ML之xgboost：利用xgboost算法(特征篩選和GridSearchCV)對數據集實現回歸預測

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實現代碼

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輸出結果

['EnterCOD', 'EnterBOD', 'EnterAD', 'EnterZL', 'EnterZD', 'EnterPH', 'EnterSS', 'M4', 'N4', 'O4', 'P4', 'Q4', 'R4']EnterCOD EnterBOD EnterAD EnterZL EnterZD EnterPH EnterSS M4 \ 0 299.0 0.0 16.7 9.63 26.5 7 354.0 4609.0 1 331.0 0.0 15.0 9.34 31.8 7 297.5 4834.0 2 326.0 0.0 19.6 11.17 33.5 7 389.5 4928.0 3 230.0 0.0 17.4 6.23 32.3 7 277.5 5073.0 4 149.0 0.0 16.8 3.59 23.7 7 106.0 4856.0 N4 O4 P4 Q4 R4 0 2346.0 1.72 32.0 69.43 17.0 1 2434.0 1.72 34.0 70.34 18.0 2 2604.0 1.70 35.0 71.02 18.0 3 2678.0 1.68 36.0 70.96 19.0 4 2452.0 1.69 37.0 76.19 19.0 mlss準確率： 0.950752699205583 特征： Index(['EnterCOD', 'EnterBOD', 'EnterAD', 'EnterZL', 'EnterZD', 'EnterPH','EnterSS', 'M4', 'N4', 'O4', 'P4', 'Q4', 'R4'],dtype='object') 每個特征的重要性： [100. 21.307432 48.90534 37.218624 26.950356 2.08140631.82239 72.88005 49.49121 61.9334 19.071848 33.44125717.745914]mlss選取重要特征后準確率： 0.9485146037853682 重要特征： Index(['EnterCOD', 'M4', 'O4', 'N4', 'EnterAD', 'EnterZL', 'Q4', 'EnterSS','EnterZD', 'EnterBOD', 'P4', 'R4'],dtype='object') 每個重要特征的重要性： [100. 92.00673 75.79092 55.387436 36.038513 32.21763642.442307 28.243927 24.789852 12.685312 18.707016 19.150238]

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實現代碼

#ML之xgboost：利用xgboost算法(特征篩選和GridSearchCV)對數據集實現回歸預測import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import metrics import pickle from xgboost.sklearn import XGBRegressor from sklearn.preprocessing import StandardScaler from clean_data import prep_water_data, normalize_water_data, normalize_data, delete_null_date from sklearn.model_selection import KFold, train_test_split, GridSearchCV, cross_val_score from sklearn.model_selection import TimeSeriesSplitdef GDBTTrain(X, y):"""xgboost用法"""train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=0) ##test_size測試集合所占比例test_preds = pd.DataFrame({"label": test_y})clf = XGBRegressor(learning_rate=0.1, # 默認0.3n_estimators=400, # 樹的個數max_depth=8,)clf.fit(train_x, train_y)test_preds['y_pred'] = clf.predict(test_x)stdm = metrics.r2_score(test_preds['label'], test_preds['y_pred'])# GridSearchCV和cross_val_score的結果一樣 # scores = cross_val_score(clf, X, y, scoring='r2') # print(scores) # gs = GridSearchCV(clf, {}, cv=3, verbose=3).fit(X, y)return stdm, clfdef XGTSearch(X, y):print("Parameter optimization")n_estimators = [50, 100, 200, 400]max_depth = [2, 4, 6, 8]learning_rate = [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3]param_grid = dict(max_depth=max_depth, n_estimators=n_estimators, learning_rate=learning_rate)xgb_model = XGBRegressor()kfold = TimeSeriesSplit(n_splits=2).get_n_splits([X, y])fit_params = {"eval_metric": "rmse"}grid_search = GridSearchCV(xgb_model, param_grid, verbose=1, fit_params=fit_params, cv=kfold)grid_result = grid_search.fit(X, y)# summarize resultsprint("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))means = grid_result.cv_results_['mean_test_score']stds = grid_result.cv_results_['std_test_score']params = grid_result.cv_results_['params']for mean, stdev, param in zip(means, stds, params):print("%f (%f) with: %r" % (mean, stdev, param))return means, grid_resultfeature_string = 'EnterCOD EnterBOD EnterAD EnterZL EnterZD EnterPH EnterSS M4 N4 O4 P4 Q4 R4' # 選取的特征 outputs_string = 'mlss mlvss sv30 OutCOD OutBOD OutAD OutZL OutZD OutPH OutSS' # 需要預測的標簽 feature = feature_string.split() outputs = outputs_string.split() print(feature)def prep_water_data(data, columns):for c in columns:data[c] = [0 if ((x in ['Not Available', 'Not Mapped', 'NULL']) | (pd.isnull(x))) else x for x in data[c]]return datadef delete_null_date(data, date_name):data = data[data[date_name].notnull()] # 刪除日期存在缺失的數據return datadata = pd.read_csv('water_a.csv', encoding="gb18030") data = prep_water_data(data, feature)print(data.iloc[:5][feature])def predict(data, out):data = delete_null_date(data, out)y = data[out]# y = y.as_matrix()X = data[feature]stdm, clf = GDBTTrain(X, y)print(out +'準確率：', stdm)feature_importance = clf.feature_importances_feature_importance = 100.0 * (feature_importance / feature_importance.max())print('特征：', X.columns)print('每個特征的重要性：', feature_importance)sorted_idx = np.argsort(feature_importance)pos = np.arange(sorted_idx.shape[0])plt.barh(pos, feature_importance[sorted_idx], align='center')plt.yticks(pos, X.columns[sorted_idx])plt.xlabel('Features')plt.ylabel('Importance')plt.title('Variable Importance')plt.show()#.......................選取重要性高的特征再次進行訓練和預測..................................#X = data[X.columns[sorted_idx][::-1][:-1]]stdm, clf = GDBTTrain(X, y)print(out +'選取重要特征后準確率：', stdm)feature_importance = clf.feature_importances_feature_importance = 100.0 * (feature_importance / feature_importance.max())print('重要特征：', X.columns)print('每個重要特征的重要性：', feature_importance)sorted_idx = np.argsort(feature_importance)pos = np.arange(sorted_idx.shape[0])plt.barh(pos, feature_importance[sorted_idx], align='center')plt.yticks(pos, X.columns[sorted_idx])plt.xlabel('Features')plt.ylabel('Importance')plt.title('重要特征 Variable Importance')plt.show()for out in outputs[:1]:sorted_idx = predict(data, out)

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