squaad 2.0

Helper functions for running queries, ml pipeline, statistical analysis on SQUAAD framework

SQUAAD ANALYSIS FRAMEWORK

Installation

pip install squaad

Releases

• V2.0 https://github.com/fostiropoulos/squaad/releases/download/v2.0/squaad-2.0.tar.gz

Install from Binary

pip install squaad-2.0.tar.gz

Usage

Creating new database connection

myConnection=db("config.json","cache")
print("Connection Status: %s"%myConnection.testConnection())

Config.json and Cache

• Config.json follows the following format:

{"pgsql":{"host":"","user":"","passwd":"","db":""} }

• Cache folder is used to save results of the queries and uses the cache next time you execute a query.

Games-Howell Statistics Test

stats.gamesHowellBinomial({"GROUP1":{True:100, False:3999}, "GROUP2":{True:2999,False:2939}})

Classification Pipeline with KFold Usage

Parameters

• X Pandas dataframe with set of data. Each column is a feature
• Y Labels for the set of data.
• split_columns (Optional) unimplemented, columns to split by. That is columns that can have bias, we take into consideration during splitting
• kfolds (Optional) number of folds to run.
• classifiers (Optional) dictionary containing classifiers to use
• balancers (Optional) the balancers you want to run

Classifiers

Default Classifiers:

• Nearest Neighbors
• Linear SVM
• RBF SVM
• Gaussian Process
• Decision Tree
• Random Forest
• Neural Net
• AdaBoost
• Naive Bayes
• QDA

Balancers

Default Classifiers:

• Unbalanced
• SMOTE
• SMOTEEN
• SMOTETomek
• RandomUnderSampler

ML Pipeline examples

X=df[['locs_inc', 'cplxs_inc', 'smls_inc', 'vuls_inc', 'fbgs_inc', 'locs_dec', 'cplxs_dec', 'smls_dec', 'vuls_dec', 'fbgs_dec']]
Y=df['affiliation']
mlPipeline.classificationPipeLineKfold(X,Y)