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%matplotlib inline


    

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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 ProbabilisticClassifierChain
from skml.datasets import load_dataset


X, y = load_dataset('yeast')
# sample down the label space to make the example faster.
# you shouldn't do this on your own data though!
y = y[:, :6]

X_train, X_test, y_train, y_test = train_test_split(X, y)

pcc = ProbabilisticClassifierChain(LogisticRegression())
pcc.fit(X_train, y_train)
y_pred = pcc.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'))