Every DAV inci
Every DAV inci

AutoGluon安装及示例

AutoGluon安装及示例

文章目录

  • AutoGluon安装及示例
    • AutoGluon_Background
    • AutoGluon安装
    • 运行示例
      • 打印相关配置信息
      • 数据预处理过程
      • 特征工程
      • 模型训练
    • NLP模型训练
      • 使用模型
    • AutoGluon模块功能
    • GPU加速
    • 包安装过程信息
    • 参考文档
    • 包安装过程信息
      • autogluon过程
      • autogluon.text过程

AutoGluon_Background

Autogluon的发明人是李沐（Mu Li）和他在亚利桑那州立大学的研究团队。

Autogluon是一种自动化机器学习工具, 可以同时支持两种模式：迁移学习模式和自动机器学习模式。

如果你已经训练好了一个模型，并且想在新的数据集上进行迁移学习，则可以使用Autogluon的迁移学习模式。在这种模式下，你可以将预先训练好的模型作为初始模型，然后使用新的数据集进行微调。Autogluon提供了API，可以将预训练的模型加载到迁移学习框架中，并进行微调和优化。

如果没有预训练的模型，想自动化地训练一个新的模型，则可以使用Autogluon的自动机器学习模式。在这种模式下，你只需要提供数据集，并指定一些基本的参数，Autogluon将自动执行整个机器学习过程，包括数据预处理、特征选择、模型选择和调整，从而为你生成一个最佳的模型。

Autogluon可以自动生成的模型任务类型包括:

  1. 二分类任务（Binary classification）：根据输入数据预测两个类别之一。
  2. 多分类任务（Multiclass classification）：根据输入数据预测多个类别之一。
  3. 回归任务（Regression）：根据输入数据预测数值型输出。
  4. 时间序列任务（Time series forecasting）：根据时间序列数据预测未来的趋势和趋势变化。
  5. 推荐任务（Recommendation）：根据用户行为历史和其他信息预测用户可能感兴趣的内容或商品。
  6. 图像分类任务（Image classification）：根据输入的图像数据对图像进行分类。
  7. 自然语言处理任务（Natural language processing）：根据输入的文本数据对文本进行分类或者生成。
  8. 异常检测任务（Anomaly detection）：根据输入的数据检测异常或者离群点。

AutoGluon安装

截至目前(2023.3.19), autoGluon现在最新的版本是0.7.0, 要求python最新的版本是3.8, 也可以是3.9或3.10

AutoGluon安装及示例_第1张图片

使用pip install autogluon进行安装.

在这之前最好使用anaconda对python环境进行管理, 并且在一个指定的环境中进行autogluon的安装

install过程中会安装大量的机器学习包, 安装过程有些缓慢, 是我安装的时间最长的一个包

AutoGluon安装及示例_第2张图片

运行示例

# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon  # autogluon==0.7.0

from autogluon.tabular import TabularDataset, TabularPredictor
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
predictor = TabularPredictor(label='class').fit(train_data, time_limit=120)  # Fit models for 120s
leaderboard = predictor.leaderboard(test_data)

PS: 以下的所有输出都是aotuGluon的功能输出

打印相关配置信息

AutoGluon安装及示例_第3张图片

数据预处理过程

image-20230319024635312

特征工程

AutoGluon安装及示例_第4张图片

模型训练

显示最优模型及各个模型的训练精度

AutoGluon安装及示例_第5张图片

NLP模型训练

要训练NLP任务, 就要用到autogluon.text包, 但是这个包不包含在autogluon包中

所以安装过autogluon包之后还要再安装autogluon.text这个包, 其中又有很多已经安装过的机器学习的包的重新安装

使用模型

Autogluon在进行文本分类任务时，会自动尝试使用多种模型，并根据数据的特点和性质自适应地选择最佳的模型。常用的模型包括：

  1. 词袋模型（Bag of Words）
  2. TF-IDF 模型
  3. FastText 模型
  4. BERT 模型
  5. RoBERTa 模型
  6. DistilBERT 模型
  7. ALBERT 模型
  8. XLNet 模型

Autogluon会自动进行模型超参数搜索和选择，以获得最佳的模型性能。

Autogluon在处理NLP任务时常用的模型：

  1. Bag-of-words (BoW) 模型：将文本转换为固定长度的向量表示，常用于文本分类等任务。

  2. 词袋模型加上n-gram特征： 在BoW模型的基础上，将相邻的n个词组成一个特征，增加了上下文信息，提高了模型性能。

  3. TF-IDF模型：在BoW的基础上，考虑了词频和文档频率，减小了一些常见但无关紧要的词对模型的影响。

  4. 词嵌入模型：将词映射为低维向量，保留了词之间的语义关系，常用于句子相似度、情感分析等任务。常见的词嵌入模型包括 Word2vec、GloVe 和 fastText 等。

  5. 卷积神经网络（CNN）模型：对于文本数据，可以将其看做一个1维信号，利用CNN提取特征，在文本分类和情感分析等任务中有良好的表现。

  6. 循环神经网络（RNN）模型：通过对历史输入进行迭代计算，可以自然地处理变长的序列数据，包括语言模型、机器翻译等任务。常见的RNN模型包括LSTM和GRU。

  7. 注意力机制（Attention）模型：通过动态地对输入进行加权，能够更好地捕捉文本中的关键信息，提高了模型的表现，在文本分类和情感分析等任务中有良好的表现。常见的注意力模型包括 Transformer 和 BERT。

chatGPT说的, 估计瞎扯了不少

AutoGluon模块功能

AutoGluon 被模块化为专门用于表格、文本或图像数据的子模块。您可以通过单独安装特定子模块来减少所需的依赖项数量：python3 -m pip install ，其中 可能是以下选项之一：

  • autogluon.tabular - 仅用于表格数据 (TabularPredictor)
  • autogluon.tabular独立的默认安装是一个骨架安装。
  • 通过 pip install autogluon.tabular[all]安装与通过 pip install autogluon安装相同
  • 可用的可选依赖项：lightgbm、catboost、xgboost、fastai。这些都包括在内。
  • 实验性可选依赖项：skex。这将使 KNN 模型在 CPU 上的训练和推理速度提高 25 倍。使用 pip install autogluon.tabular[all,skex]启用，或在标准安装 AutoGluon 后 pip install “scikit-learn-intelex<2021.3”
  • autogluon.vision - 仅用于计算机视觉（ImagePredictor、ObjectDetector）
  • autogluon.text - 仅用于自然语言处理 (TextPredictor)
  • autogluon.core - 仅对任意代码/模型的超参数调整有用的核心功能（Searcher/Scheduler）。
  • autogluon.features - 仅用于特征生成/特征预处理管道的功能（主要与表格数据相关）。
  • autogluon.extra - 各种额外功能，例如Efficient Neural Architecture Search
  • autogluon.mxnet - MXNet 的杂项额外功能。

GPU加速

  • 安装 CUDA

    安装适合的CUDA版本的GPU驱动程序和CUDA工具包, 从 NVIDIA 官方网站上下载

    安装完成后，使用nvcc --version命令检查CUDA版本

    使用nvidia-smi查看GPU使用信息

  • [torch GPU cuda11.6安装](win11配置torch GPU环境（cuda 11.6 or 11.3）_torch环境_小四01的博客-CSDN博客)

    pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 -i https://pypi.tuna.tsinghua.edu.cn/simple

    很有效

包安装过程信息

参考文档

AutoGluon GPU 版本 安装配置教程

AutoML for Image, Text, Time Series, and Tabular Data

Windows 上安装和使用 AutoGluon v0.4

AutoGluon官网

autogluon 0.7.0-pypi

包安装过程信息

autogluon过程

以下是安装的包的版本信息, 留作参考

使用Ctrl+F，对特定包版本进行搜索

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Installing collected packages: tokenizers, text-unidecode, tensorboard-plugin-wit, sentencepiece, pytz, pyasn1, py4j, msgpack, distlib, cymem, antlr4-python3-runtime, zipp, xxhash, wrapt, urllib3, ujson, typing-extensions, toolz, threadpoolctl, tensorboard-data-server, tenacity, tabulate, spacy-loggers, spacy-legacy, soupsieve, smart-open, six, rsa, regex, pyyaml, PySocks, pyrsistent, pyparsing, pygments, pyDeprecate, pyasn1-modules, psutil, protobuf, platformdirs, pkgutil-resolve-name, Pillow, packaging, ordered-set, oauthlib, numpy, networkx, murmurhash, multidict, mdurl, MarkupSafe, llvmlite, langcodes, kiwisolver, joblib, jmespath, grpcio, graphviz, future, fsspec, frozenlist, fonttools, filelock, fastprogress, dill, defusedxml, Cython, cycler, colorama, cloudpickle, charset-normalizer, catalogue, cachetools, attrs, async-timeout, absl-py, yarl, werkzeug, wasabi, virtualenv, tqdm, torch, tifffile, tensorboardX, srsly, scipy, requests, PyWavelets, python-dateutil, pytesseract, pydantic, pyarrow, preshed, plotly, patsy, omegaconf, nptyping, multiprocess, markdown-it-py, lightning-utilities, jinja2, importlib-resources, importlib-metadata, imageio, google-auth, fastcore, deprecated, contourpy, click, blis, beautifulsoup4, aiosignal, xgboost, typer, torchvision, torchmetrics, scikit-learn, scikit-image, rich, responses, requests-oauthlib, pandas, numba, nltk, matplotlib, markdown, jsonschema, hyperopt, huggingface-hub, fastdownload, fairscale, confection, botocore, aiohttp, accelerate, transformers, timm, thinc, statsmodels, seqeval, s3transfer, ray, pytorch-metric-learning, pathy, model-index, lightgbm, google-auth-oauthlib, gluonts, gdown, catboost, tensorboard, statsforecast, spacy, sktime, pytorch-lightning, pmdarima, openmim, nlpaug, datasets, boto3, tbats, fastai, evaluate, autogluon.common, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.timeseries, autogluon
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PS: 如果之前没有安装过torch, 在安装过程中应该还会带有torch-gpu版本的安装, 我这里因为之前安装过了GPU的版本所以没有附带安装.

在过程中并没有mxnet的安装

autogluon.text过程

这真的是我安装过的最麻烦的模型库

其中还涉及到一大堆的库之间的适配问题

(autog) PS C:\Users\PC> pip install autogluon.text
Looking in indexes: http://pypi.douban.com/simple
Collecting autogluon.text
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Collecting locket
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Building wheels for collected packages: fairscale, antlr4-python3-runtime
Building wheel for fairscale (pyproject.toml) … done
Created wheel for fairscale: filename=fairscale-0.4.6-py3-none-any.whl size=307237 sha256=322633f0853d8de2c58624778dd43c9f042b2358c65678f0b1c770d142906544
Stored in directory: c:\users\pc\appdata\local\pip\cache\wheels\fc\ed\fb\45837505439e660493bee33ff1aa04228fb88aac006d579750
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Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141217 sha256=335f9f89b4ecb9aabdc046f2666334c6e507c8def0203e0c53596b8440f2dde1
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Installing collected packages: typish, sortedcontainers, heapdict, antlr4-python3-runtime, zict, tornado, torch, tensorboard-data-server, tblib, smart-open, scipy, opencv-python-headless, omegaconf, nptyping, locket, grpcio, click, torchvision, torchtext, scikit-learn, partd, jsonschema, fairscale, accelerate, transformers, ray, qudida, pytorch-metric-learning, nlpaug, dask, tensorboard, openmim, distributed, albumentations, pytorch-lightning, autogluon.common, autogluon.features, autogluon.core, autogluon.multimodal, autogluon.text

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Uninstalling antlr4-python3-runtime-4.9.3:
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Attempting uninstall: fairscale
Found existing installation: fairscale 0.4.13
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Found existing installation: transformers 4.26.1
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Found existing installation: openmim 0.3.6
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Attempting uninstall: autogluon.common
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Attempting uninstall: autogluon.features
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Attempting uninstall: autogluon.core
Found existing installation: autogluon.core 0.7.0
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Attempting uninstall: autogluon.multimodal
Found existing installation: autogluon.multimodal 0.7.0
Uninstalling autogluon.multimodal-0.7.0:
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. autogluon-timeseries 0.7.0 requires autogluon.common==0.7.0, but you have autogluon-common 0.6.2 which is incompatible. autogluon-timeseries 0.7.0 requires autogluon.core[raytune]==0.7.0, but you have autogluon-core 0.6.2 which is incompatible. autogluon-tabular 0.7.0 requires autogluon.core==0.7.0, but you have autogluon-core 0.6.2 which is incompatible. autogluon-tabular 0.7.0 requires autogluon.features==0.7.0, but you have autogluon-features 0.6.2 which is incompatible. autogluon 0.7.0 requires autogluon.core[all]==0.7.0, but you have autogluon-core 0.6.2 which is incompatible. autogluon 0.7.0 requires autogluon.features==0.7.0, but you have autogluon-features 0.6.2 which is incompatible. autogluon 0.7.0 requires autogluon.multimodal==0.7.0, but you have autogluon-multimodal 0.6.2 which is incompatible.
Successfully installed accelerate-0.13.2 albumentations-1.1.0 antlr4-python3-runtime-4.8 autogluon.common-0.6.2 autogluon.core-0.6.2 autogluon.features-0.6.2 autogluon.multimodal-0.6.2 autogluon.text-0.6.2 click-8.0.4 dask-2021.11.2 distributed-2021.11.2 fairscale-0.4.6 grpcio-1.43.0 heapdict-1.0.1 jsonschema-4.8.0 locket-1.0.0 nlpaug-1.1.10 nptyping-1.4.4 omegaconf-2.1.2 opencv-python-headless-4.7.0.72 openmim-0.2.1 partd-1.3.0 pytorch-lightning-1.7.7 pytorch-metric-learning-1.3.2 qudida-0.0.4 ray-2.0.1 scikit-learn-1.1.3 scipy-1.9.3 smart-open-5.2.1 sortedcontainers-2.4.0 tblib-1.7.0 tensorboard-2.11.2 tensorboard-data-server-0.6.1 torch-1.12.1 torchtext-0.13.1 torchvision-0.13.1 tornado-6.2 transformers-4.23.1 typish-1.9.3 zict-2.2.0

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