lgbm和xgboost使用教程

# coding: utf-8
# pylint: disable = invalid-name, C0111
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV

# load or create your dataset
print('Load data...')
df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')

y_train = df_train[0].values
y_test = df_test[0].values
X_train = df_train.drop(0, axis=1).values
X_test = df_test.drop(0, axis=1).values

print('Start training...')
# train
gbm = lgb.LGBMRegressor(objective='regression',
                        num_leaves=31,
                        learning_rate=0.05,
                        n_estimators=20)
gbm.fit(X_train, y_train,
        eval_set=[(X_test, y_test)],
        eval_metric='l1',
        early_stopping_rounds=5)

print('Start predicting...')
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# eval
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)

# feature importances
print('Feature importances:', list(gbm.feature_importances_))

# other scikit-learn modules
estimator = lgb.LGBMRegressor(num_leaves=31)

param_grid = {
    'learning_rate': [0.01, 0.1, 1],
    'n_estimators': [20, 40]
}

gbm = GridSearchCV(estimator, param_grid)

gbm.fit(X_train, y_train)

print('Best parameters found by grid search are:', gbm.best_params_)

import lightgbm as lgb
from sklearn import metrics

def auc2(m, train, test): 
    return (metrics.roc_auc_score(y_train,m.predict(train)),
                            metrics.roc_auc_score(y_test,m.predict(test)))

lg = lgb.LGBMClassifier(silent=False)
param_dist = {"max_depth": [25,50, 75],
              "learning_rate" : [0.01,0.05,0.1],
              "num_leaves": [300,900,1200],
              "n_estimators": [200]
             }
grid_search = GridSearchCV(lg, n_jobs=-1, param_grid=param_dist, cv = 3, scoring="roc_auc", verbose=5)
grid_search.fit(train,y_train)
grid_search.best_estimator_

d_train = lgb.Dataset(train, label=y_train)
params = {"max_depth": 50, "learning_rate" : 0.1, "num_leaves": 900,  "n_estimators": 300}

# Without Categorical Features
model2 = lgb.train(params, d_train)
auc2(model2, train, test)

#With Catgeorical Features
cate_features_name = ["MONTH","DAY","DAY_OF_WEEK","AIRLINE","DESTINATION_AIRPORT",
                 "ORIGIN_AIRPORT"]
model2 = lgb.train(params, d_train, categorical_feature = cate_features_name)
auc2(model2, train, test)

import xgboost as xgb
from sklearn import metrics

def auc(m, train, test): 
    return (metrics.roc_auc_score(y_train,m.predict_proba(train)[:,1]),
                            metrics.roc_auc_score(y_test,m.predict_proba(test)[:,1]))

# Parameter Tuning
model = xgb.XGBClassifier()
param_dist = {"max_depth": [10,30,50],
              "min_child_weight" : [1,3,6],
              "n_estimators": [200],
              "learning_rate": [0.05, 0.1,0.16],}
grid_search = GridSearchCV(model, param_grid=param_dist, cv = 3, 
                                   verbose=10, n_jobs=-1)
grid_search.fit(train, y_train)

grid_search.best_estimator_

model = xgb.XGBClassifier(max_depth=50, min_child_weight=1,  n_estimators=200,\
                          n_jobs=-1 , verbose=1,learning_rate=0.16)
model.fit(train,y_train)

auc(model, train, test)

贝叶斯调参：

import pandas as pd

from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.preprocessing import LabelEncoder

import xgboost as xgb

from sklearn.metrics import r2_score

import numpy as np

from sklearn.decomposition import PCA, FastICA, TruncatedSVD
from sklearn.random_projection import GaussianRandomProjection
from sklearn.random_projection import SparseRandomProjection

from bayes_opt import BayesianOptimization

def average_dups(x):
    # Average value of duplicates
    Y.loc[list(x.index)] = Y.loc[list(x.index)].mean()

def xgb_r2_score(preds, dtrain):
    # Courtesy of Tilii
    labels = dtrain.get_label()
    return 'r2', r2_score(labels, preds)

def train_xgb(max_depth, subsample, min_child_weight, gamma, colsample_bytree):
    # Evaluate an XGBoost model using given params
    xgb_params = {
        'n_trees': 250,
        'eta': 0.01,
        'max_depth': int(max_depth),
        'subsample': max(min(subsample, 1), 0),
        'objective': 'reg:linear',
        'base_score': np.mean(Y), # base prediction = mean(target)
        'silent': 1,
        'min_child_weight': int(min_child_weight),
        'gamma': max(gamma, 0),
        'colsample_bytree': max(min(colsample_bytree, 1), 0)
    }
    scores = xgb.cv(xgb_params, dtrain, num_boost_round=1500, early_stopping_rounds=50, verbose_eval=False, feval=xgb_r2_score, maximize=True, nfold=5)['test-r2-mean'].iloc[-1]
    return scores


# Load the dataframes
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/test.csv")

for c in train.columns:
    if train[c].dtype == 'object':

        lbl = LabelEncoder() 
        lbl.fit(list(train[c].values) + list(test[c].values)) 
        train[c] = lbl.transform(list(train[c].values))
        test[c] = lbl.transform(list(test[c].values))


# Organize our data for training
X = train.drop(["y"], axis=1)
Y = train["y"]
X_Test = test
# X_Test = test.drop(["ID"], axis=1)

# Handling duplicate values
# First we group the duplicates and then average them
dups = X[X.duplicated(keep=False)]
dups.groupby(dups.columns.tolist()).apply(average_dups)

# Drop duplicates keeping only 1 instance of each group
train.drop(X[X.duplicated()].index.values, axis=0, inplace=True)
X = train.drop(["y"], axis=1)
Y = train["y"]

# Fix index after dropping
X.reset_index(inplace=True, drop=True)
Y.reset_index(inplace=True, drop=True)

# Handling outliers
# Y[Y > 150] = Y.quantile(0.99)


pca = PCA(n_components=5)
ica = FastICA(n_components=5, max_iter=1000)
tsvd = TruncatedSVD(n_components=5)
gp = GaussianRandomProjection(n_components=5)
sp = SparseRandomProjection(n_components=5, dense_output=True)

x_pca = pd.DataFrame(pca.fit_transform(X))
x_ica = pd.DataFrame(ica.fit_transform(X))
x_tsvd = pd.DataFrame(tsvd.fit_transform(X))
x_gp = pd.DataFrame(gp.fit_transform(X))
x_sp = pd.DataFrame(sp.fit_transform(X))

x_pca.columns = ["pca_{}".format(i) for i in x_pca.columns]
x_ica.columns = ["ica_{}".format(i) for i in x_ica.columns]
x_tsvd.columns = ["tsvd_{}".format(i) for i in x_tsvd.columns]
x_gp.columns = ["gp_{}".format(i) for i in x_gp.columns]
x_sp.columns = ["sp_{}".format(i) for i in x_sp.columns]

X = pd.concat((X, x_pca), axis=1)
X = pd.concat((X, x_ica), axis=1)
X = pd.concat((X, x_tsvd), axis=1)
X = pd.concat((X, x_gp), axis=1)
X = pd.concat((X, x_sp), axis=1)

x_test_pca = pd.DataFrame(pca.transform(X_Test))
x_test_ica = pd.DataFrame(ica.transform(X_Test))
x_test_tsvd = pd.DataFrame(tsvd.transform(X_Test))
x_test_gp = pd.DataFrame(gp.transform(X_Test))
x_test_sp = pd.DataFrame(sp.transform(X_Test))

x_test_pca.columns = ["pca_{}".format(i) for i in x_test_pca.columns]
x_test_ica.columns = ["ica_{}".format(i) for i in x_test_ica.columns]
x_test_tsvd.columns = ["tsvd_{}".format(i) for i in x_test_tsvd.columns]
x_test_gp.columns = ["gp_{}".format(i) for i in x_test_gp.columns]
x_test_sp.columns = ["sp_{}".format(i) for i in x_test_sp.columns]


X_Test = pd.concat((X_Test, x_test_pca), axis=1)
X_Test = pd.concat((X_Test, x_test_ica), axis=1)
X_Test = pd.concat((X_Test, x_test_tsvd), axis=1)
X_Test = pd.concat((X_Test, x_test_gp), axis=1)
X_Test = pd.concat((X_Test, x_test_sp), axis=1)

dtrain = xgb.DMatrix(X, Y)
dtest = xgb.DMatrix(X_Test)

# A parameter grid for XGBoost
params = {
  'min_child_weight':(1, 20),
  'gamma':(0, 10),
  'subsample':(0.5, 1),
  'colsample_bytree':(0.1, 1),
  'max_depth': (2, 10)
}

# Initialize BO optimizer
xgb_bayesopt = BayesianOptimization(train_xgb, params)

# Maximize R2 score
xgb_bayesopt.maximize(init_points=5, n_iter=25)

# Get the best params
p = xgb_bayesopt.res['max']['max_params']

xgb_params = {
    'n_trees': 250,
    'eta': 0.01,
    'max_depth': int(p['max_depth']),
    'subsample': max(min(p['subsample'], 1), 0),
    'objective': 'reg:linear',
    'base_score': np.mean(Y), # base prediction = mean(target)
    'silent': 1,
    'min_child_weight': int(p['min_child_weight']),
    'gamma': max(p['gamma'], 0),
    'colsample_bytree': max(min(p['colsample_bytree'], 1), 0)
}

model = xgb.train(xgb_params, dtrain, num_boost_round=1500, verbose_eval=False, feval=xgb_r2_score, maximize=True)

Y_Test = model.predict(dtest)

results_df = pd.DataFrame(data={'y':Y_Test}) 
ids = test["ID"]
joined = pd.DataFrame(ids).join(results_df)
joined.to_csv("mercedes.csv", index=False)

参数搜索：GridSearchCV 与 RandomizedSearchCV || 以阿里IJCAI广告推荐数据集与XGBoostClassifier分类器为例

调参

xgboost调参

xgboost入门与实战

xgboost老外调参代码

xgboost生成新特征和模型融合代码

https://juejin.im/post/5b76437ae51d45666b5d9b05