src/main/python/tests/auto_tests/test_randomForestPredict.py

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# Autogenerated By   : src/main/python/generator/generator.py
import unittest, contextlib, io
class TestRANDOMFORESTPREDICT(unittest.TestCase):
    def test_randomForestPredict(self):
        # Example test case provided in python the code block
        buf = io.StringIO()
        with contextlib.redirect_stdout(buf):
            import numpy as np
            from systemds.context import SystemDSContext
            from systemds.operator.algorithm import randomForest, randomForestPredict

            # tiny toy dataset
            X = np.array([[1],
                          [2],
                          [10],
                          [11]], dtype=np.int64)
            y = np.array([[1],
                          [1],
                          [2],
                          [2]], dtype=np.int64)

            with SystemDSContext() as sds:
                X_sds = sds.from_numpy(X)
                y_sds = sds.from_numpy(y)

                ctypes = sds.from_numpy(np.array([[1, 2]], dtype=np.int64))

                # train a 4-tree forest (no sampling)
                M = randomForest(
                        X_sds, y_sds, ctypes,
                        num_trees    = 4,
                        sample_frac  = 1.0,
                        feature_frac = 1.0,
                        max_depth    = 3,
                        min_leaf     = 1,
                        min_split    = 2,
                        seed         = 42
                     )

                preds = randomForestPredict(X_sds, ctypes, M).compute()
                print(preds)

            expected="""[[1.]
 [1.]
 [2.]
 [2.]]"""
        self.assertEqual(buf.getvalue().strip(), expected)

if __name__ == '__main__':
    unittest.main()