Different Number of Levels

HiClass supports different number of levels in the hierarchy. For this example, we will train a local classifier per node with a hierarchy similar to the following image:

Out:

/home/docs/checkouts/readthedocs.org/user_builds/hiclass/envs/v4.3.0/lib/python3.8/site-packages/sklearn/utils/_array_api.py:380: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  array = numpy.asarray(array, order=order, dtype=dtype)
[['Mammal' 'Wolf' 'Dog']
 ['Mammal' 'Cat' '']
 ['Reptile' 'Lizard' '']
 ['Reptile' 'Snake' '']
 ['Bird' '' '']]

from sklearn.linear_model import LogisticRegression

from hiclass import LocalClassifierPerNode

# Define data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
X_test = [[9, 10], [7, 8], [5, 6], [3, 4], [1, 2]]
Y_train = [
    ["Bird"],
    ["Reptile", "Snake"],
    ["Reptile", "Lizard"],
    ["Mammal", "Cat"],
    ["Mammal", "Wolf", "Dog"],
]

# Use random forest classifiers for every node
rf = LogisticRegression()
classifier = LocalClassifierPerNode(local_classifier=rf)

# Train local classifier per node
classifier.fit(X_train, Y_train)

# Predict
predictions = classifier.predict(X_test)
print(predictions)