easyML | Python Package for data processing | Machine Learning library

by aarshayj Python Version: 0.1.0 License: BSD-3-Clause

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | easyML Summary

easyML is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. easyML has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install easyML' or download it from GitHub, PyPI.

#easyML - Code Less, Do More!!. easyML is a python package designed to streamline the process of analyzing datasets using predictive models. It covers crutial aspects the of data analysis process starting from preprocessing, to feature engineering and finally predictive modeling. They key advantage of this package is the DataBlock module using which you can create a block of your data at the start of analysis. The other modules take this block as input and seemlessly work on your data together. It definitely comes at the cost of loss of generalization as compared to the raw scikit-learn features, but the idea is to incorporate typically used actions and also provide options for flexibility through user-defined tasks. The package is particularly useful for begineers and intermediate level python data science enthusiasts, who are looking to get the job done without worrying about the code.

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Quality

Security

License

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Support

easyML has a low active ecosystem.

It has 16 star(s) with 13 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

easyML has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of easyML is 0.1.0

Quality

easyML has 0 bugs and 0 code smells.

Security

easyML has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

easyML code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

easyML is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

easyML releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

easyML saves you 620 person hours of effort in developing the same functionality from scratch.

It has 1442 lines of code, 73 functions and 12 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed easyML and discovered the below as its top functions. This is intended to give you an instant insight into easyML implemented functionality, and help decide if they suit your requirements.

Imputes the mean value of a column
Check if a subset is a subset
Updates the list of column names
Check if a variable is in the given types
Fit the model
Calculates the characteristics of the classification
Performs the cross validation scoring
Scale the columns
Check that variable is of type
Exports the model
Calculate feature importances based on algo
Calculate feature importances for a given algorithm
Run feature importances
Exports the model into a model
Exports the model into an ensemble
Plots the coefficients of the Algorithm
Exports a model into a model
Function to plot the coefficients of the Algorithm
Performs recursive feature elimination
Plots the feature import score for the given algorithm
Plots the best fit of the algorithm
Set the model parameters
Recursive feature elimination
Normalize a dataset
Combine categories
Create bins for a given column

Get all kandi verified functions for this library.

easyML Key Features

No Key Features are available at this moment for easyML.

easyML Examples and Code Snippets

LinAlgError: Array must not contain infs or NaNs, but no infs of NaNs

Python

Lines of Code : 8

License : Strong Copyleft (CC BY-SA 4.0)

Copy

__main__:65: RuntimeWarning: Degrees of freedom <= 0 for slice
C:\Users\Jonat\Anaconda\lib\site-packages\numpy\lib\function_base.py:2326: 
RuntimeWarning: divide by zero encountered in true_divide
c *= np.true_divide(1, fact)
C:\Users\J

Community Discussions

Trending Discussions on easyML

ModuleNotFoundError for function in module that's been included in __init__.py file

QUESTION

ModuleNotFoundError for function in module that's been included in __init__.py file

Asked 2019-May-13 at 20:24

I'm having problems getting a function to import correctly and I'm not sure why.

Right now I have a file structure that looks like this:

...

ANSWER

Answered 2019-May-13 at 20:24

from .processing import * gets the importable names inside the processing module, but not the name processing itself.

It's kind of like opening a box, finding five more boxes inside it, putting those five boxes in your pocket, and then throwing the original box away.

The import works when you execute metrics.py as a standalone module because you're in the utils/ directory, and sys.path includes the current directory.

Source https://stackoverflow.com/questions/56119242

Community Discussions, Code Snippets contain sources that include Stack Exchange Network