本文整理汇总了Python中xgboost.cv函数的典型用法代码示例。如果您正苦于以下问题:Python cv函数的具体用法?Python cv怎么用?Python cv使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了cv函数的20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
def train(self):
print('#### preprocessing ####')
self.df = self.preprocess(self.df)
print('#### training ####')
self.predictors = [x for x in self.df.columns if x not in [self.target_column, self.id_column]]
xgb_param = self.clf.get_xgb_params()
xgtrain = xgb.DMatrix(self.df[self.predictors], label=self.df[self.target_column], missing=np.nan)
try:
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=self.clf.get_params()['n_estimators'], nfold=5,
metrics=[self.scoring], early_stopping_rounds=self.early_stopping_rounds, show_progress=self.verbose)
except:
try:
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=self.clf.get_params()['n_estimators'], nfold=5,
metrics=[self.scoring], early_stopping_rounds=self.early_stopping_rounds, verbose_eval=self.verbose)
except:
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=self.clf.get_params()['n_estimators'], nfold=5,
metrics=[self.scoring], early_stopping_rounds=self.early_stopping_rounds)
self.clf.set_params(n_estimators=cvresult.shape[0])
self.clf.fit(self.df[self.predictors], self.df[self.target_column],eval_metric=self.scoring)
#Predict training set:
train_df_predictions = self.clf.predict(self.df[self.predictors])
if self.target_type == 'binary':
train_df_predprob = self.clf.predict_proba(self.df[self.predictors])[:,1]
print("Accuracy : %.4g" % metrics.accuracy_score(self.df[self.target_column].values, train_df_predictions))
print("AUC Score (Train): %f" % metrics.roc_auc_score(self.df[self.target_column], train_df_predprob))
elif self.target_type == 'linear':
print("Mean squared error: %f" % metrics.mean_squared_error(self.df[self.target_column].values, train_df_predictions))
print("Root mean squared error: %f" % np.sqrt(metrics.mean_squared_error(self.df[self.target_column].values, train_df_predictions)))
开发者ID:softman123g,项目名称:xgbmagic,代码行数:32,代码来源:__init__.py
示例2: test_cv_explicit_fold_indices_labels
def test_cv_explicit_fold_indices_labels(self):
params = {'max_depth': 2, 'eta': 1, 'verbosity': 0, 'objective':
'reg:linear'}
N = 100
F = 3
dm = xgb.DMatrix(data=np.random.randn(N, F), label=np.arange(N))
folds = [
# Train Test
([1, 3], [5, 8]),
([7, 9], [23, 43, 11]),
]
# Use callback to log the test labels in each fold
def cb(cbackenv):
print([fold.dtest.get_label() for fold in cbackenv.cvfolds])
# Run cross validation and capture standard out to test callback result
with captured_output() as (out, err):
xgb.cv(
params, dm, num_boost_round=1, folds=folds, callbacks=[cb],
as_pandas=False
)
output = out.getvalue().strip()
solution = ('[array([5., 8.], dtype=float32), array([23., 43., 11.],' +
' dtype=float32)]')
assert output == solution
开发者ID:zhengruifeng,项目名称:xgboost,代码行数:26,代码来源:test_basic.py
示例3: test_sklearn_nfolds_cv
def test_sklearn_nfolds_cv():
tm._skip_if_no_sklearn()
from sklearn.datasets import load_digits
from sklearn.model_selection import StratifiedKFold
digits = load_digits(3)
X = digits['data']
y = digits['target']
dm = xgb.DMatrix(X, label=y)
params = {
'max_depth': 2,
'eta': 1,
'silent': 1,
'objective':
'multi:softprob',
'num_class': 3
}
seed = 2016
nfolds = 5
skf = StratifiedKFold(n_splits=nfolds, shuffle=True, random_state=seed)
cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed)
cv2 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, folds=skf, seed=seed)
cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
assert cv2.iloc[-1, 0] == cv3.iloc[-1, 0]
开发者ID:ChangXiaodong,项目名称:xgboost-withcomments,代码行数:28,代码来源:test_with_sklearn.py
示例4: test_cv
def test_cv(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
import pandas as pd
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
assert isinstance(cv, pd.DataFrame)
exp = pd.Index([u'test-error-mean', u'test-error-std',
u'train-error-mean', u'train-error-std'])
assert cv.columns.equals(exp)
# show progress log (result is the same as above)
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
show_progress=True)
assert isinstance(cv, pd.DataFrame)
exp = pd.Index([u'test-error-mean', u'test-error-std',
u'train-error-mean', u'train-error-std'])
assert cv.columns.equals(exp)
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10,
show_progress=True, show_stdv=False)
assert isinstance(cv, pd.DataFrame)
exp = pd.Index([u'test-error-mean', u'test-error-std',
u'train-error-mean', u'train-error-std'])
assert cv.columns.equals(exp)
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
assert isinstance(cv, np.ndarray)
assert cv.shape == (10, 4)
开发者ID:ndingwall,项目名称:xgboost,代码行数:29,代码来源:test_basic.py
示例5: test_custom_objective
def test_custom_objective(self):
param = {'max_depth':2, 'eta':1, 'silent':1 }
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2
def logregobj(preds, dtrain):
labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels
hess = preds * (1.0-preds)
return grad, hess
def evalerror(preds, dtrain):
labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
# test custom_objective in training
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest)
labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
# test custom_objective in cross-validation
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
obj = logregobj, feval=evalerror)
开发者ID:GongliDuan,项目名称:xgboost,代码行数:25,代码来源:test_models.py
示例6: test_sklearn_nfolds_cv
def test_sklearn_nfolds_cv():
digits = load_digits(3)
X = digits['data']
y = digits['target']
dm = xgb.DMatrix(X, label=y)
params = {
'max_depth': 2,
'eta': 1,
'silent': 1,
'objective':
'multi:softprob',
'num_class': 3
}
seed = 2016
nfolds = 5
skf = StratifiedKFold(y, n_folds=nfolds, shuffle=True, random_state=seed)
import pandas as pd
cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed)
cv2 = xgb.cv(params, dm, num_boost_round=10, folds=skf, seed=seed)
cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed)
assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0]
assert cv2.iloc[-1,0] == cv3.iloc[-1,0]
开发者ID:000Nelson000,项目名称:xgboost,代码行数:25,代码来源:test_with_sklearn.py
示例7: xgb_model
def xgb_model(all_file, num=200, debug=True):
if debug:
all_data = pd.read_csv(all_file,nrows=500, encoding='gb18030')
else:
all_data = pd.read_csv(all_file, encoding='gb18030')
train_data = all_data[all_data['tag'] ==1]
feature_data = train_data.drop(['Idx', 'ListingInfo', 'target','tag'],axis=1)
feature_data.fillna(-1, inplace=True)
labels = train_data['target']
# feature_importance = pd.read_csv(features_importance_file)
# feature_importance_columns = feature_importance['feature'].tolist()
# feature_importance_columns = feature_importance_columns[:num]
# final_train_data = feature_data[feature_importance_columns]
final_train_data = feature_data
print final_train_data.shape
labels = train_data['target']
dtrain = xgb.DMatrix(final_train_data, label=labels, missing=-1)
# xgb_params = {'subsample':0.9, 'min_child_weight': 1, 'eval_metric': 'rmse', 'fit_const': 0.5,
# 'nthread': 3, 'num_round': 700, 'gamma': 5, 'max_depth': 6, 'eta': 0.01,
# 'colsample_bytree': 0.6, 'silent': 1, 'objective': 'binary:logistic'}
# xgb_params = {'num_round': 2200, 'colsample_bytree': 0.4, 'silent': 1, 'eval_metric': 'auc', 'nthread': 3,
# 'min_child_weight': 1, 'subsample': 0.66, 'eta': 0.006, 'fit_const': 0.6, 'objective': 'binary:logistic',
# 'max_depth': 6, 'gamma': 0}
xgb_params = {'num_round': 2400, 'colsample_bytree': 0.5, 'silent': 1, 'eval_metric': 'auc', 'nthread': 3,
'min_child_weight': 6, 'subsample': 0.8, 'eta': 0.016, 'fit_const': 0.4, 'objective': 'binary:logistic',
'max_depth': 10, 'gamma': 1}
xgb.cv(xgb_params, dtrain, num_boost_round=2400, nfold=5, metrics={'auc'}, show_progress=True)
print 'finished'
开发者ID:burness,项目名称:ppd_code,代码行数:29,代码来源:xgb_model.py
示例8: test_fpreproc
def test_fpreproc(self):
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
def fpreproc(dtrain, dtest, param):
label = dtrain.get_label()
ratio = float(np.sum(label == 0)) / np.sum(label==1)
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed = 0, fpreproc = fpreproc)
开发者ID:GongliDuan,项目名称:xgboost,代码行数:10,代码来源:test_models.py
示例9: cross_validation
def cross_validation():
for k in sorted(train_y.keys()):
if k.startswith('TripType_'):
dtrain = xgboost.DMatrix(train_X, label=train_y)
params = {
'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'
}
print xgboost.cv(params, dtrain, num_round=2, nfold=5,
metrics={'error'}, seed=0)
break
开发者ID:binhngoc17,项目名称:kaggle,代码行数:10,代码来源:train.py
示例10: cross_validation
def cross_validation():
dtrain = xgb.DMatrix('dataset_dmatrix/offline_0516_sim.train.buffer')
param = {'max_depth':5, 'eta':0.08, 'silent':1, 'objective':'binary:logistic'}
param['nthread'] = 8
param['subsample'] = 0.5
num_round = 1500
print ('running cross validation')
xgb.cv(param, dtrain, num_round, nfold=3,
show_progress=True,feval=evalerror ,seed = 0,show_stdv=False,maximize=True)
开发者ID:SuixueWang,项目名称:Koubei-Recommendation,代码行数:12,代码来源:Model_cross_validation.py
示例11: cross_validate
def cross_validate(args):
"""
Usage: cv iq_training_data_svm.txt dummy --num_round=1000
https://github.com/dmlc/xgboost/blob/master/demo/kaggle-higgs/higgs-cv.py
https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py
:param args:
:return:
"""
data = xgb.DMatrix(args.input)
param = vars(args)
xgb.cv(param, data, args.num_round, nfold=int(args.nfold),
metrics={'mlogloss', 'merror'}, seed=0)
开发者ID:thepythia,项目名称:pythia,代码行数:13,代码来源:gbdt_classifier.py
示例12: xgbCV
def xgbCV(dmatrix, nfolds, eta_list, gamma_list, num_rounds = 500):
params = {'eta':'', 'gamma':'', 'objective':'binary:logistic', 'verbose':3,
'max_depth':20, 'subsample':.75, 'colsample_bytree':.75}
vals = {'eta':[], 'gamma':[], 'num_iter':[], 'mean_cv_error':[], 'std_cv_error':[]}
for e in eta_list:
for g in gamma_list:
params['eta'] = e
params['gamma'] = g
vals['eta'].append(e)
vals['gamma'].append(g)
print('Training the booster with a learning rate of', e, "and gamma of ", g)
bst = xgb.cv(params, dmatrix, num_rounds, nfolds, early_stopping_rounds = 2)
print('Stopped after', len(bst.index), "rounds.")
best_iter = bst.nsmallest(1, 'test-error-mean')
vals['num_iter'].append(best_iter.index[0])
vals['mean_cv_error'].append(best_iter['test-error-mean'])
vals['std_cv_error'].append(best_iter['test-error-std'])
cv_df = pd.DataFrame.from_dict(vals)
return(cv_df)
开发者ID:whereofonecannotspeak,项目名称:twitter_sentiment_analysis,代码行数:28,代码来源:boostCV.py
示例13: modelfit
def modelfit(alg, dtrain, predictors, target, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
if useTrainCV:
xgb_param = alg.get_xgb_params()
xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
metrics='auc', early_stopping_rounds=early_stopping_rounds)
alg.set_params(n_estimators=cvresult.shape[0])
#Fit the algorithm on the data
alg.fit(dtrain[predictors], dtrain[target],eval_metric='auc')
#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
#Print model report:
print ("\nModel Report")
print ("Accuracy : %.4g" % metrics.accuracy_score(dtrain[target].values, dtrain_predictions))
print ("AUC Score (Train): %f" % metrics.roc_auc_score(dtrain[target], dtrain_predprob))
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')
plt.show()
开发者ID:Paliking,项目名称:ML_examples,代码行数:25,代码来源:LoanPrediction2_XGB.py
示例14: model_2
def model_2(train, labels, test):
dtrain = xgb.DMatrix(train, label=labels)
dtest = xgb.DMatrix(test)
xgb_params = {}
xgb_params["objective"] = "reg:linear"
xgb_params["eta"] = 0.1
xgb_params["subsample"] = 0.7
xgb_params["silent"] = 1
xgb_params["max_depth"] = 6
xgb_params['eval_metric'] = 'rmse'
xgb_params['min_child_weight'] = 5
xgb_params['seed'] = 22424
res = xgb.cv(xgb_params, dtrain, num_boost_round=500, nfold=5, seed=2017, stratified=False,
early_stopping_rounds=25, verbose_eval=10, show_stdv=True)
best_nrounds = res.shape[0] - 1
cv_mean = res.iloc[-1, 0]
cv_std = res.iloc[-1, 1]
print('')
print('Ensemble-CV: {0}+{1}'.format(cv_mean, cv_std))
bst = xgb.train(xgb_params, dtrain, best_nrounds)
preds = np.exp(bst.predict(dtest))
return preds
开发者ID:movb,项目名称:kaggle,代码行数:28,代码来源:script.py
示例15: do_compute
def do_compute(x):
row = grid.iloc[x,:]
eta = row['eta']
min_child_weight = row['min_child_weight']
colsample_bytree = row['colsample_bytree']
max_depth = row['max_depth']
subsample = row['subsample']
_lambda = row['lambda']
nround = row['nround']
####
xgb_pars = {'min_child_weight': min_child_weight,
'eta': eta,
'colsample_bytree': colsample_bytree,
'max_depth': int(max_depth),
'subsample': subsample,
'lambda': _lambda,
'nthread': -1,
'booster' : 'gbtree',
'silent': 1,
'eval_metric': 'rmse',
'objective': 'reg:linear'}
#print(xgb_pars)
model = xgb.cv(xgb_pars, dtrain, 100000,nfold = 4, early_stopping_rounds=50,maximize=False, verbose_eval=10)
nround = model.shape[0]
rmse_cv_mean = model['test-rmse-mean'][model.shape[0]-1]
rmse_cv_std = model['test-rmse-std'][model.shape[0]-1]
# calculate the square of the value of x
grid.loc[x,'rmse_cv_mean'] = rmse_cv_mean
grid.loc[x,'rmse_cv_std'] = rmse_cv_std
grid.loc[x,'nround'] = nround
grid.to_csv('base_grid_xgb_40perc__'+str(os.getpid())+'.csv',index=False)
return rmse_cv_mean
开发者ID:gtesei,项目名称:fast-furious,代码行数:32,代码来源:base_xgb_tune_mthread.py
示例16: modelfit
def modelfit(alg, train_data, train_label, cv_folds=5, early_stopping_rounds=1):
xgb_param = alg.get_xgb_params()
xgtrain = xgb.DMatrix(train_data, label=train_label)
cvresult = xgb.cv(xgb_param,
xgtrain,
num_boost_round=alg.get_params()['n_estimators'],
nfold=cv_folds,
metrics=['auc'],
early_stopping_rounds=early_stopping_rounds,
show_progress=True)
alg.set_params(n_estimators=cvresult.shape[0]) # Goal of CV is to tune the number of rounds, which is set here
# Note: can change to a different day to see what happens
start = time.time()
alg.fit(train_data,
train_label,
eval_metric='auc')
print "Time to fit: %s" % (time.time()-start)
pickle.dump(alg, open("/home/jche/Desktop/xgboost.p", "w+")) # Save model
start = time.time()
dtrain_predprob = alg.predict_proba(train_data)[:,1]
print "Time to predict: %s" % (time.time() - start)
for cutoff in range(0, 41):
cut = cutoff/float(100) # Cutoff in decimal form
dtrain_predictions = dtrain_predprob > cut # If y values are greater than the cutoff
# Print model report:
print "\nModel Report for cutoff %s" % cut
print "Accuracy : %.4g" % metrics.accuracy_score(train_label, dtrain_predictions)
print "AUC Score (Train): %f" % metrics.roc_auc_score(train_label, dtrain_predprob)
print "Recall is: %s" % metrics.recall_score(train_label, dtrain_predictions)
print metrics.confusion_matrix(train_label, dtrain_predictions)
开发者ID:jche,项目名称:GumGum,代码行数:35,代码来源:XGBoost_CV.py
示例17: modelfit
def modelfit(alg, dtrain, predictors, dtest=None, dscore=None, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
if useTrainCV:
xgb_param = alg.get_xgb_params()
xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
metrics=['logloss'], early_stopping_rounds=early_stopping_rounds, show_progress=False)
alg.set_params(n_estimators=cvresult.shape[0])
#Fit the algorithm on the data
alg.fit(dtrain[predictors], dtrain['target'], eval_metric='logloss')
#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
if isinstance(dtest, pd.DataFrame):
dtest_predprob = alg.predict_proba(dtest[predictors])[:,1]
if isinstance(dscore, pd.DataFrame):
dscore_predprob = alg.predict_proba(dscore[predictors])[:,1]
np.savetxt('XGBoost_pred_raw.csv', dscore_predprob, delimiter=",")
#Print model report:
print "\nModel Report"
print "Accuracy : %.4g" % metrics.accuracy_score(dtrain['target'].values, dtrain_predictions)
print "Metric Score (Train): %f" % metrics.log_loss(dtrain['target'], dtrain_predprob)
if isinstance(dtest, pd.DataFrame):
print "Metric Score (Test): %f" % metrics.log_loss(dtest['target'], dtest_predprob)
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')
plt.show()
开发者ID:rschork,项目名称:schork_utilities,代码行数:32,代码来源:xgboost_crossval.py
示例18: cv
def cv(self, X, y):
X = self.build_matrix(X, y)
param = {
'silent': 1 if self.silent else 0,
'use_buffer': int(self.use_buffer),
'num_round': self.num_round,
'ntree_limit': self.ntree_limit,
'nthread': self.nthread,
'booster': self.booster,
'eta': self.eta,
'gamma': self.gamma,
'max_depth': self.max_depth,
'min_child_weight': self.min_child_weight,
'subsample': self.subsample,
'colsample_bytree': self.colsample_bytree,
'max_delta_step': self.max_delta_step,
'l': self.l,
'alpha': self.alpha,
'lambda_bias': self.lambda_bias,
'objective': self.objective,
'eval_metric': self.eval_metric,
'seed': self.seed,
'num_class': self.num_class,
}
results = xgb.cv(param, X, self.num_round, 3)
return results
开发者ID:bahrunnur,项目名称:drivendata-women-healthcare,代码行数:26,代码来源:XGBoostClassifier.py
示例19: train_crossV
def train_crossV(self, train_x, train_y, nfold=3, early_stopping_rounds=300, metrics=['auc']):
xgmat_train = xgb.DMatrix(train_x, label=train_y, missing=-9999)
params = {
'booster':'gbtree',
'objective':'binary:logistic',
'silent':self.silent,
'eta':self.eta,
'gamma':self.gamma,
'max_depth':self.max_depth,
'min_chile_weitght':self.min_chile_weight,
'subsample':self.subsample,
'lambda':self.lambda_,
'scale_pos_weight':self.scale_pos_weight,
"colsample_bytree": self.colsample_bytree,
'eval_metirc':'auc',
'seed':2014,
'nthread':self.threads
}
watchlist = [ (xgmat_train,'train') ]
num_round = self.num_boost_round
cv_result = xgb.cv(params, xgmat_train, num_boost_round=num_round, early_stopping_rounds=early_stopping_rounds, nfold=nfold, seed=1024, show_progress=True, metrics=metrics)
return cv_result
开发者ID:Sandy4321,项目名称:Xgboost_Datacastle_MoralQualityPrediction,代码行数:26,代码来源:xgb_class.py
示例20: regression_with_xgboost
def regression_with_xgboost(x_train, y_train, X_test, Y_test, features=None, use_cv=True, use_sklean=False, xgb_params=None):
train_data = xgb.DMatrix(x_train, label=y_train, missing=float('nan'))
test_data = xgb.DMatrix(X_test, Y_test, missing=float('nan'))
evallist = [(test_data,'eval'), (train_data,'train')]
#if xgb_params == None:
# xgb_params = get_default_xgboost_params()
if not use_cv:
num_rounds = 10
else:
cvresult = xgb.cv(xgb_params, train_data, num_boost_round=100, nfold=5,
metrics={'rmse'}, show_progress=True)
print cvresult
num_rounds = len(cvresult)
gbdt = None
if(use_sklean):
#gbdt = xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='reg:linear', nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=0, missing=None)
xgb_params['n_estimators'] = num_rounds
gbdt = xgboost.XGBRegressor(xgb_params)
gbdt.fit(x_train, y_train)
y_pred = gbdt.predict(X_test)
return gbdt, y_pred
else:
#gbdt = xgb.train( xgb_params, train_data, num_rounds, evallist, verbose_eval = True, early_stopping_rounds=5)
gbdt = xgb.train( xgb_params, train_data, num_rounds, evallist, verbose_eval = True)
ceate_feature_map_for_feature_importance(features)
show_feature_importance(gbdt, feature_names=features)
y_pred = gbdt.predict(xgb.DMatrix(X_test, missing=float("nan")))
return XGBoostModel(gbdt), y_pred
开发者ID:srinathperera,项目名称:mlprojects-py,代码行数:34,代码来源:__init__.py
注:本文中的xgboost.cv函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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