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ml_stacking.py
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142 lines (101 loc) · 3.86 KB
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# -*- coding:utf-8 -*-
# date:2019-04-30
# Model Stacking
# https://www.cnblogs.com/jiaxin359/p/8559029.html
# http://wulc.me/2018/01/21/stacking%20%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%80%9D%E6%83%B3%E5%8F%8A%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0/
import numpy as np
from sklearn.model_selection import KFold
def get_stacking(clf, x_train, y_train, x_test, n_folds=10):
"""
构建Stacking，使用交叉验证的方法得到次级训练集
x_train, y_train, x_test数据类型为numpy.ndarray
"""
# RawData you received~
train_num, test_num = x_train.shape[0], x_test.shape[0]
#
second_level_train_set, second_level_test_set = np.zeros((train_num, )), np.zeros((test_num, ))
test_nfolds_sets = np.zeros((test_num, n_folds))
print("-------------------------------------------")
print(second_level_train_set.shape, second_level_test_set.shape, test_nfolds_sets.shape)
kf = KFold(n_splits=n_folds)
for i, (train_index, test_index) in enumerate(kf.split(x_train)):
# print(i, train_index, test_index)
x_tra, y_tra = x_train[train_index], y_train[train_index]
x_tst, _ = x_train[test_index], y_train[test_index]
clf.fit(x_tra, y_tra)
second_level_train_set[test_index] = clf.predict(x_tst)
# print("==============================================")
# print(second_level_train_set)
test_nfolds_sets[:, i] = clf.predict(x_test)
# print(test_nfolds_sets)
second_level_test_set[:] = test_nfolds_sets.mean(axis=1)
return second_level_train_set, second_level_test_set
# 使用5个分类算法
"""
BaseModel之间的性能差异不能太大，表现差的模型会拖后腿；
BaseModel之间的相关性尽可能地小，从而能弥补模型的优势
"""
import pickle
from sklearn.ensemble import (
RandomForestClassifier,
AdaBoostClassifier,
GradientBoostingClassifier,
ExtraTreesClassifier
)
from sklearn.svm import SVC
from sklearn.externals import joblib
rf_model = RandomForestClassifier()
ada_model = AdaBoostClassifier()
gbdc_model = GradientBoostingClassifier()
ext_model = ExtraTreesClassifier()
svm_model = SVC()
# 加载数据集
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
train_x, test_x, train_y, test_y = train_test_split(iris.data, iris.target, test_size=0.2, random_state=1)
train_sets, test_sets = [], []
for clf in (rf_model, ada_model, gbdc_model, ext_model, svm_model):
train_set, test_set = get_stacking(clf, train_x, train_y, test_x)
train_sets.append(train_set)
test_sets.append(test_set)
meta_train = np.concatenate([result_set.reshape(-1, 1) for result_set in train_sets], axis=1)
meta_test = np.concatenate([y_test_set.reshape(-1, 1) for y_test_set in test_sets], axis =1)
# 次级分类器
from sklearn.tree import DecisionTreeClassifier
dt_model = DecisionTreeClassifier()
dt_model.fit(meta_train, train_y)
dt_predict = dt_model.predict(meta_test)
print("-----------------Ensumble Result-------------------------")
print(dt_predict)
joblib.dump(dt_model, './ensumble_model.pkl')
model = joblib.load('./ensumble_model.pkl')
# ------------------------------------------------------------------
from sklearn import svm
from sklearn import datasets
iris = datasets.load_iris()
X,y = iris.data,iris.target
clf = svm.SVC()
print("Before train: ", clf)
clf.fit(X,y)
print("After train: ", clf)
print(clf.dual_coef_)
print(clf.support_ )
print(clf.support_vectors_ )
print(train_sets)
import pickle
with open("svm.txt", "wb") as f:
pickle.dump(clf, f)
with open("svm.txt", "rb") as f:
clf2 = pickle.load(f)
s = pickle.dumps(clf)
f = open('svm.txt','wb')
f.write(s)
f.close()
f2 = open('svm.txt','rb')
s2 = f2.read()
clf2 = pickle.loads(s2)
clf2.score(X,y)
from sklearn.externals import joblib
joblib.dump(clf,'filename.pkl')
clf = joblib.load('filename.pkl')