Python库 | maads-3.16.6.tar.gz

**Multi-Agent Accelerator for Data Science (MAADS)** *Revolutionizing Data Science with Artificial Intelligence* **Overview** *MAADS combines Artificial Intelligence, Machine Learning and Natural Language Processing (with data engineering task automation) in one easy to use library, that allows clients to connect to the MAADS server located anywhere in the world and perform advanced analytics and embed intelligence in their organization seamlessly and fast!* This library allows users to harness the power of agent-based computing using hundreds of advanced linear and non-linear algorithms. Users can easily integrate Predictive Analytics and Prescriptive Analytics in any solution by wrapping additional code around the functions below. The system can: - Automatically analyse your data and perform feature selection to determine which variables are more important than others. - Automatically model your data for seasonality *Winter, Shoulder, and Summer seasons.* - Automatically clean your data for outliers. - Automatically make predictions using the BEST algorithm (out of hundreds of advanced algorithms) that best model your data. - Automatically optimize the optimal algorithms by MINIMIZING or MAXIMIZING them to find the *GLOBAL OPTIMAL VALUES* of the independent variables - Perform *Natural Language Processing (NLP)* on large amounts of text data - and get MAADS to summarize the text or apply deep learning for predictive outcomes. For example, you can tell it to scrape a website, read a PDF, or text data and it will return a concise summary. This summary can be used to refine your modeling and give users an integrated view of their business from a TEXT and ADVANCED ANALYTIC perspective. Or, apply machine learning to text data for deeper insights - such as analysing help desk tickets and uncovering issues before they occur. Or, apply deep learning to security logs and uncover more anomalies or threats in your networks. - Do all this in minutes. To install this library a request should be made to **[email protected]** for a username and a MAADSTOKEN. Once you have these credentials then install this Python library. **Compatibility** - Python 3.5 or greater - Minimal Python skills needed **License** - Author: Sebastian Maurice, PhD - OTICS Advanced Analytics Inc. **Installation** - At the command prompt write: **pip install maads** - This assumes you have [Downloaded Python](https://www.python.org/downloads/) and installed it on your computer. **Syntax** - There are literally two lines of code you need to write to train your data and make predictions: **Main functions:** - **dotraining** Executes hundreds of agents, running hundreds of advanced algorithms and completes in minutes. A master agent then chooses the BEST algorithm that best models your data. - **dopredictions** After training, make high quality predictions - takes 1-2 seconds. - **hyperpredictions** After training, make high quality predictions - takes less than half a second (about ~100 milliseconds). Users can also generate predictions using **non-python code** such as JAVA. Using the **maadshyperpredictions.CLASS** file, java apps can call the MAADS prediction server to return predictions very fast. Other apps, using **any** other language, can also call the MAADS prediction server using standard TCP/IP client/server communication protocols like REST: This gives MAADS users' the maximum flexibility to integrate MAADS predictions in any solution! You can also use hyperpredictions as an API (Python not needed) and make calls from any application using the following format: GET http://[maads server website]:[port]/[microserviceid]/?hyperpredict=[optimal algo key],[[input data]],[MAADSTOKEN] MAADS hyper-prediction can also be used in a MICROSERVICES architecture that utilize API gateways (reverse proxies). This allows organizations to balance loads on the server and manage millions or billions of connections, to hyper-predictions, per day without experiencing latency issues. **Support functions:** - **dolistkeys** - List all of the keys associated with the data you have analysed. - **dolistkeyswithkey** - List data associated with a single key. - **dodeletewithkey** - Permanently delete all data associated with your key. - **returndata** - Returns data from the string buffer. - **getpicklezip** - Automatically downloads a ZIP file containing the optimal algorithms. Users can modify the parameter estimates as they wish. - **sendpicklezip** - Automatically upload a ZIP file containing the optimal algorithms to MAADS. The optimal algorithms will immediately take effect for predictions. - **deploytoprod** - Automatically deploy the optimal algorithms to another MAADS server (i.e. production); MAADS will read the ZIP file, extract the algorithms and make all database updates. This function is useful when your MAADS training server(s) and MAADS prediction server(s) are separate. A powerful way to integrate MAADS in a distributed architecture is to automatically train your data, then deploy the optimal algorithms to some other server for predictions. This is a great way to scale your analytics in a large (global) entreprise setting, seamlessly and fast, with MAADS! - **algoinfo** - Get detailed information on the algorithm and other information. - **genpdf** - Retrieve the PDF containing all of the detailed information on the algorithm and other information like model explanation and feature selection, etc. - **featureselectionjson** - Returns JSON collection of feature selection results for easier programmatic manipulation. Use dotraining feature selection switch to return a CSV file of feature selections. **Optimization:** - **optimize** - Automatically perform optimization of the optimal algorithms by minimizing or maximixing them to find optimal values for the independent variables: - MAADS offers a unique and powerful way to find the optimal values of the independent variables - Users can even minimize or maximize upto THREE optimal algorithms AT THE SAME TIME by building a multi-objective equation with the optimal algorithms - By finding the optimal values of the independent variables you can "prescribe" the right combination of independent variables' values that will lead to the HIGHEST or LOWEST value for the optimal algorithms - MAADS is one of the first technologies to offer a seamless integration between PREDICTIVE and PRESCRIPTIVE analytics. **Natural Language Processing (NLP):** - **nlp** - Automatically perform NLP to summarize large amounts of text data. Specifically, there are three data sources one can use: - **Website URL:** you can pass a URL to the NLP function and it will automatically scrape the site and return a summary of the text. - **PDF:** Send a PDF to be summarized. - **Text:** Paste text to be summarized. - This allows users to integrate NLP in unique and powerful ways with advanced analytics. - **nlpgeosentiment** - Automatically perform NLP to summarize and find keywords for large amounts of twitter data and genenerate a sentiment score. Users can perform GEO based searches on tweets within a specific radius. This information could be very useful for risks and threats assessments. - **nlpaudiovideo** - Automatically extracts text from audio or video files. - This allows users to integrate text from audio and video in unique and powerful ways with machine learning (NLPCLASSIFY). - Files must be .mp3, .mp4, .wav - Users can specify how many seconds they want or get all of the recording. - **nlpocr** - Automatically extracts text from image files. - This allows