I am using RandomizedSearchCV to train an XGBoost model using the SKLearn API for XGBoost.
After training this model, how could I export it into the XGBoost internal format?
https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBRegressor.save_model
I need to then give this model to a team which uses the Java XGBoost library and are importing the model using this:
Booster booster = XGBoost.loadModel("model.bin");
Below is the RandomizedSearchCV I have with sklearn Python. The pipelines and hypeparameters currently hold only the XGBoost algorithm
pipelines = {
'xgb' : make_pipeline(xgb.XGBClassifier(random_state=777)),
}
xgb_hyperparameters = {
'xgbclassifier__objective': ['binary:logistic'],
'xgbclassifier__n_estimators': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600],
'xgbclassifier__learning_rate': [0.01, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ],
'xgbclassifier__max_depth': [ 3, 4, 5, 6, 8,9, 10,11, 12,13,14, 15,16,17,18,19,20,25,30],
'xgbclassifier__min_child_weight': [ 1, 3, 4, 5, 6, 7, 8, 9],
'xgbclassifier__gamma': [ 0.0, 0.1, 0.2 , 0.3, 0.4, 0.5 ]
}
hyperparameters = {
'xgb' : xgb_hyperparameters
}
fitted_models = {}
for model_name, model_pipeline in pipelines.items():
print('\nStarting to fit: {}'.format(model_name))
# Fit each model
model = RandomizedSearchCV(
pipelines[model_name],
hyperparameters[model_name],
n_iter = 15,
n_jobs = -1, # Use all my computer's power
cv = 5, # The default value of this will change to 5 in version 0.22
verbose = 20,
scoring = 'precision'
)
model.fit(x_train, y_train)
fitted_models[model_name] = model
# Calculate predictions
y_pred = model.predict(x_test) # y_pred = predicted Y value
y_pred_proba_prep = model.predict_proba(x_test)
y_pred_proba = [p[1] for p in y_pred_proba_prep] # y_pred_proba = the probability of the prediction
# Calculate all the performance scores:
roc_score = roc_auc_score(y_test, y_pred_proba)
precision = precision_score(y_test,y_pred)
recall = recall_score(y_test,y_pred)
f1 = f1_score(y_test,y_pred)
print('''\n\nModel {} has been fitted. \n
ROC AUC: {}\n
Precision: {}\n
Recall: {}\n
F1: {}\n
'''.format(model_name, roc_score, precision, recall, f1)
)