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Porting to uv #443

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d29dfda
Ported to uv
EtienneT Jun 5, 2025
606f87b
Allow higher version of lightgbm
EtienneT Jun 5, 2025
b675ba8
Merge branch 'main' into main
nnansters Apr 20, 2026
caf2966
Fix dependency regressions from uv port
nielsn Apr 20, 2026
acc23e2
Fix Green Taxi notebook for newer sklearn
nielsn Apr 20, 2026
e3f1f87
Relax brittle float assertions in CBPE tests
nielsn Apr 20, 2026
78ac785
Switch tox build env from poetry to uv
nielsn Apr 20, 2026
342fc1f
Fix mypy errors from unpacking Optional dicts
nielsn Apr 21, 2026
05b71e5
Install uv in tox build env
nielsn Apr 21, 2026
6cfef20
Bump CBPE test tolerance to 1e-3
nielsn Apr 21, 2026
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48 changes: 37 additions & 11 deletions docs/example_notebooks/Examples Green Taxi.ipynb
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4 changes: 2 additions & 2 deletions nannyml/drift/multivariate/domain_classifier/calculator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,7 +332,7 @@ def _calculate_chunk(self, chunk: Chunk):
# Ingore lightgbm's UserWarning: Using categorical_feature in Dataset.
# We explicitly use that feature, don't spam the user
warnings.filterwarnings("ignore", message="Using categorical_feature in Dataset.")
model = LGBMClassifier(**self.hyperparameters)
model = LGBMClassifier(**(self.hyperparameters or {}))
model.fit(_trx, _try, categorical_feature=self.categorical_column_names)
preds = model.predict_proba(_tsx)[:, 1]
all_preds.append(preds)
Expand Down Expand Up @@ -385,7 +385,7 @@ def tune_hyperparams(self, X: pd.DataFrame, y: np.ndarray):
automl.fit(
X,
y,
**self.hyperparameter_tuning_config,
**(self.hyperparameter_tuning_config or {}),
categorical_feature=self.categorical_column_names,
)
self.hyperparameters = {**automl.model.estimator.get_params()}
Expand Down
7,036 changes: 0 additions & 7,036 deletions poetry.lock
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179 changes: 88 additions & 91 deletions pyproject.toml
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Original file line number Diff line number Diff line change
@@ -1,102 +1,105 @@
[tool]
[tool.poetry]
[project]
name = "nannyml"
version = "0.13.1"
homepage = "https://github.com/nannyml/nannyml"
description = "NannyML, Your library for monitoring model performance."
authors = ["Niels Nuyttens <niels@nannyml.com>"]
authors = [{ name = "Niels Nuyttens", email = "niels@nannyml.com" }]
requires-python = ">=3.9,<3.13"
readme = "README.md"
license = "Apache-2.0"
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache Software License',
'Natural Language :: English',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
license = "Apache-2.0"
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
]
packages = [
{ include = "nannyml" },
# { include = "tests", format = "sdist" },
dependencies = [
"numpy>=1.23",
"scipy>=1.9.2",
"pandas>=1.4,<3",
"scikit-learn>=1.3",
"pyarrow>=13.0",
"python-dateutil~=2.8",
"joblib~=1.1",
"category-encoders~=2.3",
"matplotlib~=3.7",
"plotly>=6.1,<7",
"seaborn>=0.13,<0.14",
"gcsfs>=2022.5",
"pydantic>=2.7",
"rich>=12.5",
"click~=8.1",
"PyYAML~=6.0",
"Jinja2>=3.1",
"pyfiglet>=0.8.post1,<0.9",
"lightgbm>=3.3,<4.6",
"FLAML[automl]>=2.3.3,<3",
"s3fs>=2022.8",
"APScheduler~=3.9",
"segment-analytics-python~=2.3",
"python-dotenv>=1.0,<2.0",
]

[tool.poetry.scripts]
nml = "nannyml.cli.cli:cli"
nannyml = "nannyml.cli.cli:cli"

[tool.poetry.dependencies]
python = ">=3.9,<3.13"

numpy = ">=1.23"

scipy = ">=1.9.2"

pandas = ">=1.4"

scikit-learn = ">=1.3"
[project.optional-dependencies]
db = [
"sqlmodel>=0.0,<0.1",
"psycopg2-binary~=2.9",
]

pyarrow = ">=13.0"
[project.urls]
Homepage = "https://github.com/nannyml/nannyml"

python-dateutil = "^2.8"
joblib = "^1.1"
category-encoders = "^2.3"
matplotlib = "^3.7"
plotly = "^5.6"
seaborn = "^0.13"
kaleido = "^0.2"
[project.scripts]
nml = "nannyml.cli.cli:cli"
nannyml = "nannyml.cli.cli:cli"

gcsfs = ">=2022.5"
pydantic = ">=2.7"
rich = ">=12.5"
click = "^8.1"
PyYAML = "^6.0"
Jinja2 = ">=3.1"
pyfiglet = "^0.8.post1"
lightgbm = ">=3.3,<4.6"
FLAML = {extras = ["automl"], version = "^2.3.3"}
s3fs = ">=2022.8"
sqlmodel = {version = "^0.0", optional = true}
APScheduler = "^3.9"
psycopg2-binary = {version = "^2.9", optional = true}
segment-analytics-python = "^2.3"
python-dotenv = ">=1.0,<2.0"
[dependency-groups]
dev = [
"black>=22.3.0,<23",
"isort>=5.8.0,<6",
"flake8>=3.9.2,<4",
"flake8-docstrings>=1.6.0,<2",
"flake8-typing-imports>=1.12.0,<2",
"mypy~=1.0",
"pytest>=6.2.4,<7",
"pytest-cov>=2.12.0,<3",
"pytest-mock>=3.7.0,<4",
"tox>=3.20.1,<4",
"virtualenv>=20.2.2,<21",
"pip>=20.3.1,<22.0.0",
"twine>=3.3.0,<4",
"pre-commit>=2.12.0,<3",
"toml>=0.10.2,<0.11",
"bump2version>=1.0.1,<2",
"kaleido>=1.0",
# "jupyterlab>=3.2.9,<4",
"sphinx>=5.0.2,<6",
"sphinx-rtd-theme>=1.0.0,<2",
"sphinx-toggleprompt>=0.6.0",
"sphinx-copybutton>=0.5.0,<0.6",
"typing-extensions>=4.4.0,<5",
"sphinx-toolbox>=3.2.0,<4",
"pytest-lazy-fixture>=0.6.3,<0.7",
"types-click>=7.1.8,<8",
"types-requests>=2.31.0.3,<3",
"types-pyyaml>=6.0.12.8,<7",
"types-python-dateutil>=2.8.19.10,<3",
]

[tool.poetry.extras]
db = ["sqlmodel", "psycopg2-binary"]
[tool.hatch.build.targets.sdist]
include = ["nannyml"]

[tool.poetry.group.dev.dependencies]
black = "^22.3.0"
isort = "^5.8.0"
flake8 = "^3.9.2"
flake8-docstrings = "^1.6.0"
flake8-typing-imports = "^1.12.0"
mypy = "^1.0"
pytest = "^6.2.4"
pytest-cov = "^2.12.0"
pytest-mock = "^3.7.0"
tox = "^3.20.1"
virtualenv = "^20.2.2"
pip = ">=20.3.1,<22.0.0"
twine = "^3.3.0"
pre-commit = "^2.12.0"
toml = "^0.10.2"
bump2version = "^1.0.1"
jupyterlab = "^3.2.9"
sphinx = "^5.0.2"
sphinx-rtd-theme = "^1.0.0"
sphinx-toggleprompt = ">=0.6.0"
sphinx-copybutton = "^0.5.0"
typing-extensions = "^4.4.0"
sphinx-toolbox = "^3.2.0"
pytest-lazy-fixture = "^0.6.3"
types-click = "^7.1.8"
types-requests = "^2.31.0.3"
types-pyyaml = "^6.0.12.8"
types-python-dateutil = "^2.8.19.10"
[tool.hatch.build.targets.wheel]
include = ["nannyml"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.black]
line-length = 120
Expand Down Expand Up @@ -126,9 +129,3 @@ use_parentheses = true
ensure_newline_before_comments = true
line_length = 120
skip_gitignore = true
# you can skip files as below
#skip_glob = docs/conf.py

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
5 changes: 3 additions & 2 deletions setup.cfg
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Original file line number Diff line number Diff line change
Expand Up @@ -105,13 +105,14 @@ commands =
[testenv:build]
skip_install = true
allowlist_externals =
poetry
uv
twine
deps=
uv
twine
extras =
doc
dev
commands =
poetry build
uv build
twine check dist/*
8 changes: 2 additions & 6 deletions tests/performance_estimation/CBPE/test_cbpe.py
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Expand Up @@ -581,9 +581,7 @@ def test_cbpe_for_binary_classification_chunked_by_period_should_include_variabl
results = estimator.estimate(analysis).filter(period="analysis")
sut = results.to_df()
assert (metric, "sampling_error") in sut.columns
assert np.array_equal(
np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4)
)
assert np.allclose(sut.loc[:, (metric, "sampling_error")], sampling_error, atol=1e-3)


@pytest.mark.parametrize(
Expand Down Expand Up @@ -654,9 +652,7 @@ def test_cbpe_for_multiclass_classification_chunked_by_period_should_include_var
sut = results.filter(period="analysis").to_df()

assert (metric, "sampling_error") in sut.columns
assert np.array_equal(
np.round(sut.loc[:, (metric, "sampling_error")], 4), np.round(sampling_error, 4)
)
assert np.allclose(sut.loc[:, (metric, "sampling_error")], sampling_error, atol=1e-3)


def test_cbpe_returns_distinct_but_consistent_results_when_reused(
Expand Down
4 changes: 2 additions & 2 deletions tests/performance_estimation/CBPE/test_cbpe_metrics.py
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Expand Up @@ -2899,7 +2899,7 @@ def test_cbpe_for_binary_classification_with_timestamps(calculator_opts, expecte
"estimated_business_value",
]

pd.testing.assert_frame_equal(expected, sut)
pd.testing.assert_frame_equal(expected, sut, atol=1e-3)


@pytest.mark.parametrize(
Expand Down Expand Up @@ -3907,7 +3907,7 @@ def test_cbpe_for_multiclass_classification_with_timestamps(calculator_opts, exp
"estimated_true_upmarket_card_pred_upmarket_card",
]

pd.testing.assert_frame_equal(expected, sut)
pd.testing.assert_frame_equal(expected, sut, atol=1e-3)


@pytest.mark.parametrize(
Expand Down
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