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AION User Guide
(Release 2.8.5)
Copyright Notice
The information contained in this document is the property of HCL Technologies Limited. Except as specifically authorized in writing by HCL Technologies Limited, the holder of this document shall: (1) keep all information contained herein confidential and shall protec... | ||
SELECTOR- Feature Selection: The statistical analysis takes place to identify the relevant features for model training and remove the unimportant features based on correlation & importance.
LEARNER- Model Training Hyper Parameter Tuning: This stage trains configured models and selects the best parameters based on hype... | ||
Compatibility with previous version:
Note: Note: if a previous version of AION is already installed, re-training of use cases of that version is possible in the latest version, and it is backward compatible. But, use cases created in the latest version may not run the previous version, as forward compatibility is not ... | ||
Model Container
Retrained Model
MLaC: Machine Learning as Code generates code automatically based on ML Operations performed during various ML Lifecycle. Using MLaC, expert data scientists can have better control over experimentation, optimization & deployment of ML Models.
MLaC has four unique components.
Generat... | ||
Select authorization as Basic Authorization
Username: USERNAME
Password: PASSWORD
Put the data in the Body as a raw json format.
Click send to perform prediction.
Note: For more information users can follow the user guide for MLaC by clicking on the icon on the top right corner of the pop-up window.
Federated ... | ||
Model container: The model container creates a docker image of the trained model so that user can use that image anywhere, pull the image, and perform testing. If the user wants to consume the trained model for prediction purpose in some system where AION is not there, then either the user can install it as python pack... | ||
Eg: If an user clicks on Python Package icon from the Packages tab, a pop-up window will appear to confirm the model download. Click Ok and then the model gets downloaded in the form of a WHL file which can be used for further testing and prediction.
Model Re-training
After the Model got trained go to the Home.
Fro... | ||
While uploading the dataset, data size and ram size need to be checked in two cases:
If file size > ram size, file will not be uploaded for further process. File size should not be more than 80% of ram size.
If the file size > 50% of ram size than the alert will be shown and proceed for the next step.
Structured Da... | ||
Click External Data Sources.
Select different sql databases and data warehouses to ingest data. viz.- SQLite, Microsoft SQL, PostgreSQL, MySQL, Oracle, Google BigQuery, Redshift, Snowflake, Actian.
To access data from the database, you need to provide essential credentials like Database Name, Username, Host Port, e... | ||
To see the remaining details, click the EDA button.
On clicking the EDA button, a window will pop up that enables configuring the features and the sample of data that needed to be selected for EDA.
Select features and data subsampling size for exploratory data analysis. Finally, click Next.
Data Overview: The summ... | ||
Data Deep Dive: It offers an interface to inspect the correlation between data points for all the features of a dataset. Each item in the visualization represents the data point of an ingested dataset. Clicking on an individual item shows the key pairs that represent the features of that record whose values may be stri... | ||
Problem Description
Model features
Filter and Grouping
Problem Description
Select the Problem Type. The following problem types are supported:
Classification
Regression
Time Series Forecasting
Time Series Anomaly Detection
Recommender System
Clustering
Anomaly Detection
Survival Analysis
Topic Modeling
St... | ||
Clicking on the Next button will save the configuration and moves it to Advanced Configuration Tab.
AION Online Learning (Beta)
Online learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e., to further train the model even after the first fit. I... | ||
Half space trees with quantile filter
Other profiling options are the same as in regular AION profiler
STEP 3: Train the model
Now a usecase folder will be created in the target folder. All the profiler models and the best ML model are present in the Production folder
Path: C:\Users\<username>\AppData\Local\HCLT\A... | ||
Distributed Learning (Beta)
Distributed machine learning refers to multi-node/multi-processor machine learning algorithms and systems that are designed to improve performance, increase accuracy, and scale to larger input data sizes. In AION, distributed learning is supported for classification, and regression with XGB... | ||
Following are the steps to install the same:
Download and install the Microsoft C++ Build Tools through link: https://visualstudio.microsoft.com/visual-cpp-build-tools
Open the AION shell.
Install chromadb by using the command below-
Command: python -m pip install chromadb
Advance Configuration
To update the adva... | ||
Trained Model Info
It gives an information about- Training status, Deployment location of a model, problem type, best model (in case multiple algorithms is selected), Score of a best model, feature used, and the reports can be downloaded by clicking on Download Reports tab.
Evaluated Models
Evaluated models gives ... | ||
Click on the Predict icon.
Click Predict Batch tab.
Upload the dataset.
Click Submit to view the prediction result for the file selected.
Prediction can also be performed by using API which is explained in detail in the section 5.2.
Generate Script
This will generate the sample python code to call rest API for pr... | ||
Click Show Metric tab to view the graph.
Note: The acceptable threshold is highlighted in green. A class is considered biased if metrics-score is outside the threshold range.
Performance
Model performance is an assessment of the model's ability to perform a task accurately not only with training data but also in rea... | ||
First Order Sensitivity Analysis
First Order Sensitivity Analysis quantifies the impact of individual input variables on the output of a model by systematically varying one variable at a time while keeping others constant. It helps to understand the most influential factors of a model.
Total Order Sensitivity Analy... | ||
Click on Input Drift icon.
Click Performance for Performance Drift Analysis.
Enter the current data file path from Local or URL.
Click on Submit button.
Consumable API
API stands for “Application Programming Interface”. An API is a software intermediary that allows AION to communicate with other applications to... | ||
Go to directory “WHEEL file”
Install the whl package using the command:
python -m pip install usecasename_version-py3-none-any.whl
Add the following code:
from {packageName} import aion_predict as p1 p1.predict({json_data})
Using Rest API
Make Sure the AION_Service.bat is running
Click on the launch button of ... | ||
RequestType: POST Content-Type=application/json
Body: {"trainingDataLocation": "Reference Data File Path", "currentDataLocation": "Latest Data File Path"}
Output: Affected Columns. HTML File Path.
Performance: URL:/AION/AION_38_1/performance
RequestType: POST Content Type = application/jsonBody : {"trainingDataLo... | ||
In Define Label add Rule, condition, and value as shown in the figure below. Click on the Next button.
Note: It is required to add at least 3 Rules to label dataset.
In Labelled Dataset user will get Model Summary.
And user can download fully Labelled Dataset as shown below.
Data Generator
Data generation to... | ||
Configuration of Regression data
number_samples: Number of samples (records) in dummy dataset (default=100)
number_numerical_features: Number of numerical features in dummy dataset (default=25)
number_categorical_features: Number of categorical features in dummy dataset (default=2)
number_text_feature: Number o... | ||
number_informative: Number of informative features. (Default is 20, i.e out of 25 total number features, only 20 features are useful of the model, and the other 5 are uninformative or redundant.)
number_target: Number of target features
bias: The bias term (or offset/y-intercept) in the underlying linear model (de... | ||
Uncertainty Quantification
The uncertainty Quantification feature gives the user a confidence score of the model under test and recommends how the uncertainty of predictions can be reduced.
Supported Uncertainty quantification testing (MLTest) algorithms are:
Classification:
Logistic regression (Default)
Random Fo... | ||
Click ONNX runtime or TFLite runtime accordingly
Give model path and data path
Eg: Model path: C:\Users\<user_name>\Model\irisData.onnx
Data path: C:\Users\<user_name>\input_data \iris_data.csv
Click submit. By default, GUI will read the first row of the dataset.
Click Runtime Prediction for a successful pre... | ||
Click Submit.
User can modify Keywords and key Attributes for Summarization if required.
Click Submit for final Summarization.
Text Data Labelling
Text labelling generate a label from the given text using Large Language Model (LLM). The LLM takes the input text data and generate prediction for corresponding labe... | ||
Types of code clones are:
Exact clone: Two code fragments like each other with little transformation in comments, layout, or whitespaces.
Parameterized clone: Changes made in names of variables, keywords, identifiers, or bypassing parameter during function call in code fragments, result in this clone.
Near-miss clon... | ||
LLaMA-13B
LLaMA-13B is a family of large language model from meta-AI which contains 13B parameter version available for fine-tunning in GCP GCE machine. If user wants to finetune the LLaMA-13B model, actual fine tuning occurs in GCP GCE machine and user perform inferencing from the local machine.
Steps to finetune LL... | ||
Click on any of those use cases. Example: Click Engineering Analytics to explore various sectors where AION can be used.
Problem statement:
Defect reports are generated from various testing and development activities in software engineering.
Sometimes two reports are submitted that describe the same problem, lead... |
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