def greedy_markov_blanket_discovery(estimator,
data,
target_variable=None,
limit=None,
control_variables=None):
"""
Markov blanket discovery with standard greedy
"""
selected, best_score = greedy_search(estimator,
data,
target_variable=target_variable,
limit=limit,
control_variables=control_variables)
return selected, best_score
def random_markov_blanket_discovery(estimator,
data,
target_variable=None,
limit=None,
control_variables=None):
"""
Markov blanket discovery with random greedy (uniformly selects from top-3)
"""
selected, best_score = greedy_search(estimator,
data,
target_variable=target_variable,
limit=limit,
control_variables=control_variables,
select_from_top_k=3)
return selected, best_score
def test_greedy_search_with_permutation_fi():
""" Tests choose_no_overflow for simple cases """
# mock
testfile = Path(
explora.__file__).parent.parent / "datasets" / "tic_tac_toe.csv"
data = pd.read_csv(testfile)
# do
selected, best_score = greedy_search(fraction_of_information_permutation,
data)
error = 1e-10
expected = 0.4447970033469652
# assert
assert best_score == approx(expected, rel=error)
assert (selected == {1, 5, 9, 3, 7})
def test_greedy_search_with_upper_bound_mi():
""" Tests choose_no_overflow for simple cases """
# mock
testfile = Path(
explora.__file__).parent.parent / "datasets" / "tic_tac_toe.csv"
data = pd.read_csv(testfile)
# do
selected, best_score = greedy_search(
mutual_information_permutation_upper_bound, data)
error = 1e-10
expected = 0.28707781833145635
# assert
assert best_score == approx(expected, rel=error)
assert (selected == {1, 5, 9})
def test_greedy_search_with_conditional_permutation_fi():
""" Tests choose_no_overflow for simple cases """
# mock
testfile = Path(
explora.__file__).parent.parent / "datasets" / "tic_tac_toe.csv"
data = pd.read_csv(testfile)
control_var_set = {5}
# do
selected, best_score = greedy_search(
conditional_fraction_of_information_permutation,
data,
control_variables=control_var_set)
error = 1e-10
expected = 0.3561447636856704
# assert
assert best_score == approx(expected, rel=error)
assert (selected == {1, 9, 3, 7})
def grow_shrink(grow_estimator, shrink_estimator, data, shrink_threshold=0, target=None, limit=None):
"""
For a dependency measure D(XY) and
conditional D(XY|Z), it greedily finds a maximizer for D(XY), and shrinks
afterwards with the conditional. Not to be confused with the Grow Shrink for Markov
blankets (although similar)
"""
if isinstance(data, pd.DataFrame):
data = data.to_numpy()
if target is None:
target = np.size(data, 1)
# start_time = time.time()
[greedy_result, greedy_score] = greedy_search(grow_estimator, data, target, limit=limit)
# print("--- %s Time for grow---" % (time.time() - start_time))
# start_time = time.time()
greedy_result = {x - 1 for x in greedy_result}
shrink_results = shrink(shrink_estimator, greedy_result, data, shrink_threshold=shrink_threshold, target=None)
# print("--- %s Time to shrink---" % (time.time() - start_time))
return shrink_results
