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@MatthewMiddlehurst

LOF does not seem to be producing same output for fit_predict() as compared to fit().predict(). I have experimented locally as well, the prediction for anomaly scores are not same.

est = LOF(leaf_size=10, n_neighbors=5, stride=2)
est1 = _clone_estimator(est, random_state=42)
est2 = _clone_estimator(est, random_state=42)

datatype = 'UnivariateSeries-None'
X = FULL_TEST_DATA_DICT[datatype]['train'][0]
y = FULL_TEST_DATA_DICT[datatype]['train'][1]

est1.fit(X, y)
y_pred = est1.predict(X)
y_pred

>>> array([0.99658101, 0.99658101, 0.98995043, 0.98995043, 0.99216063,
       0.99216063, 0.98995043, 0.98995043, 0.99127655, 0.99127655,
       0.98862432, 0.98862432, 0.98995043, 0.98995043, 0.98774024,
       0.98774024, 0.98995043, 0.98995043, 0.98331986, 0.98331986])

y_pred2 = est2.fit_predict(X, y)
y_pred2

>>> array([1.03501792, 1.03501792, 1.02085908, 1.02085908, 1.01276639,
       1.01276639, 1.00540476, 1.00540476, 1.00364001, 1.00364001,
       0.99330039, 0.99330039, 0.98995043, 0.98995043, 0.98774024,
       0.98774024, 0.98995043, 0.98995043, 0.98331986, 0.98331986])

np.allclose(y_pred, y_pred2)

>>> False