RandomForestClassifier训练时特征名称类型不统一的报错解决

问题场景 #

处理SMSSpamCollection.tsv短信数据时，生成包含body_len、punct%特征和TF-IDF向量特征的X_features数据集，拆分训练测试集后训练RandomForestClassifier，触发如下错误：

TypeError: Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types.
If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example.
Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.

错误原因 #

你的X_features是拼接两部分生成的：

• 前两列来自原数据集，列名是字符串类型：'body_len'、'punct%'
• 后面的TF-IDF特征列由稀疏矩阵转成DataFrame生成，默认列名是整数类型（0、1、2...）
  sklearn新版本对特征名称的类型一致性有严格校验，混合int和str类型的列名会触发该错误。

解决方法 #

方案1：统一所有列名为字符串类型（推荐） #

在生成X_features后，添加一行代码将所有列名转为字符串，既满足校验要求，又能保留特征名称用于后续分析（比如查看特征重要性）：

X_features = pd.concat([data['body_len'], data['punct%'], pd.DataFrame(X_tfidf.toarray())], axis=1)
# 新增该行，统一列名为字符串类型
X_features.columns = X_features.columns.astype(str)
X_features.head()

方案2：移除特征名称，直接传入数值矩阵 #

如果不需要保留特征名称，可以在训练时直接传入数值数组，跳过列名校验：

# 训练时用.values或.to_numpy()获取纯数值矩阵
rf_model = rf.fit(X_train.values, y_train)

原始数据处理代码 #

import nltk
import pandas as pd
import re
from sklearn.feature_extraction.text import TfidfVectorizer
import string

stopwords = nltk.corpus.stopwords.words('english')
ps = nltk.PorterStemmer()

data = pd.read_csv("SMSSpamCollection.tsv", sep='\t')
data.columns = ['label', 'body_text']

def count_punct(text):
    count = sum([1 for char in text if char in string.punctuation])
    return round(count/(len(text) - text.count(" ")), 3)*100

data['body_len'] = data['body_text'].apply(lambda x: len(x) - x.count(" "))
data['punct%'] = data['body_text'].apply(lambda x: count_punct(x))

def clean_text(text):
    text = "".join([word.lower() for word in text if word not in string.punctuation])
    tokens = re.split('\W+', text)
    text = [ps.stem(word) for word in tokens if word not in stopwords]
    return text

tfidf_vect = TfidfVectorizer(analyzer=clean_text)
X_tfidf = tfidf_vect.fit_transform(data['body_text'])

X_features = pd.concat([data['body_len'], data['punct%'], pd.DataFrame(X_tfidf.toarray())], axis=1)
X_features.head()

原始训练代码 #

from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.model_selection import train_test_split


X_train, X_test, y_train, y_test = train_test_split(X_features, data['label'], test_size=0.2)


from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=50, max_depth=20, n_jobs=-1)
rf_model = rf.fit(X_train, y_train)

内容的提问来源于stack exchange，提问作者Century Egg