文章描述
- 数据分析：查看变量间相关性以及找出关键变量。
机器学习实战 —— 工业蒸汽量预测（一） - 数据特征工程对数据精进：异常值处理、归一化处理以及特征降维。
机器学习实战 —— 工业蒸汽量预测（二） - 模型训练(涉及主流ML模型)：决策树、随机森林，lightgbm等。
机器学习实战 —— 工业蒸汽量预测（三） - 模型验证：评估指标以及交叉验证等。
机器学习实战 —— 工业蒸汽量预测（四） - 特征优化：用lgb对特征进行优化。
机器学习实战 —— 工业蒸汽量预测（五） - 模型融合：进行基于stacking方式模型融合。
机器学习实战 —— 工业蒸汽量预测（六）
背景描述
- 背景介绍
火力发电的基本原理是：燃料在燃烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，然后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即燃料燃烧加热水产生高温高压蒸汽。锅炉的燃烧效率的影响因素很多，包括锅炉的可调参数，如燃烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比如锅炉床温、床压，炉膛温度、压力，过热器的温度等。
- 相关描述
经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别），根据锅炉的工况，预测产生的蒸汽量。
- 结果评估
预测结果以mean square error作为评判标准。
数据说明
数据分成训练数据（train.txt）和测试数据（test.txt），其中字段”V0”-“V37”，这38个字段是作为特征变量，”target”作为目标变量。选手利用训练数据训练出模型，预测测试数据的目标变量，排名结果依据预测结果的MSE（mean square error）。
数据来源
http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_test.txt
http://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/Industrial_Steam_Forecast/zhengqi_train.txt
实战内容
3.模型训练
3.1 回归及相关模型
导入包
from sklearn.linear_model import LinearRegression #线性回归 from sklearn.neighbors import KNeighborsRegressor #K近邻回归 from sklearn.tree import DecisionTreeRegressor #决策树回归 from sklearn.ensemble import RandomForestRegressor #随机森林回归 from sklearn.svm import SVR #支持向量回归 import lightgbm as lgb #lightGbm模型 from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import train_test_split # 切分数据 from sklearn.metrics import mean_squared_error #评价指标 from sklearn.model_selection import learning_curve from sklearn.model_selection import ShuffleSplit ## 切分训练数据和线下验证数据 #采用 pca 保留16维特征的数据 new_train_pca_16 = new_train_pca_16.fillna(0) train = new_train_pca_16[new_test_pca_16.columns] target = new_train_pca_16['target'] # 切分数据 训练数据80% 验证数据20% train_data,test_data,train_target,test_target=train_test_split(train,target,test_size=0.2,random_state=0)
3.1.1 多元线性回归模型
定义绘制模型学习曲线函数
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)): plt.figure() plt.title(title) if ylim is not None: plt.ylim(*ylim) plt.xlabel("Training examples") plt.ylabel("Score") train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) print(train_scores_mean) print(test_scores_mean) plt.grid() plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score") plt.plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score") plt.legend(loc="best") return plt
绘制学习曲线：只需要传入算法(或实例对象)、X_train、X_test、y_train、y_test
当使用该函数时传入算法，该算法的变量要进行实例化，如：PolynomialRegression(degree=2)，变量 degree 要进行实例化
def plot_learning_curve_old(algo, X_train, X_test, y_train, y_test): train_score = [] test_score = [] for i in range(10, len(X_train)+1, 10): algo.fit(X_train[:i], y_train[:i]) y_train_predict = algo.predict(X_train[:i]) train_score.append(mean_squared_error(y_train[:i], y_train_predict)) y_test_predict = algo.predict(X_test) test_score.append(mean_squared_error(y_test, y_test_predict)) plt.plot([i for i in range(1, len(train_score)+1)], train_score, label="train") plt.plot([i for i in range(1, len(test_score)+1)], test_score, label="test") plt.legend() plt.show()
线性回归模型学习曲线
3.1.2 KNN近邻回归
for i in range(3,10): clf = KNeighborsRegressor(n_neighbors=i) # 最近三个 clf.fit(train_data, train_target) score = mean_squared_error(test_target, clf.predict(test_data)) print("KNeighborsRegressor: ", score)
绘制K近邻回归学习曲线——K近邻回归
3.1.3决策树回归
clf = DecisionTreeRegressor() clf.fit(train_data, train_target) score = mean_squared_error(test_target, clf.predict(test_data)) print("DecisionTreeRegressor: ", score)
决策树回归
3.1.4 随机森林回归
clf = RandomForestRegressor(n_estimators=200) # 200棵树模型 clf.fit(train_data, train_target) score = mean_squared_error(test_target, clf.predict(test_data)) print("RandomForestRegressor: ", score) # plot_learning_curve_old(RandomForestRegressor(n_estimators=200), train_data, test_data, train_target, test_target)
X = train_data.values y = train_target.values # 随机森林 title = r"RandomForestRegressor" cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0) estimator = RandomForestRegressor(n_estimators=200) #建模 # plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)
3.1.5 Gradient Boosting
from sklearn.ensemble import GradientBoostingRegressor myGBR = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.03, loss='huber', max_depth=14, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=10, min_samples_split=40, min_weight_fraction_leaf=0.0, n_estimators=10, warm_start=False) myGBR.fit(train_data, train_target) score = mean_squared_error(test_target, clf.predict(test_data)) print("GradientBoostingRegressor: ", score) myGBR = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.03, loss='huber', max_depth=14, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=10, min_samples_split=40, min_weight_fraction_leaf=0.0, n_estimators=100, warm_start=False) plot_learning_curve_old(myGBR, train_data, test_data, train_target, test_target)
这里我为了快速展示，所以n_estimators设置较小，实战中请按需设置哦
X = train_data.values y = train_target.values # GradientBoosting title = r"GradientBoostingRegressor" cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0) estimator = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None, learning_rate=0.03, loss='huber', max_depth=14, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=10, min_samples_split=40, min_weight_fraction_leaf=0.0, n_estimators=100, warm_start=False) #建模 # plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)
3.1.6 lightgbm回归
这里我为了快速展示，所以n_estimators设置较小，实战中请按需设置哦
# lgb回归模型 clf = lgb.LGBMRegressor( learning_rate=0.01, max_depth=-1, n_estimators=100, boosting_type='gbdt', random_state=2019, objective='regression', ) # 训练模型 clf.fit( X=train_data, y=train_target, eval_metric='MSE', verbose=50 ) score = mean_squared_error(test_target, clf.predict(test_data)) print("lightGbm: ", score)
这里我为了快速展示，所以n_estimators设置较小，实战中请按需设置哦
X = train_data.values y = train_target.values # LGBM title = r"LGBMRegressor" cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0) estimator = lgb.LGBMRegressor( learning_rate=0.01, max_depth=-1, n_estimators=100, boosting_type='gbdt', random_state=2019, objective='regression' ) #建模 # plot_learning_curve(estimator, title, X, y, ylim=(0.4, 1.0), cv=cv, n_jobs=1)
