将 CSV 索引到不一致的样本数量以进行逻辑回归

Posted 2023-03-12

【中文标题】将 CSV 索引到不一致的样本数量以进行逻辑回归

【英文标题】：Indexing a CSV running into inconsistent number of samples for logistic regression

【发布时间】：2017-11-09 19:22:48

【问题描述】：

我目前正在索引具有以下值的 CSV 并遇到错误：

ValueError：发现输入变量的数量不一致 样本：[1, 514]

将其作为 1 行 514 列进行检查，强调我错误地调用了特定参数，或者是因为我删除了 NaN（大多数数据默认为？）

"Classification","DGMLEN","IPLEN","TTL","IP"
"1","0.000000","192.168.1.5","185.60.216.35","TLSv1.2"
"2","0.000160","192.168.1.5","185.60.216.35","TCP"
"3","0.000161","192.168.1.5","185.60.216.35","TLSv1.2"


import pandas  
df = pandas.read_csv('wcdemo.csv', header=0,
                  names = ["Classification", "DGMLEN", "IPLEN", "TTL", "IP"], 
                  na_values='.')

df = df.apply(pandas.to_numeric, errors='coerce')
#Data=pd.read_csv ('wcdemo.csv').reset_index()#index_col='false')
feature_cols=['Classification','DGMLEN','IPLEN','IP']

X=df[feature_cols]


    #datanewframe = pandas.Series(['Classification', 'DGMLEN', 'IPLEN', 'TTL', 'IP'], dtype='object')

#df = pandas.read_csv('wcdemo.csv')
#indexed_df = df.set_index(['Classification', 'DGMLEN','IPLEN','TTL','IP']


df['IPLEN'] = pandas.to_numeric(df['IPLEN'], errors='coerce').fillna(0)
df['TTL'] = pandas.to_numeric(df['TTL'], errors='coerce').fillna(0)

#DEFINE X TRAIN
X_train = df['IPLEN']
y_train = df['TTL']

#s = pandas.Series(['Classification', 'DGMLEN', 'IPLEN', 'TTL', 'IP'])

Y=df['TTL'] 

from sklearn.linear_model import LogisticRegression

logreg=LogisticRegression()
logreg.fit(X_train,y_train,).fillna(0.0)

#with the error being triggered here 
logreg.fit(X_train,y_train,).fillna(0.0)

【参考方案1】：

由于您的 X_train 中只有 1 个特征，其当前形状为 (n_samples,)。但是 scikit 估计器要求 X 的形状为(n_samples, n_features)。所以你需要重塑你的数据。

使用这个：

logreg.fit(X_train.reshape(-1,1), y_train).fillna(0.0)