使用XGBoost实现多分类预测的实践代码
import pandas as pd import numpy as np import xgboost as xgb from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import KFold import matplotlib.pyplot as plt import seaborn as sns import gc ## load data train_data = pd.read_csv(‘../../data/train.csv‘) test_data = pd.read_csv(‘../../data/test.csv‘) num_round = 1000 ## category feature one_hot test_data[‘label‘] = -1 data = pd.concat([train_data, test_data]) cate_feature = [‘gender‘, ‘cell_province‘, ‘id_province‘, ‘id_city‘, ‘rate‘, ‘term‘] for item in cate_feature: data[item] = LabelEncoder().fit_transform(data[item]) item_dummies = pd.get_dummies(data[item]) item_dummies.columns = [item + str(i + 1) for i in range(item_dummies.shape[1])] data = pd.concat([data, item_dummies], axis=1) data.drop(cate_feature,axis=1,inplace=True) train = data[data[‘label‘] != -1] test = data[data[‘label‘] == -1] ##Clean up the memory del data, train_data, test_data gc.collect() ## get train feature del_feature = [‘auditing_date‘, ‘due_date‘, ‘label‘] features = [i for i in train.columns if i not in del_feature] ## Convert the label to two categories train_x = train[features] train_y = train[‘label‘].astype(int).values test = test[features] params = { ‘booster‘: ‘gbtree‘, ‘objective‘: ‘multi:softmax‘, # ‘objective‘: ‘multi:softprob‘, #Multiclassification probability ‘num_class‘: 33, ‘eval_metric‘: ‘mlogloss‘, ‘gamma‘: 0.1, ‘max_depth‘: 8, ‘alpha‘: 0, ‘lambda‘: 0, ‘subsample‘: 0.7, ‘colsample_bytree‘: 0.5, ‘min_child_weight‘: 3, ‘silent‘: 0, ‘eta‘: 0.03, ‘nthread‘: -1, ‘missing‘: 1, ‘seed‘: 2019, } folds = KFold(n_splits=5, shuffle=True, random_state=2019) prob_oof = np.zeros(train_x.shape[0]) test_pred_prob = np.zeros(test.shape[0]) ## train and predict feature_importance_df = pd.DataFrame() for fold_, (trn_idx, val_idx) in enumerate(folds.split(train)): print("fold {}".format(fold_ + 1)) trn_data = xgb.DMatrix(train_x.iloc[trn_idx], label=train_y[trn_idx]) val_data = xgb.DMatrix(train_x.iloc[val_idx], label=train_y[val_idx]) watchlist = [(trn_data, ‘train‘), (val_data, ‘valid‘)] clf = xgb.train(params, trn_data, num_round, watchlist, verbose_eval=20, early_stopping_rounds=50) prob_oof[val_idx] = clf.predict(xgb.DMatrix(train_x.iloc[val_idx]), ntree_limit=clf.best_ntree_limit) fold_importance_df = pd.DataFrame() fold_importance_df["Feature"] = clf.get_fscore().keys() fold_importance_df["importance"] = clf.get_fscore().values() fold_importance_df["fold"] = fold_ + 1 feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0) test_pred_prob += clf.predict(xgb.DMatrix(test), ntree_limit=clf.best_ntree_limit) / folds.n_splits result = np.argmax(test_pred_prob, axis=1) ## plot feature importance cols = (feature_importance_df[["Feature", "importance"]].groupby("Feature").mean().sort_values(by="importance", ascending=False).index) best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)].sort_values(by=‘importance‘,ascending=False) plt.figure(figsize=(8, 15)) sns.barplot(y="Feature", x="importance", data=best_features.sort_values(by="importance", ascending=False)) plt.title(‘LightGBM Features (avg over folds)‘) plt.tight_layout() plt.savefig(‘../../result/xgb_importances.png‘)
参考代码链接为:https://github.com/ikkyu-wen/data_mining_models,这里面的xgboost实现多分类
原文地址:https://www.cnblogs.com/wyhluckdog/p/12194457.html
时间: 2024-10-08 12:30:48