Fix bad data by VincentAuriau · Pull Request #312 · artefactory/choice-learn

VincentAuriau · 2026-04-14T08:29:30Z

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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

gemini-code-assist

Code Review

This pull request renames SyntheticDataGenerator to BadmintonDataGenerator across the codebase and documentation, updates pre-commit hook versions, and fixes type mismatch issues in metrics. Feedback highlights an opportunity to simplify user ID selection logic for better robustness and points out a performance regression in the basic attention notebook where a vectorized prediction call was replaced by an inefficient loop.

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

The selection of a random user ID is improved by using np.random.choice, which supports non-sequential keys. However, list(self.user_profile.keys()) can be simplified to list(self.user_profile). Additionally, using if self.user_profile: instead of if self.user_profile is not None would safely handle cases where an empty dictionary is provided, avoiding a ValueError in np.random.choice.

            np.random.choice(list(self.user_profile)) if self.user_profile else 0

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

Replacing the vectorized model.predict call with a Python loop that calls compute_item_likelihood for each context individually is significantly less efficient. If model.predict is still available in the API, it should be used to leverage TensorFlow's batch processing capabilities. If the API has changed, consider providing a vectorized way to compute these likelihoods in the tutorial.

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

github-actions · 2026-04-14T08:37:43Z

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 22s ⏱️

github-actions · 2026-04-14T08:38:14Z

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	36	89%	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, 908, 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	854	85%

Tests	Skipped	Failures	Errors	Time
222	0 💤	0 ❌	0 🔥	6m 2s ⏱️

github-actions · 2026-04-14T08:38:47Z

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 🔥	8m 1s ⏱️

github-actions · 2026-04-14T08:39:36Z

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	34	73%	153, 172, 176–192, 341, 360, 364–380
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	885	84%

Tests	Skipped	Failures	Errors	Time
222	0 💤	2 ❌	0 🔥	7m 15s ⏱️

VincentAuriau and others added 16 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

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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

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small changes

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merge

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

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ruffed

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

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…to fix-bad-data

fix notebook

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fix: mkdoc

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fix notebook

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Merge branch 'main' into fix-bad-data

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VincentAuriau merged commit 27d7c7d into main Apr 14, 2026
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VincentAuriau deleted the fix-bad-data branch April 14, 2026 08:43

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Fix bad data - #312

Fix bad data#312
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choice_learn/basket_models/datasets/badminton.py (275)

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

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