def __init__(self,
n_estimators=10,
criterion='gini',
max_depth=None,
min_samples_leaf=1,
max_features='auto',
bootstrap=True,
n_jobs=None,
random_state=None,
verbose=False,
use_histograms=False,
hist_nbins=256,
use_gpu=False,
gpu_ids=None):
assert pai4sk_installed, """Your Python environment does not have pai4sk installed. For installation instructions see: https://www.zurich.ibm.com/snapml/"""
self._hyperparams = {
'n_estimators': n_estimators,
'criterion': criterion,
'max_depth': max_depth,
'min_samples_leaf': min_samples_leaf,
'max_features': max_features,
'bootstrap': bootstrap,
'n_jobs': n_jobs,
'random_state': random_state,
'verbose': verbose,
'use_histograms': use_histograms,
'hist_nbins': hist_nbins,
'use_gpu': use_gpu,
'gpu_ids': gpu_ids
}
modified_hps = {**self._hyperparams}
if modified_hps['gpu_ids'] is None:
modified_hps['gpu_ids'] = [0] #TODO: support list as default
self._wrapped_model = pai4sk.RandomForestClassifier(**modified_hps)
def test_without_lale(self):
import pai4sk
clf = pai4sk.RandomForestClassifier()
self.assertIsInstance(clf, pai4sk.RandomForestClassifier)
fit_result = clf.fit(self.train_X, self.train_y)
self.assertIsNone(fit_result)
scorer = sklearn.metrics.make_scorer(sklearn.metrics.accuracy_score)
accuracy = scorer(clf, self.test_X, self.test_y)
def __init__(
self,
n_estimators=10,
criterion="gini",
max_depth=None,
min_samples_leaf=1,
max_features="auto",
bootstrap=True,
n_jobs=None,
random_state=None,
verbose=False,
use_histograms=False,
hist_nbins=256,
use_gpu=False,
gpu_ids=None,
):
assert (
pai4sk_installed
), """Your Python environment does not have pai4sk installed. For installation instructions see: https://www.zurich.ibm.com/snapml/"""
self._hyperparams = {
"n_estimators": n_estimators,
"criterion": criterion,
"max_depth": max_depth,
"min_samples_leaf": min_samples_leaf,
"max_features": max_features,
"bootstrap": bootstrap,
"n_jobs": n_jobs,
"random_state": random_state,
"verbose": verbose,
"use_histograms": use_histograms,
"hist_nbins": hist_nbins,
"use_gpu": use_gpu,
"gpu_ids": gpu_ids,
}
modified_hps = {**self._hyperparams}
if modified_hps["gpu_ids"] is None:
modified_hps["gpu_ids"] = [0] # TODO: support list as default
self._wrapped_model = pai4sk.RandomForestClassifier(**modified_hps)