#coding:utf-8
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
import pandas as pd # 数据分析
import numpy as np # 科学计算
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.ensemble import RandomForestRegressor
import sklearn.preprocessing as preprocessing
from sklearn import linear_model
from sklearn.ensemble import BaggingRegressor
from sklearn import cross_validation
import time
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier,AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.metrics import precision_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve,KFold
import time
import os
from sklearn.feature_extraction.text import TfidfTransformer# warnings.simplefilter('ignore', DeprecationWarning)
import warnings
warnings.filterwarnings('ignore')# import tensorflow as tf
#
# tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")
# FLAGS = tf.flags.FLAGS
#
#
# class TextCNN(object):
# """
# A CNN for text classification.
# Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
# """
#
# def __init__(
# self, sequence_length, num_classes, vocab_size,
# embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.8):
# # Placeholders for input, output and dropout
# self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
# self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
# self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
#
# # Keeping track of l2 regularization loss (optional)
# l2_loss = tf.constant(0.0)
# print(l2_reg_lambda)
#
# # def pri(self):
# # print(sequence_length)
#
#
# cnn = TextCNN(
# sequence_length=1,
# num_classes=1,
# vocab_size=1,
# embedding_size=1,
# filter_sizes=1,
# num_filters=1,
# )
#
# # cnn.pri()data_train = pd.read_csv("D:/titanic/train.csv")
data_test = pd.read_csv("D:/titanic/test.csv")# print(data_train.Sex.value_counts())
# print('***************')
# print(pd.DataFrame(data_train.groupby(['Sex','Survived']).count()['PassengerId']))
# print(pd.DataFrame(data_train.groupby(['Sex']).mean()['Survived']))
# print(data_train[data_train.Sex == 'male'].Survived.value_counts())
# print('*******************************************************')# df = pd.DataFrame({'key1':list('aabba'),
# 'key2': ['one','two','one','two','one'],
# 'data1': np.random.randn(5),
# 'data2': np.random.randn(5)})
# print(df)
# g = df.groupby(['key1','key2'])
# print(pd.DataFrame(g.count()))# data_train.Age.value_counts().plot(kind = 'kde')
# plt.show()
#
# data_train.Age[data_train.Survived == 0].plot(kind = 'kde')
# data_train.Age[data_train.Survived == 1].plot(kind = 'kde')
# plt.legend((u'1',u'0'),loc = 'best')
# plt.show()
#
# data_train.Fare[data_train.Survived == 0].plot(kind = 'kde')
# data_train.Fare[data_train.Survived == 1].plot(kind = 'kde')
# plt.legend((u'0',u'1'),loc = 'best')
# plt.gca().set_xticklabels(('0','50','100','150','200','250'))
# plt.show()# data_train.Age[data_train.Embarked == 'C'].plot(kind = 'kde')
# data_train.Age[data_train.Embarked == 'Q'].plot(kind = 'kde')
# data_train.Age[data_train.Embarked == 'S'].plot(kind = 'kde')
# plt.legend((u'C',u'Q',u'S'),loc = 'best')
# plt.show()# for i in ['C','Q','S']:
# for j in range(1,4,1):
# data_train.Survived[data_train.Embarked == i][data_train.Pclass == j].value_counts().plot(kind = 'bar',label = '{}/{}'.format(i,j))
#
# plt.show()# fig=plt.figure()
# fig.set(alpha=0.65) # 设置图像透明度，无所谓
# plt.title(u"根据舱等级和性别的获救情况")
#
# ax1=fig.add_subplot(141)
# data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label="female highclass", color='#FA2479')
# print(data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts())
# ax1.set_xticklabels([u"获救", u"未获救"], rotation=0)
# ax1.legend([u"女性/高级舱"], loc='best')
#
# ax2=fig.add_subplot(142, sharey=ax1)
# data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink')
# print(data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts())
# ax2.set_xticklabels([u"未获救", u"获救"], rotation=0)
# plt.legend([u"女性/低级舱"], loc='best')
#
# ax3=fig.add_subplot(143, sharey=ax1)
# data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(kind='bar', label='male, high class',color='lightblue')
# print(data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts())
# ax3.set_xticklabels([u"未获救", u"获救"], rotation=0)
# plt.legend([u"男性/高级舱"], loc='best')
#
# ax4=fig.add_subplot(144, sharey=ax1)
# data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='male low class', color='steelblue')
# print(data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts())
# ax4.set_xticklabels([u"未获救", u"获救"], rotation=0)
# plt.legend([u"男性/低级舱"], loc='best')
# print(type(data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts()))
# plt.show()#
# survived_1 = data_train.SibSp[data_train.Survived == 1].value_counts()
# survived_0 = data_train.SibSp[data_train.Survived == 0].value_counts()
# df = pd.DataFrame({u'1':survived_1,u'0':survived_0})
# df.plot(kind = 'bar',stacked = True)
# plt.show()def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1,train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):"""画出data在某模型上的learning curve.参数解释----------estimator : 你用的分类器。title : 表格的标题。X : 输入的feature，numpy类型y : 输入的target vectorylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)n_jobs : 并行的的任务数(默认1)"""train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)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)if plot:plt.figure()plt.title(title)if ylim is not None:plt.ylim(*ylim)plt.xlabel(u"训练样本数")plt.ylabel(u"得分")plt.gca().invert_yaxis()plt.grid()plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,alpha=0.1, color="b")plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,alpha=0.1, color="r")plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label=u"训练集上得分")plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label=u"交叉验证集上得分")plt.legend(loc="best")plt.draw()plt.show()plt.gca().invert_yaxis()midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])return midpoint, diff# #知乎的stacking方法，但没有使用5-折交叉训练，所以效果略差
# class Ensemble(object):
# def __init__(self, estimators):
# self.estimator_names = []
# self.estimators = []
# for i in estimators:
# self.estimator_names.append(i[0])
# self.estimators.append(i[1])
# self.clf = LogisticRegression()
#
# def fit(self, train_x, train_y):
# for i in self.estimators:
# i.fit(train_x, train_y)
# x = np.array([i.predict(train_x) for i in self.estimators]).T
# y = train_y
# self.clf.fit(x, y)
#
# def predict(self, x):
# x = np.array([i.predict(x) for i in self.estimators]).T
# # print(x)
# return self.clf.predict(x)
#
# def score(self, x, y):
# s = precision_score(y, self.predict(x))
# return s#使用了k-折 交叉训练
class Stacking(object):def __init__(self, first_estimators):self.estimators = []self.first_esti_nums = len(first_estimators)for i in first_estimators:self.estimators.append(i[1])def get_oof_predictions(self,clf,train_x,train_y,test,Kfold):ntrain = train_x.shape[0]ntest = test.shape[0]oof_train = np.zeros((ntrain,))oof_test = np.zeros((ntest,))oof_test_skf = np.empty((Kfold, ntest))kf = KFold(n_splits=Kfold)for i, (train_index, test_index) in enumerate(kf.split(train_x)):kf_x_train = train_x[train_index]kf_y_train = train_y[train_index]kf_x_test = train_x[test_index]clf.fit(kf_x_train, kf_y_train)oof_train[test_index] = clf.predict(kf_x_test)oof_test_skf[i, :] = clf.predict(test)oof_test = oof_test_skf.mean(axis = 0)return oof_train.reshape(-1,1)[:,0],oof_test.reshape(-1,1)[:,0]def fit_predict(self, train_x, train_y,test,Kfold):# print('Kfold = {}'.format(Kfold))ntrain = train_x.shape[0]ntest = test.shape[0]self.middle_train = np.zeros((ntrain, self.first_esti_nums))self.middle_test = np.zeros((ntest, self.first_esti_nums))for i, clf in enumerate(self.estimators):self.middle_train[:, i], self.middle_test[:, i] = self.get_oof_predictions(clf, train_x, train_y, test,Kfold)second_estimator = GridSearchCV(estimator=LogisticRegression(), param_grid=param_lr, cv=5, n_jobs=-1)second_estimator.fit(self.middle_train,train_y)second_estimator.best_estimator_.fit(self.middle_train, train_y)predictions = second_estimator.best_estimator_.predict(self.middle_test)return predictionsdef score(self, x, y):s = precision_score(y,self.fit_predict(x,y,x,10))return sif __name__ =='__main__':combine_df = pd.concat([data_train,data_test])#Name特征combine_df['Name_Len'] = combine_df.Name.apply(lambda x:len(x))combine_df['Name_Len'] = pd.qcut(combine_df.Name_Len,5)df_name = pd.get_dummies(combine_df.Name_Len, prefix='Name_Len')combine_df = pd.concat([combine_df, df_name], axis=1).drop('Name_Len', axis=1)#不同的称谓类似，有显著不同的获救概率# combine_df.groupby(combine_df['Name'].apply(lambda x: x.split(', ')[1]).apply(lambda x: x.split('.')[0]))[# 'Survived'].mean().plot(kind = 'bar')# plt.show()combine_df['Title'] = combine_df['Name'].apply(lambda x: x.split(', ')[1]).apply(lambda x: x.split('.')[0])combine_df['Title'] = combine_df['Title'].replace(['Don', 'Dona', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col', 'Sir', 'Dr'], 'Mr')combine_df['Title'] = combine_df['Title'].replace(['Mlle', 'Ms'], 'Miss')combine_df['Title'] = combine_df['Title'].replace(['the Countess', 'Mme', 'Lady', 'Dr'], 'Mrs')df = pd.get_dummies(combine_df['Title'], prefix='Title')combine_df = pd.concat([combine_df, df], axis=1)# print(combine_df.Title)#同一个family下的生存死亡模式有很大程度上是相同的，例如：有一个family有一个女性死亡，这个family其他的女性的死亡概率也比较高。# 因此，我们标注出这些特殊的family即可combine_df['Fname'] = combine_df['Name'].apply(lambda x: x.split(',')[0])combine_df['Familysize'] = combine_df['SibSp'] + combine_df['Parch']dead_female_Fname = list(set(combine_df[(combine_df.Sex == 'female') & (combine_df.Age >= 12)& (combine_df.Survived == 0) & (combine_df.Familysize > 1)]['Fname'].values))survive_male_Fname = list(set(combine_df[(combine_df.Sex == 'male') & (combine_df.Age >= 12)& (combine_df.Survived == 1) & (combine_df.Familysize > 1)]['Fname'].values))combine_df['Dead_female_family'] = np.where(combine_df['Fname'].isin(dead_female_Fname), 1, 0)combine_df['Survive_male_family'] = np.where(combine_df['Fname'].isin(survive_male_Fname), 1, 0)combine_df = combine_df.drop(['Name', 'Fname'], axis=1)#Age 添加一个小孩子标签# print(combine_df.Age)group = combine_df.groupby(['Title', 'Pclass'])['Age']# print(group.count())print('******************************************')combine_df['Age'] = group.transform(lambda x: x.fillna(x.median()))combine_df = combine_df.drop('Title', axis=1)combine_df['IsChild'] = np.where(combine_df['Age'] <= 12, 1, 0)# print(combine_df.columns)combine_df['Age'] = pd.cut(combine_df['Age'], 5)combine_df = combine_df.drop('Age', axis=1)#Familysize 我们将上面提取过的Familysize再离散化combine_df['Familysize'] = np.where(combine_df['Familysize'] == 0, 'solo',np.where(combine_df['Familysize'] <= 3, 'normal', 'big'))df = pd.get_dummies(combine_df['Familysize'], prefix='Familysize')combine_df = pd.concat([combine_df, df], axis=1).drop(['SibSp', 'Parch', 'Familysize'],axis = 1)# print(combine_df.columns)# print(len(combine_df.columns))#Ticketcombine_df['Ticket_Lett'] = combine_df['Ticket'].apply(lambda x: str(x)[0])combine_df['Ticket_Lett'] = combine_df['Ticket_Lett'].apply(lambda x: str(x))combine_df['High_Survival_Ticket'] = np.where(combine_df['Ticket_Lett'].isin(['1', '2', 'P']), 1, 0)combine_df['Low_Survival_Ticket'] = np.where(combine_df['Ticket_Lett'].isin(['A', 'W', '3', '7']), 1, 0)combine_df = combine_df.drop(['Ticket', 'Ticket_Lett'], axis=1)#Embarkedcombine_df.Embarked = combine_df.Embarked.fillna('S')df = pd.get_dummies(combine_df['Embarked'], prefix='Embarked')combine_df = pd.concat([combine_df, df], axis=1).drop('Embarked', axis=1)#Cabincombine_df['Cabin_isNull'] = np.where(combine_df['Cabin'].isnull(), 0, 1)combine_df = combine_df.drop('Cabin', axis=1)#Pclassdf = pd.get_dummies(combine_df['Pclass'],prefix = 'Pclass')combine_df = pd.concat([combine_df,df],axis = 1).drop('Pclass',axis = 1)#Sexdf = pd.get_dummies(combine_df['Sex'], prefix='Sex')combine_df = pd.concat([combine_df, df], axis=1).drop('Sex', axis=1)## #Fare 缺省值用众数填充，之后进行离散化# combine_df['Fare'] = pd.qcut(combine_df.Fare, 3)# # print(combine_df.Fare)# df = pd.get_dummies(combine_df.Fare, prefix='Fare').drop('Fare_(-0.001, 8.662]', axis=1)# combine_df = pd.concat([combine_df, df], axis=1).drop('Fare', axis=1)# Farecombine_df['Fare'].fillna(combine_df['Fare'].dropna().median(), inplace=True)combine_df['Low_Fare'] = np.where(combine_df['Fare'] <= 8.662, 1, 0)combine_df['High_Fare'] = np.where(combine_df['Fare'] >= 26, 1, 0)combine_df = combine_df.drop('Fare', axis=1)print(combine_df.columns.values)print(len(combine_df.columns.values))print('before LabelEncoder.....')print(combine_df)print(len(combine_df.columns))#所有特征转化成数值型编码features = combine_df.drop(["PassengerId", "Survived"], axis=1).columnsle = LabelEncoder()for feature in features:le = le.fit(combine_df[feature])combine_df[feature] = le.transform(combine_df[feature])print('After LabelEncoder....')print(combine_df)# 防止xgboost 不识别特征中的 ']'combine_df.rename(columns=lambda x: x.replace('[', '').replace(']',''), inplace=True)#得到训练/测试数据X_all = combine_df.iloc[:891, :].drop(["PassengerId", "Survived"], axis=1)Y_all = combine_df.iloc[:891, :]["Survived"]X_test = combine_df.iloc[891:, :].drop(["PassengerId", "Survived"], axis=1)# print('X_all = ')# print(X_all)# print(Y_all)# print(X_test)# print(X_all.columns.values)# print(X_test.columns.values)print('*******************************************************')##分别考察逻辑回归、支持向量机、最近邻、决策树、随机森林、gbdt、xgbGBDT几类算法的性能。param_lr = {'penalty': ['l1', 'l2'], 'C': [0.1, 0.5, 5.0]}grd_lr = GridSearchCV(estimator = LogisticRegression(),param_grid = param_lr,cv = 5,n_jobs = -1)grd_lr.fit(X_all,Y_all)# print(grd_lr.best_estimator_,grd_lr.best_score_)lr = grd_lr.best_estimator_param_svc = {'C':[0.1,1.0,10.0],'gamma':['auto',1,0.1,0.01],'kernel':['linear','rbf','sigmoid']}grd_svc = GridSearchCV(estimator = SVC(),param_grid = param_svc,cv = 5,n_jobs = -1)grd_svc.fit(X_all,Y_all)# print(grd_svc.best_estimator_,grd_svc.best_score_)svc = grd_svc.best_estimator_k_range = list(range(1, 10))leaf_range = list(range(1, 2))weight_options = ['uniform', 'distance']algorithm_options = ['auto', 'ball_tree', 'kd_tree', 'brute']param_knn = dict(n_neighbors=k_range, weights=weight_options, algorithm=algorithm_options)grd_knn = GridSearchCV(estimator = KNeighborsClassifier(),param_grid = param_knn,cv = 5,n_jobs = -1)grd_knn.fit(X_all,Y_all)# print(grd_knn.best_estimator_,grd_knn.best_score_)knn = grd_knn.best_estimator_param_dt = {'max_depth': [1, 2, 3, 4, 5], 'max_features': [1, 2, 3, 4]}grd_dt = GridSearchCV(estimator=DecisionTreeClassifier(), param_grid=param_dt, cv=5, n_jobs=-1)grd_dt.fit(X_all, Y_all)# print(grd_dt.best_estimator_, grd_dt.best_score_)dt = grd_dt.best_estimator_param_rf = {'n_estimators': [50,100,300,500], 'min_samples_leaf': [1, 2, 3, 4],'class_weight':[{0: 0.745, 1: 0.255}]}grd_rf = GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_rf, cv=5, n_jobs=-1)grd_rf.fit(X_all, Y_all)# print(grd_rf.best_estimator_, grd_rf.best_score_)rf = grd_rf.best_estimator_gbdt = GradientBoostingClassifier(n_estimators=500, learning_rate=0.03, max_depth=3)xgb = XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)# clfs = [('lr', lr), ('svc', svc), ('knn', knn), ('dt', dt), ('rf', rf), ('gbdt', gbdt),('xgb',xgb)]## kfold = 10# cv_results = []# for classifier in clfs:# cv_results.append(cross_val_score(classifier, X_all, y=Y_all, scoring="accuracy", cv=kfold, n_jobs=4))# for classifier in clfs:# print('the cross_value of {} is:'.format(classifier[0]))# # print(cross_val_score(classifier[1], X_all, y=Y_all, scoring="accuracy", cv=kfold, n_jobs=4))# cross_result = list(cross_validation.cross_val_score(classifier[1], X_all, Y_all, cv=5))# cross_result.append(np.mean(cross_result))# print(cross_result)## #XGBClassifier 很坑，属性名中不能有 '[,]'# demo_train = X_all.iloc[:,:] #是深拷贝，怎么实现浅拷贝？# demo_train['Fare_1'] = X_all['Fare_(8.662, 26.0]']# demo_train['Fare_2'] = X_all['Fare_(26.0, 512.329]']# demo_test = X_test.iloc[:,:]# demo_test['Fare_1'] = X_all['Fare_(8.662, 26.0]']# demo_test['Fare_2'] = X_all['Fare_(26.0, 512.329]']# demo_test = demo_test.drop(['Fare_(8.662, 26.0]', 'Fare_(26.0, 512.329]'], axis=1)# print(X_all.columns.values)# print(demo_train.columns.values)# demo_train = demo_train.drop(['Fare_(8.662, 26.0]','Fare_(26.0, 512.329]'],axis = 1)# print(demo_train.columns.values)# print(cross_val_score(rf, demo_train, y=Y_all, scoring="accuracy", cv=kfold, n_jobs=4))# rf.fit(demo_train,Y_all)# print(rf.feature_importances_)# print(type(rf.feature_importances_))# print(rf.feature_importances_.sum())## #实现浅拷贝X_all# demo = X_all.drop(['Fare_(8.662, 26.0]','Fare_(26.0, 512.329]'],axis = 1)# print(demo.columns.values)# print(X_all.columns.values)# demo['Fare1'] = X_all['Fare_(8.662, 26.0]']# demo['Fare2'] = X_all['Fare_(26.0, 512.329]']# print(demo.columns.values)# print(X_all.columns.values)# cv_means = []# cv_std = []# for cv_result in cv_results:# cv_means.append(cv_result.mean())# cv_std.append(cv_result.std())# cv_res = pd.DataFrame({"CrossValMeans": cv_means, "CrossValerrors": cv_std,# "Algorithm": ["LR", "SVC", 'KNN', 'decision_tree', "random_forest", "GBDT", "xgbGBDT"]})# # g = sns.barplot("CrossValMeans", "Algorithm", data=cv_res, palette="Set3", orient="h", **{'xerr': cv_std})# # g.set_xlabel("Mean Accuracy")# # g = g.set_title("Cross validation scores")#观察发现不同的模型的feature importance 有比较大的差别，，，把他们组合再一起会不会更好呢#集成框架Ensemble，我们把基分类器丢进去。# bag = Ensemble([('xgb', xgb), ('lr', lr), ('rf', rf), ('svc', svc), ('gbdt', gbdt)])# bag = Ensemble([('xgb', xgb), ('lr', lr), ('gbdt', gbdt), ('rf', rf)])# score = 0# for i in range(0, 10):# num_test = 0.20# X_train, X_cv, Y_train, Y_cv = train_test_split(X_all, Y_all, test_size=num_test)# bag.fit(X_train, Y_train)# # Y_test = bag.predict(X_test)# acc_xgb = round(bag.score(X_cv, Y_cv) * 100, 2)# score += acc_xgb# print(score / 10) # 0.8786## print(X_all.values)x = X_all.as_matrix()y = Y_all.as_matrix()test = X_test.as_matrix()# stackings = [('lr', lr), ('svc', svc), ('knn', knn), ('dt', dt), ('rf', rf), ('gbdt', gbdt),('xgb',xgb)]first_estimators =[('lr', lr), ('svc', svc), ('knn', knn), ('dt', dt), ('rf', rf), ('gbdt', gbdt),('xgb',xgb)]# param = {'penalty':['l1','l2'],'C':[0.1,0.5,5.0]}# #use best estimator# my_lr = lr2.best_estimator_# my_lr.fit(middle_train,y)# predictions = my_lr.predict(middle_test)sta = Stacking(first_estimators)print(sta.score(x,y))predictions = sta.fit_predict(x,y,test,Kfold = 10)# lr2.fit(middle_train,y)# predictions = lr2.predict(middle_test)# print(predictsions)# print(predictsions.shape)# print(cross_validation.cross_val_score(lr2, middle_train, y, cv=5))#stacking saving# result = pd.DataFrame(# {'PassengerId': data_test['PassengerId'].as_matrix(), 'Survived': predictions.astype(np.int32)})# result.to_csv('D:/titanic/results/' +'stacking'+'_predictions' + time.strftime('%Y-%m-%d %H-%M-%S',# time.localtime(time.time())) + '.csv',# index=False)## bag.fit(X_all,Y_all)# predictions = bag.predict(X_test)# result = pd.DataFrame(# {'PassengerId': data_test['PassengerId'].as_matrix(), 'Survived': predictions.astype(np.int32)})# result.to_csv('D:/titanic/results/' +'bag' +'_predictions' + time.strftime('%Y-%m-%d %H-%M-%S',# time.localtime(time.time())) + '.csv',# index=False)## 预测并打印结果到csv# for i in clfs:# i[1].fit(X_all,Y_all)# predictions = i[1].predict(X_test)# result = pd.DataFrame(# {'PassengerId': data_test['PassengerId'].as_matrix(), 'Survived': predictions.astype(np.int32)})# result.to_csv('D:/titanic/results/' +i[0] +'_predictions' + time.strftime('%Y-%m-%d %H-%M-%S',# time.localtime(time.time())) + '.csv',# index=False)#
# class A(object):
# def __init__(self,c = 1):
# self.b = 0
# def cccccc(self):
# self.cccccc = 10
# def print_(self):
# print('b = {}'.format(self.b))
# print('ccccccc = {}'.format(self.cccccc))
# print('c = {}'.format(c))
#
#
# aaa = A(100)
# aaa.print_()
#
#
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