in python/pipelines/components/python/component.py [0:0]
def _create_model(params):
model = Pipeline([
('transform', ColumnTransformer(
transformers=[
('tfidf',
TfidfTransformer(norm='l2'),
list(range(columns_to_skip, len(features) + columns_to_skip)) # Skipping the first n columns
)
]
)),
('model', KMeans(
init='k-means++', n_init='auto',
random_state=42,
**params)
)
])
return model