Distributed Analytics Service (DAS)

Pre-defined set of A-Stack Activities

A set of simple activities which collects commands from DASScript and executes in a python runtime as part of Exec Activity

DAS Configuraton Model

Configuration parameters for DAS

DAS Python SDK

Offers basic functions that can called from DASConfig’s DAS Script.

Functions:

1. broadcastmsg

2. savemodel

3. readmodel

Currently Supports following libraries

import numpy as np
from numpy import nan
import datetime
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import make_pipeline
import statsmodels.api as sm
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from collections import namedtuple
from sklearn import linear_model

Feeding Training or Testing (To-be-Predicted) Data

InputDataSource - DAS Provides InputDataSource parameter as part of the DASConfig Parameter

Training Data Preparation

1. Data_Shape - DAS Provides Data_Shape Section as part of the DASConfig’s DASScript where TQL developers can add their Data Shaping code.

2. Data_Split_TrainAndValidate - DAS Provides Data_Split_TrainAndValidate Section as part of the DASConfig’s DASScript where TQL developers can add further steps their Data Shaping code.

Providing Algorithm Details

PredictiveModels - DAS Provides PredictiveModels Section as part of the DASConfig’s DASScript where TQL developers can add specific algorithm steps

Handle Model Output

Saving Trained Model for Prediction re-use

Reading Saved model

Real-time viewing of algorithm execution logs

All broadcastmsg statements from DAS Script are published to DASLog websocket endpoint