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来自天秤座的梦想_天秤座：单线全自动机器学习

發(fā)布時間：2023/12/15 编程问答 28 豆豆

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來自天秤座的夢想

Libra is one of the python package, which helps in performing deep learning on a given data set with minimum no of lines of code. The recent usages of Machine Learning in many of daily applications. There has been many platforms for performing Machine Learning on a given data set. The Libra python package is used to automate the end-to-end machine learning process in just few line of code. It is built for both non-technical users and software professionals of all kinds. The Libra package can also be used by experienced developer to perform tuning and identify the best parameters of the model.

Libra是python軟件包之一，它有助于以最少的代碼行對給定的數(shù)據(jù)集執(zhí)行深度學(xué)習(xí)。機器學(xué)習(xí)在許多日常應(yīng)用中的最新用法。有許多平臺可以在給定的數(shù)據(jù)集上執(zhí)行機器學(xué)習(xí)。 Libra python軟件包僅需幾行代碼即可用于自動化端到端機器學(xué)習(xí)過程。它是為非技術(shù)用戶和各種軟件專業(yè)人員而構(gòu)建的。經(jīng)驗豐富的開發(fā)人員也可以使用Libra軟件包執(zhí)行調(diào)整并確定模型的最佳參數(shù)。

Libra website天秤座網(wǎng)站的訓(xùn)練模型所需的行數(shù)

We have to understand only the API’s used to perform the machine learning operations without worrying anything about the instructions. Libra also has large community and many resources available to get help of the package usage.

我們只需要了解用于執(zhí)行機器學(xué)習(xí)操作的API，而不必?fù)?dān)心任何說明。天秤座還擁有龐大的社區(qū)，可利用許多資源來獲得有關(guān)軟件包使用的幫助。

安裝天秤座 (Installing Libra)

In order to use the Libra API, install the latest Libra python package from command line is using pip.

為了使用Libra API，請從命令行使用pip安裝最新的Libra python軟件包。

pip install -U libra

搭建環(huán)境 (Setting up the environment)

離線工作 (Working Offline)

Install Python 3.6+

安裝Python 3.6+

For Libra, and other libraries, pip is the best option for installing libraries. Install the most recent version.

對于Libra和其他庫，pip是安裝庫的最佳選擇。安裝最新版本。

You can you use Jupyter Notebook or any Python IDE (PyCharm) for coding.

您可以使用Jupyter Notebook或任何Python IDE( PyCharm )進行編碼。

在線預(yù)配置環(huán)境 (Online Pre Configured environment)

Google Collaboratory can be used for online usage of the Libra package, as it supports intensive processing and also configures an environment for you.

Google Collaboratory可用于在線使用Libra軟件包，因為它支持密集處理并還為您配置了環(huán)境。

登錄到您的Google帳戶，然后在collab中打開一個筆記本。

Use !pip install libra to get all the package’s.

使用！pip install libra獲取所有軟件包。

Upload your files for data and you are ready to go.

上傳您的文件以獲取數(shù)據(jù)，您就可以開始了。

Libra API的結(jié)構(gòu) (Libra API’s structure)

All the operation are built around the client() object in Libra. We can call different queries on it and everything will be stored under the models field of the object. All the information can be accessed has a dictionary. The latest model called will automatically be the latest.

所有操作都圍繞Libra中的client()對象構(gòu)建。我們可以對其調(diào)用不同的查詢，所有內(nèi)容都將存儲在對象的models字段下。所有可以訪問的信息都有一個字典。最新型號將自動為最新型號。

機器學(xué)習(xí)模型 (Machine Learning models)

Below are the different Machine Learning models supported in the Libra python package,

以下是Libra python軟件包中支持的不同機器學(xué)習(xí)模型，

＃1神經(jīng)網(wǎng)絡(luò) (#1 Neural Network)

The neural_network_query() method can be used to automatically fit a Neural Network to the dataset. Target detection, preprocessing, and scoring are done by default. The neural_network_query() method accepts certain parameters in order to perform the operation on the data set.Below is the sample code for performing the Neural Network operation on the dataset.

neural_network_query()方法可用于將神經(jīng)網(wǎng)絡(luò)自動擬合到數(shù)據(jù)集。目標(biāo)檢測，預(yù)處理和評分默認(rèn)情況下完成。 neural_network_query()方法接受某些參數(shù)以對數(shù)據(jù)集執(zhí)行操作。下面是對數(shù)據(jù)集執(zhí)行神經(jīng)網(wǎng)絡(luò)操作的示例代碼。

new_client = client(‘path_to_dataset’)
new_client.neural_network_query(‘Please estimate the number of households.’)
new_client.models[‘regression_ANN’].plots() #access plots

＃2卷積神經(jīng)網(wǎng)絡(luò) (#2 Convolutional Neural Network)

The convolutional_query() method can be used to automatically fit a Convolutional Neural Network to the dataset. Images are automatically interpolated to the median height and width. Three types of dataset structures(‘setwise’, ‘classwise’ and ‘csvwise’) are supported. Below is the sample code for performing the CNN operation on the dataset.

convolutional_query()方法可用于將卷積神經(jīng)網(wǎng)絡(luò)自動擬合到數(shù)據(jù)集。圖像會自動插值到中間的高度和寬度。支持三種類型的數(shù)據(jù)集結(jié)構(gòu)(“ setwise”，“ classwise”和“ csvwise”)。以下是用于對數(shù)據(jù)集執(zhí)行CNN操作的示例代碼。

newClient = client('path_to_directory_with_image_folders')
newClient.convolutional_query("Please classify my images", pretrained={'arch':'vggnet19', 'weights':'imagenet'})

＃3支持向量機 (#3 Support Vector Machine)

The svm_query() method automatically fits a Support Vector Machine to the dataset. Currently classification is only supported. Target detection, preprocessing, and scoring are done by default. Below is the sample code for performing the support vector machine operation on the dataset.

svm_query()方法自動將支持向量機擬合到數(shù)據(jù)集。當(dāng)前僅支持分類。目標(biāo)檢測，預(yù)處理和評分默認(rèn)情況下完成。以下是用于對數(shù)據(jù)集執(zhí)行支持向量機操作的示例代碼。

newClient = client('path_to_file')
newClient.svm_query('Model the type of credit card')

＃4最近的鄰居 (#4 Nearest Neighbors)

The nearest_neighbor_query() method automatically fits the neural network to the dataset. Target detection, preprocessing, and scoring are done by default. Below is the sample code for performing the nearest neighbors operation on the dataset.

最近的nearest_neighbor_query()方法自動使神經(jīng)網(wǎng)絡(luò)適合數(shù)據(jù)集。目標(biāo)檢測，預(yù)處理和評分默認(rèn)情況下完成。以下是用于對數(shù)據(jù)集執(zhí)行最近鄰操作的示例代碼。

newClient = client('path_to_file')
newClient.nearest_neighbors_query('Model the type of credit card')

＃5決策樹 (#5 Decision Tree)

The decision_tree_query() method automatically fits a Decision Tree algorithm to the dataset. Target detection, preprocessing, and scoring are done by default. Below is the sample code for performing the decision tree operation on the dataset.

Decision_tree_query decision_tree_query()方法自動將決策樹算法擬合到數(shù)據(jù)集。目標(biāo)檢測，預(yù)處理和評分默認(rèn)情況下完成。以下是用于對數(shù)據(jù)集執(zhí)行決策樹操作的示例代碼。

newClient = client('path_to_file')
newClient.decision_tree_query('please estimate ocean proximity')

＃6 K-Means聚類 (#6 K-Means Clustering)

The kmeans_clustering_query() method automatically fits a clustering algorithm to the dataset. Target detection, preprocessing, and scoring are done by default. Below is the sample code for performing the K-Means clustering operation on the dataset.

kmeans_clustering_query()方法自動將聚類算法擬合到數(shù)據(jù)集。目標(biāo)檢測，預(yù)處理和評分默認(rèn)情況下完成。以下是用于對數(shù)據(jù)集執(zhí)行K-Means聚類操作的示例代碼。

newClient = client('path_to_file')
newClient.kmeans_clustering_query(preprocess=True, generate_plots=True, drop=[])

Each of the methods takes additional arguments for performing the individual operations. The more parameters you specify the more optimized the model will be generated.

每個方法都采用其他參數(shù)來執(zhí)行各個操作。您指定的參數(shù)越多，將生成的模型越優(yōu)化。

自然語言處理 (Natural Language Processing)

Below are the different Natural Language Processing supported in the Libra python package,

以下是Libra python軟件包中支持的不同自然語言處理，

＃1文字分類 (#1 Text Classification)

The text_classification_query() method automatically fits a Text Classification model to the dataset. All standard text modification procedures are applied automatically if applicable. It is stored as ‘text_classification’ in models dictionary. Below are the Dataset Guidelines* One column in the file should contain the text to be classified* One column should contain the label of each text and SHOULD BE NAMED LABEL. If it is named something else, the name should be provided in the label_column parameter.

text_classification_query()方法自動將文本分類模型擬合到數(shù)據(jù)集。如果適用，將自動應(yīng)用所有標(biāo)準(zhǔn)文本修改程序。它以“ text_classification”存儲在模型字典中。以下是數(shù)據(jù)集準(zhǔn)則*文件中的一列應(yīng)包含要分類的文本*每一列應(yīng)包含每個文本的標(biāo)簽，并應(yīng)命名為LABEL。如果使用其他名稱，則應(yīng)在label_column參數(shù)中提供該名稱。

Below is the sample code for performing the Text Classification operation on the dataset.

以下是用于對數(shù)據(jù)集執(zhí)行“文本分類”操作的示例代碼。

new_client = client('path_to_csv')
new_client.text_classification_query('Please estimate the sentiment')
new_client.classify_text('new text to classify')

＃2文件摘要 (#2 Document Summarization)

The summarization_query() method automatically fits a transfer-learning Document Summarization model to the dataset. The model will have frozen layers with pretrained weights to help with small dataset sizes. It is stored as ‘doc_summarization’ in models dictionary.

summarization_query()方法自動將轉(zhuǎn)移學(xué)習(xí)文檔摘要模型擬合到數(shù)據(jù)集。該模型將具有經(jīng)過預(yù)訓(xùn)練權(quán)重的凍??結(jié)圖層，以幫助實現(xiàn)較小的數(shù)據(jù)集大小。它在模型字典中存儲為“ doc_summarization”。

Below is the sample code for performing the Document Summarization operation on the dataset.

下面是用于對數(shù)據(jù)集執(zhí)行“文檔匯總”操作的示例代碼。

newClient = client('path_to_csv')
newClient.summarization_query("Please summarize original text")
newClient.get_summary('new text to summarize')

＃3圖片說明生成 (#3 Image Caption Generation)

The image_caption_query() method automatically fits an caption generation transfer learning model to your dataset. The model will have frozen layers with pretrained weights to help with small dataset sizes. It is stored as ‘image_caption’ in models dictionary.

image_caption_query()方法自動將字幕生成轉(zhuǎn)移學(xué)習(xí)模型適合您的數(shù)據(jù)集。該模型將包含具有預(yù)訓(xùn)練權(quán)重的凍??結(jié)圖層，以幫助實現(xiàn)較小的數(shù)據(jù)集大小。它在模型字典中存儲為“ image_caption”。

Below is the sample code for performing the Image Caption Generation operation on the dataset.

以下是用于對數(shù)據(jù)集執(zhí)行“圖像標(biāo)題生成”操作的示例代碼。

newClient = client('path_to_csv')
newClient.image_caption_query('Generate image captions')
newClient.generate_caption('path to image')

＃4文字生成 (#4 Text Generation)

The generate_text() method automatically generates text of specified length based on initial prefix text. It is stored as ‘generated_text’ in models dictionary.

generate_text()方法根據(jù)初始前綴文本自動生成指定長度的文本。它在模型字典中存儲為“ generated_text”。

Below is the sample code for performing the Text Generation operation on the dataset.

以下是用于對數(shù)據(jù)集執(zhí)行“文本生成”操作的示例代碼。

newClient = client('path_to_txt’)
newClient.generate_text(“generate text” file_data=False, prefix=“Hello there!”)

＃5命名實體識別 (#5 Named Entity Recognition)

The get_named_entities() method automatically detects name entities like persons name, geographic locations, organization/companies and addresses from label column containing text. It is stored as ‘named_entity_recognition’ in models dictionary.

get_named_entities()方法自動從包含文本的標(biāo)簽列中檢測姓名實體，例如人員姓名，地理位置，組織/公司和地址。它在模型字典中存儲為“ named_entity_recognition”。

Below is the sample code for performing the Named Entity Recognition operation on the dataset.

以下是用于對數(shù)據(jù)集執(zhí)行“命名實體識別”操作的示例代碼。

newClient = client('path_to_txt’)
newClient.get_named_entities('detect from text')

附加方法 (Additional Methods)

Libra also supports some additional methods to perform below operations on the data set.

Libra還支持一些其他方法來對數(shù)據(jù)集執(zhí)行以下操作。

更深入的分析 (Deeper Analysis)

The below operations can be performed on the data set for deeper analysis of the data.

可以對數(shù)據(jù)集執(zhí)行以下操作，以更深入地分析數(shù)據(jù)。

#1 Data Analysis [dashboard()]— Launch the fully functional UI to perform data analysis and dimensionality reduction live.

＃1數(shù)據(jù)分析 [ dashboard() ]-啟動功能齊全的UI，以實時執(zhí)行數(shù)據(jù)分析和降維。

#2 Analyzing [analyze() ]— Generate in-depth statistics about the dataset.

＃2分析 [ analyze() ]-生成有關(guān)數(shù)據(jù)集的深入統(tǒng)計信息。

信息檢索 (Information Retrieval)

The below operations can be performed for information retrieval from the model for the dataset.

可以執(zhí)行以下操作，以從數(shù)據(jù)集的模型中檢索信息。

#1 Information [ info() ] — Used to represent each category of data generated for the dataset.

＃1信息 [ info() ] —用于表示為數(shù)據(jù)集生成的每種數(shù)據(jù)類別。

#2 Plots[plots()] — Display all of the plots generated for the model

＃2 Plots [ plots() ] —顯示為模型生成的所有圖

#3 Vocabulary[vocab()] — Used for document summarization and image caption

＃3詞匯表 [ vocab() ] —用于文檔摘要和圖像標(biāo)題

#4 Prediction[predict()] — It automatically fits a neural network for the dataset.

＃4 Prediction [ predict() ] —它自動適合數(shù)據(jù)集的神經(jīng)網(wǎng)絡(luò)。

#5 Model[model()] — It returns the entire dictionary of a specific model

＃5 Model [ model() ] —返回特定模型的整個字典

Demo of using the Libra from the Github Demo section Github演示部分中的使用天秤座的演示

開發(fā)團隊 (Developer Team)

Below is the team behind the Libra python package.

以下是Libra python軟件包背后的團隊。

Founder — Palash Shah

創(chuàng)始人-Palash Shah

Developers — Siddharth Akalwadi , Rostam Vakhshoori , Ramya Bhaskara , Pragun Ananda , Pranav Teegavarapu , Anas Awadalla , Juan Bofill , Pratham Chhabria , Sarthak Chauhan, Goral Pahuja, Yash Himmatraka

開發(fā)商-Siddharth Akalwadi ， Rostam Vakhshoori ， Ramya Bhaskara ， Pragun Ananda ， Pranav Teegavarapu ， Anas Awadalla ， Juan Bofill ， Pratham Chhabria ， Sarthak Chauhan ， Goral Pahuja ， Yash Himmatraka

最后的想法 (Final Thoughts)

Libra is the nexus of modern machine learning. It combines the technology from most of the popular machine learning platforms to create a complete experience. Libra integrates the below capabilities of the Machine learning platform for making it easier to use for anyone.

天秤座是現(xiàn)代機器學(xué)習(xí)的紐帶。它結(jié)合了大多數(shù)流行的機器學(xué)習(xí)平臺中的技術(shù)，以創(chuàng)建完整的體驗。 Libra集成了機器學(xué)習(xí)平臺的以下功能，從而使任何人都更容易使用。

Keras: straightforward model building techniques for improved modularity and ease of deployment.
Keras：簡單的模型構(gòu)建技術(shù)，可改善模塊化和易于部署。
TensorFlow: core computational fundamentals and detailed functionality.
TensorFlow：核心計算基礎(chǔ)和詳細(xì)功能。
PyTorch: scalable training for highly-dimensional processes.
PyTorch：針對高維度流程的可擴展培訓(xùn)。
Scikit-Learn: one-line quick model building capabilities.
Scikit-Learn：一線快速模型構(gòu)建功能。
Keras-Tuner: class-wise structure for intelligent neural network tuning.
Keras-Tuner：智能神經(jīng)網(wǎng)絡(luò)調(diào)整的類結(jié)構(gòu)。

It can be used by complete novice user without worrying about the different instructions. It also helps experienced developers to perform the tuning and identify the best parameters quickly. Even Though, there are other platforms for Machine Learning, Libra helps us in building the model very quickly and perform the required machine learning task in fewer lines of code.

完全的新手用戶都可以使用它，而無需擔(dān)心不同的說明。它還可以幫助有經(jīng)驗的開發(fā)人員快速執(zhí)行調(diào)整并確定最佳參數(shù)。盡管還有其他用于機器學(xué)習(xí)的平臺，但Libra可以幫助我們快速構(gòu)建模型并以更少的代碼行執(zhí)行所需的機器學(xué)習(xí)任務(wù)。

其他參考： (Further References:)

影片教學(xué) (Video Tutorials)

Machine Learning in One Line of Code by Ahmad Bazzi.

艾哈邁德·巴齊(Ahmad Bazzi)編寫的《代碼集中的機器學(xué)習(xí)》

Introduction to Machine Learning using Libra by Palash Shah.

Palash Shah撰寫的使用Libra進行機器學(xué)習(xí)簡介。

Libra — Your Data Talks Meetup by Palash Shah

天秤座— Palash Shah的數(shù)據(jù)對話聚會

文章 (Articles)

Libra: A Python tool that Automates Machine Learning Process in a Few Lines of Code by marktechpost.

Libra：由marktechpost 使用幾行代碼自動執(zhí)行機器學(xué)習(xí)過程的Python工具。

One liner Machine learning and Deep Learning using Libra by Ali Aryan.

Ali Aryan 使用Libra進行了一次線性機器學(xué)習(xí)和深度學(xué)習(xí) 。

Create a complex Machine Learning model in one line with Libra by Cornellius Yudha Wijaya.

與 Cornellius Yudha Wijaya的Libra一起在一行中創(chuàng)建一個復(fù)雜的機器學(xué)習(xí)模型。

Fully Automated Machine Learning in One-Liners by Gagandeep Singh.

Gagandeep Singh編寫的單線全自動機器學(xué)習(xí) 。

Machine Learning in One-Minute with Libra by Pranav Teegavarapu.

Pranav Teegavarapu撰寫的《天秤座一分鐘的機器學(xué)習(xí)》。

網(wǎng)絡(luò)研討會 (Webinars)

Become a machine learning expert at Cloud Computing, AI, Big Data.

成為云計算，人工智能，大數(shù)據(jù)領(lǐng)域的機器學(xué)習(xí)專家。

Become a machine learning expert in 45 minutes at Hyphora

在Hyphora中成為45分鐘內(nèi)的機器學(xué)習(xí)專家

其他 (Other)

Trending Project on Made with ML in August

八月的ML制造趨勢項目

Libra Documentation

天秤座文檔

Tutorial Notebook on Colaboratory

協(xié)作教程筆記本

Slack Channel

松弛通道

Get Started with a Step-by-Step Guide in Medium by Palash Shah

Palash Shah撰寫的 Medium中的分步指南入門

“嬰兒學(xué)會了爬行，走路和奔跑。應(yīng)用機器學(xué)習(xí)時，我們正處于爬行階段。” ? 戴夫·沃特斯 (“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” ~Dave Waters)

翻譯自: https://medium.com/@ravi07/libra-fully-automated-machine-learning-in-one-liners-27ca352339ed