def test_init_fit_predict_spark_pandas(self):
from lale.datasets import pandas2spark
from lale.datasets.util import spark_installed
if spark_installed:
trainable_cf = ConcatFeatures()
A = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
B = [[14, 15], [24, 25], [34, 35]]
A = pd.DataFrame(A, columns=["a", "b", "c"])
B = pd.DataFrame(B, columns=["d", "e"])
A = pandas2spark(A, add_index=True)
A = add_table_name(A, "A")
B = add_table_name(B, "B")
trained_cf = trainable_cf.fit(X=[A, B])
transformed = trained_cf.transform([A, B])
expected = [
[11, 12, 13, 14, 15],
[21, 22, 23, 24, 25],
[31, 32, 33, 34, 35],
]
expected = pd.DataFrame(expected,
columns=["a", "b", "c", "d", "e"])
for c in expected.columns:
self.assertEqual(list(transformed[c]), list(expected[c]))
def test_init_fit_predict(self):
trainable_cf = ConcatFeatures()
A = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
B = [[14, 15], [24, 25], [34, 35]]
trained_cf = trainable_cf.fit(X=[A, B])
transformed = trained_cf.transform([A, B])
expected = [[11, 12, 13, 14, 15], [21, 22, 23, 24, 25],
[31, 32, 33, 34, 35]]
for i_sample in range(len(transformed)):
for i_feature in range(len(transformed[i_sample])):
self.assertEqual(transformed[i_sample][i_feature],
expected[i_sample][i_feature])
def test_init_fit_predict_pandas_series(self):
trainable_cf = ConcatFeatures()
A = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]
B = [14, 24, 34]
A = pd.DataFrame(A, columns=["a", "b", "c"])
B = pd.Series(B, name="d")
A = add_table_name(A, "A")
B = add_table_name(B, "B")
trained_cf = trainable_cf.fit(X=[A, B])
transformed = trained_cf.transform([A, B])
expected = [
[11, 12, 13, 14],
[21, 22, 23, 24],
[31, 32, 33, 34],
]
expected = pd.DataFrame(expected, columns=["a", "b", "c", "d"])
for c in expected.columns:
self.assertEqual(list(transformed[c]), list(expected[c]))
