semvec/pypi-packages huggingface dataset:
sklearning 0.0.1
metrics module
License: MIT
Keywords: metrics

This module is used to get metrics of Machine Learning/Deep Learning Models.It consists of all sklearn.metrics and stats module methods.Using this module you can also use all all different distances obtained in metrics.pairwise.cosine_distance etc.

from sklearning.metrics import *

y_test = [0,1,2,3,4]

y_pred = [0,1,2,3,5]

#Root Mean Squared Error

rmse = rootMeanSquaredError(y_test,y_pred)

print(rmse)

o/p:0.4472135954999579



#Regressor Summary

summary = regressorSummary(y_test,y_pred)

print(summary)

#Stats Value

statsValue(y_test,y_pred)

o/p:

statsValue(y_test,y_pred)

pvalues

 [0.53047777 0.00190127]

tvalues

 [-0.70710678 10.39230485]

rsquared

 0.972972972972973

rsquared_adj

 0.963963963963964


#All methods of sklean.metrics can be used by just giving the name of methods ex:

mse = mean_squared_error(y_test,y_pred)

print(mse)

o/p: 0.2
makedalytics 0.0.9
Python library for reproducible data analytics of dataframes, text and images
License: MIT

# makedalytics
Currrent version: <a href="https://zenodo.org/badge/latestdoi/311430172"><img src="https://zenodo.org/badge/311430172.svg" alt="DOI"></a>
A python library for data analytics on data including text and/or images with an emphasis on scientific reproducibility. There is only a dependancy on pip (although some versions of pip may have dependancies). Please note the library is currently evolving, and I am testing new functions.  At present the tested functional functions of makedalytics are the following: 

## general functions

### remind_ds_libraries():
   This function takes no arguments and reminds what libraries you may want to import by printing them.

### packages_to():
   This function takes no arguments and displays what your system and packages are, and writes them to a file called "requirementscontext.txt"  

### check_requirements(test2list):
   Function takes arguments of a list e.g. ['absl-py==0.7.0','aiofiles==0.4.0','altair==4.1.1'] of packages you will have as requirements as test2list. 
    Returns which you are missing in your machine environment. Missing may be a difference of version number. 

### biopsy_df(start_row,end_row,column, df):
   Function takes arguments of starting row (start row), end row and column or [columns] listed in []. Returns only specified area of dataframe.

### understand_df(df):
   This function takes the dataframe as an argument, and produces information on number of columns, rows, data types, existance of nulls etc.

### pristine(df,axis_to_zap, modify_index):
   Takes arguments of dataframe(df), 'columns' or 'rows' (axis to zap) and modify_index which can be set to true or false.
      Returns a cleaned dataframe without columns or rows that have nulls.

## text and NLP related functions

### show_duplicates(df):
   If there are duplicates it will show and tell. 

### tag_text(text, column):
   Shows you where (what row) certain text is in a columns. Works like nuclear medicine tagging. 

### wordish_count(str):
   Counts split up elements of a string. In the case ofan English sentance that is a count for each word with some counting of certain punctuation..

## image related functions   

### create_matrix(width, height, default_element):
Helper function to create matrices `width' x `height' y
    fills with default element which can be set to a number or a boolean (True or False)
TOPSIS-Tannishtha-101803201 1.0
Topsis score calculator
License: MIT
Keywords: topsis,thapar,rank,topsis score

# TOPSIS_Tannishtha_101803201
Topsis score and rank generator python package
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

########################

This is a package to find topsis score and rank of a dataframe with only numerical values.


########################

Command Line Usage :

>>python topsis.py "input.csv" "1,1,1,2" "+,+,+,-" "out.csv"