泰坦尼克号问题

from sklearn.ensemble import RandomForestRegressor

### 使用 RandomForestClassifier 填补缺失的年龄属性
def set_missing_ages(df):

    # 把已有的数值型特征取出来丢进Random Forest Regressor中
    age_df = df[[‘Age‘,‘Fare‘, ‘Parch‘, ‘SibSp‘, ‘Pclass‘]]

    # 乘客分成已知年龄和未知年龄两部分
    known_age = age_df[age_df.Age.notnull()].as_matrix()
    unknown_age = age_df[age_df.Age.isnull()].as_matrix()

    # y即目标年龄
    y = known_age[:, 0]

    # X即特征属性值
    X = known_age[:, 1:]

    # fit到RandomForestRegressor之中
    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)
    rfr.fit(X, y)

    # 用得到的模型进行未知年龄结果预测
    predictedAges = rfr.predict(unknown_age[:, 1::])

    # 用得到的预测结果填补原缺失数据
    df.loc[ (df.Age.isnull()), ‘Age‘ ] = predictedAges 

    return df, rfr

def set_Cabin_type(df):
    df.loc[ (df.Cabin.notnull()), ‘Cabin‘ ] = "Yes"
    df.loc[ (df.Cabin.isnull()), ‘Cabin‘ ] = "No"
    return df

data_train, rfr = set_missing_ages(data_train)
data_train = set_Cabin_type(data_train)

import numpy as np  
from sklearn.feature_selection import SelectKBest,f_classif  
import matplotlib.pyplot as plt  
predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked","FamilySize","Title","NameLength"]  
  
#Perform feature selection  
selector=SelectKBest(f_classif,k=5)  
selector.fit(titanic[predictors],titanic["Survived"])  
  
#Plot the raw p-values for each feature,and transform from p-values into scores  
scores=-np.log10(selector.pvalues_)  
  
#Plot the scores.   See how "Pclass","Sex","Title",and "Fare" are the best?  
plt.bar(range(len(predictors)).scores)  
plt.xticks(range(len(predictors)).predictors,rotation=‘vertical‘)  
plt.show()  
  
#Pick only the four best features.  
predictors=["Pclass","Sex","Fare","Title"]  
  
alg=RandomForestClassifier(random_state=1,n_estimators=50,min_samples_split=8,min_samples_leaf=4)  

//集成多种算法求平均的方法来进行机器学习求解  
from sklearn.ensemble import GradientBoostingClassifier  
import numpy as  np  
  
#The algorithms we want to ensemble.  
#We‘re using the more linear predictors for the logistic regression,and everything with the gradient boosting classifier  
algorithms=[  
    [GradientBoostingClassifier(random_state=1,n_estimators=25,max_depth=3, ["Pclass","Sex","Age","Fare","FamilySize","Title","Age","Embarked"]]  
    [LogisticRegression(random_state=1),["Pclass","Sex","Fare","FamilySize","Title","Age","Embarked"]]  
]  
  
#Initialize the cross validation folds  
kf=KFold(titanic.shape[0],n_folds=3,random_state=1)  
  
predictions=[]  
for train,test in kf:  
    train_target=titanic["Survived"].iloc[train]  
    full_test_predictions=[]  
    #Make predictions for each algorithm on each fold  
    for alg,predictors in algorithms:  
        #Fit the algorithm on the training data  
        alg.fit(titanic[predictors].iloc[train,:],train_targegt)  
        #Select and predict on the test fold  
        #The .astype(float) is necessary to convert the dataframe to all floats and sklearn error.  
        test_predictions=alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]  
    #Use a simple ensembling scheme -- just  average the predictions to get the final classification.  
    test_predictions=(full_test_predictions[0]+full_test_predictions[1])/2  
    #Any value over .5 is assumed to be a 1 prediction,and below .5 is a 0 prediction.  
    test_predictions[test_predictions<=0.5]=0  
    test_predictions[test_predictions>0.5]=1  
    predictions.append(test_predictions)  
  
#Put all the predictions together into one array.  
predictions=np.concatenate(predictions,axis=0)  
  
#Compute accuracy by comparing to the training data  
accuracy=sum(predictions[predictions==titanic["Survived"]])/len(predictions)  
print(accuracy)  



#The gradient boosting classifier generates better predictions,so we weight it higher  
predictions=(full_predictions[0]*3+full_predictions[1]*1)/4  
predictions