def test_model_def_factory(self):
base = ModelDefinition(features=["a"], estimator="model", target="y")
factory = model_definition_factory(
base, features=[["a", "b"], ["a", "b", "c"], ["a", "b", "c", "y"]], estimator=["model2", "model3"]
)
mds = list(factory)
self.assertEqual(len(mds), 6)
def cv_factory(data=None,
folds=5,
repeat=1,
reporters=[],
metrics=None,
cv_runner=None,
**kwargs):
"""Shortcut to iterate and cross-validate models.
All ModelDefinition kwargs should be iterables that can be
passed to model_definition_factory.
Parameters:
___________
data:
Raw DataFrame
folds:
If an int, than basic k-fold cross-validation will be done.
Otherwise must be an iterable of tuples of pandas Indexes
[(train_index, test_index), ...]
repeat:
How many times to repeat each cross-validation run of each model. Only
makes sense if cross-validation folds are randomized.
kwargs:
Can be any keyword accepted by `ModelDefinition`.
Values should be iterables.
"""
cv_runner = cv_runner or cross_validate
md_kwargs = {}
for arg in ModelDefinition.params:
if arg in kwargs:
md_kwargs[arg] = kwargs.pop(arg)
model_def_fact = model_definition_factory(ModelDefinition(), **md_kwargs)
results = []
model_defs = list(model_def_fact)
for model_def in model_defs:
reporters = [reporter.copy() for reporter in reporters]
cvr = cv_runner(model_def=model_def,
data=data,
folds=folds,
repeat=repeat,
reporters=reporters,
metrics=metrics,
**kwargs)
results.append(cvr)
return CVComparisonResult(model_defs, results)
def test_model_def_factory(self):
base = ModelDefinition(features=['a'],
estimator=estimators.Estimator('dummy'),
target='y')
factory = model_definition_factory(base,
features=[
['a', 'b'],
['a', 'b', 'c'],
['a', 'b', 'c', 'y'],
],
estimator=[
estimators.Estimator('dummy'),
estimators.Estimator('dummy2'),
])
mds = list(factory)
self.assertEqual(len(mds), 6)
def cv_factory(data=None, folds=5, repeat=1, reporters=[], metrics=None,
cv_runner=None, **kwargs):
"""Shortcut to iterate and cross-validate models.
All ModelDefinition kwargs should be iterables that can be
passed to model_definition_factory.
Parameters:
___________
data:
Raw DataFrame
folds:
If an int, than basic k-fold cross-validation will be done.
Otherwise must be an iterable of tuples of pandas Indexes
[(train_index, test_index), ...]
repeat:
How many times to repeat each cross-validation run of each model. Only
makes sense if cross-validation folds are randomized.
kwargs:
Can be any keyword accepted by `ModelDefinition`.
Values should be iterables.
"""
cv_runner = cv_runner or cross_validate
md_kwargs = {}
for arg in ModelDefinition.params:
if arg in kwargs:
md_kwargs[arg] = kwargs.pop(arg)
model_def_fact = model_definition_factory(ModelDefinition(), **md_kwargs)
results = []
model_defs = list(model_def_fact)
for model_def in model_defs:
reporters = [reporter.copy() for reporter in reporters]
cvr = cv_runner(model_def=model_def,
data=data,
folds=folds,
repeat=repeat,
reporters=reporters,
metrics=metrics,
**kwargs)
results.append(cvr)
return CVComparisonResult(model_defs, results)
def test_model_def_factory(self):
base = ModelDefinition(
features=['a'],
estimator=estimators.Estimator('dummy'),
target='y'
)
factory = model_definition_factory(base,
features=[
['a','b'],
['a','b','c'],
['a','b','c','y'],
],
estimator=[
estimators.Estimator('dummy'),
estimators.Estimator('dummy2'),
]
)
mds = list(factory)
self.assertEqual(len(mds), 6)
def cv_factory(data=None, folds=None, repeat=1, reporter_factories=[], **kwargs):
"""Shortcut to iterate and cross-validate models.
All ModelDefinition kwargs should be iterables that can be
passed to model_definition_factory.
"""
cv_runner = kwargs.pop('cv_runner', modeling.cross_validate)
md_kwargs = {}
for arg in ModelDefinition.params:
if arg in kwargs:
md_kwargs[arg] = kwargs.pop(arg)
model_def_fact = model_definition_factory(ModelDefinition(), **md_kwargs)
ret = {}
for model_def in model_def_fact:
results, reporters = cv_runner(model_def, data, folds, repeat=repeat, reporters=[factory() for factory in reporter_factories], **kwargs)
ret[model_def.summary] = {'model_def': model_def,
'results': results,
'reporters': reporters}
return ret
