3 examples of 'xgbclassifier python' in Python

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327def run_xgb(train_X, test_X, train_y, test_y, depth=6, a=0.0, l=1.5, seed=0):
328 param = {'max_depth':depth, 'num_round':20, 'eta':0.3, 'silent':1,
329 'objective':'binary:logistic', 'eval_metric':['auc', 'error'],
330 'alpha': a, 'lambda':l }
331 if seed != 0: # specific random seed entered
332 param['seed'] = seed
333 param['colsample_bytree'] = 0.5
334 param['colsample_bylevel'] = 0.5
335 train_xgb = xgb.DMatrix(train_X, label=train_y)
336 test_xgb = xgb.DMatrix(test_X, label=test_y)
337 bst = xgb.train(param, train_xgb)
338 ypred = bst.predict(test_xgb)
339 metrics = gen_eval_metrics(test_y, ypred)
340 accuracy = metrics[0]
341
342 #cor = sum([int(ypred[i] + 0.5) == test_y[i] for i in range(len(ypred))])
343 #accuracy = cor / len(test_y)
344 print('Fold accuracy: ' + str(accuracy))
345 return metrics
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129def test_xgboost_multiclass():
130 try:
131 import xgboost
132 except Exception as e:
133 print("Skipping test_xgboost_multiclass!")
134 return
135 import shap
136
137 # train XGBoost model
138 X, Y = shap.datasets.iris()
139 model = xgboost.XGBClassifier(objective="binary:logistic", max_depth=4)
140 model.fit(X, Y)
141
142 # explain the model's predictions using SHAP values (use pred_contrib in LightGBM)
143 shap_values = shap.TreeExplainer(model).shap_values(X)
144
145 # ensure plot works for first class
146 shap.dependence_plot(0, shap_values[0], X, show=False)
22def train_model(self, train_x, train_y):
23 """
24 use Feature vector
25 :param train_x:
26 :param train_y:
27 :return:
28 """
29 self.clf = xgb.XGBClassifier()
30 self.clf.fit(train_x, train_y, eval_metric=self.eval_metric,
31 eval_set=[(train_x, train_y)])
32 self.init = True
33 evals_result = self.clf.evals_result()
34 print('evals_result:', evals_result)
35 with open(self.xgb_model_name, 'wb')as f:
36 pickle.dump(self.clf, f, True)

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