slacgs 0.1.9

A Simulator for Loss Analysis of Classifiers on Gaussian Samples.

SLACGS

A Simulator for Loss Analysis of Linear Classifiers on Gaussian Samples in order to evaluate Trade Off Between Samples and Features sizes in Classification Problems on gaussian Samples.

Documentation: https://slacgs.netlify.app/

• Reports with results will be stored in a Google Spreadsheet for each: Experiment Scenario, Custom Experiment Scenario and another one for the Custom Simulations.

• The Spreadsheets are stored in a Google Drive folder named 'slacgs.demo.<user_email>' owned by slacgs' google service account and shared with the user's Google Drive account.

• Also, images with data visualization will be exported to a local folder inside user's local folder (/slacgs/images/ or /content/slacgs/images (for G-colab) )

• Reports Exported:

  • Loss Report: Contains mainly results focused on Loss Functions evaluations for each dimensionality of the model.
  • Compare Resport: Contains mainly results focused on comparing the performance of the Model using 2 features and 3 features.
  • Home Report (Scenario): Contains results from all simulations in a Scenario and links to the other reports. (available only for comparison between 2D and 3D)

• Images Exported (<user_home>/slacgs/images/ or /content/slacgs/images [for G-colab] ):

  • Scenario Data plots .gif: Contains a gif with all plots with the data points (n = 1024, dims=[2,3] ) generated for all Models in an Experiment Scenario.
  • Simulation Data plot .png: Contains a plot with the data points (n = 1024, dims=[2,3] ) generated for a Model in a Simulation.
  • Simulation Loss plot .png: Contains a plot with the loss values (Theoretical, Empirical with Train Data, Empirical with Test data) generated for a Model in a Simulation.

• Loss Functions:

  • Theoretical Loss: estimated using probability theory
  • Empirical Loss with Train Data: estimated using empirical approach with train data
  • Empirical Loss with Test Data: estimated using empirical approach with test data

Experiment

Download Experiment PDF

Demo

1. Download and Install
2. Set/Start Report Service
3. Experiment Scenarios
4. Demo Functions:
   • Run an Experiment Simulation
     • run a simulation for one of the experiment scenarios and return True if there are still parameters to be simulated and False otherwise
   • Add a Simulation to an Experiment Scenario
     • add simulation results to one of the experiment scenario spreadsheets
   • Run a Custom Scenario
     • run a custom scenario and write the results to a Google Spreadsheet shared with the user
   • Add a Simulation to a Custom Scenario
     • add a simulation to a custom scenario spreadsheet
   • Run a Custom Simulation
     • run a custom simulation for any dimensionality and cardinality
   • Run All Experiment Simulations
     • run all simulations in all experiment scenarios

1. Download And Install

pip install slacgs

2. Set Report Service

from slacgs.demo import *

## opt-1: set report service configuration with your own google cloud service account key file
path_to_google_cloud_service_account_api_key = 'path/to/key.json'
set_report_service_conf(path_to_google_cloud_service_account_api_key)

# opt-2 set report service configuration to use slacgs' server if you have the access password
set_report_service_conf()

3. Experiment Scenarios

from slacgs.demo import print_experiment_scenarios

print_experiment_scenarios()

4 Demo Functions

from slacgs.demo import *

## 1. Run an Experiment Simulation ##
run_experiment_simulation()
  
## 2. Add a Simulation to an Experiment Scenario Spreadsheet ##
### Scenario 1
scenario_number = 1
params = [1, 1, 2.1, 0, 0, 0]
add_simulation_to_experiment_scenario_spreadsheet(params, scenario_number)

### Scenario 2
scenario_number = 2
params = [1, 1, 2, -0.15, 0, 0]
add_simulation_to_experiment_scenario_spreadsheet(params, scenario_number)

### Scenario 3
scenario_number = 3
params = [1, 1, 2, 0, 0.15, 0.15]
add_simulation_to_experiment_scenario_spreadsheet(params, scenario_number)

### Scenario 4
scenario_number = 4
params = [1, 1, 2, -0.1, 0.15, 0.15]
add_simulation_to_experiment_scenario_spreadsheet(params, scenario_number)

## 3. Run a Custom Scenario ##
scenario_list = [[1,1,3,round(0.1*rho,1),0,0] for rho in range(-5,6)]
scenario_number = 5
run_custom_scenario(scenario_list, scenario_number)
  
## 4. Add a Simulation to a Custom Scenario Spreadsheet ##
params = (1, 1, 3, -0.7, 0, 0)
scenario_number = 5
add_simulation_to_custom_scenario_spreadsheet(params, scenario_number)

## 5. Run a Custom Simulation ##
### 2 features
params = [1, 2, 0.4]
run_custom_simulation(params)

### 3 features
params = [1, 1, 4, -0.2, 0.1, 0.1]
run_custom_simulation(params)

### 4 features
params = [1, 1, 1, 2, 0, 0, 0, 0, 0, 0]
run_custom_simulation(params)

### 5 features
params = [1, 1, 2, 2, 2, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.2, 0, 0, 0]
dims_to_compare = (2, 5)
run_custom_simulation(params, dims_to_compare)

### 6 features
params = [1, 2, 3, 4, 5, 6, -0.3, -0.3, -0.2, -0.2, -0.1, -0.1, 0, 0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4]
run_custom_simulation(params)

## 6. Run All Experiment Simulations ##
run_experiment()

4.1 Demo Test Functions (simulations running at 1% of its default number of iterations)

from slacgs.demo import *

## 1. Run an Experiment Simulation ##
run_experiment_simulation_test()

## 2. Add a Simulation to an Experiment Scenario Spreadsheet ##
### scenario 1
scenario_number = 1
params = [1, 1, 2.1, 0, 0, 0]
add_simulation_to_experiment_scenario_spreadsheet_test(params, scenario_number)

### scenario 2
scenario_number = 2
params = [1, 1, 2, -0.15, 0, 0]
add_simulation_to_experiment_scenario_spreadsheet_test(params, scenario_number)

### scenario 3
scenario_number = 3
params = [1, 1, 2, 0, 0.15, 0.15]
add_simulation_to_experiment_scenario_spreadsheet_test(params, scenario_number)

### scenario 4
scenario_number = 4
params = [1, 1, 2, -0.1, 0.15, 0.15]
add_simulation_to_experiment_scenario_spreadsheet_test(params, scenario_number)

## 3. Run a Custom Scenario ##
scenario_list = [[1, 1, 3, round(0.1 * rho, 1), 0, 0] for rho in range(-5, 6)]
scenario_number = 5
run_custom_scenario_test(scenario_list, scenario_number)

## 4. Add a Simulation to a Custom Scenario Spreadsheet ##
params = (1, 1, 3, -0.7, 0, 0)
scenario_number = 5
add_simulation_to_custom_scenario_spreadsheet_test(params, scenario_number)

## 5. Run a Custom Simulation ##
### 2 features
params = [1, 2, 0.4]
run_custom_simulation_test(params)

### 3 features
params = [1, 1, 4, -0.2, 0.1, 0.1]
run_custom_simulation_test(params)

### 4 features
params = [1, 1, 1, 2, 0, 0, 0, 0, 0, 0]
run_custom_simulation_test(params)

### 5 features
params = [1, 1, 2, 2, 2, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.2, 0, 0, 0]
dims_to_compare = _test(2, 5)
run_custom_simulation_test(params, dims_to_compare)

### 6 features
params = [1, 2, 3, 4, 5, 6, -0.3, -0.3, -0.2, -0.2, -0.1, -0.1, 0, 0, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4]
run_custom_simulation_test(params)

## 6. Run All Experiment Simulations ##
run_experiment_test()