AutoGluon安装及示例

文章目录

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:

二分类任务（Binary classification）：根据输入数据预测两个类别之一。
多分类任务（Multiclass classification）：根据输入数据预测多个类别之一。
回归任务（Regression）：根据输入数据预测数值型输出。
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。
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。
图像分类任务（Image classification）：根据输入的图像数据对图像进行分类。
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。
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

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

运行示例

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

数据预处理过程

特征工程

模型训练

显示最优模型及各个模型的训练精度

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：

词袋模型（Bag of Words）
TF-IDF 模型
FastText 模型
BERT 模型
RoBERTa 模型
DistilBERT 模型
ALBERT 模型
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：

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。
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。
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。
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。
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。
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。
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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beautifulsoup4-4.11.2 blis-0.7.9 boto3-1.26.92 botocore-1.29.94 cachetools-5.3.0 catalogue-2.0.8 catboost-1.1.1 charset-normalizer-3.1.0 click-8.1.3 cloudpickle-2.2.1 colorama-0.4.6 confection-0.0.4 contourpy-1.0.7 cycler-0.11.0 cymem-2.0.7 datasets-2.10.1 defusedxml-0.7.1 deprecated-1.2.13 dill-0.3.6 distlib-0.3.6 evaluate-0.3.0 fairscale-0.4.13 fastai-2.7.11 fastcore-1.5.28 fastdownload-0.0.7 fastprogress-1.0.3 filelock-3.10.0 fonttools-4.39.0 frozenlist-1.3.3 fsspec-2023.3.0 future-0.18.3 gdown-4.6.4 gluonts-0.12.4 google-auth-2.16.2 google-auth-oauthlib-0.4.6 graphviz-0.20.1 grpcio-1.51.3 huggingface-hub-0.13.2 hyperopt-0.2.7 imageio-2.26.0 importlib-metadata-6.0.0 importlib-resources-5.12.0 jinja2-3.1.2 jmespath-1.0.1 joblib-1.2.0 jsonschema-4.17.3 kiwisolver-1.4.4 langcodes-3.3.0 lightgbm-3.3.5 lightning-utilities-0.8.0 llvmlite-0.39.1 markdown-3.4.1 markdown-it-py-2.2.0 matplotlib-3.7.1 mdurl-0.1.2 model-index-0.1.11 msgpack-1.0.5 multidict-6.0.4 multiprocess-0.70.14 murmurhash-1.0.9 networkx-2.8.8 nlpaug-1.1.11 nltk-3.8.1 nptyping-2.4.1 numba-0.56.4 numpy-1.23.5 oauthlib-3.2.2 omegaconf-2.2.3 openmim-0.3.6 ordered-set-4.1.0 packaging-23.0 pandas-1.5.3 pathy-0.10.1 patsy-0.5.3 pkgutil-resolve-name-1.3.10 platformdirs-3.1.1 plotly-5.13.1 pmdarima-1.8.5 preshed-3.0.8 protobuf-3.20.3 psutil-5.9.4 py4j-0.10.9.7 pyDeprecate-0.3.2 pyarrow-11.0.0 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydantic-1.10.6 pygments-2.14.0 pyparsing-3.0.9 pyrsistent-0.19.3 pytesseract-0.3.10 python-dateutil-2.8.2 pytorch-lightning-1.9.4 pytorch-metric-learning-1.7.3 pytz-2022.7.1 pyyaml-6.0 ray-2.2.0 regex-2022.10.31 requests-2.28.2 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.3.2 rsa-4.9 s3transfer-0.6.0 scikit-image-0.19.3 scikit-learn-1.2.2 scipy-1.10.1 sentencepiece-0.1.97 seqeval-1.2.2 six-1.16.0 sktime-0.15.1 smart-open-6.3.0 soupsieve-2.4 spacy-3.5.1 spacy-legacy-3.0.12 spacy-loggers-1.0.4 srsly-2.4.6 statsforecast-1.4.0 statsmodels-0.13.5 tabulate-0.9.0 tbats-1.1.2 tenacity-8.2.2 tensorboard-2.12.0 tensorboard-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 tensorboardX-2.6 text-unidecode-1.3 thinc-8.1.9 threadpoolctl-3.1.0 tifffile-2023.3.15 timm-0.6.12 tokenizers-0.13.2 toolz-0.12.0 **
torch-1.13.1
** torchmetrics-0.8.2 torchvision-0.14.1 tqdm-4.65.0 transformers-4.26.1 typer-0.7.0 typing-extensions-4.5.0 ujson-5.7.0 urllib3-1.26.15 virtualenv-20.21.0 wasabi-1.1.1 werkzeug-2.2.3 wrapt-1.15.0 xgboost-1.7.4 xxhash-3.2.0 yarl-1.8.2 zipp-3.15.0

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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Building wheels for collected packages: fairscale, antlr4-python3-runtime
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Created wheel for fairscale: filename=fairscale-0.4.6-py3-none-any.whl size=307237 sha256=322633f0853d8de2c58624778dd43c9f042b2358c65678f0b1c770d142906544
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Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141217 sha256=335f9f89b4ecb9aabdc046f2666334c6e507c8def0203e0c53596b8440f2dde1
Stored in directory: c:\users\pc\appdata\local\pip\cache\wheels\97\00\56\ee5a640976aa25f5ed767e5f195e3de51c4a04b8bc3b67e96c
Successfully built fairscale antlr4-python3-runtime
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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Found existing installation: ray 2.2.0
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Attempting uninstall: pytorch-metric-learning
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Attempting uninstall: autogluon.core
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Successfully uninstalled autogluon.core-0.7.0
Attempting uninstall: autogluon.multimodal
Found existing installation: autogluon.multimodal 0.7.0
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Successfully uninstalled autogluon.multimodal-0.7.0
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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AutoGluon安装及示例

AutoGluon安装及示例

文章目录

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

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