不移除数据帧头的Sklearn随机林模型

Question

我试图将下面的数据输入到使用sklearn的随机森林算法中。

数据(作为csv提出)：

id,CAP,astroturf,fake_follower,financial,other,overall,self-declared,labels
3039154799,0.7828265255249504,0.1,1.8,1.4,3.2,1.4,0.4,1
390617262,1.0,0.8,1.4,1.0,5.0,5.0,0.2,0
4611389296,0.7334998320027682,0.2,0.6,0.1,1.8,1.1,0.0,1

我的代码：

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import numpy as np

master_training_set_path = "data_bank/cleaning_data/master_training_data_id/master_train_one_hot.csv"
df = pd.read_csv(master_training_set_path)
labels = np.array(df["labels"].values)

train, test, train_labels, test_labels = train_test_split(df, labels,
                                                      stratify=labels,
                                                      test_size=0.3)
model = RandomForestClassifier(n_estimators=100, bootstrap=True, max_features='sqrt')

# this is the problematic line
model.fit(train, train_labels)

有问题的行是最后一行，当我运行它时，它返回以下回溯：

Traceback (most recent call last):
  File "path\random_forest.py", line 39, in 
<module>
    model.fit(train, train_labels)
  File "path\sklearn\ensemble\forest.py", line 247, in fit
    X = check_array(X, accept_sparse="csc", dtype=DTYPE)
  File "path\sklearn\utils\validation.py", line 434, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)

ValueError: could not convert string to float: 'self-declared'

我已经尝试过确保'train‘和'train_label’变量是numpy 2d数组，但是我仍然得到相同的错误

我的困惑来自这样一个事实，即“自我声明”功能不是一个值，而是数据集中某个功能的名称。为什么滑雪板在训练数据之前不放弃头呢？

Answer 1

代码工作在scikit-学习版本：0.23.1上。如果您使用的是旧版本，则可以尝试更新以下内容：

conda install scikit-learn=0.23.1

问题可能是您将df提供给train_test_split。但是，这将为模型带来问题，因为train和test数据格式(带有标题)是创建的，而不是功能矩阵。因此，您可以尝试替换：

train, test, train_labels, test_labels = train_test_split(df, labels,
                                                      stratify=labels,
                                                      test_size=0.3)

在这方面：

df.drop(['labels'],axis=1,inplace=True) #you have labels in the training set as well.
train, test, train_labels, test_labels = train_test_split(df.values, labels,
                                                      stratify=labels,
                                                      test_size=0.3)