标签: kaggle

我正在将以下Kaggle代码翻译成Python3.4:

在输出CSV文件的最后一行中,

predictions_file = open("myfirstforest.csv", "wb")
open_file_object = csv.writer(predictions_file)
open_file_object.writerow(["PassengerId","Survived"])
open_file_object.writerows(zip(ids, output))
predictions_file.close()
print('Done.')

有一个类型错误

TypeError: 'str' does not support the buffer interface

发生在线上open_file_object.writerow(["PassengerId","Survived"]).

我相信这是因为在二进制模式打开文件写入CSV数据不会出现在Python 3的工作.然而,增加encoding='utf8'的open()行也不行.

在Python3.4中执行此操作的标准方法是什么？

这个问题真的很奇怪,因为这件事对其他数据集来说效果很好.

完整代码:

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.cross_validation import train_test_split

# # Split the Learning Set
X_fit, X_eval, y_fit, y_eval= train_test_split(
    train, target, test_size=0.2, random_state=1
)

clf = xgb.XGBClassifier(missing=np.nan, max_depth=6, 
                        n_estimators=5, learning_rate=0.15, 
                        subsample=1, colsample_bytree=0.9, seed=1400)

# fitting
clf.fit(X_fit, y_fit, early_stopping_rounds=50, eval_metric="logloss", eval_set=[(X_eval, y_eval)])
#print y_pred
y_pred= clf.predict_proba(test)[:,1]

最后一行导致下面的错误(提供完整输出):

Will train until validation_0 error hasn't decreased in 50 rounds.
[0] validation_0-logloss:0.554366
[1] validation_0-logloss:0.451454
[2] validation_0-logloss:0.372142
[3] validation_0-logloss:0.309450
[4] validation_0-logloss:0.259002
Traceback (most recent call …

我一直在使用Jupyter Notebook 从kaggle学习主成分分析),但是当我运行这段代码时

     from subprocess import check_output
     print(check_output(["ls", "../input"]).decode("utf8"))

我在下面收到了一个错误

FileNotFoundError Traceback (most recent call last)
<ipython-input-3-de0e39ca3ab8> in <module>()
      1 from subprocess import check_output
----> 2 print(check_output(["ls", "C:/Users/wanglei/Documents/input"]).decode("utf8"))

D:\Anaconda3\lib\subprocess.py in check_output(timeout, *popenargs, **kwargs)
    624 
    625     return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
--> 626                **kwargs).stdout
    627 
    628 

D:\Anaconda3\lib\subprocess.py in run(input, timeout, check, *popenargs, **kwargs)
    691         kwargs['stdin'] = PIPE
    692 
--> 693     with Popen(*popenargs, **kwargs) as process:
    694         try:
    695             stdout, stderr = process.communicate(input, timeout=timeout)

D:\Anaconda3\lib\subprocess.py in __init__(self, args, bufsize, …

我安装了kaggle-cli就好......一切顺利,或者pip说.

但是,当我尝试运行kg命令或只是kg --version

我明白了 kg command not found

我可以在python系统包中看到kaggle,所有py和pyc文件都在那里.但是没有bin目录或任何东西.

试图在网上找到类似的问题没有成功 - 所以我想在这里试试.

我在Windows 10的ubuntu应用程序上.对于我的机器学习装备,python,keras,theano..etc等其他所有工作都很好.

我正在尝试使用 Kaggle API 从 kaggle 网站提取数据。我使用的是 Ubuntu 18.04。当我输入以下命令时：

kaggle competitions download -c home-credit-default-risk

我收到以下错误：

Traceback (most recent call last):
  File "/home/hduser/anaconda3/bin/kaggle", line 5, in <module>
    from kaggle.cli import main
  File "/home/hduser/anaconda3/lib/python3.7/site-packages/kaggle/__init__.py", line 23, in <module>
    api.authenticate()
  File "/home/hduser/anaconda3/lib/python3.7/site-packages/kaggle/api/kaggle_api_extended.py", line 149, in authenticate
    self.config_file, self.config_dir))
OSError: Could not find kaggle.json. Make sure it's located in /home/hduser/.kaggle/kaggle.json. Or use the environment method.

这很奇怪，因为我将 kaggle.json 文件保存在正确的目录中，并且我拥有为其授予的所有权限。

ls ~/.kaggle

返回：

kaggle.json

我在这里错过了什么吗？

我的数据集来自Kaggle 的MNIST

我正在尝试使用该image功能可视化说出训练集中的第一个数字.不幸的是我收到以下错误:

>image(1:28, 1:28, im, col=gray((0:255)/255))
Error in image.default(1:28, 1:28, im, col = gray((0:255)/255)) : 
'z' must be numeric or logical

添加几个代码:

rawfile<-read.csv("D://Kaggle//MNIST//train.csv",header=T) #Reading the csv file
im<-matrix((rawfile[1,2:ncol(rawfile)]), nrow=28, ncol=28) #For the 1st Image

image(1:28, 1:28, im, col=gray((0:255)/255))

Error in image.default(1:28, 1:28, im, col = gray((0:255)/255)) : 
'z' must be numeric or logical

在使用Kaggle笔记本时,我遇到了一个问题.以下代码块:

from nltk import ngrams
def grams(tokens):
    return list(ngrams(tokens, 3))
negative_grams = preprocessed_negative_tweets.apply(grams)

结果出现了一个红色的盒子

/opt/conda/bin/ipython:5: DeprecationWarning: generator 'ngrams' raised StopIteration

该变量preprocessed_negative_tweets是包含令牌的Pandas数据帧.

有谁知道如何让它消失？

(这里有完整笔记本)

尝试使用该模块下载“Cats_vs_Dogs”TensorFlow 数据集时tfds，出现以下错误

DownloadError                             Traceback (most recent call last)
<ipython-input-2-244305a07c33> in <module>()
      7     split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
      8     with_info=True,
----> 9     as_supervised=True,
     10 )

21 frames
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/download/downloader.py in _assert_status(response)
    257   if response.status_code != 200:
    258     raise DownloadError('Failed to get url {}. HTTP code: {}.'.format(
--> 259         response.url, response.status_code))

DownloadError: Failed to get url https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip. HTTP code: 404.

我使用的代码是

import tensorflow_datasets as tfds
tfds.disable_progress_bar()

# split the data manually into 80% training, 10% testing, 10% validation
(raw_train, raw_validation, raw_test), metadata = …

我是 colab 的新手。我最近将数据集从 kaggle 下载到了 google colab。在我下次访问时，数据集不在那里，我的 kaggle 安装也被删除了。有谁知道为什么？

我试过在 /content 中查看我记得保存和编辑它的地方！

我设法使用 Kaggle API 从 Kaggle 下载数据集。数据存储在/databricks/driver目录下。

%sh pip install kaggle
%sh
export KAGGLE_USERNAME=my_name
export KAGGLE_KEY=my_key
kaggle competitions download -c ncaaw-march-mania-2021
%sh unzip ncaaw-march-mania-2021.zip

问题是：如何在 DBFS 中使用它们？以下是我读取数据的方式以及尝试使用pyspark读取csv文件时遇到的错误：

spark.read.csv('/databricks/driver/WDataFiles_Stage1/Cities.csv')
AnalysisException: Path does not exist: dbfs:/databricks/driver/WDataFiles_Stage1/Cities.csv