def __init__(self, globals_dict: dict) -> None:
logger = get_logger()
np.random.seed(globals_dict["seed"])
gpu_imported = False
from sklearn.linear_model import RidgeClassifier
if globals_dict["gpu_param"] == "force":
import cuml.linear_model
logger.info("Imported cuml.linear_model")
gpu_imported = True
elif globals_dict["gpu_param"]:
try:
import cuml.linear_model
logger.info("Imported cuml.linear_model")
gpu_imported = True
except ImportError:
logger.warning("Couldn't import cuml.linear_model")
args = {}
tune_args = {}
tune_grid = {}
tune_distributions = {}
if gpu_imported:
RidgeClassifier = pycaret.internal.cuml_wrappers.get_ridge_classifier(
)
else:
args = {"random_state": globals_dict["seed"]}
tune_grid = {
"normalize": [True, False],
}
tune_grid["alpha"] = np_list_arange(0.01, 10, 0.01, inclusive=False)
tune_grid["fit_intercept"] = [True, False]
tune_distributions["alpha"] = UniformDistribution(0.001, 10)
leftover_parameters_to_categorical_distributions(
tune_grid, tune_distributions)
super().__init__(
id="ridge",
name="Ridge Classifier",
class_def=RidgeClassifier,
args=args,
tune_grid=tune_grid,
tune_distribution=tune_distributions,
tune_args=tune_args,
shap=False,
is_gpu_enabled=gpu_imported,
)
if gpu_imported:
self.reference = get_class_name(cuml.linear_model.Ridge)
def __init__(self, globals_dict: dict) -> None:
logger = get_logger()
np.random.seed(globals_dict["seed"])
gpu_imported = False
from sklearn.ensemble import RandomForestClassifier
if globals_dict["gpu_param"] == "force":
import cuml.ensemble
logger.info("Imported cuml.ensemble")
gpu_imported = True
elif globals_dict["gpu_param"]:
try:
import cuml.ensemble
logger.info("Imported cuml.ensemble")
gpu_imported = True
except ImportError:
logger.warning("Couldn't import cuml.ensemble")
if gpu_imported:
RandomForestClassifier = (
pycaret.internal.cuml_wrappers.get_random_forest_classifier())
args = ({
"random_state": globals_dict["seed"],
"n_jobs": globals_dict["n_jobs_param"],
} if not gpu_imported else {
"seed": globals_dict["seed"]
})
tune_args = {}
tune_grid = {
"n_estimators":
np_list_arange(10, 300, 10, inclusive=True),
"max_depth":
np_list_arange(1, 11, 1, inclusive=True),
"min_impurity_decrease": [
0,
0.0001,
0.001,
0.01,
0.0002,
0.002,
0.02,
0.0005,
0.005,
0.05,
0.1,
0.2,
0.3,
0.4,
0.5,
],
"max_features": [1.0, "sqrt", "log2"],
"bootstrap": [True, False],
}
tune_distributions = {
"n_estimators":
IntUniformDistribution(10, 300),
"max_depth":
IntUniformDistribution(1, 11),
"min_impurity_decrease":
UniformDistribution(0.000000001, 0.5, log=True),
"max_features":
UniformDistribution(0.4, 1),
}
if gpu_imported:
tune_grid["split_criterion"] = [0, 1]
else:
tune_grid["criterion"] = ["gini", "entropy"]
tune_grid["class_weight"] = ["balanced", "balanced_subsample", {}]
tune_grid["min_samples_split"] = [2, 5, 7, 9, 10]
tune_grid["min_samples_leaf"] = [2, 3, 4, 5, 6]
tune_distributions["min_samples_split"] = IntUniformDistribution(
2, 10)
tune_distributions["min_samples_leaf"] = IntUniformDistribution(
2, 6)
leftover_parameters_to_categorical_distributions(
tune_grid, tune_distributions)
super().__init__(
id="rf",
name="Random Forest Classifier",
class_def=RandomForestClassifier,
args=args,
tune_grid=tune_grid,
tune_distribution=tune_distributions,
tune_args=tune_args,
shap="type1",
is_gpu_enabled=gpu_imported,
)
if gpu_imported:
self.reference = get_class_name(
cuml.ensemble.RandomForestClassifier)