kaggle-kernel packages

这篇博客记录了在Kaggle kernel环境中运行时加载的各种Python包及其警告和弃用信息。其中包括了Bio库的弃用警告,IPython和matplotlib的相关警告,以及一些未安装或过时的库如clusterpy、tensorflow和Keras等的提示。此外,还列出了环境中可用的其他库,如numpy、pandas和scikit-learn等。

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/opt/conda/lib/python3.6/site-packages/Bio/PopGen/Async/__init__.py:17: BiopythonDeprecationWarning: Bio.PopGen.Async has been deprecated, and we intend to remove it in a future release of Biopython. If you would like to continue using it, please contact the Biopython developers via the mailing list.
  " via the mailing list.", BiopythonDeprecationWarning)
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/opt/conda/lib/python3.6/site-packages/Bio/PopGen/FDist/__init__.py:28: BiopythonDeprecationWarning: Bio.PopGen.FDist has been deprecated, and we intend to remove it in a future release of Biopython. If you would like to continue using it, please contact the Biopython developers via the mailing list.
  " via the mailing list.", BiopythonDeprecationWarning)
/opt/conda/lib/python3.6/site-packages/Bio/PopGen/SimCoal/__init__.py:17: BiopythonDeprecationWarning: Bio.PopGen.SimCoal has been deprecated, and we intend to  remove it in a future release of Biopython. If you would like to continue using it, please contact the Biopython developers via the mailing list.
  " via the mailing list.", BiopythonDeprecationWarning)
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/opt/conda/lib/python3.6/site-packages/Bio/SearchIO/__init__.py:211: BiopythonExperimentalWarning: Bio.SearchIO is an experimental submodule which may undergo significant changes prior to its future official release.
  BiopythonExperimentalWarning)
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/opt/conda/lib/python3.6/site-packages/Bio/codonalign/__init__.py:31: BiopythonExperimentalWarning: Bio.codonalign is an experimental module which may undergo significant changes prior to its future official release.
  BiopythonExperimentalWarning)
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/opt/conda/lib/python3.6/site-packages/Bio/phenotype/__init__.py:102: BiopythonExperimentalWarning: Bio.phenotype is an experimental submodule which may undergo significant changes prior to its future official release.
  BiopythonExperimentalWarning)
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/opt/conda/lib/python3.6/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.
  "You should import from ipykernel or jupyter_client instead.", ShimWarning)
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/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.


  warnings.warn(_use_error_msg)
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/opt/conda/lib/python3.6/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.
  warnings.warn(message, mplDeprecation, stacklevel=1)
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/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning:  This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported f
### 如何在Kaggle上使用特定的训练包进行机器学习模型训练 #### 登录并创建新Notebook 要在Kaggle平台上使用特定的Python库或R包来进行机器学习模型训练,首先需要完成登录过程[^1]。之后可以在Kernels页面点击“New Notebook”,从而进入Jupyter风格的工作环境。 #### 安装额外软件包 对于那些不在默认环境中预先安装好的第三方库,可以通过运行`!pip install package_name`命令来临时安装所需的Python包;如果是R语言,则可使用`install.packages('package')`语句实现相同目的。需要注意的是,这些更改仅适用于当前会话期间,并不会影响其他用户的Kernel实例或者持久化到平台的基础镜像中。 ```python # Python example of installing a new library within the Kaggle kernel environment. !pip install some-python-package --quiet ``` #### 导入必要的模块 一旦确认所需工具已经可用,下一步就是导入它们以便后续操作: ```python import pandas as pd from sklearn.model_selection import train_test_split, KFold from sklearn.metrics import accuracy_score from keras.models import Sequential from keras.layers import Dense ``` 上述代码片段展示了如何加载Pandas用于数据处理、Scikit-Learn支持的数据分割功能以及构建简单的前馈神经网络所需要的Keras组件。 #### 数据准备与预处理 按照惯例,在实际建模之前应当先准备好干净整齐的数据集。这通常涉及到读取原始文件、清理异常值、填补缺失项等一系列步骤。此外,还应该考虑执行标准化/归一化转换以提高某些算法的表现效果。 #### 构建和配置模型架构 定义好输入层尺寸后可以根据具体应用场景灵活调整隐藏层数量及其单元数,同时指定激活函数类型等超参数设置。例如,当面对二分类任务时可以选择sigmoid作为最后一层输出节点处应用的非线性映射关系。 #### 训练流程控制 借助于内置回调机制(Callback),能够方便地监控整个迭代过程中各项指标的变化趋势,进而采取适当措施防止过早停止现象发生或是保存最佳权重副本供最终部署阶段调用。另外,由于题目提到想要增大batch size却受限于本地硬件条件不足的情况,此时正好可以充分利用云端资源的优势——即允许设定较大的批量大小而不用担心内存溢出风险。 #### 验证策略实施 考虑到泛化能力的重要性,建议采用交叉验证方法论[K折交叉验证](https://en.wikipedia.org/wiki/Cross-validation_(statistics))对候选方案进行全面检验。这种方式有助于更稳定可靠地估计未知样本上的预期误差范围,同时也便于识别潜在模式偏差问题的存在与否[^3]。 #### 性能评估标准确立 最后一步则是挑选合适的度量准则用来量化预测质量的好坏程度。针对不同的业务需求可能会有不同的侧重点,比如精确率(Precision)、召回率(Recall)或者是F1 Score等等。当然也可以综合多个方面给出更加全面的成绩报告单。
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