uber / causalml - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

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Conditional Complexity Overall

There are 430 units with 6,275 lines of code in units (42.9% of code).

1 very complex units (297 lines of code)
2 complex units (248 lines of code)
15 medium complex units (1,035 lines of code)
23 simple units (813 lines of code)
389 very simple units (3,882 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def growDecisionTreeFrom() in causalml/inference/tree/uplift.pyx	297	72	14
def pruneTree() in causalml/inference/tree/uplift.pyx	108	28	9
def cat_continuous() in causalml/inference/tree/utils.py	140	28	2
def uplift_tree_plot() in causalml/inference/tree/plot.py	195	19	2
def fit() in causalml/inference/tree/uplift.pyx	49	18	7
def fit() in causalml/inference/meta/drlearner.py	92	16	6
def classify() in causalml/inference/tree/uplift.pyx	88	15	3
def match() in causalml/match.py	58	15	4
def check_table_one() in causalml/match.py	48	15	6
def compute_partial_dependence() in causalml/inference/tree/_tree/_tree.pyx	58	14	5
def get_fill_color() in causalml/inference/tree/plot.py	17	13	3
def group_counts_by_divide() in causalml/inference/tree/uplift.pyx	63	12	9
def __init__() in causalml/inference/tree/uplift.pyx	47	12	13
def plot() in causalml/metrics/sensitivity.py	63	11	5
def arr_evaluate_CIT() in causalml/inference/tree/uplift.pyx	57	11	12
def fit() in causalml/inference/meta/rlearner.py	92	11	7
def estimate_ate() in causalml/inference/meta/tmle.py	73	11	7
def search_best_match() in causalml/match.py	35	11	2
def get_synthetic_summary_holdout() in causalml/dataset/synthetic.py	69	10	4
def fillTree() in causalml/inference/tree/uplift.pyx	22	10	5
def predict() in causalml/inference/iv/drivlearner.py	29	9	6
def fit() in causalml/inference/meta/rlearner.py	52	9	7
def fit() in causalml/inference/meta/rlearner.py	54	9	7
def fit() in causalml/inference/meta/xlearner.py	41	9	5
def fit() in causalml/inference/meta/xlearner.py	49	9	5
def _filter_F_one_feature() in causalml/feature_selection/filters.py	57	9	5
def group_uniqueCounts_to_arr() in causalml/inference/tree/uplift.pyx	36	8	6
def fit() in causalml/inference/tree/uplift.pyx	28	8	7
def predict() in causalml/inference/tree/uplift.pyx	38	8	3
def _all_compatible_dtype_dicts() in causalml/inference/tree/_tree/_tree.pyx	13	8	1
def load_data() in causalml/features.py	14	8	3
def get_synthetic_preds() in causalml/dataset/synthetic.py	41	7	3
def _count_groups_distribution() in causalml/inference/tree/causal/causaltree.py	20	7	3
def get_std_diffs() in causalml/metrics/visualize.py	44	6	5
def uplift_tree_string() in causalml/inference/tree/plot.py	26	6	2
def __init__() in causalml/inference/tree/uplift.pyx	17	6	15
def tree_node_summary() in causalml/inference/tree/uplift.pyx	37	6	6
def tree_node_summary_to_arr() in causalml/inference/tree/uplift.pyx	74	6	15
def _check_n_classes() in causalml/inference/tree/_tree/_tree.pyx	15	6	2
def _validate_X_predict() in causalml/inference/tree/_tree/_classes.py	20	6	3
def check_p_conditions() in causalml/inference/meta/utils.py	17	6	2
def _get_numeric_vars() in causalml/metrics/visualize.py	17	5	2
def get_synthetic_summary() in causalml/dataset/synthetic.py	32	5	4
def bootstrap() in causalml/inference/iv/drivlearner.py	26	5	9
def divideSet() in causalml/inference/tree/uplift.pyx	25	5	5
def divideSet_len() in causalml/inference/tree/uplift.pyx	29	5	5
def _any_isnan_axis0() in causalml/inference/tree/_tree/_utils.pyx	15	5	2
def _check_value_ndarray() in causalml/inference/tree/_tree/_tree.pyx	19	5	3
def _check_node_ndarray() in causalml/inference/tree/_tree/_tree.pyx	22	5	2
def __cinit__() in causalml/inference/tree/_tree/_criterion.pyx	24	5	4