DictVectorizer问题：为不同的输入创建不同数量的特性

Question

我试图写一个机器学习算法，我试图预测输出是+50000还是-50000。在这样做时，我利用了11个字符串特征使用随机森林分类器。但是由于随机森林分类器需要以float/numbers的形式输入，所以我使用DictVectorizer将字符串特性转换为float/numbers。但是对于数据中的不同行，DictVectorizer创建了不同数量的特性（240-260）。这会导致预测模型输出的错误。一个示例输入行是：

{'detailed household summary in household': ' Spouse of householder',
 'tax filer stat': ' Joint both under 65',
 'weeks worked in year': ' 52',
 'age': '32', 
 'sex': ' Female',
 'marital status': ' Married-civilian spouse present',
 'full or part time employment stat': ' Full-time schedules',
 'detailed household and family stat': ' Spouse of householder', 
 'education': ' Bachelors degree(BA AB BS)',
 'num persons worked for employer': ' 3',
 'major occupation code': ' Adm support including clerical'}

是否有一些方法我可以转换输入，以便我可以使用随机森林分类器预测输出。

编辑：我使用的代码是：

    X,Y=[],[]
    features=[0,4,7,9,12,15,19,22,23,30,39]
    with open("census_income_learn.csv","r") as fl:
        reader=csv.reader(fl)
        for row in reader:
            data={}
            for i in features:
                data[columnNames[i]]=str(row[i])
            X.append(data)
            Y.append(str(row[41]))

    X_train, X_validate, Y_train, Y_validateActual = train_test_split(X, Y, test_size=0.2, random_state=32)

    vec = DictVectorizer()
    X_train=vec.fit_transform(X_train).toarray()
    X_validate=vec.fit_transform(X_validate).toarray()
    print("data ready")

    forest = RandomForestClassifier(n_estimators = 100)
    forest = forest.fit( X_train, Y_train )
    print("model created")

    Y_predicted=forest.predict(X_validate)
    print(Y_predicted)

所以在这里，如果我试图打印训练集和验证集的第一个元素，我在x_train[0]中得到252个特性，而在x_validate[0]中有249个特性。

Answer 1

试试这个：

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

cols = [0,4,7,9,12,15,19,22,23,30,39,  41]
names = [
 'detailed household summary in household',
 'sex',
 'full or part time employment stat',
 'age',
 'detailed household and family stat',
 'weeks worked in year',
 'num persons worked for employer',
 'major occupation code',
 'tax filer stat',
 'education',
 'marital status',
 'TARGET'
]

fn = r'D:\temp\.data\census_income_learn.csv'
data = pd.read_csv(fn, header=None, usecols=cols, names=names)

# http://stackoverflow.com/questions/24458645/label-encoding-across-multiple-columns-in-scikit-learn    
df = data.apply(LabelEncoder().fit_transform)

X, Y = np.split(df, [11], axis=1)
X_train, X_validate, Y_train, Y_validateActual = train_test_split(X, Y, test_size=0.2, random_state=32)

forest = RandomForestClassifier(n_estimators = 100)
forest = forest.fit( X_train, Y_train )

Y_predicted=forest.predict(X_validate)