abtest 0.0.1

allows to run AB Test for any problem, automatically decides which test must be applied and represents the results

A - B Test Platform

Key Features

• allows you to find the Dsitribution of the testing values.
• Time period detection (year, quarter, month, week, week-part, day, hour) adding as subgroups
• subgroups og testing is available
• schedule your test daily, monthly, weekly, hourly.
• Confidence level can aoutmatically assigns and tests for each Confidencce levels (e.g. 0.01, 0.05 appying for testes individually)

Running Platform

• Test Parameters

  test_groups : if there are any sub-groups of the active and control group, framework can run Test results for each subgroups. This parameter must be the column name which exits on given data set for both Active and Control groups.

  groups :* The column name represents active and control group flag.

  date :* If it needs, it is able to be trigger related to a date that data is goint to be filtered as before the given date.

  feature :* the column name that represents actual values that are tested according to two main groups.

  data_source :* The location where the data is stored or the query (checck data source for details).

  data_query_path :* type of data source to import data to the platform (optinal Ms SQL, PostgreSQL, AWS RedShift, Google BigQuery, csv, json, pickle).

  time_period :* additional time period which c(optinal year, month, day, hour, week, week day, day part quarter) (check details time periods).

  time_indicator :* If test is running periodicly, the column name that related to time must be assigned.

  export_path :* Output results of export as csv format (optional).

  connector :* if there is a connection paramters as user, pasword, host port, this allows us to assign it as dictionary format (e.g {"user": ***, "pw": ****}).

  confidence_level :* Confidence level of test results (list or float).

  boostrap_sample_ratio :* Boostraping randomly selected sample data raio (between 0 and 1).

  boostrap_iteration :** Number of iteration for boostraping.

Data Source

Here is the data source that you can connect with your SQL queries:

• Ms SQL Server

• PostgreSQL

• AWS RedShift

• Google BigQuery

• .csv

• .json

• pickle

• Connection PostgreSQL - MS SQL - AWS RedShift

  data_source = "postgresql"
  connector = {"user": ***, "password": ***, "server": "127.0.0.1", 
               "port": "5440", "db": ***}
  data_main_path ="""
                     SELECT                             
                      groups,
                      test_groups
                      feature,
                      time_indicator
                     FROM table
                 """
  

• Connection Google BigQuery

  data_source = "googlebigquery"
  connector = {"data_main_path": "./json_file_where_you_stored", 
               "db": "flash-clover-*********.json"}
  data_main_path ="""
             SELECT                             
              groups,
              test_groups
              feature,
              time_indicator
             FROM table
         """
  

• Connection csv - .json - .pickle

  data_source = "csv"
  data_main_path = "./data_where_you_store/***.csv"