python - 值错误 : could not convert string to float: med

Question

我正在编写一个非常简单的脚本。我所要做的就是使用 Pandas 读取数据，然后根据数据训练决策树。我使用的数据是:

https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data

下面是我的脚本

import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn import preprocessing
import pandas as pd
balance_data=pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data",
                           sep= ',', header= None)
#print "Dataset:: "

#df1.head()

X = balance_data.values[:, 0:5]
Y = balance_data.values[:,6]
X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.2, random_state = 100)
clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100,
                               max_depth=3, min_samples_leaf=5)

clf_gini.fit(X_train, y_train)

根据错误，我猜测它无法将“med”属性值转换为 float 。通过查看数据，我随机猜测 low 前面有一个空格，而 med 没有。这就是它变得困惑的原因。但我不确定。请告诉它可能有什么问题。PS:错误发生在最后一行，这里是回溯

ValueError                                Traceback (most recent call last)
<ipython-input-26-b495e5f26174> in <module>()
     18                                max_depth=3, min_samples_leaf=5)
     19 X_train[X_train != '']
---> 20 clf_gini.fit(X_train, y_train)

/home/fatima/anaconda2/lib/python2.7/site-packages/sklearn/tree/tree.pyc in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    788             sample_weight=sample_weight,
    789             check_input=check_input,
--> 790             X_idx_sorted=X_idx_sorted)
    791         return self
    792 

/home/fatima/anaconda2/lib/python2.7/site-packages/sklearn/tree/tree.pyc in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    114         random_state = check_random_state(self.random_state)
    115         if check_input:
--> 116             X = check_array(X, dtype=DTYPE, accept_sparse="csc")
    117             y = check_array(y, ensure_2d=False, dtype=None)
    118             if issparse(X):

/home/fatima/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.pyc in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    400                                       force_all_finite)
    401     else:
--> 402         array = np.array(array, dtype=dtype, order=order, copy=copy)
    403 
    404         if ensure_2d:

ValueError: could not convert string to float: med

Answer 1

数据集如下所示:

       0      1  2  3      4     5      6
0  vhigh  vhigh  2  2  small   low  unacc
1  vhigh  vhigh  2  2  small   med  unacc
2  vhigh  vhigh  2  2  small  high  unacc
3  vhigh  vhigh  2  2    med   low  unacc
4  vhigh  vhigh  2  2    med   med  unacc

其中数据类型(dtypes)都是对象。然而，机器学习算法只能从数字(int、float、doubles ..)中学习，因此，您需要在将数据用于训练之前对其进行编码。

有几种方法可以对您的数据进行编码，一种方法是使用标签编码，为此，在加载数据集后将以下行添加到您的代码中:

le = preprocessing.LabelEncoder()
balance_data = balance_data.apply(le.fit_transform)

现在 balance_data 中的数据如下所示:

   0  1  2  3  4  5  6
0  3  3  0  0  2  1  2
1  3  3  0  0  2  2  2
2  3  3  0  0  2  0  2
3  3  3  0  0  1  1  2
4  3  3  0  0  1  2  2

其中所有数据类型都是 int。

通常，您需要在训练/拟合模型之前执行一些数据预处理。为此，我建议您通过一些教程来了解该过程。例如，检查这个:

• practical-guide-data-preprocessing-python-scikit-learn

---

这是修复后的整体代码:

import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn import preprocessing
import pandas as pd
balance_data=pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data",
                           sep= ',', header= None)
#print "Dataset:: "

#df1.head()

le = preprocessing.LabelEncoder()
balance_data = balance_data.apply(le.fit_transform)

X = balance_data.values[:, 0:5]
Y = balance_data.values[:,6]
X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.2, random_state = 100)
clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100,
                               max_depth=3, min_samples_leaf=5)

clf_gini.fit(X_train, y_train)