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single_xgboost.py
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single_xgboost.py
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import os
import time
import torch
import numpy as np
import xgboost as xgb
import matplotlib.pyplot as plt
class SingleXGBoost:
def __init__(self, params, num_boost_round, early_stopping_rounds):
self.params = params
self.num_boost_round = num_boost_round
self.early_stopping_rounds = early_stopping_rounds
self.children = []
def fit(self, DMatrix_train, DMatrix_val, *args, **kwargs):
self.evals_result = {}
watchlist = [(DMatrix_train, 'train'), (DMatrix_val, 'val')]
self.bst = xgb.train(self.params, DMatrix_train, self.num_boost_round,
evals=watchlist, early_stopping_rounds=self.early_stopping_rounds,
evals_result=self.evals_result, verbose_eval=True, *args, **kwargs)
return self
def predict(self, X, iteration_range=None):
start = time.time()
if isinstance(X, np.ndarray) or isinstance(X, torch.Tensor):
X = xgb.DMatrix(X)
if iteration_range is None:
iteration_range = (0, self.bst.best_iteration+1)
# print(iteration_range)
output = self.bst.predict(X, iteration_range=iteration_range)
# print("XGBoost Predict Finished in", time.time() - start, "seconds.")
return output
def inplace_predict(self, X, iteration_range=None):
start = time.time()
if iteration_range is None:
iteration_range = (0, self.bst.best_iteration+1)
print(iteration_range)
output = self.bst.inplace_predict(X, iteration_range=iteration_range)
print("XGBoost Inplace Predict Finished in", time.time() - start, "seconds.")
return output
def cuda(self, gpu_id=0):
self.bst.set_param({"predictor": "gpu_predictor"})
self.bst.set_param({"gpu_id": gpu_id})
def cpu(self):
self.bst.set_param({"predictor": "cpu_predictor"})
def to(self, device):
if device == 'cuda':
self.cuda()
else:
self.cpu()
def plot_learning_curve(self, eval_metric='logloss', path=None):
plt.plot(self.evals_result['train'][eval_metric], label='train')
plt.plot(self.evals_result['val'][eval_metric], label='val')
plt.xlabel('Iteration')
plt.ylabel(eval_metric)
plt.legend()
if path is not None:
plt.savefig(path)
# plt.show()
plt.close()
else:
plt.show()
def get_feature_importance(self, importance_type='gain'):
feature_importance = self.bst.get_score(importance_type=importance_type)
feature_importance = sorted(feature_importance.items(), key=lambda x: x[1], reverse=True)
feature_importance = np.array(feature_importance)
feature_importance = [int(i[1:]) for i in feature_importance[:, 0]]
return feature_importance
def get_feature_importance_score(self, importance_type='gain'):
feature_importance = self.bst.get_score(importance_type=importance_type)
feature_importance = feature_importance.items()
feature_importance = [y for x, y in feature_importance]
return feature_importance
def get_num_boost_round(self):
return self.bst.num_boost_round
def save_model(self, model_path):
start = time.time()
self.bst.save_model(model_path)
print("Save XGBoost Finished in", time.time() - start, "seconds.")
def plot_distribution(y, prob):
plt.hist(prob[y==0], bins=100, alpha=0.5, label='0')
plt.hist(prob[y==1], bins=100, alpha=0.5, label='1')
plt.legend()
plt.show()
if __name__ == "__main__":
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
X, y = make_classification(n_samples=1000000, n_features=256, n_informative=10, n_classes=2, class_sep=0.1, random_state=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
params = {
'objective': 'binary:logistic',
'eval_metric': 'logloss',
'max_depth': 3,
'eta': 0.1,
'nthread': 4,
'seed': 0,
'max_delta_step': 1,
}
num_boost_round = 10000
early_stopping_rounds = 100
sxgb = SingleXGBoost(params, num_boost_round, early_stopping_rounds)
sxgb.fit(X_train, y_train, X_test, y_test)
sxgb.plot_learning_curve()
y_pred = sxgb.bst.predict(xgb.DMatrix(X_test))
plot_distribution(y_test, y_pred)
# y_pred = np.where(y_pred > 0.5, 1, 0)
# print(accuracy_score(y_test, y_pred))
print(log_loss(y_test, y_pred))