quick documentation fix by VincentAuriau · Pull Request #313 · artefactory/choice-learn

VincentAuriau · 2026-04-14T08:50:41Z

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for more information, see https://pre-commit.ci

…to fix-bad-data

for more information, see https://pre-commit.ci

…to fix-bad-data

for more information, see https://pre-commit.ci

gemini-code-assist

Code Review

This pull request renames SyntheticDataGenerator to BadmintonDataGenerator, updates documentation and tests to reflect this change, and refines TensorFlow metric updates with explicit type casting. It also updates pre-commit hook versions and modifies notebook examples to include user parameters and updated model interaction patterns. Feedback highlights a broken documentation link in mkdocs.yaml and performance inefficiencies in BadmintonDataGenerator.generate_trip and notebook likelihood computations.

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mkdocs.yaml (105)

The documentation path for the Badminton Dataset is incorrect. The file added in this pull request is named references_badminton_dataset.md, but the path in mkdocs.yaml is badminton_dataset.md. This mismatch will break the documentation link. To follow the project's naming convention (e.g., references_bakery.md), the path should include the references_ prefix.

      - Badminton Dataset: references/basket_models/datasets/references_badminton_dataset.md

choice_learn/basket_models/datasets/badminton.py (275)

Converting self.user_profile.keys() to a list on every call to generate_trip is inefficient, especially for large datasets or simulations. It is recommended to cache the list of user IDs in the __init__ method and reuse it here. Additionally, list(self.user_profile) is more idiomatic than list(self.user_profile.keys()) in Python.

notebooks/basket_models/basic_attention.ipynb (175-180)

This loop is inefficient because it repeatedly creates several arrays (np.array([0]), np.zeros(...)) and performs redundant type conversions (np.array(available_items)) for every context in the batch. These should be initialized once outside the list comprehension to improve performance.

github-actions · 2026-04-14T08:58:01Z

Coverage Report for Python 3.11

File	Stmts	Miss	Cover	Missing
choice_learn
__init__.py	2	0	100%
tf_ops.py	62	1	98%	283
choice_learn/basket_models
__init__.py	4	0	100%
alea_carta.py	148	22	85%	86–90, 92–96, 98–102, 106, 109, 131, 159, 308, 431–455
base_basket_model.py	235	27	89%	111–112, 123, 141, 185, 255, 377, 485, 585–587, 676, 762, 772, 822–830, 891–894, 934–935
basic_attention_model.py	89	4	96%	424, 427, 433, 440
self_attention_model.py	133	9	93%	71, 73, 75, 450–454, 651
shopper.py	184	9	95%	130, 159, 325, 345, 360, 363, 377, 489, 618
choice_learn/basket_models/data
__init__.py	2	0	100%
basket_dataset.py	190	30	84%	74–77, 295–297, 407, 540–576, 636, 658–661, 700–705, 790–801, 849
preprocessing.py	94	78	17%	43–45, 128–364
choice_learn/basket_models/datasets
__init__.py	3	0	100%
badminton.py	81	6	93%	62, 194–199, 247
bakery.py	38	3	92%	47, 51, 61
choice_learn/basket_models/utils
__init__.py	0	0	100%
permutation.py	22	1	95%	37
choice_learn/data
__init__.py	3	0	100%
choice_dataset.py	649	33	95%	198, 250, 283, 421, 463–464, 589, 724, 738, 840, 842, 937, 957–961, 1140, 1159–1161, 1179–1181, 1209, 1214, 1223, 1240, 1281, 1293, 1307, 1346, 1361, 1366, 1395, 1408, 1443–1444
indexer.py	241	23	90%	20, 31, 45, 60–67, 202–204, 219–230, 265, 291, 582
storage.py	161	6	96%	22, 33, 51, 56, 61, 71
store.py	72	72	0%	3–275
choice_learn/datasets
__init__.py	4	0	100%
base.py	400	5	99%	42–43, 153–154, 714
expedia.py	102	83	19%	37–301
tafeng.py	49	0	100%
choice_learn/datasets/data
__init__.py	0	0	100%
choice_learn/models
__init__.py	14	2	86%	15–16
base_model.py	335	35	90%	145, 187, 289, 297, 303, 312, 352, 356–357, 362, 391, 395–396, 413, 426, 434, 475–476, 485–486, 587, 589, 605, 609, 611, 734–735, 935, 939–953
baseline_models.py	49	0	100%
conditional_logit.py	269	26	90%	49, 52, 54, 85, 88, 91–95, 98–102, 136, 206, 212–216, 351, 388, 445, 520–526, 651, 685, 822, 826
halo_mnl.py	124	2	98%	186, 374
latent_class_base_model.py	286	39	86%	55–61, 273–279, 288, 325–330, 497–500, 605, 624, 665–701, 715, 720, 751–752, 774–775, 869–870, 974
latent_class_mnl.py	62	6	90%	257–261, 296
learning_mnl.py	67	3	96%	157, 182, 188
nested_logit.py	291	12	96%	55, 77, 160, 269, 351, 484, 530, 600, 679, 848, 900, 904
reslogit.py	132	6	95%	285, 360, 369, 374, 382, 432
rumnet.py	236	3	99%	748–751, 982
simple_mnl.py	139	6	96%	167, 275, 347, 355, 357, 359
tastenet.py	94	3	97%	142, 180, 188
choice_learn/toolbox
__init__.py	0	0	100%
assortment_optimizer.py	27	6	78%	28–30, 93–95, 160–162
gurobi_opt.py	238	238	0%	3–675
or_tools_opt.py	230	11	95%	103, 107, 296–305, 315, 319, 607, 611
choice_learn/utils
metrics.py	85	43	49%	74, 126–130, 147–166, 176, 190–199, 211–232, 242
TOTAL	5646	853	85%

Tests	Skipped	Failures	Errors	Time
222	0 💤	0 ❌	0 🔥	5m 59s ⏱️

github-actions · 2026-04-14T08:58:51Z

Coverage Report for Python 3.9

File	Stmts	Miss	Cover	Missing
choice_learn
__init__.py	2	0	100%
tf_ops.py	62	1	98%	283
choice_learn/basket_models
__init__.py	4	0	100%
alea_carta.py	148	22	85%	86–90, 92–96, 98–102, 106, 109, 131, 159, 308, 431–455
base_basket_model.py	235	27	89%	111–112, 123, 141, 185, 255, 377, 485, 585–587, 676, 762, 772, 822–830, 891–894, 934–935
basic_attention_model.py	89	4	96%	424, 427, 433, 440
self_attention_model.py	133	9	93%	71, 73, 75, 450–454, 651
shopper.py	184	9	95%	130, 159, 325, 345, 360, 363, 377, 489, 618
choice_learn/basket_models/data
__init__.py	2	0	100%
basket_dataset.py	190	30	84%	74–77, 295–297, 407, 540–576, 636, 658–661, 700–705, 790–801, 849
preprocessing.py	94	78	17%	43–45, 128–364
choice_learn/basket_models/datasets
__init__.py	3	0	100%
badminton.py	81	6	93%	62, 194–199, 247
bakery.py	38	3	92%	47, 51, 61
choice_learn/basket_models/utils
__init__.py	0	0	100%
permutation.py	22	1	95%	37
choice_learn/data
__init__.py	3	0	100%
choice_dataset.py	649	33	95%	198, 250, 283, 421, 463–464, 589, 724, 738, 840, 842, 937, 957–961, 1140, 1159–1161, 1179–1181, 1209, 1214, 1223, 1240, 1281, 1293, 1307, 1346, 1361, 1366, 1395, 1408, 1443–1444
indexer.py	241	23	90%	20, 31, 45, 60–67, 202–204, 219–230, 265, 291, 582
storage.py	161	6	96%	22, 33, 51, 56, 61, 71
store.py	72	72	0%	3–275
choice_learn/datasets
__init__.py	4	0	100%
base.py	400	5	99%	42–43, 153–154, 714
expedia.py	102	83	19%	37–301
tafeng.py	49	0	100%
choice_learn/datasets/data
__init__.py	0	0	100%
choice_learn/models
__init__.py	14	2	86%	15–16
base_model.py	335	35	90%	145, 187, 289, 297, 303, 312, 352, 356–357, 362, 391, 395–396, 413, 426, 434, 475–476, 485–486, 587, 589, 605, 609, 611, 734–735, 935, 939–953
baseline_models.py	49	0	100%
conditional_logit.py	269	26	90%	49, 52, 54, 85, 88, 91–95, 98–102, 136, 206, 212–216, 351, 388, 445, 520–526, 651, 685, 822, 826
halo_mnl.py	124	2	98%	186, 374
latent_class_base_model.py	286	39	86%	55–61, 273–279, 288, 325–330, 497–500, 605, 624, 665–701, 715, 720, 751–752, 774–775, 869–870, 974
latent_class_mnl.py	62	6	90%	257–261, 296
learning_mnl.py	67	3	96%	157, 182, 188
nested_logit.py	291	12	96%	55, 77, 160, 269, 351, 484, 530, 600, 679, 848, 900, 904
reslogit.py	132	6	95%	285, 360, 369, 374, 382, 432
rumnet.py	236	3	99%	748–751, 982
simple_mnl.py	139	6	96%	167, 275, 347, 355, 357, 359
tastenet.py	94	3	97%	142, 180, 188
choice_learn/toolbox
__init__.py	0	0	100%
assortment_optimizer.py	27	6	78%	28–30, 93–95, 160–162
gurobi_opt.py	236	236	0%	3–675
or_tools_opt.py	230	11	95%	103, 107, 296–305, 315, 319, 607, 611
choice_learn/utils
metrics.py	85	43	49%	74, 126–130, 147–166, 176, 190–199, 211–232, 242
TOTAL	5644	851	85%

Tests	Skipped	Failures	Errors	Time
222	0 💤	0 ❌	0 🔥	6m 38s ⏱️

github-actions · 2026-04-14T08:59:14Z

Coverage Report for Python 3.12

File	Stmts	Miss	Cover	Missing
choice_learn
__init__.py	2	0	100%
tf_ops.py	62	1	98%	283
choice_learn/basket_models
__init__.py	4	0	100%
alea_carta.py	148	22	85%	86–90, 92–96, 98–102, 106, 109, 131, 159, 308, 431–455
base_basket_model.py	235	27	89%	111–112, 123, 141, 185, 255, 377, 485, 585–587, 676, 762, 772, 822–830, 891–894, 934–935
basic_attention_model.py	89	4	96%	424, 427, 433, 440
self_attention_model.py	133	9	93%	71, 73, 75, 450–454, 651
shopper.py	184	9	95%	130, 159, 325, 345, 360, 363, 377, 489, 618
choice_learn/basket_models/data
__init__.py	2	0	100%
basket_dataset.py	190	30	84%	74–77, 295–297, 407, 540–576, 636, 658–661, 700–705, 790–801, 849
preprocessing.py	94	78	17%	43–45, 128–364
choice_learn/basket_models/datasets
__init__.py	3	0	100%
badminton.py	81	6	93%	62, 194–199, 247
bakery.py	38	3	92%	47, 53, 61
choice_learn/basket_models/utils
__init__.py	0	0	100%
permutation.py	22	1	95%	37
choice_learn/data
__init__.py	3	0	100%
choice_dataset.py	649	33	95%	198, 250, 283, 421, 463–464, 589, 724, 738, 840, 842, 937, 957–961, 1140, 1159–1161, 1179–1181, 1209, 1214, 1223, 1240, 1281, 1293, 1307, 1346, 1361, 1366, 1395, 1408, 1443–1444
indexer.py	241	23	90%	20, 31, 45, 60–67, 202–204, 219–230, 265, 291, 582
storage.py	161	6	96%	22, 33, 51, 56, 61, 71
store.py	72	72	0%	3–275
choice_learn/datasets
__init__.py	4	0	100%
base.py	400	5	99%	42–43, 153–154, 714
expedia.py	102	83	19%	37–301
tafeng.py	49	0	100%
choice_learn/datasets/data
__init__.py	0	0	100%
choice_learn/models
__init__.py	14	2	86%	15–16
base_model.py	335	35	90%	145, 187, 289, 297, 303, 312, 352, 356–357, 362, 391, 395–396, 413, 426, 434, 475–476, 485–486, 587, 589, 605, 609, 611, 734–735, 935, 939–953
baseline_models.py	49	0	100%
conditional_logit.py	269	26	90%	49, 52, 54, 85, 88, 91–95, 98–102, 136, 206, 212–216, 351, 388, 445, 520–526, 651, 685, 822, 826
halo_mnl.py	124	2	98%	186, 374
latent_class_base_model.py	286	39	86%	55–61, 273–279, 288, 325–330, 497–500, 605, 624, 665–701, 715, 720, 751–752, 774–775, 869–870, 974
latent_class_mnl.py	62	6	90%	257–261, 296
learning_mnl.py	67	3	96%	157, 182, 188
nested_logit.py	291	12	96%	55, 77, 160, 269, 351, 484, 530, 600, 679, 848, 900, 904
reslogit.py	132	6	95%	285, 360, 369, 374, 382, 432
rumnet.py	236	3	99%	748–751, 982
simple_mnl.py	139	6	96%	167, 275, 347, 355, 357, 359
tastenet.py	94	3	97%	142, 180, 188
choice_learn/toolbox
__init__.py	0	0	100%
assortment_optimizer.py	27	6	78%	28–30, 93–95, 160–162
gurobi_opt.py	238	238	0%	3–675
or_tools_opt.py	230	11	95%	103, 107, 296–305, 315, 319, 607, 611
choice_learn/utils
metrics.py	85	43	49%	74, 126–130, 147–166, 176, 190–199, 211–232, 242
TOTAL	5646	853	85%

Tests	Skipped	Failures	Errors	Time
222	0 💤	0 ❌	0 🔥	7m 51s ⏱️

github-actions · 2026-04-14T08:59:25Z

Coverage Report for Python 3.10

File	Stmts	Miss	Cover	Missing
choice_learn
__init__.py	2	0	100%
tf_ops.py	62	1	98%	283
choice_learn/basket_models
__init__.py	4	0	100%
alea_carta.py	148	22	85%	86–90, 92–96, 98–102, 106, 109, 131, 159, 308, 431–455
base_basket_model.py	235	27	89%	111–112, 123, 141, 185, 255, 377, 485, 585–587, 676, 762, 772, 822–830, 891–894, 934–935
basic_attention_model.py	89	4	96%	424, 427, 433, 440
self_attention_model.py	133	9	93%	71, 73, 75, 450–454, 651
shopper.py	184	9	95%	130, 159, 325, 345, 360, 363, 377, 489, 618
choice_learn/basket_models/data
__init__.py	2	0	100%
basket_dataset.py	190	30	84%	74–77, 295–297, 407, 540–576, 636, 658–661, 700–705, 790–801, 849
preprocessing.py	94	78	17%	43–45, 128–364
choice_learn/basket_models/datasets
__init__.py	3	0	100%
badminton.py	81	6	93%	62, 194–199, 247
bakery.py	38	3	92%	47, 51, 61
choice_learn/basket_models/utils
__init__.py	0	0	100%
permutation.py	22	1	95%	37
choice_learn/data
__init__.py	3	0	100%
choice_dataset.py	649	33	95%	198, 250, 283, 421, 463–464, 589, 724, 738, 840, 842, 937, 957–961, 1140, 1159–1161, 1179–1181, 1209, 1214, 1223, 1240, 1281, 1293, 1307, 1346, 1361, 1366, 1395, 1408, 1443–1444
indexer.py	241	23	90%	20, 31, 45, 60–67, 202–204, 219–230, 265, 291, 582
storage.py	161	6	96%	22, 33, 51, 56, 61, 71
store.py	72	72	0%	3–275
choice_learn/datasets
__init__.py	4	0	100%
base.py	400	5	99%	42–43, 153–154, 714
expedia.py	102	83	19%	37–301
tafeng.py	49	0	100%
choice_learn/datasets/data
__init__.py	0	0	100%
choice_learn/models
__init__.py	14	2	86%	15–16
base_model.py	335	35	90%	145, 187, 289, 297, 303, 312, 352, 356–357, 362, 391, 395–396, 413, 426, 434, 475–476, 485–486, 587, 589, 605, 609, 611, 734–735, 935, 939–953
baseline_models.py	49	0	100%
conditional_logit.py	269	26	90%	49, 52, 54, 85, 88, 91–95, 98–102, 136, 206, 212–216, 351, 388, 445, 520–526, 651, 685, 822, 826
halo_mnl.py	124	2	98%	186, 374
latent_class_base_model.py	286	39	86%	55–61, 273–279, 288, 325–330, 497–500, 605, 624, 665–701, 715, 720, 751–752, 774–775, 869–870, 974
latent_class_mnl.py	62	6	90%	257–261, 296
learning_mnl.py	67	3	96%	157, 182, 188
nested_logit.py	291	12	96%	55, 77, 160, 269, 351, 484, 530, 600, 679, 848, 900, 904
reslogit.py	132	6	95%	285, 360, 369, 374, 382, 432
rumnet.py	236	3	99%	748–751, 982
simple_mnl.py	139	6	96%	167, 275, 347, 355, 357, 359
tastenet.py	94	3	97%	142, 180, 188
choice_learn/toolbox
__init__.py	0	0	100%
assortment_optimizer.py	27	6	78%	28–30, 93–95, 160–162
gurobi_opt.py	238	238	0%	3–675
or_tools_opt.py	230	11	95%	103, 107, 296–305, 315, 319, 607, 611
choice_learn/utils
metrics.py	85	43	49%	74, 126–130, 147–166, 176, 190–199, 211–232, 242
TOTAL	5646	853	85%

Tests	Skipped	Failures	Errors	Time
222	0 💤	0 ❌	0 🔥	7m 22s ⏱️

VincentAuriau and others added 18 commits April 10, 2026 18:41

fix: user_id None

b377247

update notebook

0345c73

[pre-commit.ci] auto fixes from pre-commit.com hooks

620aa48

for more information, see https://pre-commit.ci

small fix

0f5cece

Merge branch 'fix-bad-data' of github.com:artefactory/choice-learn in…

d78631a

…to fix-bad-data

fix texts

ada3224

[pre-commit.ci] auto fixes from pre-commit.com hooks

4b3b2bc

for more information, see https://pre-commit.ci

small changes

21ebf79

merge

de2f31c

[pre-commit.ci] auto fixes from pre-commit.com hooks

35b3aa3

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ruffed

2aa8627

Merge branch 'fix-bad-data' of github.com:artefactory/choice-learn in…

15f1939

…to fix-bad-data

fix notebook

f165583

fix: mkdoc

9ca54ff

fix notebook

cf6090a

FIX: doc

0776efb

Merge branch 'main' into fix-bad-data

38a3033

[pre-commit.ci] auto fixes from pre-commit.com hooks

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VincentAuriau merged commit 5ed3b19 into main Apr 14, 2026
7 checks passed

VincentAuriau deleted the fix-bad-data branch April 14, 2026 08:51

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quick documentation fix#313

quick documentation fix#313
VincentAuriau merged 18 commits intomainfrom
fix-bad-data

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Code Review

mkdocs.yaml (105)

choice_learn/basket_models/datasets/badminton.py (275)

notebooks/basket_models/basic_attention.ipynb (175-180)

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