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Update BTB Python and dependency versions #217

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merged 4 commits into from
Jul 21, 2023
Merged

Update BTB Python and dependency versions #217

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3bc789d
Replaced deprecated np.float with np.float64
PatrikDurdevic Jul 14, 2023
887fddb
Add support for newer versions
PatrikDurdevic Jul 14, 2023
c95fef3
Deprecated older Python versions (<3.8)
PatrikDurdevic Jul 14, 2023
ab32286
Update sparse to sparse_output
PatrikDurdevic Jul 14, 2023
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2 changes: 1 addition & 1 deletion btb/tuning/hyperparams/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,7 +127,7 @@ def _within_search_space(self, values):
values (numpy.ndarray):
2D array of values that will be validated.
"""
self._within_range(values.astype(np.float), min=0, max=1)
self._within_range(values.astype(np.float64), min=0, max=1)

@abstractmethod
def _inverse_transform(self, values):
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2 changes: 1 addition & 1 deletion btb/tuning/hyperparams/categorical.py
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Expand Up @@ -49,7 +49,7 @@ def __init__(self, choices, default=NO_DEFAULT):
self.dimensions = len(choices)
self.cardinality = self.dimensions
choices = np.array(choices, dtype='object')
self._encoder = OneHotEncoder(categories=[choices], sparse=False)
self._encoder = OneHotEncoder(categories=[choices], sparse_output=False)
self._encoder.fit(choices.reshape(-1, 1))

def _within_hyperparam_space(self, values):
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6 changes: 3 additions & 3 deletions btb/tuning/tuners/base.py
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Expand Up @@ -44,10 +44,10 @@ class BaseTuner:

def __init__(self, tunable, maximize=True):
self.tunable = tunable
self.trials = np.empty((0, self.tunable.dimensions), dtype=np.float)
self.trials = np.empty((0, self.tunable.dimensions), dtype=np.float64)
self._trials_set = set()
self.raw_scores = np.empty((0, 1), dtype=np.float)
self.scores = np.empty((0, 1), dtype=np.float)
self.raw_scores = np.empty((0, 1), dtype=np.float64)
self.scores = np.empty((0, 1), dtype=np.float64)
self.maximize = maximize
LOGGER.debug(
('Creating %s instance with %s hyperparameters and cardinality %s.'),
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13 changes: 7 additions & 6 deletions setup.py
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Expand Up @@ -23,11 +23,11 @@


install_requires = [
'copulas>=0.3.2,<0.4',
'numpy>=1.14.0',
'copulas>=0.3.2,<1',
'numpy>=1.20.0',
'scikit-learn>=0.20.0',
'scipy>=1.2,<2',
'pandas>=1,<2',
'pandas>=1',
'tqdm>=4.36.1,<5',
]

Expand Down Expand Up @@ -90,9 +90,10 @@
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
],
description='Bayesian Tuning and Bandits',
extras_require={
Expand All @@ -107,7 +108,7 @@
long_description_content_type='text/markdown',
name='baytune',
packages=find_packages(include=['btb', 'btb.*']),
python_requires='>=3.6,<3.9',
python_requires='>=3.8,<3.12',
setup_requires=setup_requires,
test_suite='tests',
tests_require=tests_require,
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12 changes: 6 additions & 6 deletions tests/tuning/tuners/test_base.py
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Expand Up @@ -244,11 +244,11 @@ def test_record_list_maximize_true(self):
instance = MagicMock()
instance.tunable = MagicMock(spec_set=Tunable)
instance.tunable.transform.return_value = np.array([[1, 0]])
instance.trials = np.empty((0, 2), dtype=np.float)
instance.trials = np.empty((0, 2), dtype=np.float64)
instance._trials_set = set()
instance.scores = None
instance.maximize = True
instance.raw_scores = np.empty((0, 1), dtype=np.float)
instance.raw_scores = np.empty((0, 1), dtype=np.float64)

# run
BaseTuner.record(instance, [1], [0.1])
Expand All @@ -268,11 +268,11 @@ def test_record_list_maximize_false(self):
instance = MagicMock()
instance.tunable = MagicMock(spec_set=Tunable)
instance.tunable.transform.return_value = np.array([[1, 0]])
instance.trials = np.empty((0, 2), dtype=np.float)
instance.trials = np.empty((0, 2), dtype=np.float64)
instance._trials_set = set()
instance.scores = None
instance.maximize = False
instance.raw_scores = np.empty((0, 1), dtype=np.float)
instance.raw_scores = np.empty((0, 1), dtype=np.float64)

# run
BaseTuner.record(instance, [1], [0.1])
Expand All @@ -292,8 +292,8 @@ def test_record_scalar_values(self):
# setup
instance = MagicMock()
instance.tunable = MagicMock(spec_set=Tunable)
instance.trials = np.empty((0, 2), dtype=np.float)
instance.raw_scores = np.empty((0, 1), dtype=np.float)
instance.trials = np.empty((0, 2), dtype=np.float64)
instance.raw_scores = np.empty((0, 1), dtype=np.float64)
instance._trials_set = set()
instance.tunable.transform.return_value = np.array([[1, 0]])

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