[英]Impute missing values for testing set
I used adult data here , impute missing values for training data, while I want to apply the same number I get from training data to test data. 我在这里使用了成人数据 ,为训练数据估算了缺失值,而我想将从训练数据中获得的相同数字应用于测试数据。 I must miss something and cannot get it right.
我必须错过一些东西,不能做对。 My code is as following:
我的代码如下:
import numpy as np
import pandas as pd
from sklearn.base import TransformerMixin
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
features = ['age','workclass','fnlwgt','education','educationNum','maritalStatus','occupation','relationship','race','sex','capitalGain','capitalLoss','hoursPerWeek','nativeCountry']
x_train = train[list(features)]
y_train = train['class']
x_test = test[list(features)]
y_test = test['class']
class DataFrameImputer(TransformerMixin):
def _init_(self):
"""Impute missing values.
Columns of dtype object are imputed with the most frequent value in column.
columns of other types are imputed with mean of column"""
def fit(self, X, y=None):
self.fill = pd.Series([X[c].value_counts().index[0]
if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],
index=X.columns)
return self
def transform(self, X, y=None):
return X.fillna(self.fill)
# 2 step transformation, fit and transform
# -------Impute missing values-------------
x_train = pd.DataFrame(x_train) # x_train is class
x_test = pd.DataFrame(x_test)
x_train_new = DataFrameImputer().fit_transform(x_train)
x_train_new = pd.DataFrame(x_train_new)
# use same value fitted training data to fit test data
for c in x_test:
if x_test[c].dtype==np.dtype('O'):
x_test.fillna(x_train[c].value_counts().index[0])
else:
x_test.fillna(x_train[c].mean(),inplace=True)
We want to use what we get from training data, apply it to test data, in the previous piece of code, the loop doesn't work, the first column is a column of numbers, so it will fill all the NaNs in the test data as the mean of the first column of training data. 我们想使用从训练数据中获得的信息,将其应用于测试数据,在前面的代码中,循环不起作用,第一列是一列数字,因此它将填充测试中的所有NaN数据作为训练数据第一列的平均值。 Instead, if I use fillna with values, here values is a dictionary, test data will match training data according to categories.
相反,如果我使用带值的fillna,这里的值是一个字典,则测试数据将根据类别匹配训练数据。
values = {} #declare dict
for c in x_train:
if x_train[c].dtype==np.dtype('O'):
values[c]=x_train[c].value_counts().index[0]
else:
values[c]=x_train[c].mean()
values.update({c:values[c]})
x_test_new = x_test.fillna(value=values)
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