def loadDataFrame():
iris = load_iris()
iris_data = iris.data
iris_target = iris.target
iris_df = pd.DataFrame(iris_data, columns=iris.feature_names)
iris_df['target'] = pd.Series(iris_target)
return iris_df
if __name__ == "__main__":
iris_df = loadDataFrame()
value_counts = iris_df['target'].value_counts()
print(value_counts)
X_iris = iris_df.iloc[:, :-1].values
y_iris = iris_df.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X_iris,
y_iris,
test_size=0.3,
stratify=y_iris)
model = LGBMClassifier()
model.fit(X_train, y_train)
print(model.get_params())
y_pred = model.predict(X_test)
print("accuracy: %s" % np.mean(y_pred == y_test))
random_state=10)
lgb = joblib.load('LightGBM_model.pkl')
dtime1 = time.time() - time1
# 预测测试集1
time2 = time.time()
test_pre = lgb.predict(data['test_data'])
dtime2 = time.time() - time2
test_preb = lgb.predict_proba(data['test_data'])
acc = metrics.accuracy_score(data['test_label'], test_pre)
t = [i[1] for i in test_preb]
auc = metrics.roc_auc_score(data['test_label'], t)
recall = metrics.recall_score(data['test_label'], test_pre)
prec = metrics.precision_score(data['test_label'], test_pre)
f1 = metrics.f1_score(data['test_label'], test_pre)
print(lgb.get_params())
print(lgb.feature_importances_)
print('''train time: %d
predict time: %d
acc: %f
auc: %f
recall: %f
precision: %f
f1: %f''' % (dtime1, dtime2, acc, auc, recall, prec, f1))
joblib.dump(lgb, 'LightGBM_model.pkl')
pre_list = []
for p in test_preb:
if p[1] < 0.5:
pre_list.append(0)
else:
