def test_one_hot_encoder_not_fitted():
X = np.array([['a'], ['b']])
enc = OneHotEncoder(categories=['a', 'b'])
msg = ("This OneHotEncoder instance is not fitted yet. "
"Call 'fit' with appropriate arguments before using this method.")
with pytest.raises(NotFittedError, match=msg):
enc.transform(X)
def test_one_hot_encoder_handle_unknown():
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
X2 = np.array([[4, 1, 1]])
# Test that one hot encoder raises error for unknown features
# present during transform.
oh = OneHotEncoder(handle_unknown='error')
oh.fit(X)
with pytest.raises(ValueError, match='Found unknown categories'):
oh.transform(X2)
# Test the ignore option, ignores unknown features (giving all 0's)
oh = OneHotEncoder(handle_unknown='ignore')
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0., 0., 0., 0., 1., 0., 0.]]))
# ensure transformed data was not modified in place
assert_allclose(X2, X2_passed)
# Raise error if handle_unknown is neither ignore or error.
oh = OneHotEncoder(handle_unknown='42')
with pytest.raises(ValueError, match='handle_unknown should be either'):
oh.fit(X)
def test_encoder_dtypes():
# check that dtypes are preserved when determining categories
enc = OneHotEncoder(categories='auto')
exp = np.array([[1., 0., 1., 0.], [0., 1., 0., 1.]], dtype='float64')
for X in [
np.array([[1, 2], [3, 4]], dtype='int64'),
np.array([[1, 2], [3, 4]], dtype='float64'),
np.array([['a', 'b'], ['c', 'd']]), # string dtype
np.array([[1, 'a'], [3, 'b']], dtype='object')
]:
enc.fit(X)
assert all([enc.categories_[i].dtype == X.dtype for i in range(2)])
assert_array_equal(enc.transform(X).toarray(), exp)
X = [[1, 2], [3, 4]]
enc.fit(X)
assert all([
np.issubdtype(enc.categories_[i].dtype, np.integer) for i in range(2)
])
assert_array_equal(enc.transform(X).toarray(), exp)
X = [[1, 'a'], [3, 'b']]
enc.fit(X)
assert all([enc.categories_[i].dtype == 'object' for i in range(2)])
assert_array_equal(enc.transform(X).toarray(), exp)
def test_one_hot_encoder_diff_n_features():
X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
X2 = np.array([[1, 0]])
enc = OneHotEncoder()
enc.fit(X)
err_msg = ("The number of features in X is different to the number of "
"features of the fitted data.")
with pytest.raises(ValueError, match=err_msg):
enc.transform(X2)
def test_encoder_dtypes_pandas():
# check dtype (similar to test_categorical_encoder_dtypes for dataframes)
pd = pytest.importorskip('pandas')
enc = OneHotEncoder(categories='auto')
exp = np.array([[1., 0., 1., 0., 1., 0.], [0., 1., 0., 1., 0., 1.]],
dtype='float64')
X = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}, dtype='int64')
enc.fit(X)
assert all([enc.categories_[i].dtype == 'int64' for i in range(2)])
assert_array_equal(enc.transform(X).toarray(), exp)
X = pd.DataFrame({'A': [1, 2], 'B': ['a', 'b'], 'C': [3., 4.]})
X_type = [X['A'].dtype, X['B'].dtype, X['C'].dtype]
enc.fit(X)
assert all([enc.categories_[i].dtype == X_type[i] for i in range(3)])
assert_array_equal(enc.transform(X).toarray(), exp)
def test_one_hot_encoder_raise_missing(X, as_data_frame, handle_unknown):
if as_data_frame:
pd = pytest.importorskip('pandas')
X = pd.DataFrame(X)
ohe = OneHotEncoder(categories='auto', handle_unknown=handle_unknown)
with pytest.raises(ValueError, match="Input contains NaN"):
ohe.fit(X)
with pytest.raises(ValueError, match="Input contains NaN"):
ohe.fit_transform(X)
if as_data_frame:
X_partial = X.iloc[:1, :]
else:
X_partial = X[:1, :]
ohe.fit(X_partial)
with pytest.raises(ValueError, match="Input contains NaN"):
ohe.transform(X)
def test_one_hot_encoder_handle_unknown_strings():
X = np.array(['11111111', '22', '333', '4444']).reshape((-1, 1))
X2 = np.array(['55555', '22']).reshape((-1, 1))
# Non Regression test for the issue #12470
# Test the ignore option, when categories are numpy string dtype
# particularly when the known category strings are larger
# than the unknown category strings
oh = OneHotEncoder(handle_unknown='ignore')
oh.fit(X)
X2_passed = X2.copy()
assert_array_equal(
oh.transform(X2_passed).toarray(),
np.array([[0., 0., 0., 0.], [0., 1., 0., 0.]]))
# ensure transformed data was not modified in place
assert_array_equal(X2, X2_passed)
n_estimators=n_estimator,
random_state=0)
rt_lm = LogisticRegression(max_iter=1000)
pipeline = make_pipeline(rt, rt_lm)
pipeline.fit(X_train, y_train)
y_pred_rt = pipeline.predict_proba(X_test)[:, 1]
fpr_rt_lm, tpr_rt_lm, _ = roc_curve(y_test, y_pred_rt)
# Supervised transformation based on random forests
rf = RandomForestClassifier(max_depth=3, n_estimators=n_estimator)
rf_enc = OneHotEncoder()
rf_lm = LogisticRegression(max_iter=1000)
rf.fit(X_train, y_train)
rf_enc.fit(rf.apply(X_train))
rf_lm.fit(rf_enc.transform(rf.apply(X_train_lr)), y_train_lr)
y_pred_rf_lm = rf_lm.predict_proba(rf_enc.transform(rf.apply(X_test)))[:, 1]
fpr_rf_lm, tpr_rf_lm, _ = roc_curve(y_test, y_pred_rf_lm)
# Supervised transformation based on gradient boosted trees
grd = GradientBoostingClassifier(n_estimators=n_estimator)
grd_enc = OneHotEncoder()
grd_lm = LogisticRegression(max_iter=1000)
grd.fit(X_train, y_train)
grd_enc.fit(grd.apply(X_train)[:, :, 0])
grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)[:, :, 0]), y_train_lr)
y_pred_grd_lm = grd_lm.predict_proba(
grd_enc.transform(grd.apply(X_test)[:, :, 0]))[:, 1]
fpr_grd_lm, tpr_grd_lm, _ = roc_curve(y_test, y_pred_grd_lm)