statds 1.1.5

Library for statistical testing and comparison of algorithm results

Hi, StaTDS is a library for statistical testing and comparison of algorithm results 👋

Statistical Tests for Data Science (StaTDS)

StaTDS is a library for mathematicians, scientists, and engineers. It includes various tools to facilitate statistical analysis given a set of data samples. Within this library, you will find a wide range of statistical tests to streamline the process when conducting comparative or sample studies.

Available statistical test

Normality

Name	Function
Shapiro-Wilk	normality.shapiro_wilk_normality
D'Agostino-Pearson	normality.d_agostino_pearson
Kolmogorov-Smirnov	normality.kolmogorov_smirnov

Homoscedasticity

Name	Function
Levene	homoscedasticity.levene
Bartlett	homoscedasticity.bartlett

Parametrics

Name	Function	Type Comparisons
T Test paired	parametrics.t_test_paired	Paired
T Test unpaired	parametrics.t_test_unpaired	Paired
ANOVA between cases	parametrics.anova_cases	Multiple
ANOVA within cases	parametrics.anova_within_cases	Multiple

Non Parametrics

Name	Function	Type Comparisons
Wilcoxon	no_parametrics.wilconxon	Paired
Binomial Sign	no_parametrics.binomial	Paired
Mann-Whitney U	no_parametrics.mannwhitneyu	Paired
Friedman	no_parametrics.friedman	Multiple
Friedman + Iman-Davenport	no_parametrics.iman_davenport	Multiple
Friedman Aligned Ranks	no_parametrics.friedman_aligned_ranks	Multiple
Quade	no_parametrics.quade	Multiple
Kruskal-Wallis	no_parametrics.kruskal_wallis	Multiple

Post-hoc

Name	Function
Nemenyi	no_parametrics.nemenyi
Bonferroni	no_parametrics.bonferroni
Li	no_parametrics.li
Holm	no_parametrics.holm
Holland	no_parametrics.holland
Finner	no_parametrics.finner
Hochberg	no_parametrics.hochberg
Hommel	no_parametrics.hommel
Rom	no_parametrics.rom
Shaffer	no_parametrics.shaffer

Developed in:

Authors

• @ChrisLe7
• @anmoya2
• @jmluna
• @sebastianventura

Documentación

You can find all documentation in Documentation Folder, Web Docs or Youtube Channel.

Installation

StaTDS could be downloaded using two different ways: using pip or git as command line or docker container.

Using Git repository

The installation process for Git is detailed for each supported operating system in [1]. Additionally, a comprehensive guide on downloading StaTDS is provided. Git can be easily installed on widely used operating systems such as Windows, Mac, and Linux. It is worth noting that Git comes pre-installed on the majority of Mac and Linux machines by default.

 $ git clone https//github.com/kdis-lab/StaTDS 

    $ cd StaTDS
    $ python -m pip install --upgrade pip # To update pip
    $ python -m pip install --upgrade build # To update build
    $ python -m build 
    $ pip install dist/statds-1.0-py3-none-any.whl

Using pip

Ensure that Python and pip are correctly installed on your operating system before proceeding. Once you have completed this step, utilize the following commands for library installation according to your preferred configuration:

• If you only want to use the statistical tests:

  $ pip install statds
  

• If you also want to generate PDFs:

  $ pip install statds[pdf]
  

• If you want all the features:

  $ pip install statds[full-app]
  

Quick start

• If you have questions, please ask them in GitHub Discussions.
• If you want to report a bug, please open an issue on the GitHub repository.
• If you want to see StaTDS in action, please click on the link below and navigate to the notebooks/ folder to open a collection of interactive Jupyter notebooks.

Using StaTDS Library - API

Normality tests: Shapiro Test

from statds.normality import shapiro_wilk_normality
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
results = []

for i in range(1, len(columns)): 
    results.append(shapiro_wilk_normality(dataset[columns[i]].to_numpy(), alpha))

statistic_list, p_value_list, cv_value_list, hypothesis_list = zip(*results)

results_test = pd.DataFrame({"Algorithm": columns[1:], "Statistic": statistic_list, "p-value": p_value_list, "Results": hypothesis_list})
print(results_test)

Homoscedasticy tests: Levene

from statds.homoscedasticity import levene_test
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
statistic, p_value, rejected_value, hypothesis = levene_test(dataset, alpha, center='mean')
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}")

Parametrics tests: T-test

from statds.parametrics import t_test_paired
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
selected_columns = [columns[1], columns[2]]
statistic, rejected_value, p_value, hypothesis = t_test_paired(dataset[selected_columns], alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Parametrics tests: ANOVA

from statds.parametrics import anova_test
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
statistic, p_value, rejected_value, hypothesis = anova_test(dataset, alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Non-parametrics tests: Wilcoxon

from statds.no_parametrics import wilconxon
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
selected_columns = [columns[1], columns[2]]
statistic, p_value, rejected_value, hypothesis = wilconxon(dataset[selected_columns], alpha)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")

Non-parametrics tests: Friedman Test

import pandas as pd
from statds.no_parametrics import friedman

dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(ff"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)

Post-hoc tests: Bonferroni

from statds.no_parametrics import friedman, bonferroni
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)
num_cases = dataset.shape[0]
results, figure = bonferroni(rankings, num_cases, alpha, control = None, type_rank = "Friedman")
print(results)
figure.show()

Post-hoc tests: Nemenyi

from statds.no_parametrics import friedman, nemenyi
dataset = pd.read_csv("dataset.csv")
alpha = 0.05
columns = list(dataset.columns)
rankings, statistic, p_value, critical_value, hypothesis = friedman(dataset, alpha, criterion=False)
print(hypothesis)
print(f"Statistic {statistic}, Rejected Value {rejected_value}, p-value {p_value}")
print(rankings)
num_cases = dataset.shape[0]
ranks_values, critical_distance_nemenyi, figure = nemenyi(rankings, num_cases, alpha)
print(ranks_values)
print(critical_distance_nemenyi)
figure.show()

Using StaTDS Web Client

Local with Python

You only need create a python script with next code:

from statds import app

app.start_app(port=8050)

Now, you can access to the interface with your Web navigator through the following url: http://localhost:8050

Local Using Docker

Firstly, to begin with, it is essential to download the repository from GitHub to obtain the Dockerfile. Before this step, ensure that Docker is installed on your computer [2]. With Docker ready to use, you can build the application's image by executing the following command:

docker build -t name-lib ./

After the image has been successfully created, the next step is to instantiate a container using that image.

docker run -p 8050:8050 --name container name-lib

Now, you can access to the interface with your Web navigator through the following url: http://localhost:8050

References

[1] 1.5 getting started - installing git. Git. (n.d.). https://git-scm.com/book/en/v2/Getting-Started-Installing-Git [2] Get Docker — Docker Docs. Docker Inc. 2023. url: https://docs.docker.com/get-docker