def test_class_enumerator_actual_module():
module = ClassEnumerator(module=cuml.linear_model,
exclude_classes=[
cuml.LinearRegression, cuml.MBSGDClassifier,
cuml.MBSGDRegressor
],
custom_constructors={
'LogisticRegression':
lambda: cuml.LogisticRegression(handle=1)
})
models = module.get_models()
ref = {
'ElasticNet': cuml.ElasticNet,
'Lasso': cuml.Lasso,
'LogisticRegression': lambda: cuml.LogisticRegression(handle=1),
'Ridge': cuml.Ridge
}
assert models['LogisticRegression']().handle == ref['LogisticRegression'](
).handle
models.pop('LogisticRegression')
ref.pop('LogisticRegression')
assert models == ref
def test_class_enumerator():
class SomeModule:
class SomeClass(cuml.Base):
pass
class ExcludedClass(cuml.Base):
pass
class CustomConstructorClass(cuml.Base):
def __init__(self, *, some_parameter):
self.some_parameter = some_parameter
def __eq__(self, other):
return self.some_parameter == other.some_parameter
module = ClassEnumerator(
module=SomeModule,
exclude_classes=[SomeModule.ExcludedClass],
custom_constructors={
"CustomConstructorClass":
lambda: SomeModule.CustomConstructorClass(some_parameter=1)
})
models = module.get_models()
ref = {
"SomeClass":
SomeModule.SomeClass,
"CustomConstructorClass":
lambda: SomeModule.CustomConstructorClass(some_parameter=1)
}
# Here we don't do `assert models == ref` because CustomConstructorClass is
# a lambda.
assert len(models) == len(ref) == 2
assert models['SomeClass'] == ref['SomeClass']
assert models['CustomConstructorClass']() == ref['CustomConstructorClass'](
)
from cuml import LinearRegression as reg
from cuml import PCA
from cuml.experimental.explainer.common import get_cai_ptr
from cuml.experimental.explainer.common import get_dtype_from_model_func
from cuml.experimental.explainer.common import get_handle_from_cuml_model_func
from cuml.experimental.explainer.common import get_link_fn_from_str_or_fn
from cuml.experimental.explainer.common import get_tag_from_model_func
from cuml.experimental.explainer.common import link_dict
from cuml.experimental.explainer.common import model_func_call
from cuml.test.utils import ClassEnumerator
from cuml.datasets import make_regression
from sklearn.linear_model import LinearRegression as skreg
models_config = ClassEnumerator(module=cuml)
models = models_config.get_models()
_default_tags = [
'preferred_input_order',
'X_types_gpu',
'non_deterministic',
'requires_positive_X',
'requires_positive_y',
'X_types',
'poor_score',
'no_validation',
'multioutput',
'allow_nan',
'stateless',
'multilabel',
'_skip_test',
import numpy as np
import pickle
import pytest
from cuml.test import test_arima
from cuml.tsa.arima import ARIMA
from cuml.test.utils import array_equal, unit_param, stress_param, \
ClassEnumerator, get_classes_from_package
from cuml.test.test_svm import compare_svm
from sklearn.base import clone
from sklearn.datasets import load_iris, make_classification, make_regression
from sklearn.manifold.t_sne import trustworthiness
from sklearn.model_selection import train_test_split
regression_config = ClassEnumerator(module=cuml.linear_model)
regression_models = regression_config.get_models()
solver_config = ClassEnumerator(
module=cuml.solvers,
# QN uses softmax here because some of the tests uses multiclass
# logistic regression which requires a softmax loss
custom_constructors={"QN": lambda: cuml.QN(loss="softmax")})
solver_models = solver_config.get_models()
cluster_config = ClassEnumerator(module=cuml.cluster,
exclude_classes=[cuml.DBSCAN])
cluster_models = cluster_config.get_models()
decomposition_config = ClassEnumerator(module=cuml.decomposition)
decomposition_models = decomposition_config.get_models()