Specifying dim_contiguous causes seg faults in PatchObliqueForestClassifier

[j1c]

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Description

When there are non-contiguous dimensions, there are segmentation faults when fitting. See below for example.

Setting dim_contiguous=(True, True) runs fine, but dim_contiguous=(False, True) leads to seg faults.

Python traceback:

Related issues or possible duplicates

• None

Environment

OS: Ubuntu

Python version: 3.9.16

scikit-tree version: 0.6.1

Output of pip freeze:

Steps to reproduce

Example source:

import numpy as np
from sktree import PatchObliqueRandomForestClassifier

n, a, b = 100, 10, 10

x = np.random.normal(size=(n, a, b))
y = np.random.binomial(1, 0.5, size=(n))

rf = PatchObliqueRandomForestClassifier(
    n_estimators=100,
    min_patch_dims=[1, 1],
    max_patch_dims=[4, 4],
    dim_contiguous=(False, True),
    data_dims=(a, b),
    n_jobs=-1,
)

rf.fit(x.reshape(100, -1), y)

[adam2392]

It seems this test works when n_estimators is low, but not when I ramp to 50 or maybe 100. This makes me think there is a sampling edge case error where we go out of the bounds of the image:

def test_contiguous_and_discontiguous_patch_forest():
    """Test regression reported in https://github.com/neurodata/scikit-tree/issues/215."""
    n, a, b = 100, 10, 10
    x = np.random.normal(size=(n, a, b))
    y = np.random.binomial(1, 0.5, size=(n))

    est = PatchObliqueRandomForestClassifier(
        n_estimators=20,
        min_patch_dims=[1, 1],
        max_patch_dims=[4, 4],
        dim_contiguous=(False, True),
        data_dims=(a, b),
        n_jobs=-1
    )

    est.fit(x.reshape(100, -1), y)

[adam2392]

I think the issue is isolated to when dim_contiguous=False you can test this with a single tree iterated over 100-1000 different random seeds.
When dim_contiguous=True , seems there is consistently no error.

It must occur in accessing an index that is not valid in one of two areas:

• https://github.com/neurodata/scikit-tree/blob/18c2f457b7fe35d3ab95fdd48e4212d325321b09/sktree/tree/manifold/_morf_splitter.pyx#L339-L350
• https://github.com/neurodata/scikit-tree/blob/18c2f457b7fe35d3ab95fdd48e4212d325321b09/sktree/tree/manifold/_morf_splitter.pyx#L375-L401