ML模型无法估算值 [英] ML model is failing to impute values

问题描述

我试图创建一个ML模型来做出一些预测，但是我一直遇到绊脚石.即，代码似乎忽略了我给它的插补指令，从而导致以下错误:

I've tried creating an ML model to make some predictions, but I keep running into a stumbling block. Namely, the code seems to be ignoring the imputation instructions I give it, resulting in the following error:

ValueError:输入包含NaN，无穷大或对于dtype('float64')而言太大的值.

这是我的代码:

import pandas as pd
import numpy as np
from sklearn.ensemble import AdaBoostRegressor
from category_encoders import CatBoostEncoder
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer

data = pd.read_csv("data.csv",index_col=("Unnamed: 0"))
y = data.Installs
x = data.drop("Installs",axis=1)


strat = ["mean","median","most_frequent","constant"]
num_imp = SimpleImputer(strategy=strat[0])
obj_imp = SimpleImputer(strategy=strat[2])

# Set up the scaler
sc = StandardScaler()

# Set up Encoders
cb = CatBoostEncoder()
oh = OneHotEncoder(sparse=True)


# Set up columns
obj = list(x.select_dtypes(include="object"))
num = list(x.select_dtypes(exclude="object"))


cb_col = [i for i in obj if len(x[i].unique())>30]
oh_col = [i for i in obj if len(x[i].unique())<10]

# First Pipeline
imp = make_pipeline((num_imp))
enc_cb = make_pipeline((obj_imp),(cb))
enc_oh = make_pipeline((obj_imp),(oh))

# Col Transformation
col = make_column_transformer((imp,num),
                              (sc,num),
                              (enc_oh,oh_col),
                              (enc_cb,cb_col))
model = AdaBoostRegressor(random_state=(0))

run = make_pipeline((col),(model))
run.fit(x,y)

这是链接在代码中用于复制目的.你能说出什么问题吗?谢谢您的宝贵时间.

And here's a link to the data used in the code for reproduction purposes. Can you tell what's wrong? Thanks for your time.

推荐答案

您的数字缩放转换器可能是一个抱怨:​​您没有在应用 StandardScaler 之前进行估算.可能您想要这样的东西:

Your numeric scaling transformer is probably the one complaining: you haven't imputed before the StandardScaler is applied. Probably you wanted something like this:

imp_sc = make_pipeline((num_imp),(sc))

# Col Transformation
col = make_column_transformer((imp_sc,num),
                              (enc_oh,oh_col),
                              (enc_cb,cb_col))

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