python模型lgbm调优工具_Pythonlightgbm.LGBMClassifie。。。

python模型lgbm调优⼯具_Pythonlightgbm.LGBMClassifie。

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本⽂整理汇总了Python中lightgbm.LGBMClassifier⽅法的典型⽤法代码⽰例。如果您正苦于以下问题：Python

lightgbm.LGBMClassifier⽅法的具体⽤法？Python lightgbm.LGBMClassifier怎么⽤？Python lightgbm.LGBMClassifier使⽤的例⼦？那么恭喜您, 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模块lightgbm的⽤法⽰例。

在下⽂中⼀共展⽰了lightgbm.LGBMClassifier⽅法的30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，您的评价将有助于我们的系统推荐出更棒的Python代码⽰例。

⽰例1: Train

# 需要导⼊模块: import lightgbm [as 别名]

酸洗缓蚀剂

# 或者: from lightgbm import LGBMClassifier [as 别名]

def Train(data, modelcount, censhu, yanzhgdata):

model = lgbm.LGBMClassifier(boosting_type='gbdt', objective='binary', num_leaves=50,

learning_rate=0.1, n_estimators=modelcount, max_depth=censhu,

bagging_fraction=0.9, feature_fraction=0.9, reg_lambda=0.2)

model.fit(data[:, :-1], data[:, -1])

# 给出训练数据的预测值

train_out = model.predict(data[:, :-1])

# 计算f1度量

train_mse = fmse(data[:, -1], train_out)[0]

# 给出验证数据的预测值

add_yan = model.predict(yanzhgdata[:, :-1])

# 计算f1度量

add_mse = fmse(yanzhgdata[:, -1], add_yan)[0]

print(train_mse, add_mse)

return train_mse, add_mse

# 最终确定组合的函数

开发者ID:Anfany，项⽬名称:Machine-Learning-for-Beginner-by-Python3，代码⾏数:21，

⽰例2: recspre

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def recspre(estrs, predata, datadict, zhe):

mo, ze = estrs.split('-')

model = lgbm.LGBMClassifier(boosting_type='gbdt', objective='binary', num_leaves=50,

learning_rate=0.1, n_estimators=int(mo), max_depth=int(ze),

bagging_fraction=0.9, feature_fraction=0.9, reg_lambda=0.2)

model.fit(datadict[zhe]['train'][:, :-1], datadict[zhe]['train'][:, -1])

# 预测

yucede = model.predict(predata[:, :-1])

# 计算混淆矩阵

print(ConfuseMatrix(predata[:, -1], yucede))

return fmse(predata[:, -1], yucede)

# 主函数

开发者ID:Anfany，项⽬名称:Machine-Learning-for-Beginner-by-Python3，代码⾏数:20，

⽰例3: test_cv_lgbm

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def test_cv_lgbm():

X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

models = [LGBMClassifier(n_estimators=300) for _ in range(5)]

pred_oof, pred_test, scores, importance = cross_validate(models, X_train, y_train, X_test, cv=5, eval_func=roc_auc_score,

fit_params={'early_stopping_rounds': 200})

运维流程管理

print(scores)

assert len(scores) == 5 + 1

assert scores[-1] >= 0.85 # overall roc_auc

assert roc_auc_score(y_train, pred_oof) == scores[-1]

assert roc_auc_score(y_test, pred_test) >= 0.85 # test roc_auc

assert roc_auc_score(y, models[0].predict_proba(X)[:, 1]) >= 0.85 # make sure models are trained assert len(importance) == 5

assert list(importance[0].columns) == ['feature', 'importance']

assert len(importance[0]) == 20

开发者ID:nyanp，项⽬名称:nyaggle，代码⾏数:21，

⽰例4: test_cv_lgbm_df

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def test_cv_lgbm_df():

X, y = make_classification_df(n_samples=1024, n_num_features=20, n_cat_features=1, class_sep=0.98, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

models = [LGBMClassifier(n_estimators=300) for _ in range(5)]

pred_oof, pred_test, scores, importance = cross_validate(models, X_train, y_train, X_test, cv=5,

eval_func=roc_auc_score)

print(scores)

assert len(scores) == 5 + 1

assert scores[-1] >= 0.85 # overall roc_auc

assert roc_auc_score(y_train, pred_oof) == scores[-1]

assert roc_auc_score(y_test, pred_test) >= 0.85 # test roc_auc

assert roc_auc_score(y_test, models[0].predict_proba(X_test)[:, 1]) >= 0.85 # make sure models are trained

assert len(importance) == 5

assert list(importance[0].columns) == ['feature', 'importance']

assert len(importance[0]) == 20 + 1

assert models[0].booster_.num_trees() < 300 # making sure early stopping worked

开发者ID:nyanp，项⽬名称:nyaggle，代码⾏数:21，

⽰例5: test_fit_params_callback

# 需要导⼊模块: import lightgbm [as 别名]

绝缘法兰

# 或者: from lightgbm import LGBMClassifier [as 别名]

def test_fit_params_callback():

X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

models = [LGBMClassifier(n_estimators=300) for _ in range(5)]

sample_weights = np.random.randint(1, 10, size=len(X_train))

sample_weights = sample_weights / sample_weights.sum()

def fit_params(n: int, train_index: List[int], valid_index: List[int]):

return {

'early_stopping_rounds': 100,

'sample_weight': list(sample_weights[train_index]),

'eval_sample_weight': [list(sample_weights[valid_index])]

}

result_w_weight = cross_validate(models, X_train, y_train, X_test, cv=5,

eval_func=roc_auc_score, fit_params=fit_params)

result_wo_weight = cross_validate(models, X_train, y_train, X_test, cv=5,

eval_func=roc_auc_score, fit_params={'early_stopping_rounds': 50})

assert result_w_weight.scores[-1] != result_wo_weight.scores[-1]

开发者ID:nyanp，项⽬名称:nyaggle，代码⾏数:25，

⽰例6: __init__

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def __init__(self):

self._models = dict()

try:

semble

self._models['RandomForestClassifier'] = semble.RandomForestClassifier except ImportError:

pass

try:

import xgboost

课堂教学模式

self._models['XGBClassifier'] = xgboost.XGBClassifier

except ImportError:

pass

try:

import lightgbm

self._models['LGBMClassifier'] = lightgbm.LGBMClassifier

except ImportError:

pass

try:

import catboost

self._models['CatBoostClassifier'] = catboost.CatBoostClassifier

except ImportError:

pass

开发者ID:m3dev，项⽬名称:redshells，代码⾏数:27，

⽰例7: __call__

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def __call__(self, estimator):

fitted_estimator = estimator.fit(self.X_train, self.y_train)

if isinstance(estimator, (LinearClassifierMixin, SVC, NuSVC, LightBaseClassifier)):

y_pred = estimator.decision_function(self.X_test)

elif isinstance(estimator, DecisionTreeClassifier):

y_pred = estimator.predict_proba(self.X_test.astype(np.float32))

elif isinstance(

gu10灯头

nfs5estimator,

(ForestClassifier, XGBClassifier, LGBMClassifier)):

y_pred = estimator.predict_proba(self.X_test)

else:

y_pred = estimator.predict(self.X_test)

return self.X_test, y_pred, fitted_estimator

开发者ID:BayesWitnesses，项⽬名称:m2cgen，代码⾏数:18，

⽰例8: test_multi_class

# 需要导⼊模块: import lightgbm [as 别名]

# 或者: from lightgbm import LGBMClassifier [as 别名]

def test_multi_class():

estimator = lightgbm.LGBMClassifier(n_estimators=1, random_state=1, max_depth=1)

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