Multi-Agent Accelerator for Data Science (MAADS) Batch AutoML (MAADSBML)
Project description
**Multi-Agent Accelerator for Data Science: Batch AutoML (MAADSBML)
Revolutionizing Data Science with Artificial Intelligence
Overview
*MAADSBML combines Artificial Intelligence, Docker, Machine Learning. It automates the machine learning process, and finds the BEST algorithm for your data. It also produces a very detailed PDF report. MAADSBML is integrated with Docker Container.
This library allows users to harness the power of agent-based computing using hundreds of advanced linear and non-linear algorithms.
FOR MORE INFORMATION GO TO: https://maadsbml.readthedocs.io/en/latest/
Compatibility - Python 3.8 or greater - Minimal Python skills needed
Copyright
- Author: Sebastian Maurice, PhD
Installation
- At the command prompt write:
pip install maadsbml
- This assumes you have Downloaded Python and installed it on your computer.
- You will also need to pull the MAADSBML Docker container: maadsdocker/maads-batch-automl-otics
Syntax
- There are literally two lines of code you need to write to train your data and make predictions:
Main functions:
- hypertraining 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.
- 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.
- algodescription Get detailed information on the optimal algorithm found during hypertraining
- abort Abort the training process.
- rundemo To run canned demo of the system to see how it works.
- finddistribution Finds the best distribution for your continuous data
First import the Python library.
import maadsbml
- maadsbml.hypertraining(host,port,filename,dependentvariable,removeoutliers=0,hasseasonality=0,summer='6,7,8',winter='11,12,1,2',shoulder='3,4,5,9,10', trainingpercentage=70,shuffle=0,deepanalysis=0,username='admin',timeout=1200,company='otics',password='123',email='support@otics.ca',usereverseproxy=0, microserviceid='',maadstoken='123',mode=0)
Parameters:
host : string, required
- This is the IP address of the running Docker container - it is usually http://localhost
port : int, required
- This is the TRAINING PORT in the container. The default is port==5595
filename : string, required
- This is the raw data file in csv format - Note this file is stored on your host machine - the DOCKER container needs to be mapped to this volume using -v
dependentvariable : string, required
- This is the dependent variable in your csv file.
removeoutliers : int, optional, 1 or 0
- If 1, then outliers will be removed from your data. If 0, no outliers are removed.
hasseasonality : int, optional, 1 or 0
- If 1, then your data will be modeled for seasonality: Winter, Summer, Shoulder. If 0, then your data will not be modeled for seasonality. If modeling for seasonality, ensure you have enough data points that covers the seasons, usually 1 year of data.
summer : string, optional
- Definition for summer months. This can be changed.
winter : string, optional
- Definition for winter months. This can be changed.
shoulder : string, optional
- Definition for shoulder months. This can be changed.
trainingpercentage : int, optional, Default=70
- This is the split percentage between Training and Test data sets. It is defaulted to 70 (70% for training, 30% test).
shuffle : number, 0 or 1, optional
- Indicates whether to shuffle the training dataset or not, default=0.
deepanalysis : int, optional
- This will force MAADSBML to perform deeper analysis on your data. This could take 30-40 minutes. Set to 1 for deepanalysis, 0 for no deep analysis.
username : string, optional
- This identifies a user. You may want to change this if multiple users are running the same file.
company : string, optional
- This identifies your company. You may want to change this for the Report.
timeout : int, optional
- You can increase this if you receive a timeout error before the training is taking too long. The setting is in seconds.
password : string, optional
- leave as is
email : string, optional
- leave as is
usereverseproxy : int, optional
- leave as is
microserviceid : string, optional
- leave as is if not using a pass through service.
mode : int, optional
- leave as is
maadstoken : string, optional
- leave as is
Returns: string JSON buffer, with the algorithm key (PKEY) and other details:
- PKEY: : This is the key to the BEST algorithm and must be used when making predictions.
2. maadsbml.hyperpredictions(pkey,theinputdata,host,port,username,algoname='',seasonname='',usereverseproxy=0,microserviceid='',password='123',company='otics', email='support@otics.ca',maadstoken='123')
Parameters:
pkey : string, required
- This is the PKEY you received from the hypertraining function.
theinputdata : string, required
- These are the Xs for your model: For example if my model had 3 Xs then inputdata='5/21/2010,-14.3,-32.0,-12.0', with the first entry as Date: Date must be in the format: MM/DD/YYYY
host : string, required
- This is the IP address of the running Docker container - it is usually http://localhost
port : int, required
- This is the PREDICTION PORT in the container. The default is port==5495 (or 5595)
username : string, required
- The username you used in the hypertraining functions. Default is admin.
algoname : string, optional
- Enter the name of the algorithm to use, this can be retrieved from the hypertraining function. If this is empty, the BEST algorithm will be used by default.
seasonname : string, optional
- Enter the season to use (winter,summer,shoulder), this can be retrieved from the hypertraining function. If this is empty, the default season is used.
usereverseproxy : int, optional
- leave as is
microserviceid : int, optional
- leave as is
password : string, optional
- leave as is
company : string, optional
- change for reporting.
email : string, optional
- leave as is
maadstoken : string, optional
- leave as is
Returns: string buffer containing the prediction, and other details.
3. maadsbml.abort(host,port=10000)
Parameters:
host : string, required
- This is the IP address of the Docker container: http://localhost
port : string, optional
- Port is fixed at 10000
Returns: Abort will shutdown and re-start your system.
4. maadsbml.rundemo(host,port,demotype=1,timeout=1200,usereverseproxy=0,microserviceid='')
Parameters:
host : string, required
- This is the IP address of the Docker container: http://localhost
port : string, required
- This is the TRAININGPORT, it is usually 5595.
demotype : int, required
- If demotype is 1, then a regression models is run; if demotype is 0 then a classification model is run.
timeout : int, optional
- The connection timeout between Python and the container, in seconds
usereverseproxy : int, optional
- leave as is
microserviceid : string, optional
- leave as is
Returns: null
5. maadsbml.algodescription(host,port,pkey,timeout=300,usereverseproxy=0,microserviceid='')
Parameters:
host : string, required
- This is the IP address of the Docker container: http://localhost
port : string, required
- This is the TRAININGPORT, it is usually 5595.
pkey : string, required
- This is the PKEY from hypertraining.
timeout : int, optional
- The connection timeout between Python and the container, in seconds
usereverseproxy : int, optional
- leave as is
microserviceid : string, optional
- leave as is
Returns: null
6. maadsbml.finddistribution(filename,varname,dataarray=[],folderpath='',imgname='distimage',common=1,topdist=5)
Parameters:
filename : string, required
- Filename containing the raw data. This must be a CSV file.
varname : string, required
- Name of the variable for your data.
dataarray : array_like, optional
- Numpy array. Rather than pass a filename, you can pass in an array.
folderpath : string, optional
- Folder path to store the output data, and distribution image file.
imgname : string, optional
- Name of the image and json data.
common : int, optional
-
If set to 1, this will apply common distributions to your data.
If Set to 0, it will iterate through roughly 80 distributions.
topdist : int, optional
- The number of the TOP distributions to print.
Returns: status,dist dataframe,name of best distribution,all JSON data
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