def test_refit_always():
lr = LogisticRegression()
mod = Thresholder(lr, threshold=0.5, refit=True)
np.random.seed(42)
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
assert mod.fit(X, y).predict(X).shape == y.shape
def test_raise_error2():
with pytest.raises(ValueError):
mod = Thresholder(LinearRegression(), threshold=0.7)
np.random.seed(42)
X = np.random.normal(0, 1, (1000, 3))
# we only support two classes
y = np.random.choice(["a", "b", "c"], 1000)
mod.fit(X, y)
def test_raise_error1():
with pytest.raises(ValueError):
# we only support classification models
mod = Thresholder(LinearRegression(), threshold=0.7)
np.random.seed(42)
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
mod.fit(X, y)
def test_diff_threshold():
mod1 = Thresholder(LogisticRegression(), threshold=0.5)
mod2 = Thresholder(LogisticRegression(), threshold=0.7)
mod3 = Thresholder(LogisticRegression(), threshold=0.9)
np.random.seed(42)
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
assert mod1.fit(X, y).predict(X).sum() >= mod2.fit(X, y).predict(X).sum()
assert mod2.fit(X, y).predict(X).sum() >= mod3.fit(X, y).predict(X).sum()
def test_refit_fits_underlying():
X = np.array([1, 2, 3, 4]).reshape(-1, 1)
y_ones = np.array([0, 1, 1, 1]).reshape(-1, )
y_zeros = np.array([0, 0, 0, 1]).reshape(-1, )
clf = DummyClassifier(strategy="most_frequent")
clf.fit(X, y_ones)
a = Thresholder(clf, threshold=0.2, refit=True)
a.fit(X, y_zeros)
assert a.predict(np.array([[1]])) == 0
def test_passes_sample_weight(refit):
class CustomLR(LogisticRegression):
def fit(self, X, y, sample_weight=None):
assert sample_weight is not None
super().fit(X, y)
mod = Thresholder(CustomLR(), threshold=0.5, refit=refit)
np.random.seed(42)
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
weight = np.random.random(100)
mod.fit(X, y, sample_weight=weight)
def test_stacking_classifier():
'''
Tests issue https://github.com/koaning/scikit-lego/issues/501
No asserts are added as we only test for being exception free.
When cloning the model in Thresholder an unfitted model is generated
where no predict_proba exists
'''
estimators = [("dummy", DummyClassifier(strategy="constant", constant=0))]
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
clf = StackingClassifier(estimators=estimators,
final_estimator=DummyClassifier(
strategy="constant", constant=0))
clf.fit(X, y)
a = Thresholder(clf, threshold=0.2)
a.fit(X, y)
a.predict(X)
def test_same_threshold():
mod1 = Thresholder(LogisticRegression(), threshold=0.5)
mod2 = LogisticRegression()
X = np.random.normal(0, 1, (100, 3))
y = np.random.normal(0, 1, (100, )) < 0
assert (mod1.fit(X, y).predict(X) == mod2.fit(X, y).predict(X)).all()
def test_standard_checks(test_fn):
trf = Thresholder(LogisticRegression(), threshold=0.5)
test_fn(Thresholder.__name__, trf)
def test_nans_could_work():
X = np.array([[np.nan, 4], [7, 3], [5, 5], [7, 2], [5, 7]])
y = np.array([1, 0, 1, 0, 1])
model = Thresholder(HistGradientBoostingClassifier(), 0.6)
model.fit(X, y)