning_table.js == approx(0.10989515, rel=1e-6)
assert optb.binning_table.quality_score == approx(0.05279822, rel=1e-6)
optb.binning_table.plot(savefig="test_multiclass_binning.png")
optb.binning_table.plot(add_special=False,
savefig="test_multiclass_binning_no_special.png")
optb.binning_table.plot(add_missing=False,
savefig="test_multiclass_binning_no_missing.png")
def test_default_transform_multiclass():
data = load_wine()
variable_names = data.feature_names
X = data.data
y = data.target
process = BinningProcess(variable_names)
process.fit(X, y)
X_transform = process.transform(X)
optb = process.get_binned_variable(variable_names[0])
assert isinstance(optb, MulticlassOptimalBinning)
optb = MulticlassOptimalBinning()
x = X[:, 5]
optb.fit(x, y)
assert optb.transform(x) == approx(X_transform[:, 5], rel=1e-6)
def test_params():
with raises(TypeError):
optb = MulticlassOptimalBinning(name=1)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(prebinning_method="new_method")
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(solver="new_solver")
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(max_n_prebins=-2)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(min_prebin_size=0.6)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(min_n_bins=-2)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(max_n_bins=-2.2)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(min_n_bins=3, max_n_bins=2)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(min_bin_size=0.6)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(max_bin_size=-0.6)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(min_bin_size=0.5, max_bin_size=0.3)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(monotonic_trend=["new_trend", "auto"])
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(monotonic_trend="new_trend")
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(max_pvalue=1.1)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(max_pvalue_policy="new_policy")
optb.fit(x, y)
with raises(TypeError):
optb = MulticlassOptimalBinning(user_splits={"a": [1, 2]})
optb.fit(x, y)
with raises(TypeError):
optb = MulticlassOptimalBinning(special_codes={1, 2, 3})
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(split_digits=9)
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(mip_solver="new_solver")
optb.fit(x, y)
with raises(ValueError):
optb = MulticlassOptimalBinning(time_limit=-2)
optb.fit(x, y)
with raises(TypeError):
optb = MulticlassOptimalBinning(verbose=1)
optb.fit(x, y)
def test_verbose():
optb = MulticlassOptimalBinning(verbose=True)
optb.fit(x, y)
assert optb.status == "OPTIMAL"
def test_classes():
optb = MulticlassOptimalBinning()
optb.fit(x, y)
assert optb.classes == approx([0, 1, 2])
def test_numerical_user_splits_fixed():
user_splits = [2.1, 2.2, 2.3, 2.6, 2.9]
with raises(ValueError):
user_splits_fixed = [False, False, False, True, False]
optb = MulticlassOptimalBinning(user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
with raises(TypeError):
user_splits_fixed = (False, False, False, True, False)
optb = MulticlassOptimalBinning(user_splits=user_splits,
user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
with raises(ValueError):
user_splits_fixed = [0, 0, 0, 1, 0]
optb = MulticlassOptimalBinning(user_splits=user_splits,
user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
with raises(ValueError):
user_splits_fixed = [False, False, False, False]
optb = MulticlassOptimalBinning(user_splits=user_splits,
user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
user_splits_fixed = [False, False, False, True, True]
with raises(ValueError):
# pure pre-bins
optb = MulticlassOptimalBinning(user_splits=user_splits,
user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
user_splits = [2.1, 2.2, 2.3, 2.6, 2.7]
optb = MulticlassOptimalBinning(user_splits=user_splits,
user_splits_fixed=user_splits_fixed)
optb.fit(x, y)
assert optb.status == "OPTIMAL"
assert 2.7 in optb.splits
def test_numerical_user_splits_non_unique():
user_splits = [2.1, 2.2, 2.2, 2.6, 2.9]
optb = MulticlassOptimalBinning(user_splits=user_splits)
with raises(ValueError):
optb.fit(x, y)