mozilla / smart_intent

Intro

Unit size measurements show the distribution of size of units of code (methods, functions...).
Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.

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Unit Size Overall

There are 47 units with 467 lines of code in units (2.0% of code).

0 very long units (0 lines of code)
1 long units (52 lines of code)
3 medium size units (74 lines of code)
11 small units (162 lines of code)
32 very small units (179 lines of code)

Legend:

101+

51-100

21-50

11-20

1-10

Unit Size per Extension

101+

51-100

21-50

11-20

1-10

Unit Size per Logical Component

primary logical decomposition

101+

51-100

21-50

11-20

1-10

Alternative Visuals

3D view of all units

Longest Units

Top 47 longest units

Unit	# lines	McCabe index	# params
def find_location() in src/infer_location.py	52	27	3
async function fetchAndProcessData() in src/js/MLSuggest_inference.js	30	6	0
def __init__() in src/infer_intent.py	23	4	1
def _tokenize_function() in src/train_ner.py	21	5	2
def compute_metrics() in src/train_ner.py	18	2	2
def postprocess_predictions_and_labels() in src/train_ner.py	17	14	3
def compute_metrics() in src/utils.py	17	1	1
def find_intent() in src/infer_intent.py	17	4	3
def __init__() in src/train_intent.py	16	2	1
def main() in src/infer_intent.py	15	2	0
def __init__() in src/infer_location.py	14	3	1
def show_explanation() in src/infer_location.py	13	5	2
def predict_proba() in src/infer_location.py	12	1	2
def run() in src/train_intent.py	12	1	2
def _tokenize_function() in src/train_intent.py	11	2	2
def __init__() in src/train_ner.py	10	2	1
def run() in src/train_ner.py	10	1	2
def __init__() in src/upload_intent.py	10	2	1
def __init__() in src/upload_ner.py	10	2	1
async function writeResultsToFile() in src/js/MLSuggest_inference.js	9	2	1
def _sample_tgt_by_percentages() in src/data_prep_intent.py	9	1	2
async function get_yelp_keywords() in src/js/MLSuggest_inference.js	8	2	0
async function get_yelp_val_data() in src/js/MLSuggest_inference.js	8	2	0
async function get_ner_val_data() in src/js/MLSuggest_inference.js	8	2	0
def prepare_train_test_datasets() in src/data_prep_ner.py	8	1	2
def prepare_train_test_datasets() in src/data_prep_intent.py	8	1	2
def get_geonames_city_state_data() in src/intent_app.py	7	5	0
def upload_to_hf() in src/data_prep_ner.py	7	2	3
def run_pipeline() in src/data_prep_ner.py	7	3	1
def predict() in src/infer_location.py	7	1	2
def upload_to_hf() in src/data_prep_intent.py	7	2	3
def run_pipeline() in src/data_prep_intent.py	6	3	1
def shap_predict_wrapper() in src/infer_location.py	5	3	2
def get_intent_classifier() in src/intent_app.py	3	1	0
def get_location_finder() in src/intent_app.py	3	1	0
def check_location_in_query() in src/intent_app.py	3	7	3
def upload_model_artifacts() in src/upload_intent.py	3	1	1
def upload_model_artifacts() in src/upload_ner.py	3	1	1
def preprocess() in src/infer_location.py	3	1	2
def preprocess_data() in src/train_intent.py	3	1	2
def _load_data() in src/train_ner.py	2	1	1
def preprocess_data() in src/train_ner.py	2	1	2
def _read_data() in src/data_prep_ner.py	2	1	1
def get_data() in src/data_prep_ner.py	2	1	1
def _load_data() in src/train_intent.py	2	1	1
def _read_data() in src/data_prep_intent.py	2	1	1
def get_data() in src/data_prep_intent.py	2	1	1