def test_sample_down_label_space(self):
_, y = load_dataset('yeast')
sample10 = sample_down_label_space(y, 10)
assert sample10.shape[1] == 10
sample5 = sample_down_label_space(y, 5)
assert sample5.shape[1] == 5
self.assert_raises(ValueError, sample_down_label_space, y, 20)
def test_load_enron(self):
X, y = load_dataset('enron', 'undivided')
def test_load_yeast(self):
X, y = load_dataset('yeast')
An example of :class:`skml.problem_transformation.LabelPowerset`
"""
from sklearn.metrics import hamming_loss
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np
from skml.problem_transformation import LabelPowerset
from skml.datasets import load_dataset
X, y = load_dataset('yeast')
X_train, X_test, y_train, y_test = train_test_split(X, y)
clf = LabelPowerset(LogisticRegression())
clf.fit(X_test, y_test)
y_pred = clf.predict(X_test)
print("hamming loss: ")
print(hamming_loss(y_test, y_pred))
print("accuracy:")
print(accuracy_score(y_test, y_pred))
print("f1 score:")
print("micro")
print(f1_score(y_test, y_pred, average='micro'))