本文整理汇总了Python中sklearn.ensemble.AdaBoostRegressor方法的典型用法代码示例。如果您正苦于以下问题:Python ensemble.AdaBoostRegressor方法的具体用法?Python ensemble.AdaBoostRegressor怎么用?Python ensemble.AdaBoostRegressor使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble
的用法示例。
在下文中一共展示了ensemble.AdaBoostRegressor方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Train
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def Train(data, modelcount, censhu, yanzhgdata):
model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=censhu),
n_estimators=modelcount, learning_rate=0.8)
model.fit(data[:, :-1], data[:, -1])
# 给出训练数据的预测值
train_out = model.predict(data[:, :-1])
# 计算MSE
train_mse = mse(data[:, -1], train_out)
# 给出验证数据的预测值
add_yan = model.predict(yanzhgdata[:, :-1])
# 计算MSE
add_mse = mse(yanzhgdata[:, -1], add_yan)
print(train_mse, add_mse)
return train_mse, add_mse
# 最终确定组合的函数
示例2: test_gridsearch
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_gridsearch():
# Check that base trees can be grid-searched.
# AdaBoost classification
boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
parameters = {'n_estimators': (1, 2),
'base_estimator__max_depth': (1, 2),
'algorithm': ('SAMME', 'SAMME.R')}
clf = GridSearchCV(boost, parameters)
clf.fit(iris.data, iris.target)
# AdaBoost regression
boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(),
random_state=0)
parameters = {'n_estimators': (1, 2),
'base_estimator__max_depth': (1, 2)}
clf = GridSearchCV(boost, parameters)
clf.fit(boston.data, boston.target)
示例3: test_sample_weight_adaboost_regressor
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_sample_weight_adaboost_regressor():
"""
AdaBoostRegressor should work without sample_weights in the base estimator
The random weighted sampling is done internally in the _boost method in
AdaBoostRegressor.
"""
class DummyEstimator(BaseEstimator):
def fit(self, X, y):
pass
def predict(self, X):
return np.zeros(X.shape[0])
boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
boost.fit(X, y_regr)
assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_))
示例4: test_multidimensional_X
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_multidimensional_X():
"""
Check that the AdaBoost estimators can work with n-dimensional
data matrix
"""
from sklearn.dummy import DummyClassifier, DummyRegressor
rng = np.random.RandomState(0)
X = rng.randn(50, 3, 3)
yc = rng.choice([0, 1], 50)
yr = rng.randn(50)
boost = AdaBoostClassifier(DummyClassifier(strategy='most_frequent'))
boost.fit(X, yc)
boost.predict(X)
boost.predict_proba(X)
boost = AdaBoostRegressor(DummyRegressor())
boost.fit(X, yr)
boost.predict(X)
示例5: run_sklearn
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def run_sklearn():
n_trees = 100
n_folds = 3
# https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/
alg_list = [
['lreg',LinearRegression()],
['rforest',RandomForestRegressor(n_estimators=1000, n_jobs=-1, max_depth=3)],
['extree',ExtraTreesClassifier(n_estimators = 1000,max_depth=2)],
['adaboost',AdaBoostRegressor(base_estimator=None, n_estimators=600, learning_rate=1.0)],
['knn', sklearn.neighbors.KNeighborsRegressor(n_neighbors=5)]
]
start_time = time.time()
for name,alg in alg_list:
train = jhkaggle.train_sklearn.TrainSKLearn("1",name,alg,False)
train.run()
train = None
elapsed_time = time.time() - start_time
print("Elapsed time: {}".format(jhkaggle.util.hms_string(elapsed_time)))
示例6: sample_1031_4
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def sample_1031_4():
"""
10.3.1_4 猪老三使用回归预测股价:使用集成学习算法预测股价AdaBoost与RandomForest
:return:
"""
train_x, train_y_regress, train_y_classification, pig_three_feature, \
test_x, test_y_regress, test_y_classification, kl_another_word_feature_test = sample_1031_1()
# AdaBoost
from sklearn.ensemble import AdaBoostRegressor
estimator = AdaBoostRegressor(n_estimators=100)
regress_process(estimator, train_x, train_y_regress, test_x,
test_y_regress)
plt.show()
# RandomForest
from sklearn.ensemble import RandomForestRegressor
estimator = RandomForestRegressor(n_estimators=100)
regress_process(estimator, train_x, train_y_regress, test_x, test_y_regress)
plt.show()
示例7: test_sample_weight_adaboost_regressor
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_sample_weight_adaboost_regressor():
"""
AdaBoostRegressor should work without sample_weights in the base estimator
The random weighted sampling is done internally in the _boost method in
AdaBoostRegressor.
"""
class DummyEstimator(BaseEstimator):
def fit(self, X, y):
pass
def predict(self, X):
return np.zeros(X.shape[0])
boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
boost.fit(X, y_regr)
assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_))
示例8: Adaboost_First
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def Adaboost_First(self, data, max_depth=5, n_estimators=320):
model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=max_depth),
n_estimators=n_estimators, learning_rate=0.8)
model.fit(data['train'][:, :-1], data['train'][:, -1])
# 注意存储验证数据集结果和预测数据集结果的不同
# 训练数据集的预测结果
xul = model.predict(data['train'][:, :-1])
# 验证的预测结果
yanre = model.predict(data['test'][:, :-1])
# 预测的预测结果
prer = model.predict(data['predict'][:, :-1])
# 储存
self.yanzhneg_pr.append(yanre)
self.predi.append(prer)
# 分别计算训练、验证、预测的误差
# 每计算一折后,要计算训练、验证、预测数据的误差
xx = self.RMSE(xul, data['train'][:, -1])
yy = self.RMSE(yanre, data['test'][:, -1])
pp = self.RMSE(prer, data['predict'][:, -1])
# 储存误差
self.error_dict['AdaBoost'] = [xx, yy, pp]
# 验证数据集的真实输出结果
self.yanzhneg_real = data['test'][:, -1]
# 预测数据集的真实输出结果
self.preal = data['predict'][:, -1]
return print('1层中的AdaBoost运行完毕')
# GBDT
示例9: recspre
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def recspre(exstr, predata, datadict, zhe, count=100):
tree, te = exstr.split('-')
model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=int(te)),
n_estimators=int(tree), learning_rate=0.8)
model.fit(datadict[zhe]['train'][:, :-1], datadict[zhe]['train'][:, -1])
# 预测
yucede = model.predict(predata[:, :-1])
# 为了便于展示,选100条数据进行展示
zongleng = np.arange(len(yucede))
randomnum = np.random.choice(zongleng, count, replace=False)
yucede_se = list(np.array(yucede)[randomnum])
yuce_re = list(np.array(predata[:, -1])[randomnum])
# 对比
plt.figure(figsize=(17, 9))
plt.subplot(2, 1, 1)
plt.plot(list(range(len(yucede_se))), yucede_se, 'r--', label='预测', lw=2)
plt.scatter(list(range(len(yuce_re))), yuce_re, c='b', marker='.', label='真实', lw=2)
plt.xlim(-1, count + 1)
plt.legend()
plt.title('预测和真实值对比[最大树数%d]' % int(tree))
plt.subplot(2, 1, 2)
plt.plot(list(range(len(yucede_se))), np.array(yuce_re) - np.array(yucede_se), 'k--', marker='s', label='真实-预测', lw=2)
plt.legend()
plt.title('预测和真实值相对误差')
plt.savefig(r'C:\Users\GWT9\Desktop\duibi.jpg')
return '预测真实对比完毕'
# 主函数
示例10: test_regression_toy
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_regression_toy():
# Check classification on a toy dataset.
clf = AdaBoostRegressor(random_state=0)
clf.fit(X, y_regr)
assert_array_equal(clf.predict(T), y_t_regr)
示例11: test_boston
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_boston():
# Check consistency on dataset boston house prices.
reg = AdaBoostRegressor(random_state=0)
reg.fit(boston.data, boston.target)
score = reg.score(boston.data, boston.target)
assert score > 0.85
# Check we used multiple estimators
assert len(reg.estimators_) > 1
# Check for distinct random states (see issue #7408)
assert_equal(len(set(est.random_state for est in reg.estimators_)),
len(reg.estimators_))
示例12: test_pickle
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_pickle():
# Check pickability.
import pickle
# Adaboost classifier
for alg in ['SAMME', 'SAMME.R']:
obj = AdaBoostClassifier(algorithm=alg)
obj.fit(iris.data, iris.target)
score = obj.score(iris.data, iris.target)
s = pickle.dumps(obj)
obj2 = pickle.loads(s)
assert_equal(type(obj2), obj.__class__)
score2 = obj2.score(iris.data, iris.target)
assert_equal(score, score2)
# Adaboost regressor
obj = AdaBoostRegressor(random_state=0)
obj.fit(boston.data, boston.target)
score = obj.score(boston.data, boston.target)
s = pickle.dumps(obj)
obj2 = pickle.loads(s)
assert_equal(type(obj2), obj.__class__)
score2 = obj2.score(boston.data, boston.target)
assert_equal(score, score2)
示例13: test_sample_weight_missing
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def test_sample_weight_missing():
from sklearn.cluster import KMeans
clf = AdaBoostClassifier(KMeans(), algorithm="SAMME")
assert_raises(ValueError, clf.fit, X, y_regr)
clf = AdaBoostRegressor(KMeans())
assert_raises(ValueError, clf.fit, X, y_regr)
示例14: setClf
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def setClf(self):
# min_samples_split = 3
self.clf = AdaBoostRegressor()
return
示例15: __init__
# 需要导入模块: from sklearn import ensemble [as 别名]
# 或者: from sklearn.ensemble import AdaBoostRegressor [as 别名]
def __init__(self, options):
self.handle_options(options)
params = options.get('params', {})
out_params = convert_params(
params,
strs=['loss', 'max_features'],
floats=['learning_rate'],
ints=['n_estimators'],
)
self.estimator = _AdaBoostRegressor(**out_params)