To get a score on system performace of your machine in the field of analytics
#Running the benchmark
- Clone or Download this repo
- cd to the cloned or Downloaded(Might need to unzip if downloaded) repo
- issue python run.py from commandline
If running this code on bash on ubuntu on windows, you need to do export KMP_AFFINITY=disabled
#Requirements
- Python 3.5 or above
- statsmodels
- pandas
- numpy-mkl
**Best way to run this is install Anaconda 3 from Continuum Analytics and run it **
Some Scores: ####System 1:
- OS: Ubuntu
- Processor: intel i5-5th gen MQ processor
- Ram:- 8 GB DDR3
- System Type: Desktop
- Score : 5.6
####System 2:
- OS: Windows 10
- Processor: intel i5-6th gen U processor
- Ram: 8 GB
- System Type: Laptop
- Score: 2.5
####System 3:
- OS: Ubuntu on Windows 10 (After anniversary update)
- Processor: intel i5-5th gen U processor
- Ram: 12 GB
- System Type: Laptop
- Score: 2.15
####System 4:
- OS: Windows 10
- Processor: intel i5-5th gen U processor
- Ram: 12 GB
- System Type: Laptop
- Score: 2.0
####System 5:
- OS: Windows 10
- Processor: intel i3-5th gen U processor
- Ram: 4 GB
- System Type: Laptop
- Score: 1.3
#Introduction
##Methodology It works by calculating various statistical values and models (explained later) and compares with a base and it uses Hyperbolic curve formulae which highly penalizes slow machines and at the same time highly rewards good machines.
##Score Range Theoratically score will range from [0, infi)
##Python version Built using python 3
#Functions used
##Generating list Using numpy to generate a very large list of random floats
The folwing functions are run several times and score is calculated
##Sorting the List Using self implemented quicksort to sort.
##Min Finding the minimum element of the list.
##Max Finding the max element of the lis.
##Mean Finding the mean of the list.
##Standard Deviation Finding the sample standard deviation of the list
##Kurtosis Finding the excess Kurtosis of the list
##Pandas Analysis and Training several models The sample data in the "Data/data.xlsx" file is used as the sample data and various operations are performed on the data and the following models are trained: ###1)Ordinary Least Sqaure ###2)Weighted Least Square ###3)GLSAR (Generalized least square with AR(p) convergence) ###4)Robust Linear Model
Except model creation, all methods are self implemented