[英]Bag of Words encoding for Python with vocabulary
I am trying to implement new columns into my ML model.我正在尝试在我的 ML 模型中实现新列。 A numeric column should be created if a specific word is found in the text of the scraped data.
如果在抓取数据的文本中找到特定单词,则应创建数字列。 For this I created a dummy script for testing.
为此,我创建了一个虚拟脚本进行测试。
import pandas as pd
bagOfWords = ["cool", "place"]
wordsFound = ""
mystring = "This is a cool new place"
mystring = mystring.lower()
for word in bagOfWords:
if word in mystring:
wordsFound = wordsFound + word + " "
print(wordsFound)
pd.get_dummies(wordsFound)
The output is输出是
cool place
0 1
This means there is one sentence "0" and one entry of "cool place".这意味着有一个句子“0”和一个条目“cool place”。 This is not correct.
这是不正确的。 Expectations would be like this:
期望是这样的:
cool place
0 1 1
Found a different solution, as I cound not find any way forward.找到了一个不同的解决方案,因为我找不到任何前进的道路。 Its a simple direct hot encoding.
它是一种简单的直接热编码。 For this I enter for every word I need a new column into the dataframe and create the encoding directly.
为此,我为每个需要在数据框中添加一个新列的单词输入并直接创建编码。
vocabulary = ["achtung", "suchen"]
for word in vocabulary:
df2[word] = 0
for index, row in df2.iterrows():
if word in row["title"].lower():
df2.set_value(index, word, 1)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.