問題描述

當我嘗試將 svmlight python 包 與我已轉換為 svmlight 格式的數據一起使用時我得到一個錯誤.它應該是非常基本的，我不明白發生了什么.代碼如下:

When I try to use the svmlight python package with data I already converted to svmlight format I get an error. It should be pretty basic, I don't understand what's happening. Here's the code:

import svmlight
training_data = open('thedata', "w")
model=svmlight.learn(training_data, type='classification', verbosity=0)

我也試過了:

training_data = numpy.load('thedata')

和

training_data = __import__('thedata')

推薦答案

一個明顯的問題是您在打開數據文件時會截斷它，因為您指定了寫入模式 "w".這意味著將沒有要讀取的數據.

One obvious problem is that you are truncating your data file when you open it because you are specifying write mode "w". This means that there will be no data to read.

無論如何，如果您的數據文件類似于此 example，因為是python文件，所以需要導入.這應該有效:

Anyway, you don't need to read the file like that if your data file is like the one in this example, you need to import it because it is a python file. This should work:

import svmlight
from data import train0 as training_data    # assuming your data file is named data.py
# or you could use __import__()
#training_data = __import__('data').train0

model = svmlight.learn(training_data, type='classification', verbosity=0)

您可能希望將您的數據與示例的數據進行比較.

You might want to compare your data against that of the example.

數據文件格式明確后編輯

輸入文件需要被解析成這樣的元組列表:

The input file needs to be parsed into a list of tuples like this:

[(target, [(feature_1, value_1), (feature_2, value_2), ... (feature_n, value_n)]),
 (target, [(feature_1, value_1), (feature_2, value_2), ... (feature_n, value_n)]),
 ...
]

svmlight 包似乎不支持讀取 SVM 文件格式的文件，并且沒有任何解析功能，因此必須在 Python 中實現.SVM 文件如下所示:

The svmlight package does not appear to support reading from a file in the SVM file format, and there aren't any parsing functions, so it will have to be implemented in Python. SVM files look like this:

<target> <feature>:<value> <feature>:<value> ... <feature>:<value> # <info>

所以這里有一個解析器，可以將文件格式轉換為 svmlight 包所需的格式:

so here is a parser that converts from the file format to that required by the svmlight package:

def svm_parse(filename):

    def _convert(t):
        """Convert feature and value to appropriate types"""
        return (int(t[0]), float(t[1]))

    with open(filename) as f:
        for line in f:
            line = line.strip()
            if not line.startswith('#'):
                line = line.split('#')[0].strip() # remove any trailing comment
                data = line.split()
                target = float(data[0])
                features = [_convert(feature.split(':')) for feature in data[1:]]
                yield (target, features)

你可以這樣使用它:

import svmlight

training_data = list(svm_parse('thedata'))
model=svmlight.learn(training_data, type='classification', verbosity=0)

這篇關于加載 svmlight 格式錯誤的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！