Question

我有以下代码实现了scikit-learn的决策树分类器：

import numpy as np
import pandas as pd
from sklearn import tree

# #---------------------------------------------------------------------------------------------------
with open('data/training.csv', 'r') as f:

    df = pd.read_csv(f, index_col=None)

Subset = df.iloc[:, 32:33]  # Just the labels
df['Num_Labels'] = df.Label.map(lambda x: '-1' if x == 's' else '1')  # Convert labels to '0' or '1'.

Z = df.iloc[:, 32:34]  # the letter labels & numerical labels
Train_values = df.iloc[:, 1:31].values
Train_labels = df.iloc[:, 33:34].values

with open('data/test.csv', 'r') as f2:

    df2 = pd.read_csv(f2, index_col=None)

Test_values = df2.iloc[:, 1:31].values

# #----------------------------------------------------------------------------------------------

X = Train_values
Y = Train_labels.astype(np.float)

print X.dtype
print Y.dtype

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

Pred = clf.predict(Test_values)

print Pred.dtype

Out = Pred.astype(np.float)
np.savetxt('Output_Numerical.csv', Out, delimiter=' ')

到目前为止，代码按预期工作。然而，之后我想将标签转换回原来的字符值，＆＃39; s＆＃39;并且＆＃39; h＆＃39;。我写了以下内容：

Out2 = Pred.astype(str)  # Initialize

print "Out2's type is:"
print Out2.dtype

for i in range(0, len(Out)):
    if Out[i] == -1:
        Out2[i] == 's'
    else:
        Out2[i] == 'h'

print Out2

但它并没有改变Out2的值。

Answer 1

这很简单，即使错误不是你想的那样：

for i in range(0, len(Out)):
    if Out[i] == -1:
        Out2[i] == 's'
    else:
        Out2[i] == 'h'

在过去两次使用单=而不是==！现在发生的是声明Out2[1] == 's'等于False，没有人有兴趣使用它。因此，它不是一个非法的构造，并且口译员没有理由抱怨它。

在Python中将整数转换为char

1 个答案: