hnet is a Python package for probability density fitting of univariate distributions for random variables. The hnet library can determine the best fit for over 90 theoretical distributions. The goodness-of-fit test is used to score for the best fit and after finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned. It can be used for parametric, non-parametric, and discrete distributions. ⭐️Star it if you like it⭐️

Key Features

Resources and Links

• Example Notebooks: Examples
• Medium Blogs Medium
• Gumroad Blogs with podcast: GumRoad
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues
• Article: arXiv
• Article: PDF

Background

• HNet stands for graphical Hypergeometric Networks, which is a method where associations across variables are tested for significance by statistical inference. The aim is to determine a network with significant associations that can shed light on the complex relationships across variables. Input datasets can range from generic dataframes to nested data structures with lists, missing values and enumerations.

• Real-world data often contain measurements with both continuous and discrete values. Despite the availability of many libraries, data sets with mixed data types require intensive pre-processing steps, and it remains a challenge to describe the relationships between variables. The data understanding phase is crucial to the data-mining process, however, without making any assumptions on the data, the search space is super-exponential in the number of variables. A thorough data understanding phase is therefore not common practice.

• Graphical hypergeometric networks (HNet), a method to test associations across variables for significance using statistical inference. The aim is to determine a network using only the significant associations in order to shed light on the complex relationships across variables. HNet processes raw unstructured data sets and outputs a network that consists of (partially) directed or undirected edges between the nodes (i.e., variables). To evaluate the accuracy of HNet, we used well known data sets and generated data sets with known ground truth. In addition, the performance of HNet is compared to Bayesian association learning.

• HNet showed high accuracy and performance in the detection of node links. In the case of the Alarm data set we can demonstrate on average an MCC score of 0.33 + 0.0002 (P<1x10-6), whereas Bayesian association learning resulted in an average MCC score of 0.52 + 0.006 (P<1x10-11), and randomly assigning edges resulted in a MCC score of 0.004 + 0.0003 (P=0.49). HNet overcomes processes raw unstructured data sets, it allows analysis of mixed data types, it easily scales up in number of variables, and allows detailed examination of the detected associations.

Installation

Install hnet from PyPI

pip install hnet

Install from Github source

pip install git+https://github.com/erdogant/hnet

Imort Library

import hnet
print(hnet.__version__)

# Import library
from hnet import hnet

Installation

• Install hnet from PyPI (recommended).

pip install -U hnet

Examples

• Simple example for the Titanic data set

# Initialize hnet with default settings
from hnet import hnet
# Load example dataset
df = hnet.import_example('titanic')
# Print to screen
print(df)

#      PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
# 0              1         0       3  ...   7.2500   NaN         S
# 1              2         1       1  ...  71.2833   C85         C
# 2              3         1       3  ...   7.9250   NaN         S
# 3              4         1       1  ...  53.1000  C123         S
# 4              5         0       3  ...   8.0500   NaN         S
# ..           ...       ...     ...  ...      ...   ...       ...
# 886          887         0       2  ...  13.0000   NaN         S
# 887          888         1       1  ...  30.0000   B42         S
# 888          889         0       3  ...  23.4500   NaN         S
# 889          890         1       1  ...  30.0000  C148         C
# 890          891         0       3  ...   7.7500   NaN         Q

Play with the interactive Titanic results.

Example: Learn association learning on the titanic dataset

Example: Summarize results

Networks can become giant hairballs and heatmaps unreadable. You may want to see the general associations between the categories, instead of the label-associations. With the summarize functionality, the results will be summarized towards categories.

Example: Feature importance

Performance

Contributors

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• This library is free. But powered by caffeine! Like it? Chip in what it's worth, and keep me creating new functionalities!🙂