学习目标
将之前建模调参的结果进行模型融合。 尝试多种融合方案，提交融合结果并打卡。（模型融合一般用于A榜比赛的尾声和B榜比赛的全程）
内容介绍
- 平均：
- 简单平均法
- 加权平均法
- 投票：
- 简单投票法
- 加权投票法
- 综合：
- 排序融合
- log融合
- stacking:
- 构建多层模型，并利用预测结果再拟合预测。
- blending：
- 选取部分数据预测训练得到预测结果作为新特征，带入剩下的数据中预测。
- boosting/bagging（在Task4中已经提及，就不再赘述）
代码
到这一步的话，单独的模型已经都有基本的结果了，剩下的就是将模型进行融合，争取拿到更好的结果。
# 平均
pre = (pre1 + pre2 + pre3 +...+pren )/n
pre = 0.3pre1 + 0.3pre2 + 0.4pre3 # 投票
# 简单投票
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = XGBClassifier(learning_rate=0.1, n_estimators=150, max_depth=4, min_child_weight=2, subsample=0.7,objective='binary:logistic')vclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('xgb', clf3)])
vclf = vclf .fit(x_train,y_train)
print(vclf .predict(x_test))# 加权投票
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
clf1 = LogisticRegression(random_state=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = XGBClassifier(learning_rate=0.1, n_estimators=150, max_depth=4, min_child_weight=2, subsample=0.7,objective='binary:logistic')vclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('xgb', clf3)], voting='soft', weights=[2, 1, 1])
vclf = vclf .fit(x_train,y_train)
print(vclf .predict(x_test))# Stacking
import warnings
warnings.filterwarnings('ignore')
import itertools
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingClassifier
from sklearn.model_selection import cross_val_score, train_test_split
from mlxtend.plotting import plot_learning_curves
from mlxtend.plotting import plot_decision_regions# 以python自带的鸢尾花数据集为例
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.targetclf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], meta_classifier=lr)label = ['KNN', 'Random Forest', 'Naive Bayes', 'Stacking Classifier']
clf_list = [clf1, clf2, clf3, sclf]fig = plt.figure(figsize=(10,8))
gs = gridspec.GridSpec(2, 2)
grid = itertools.product([0,1],repeat=2)clf_cv_mean = []
clf_cv_std = []
for clf, label, grd in zip(clf_list, label, grid):scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')print("Accuracy: %.2f (+/- %.2f) [%s]" %(scores.mean(), scores.std(), label))clf_cv_mean.append(scores.mean())clf_cv_std.append(scores.std())clf.fit(X, y)ax = plt.subplot(gs[grd[0], grd[1]])fig = plot_decision_regions(X=X, y=y, clf=clf)plt.title(label)plt.show()# blending
# 以python自带的鸢尾花数据集为例
data_0 = iris.data
data = data_0[:100,:]target_0 = iris.target
target = target_0[:100]#模型融合中基学习器
clfs = [LogisticRegression(),RandomForestClassifier(),ExtraTreesClassifier(),GradientBoostingClassifier()]#切分一部分数据作为测试集
X, X_predict, y, y_predict = train_test_split(data, target, test_size=0.3, random_state=914)#切分训练数据集为d1,d2两部分
X_d1, X_d2, y_d1, y_d2 = train_test_split(X, y, test_size=0.5, random_state=914)
dataset_d1 = np.zeros((X_d2.shape[0], len(clfs)))
dataset_d2 = np.zeros((X_predict.shape[0], len(clfs)))for j, clf in enumerate(clfs):#依次训练各个单模型clf.fit(X_d1, y_d1)y_submission = clf.predict_proba(X_d2)[:, 1]dataset_d1[:, j] = y_submission#对于测试集，直接用这k个模型的预测值作为新的特征。dataset_d2[:, j] = clf.predict_proba(X_predict)[:, 1]print("val auc Score: %f" % roc_auc_score(y_predict, dataset_d2[:, j]))#融合使用的模型
clf = GradientBoostingClassifier()
clf.fit(dataset_d1, y_d2)
y_submission = clf.predict_proba(dataset_d2)[:, 1]
print("Val auc Score of Blending: %f" % (roc_auc_score(y_predict, y_submission)))