def test_n_estimators(self):
clf = ForestClassifier()
assert clf.n_estimators == 10
clf = ForestClassifier(n_estimators=17)
assert clf.n_estimators == 17
clf.n_estimators = 42
assert clf.n_estimators == 42
with pytest.raises(ValueError,
match="n_estimators must be an integer number"):
clf.n_estimators = 0.42
with pytest.raises(ValueError,
match="n_estimators must be an integer number"):
clf.n_estimators = None
with pytest.raises(ValueError,
match="n_estimators must be an integer number"):
clf.n_estimators = "4"
with pytest.raises(ValueError, match="n_estimators must be >= 1"):
clf.n_estimators = 0
with pytest.raises(ValueError, match="n_estimators must be >= 1"):
clf.n_estimators = -3
clf = ForestClassifier()
# Check that the properties checks the _fitted flag
clf._fitted = True
with pytest.raises(
ValueError,
match="You cannot change n_estimators after calling fit"):
clf.n_estimators = 0.42
def test_n_samples_in_(self):
clf = ForestClassifier()
with pytest.raises(
ValueError,
match="You must call fit before asking for n_samples_in_"):
_ = clf.n_samples_in_
clf.n_samples_in_ = 42
with pytest.raises(
ValueError,
match="You must call fit before asking for n_samples_in_"):
_ = clf.n_samples_in_
clf._fitted = True
assert clf.n_samples_in_ == 42
assert clf.n_samples_in_ == 42
np.random.seed(42)
X = np.random.randn(10, 3)
y = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
clf = ForestClassifier().fit(X, y)
assert clf.n_samples_in_ == 10
