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).

Learn more...

Conditional Complexity Overall

There are 458 units with 6,697 lines of code in units (42.2% of code).

1 very complex units (349 lines of code)
2 complex units (253 lines of code)
14 medium complex units (987 lines of code)
27 simple units (1,010 lines of code)
414 very simple units (4,098 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	349	76	14
def pruneTree() in causalml/inference/tree/uplift.pyx	113	28	9
def cat_continuous() in causalml/inference/tree/utils.py	140	28	2
def fit() in causalml/inference/tree/uplift.pyx	63	20	7
def uplift_tree_plot() in causalml/inference/tree/plot.py	195	19	2
def classify() in causalml/inference/tree/uplift.pyx	92	17	3
def fit() in causalml/inference/meta/drlearner.py	92	16	6
def group_counts_by_divide() in causalml/inference/tree/uplift.pyx	70	15	10
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 __init__() in causalml/inference/tree/uplift.pyx	47	12	13
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 search_best_match() in causalml/match.py	35	11	2
def plot() in causalml/metrics/sensitivity.py	63	11	5
def fillTree() in causalml/inference/tree/uplift.pyx	27	10	5
def estimate_ate() in causalml/inference/meta/tmle.py	70	10	7
def get_synthetic_summary_holdout() in causalml/dataset/synthetic.py	69	10	4
def predict() in causalml/inference/tree/uplift.pyx	39	9	3
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	29	8	7
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	44	8	3
def divideSet() in causalml/inference/tree/uplift.pyx	31	7	6
def _count_groups_distribution() in causalml/inference/tree/causal/causaltree.py	21	7	3
def generate() in causalml/dataset/semiSynthetic.py	41	7	3
def uplift_tree_string() in causalml/inference/tree/plot.py	26	6	2
def __init__() in causalml/inference/tree/uplift.pyx	18	6	16
def divideSet_len() in causalml/inference/tree/uplift.pyx	42	6	6
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 _validate_X_predict() in causalml/inference/tree/_tree/_classes.py	21	6	3
def _check_n_classes() in causalml/inference/tree/_tree/_tree.pyx	15	6	2
def check_p_conditions() in causalml/inference/meta/utils.py	17	6	2
def get_std_diffs() in causalml/metrics/visualize.py	44	6	5
def cat_transform() in causalml/inference/tree/utils.py	11	5	3
def __cinit__() in causalml/inference/tree/_tree/_criterion.pyx	24	5	4
def _any_isnan_axis0() in causalml/inference/tree/_tree/_utils.pyx	15	5	2
def _compute_missing_values_in_feature_mask() in causalml/inference/tree/_tree/_classes.py	14	5	3
def predict() in causalml/inference/tree/_tree/_classes.py	21	5	3
def _check_value_ndarray() in causalml/inference/tree/_tree/_tree.pyx	19	5	3