def test_classifier(self): index = [i for i in range(len(self.iris.data))] rf = RandomForestClassifier() jrf = JoblibedClassifier(rf, "rf", cache_dir='') jrf.fit(self.iris.data, self.iris.target, index) prediction = jrf.predict(self.iris.data, index) score = accuracy_score(self.iris.target, prediction) self.assertGreater(score, 0.9, "Failed with score = {0}".format(score)) rf = RandomForestClassifier(n_estimators=20) jrf = JoblibedClassifier(rf, "rf", cache_dir='') jrf.fit(self.iris.data, self.iris.target) index = [i for i in range(len(self.iris.data))] prediction2 = jrf.predict(self.iris.data, index) self.assertTrue((prediction == prediction2).all())
def test_classifier(self): index = [i for i in range(len(self.iris.data))] rf = RandomForestClassifier() jrf = JoblibedClassifier(rf, "rf") jrf.fit(self.iris.data, self.iris.target, index) prediction = jrf.predict(self.iris.data, index) score = accuracy_score(self.iris.target, prediction) self.assertGreater(score, 0.9, "Failed with score = {0}".format(score)) rf = RandomForestClassifier(n_estimators=20) jrf = JoblibedClassifier(rf, "rf") jrf.fit(self.iris.data, self.iris.target) index = [i for i in range(len(self.iris.data))] prediction2 = jrf.predict(self.iris.data, index) self.assertTrue((prediction == prediction2).all())
from sklearn.ensemble import RandomForestClassifier from sklearn.utils.validation import check_random_state from stacked_generalization.lib.joblibed import JoblibedClassifier iris = datasets.load_iris() rng = check_random_state(0) perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] # Joblibed model rf = RandomForestClassifier(n_estimators=40, criterion='gini', random_state=1) clf = JoblibedClassifier(rf, "rf") train_idx, test_idx = list(StratifiedKFold(iris.target, 3))[0] xs_train = iris.data[train_idx] y_train = iris.target[train_idx] xs_test = iris.data[test_idx] y_test = iris.target[test_idx] print("First fit and prediction (not cached).") clf.fit(xs_train, y_train, train_idx) score = clf.score(xs_test, y_test, test_idx) print('Classfier score: {0}'.format(score))
from sklearn import datasets from sklearn.model_selection import StratifiedKFold from sklearn.ensemble import RandomForestClassifier from sklearn.utils.validation import check_random_state from stacked_generalization.lib.joblibed import JoblibedClassifier iris = datasets.load_iris() rng = check_random_state(0) perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] # Joblibed model rf = RandomForestClassifier(n_estimators=40, criterion='gini', random_state=1) clf = JoblibedClassifier(rf, "rf") train_idx, test_idx = list(StratifiedKFold(3).split(iris.data, iris.target))[0] xs_train = iris.data[train_idx] y_train = iris.target[train_idx] xs_test = iris.data[test_idx] y_test = iris.target[test_idx] print("First fit and prediction (not cached).") clf.fit(xs_train, y_train, train_idx) score = clf.score(xs_test, y_test, test_idx) print('Classfier score: {0}'.format(score)) print("Second fit and prediction (load cache).") clf.fit(xs_train, y_train, train_idx) score = clf.score(xs_test, y_test, test_idx)