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)