scikit学习逻辑回归模型tfidfvectoriz

Question

我正在尝试使用scikit learn创建一个logistic回归模型，代码如下。我用9列表示特性（X）和一列表示标签（Y）。当尝试拟合时，我得到一个错误“ValueError:找到的输入变量的样本数不一致：[9560000]”即使以前X和Y的长度相同，如果我使用X.transpose（）我会得到一个不同的错误“AttributeError:'int'object has no attribute'lower'”。我假设这可能与tfidfvectorizer有关，我这样做是因为其中3列包含单个单词，并且不起作用。这是正确的方法吗？还是应该分别转换列中的单词，然后使用train_test_split？如果不是的话，我为什么会犯错误，我怎么才能把它们弄错呢。这里有一个csv的例子。

df = pd.read_csv("UNSW-NB15_1.csv",header=None, names=cols, encoding = "UTF-8",low_memory=False) 

df.to_csv('netraf.csv')
csv = 'netraf.csv'
my_df = pd.read_csv(csv)

x_features = my_df.columns[1:10]
x_data = my_df[x_features]
Y = my_df["Label"]

x_train, x_validation, y_train, y_validation = 
model_selection.train_test_split(x_data, Y, test_size=0.2, random_state=7)

tfidf_vectorizer = TfidfVectorizer()
lr = LogisticRegression()
tfidf_lr_pipe = Pipeline([('tfidf', tfidf_vectorizer), ('lr', lr)])

tfidf_lr_pipe.fit(x_train, y_train)  

Answer 1

您要做的是不同寻常的，因为TfidfVectorizer是用来从文本中提取数字特征的。但是，如果您并不真正关心并且只想让您的代码正常工作，一种方法是将您的数字数据转换为字符串并配置TfidfVectorizer以接受标记化的数据：

import pandas as pd
from sklearn import model_selection
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

cols = ['srcip','sport','dstip','dsport','proto','service','smeansz','dmeansz','attack_cat','Label']
df = pd.read_csv("UNSW-NB15_1.csv",header=None, names=cols, encoding = "UTF-8",low_memory=False) 

df.to_csv('netraf.csv')
csv = 'netraf.csv'
my_df = pd.read_csv(csv)

# convert all columns to string like we don't care
for col in my_df.columns:
    my_df[col] = my_df[col].astype(str)

# replace nan with empty string like we don't care
for col in my_df.columns[my_df.isna().any()].tolist():
    my_df.loc[:, col].fillna('', inplace=True)

x_features = my_df.columns[1:10]
x_data = my_df[x_features]
Y = my_df["Label"]

x_train, x_validation, y_train, y_validation = model_selection.train_test_split(
    x_data.values, Y.values, test_size=0.2, random_state=7)

# configure TfidfVectorizer to accept tokenized data
# reference http://www.davidsbatista.net/blog/2018/02/28/TfidfVectorizer/
tfidf_vectorizer = TfidfVectorizer(
    analyzer='word',
    tokenizer=lambda x: x,
    preprocessor=lambda x: x,
    token_pattern=None)

lr = LogisticRegression()
tfidf_lr_pipe = Pipeline([('tfidf', tfidf_vectorizer), ('lr', lr)])
tfidf_lr_pipe.fit(x_train, y_train)

尽管如此，我建议您使用另一种方法对数据集进行功能工程。例如，您可以尝试将to encode your nominal data（例如IP，port）转换为数值。在