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)))
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
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]
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)
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)
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]
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'
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)
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
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)
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)
def model_param_select(self):
"""
k-folds cross validation to select the best param
"""
params = {'max_depth': self.max_depth,
'eta': self.eta,
'subsample': self.subsample,
'objective': self.objective,
'silent': self.silent}
best_auc, best_param, best_iter_round = 0, {}, 0
param_grid = ParameterGrid(params)
for i, param in enumerate(param_grid):
cv_result = xgb.cv(param, self.train_matrix,
num_boost_round=self.num_boost_round, # max iter round
nfold=self.nfold,
stratified=self.stratified,
metrics=self.metrics, # metrics focus on
early_stopping_rounds=self.early_stopping_rounds) # stop when metrics not get better
cur_auc = cv_result.ix[len(cv_result)-1, 0]
cur_iter_round = len(cv_result)
if cur_auc > best_auc:
best_auc, best_param, best_iter_round = cur_auc, param, cur_iter_round
print('Param select {}, auc: {}, iter_round: {}, params: {}, now best auc: {}'
.format(i, cur_auc, cur_iter_round, param, best_auc))
return best_auc, best_param, best_iter_round
def train_cv(self, dtrain, **args):
cv_args = self.parse_args(args, method='cv')
print(cv_args)
cv_xgb = xgb.cv(dtrain=dtrain,
**cv_args)
results = self.persit_result_cv(cv_xgb, args)
return results
def modelfit(alg, data, predictors, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
'''
a variation of:
http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
'''
if useTrainCV:
xgb_param = alg.get_xgb_params()
xgtrain = xgb.DMatrix(data['x_train'][predictors], label=data['y_train'])
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(data['x_train'][predictors], data['y_train'], eval_metric='auc')
#Predict training set:
dtrain_predictions = alg.predict(data['x_train'][predictors])
dtrain_predprob = alg.predict_proba(data['x_train'][predictors])[:,1]
#Print model report:
print ("\nModel Report")
print ("Accuracy : %.4g" % metrics.accuracy_score(data['y_train'].values, dtrain_predictions))
print ("AUC Score (Train): %f" % metrics.roc_auc_score(data['y_train'], dtrain_predprob))
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
feat_imp[0:20].plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')
plt.show()
return alg
def xgbTuning(self, pX, change = 3):
w = self.getWeight(self.y)
dm = xgb.DMatrix(pX, self.y, weight=w)
best_auc = 0
n = pX.shape[0]
best_params = None
for i in range(change):
randp = np.random.random_sample(3)
param = {
'bst:eta': randp[0],
'max_depth': int(3+6*randp[1]) ,
'nthread':4,
'silent':1,
'alpha':randp[2],
'eval_metric':'auc',
'objective': 'binary:logistic'
}
m = xgb.cv(param, dm, metrics='auc', nfold=3, num_boost_round = 50,early_stopping_rounds=5)
auc = m['test-auc-mean'].max()
if auc > best_auc :
print 'xgb:' + str(auc)
best_auc = auc
best_params = param
Xtrain, Xtest, ytrain, ytest = train_test_split(pX, self.y, test_size=.33)
trainw = self.getWeight(ytrain)
testw = self.getWeight(ytest)
dtrain = xgb.DMatrix(Xtrain, label = ytrain, feature_names=Xtrain.columns, weight = trainw)
dtest = xgb.DMatrix(Xtest, label = ytest, feature_names=Xtest.columns, weight = testw)
evallist = [(dtrain, 'train'), (dtest, 'eval')]
booster = xgb.train(best_params, dtrain, evals=evallist, num_boost_round=100,early_stopping_rounds=10)
rounds = booster.attr("best_iteration")
best_auc = booster.attr("best_score")
return float(best_auc), xgb.train(best_params, dtrain, num_boost_round=int(rounds))
def modelfit(alg, dtrain, predictors, 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 DataFrame
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)
importances = alg.booster().get_fscore()
importances = sorted(importances.items(), key=operator.itemgetter(1), reverse=True)
df = pd.DataFrame(importances, columns=['feature', 'fscore'])
df['fscore'] = df['fscore'] / df['fscore'].sum()
df.to_csv("ips.csv", index=False)
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
print feat_imp
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
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
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
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)
def fit(self, X, y):
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
self._le = XGBLabelEncoder().fit(y)
training_labels = self._le.transform(y)
xgdmat = xgb.DMatrix(X, label=training_labels)
if self.n_classes_ > 2:
self.param_map.update({'num_class':self.n_classes_})
self.param_map.update({'objective':'multi:softprob'})
self.results = xgb.cv(self.param_map,
xgdmat,
self.num_boost_round,
self.nfold,
self.stratified,
self.folds,
self.metrics,
self.obj,
self.feval,
self.maximize,
self.early_stopping_rounds,
self.fpreproc,
self.as_pandas,
self.verbose_eval,
self.show_stdv,
self.seed,
self.callbacks)
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()
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
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)
def modelfit(alg, train, target, test, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
if useTrainCV:
xgboost_params = alg.get_xgb_params()
xgtrain = xgb.DMatrix(train.values, label=target.values)
xgtest = xgb.DMatrix(test.values)
watchlist = [(xgtrain, 'train')] # Specify validations set to watch performance
cvresult = xgb.cv(xgboost_params, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, early_stopping_rounds=early_stopping_rounds) #metrics='auc',show_progress=False
alg.set_params(n_estimators=cvresult.shape[0])
# Fit the algorithm on the data
alg.fit(train, target, eval_metric='auc')
# Predict training set:
train_preds = alg.predict(train)
train_predprob = alg.predict_proba(train)[:,1]
# Print model report:
print "\nModel Report"
print "Accuracy : %.4g" % metrics.accuracy_score(target.values, train_preds)
print "AUC Score (Train): %f" % metrics.roc_auc_score(target, train_predprob)
# Make a prediction:
print('Predicting......')
test_predprob = alg.predict_proba(test)[:,1]
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()
return test_predprob
def testInstance(population,i,dtrain):
params = {"objective": "reg:linear",
"eta": population.eta[i],
"max_depth": population.depth[i],
"subsample": population.subsample[i],
"colsample_bytree": population.colsample_bytree[i],
"num_boost_round":int(population.nRound[i]),
"lambda":population.lamda[i],
"alpha":population.alpha[i],
"gamma":population.gamma[i],
"min_child_weight":population.min_child_weight[i],
"silent": 1,
#"seed": 1301
}
history = xgb.cv(
params,
dtrain,
#early_stopping_rounds=30, #no early stopping in Python yet!!!
num_boost_round =int(population.nRound[i]),
nfold=5, # number of CV folds
#nthread=12, # number of CPU threads
show_progress=False,
feval=rmspe_xg, # custom evaluation metric
obj=RMSPE_objective
#maximize=0 # the lower the evaluation score the better
)
return history["test-rmspe-mean"].iget(-1)
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()
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
def test_cv(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
# 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)
def test_cv_no_shuffle(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
# return np.ndarray
cv = xgb.cv(params, dm, num_boost_round=10, shuffle=False, nfold=10, as_pandas=False)
assert isinstance(cv, dict)
assert len(cv) == (4)
import numpy as np
import xgboost as xgb
### load data in do training
dtrain = xgb.DMatrix('data/aga.train')
param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
num_round = 2
print('running cross validation')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value+std_value
# std_value is standard deviation of the metric
xgb.cv(param,
dtrain,
num_round,
nfold=5,
metrics={'error'},
seed=0,
callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
print('running cross validation, disable standard deviation display')
# do cross validation, this will print result out as
# [iteration] metric_name:mean_value
res = xgb.cv(param,
dtrain,
num_boost_round=10,
nfold=5,
metrics={'error'},
seed=0,
callbacks=[
xgb.callback.print_evaluation(show_stdv=False),
def test_cv_as_pandas(self):
dm = xgb.DMatrix(dpath + 'agaricus.txt.train')
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic'
}
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 len(cv.columns.intersection(exp)) == 4
# show progress log (result is the same as above)
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
verbose_eval=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 len(cv.columns.intersection(exp)) == 4
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
verbose_eval=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 len(cv.columns.intersection(exp)) == 4
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': 'auc'
}
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
assert 'eval_metric' in params
assert 'auc' in cv.columns[0]
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': ['auc']
}
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
assert 'eval_metric' in params
assert 'auc' in cv.columns[0]
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': ['auc']
}
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
early_stopping_rounds=1)
assert 'eval_metric' in params
assert 'auc' in cv.columns[0]
assert cv.shape[0] < 10
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic'
}
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
metrics='auc')
assert 'auc' in cv.columns[0]
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic'
}
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
metrics=['auc'])
assert 'auc' in cv.columns[0]
params = {
'max_depth': 2,
'eta': 1,
'verbosity': 0,
'objective': 'binary:logistic',
'eval_metric': ['auc']
}
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
metrics='error')
assert 'eval_metric' in params
assert 'auc' not in cv.columns[0]
assert 'error' in cv.columns[0]
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
metrics=['error'])
assert 'eval_metric' in params
assert 'auc' not in cv.columns[0]
assert 'error' in cv.columns[0]
params = list(params.items())
cv = xgb.cv(params,
dm,
num_boost_round=10,
nfold=10,
as_pandas=True,
metrics=['error'])
assert isinstance(params, list)
assert 'auc' not in cv.columns[0]
assert 'error' in cv.columns[0]
'subsample': 0.8,
'learning_rate': 0.06,
'colsample_bytree': 0.8,
'eta': 0.02,
'objective': 'binary:logistic',
'sample_type': 'uniform',
'normalize': 'tree',
'rate_drop': 0.15, # 0.1
'skip_drop': 0.85, # 0.9
'nthread': -1,
}
dtrain = xgb.DMatrix(X_train, label=y_train)
xgb.cv(xgb_params,
dtrain,
num_boost_round=10000,
nfold=5,
verbose_eval=5,
early_stopping_rounds=100)
model = xgb.train(xgb_params,
dtrain=dtrain,
num_boost_round=300,
evals=[(dtrain, 'train')],
verbose_eval=5)
dtest = xgb.DMatrix(X_test)
preds = model.predict(dtest)
print dtest
StackingSubmission = pd.DataFrame({'score': preds})
def _train_model(self):
self.log.info("Training models...")
if self.date_obs_weights:
date_min = min(self.data['train']['data']['disp_date'])
date_max = max(self.data['train']['data']['disp_date'])
span = date_max - date_min
obs_weights = [(i - date_min) / span
for i in self.data['train']['data']['disp_date']]
else:
obs_weights = [1] * len(self.data['train']['data'].index)
# Tune Hyperparameters ----------------------------------------------------------------
if not os.path.exists(f"{self.code_dir}/models/tune_logs.json"):
self.log.info(
"No hyperparameter tuning logs found! Finding optimal hyperparameters... (this will take a while)"
)
#
log_params = {}
# For each label in the training dataset...
for name, values in self.data['train']['labels'].iteritems():
# Generate dmatrix for xgb model
train_dmatrix = xgboost.DMatrix(self.data['train']['data'],
label=values,
weight=obs_weights)
# Optomiz parameters and save to log
log_params[name] = bayes_opt_xgb(
dmatrix=train_dmatrix,
log=self.log,
opt_fun=xgb_cv_fun,
opt_rounds=self.opt_rounds,
init_rounds=self.init_rounds,
params_ranges=self.params_ranges,
max_estimators=self.max_estimators)
# Save log to file
json.dump(log_params,
open(f"{self.code_dir}/models/tune_logs.json", "w"),
indent=4)
else:
# If a file already exists, load it
self.log.info("Hyperparameter tuning logs found, loading...")
log_params = json.load(
open(f"{self.code_dir}/models/tune_logs.json", "r"))
# Train models -------------------------------------------------------------------------
fits = {}
for name, values in self.data['train']['labels'].iteritems():
self.log.info(f"Training {name}")
train_dmatrix = xgboost.DMatrix(
self.data['train']['data'],
label=values,
weight=obs_weights,
feature_names=self.data['train']['data'].columns)
# Get best hyperparameters for label from log
log_best = log_params[name][max(log_params[name].keys())]
best_params = log_best['params']
fits[name] = xgboost.train(
best_params,
train_dmatrix,
num_boost_round=log_best['fit_props']['n_estimators'])
if len(self.data['test']['data'].index) > 0:
# Print some quick "sanity check" results in test data
test_dmatrix = xgboost.DMatrix(
self.data['test']['data'],
label=self.data['test']['labels'][name])
self.log.info("Mean pred: " +
str(np.mean(fits[name].predict(test_dmatrix))) +
" (" +
str(np.mean(self.data['test']['labels'][name])) +
")"
" Test AUC: " + str(
metrics.roc_auc_score(
self.data['test']['labels'][name],
fits[name].predict(test_dmatrix))))
else:
# If no test data, check results using CV
cv_results = xgboost.cv(
best_params,
train_dmatrix,
metrics='auc',
num_boost_round=log_best['fit_props']['n_estimators'])
self.log.info(cv_results.iloc[-1])
model_props = get_model_props(fits,
data=self.data,
out_weights=self.out_weights,
instrument_trans=self.instrument_trans,
log=self.log)
if self.refit_full_model:
for name, values in self.data['train']['labels'].iteritems():
self.log.info(f"Training {name} on training and test data")
train_dmatrix = xgboost.DMatrix(
self.data['train']['data'],
label=values,
weight=obs_weights,
feature_names=self.data['train']['data'].columns)
# Get best hyperparameters for label from log
log_best = log_params[name][max(log_params[name].keys())]
best_params = log_best['params']
fits[name] = xgboost.train(
best_params,
train_dmatrix,
num_boost_round=log_best['fit_props']['n_estimators'])
model_dict = {'models': fits, 'model_props': model_props}
return model_dict
def train(self):
params = {
'eta': self.eta,
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'nthread': self.threads,
'num_class': len(self.incl)
}
print('\033[92m' + 'Using the following parameters:' + '\033[0m')
print('eta: {}'.format(self.eta))
print('num_rounds: {}'.format(self.num_rounds))
print('early_stopping_rounds: {}'.format(self.early_stop))
grid_params = [(max_depth, subsample, colsample_bytree)
for max_depth in self.max_depth
for subsample in self.subsample
for colsample_bytree in self.colsample_bytree]
print('\033[92m' +
'Cross-validating with {} folds:'.format(self.nfold) + '\033[0m')
min_mlogloss = float("Inf")
best_params = None
for max_depth, subsample, colsample_bytree in grid_params:
print("CV with max_depth={}, subsample={}, colsample_bytree={}".
format(max_depth, subsample, colsample_bytree))
params['max_depth'] = max_depth
params['subsample'] = subsample
params['colsample_bytree'] = colsample_bytree
cv_results = xgb.cv(params,
self.dtrain,
num_boost_round=self.num_rounds,
seed=self.rnd_seed,
nfold=self.nfold,
metrics={'mlogloss'},
early_stopping_rounds=self.early_stop)
# Update best mlogloss
mean_mlogloss = cv_results['test-mlogloss-mean'].min()
boost_rounds = cv_results['test-mlogloss-mean'].argmin()
print("\tmlogloss {} for {} rounds".format(mean_mlogloss,
boost_rounds))
if mean_mlogloss < min_mlogloss:
min_mlogloss = mean_mlogloss
best_params = (max_depth, subsample, colsample_bytree,
boost_rounds)
print("Best params: {}, {}, {}, mlogloss: {}".format(
best_params[0], best_params[1], best_params[2], min_mlogloss))
print('\033[92m' + 'Training final model' + '\033[0m')
params['max_depth'] = best_params[0]
params['subsample'] = best_params[1]
params['colsample_bytree'] = best_params[2]
self.model = xgb.train(params,
self.dtrain,
num_boost_round=self.num_rounds,
evals=[(self.dtest, "Test")],
early_stopping_rounds=self.early_stop)
self.boost_rounds = self.model.best_iteration
self.model.save_model(self.out + 'xgb_repeats.model')
best_params = None
for max_depth, min_child_weight in gridsearch_params:
print("CV with max_depth={}, min_child_weight={}".format(
max_depth,
min_child_weight))
# Update our parameters
params['max_depth'] = max_depth
params['min_child_weight'] = min_child_weight
# Run CV
cv_results = xgb.cv(
params,
dtrain=dt,
num_boost_round=300,
seed=2,
nfold=5,
metrics={'mae'},
early_stopping_rounds=10
)
mean_mae = cv_results['test-mae-mean'].min()
boost_rounds = cv_results['test-mae-mean'].argmin()
print("\tMAE {} for {} rounds".format(mean_mae, boost_rounds))
if mean_mae < min_mae:
min_mae = mean_mae
best_params = (max_depth,min_child_weight)
print("Best params: {}, {}, MAE: {}".format(best_params[0], best_params[1], min_mae))
# Best (11,4)
reg_alpha=0.005,
learning_rate=0.21,
max_depth=1,
alpha=10,
n_estimators=1000,
min_child_weight=10,
gamma=0.2,
nthread=4,
scale_pos_weight=1)
datamatrix = xgb.DMatrix(data=X, label=Y)
xgpara = xg_reg.get_xgb_params()
cv_results = xgb.cv(
dtrain=datamatrix,
params=xgpara,
nfold=6,
num_boost_round=xg_reg.get_params()['n_estimators'],
early_stopping_rounds=30,
metrics="rmse",
as_pandas=True,
seed=123)
xg_reg.set_params(n_estimators=cv_results.shape[0])
sc = MinMaxScaler(feature_range=(0, 1200))
ta = sc.fit_transform(ta[:, :])
xg_reg.fit(ta[:, :-1], ta[:, -1])
#Getting the Predictions For Non-Linear Residue
xgpred = xg_reg.predict(ta[:, :-1])
xg_reg.fit(ta1[:, :-1], ta1[:, -1])
xgpred1 = xg_reg.predict(ta1[:, :-1])
arimaPreds1 = arima_predictions[start:end, 1]
arimaPreds2 = arimaPreds1.reshape((arimaPreds1.shape[0], 1))
min_mae = float("Inf")
best_params = None
params['silent'] = 1
for eta in [.3, .2, .1, .05, .01, .005]:
print("CV with eta={}".format(eta))
# We update our parameters
params['eta'] = eta
# Run and time CV
cv_results = xgb.cv(
params,
dtrain,
num_boost_round=num_boost_round,
seed=42,
nfold=5,
metrics=['mae'],
early_stopping_rounds=10
)
# Update best score
mean_mae = cv_results['test-mae-mean'].min()
boost_rounds = cv_results['test-mae-mean'].argmin()
print("\tMAE {} for {} rounds\n".format(mean_mae, boost_rounds))
if mean_mae < min_mae:
min_mae = mean_mae
best_params = eta
print("Best params: {}, MAE: {}".format(best_params, min_mae))
print('================\n')
'eta': .01,
'colsample_bytree': .8,
'subsample': .8,
'seed': 0,
'nthread': 16,
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'num_class': 3,
'silent': 0
}
dtrain = xgb.DMatrix(data=x_train, label=y_train)
dtest = xgb.DMatrix(data=x_test)
bst = xgb.cv(params,
dtrain,
10000,
NFOLDS,
early_stopping_rounds=50,
verbose_eval=25)
best_rounds = np.argmin(bst['test-mlogloss-mean'])
bst = xgb.train(params, dtrain, best_rounds)
bst.save_model('xgboostreduced2.model')
preds = bst.predict(dtest)
preds = pd.DataFrame(preds)
cols = ['high', 'medium', 'low']
preds.columns = cols
preds['listing_id'] = test.listing_id.values
preds.to_csv('xgboostreduced2.csv', index=None)
best_params = None
t = time.time()
#search for max depth and min_child_weight
for max_depth, min_child_weight in gridsearch_params:
print("CV with max_depth={}, min_child_weight={}".format(
max_depth, min_child_weight))
params = initializeDefaultParamsGPU()
params['max_depth'] = max_depth
params['min_child_weight'] = min_child_weight
cv_result = xgb.cv(params,
train_dmat,
num_boost_round=num_boost_round_default,
seed=seed,
nfold=nfold,
metrics=metrics,
early_stopping_rounds=early_stopping_round_default)
mean_mlogloss = cv_result['test-mlogloss-mean'].min()
boost_rounds = cv_result['test-mlogloss-mean'].argmin()
print("\tMAE {} for {} rounds".format(mean_mlogloss, boost_rounds))
if mean_mlogloss < min_mlogloss:
min_mlogloss = mean_mlogloss
best_params = (max_depth, min_child_weight)
print("Best max_depth and min_child_weight: {}, mlogloss: {}".format(
best_params, min_mlogloss))
print("Time: {}".format(time.time() - t))
max_depth = best_params[0]
'normalize': 'tree',
'rate_drop': 0.1,
'skip_drop': 0.9,
'seed': 87,
'nthread': 12,
'slice': 0
}
watchlist = [(dtrain, 'train')]
print 'cv'
# #通过cv找最佳的nround
cv_log = xgb.cv(params,
dtrain,
num_boost_round=1000,
nfold=5,
metrics='rmse',
early_stopping_rounds=50,
seed=1024)
bst_rmse = cv_log['test-rmse-mean'].min()
cv_log['nb'] = cv_log.index
cv_log.index = cv_log['test-rmse-mean']
bst_nb = cv_log.nb.to_dict()[bst_rmse]
# watchlist = [(dtrain,'train')]
model = xgb.train(params, dtrain, num_boost_round=bst_nb + 50, evals=watchlist)
#predict test set
test_y = model.predict(dtest)
print test_y
#
X_train_b = X_train[cols_b]
X_test_a = X_test[cols_a]
X_test_b = X_test[cols_b]
dtrain = xgb.DMatrix(X_train,y)
dtest= xgb.DMatrix(X_test)
dtrain_b = xgb.DMatrix(X_train_b,y)
dtest_b = xgb.DMatrix(X_test_b)
param_a = {'subsample':0.55, 'eta':0.05, 'seed': 10, 'max_depth': 4, 'gamma': 0.75,'objective':'reg:linear','colsample_bytree':0.7,'eval_metric': 'rmse','nthread': 8, 'min_child_weight': 4.0 ,'early_stopping_rounds':10,'verbose_eval':10,'booster':'gbtree'}
num_round_a = 3000
n = int(0.3*74067)
df_train_train = df_train.iloc[:n]
df_train_test = df_train.iloc[n:]
id_test = df_train_test['id'].astype(int)
clf_a = xgb.cv(param_a, dtrain, num_round_a,nfold = 5,metrics={'rmse'}, seed = 0)
clf_a[clf_a['test-rmse-mean']==min(clf_a['test-rmse-mean'])].index.tolist()
param_b = {'subsample':0.55, 'eta':0.05, 'seed': 10, 'max_depth': 4, 'gamma': 0.75,'objective':'reg:linear','colsample_bytree':0.7,'eval_metric': 'rmse','nthread': 8, 'min_child_weight': 4.0 ,'early_stopping_rounds':10,'verbose_eval':10,'booster':'gblinear'}
num_round_b = 8000
clf_b = xgb.cv(param_b, dtrain_b, num_round_b,nfold = 5,metrics={'rmse'}, seed = 0)
clf_b[clf_b['test-rmse-mean']==min(clf_b['test-rmse-mean'])].index.tolist()
########################## xgboost model 1 ##################################
num_round = 8000
bst = xgb.train(param_a,dtrain_a,num_round_a)
print("predict...")
y_pred = bst.predict(dtest_a)
for i in range(len(y_pred)):
if y_pred[i]<1.0:
y_pred[i] = 1.0
clf=xgb.train(params, dtrain, num_boost_round=10, evals=[], obj=None, feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None)
'''
num_boost_round：boost迭代次数
evals：一对对 (DMatrix, string)组成的列表，培训期间将评估哪些指标的验证集列表。验证指标将帮助我们跟踪模型的性能。用evallist = [(dtest, 'eval'), (dtrain, 'train')]指定。
obj
feval：自定义评价函数
maximize
early_stopping_rounds：验证指标需要至少在每轮early_stopping_rounds中改进一次才能继续训练，例如early_stopping_rounds=200表示每200次迭代将会检查验证指标是否有改进，如果没有就会停止训练，如果有多个指标，则只判断最后一个指标
evals_result
verbose_eval：取值可以是bool型也可以是整数，当取值为True时，表示每次迭代都显示评价指标，当取值为整数时，表示每该取值次数轮迭代后显示评价指标
xgb_model
callbacks
learning_rates
'''
xgb.cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None, metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True)
'''
model = xgb.cv(params, dtrain, num_boost_round=500, early_stopping_rounds=100)
model.loc[30:,["test-rmse-mean", "train-rmse-mean"]].plot()
'''
bst.save_model('0001.model')
ypred = clf.predict(data, output_margin=False, ntree_limit=None, validate_features=True)
'''
ntree_limit：限制预测中的树数；如果定义了最佳树数限制，则默认为最佳树数限制，否则为0（使用所有树）
'''
xgb.plot_tree(bst, num_trees=2)
xgb.to_graphviz(bst, num_trees=2)
mdl = xgb.train(params, d_train, 1600, watchlist, early_stopping_rounds=150, maximize=True, verbose_eval=100)
# Add model to the list of models (one for each fold)
models_by_fold.append(mdl)
# In[ ]:
n_folds = 5
early_stopping = 10
params = {'eta': 0.02, 'max_depth': 5, 'subsample': 0.7, 'colsample_bytree': 0.7, 'objective': 'binary:logistic', 'seed': 99, 'silent': 1, 'eval_metric':'auc', 'nthread':4}
xg_train = xgb.DMatrix(Xtrain_hashed, label=y_train);
cv = xgb.cv(params, xg_train, 5000, nfold=n_folds, early_stopping_rounds=early_stopping, verbose_eval=1)
# ## Work in progess-K folds
# In[ ]:
K = 5
kf = KFold(n_splits = K, random_state = 3228, shuffle = True)
# In[ ]:
for train_index, test_index in kf.split(train):
help=
"path to CSV file with labels reflecting relevances of pairs (theorem, premise)"
)
parser.add_argument(
"output_directory",
help=
"path to directory where performance of tested model should be saved")
args = parser.parse_args()
y = read_csv(os.path.abspath(args.y), type_of_records="int")
X = load_obj(os.path.abspath(args.X))
output_directory = os.path.abspath(args.output_directory)
dtrain = xgb.DMatrix(X, label=y)
params = {
"max_depth": p["max_depth"],
"eta": p["eta"],
"gamma": p["gamma"],
"objective": "binary:logistic"
}
x = xgb.cv(params=params,
dtrain=dtrain,
num_boost_round=p["num_boost_round"],
early_stopping_rounds=p["early_stopping_rounds"],
nfold=4,
metrics={"error", "auc", "logloss"})
output_name = os.path.join(output_directory,
"_".join(map(str, list(p.values()))) + ".pkl")
save_obj({"params": p, "stats": x}, output_name)
# Compute the accuracy of the predictions: accuracy
accuracy = float(np.sum(y_pred_4 == y_test)) / y_test.shape[0]
print("accuracy:", accuracy)
# Create the DMatrix: churn_dmatrix
churn_dmatrix = xgb.DMatrix(data=X, label=y)
# Create the parameter dictionary: params
params = {"objective": "reg:logistic", "max_depth": 3}
# Perform cross-validation: cv_results
cv_results = xgb.cv(dtrain=churn_dmatrix,
params=params,
nfold=3,
num_boost_round=5,
metrics="error",
as_pandas=True,
seed=123)
# Print cv_results
print(cv_results)
# Print the accuracy
print(((1 - cv_results["test-error-mean"]).iloc[-1]))
# Perform cross_validation: cv_results
cv_results = xgb.cv(dtrain=churn_dmatrix,
params=params,
nfold=3,
num_boost_round=5,
# get the parameters for xgboost
file_name = sys.argv[1]
folder_name = file_name.split('.')[0]
with open('../data/params_0119/' + file_name) as fread:
jj = 0
for line in fread.readlines():
params = line.split(',')
params[-1] = params[-1].strip()
jj += 1
# get the parameters for xgboost
plst = get_params(eta=params[0],
min_child_weight=params[1],
subsample=params[2],
colsample_bytree=params[3],
max_depth=params[4])
# print 'file: '+file_name+",params: "+str(jj)
# train model
model = xgb.cv(plst,
xgtrain0,
num_boost_round=xgb_num_rounds,
metrics=['auc'],
show_progress=True,
show_stdv=True)
# get preds
# train_validation_preds = model.predict(xgtrain_validation, ntree_limit=model.best_iteration)
# print 'file: '+file_name+",params: "+str(jj)+' Train score is:', eval_wrapper(train_validation_preds, train_validation['Response'])
def kfold(self, x_train, y_train, nfold=5):
dtrain = xgb.DMatrix(x_train, y_train)
cv_rounds = xgb.cv(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round,
nfold=nfold, feval=xg_eval_mae, maximize=False, early_stopping_rounds=10)
return cv_rounds.iloc[-1,:]
def xgb_evaluate(max_depth,
min_child_weight,
colsample_bytree,
subsample,
gamma,
colsample_bylevel,
max_delta_step,
eta,
reg_alpha,
reg_lambda
):
global AUCbest
global ITERbest
params={}
params['booster'] = 'gbtree'
params['min_child_weight'] = int(min_child_weight)
params['cosample_bytree'] = max(min(colsample_bytree, 1), 0)
params['max_depth'] = int(max_depth)
params['subsample'] = max(min(subsample, 1), 0)
params['gamma'] = gamma
params['colsample_bylevel'] = max(min(colsample_bylevel, 1), 0)
params['max_delta_step']=max(int(max_delta_step),0)
params['eta']=max(min(eta,1), 0)
params['reg_alpha'] = max(reg_alpha, 0)
params['reg_lambda']=max(min(reg_lambda, 1), 0)
params['eval_metric']='auc'
params['silent']=True
params['objective']='binary:logistic'
params['seed'] =42
print("\n Search parameters (%d-fold validation):\n %s" % (folds, params), file=log_file )
log_file.flush()
xgbc = xgb.cv(
params,
dtrain,
num_boost_round = nrounds,
stratified = True,
nfold = folds,
early_stopping_rounds = 100,
metrics = 'auc',
show_stdv = True
)
val_score = xgbc['test-auc-mean'].iloc[-1]
train_score = xgbc['train-auc-mean'].iloc[-1]
print(' Stopped after %d iterations with train-auc = %f val-auc = %f ( diff = %f ) train-gini = %f val-gini = %f'
% ( len(xgbc), train_score, val_score, (train_score - val_score), (train_score*2-1),
(val_score*2-1)) , file=log_file)
if ( val_score > AUCbest ):
AUCbest = val_score
ITERbest = len(xgbc)
print('\n\nBest Valid AUC changed to %f'%AUCbest, file=log_file)
log_file.flush()
#
print("\n Best parameters (%d-fold validation):\n %s" % (folds, params), file=log_file_bestparam )
print('\n Best Valid AUC changed to %f'%AUCbest, file=log_file_bestparam)
print('\n Train AUC is %f'%train_score, file=log_file_bestparam)
log_file_bestparam.flush()
#
del xgbc
gc.collect()
return (val_score*2) - 1
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()
##################################################################
## Random Forest; Spend 7 mins
# Let's try a random forest with the features we just created.
rf_clf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=0) # Initialize a Random Forest classifier with 100 trees
# Use cross validation to evaluate the performance of Random Forest
rf_clf_error = 1 - cross_val_score(rf_clf, train_data_features, train['sentiment'], cv=5, scoring='accuracy', n_jobs=-1).mean()
print(rf_clf_error) # Random Forest training error: 0.1573
##################################################################
## XGBoost; 比上面的还要慢好多
# Let's try a XGBoost with the features we created.
dtrain = xgb.DMatrix(train_data_features, label=train['sentiment']) # Create xgb trianing set and parameters
params = {'silent': 1, 'nthread': -1, 'eval_metric': 'error'}
print('The cross validation may take a while...') # Use cross validation to evaluate the performance of XGBoost
xgb_cv_results = xgb.cv(params, dtrain, num_boost_round=100, nfold=5, show_stdv=False, seed=0) # 好慢啊, 忍不住 kill 了
xgb_error = xgb_cv_results['test-error-mean'].mean()
print(xgb_error) # XGBoost trianing error: 0.1829
# It seems that Random Forest out-performed XGBoost. Thus we'll create a submission file with Random Forest.
##################################################################
## Creating a Submission of Random Forest; 经过上面在 test 上面的对比, 最终采用 Random Forest
# Fit the forest to the training set, using the bag of words as features and the sentiment labels as labels
rf_clf.fit(train_data_features, train['sentiment']) # This may take a few minutes to run, 比上面的 XGBoost 快多了
# Use the random forest to make sentiment label predictions
result = rf_clf.predict(test_data_features); print(result)
# Copy the results to a pandas dataframe with an "id" column an a "sentiment" column
output = pd.DataFrame(data={"id":test["id"], "sentiment":result})
##################################################################
## 保存结果, 提交到比赛页面
# Use pandas to write the comma-separated output file
output.to_csv("tmp-Bag_of_Words_rf_clf_results.csv", index=False, quoting=3)
# Fitting the ANN to the Training set
model_history=classifier.fit(X_train.values, y_train.values,validation_split=0.20, batch_size = 10, epochs = 760)
import xgboost as xgb
data_dmatrix = xgb.DMatrix(data=X_train,label=y_train)
xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,max_depth = 5, alpha = 10, n_estimators = 500)
xg_reg.fit(X_train,y_train)
y_pred = xg_reg.predict(X_test)
params = {"objective":"reg:linear",'colsample_bytree': 0.3,'learning_rate': 0.1,
'max_depth': 5, 'alpha': 10}
cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,
num_boost_round=50,early_stopping_rounds=10,metrics="rmse", as_pandas=True, seed=123)
cv_results.head()
y_pred=classifier.predict(test)
pred=pd.DataFrame(y_pred)
sub_df=pd.read_csv('sample_submission.csv')
datasets=pd.concat([sub_df['Id'],pred],axis=1)
datasets.columns=['Id','SalePrice']
datasets.to_csv('sample_submission.csv',index=False)
datasets.head()
# Author: 杨秀隆 sndnyang <*****@*****.**>
# sndnyang.github.io
# Description:
#
#####################################################
import xgboost as xgb
if __name__ == '__main__':
dtrain = xgb.DMatrix("train.buffer")
param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }
param['nthread'] = 4
num_round = 10
print xgb.cv( param, dtrain, num_round, nfold=5,
metrics={'error'}, seed = 0 )
"""test_features = pd.read_csv('test/orig_feature.csv')
test_features = pd.read_csv('test/test_features.csv')
test_features = test_features.sort('enrollment_id')
columns = test_features.columns[2:]
test_features[columns].values
#
print columns
fp = file('lsvm_submit.csv', 'w')
prd = clf.predict(test_features[columns].values)
for eid, cls in zip(test_features['enrollment_id'].values, prd):
######################################### Modeling ###############################################
### PROC REG -- stepwise selection -- predicting TARGET
### PROC GENMOD -- using stepwise selected variables -- log link dist=nb
### PROC Logisitc -- using stepwise selected variables --
######################################## XGBoost ###############################################
X = data.iloc[:, 3:]
y = data.iloc[:, 0]
dTrain = xgb.DMatrix(data=X, label=y)
params = {'objective': 'count:poisson', 'max_depth': 4}
cv_results = xgb.cv(dtrain=dTrain,
params=params,
nfold=4,
num_boost_round=10,
metrics='error',
as_pandas=True)
print("Accuracy: %f" % ((1 - cv_results["test-error-mean"]).iloc[-1]))
bst = xgb.train(params, dTrain)
preds = bst.predict(dTrain)
print("RMSE: %f" % np.sqrt(mean_squared_error(y, preds)))
X = data.iloc[:, 3:]
y = data.iloc[:, 0]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=123)
xg_reg = xgb.XGBRegressor(objective='reg:linear', n_estimators=10, seed=123)
xg_reg.fit(X_train, y_train)
subsample=0.65,
colsample_bytree=0.7,
learning_rate=0.01,
objective='multi:softmax', #需要被最小化的损失函数，选的是多分类预测类别
num_class=5, #指定类别数目
gamma=0, #惩罚参数
reg_alpha=0.05,
reg_lambda=0.05,
nthread=4,
seed=27)
xgtrain = xgb.DMatrix(X, label=y)
xgb_param = clf.get_xgb_params()
cvresult = xgb.cv(xgb_param,
xgtrain,
num_boost_round=5000,
nfold=5,
metrics=['mlogloss'],
early_stopping_rounds=50,
stratified=True,
seed=1301)
print('Best number of trees = {}'.format(cvresult.shape[0]))
clf.set_params(n_estimators=cvresult.shape[0],
use_label_encoder=False) #把clf的参数设置成最好的树对应的参数
clf.fit(X, y, eval_metric='merror')
dtest_x = xgb.DMatrix(X1)
pre = clf.predict(X1)
w_score(y1, pre)
#--------------------------------------------------------------
#number Two
#sklearn的CV调优
param_test1 = {
subsample=0.77,
colsample_bytree=0.7,
objective='reg:linear',
nthread=4,
alpha=.1,
lamda=1)
# ### Build xgboost model
# In[21]:
xgb_param = model.get_xgb_params()
xgtrain = xgb.DMatrix(features_filtered, label=y)
cvresult = xgb.cv(xgb_param,
xgtrain,
num_boost_round=model.get_params()['n_estimators'],
nfold=5,
early_stopping_rounds=50)
model.set_params(n_estimators=cvresult.shape[0])
model.fit(features_filtered, y)
model.predict(features_filtered)
# In[30]:
path = 'C:\\Users\\DALab\\Desktop\\data contest\\test data'
test = pd.DataFrame(np.nan,
index=range(75000),
columns=['id', 'time', '1st', '2nd', '3rd', '4th'])
index = 0
for file in listdir(path):
temp = pd.read_excel(path + '\\' + file, header=None)
plt.show()
#The residual plot looks pretty good.To wrap it up let's predict on the test set
# and submit on the leaderboard:
#Adding an xgboost model:
#Let's add an xgboost model to our linear model to see if we can improve our score:
import xgboost as xgb
dtrain = xgb.DMatrix(X_train, label=y)
dtest = xgb.DMatrix(X_test)
params = {"max_depth": 2, "eta": 0.1}
model = xgb.cv(params, dtrain, num_boost_round=500, early_stopping_rounds=100)
model.loc[30:, ["test-rmse-mean", "train-rmse-mean"]].plot(
) # will plot train and test errors
plt.show()
model_xgb = xgb.XGBRegressor(
n_estimators=360, max_depth=2,
learning_rate=0.1) #the params were tuned using xgb.cv
model_xgb.fit(X_train, y)
# Output: XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,
# learning_rate=0.1, max_delta_step=0, max_depth=2,
# min_child_weight=1, missing=None, n_estimators=360, nthread=-1,
# objective='reg:linear', reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, seed=0, silent=True, subsample=1)
def xgb_r2_score(preds, dtrain):
labels = dtrain.get_label()
return 'rmse', r2_score(labels, preds)
# form DMatrices for Xgboost training
dtrain = xgb.DMatrix(train.drop('y', axis=1), y_train)
dtest = xgb.DMatrix(test)
# xgboost, cross-validation
cv_result = xgb.cv(xgb_params,
dtrain,
obj=logcoshobj,
feval=xgb_r2_score,
num_boost_round=10000, # increase to have better results (~700)
early_stopping_rounds=100,
verbose_eval=50,
show_stdv=False,
maximize=True
)
best_iteration = cv_result.shape[0] - 1
print(best_iteration)
cv_mean = cv_result.iloc[-1, 2]
cv_std = cv_result.iloc[-1, 3]
print('CV-Mean: {0}+{1}'.format(cv_mean, cv_std))
#num_boost_rounds = len(cv_result)
#print('num_boost_rounds=' + str(num_boost_rounds))
# train model
model = xgb.train(dict(xgb_params, silent=1), dtrain, obj=logcoshobj, num_boost_round=best_iteration)
def train_test_xgb(dataset='data/dataset_for_analysis_test.csv',
finetuning=False,
force_data_prep=True):
""" train and test a xgb model
1) define target and predictors, prepare dataset
2) OPTIONAL: hyper-parameter tuning
3) train and test model, save CV-scores
4) print scores and feature importance
"""
# 1) define target and predictors, prepare dataset
# notes: f*****g FLDAS updates after 2 months
# alternatives
# precipitation: NASA/GPM_L3/IMERG_V06
# define labeld of predictors and target
X_labels_base = [
'MODIS_006_MOD11A1_LST_Day_1km',
'MODIS_006_MOD11A1_LST_Night_1km',
# 'MODIS_006_MYD13A1_EVI',
'NASA_FLDAS_NOAH01_C_GL_M_V001_Qair_f_tavg',
'NASA_FLDAS_NOAH01_C_GL_M_V001_Rainf_f_tavg',
'NASA_FLDAS_NOAH01_C_GL_M_V001_SoilMoi00_10cm_tavg',
'NASA_FLDAS_NOAH01_C_GL_M_V001_SoilTemp00_10cm_tavg',
'NASA_FLDAS_NOAH01_C_GL_M_V001_Wind_f_tavg',
'NASA_FLDAS_NOAH01_C_GL_M_V001_Tair_f_tavg',
'JAXA_GPM_L3_GSMaP_v6_operational_hourlyPrecipRateGC'
]
y_label = 'mean_ovi'
# define number of time steps to use (past observations)
n_time_steps = 3
# define labels of predictors at different time steps
X_labels = []
for ts in range(n_time_steps):
X_labels += [x + '_' + str(ts) for x in X_labels_base]
# prepare dataset
if not os.path.exists(dataset) or force_data_prep:
print(
'dataset not found, preparing from raw data (this might take a wile)'
)
# define input data path
ovitrap_data = 'data/ovitrap_data_month_adm2.csv'
weather_data = 'data/merged_adm2.csv'
prepare_dataset_for_training(ovitrap_data,
weather_data,
X_labels_base,
filename=dataset,
n_time_steps=n_time_steps)
df = pd.read_csv(dataset)
df = df.reset_index()
df.date = pd.to_datetime(df.date)
# save original dataset for later (inference)
df_uncut = df.copy()
df_uncut = df_uncut.dropna(subset=X_labels)
df = df.dropna(subset=[y_label])
# QA cuts
# removing a few data points based on poor correlation with observed "good" predictors
df = df[(df.date.dt.year >= 2013) & (df.date.dt.year <= 2017)]
bad_provinces = [
'Nueva Vizcaya', 'Surigao del Norte', 'Sarangani', 'Siquijor',
'Dinagat Islands', 'Isabela', 'Capiz', 'South Cotabato', 'Maguindanao',
'Biliran', 'Davao Occidental', 'Quirino', 'Guimaras', 'Aurora'
]
df = df[~df.adm_level.isin(bad_provinces)]
df = df[df.count_ovi > 5]
# 2) OPTIONAL: hyper-parameter tuning
if finetuning:
# define training data
dtrain = xgb.DMatrix(data=df[X_labels], label=df[y_label])
# define grid of possible hyper-parameter values
gridsearch_params = [{
'max_depth': max_depth,
'learning_rate': learning_rate,
'n_estimators': n_estimators,
'min_split_loss': min_split_loss,
'subsample': subsample,
'colsample_bytree': colsample_bytree,
'reg_lambda': reg_lambda,
'reg_alpha': reg_alpha,
'booster': booster
} for max_depth in [2, 5, 10, 20]
for learning_rate in [0.05, 0.1, 0.2]
for n_estimators in [1000, 2000, 5000]
for min_split_loss in [0., 0.1, 1.]
for subsample in [0.5, 0.8, 1.]
for colsample_bytree in [0.3, 0.6, 1.]
for reg_lambda in [1, 1.5, 2]
for reg_alpha in [0., 0.1, 1.]
for booster in ['gbtree']]
# fixed hyper-parameters
params = {
'objective': 'reg:squarederror',
'eval_metric': 'rmse',
'booster': 'gbtree',
'num_boost_round': 1000
}
# loop over grid, find best hyper-parameters
min_rmse = float("Inf")
best_params = None
for params_grid in tqdm(gridsearch_params):
# Update parameters
for name, value in params_grid.items():
params[name] = value
# Run CV
cv_results = xgb.cv(dtrain=dtrain,
params=params,
num_boost_round=params['num_boost_round'],
early_stopping_rounds=50,
seed=42,
nfold=5,
as_pandas=True)
# Update best MAE
mean_rmse = cv_results['test-rmse-mean'].min()
boost_rounds = cv_results['test-rmse-mean'].values.argmin() + 1
print("\trmse {} for {} / {} rounds".format(
mean_rmse, boost_rounds, len(cv_results)))
if mean_rmse < min_rmse:
min_rmse = mean_rmse
best_params = params
# print results
print("best params: {}".format(best_params))
print("min rmse: {}".format(min_rmse))
else:
# used fixed hyper-parameters
best_params = {
'objective': 'reg:squarederror',
'eval_metric': 'rmse',
'booster': 'gbtree',
'num_boost_round': 1000,
'max_depth': 20,
'learning_rate': 0.2,
'n_estimators': 5000,
'subsample': 1.0,
'colsample_bytree': 1.0,
'reg_lambda': 2,
'reg_alpha': 1.0,
'min_split_loss': 0.
}
# 3) train and test model, save CV-scores
nfolds = 5
X, y = df[X_labels], df[y_label]
dmdata = xgb.DMatrix(data=X, label=y)
cv_results = xgb.cv(dtrain=dmdata,
params=best_params,
nfold=nfolds,
metrics=["rmse", "mae"],
num_boost_round=best_params['num_boost_round'],
early_stopping_rounds=50,
as_pandas=True,
seed=42)
boost_rounds = len(cv_results)
# 4) print scores and feature importance
# print average performance & feature importance
print("cv-performance: MAE {}, RMSE {}".format(
cv_results["test-mae-mean"].tail(1).values[0],
cv_results["test-rmse-mean"].tail(1).values[0]))
xg_reg = xgb.train(dtrain=dmdata,
params=best_params,
num_boost_round=boost_rounds)
dpredict = xgb.DMatrix(data=df_uncut[X_labels], label=df_uncut[y_label])
predictions = xg_reg.predict(dpredict)
df_pred = df_uncut.copy()
df_pred[y_label] = predictions
df_pred = inverse_transform(df_pred, dataset, [y_label] + X_labels)
df_pred.to_csv('output/dataset_predictions.csv')
xg_reg.save_model('models/best_model.json')
df_uncut = df_uncut.dropna(subset=[y_label])
dpredict = xgb.DMatrix(data=df_uncut[X_labels], label=df_uncut[y_label])
predictions = xg_reg.predict(dpredict)
print('R2 train', r2_score(df_uncut[y_label].values, predictions))
xgb.plot_importance(xg_reg)
plt.tight_layout()
plt.show()
def main():
print 'load datas...'
train, test = data_util.load_dataset()
y_train_all = train['y']
del train['ID']
del train['y']
id_test = test['ID']
del test['ID']
print 'train:', train.shape, ', test:', test.shape
train_r2_scores = []
val_r2_scores = []
num_boost_roundses = []
X_test = test
df_columns = train.columns.values
dtest = xgb.DMatrix(X_test, feature_names=df_columns)
xgb_params = {
'eta': 0.005,
'max_depth': 4,
'subsample': 0.93,
'objective': 'reg:linear',
'eval_metric': 'rmse',
'silent': 1
}
for i in range(0, 5):
random_state = 42 + i
X_train, X_val, y_train, y_val = train_test_split(
train, y_train_all, test_size=0.25, random_state=random_state)
dtrain = xgb.DMatrix(X_train, y_train, feature_names=df_columns)
dval = xgb.DMatrix(X_val, y_val, feature_names=df_columns)
y_mean = np.mean(y_train)
cv_result = xgb.cv(
dict(xgb_params,
base_score=y_mean), # base prediction = mean(target)
dtrain,
num_boost_round=2000, # increase to have better results (~700)
early_stopping_rounds=50,
)
num_boost_rounds = len(cv_result)
num_boost_roundses.append(num_boost_rounds)
model = xgb.train(dict(xgb_params, base_score=y_mean),
dtrain,
num_boost_round=num_boost_rounds)
train_r2_score = r2_score(dtrain.get_label(), model.predict(dtrain))
val_r2_score = r2_score(dval.get_label(), model.predict(dval))
print 'perform {} cross-validate: train r2 score = {}, validate r2 score = {}'.format(
i + 1, train_r2_score, val_r2_score)
train_r2_scores.append(train_r2_score)
val_r2_scores.append(val_r2_score)
print '\naverage train r2 score = {}, average validate r2 score = {}'.format(
sum(train_r2_scores) / len(train_r2_scores),
sum(val_r2_scores) / len(val_r2_scores))
best_num_boost_rounds = sum(num_boost_roundses) // len(num_boost_roundses)
print 'best_num_boost_rounds =', best_num_boost_rounds
# train model
print 'training on total training data...'
dtrain_all = xgb.DMatrix(train, y_train_all, feature_names=df_columns)
model = xgb.train(dict(xgb_params, base_score=np.mean(y_train_all)),
dtrain_all,
num_boost_round=best_num_boost_rounds)
print 'predict submit...'
xgb_result = model.predict(dtest)
# ===================================== model stacking =================================
stacked_pipeline = make_pipeline(
StackingEstimator(estimator=LassoLarsCV(normalize=True)),
StackingEstimator(
estimator=GradientBoostingRegressor(learning_rate=0.001,
loss="huber",
max_depth=3,
max_features=0.55,
min_samples_leaf=18,
min_samples_split=14,
subsample=0.7)), LassoLarsCV())
stacked_pipeline.fit(train.values, y_train_all)
stack_results = stacked_pipeline.predict(X_test.values)
df_sub = pd.DataFrame({'ID': id_test, 'y': stack_results})
df_sub.to_csv('model_stacking_result.csv', index=False)
y_pred = xgb_result * 0.784 + stack_results * 0.216
df_sub = pd.DataFrame({'ID': id_test, 'y': y_pred})
df_sub.to_csv(Configure.submission_path, index=False)
'colsample_bytree': 0.8,
'silent': 1,
'subsample': 0.6,
'learning_rate': 0.01,
'objective': 'reg:linear',
'max_depth': 1,
'num_parallel_tree': 1,
'min_child_weight': 1,
'eval_metric': 'rmse',
}
res = xgb.cv(xgb_params,
dtrain,
num_boost_round=1000,
nfold=4,
seed=SEED,
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('Ensemble-CV: {0}±{1}'.format(cv_mean, cv_std))
gbdt = xgb.train(xgb_params, dtrain, best_nrounds)
submission = pd.read_csv(SUBMISSION_FILE)
submission.iloc[:, 1] = gbdt.predict(dtest)