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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.ensemble import RandomForestClassifier
import numpy as np


from skml.ensemble import EnsembleClassifierChain
from skml.datasets import load_dataset

X, y = load_dataset('yeast')
X_train, X_test, y_train, y_test = train_test_split(X, y)

ensemble = EnsembleClassifierChain(RandomForestClassifier())
ensemble.fit(X, y)
y_pred = ensemble.predict(X)

print("hamming loss: ")
print(hamming_loss(y, y_pred))

print("accuracy:")
print(accuracy_score(y, y_pred))

print("f1 score:")
print("micro")
print(f1_score(y, y_pred, average='micro'))
print("macro")
print(f1_score(y, y_pred, average='macro'))

print("precision:")
print("micro")
print(precision_score(y, y_pred, average='micro'))
print("macro")
print(precision_score(y, y_pred, average='macro'))

print("recall:")
print("micro")
print(recall_score(y, y_pred, average='micro'))
print("macro")
print(recall_score(y, y_pred, average='macro'))