def test_leave_one_out_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
enc = Encoder(method='leave_one_out')
fm_encoded = enc.fit_transform(feature_matrix, features, feature_matrix['value'])
fm_encoded_result = [[7.50001, 5.00001, 2.50001, 20.00001, 15.00001, 8.33333],
[True, True, True, True, True, True],
[0.00001, 5.00001, 10.00001, 15.00001, 20.00001, 0.00001],
[12.50001, 11.250001, 10.00001, 8.750001, 7.50001, 8.33333]]
fm_encoded_result = np.swapaxes(fm_encoded_result, 0, 1)
np.testing.assert_almost_equal(fm_encoded.values, fm_encoded_result, decimal=1)
encoder = LeaveOneOutEnc(fitted_encoder=enc, category='product_id')
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [17.5, 8.33333, 5, 5]
np.testing.assert_almost_equal(encoded, encoded_results, decimal=4)
product_feature = ft.Feature([f1], primitive=LeaveOneOutEnc(enc, 'product_id'))
cc_feature = ft.Feature([f4], primitive=LeaveOneOutEnc(enc, 'countrycode'))
features = [product_feature, f2, f3, cc_feature]
assert features == enc.get_features()
features = enc.get_features()
feature_matrix_new = ft.calculate_feature_matrix(features, es, instance_ids=ids)
new_data = [[5.00001, 5.00001, 5.00001, 17.50001, 17.50001, 8.33333],
[True, True, True, True, True, True],
[0.00001, 5.00001, 10.00001, 15.00001, 20.00001, 0.00001],
[10.00001, 10.00001, 10.00001, 10.00001, 10.00001, 8.33333]]
new_result = np.swapaxes(new_data, 0, 1)
np.testing.assert_almost_equal(feature_matrix_new.values, new_result, decimal=1)
def test_ordinal_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
enc = Encoder(method='ordinal')
fm_encoded = enc.fit_transform(feature_matrix, features)
encoder = OrdinalEnc(fitted_encoder=enc, category='product_id')
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [2, 3, 1, 1]
assert (encoded == encoded_results).all()
product_feature = ft.Feature([f1], primitive=OrdinalEnc(enc, 0))
cc_feature = ft.Feature([f4], primitive=OrdinalEnc(enc, 1))
features = [product_feature, f2, f3, cc_feature]
assert features == enc.get_features()
features = enc.get_features()
feature_matrix_new = ft.calculate_feature_matrix(features, es, instance_ids=ids)
assert (fm_encoded == feature_matrix_new).all().all()
def test_target_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
enc = Encoder(method='target')
fm_encoded = enc.fit_transform(feature_matrix, features, feature_matrix['value'])
encoder = TargetEnc(fitted_encoder=enc, category='product_id')
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [15.034704, 8.333333, 5.397343, 5.397343]
np.testing.assert_almost_equal(encoded, encoded_results, decimal=5)
product_feature = ft.Feature([f1], primitive=TargetEnc(enc, 'product_id'))
cc_feature = ft.Feature([f4], primitive=TargetEnc(enc, 'countrycode'))
features = [product_feature, f2, f3, cc_feature]
assert features == enc.get_features()
features = enc.get_features()
feature_matrix_new = ft.calculate_feature_matrix(features, es, instance_ids=ids)
assert (fm_encoded == feature_matrix_new).all().all()
def test_hashing_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
enc = Encoder(method='hashing')
fm_encoded = enc.fit_transform(feature_matrix, features)
encoder = HashingEnc(fitted_encoder=enc)
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [[0, 0, 0, 0],
[1, 0, 0, 0],
[0, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]]
assert (encoded == encoded_results).all()
product_feature = ft.Feature([f1], primitive=HashingEnc(enc))
cc_feature = ft.Feature([f4], primitive=HashingEnc(enc))
features = [product_feature, f2, f3, cc_feature]
assert len(features) == len(enc.get_features())
for i in range(len(features)):
assert features[i].unique_name() == enc.get_features()[i].unique_name()
features = enc.get_features()
feature_matrix = ft.calculate_feature_matrix(features, es, instance_ids=ids)
assert (fm_encoded == feature_matrix).all().all()
def test_one_hot_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
feature_matrix['countrycode'][0] = np.nan
enc = Encoder(method='one_hot')
fm_encoded = enc.fit_transform(feature_matrix, features)
encoder = OneHotEnc(value='coke zero')
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [0, 0, 1, 1]
assert (encoded == encoded_results).all()
encoder = OneHotEnc(value=np.nan)
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero', np.nan])
encoded_results = [0, 0, 0, 0, 1]
assert (encoded == encoded_results).all()
f1_1 = ft.Feature([f1], primitive=OneHotEnc('coke zero'))
f1_2 = ft.Feature([f1], primitive=OneHotEnc('car'))
f1_3 = ft.Feature([f1], primitive=OneHotEnc('toothpaste'))
f4_1 = ft.Feature([f4], primitive=OneHotEnc('US'))
f4_2 = ft.Feature([f4], primitive=OneHotEnc('AL'))
f4_3 = ft.Feature([f4], primitive=OneHotEnc(np.nan))
features_encoded = [f1_1, f1_2, f1_3, f2, f3, f4_1, f4_2, f4_3]
assert len(features_encoded) == len(enc.get_features())
for i in range(len(features_encoded)):
assert features_encoded[i].unique_name() == enc.get_features()[i].unique_name()
features_encoded = enc.get_features()
feature_matrix = ft.calculate_feature_matrix(features_encoded, es, instance_ids=[6, 7])
data = {'product_id = coke zero': [0, 0],
'product_id = car': [0, 0],
'product_id = toothpaste': [1, 1],
'purchased': [True, True],
'value': [1.0, 2.0],
'countrycode = US': [0, 0],
'countrycode = AL': [1, 1],
'countrycode = nan': [0, 0]}
fm_encoded = pd.DataFrame(data, index=[6, 7])
assert feature_matrix.eq(fm_encoded).all().all()
def test_binary_encoding():
feature_matrix, features, f1, f2, f3, f4, es, ids = create_feature_matrix()
enc = Encoder(method='binary')
fm_encoded = enc.fit_transform(feature_matrix, features)
encoder = BinaryEnc(fitted_encoder=enc, category='product_id')
encoded = encoder(['car', 'toothpaste', 'coke zero', 'coke zero'])
encoded_results = [[0, 0, 0, 0],
[1, 1, 0, 0],
[0, 1, 1, 1]]
assert (encoded == encoded_results).all()
product_feature = ft.Feature([f1], primitive=BinaryEnc(enc, 0))
cc_feature = ft.Feature([f4], primitive=BinaryEnc(enc, 1))
features = [product_feature, f2, f3, cc_feature]
assert len(features) == len(enc.get_features())
# __eq__ does not support multioutput columns yet
for i in range(len(enc.get_features())):
assert features[i].unique_name() == enc.get_features()[i].unique_name()
features = enc.get_features()
feature_matrix = ft.calculate_feature_matrix(features, es, instance_ids=ids)
assert (fm_encoded == feature_matrix).all().all()