wildonion/unixerr

wildonionunixerr

Export cassandra table into csv file:

AI Core Development Guide

⚠️ If you are working on development part, remember to change the local host(127.0.0.1) inside /etc/hosts/ to api.unixerr.com and tensorboard.api.unixerr.com for API and TensorBoard server respectively.
⚠️ Remember to call /users/add/info and /users/add/positions routes of API server after the classification is done on csv file of input data.
⚠️ You can't create an environment if the environment was exported on a different platform than the target machine.
❗️ Both core and server folders can only be controlled using controller.py middleware.

Setup

  • Start an Apache Cassandra server and fill out .env file with necessary variables
  • Create an environment with a specific python version:
  • Create the environment using the uniXerr.yml file:
  • Activate uniXerr environment:
  • Update the environment using uniXerr.yml file:
  • Export your active environment to uniXerr.yml file:
  • Install pm2:
  • Install completion for typer-cli:
  • Create a docs file from uniXerr CLI:

Usage

Running in development mode: __ API docs

Export cassandra table into csv file:

Import exported csv file into cassandra table:

Running TensorBoard for visualization of training and testing DL models:

You can also install uniXerr from pip if you are the commander guy: __ uniXerr CLI usage


Results

📌 Position Clustering Process

Dataloader Object - MinMax Scaler

Fake Dataset for Offline Training

📊 Plotted Dataset before Clustering using PCA - Standard Scaler

📊 Plotted Dataset before Clustering using TSNE - Standard Scaler

Clustered Dataset Based on Latent Space of Pre-trained VAE model

Clustered Dataset Based on Position Clustering data

VAE Pre-trained Model - Normal PDF

📊 Clusters Found by KMeans on Latent Space of Pre-trained VAE model

📊 Clusters Found by KMeans on Position Clustering Dataset - Plotted using PCA | Standard Scaler

📊 Clusters Found by KMeans on Position Clustering Dataset - Plotted using TSNE | Standard Scaler

📊 VAE Model Training Loss

📌 Position Classification Process

Training Dataloader Object of Clustered Dataset Based on Latent Space of Pre-trained VAE model

Testing Dataloader Object of Clustered Dataset Based on Latent Space of Pre-trained VAE model

Training Dataloader Object of Clustered Dataset Based on Position Clustering data

Testing Dataloader Object of Clustered Dataset Based on Position Clustering data

📊 Percentage of Positions before Classification on Clustered Dataset Based on Latent Space of Pre-trained VAE model

📊 Percentage of Positions before Classification on Clustered Dataset Based on Position Clustering data

Classifier Pre-trained Model - Trained and Tested on Clustered Dataset Based on Latent Space of Pre-trained VAE model

Classifier Pre-trained Model - Trained and Tested on Clustered Dataset Based on Position Clustering data

📊 Classifier Model Training Accuracy - Clustered Dataset Based on Latent Space of Pre-trained VAE model

📊 Classifier Model Testing Accuracy - Clustered Dataset Based on Latent Space of Pre-trained VAE model

📊 Classifier Model Training Loss - Clustered Dataset Based on Latent Space of Pre-trained VAE model

📊 Classifier Model Training Accuracy - Clustered Dataset Based on Position Clustering data

📊 Classifier Model Testing Accuracy - Clustered Dataset Based on Position Clustering data

📊 Classifier Model Training Loss - Clustered Dataset Based on Position Clustering data

Classification Results on Arbitrary Inputs - Classified using Pre-trained Model of Clustered Dataset Based on Latent Space of Pre-trained VAE model and Clustered Dataset Based on Position Clustering data

Information - Updated Sep 07, 2022

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