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)
