GridSearchCV - XGBoost - Early Stopping - python-3.x

Question

i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and (expecting to) have better results on my prediction/regression task. I am using XGBoost via its Scikit-Learn API.
model = xgb.XGBRegressor()
GridSearchCV(model, paramGrid, verbose=verbose ,fit_params={'early_stopping_rounds':42}, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX,trainY)
I tried to give early stopping parameters with using fit_params, but then it throws this error which is basically because of lack of validation set which is required for early stopping:
/opt/anaconda/anaconda3/lib/python3.5/site-packages/xgboost/callback.py in callback(env=XGBoostCallbackEnv(model=<xgboost.core.Booster o...teration=4000, rank=0, evaluation_result_list=[]))
187 else:
188 assert env.cvfolds is not None
189
190 def callback(env):
191 """internal function"""
--> 192 score = env.evaluation_result_list[-1][1]
score = undefined
env.evaluation_result_list = []
193 if len(state) == 0:
194 init(env)
195 best_score = state['best_score']
196 best_iteration = state['best_iteration']
How can i apply GridSearch on XGBoost with using early_stopping_rounds?
note: model is working without gridsearch, also GridSearch works without 'fit_params={'early_stopping_rounds':42}

Answer 1

When using early_stopping_rounds you also have to give eval_metric and eval_set as input parameter for the fit method. Early stopping is done via calculating the error on an evaluation set. The error has to decrease every early_stopping_rounds otherwise the generation of additional trees is stopped early.
See the documentation of xgboosts fit method for details.
Here you see a minimal fully working example:
import xgboost as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
cv = 2
trainX= [[1], [2], [3], [4], [5]]
trainY = [1, 2, 3, 4, 5]
# these are the evaluation sets
testX = trainX
testY = trainY
paramGrid = {"subsample" : [0.5, 0.8]}
fit_params={"early_stopping_rounds":42,
"eval_metric" : "mae",
"eval_set" : [[testX, testY]]}
model = xgb.XGBRegressor()
gridsearch = GridSearchCV(model, paramGrid, verbose=1 ,
fit_params=fit_params,
cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX,trainY]))
gridsearch.fit(trainX,trainY)

Answer 2

An update to #glao's answer and a response to #Vasim's comment/question, as of sklearn 0.21.3 (note that fit_params has been moved out of the instantiation of GridSearchCV and been moved into the fit() method; also, the import specifically pulls in the sklearn wrapper module from xgboost):
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
cv = 2
trainX= [[1], [2], [3], [4], [5]]
trainY = [1, 2, 3, 4, 5]
# these are the evaluation sets
testX = trainX
testY = trainY
paramGrid = {"subsample" : [0.5, 0.8]}
fit_params={"early_stopping_rounds":42,
"eval_metric" : "mae",
"eval_set" : [[testX, testY]]}
model = xgb.XGBRegressor()
gridsearch = GridSearchCV(model, paramGrid, verbose=1,
cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]))
gridsearch.fit(trainX, trainY, **fit_params)

Answer 3

Here's a solution that works in a Pipeline with GridSearchCV. The challenge occurs when you have a pipeline that is required to pre-process your training data. For example, when X is a text document and you need TFTDFVectorizer to vectorize it.
Over-ride the XGBRegressor or XGBClssifier.fit() Function
This step uses train_test_split() to select the specified number of
validation records from X for the eval_set and then passes the
remaining records along to fit().
A new parameter eval_test_size is added to .fit() to control the number of validation records. (see train_test_split test_size documenation)
**kwargs passes along any other parameters added by the user for the XGBRegressor.fit() function.
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import train_test_split
class XGBRegressor_ES(XGBRegressor):
def fit(self, X, y, *, eval_test_size=None, **kwargs):
if eval_test_size is not None:
params = super(XGBRegressor, self).get_xgb_params()
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=eval_test_size, random_state=params['random_state'])
eval_set = [(X_test, y_test)]
# Could add (X_train, y_train) to eval_set
# to get .eval_results() for both train and test
#eval_set = [(X_train, y_train),(X_test, y_test)]
kwargs['eval_set'] = eval_set
return super(XGBRegressor_ES, self).fit(X_train, y_train, **kwargs)
Example Usage
Below is a multistep pipeline that includes multiple transformations to X. The pipeline's fit() function passes the new evaluation parameter to the XGBRegressor_ES class above as xgbr__eval_test_size=200. In this example:
X_train contains text documents passed to the pipeline.
XGBRegressor_ES.fit() uses train_test_split() to select 200 records from X_train for the validation set and early stopping. (This could also be a percentage such as xgbr__eval_test_size=0.2)
The remaining records in X_train are passed along to XGBRegressor.fit() for the actual fit().
Early stopping may now occur after 75 rounds of unchanged boosting for each cv fold in a gridsearch.
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import VarianceThreshold
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectPercentile, f_regression
xgbr_pipe = Pipeline(steps=[('tfidf', TfidfVectorizer()),
('vt',VarianceThreshold()),
('scaler', StandardScaler()),
('Sp', SelectPercentile()),
('xgbr',XGBRegressor_ES(n_estimators=2000,
objective='reg:squarederror',
eval_metric='mae',
learning_rate=0.0001,
random_state=7)) ])
X_train = train_idxs['f_text'].values
y_train = train_idxs['Pct_Change_20'].values
Example Fitting the Pipeline:
%time xgbr_pipe.fit(X_train, y_train,
xgbr__eval_test_size=200,
xgbr__eval_metric='mae',
xgbr__early_stopping_rounds=75)
Example Fitting GridSearchCV:
learning_rate = [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3]
param_grid = dict(xgbr__learning_rate=learning_rate)
grid_search = GridSearchCV(xgbr_pipe, param_grid, scoring="neg_mean_absolute_error", n_jobs=-1, cv=10)
grid_result = grid_search.fit(X_train, y_train,
xgbr__eval_test_size=200,
xgbr__eval_metric='mae',
xgbr__early_stopping_rounds=75)