我正在尝试阅读Amazon S3上可用的文件,因为问题解释了问题.我找不到已弃用的构造函数的替代调用.
这是代码:
private String AccessKeyID = "xxxxxxxxxxxxxxxxxxxx";
private String SecretAccessKey = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx";
private static String bucketName = "documentcontainer";
private static String keyName = "test";
//private static String uploadFileName = "/PATH TO FILE WHICH WANT TO UPLOAD/abc.txt";
AWSCredentials credentials = new BasicAWSCredentials(AccessKeyID, SecretAccessKey);
void downloadfile() throws IOException
{
// Problem lies here - AmazonS3Client is deprecated
AmazonS3 s3client = new AmazonS3Client(credentials);
try {
System.out.println("Downloading an object...");
S3Object s3object = s3client.getObject(new GetObjectRequest(
bucketName, keyName));
System.out.println("Content-Type: " +
s3object.getObjectMetadata().getContentType());
InputStream input = …由于问题解释了问题,我一直在尝试生成嵌套的JSON对象.在这种情况下,我有for循环从字典中获取数据dic.以下是代码:
f = open("test_json.txt", 'w')
flag = False
temp = ""
start = "{\n\t\"filename\"" + " : \"" +initial_filename+"\",\n\t\"data\"" +" : " +" [\n"
end = "\n\t]" +"\n}"
f.write(start)
for i, (key,value) in enumerate(dic.iteritems()):
f.write("{\n\t\"keyword\":"+"\""+str(key)+"\""+",\n")
f.write("\"term_freq\":"+str(len(value))+",\n")
f.write("\"lists\":[\n\t")
for item in value:
f.write("{\n")
f.write("\t\t\"occurance\" :"+str(item)+"\n")
#Check last object
if value.index(item)+1 == len(value):
f.write("}\n"
f.write("]\n")
else:
f.write("},") # close occurrence object
# Check last item in dic
if i == len(dic)-1:
flag = True
if(flag):
f.write("}")
else:
f.write("},") …这不是一个新问题，我找到了没有任何解决方案的参考文献first和second。我是 PyTorch 的新手，AttributeError: 'Field' object has no attribute 'vocab'在PyTorch使用torchtext.
继本书之后，Deep Learning with PyTorch我编写了与书中解释的相同的示例。
这是片段：
from torchtext import data
from torchtext import datasets
from torchtext.vocab import GloVe
TEXT = data.Field(lower=True, batch_first=True, fix_length=20)
LABEL = data.Field(sequential=False)
train, test = datasets.IMDB.splits(TEXT, LABEL)
print("train.fields:", train.fields)
print()
print(vars(train[0])) # prints the object
TEXT.build_vocab(train, vectors=GloVe(name="6B", dim=300),
max_size=10000, min_freq=10)
# VOCABULARY
# print(TEXT.vocab.freqs) # freq
# print(TEXT.vocab.vectors) # vectors
# print(TEXT.vocab.stoi) # Index
train_iter, test_iter …我尝试对我的数据使用 dask DummyEncoderOneHotEncoding。但结果并不如预期。
dask 的 DummyEncoder 示例：
from dask_ml.preprocessing import DummyEncoder
import pandas as pd
data = pd.DataFrame({
'B': ['a', 'a', 'a', 'b','c']
})
de = DummyEncoder()
de = de.fit(data)
testD = pd.DataFrame({'B': ['a','a']})
trans = de.transform(testD)
print(trans)
输出：
B_a
0 1
1 1
为什么不显示B_b，B_c？但是当我将其更改testD为：
testD = pd.DataFrame({'B': ['a','a', 'b', 'c']})
结果是：
B_a B_b B_c
0 1 0 0
1 1 0 0
2 0 1 0
3 0 0 1
sklearn …