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train.py
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train.py
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#import sklearn
from plot_learning_curve import *
# do some prepare work
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)
def train(X_train, Y_train, learner, title):
print('start training...')
learner.fit(X_train, Y_train)
#plot_learning_curve(learner, title, X_train, Y_train, cv=5, n_jobs=4)
#plt.show()
print('saving model...')
from sklearn.externals import joblib
with open('trained_' + title +'.fzy', 'wb') as fo:
joblib.dump(learner, fo)
print('done')
def test(X_test, Y_test, title):
from sklearn.externals import joblib
learner = joblib.load('trained_' + title +'.fzy')
Y_pred = learner.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(Y_test, Y_pred)
from plot_precision_recall_curve import plot_precision_recall_curve
precision, recall = plot_precision_recall_curve(Y_test, Y_pred)
from sklearn.metrics import f1_score
f1 = f1_score(Y_test, Y_pred)
from sklearn.metrics import confusion_matrix
cnf_matrix = confusion_matrix(Y_test, Y_pred)
from plot_confusion_matrix import plot_confusion_matrix
plot_confusion_matrix(cnf_matrix, classes=['pular', 'non-pular'], normalize=True, title='Normalized confusion matrix')
from plot_roc_curve import plot_roc_curve
plot_roc_curve(Y_test, Y_pred)
print('accuracy: ' + str(accuracy))
print('f1: ' + str(f1))
print('precision: ' + str(precision))
print('recall: ' + str(recall))
#print('threshold: ' + str(threshold))