An example of skml.problem_transformation.BinaryRelevance
from __future__ import print_function
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 BinaryRelevance
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 = BinaryRelevance(LogisticRegression())
clf.fit(X_train, y_train)
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'))
print("macro")
print(f1_score(y_test, y_pred, average='macro'))
print("precision:")
print("micro")
print(precision_score(y_test, y_pred, average='micro'))
print("macro")
print(precision_score(y_test, y_pred, average='macro'))
print("recall:")
print("micro")
print(recall_score(y_test, y_pred, average='micro'))
print("macro")
print(recall_score(y_test, y_pred, average='macro'))Total running time of the script: ( 0 minutes 0.000 seconds)
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