semvec/pypi-packages huggingface dataset:
topsis-naveen 1.3
A package used to implement topsis functionality
Requires Python: >=3.6

# Package for implementing topsis functionality

```
Naveen Budhwal  
COE-16
Roll number: 101703362
```
## Description
Package for implementing the topsis functionality easily.

## Installation
`pip install topsis_naveen`

## Command Line Arguments
`topsis_naveen <dataset>.csv "<weigths> "<levels>"`

First argument is the input csv filename containing the dataset. 
The second argument is the weigths assigned to each column.
The third argument is a string of +,- denoting whether the particular column/feature needs to be increased(+) or decreased(-)

## Use via py script
```
from topsis_naveen import topsis
topsis('<input_filename>.csv', '<Weights>', '<Levels>')
```
piprec 1.0.0
Recommender system for Python libraries
Requires Python: >=3

# PipRec - Recommender system for Python libraries

PipRec is a robust recommender system targeted for users starting on new projects. Often people want to install a number of libraries when they start working on a project. With so many libraries to choose from (over 200 thousand from PyPI alone) it is often difficult to make an informed decision. Our recommender system is aimed at solving this problem.

### Background

We use data provided by libraries.io (https://libraries.io/data) to compute our recommendations. From the data we compute association rules which are then used as a basis for our recommendations.

The number of association rules is relatively small. Around 8500 libraries have recommendations based on these rules. Compared to the amount of libraries out there we employ another method to increase our range of recommendations.

For libraries which we do not have information for, we try to find a similar library for which we already have recommendations. This is based purely on library descriptions.

### Features

- Each recommendation has a quality indicator
- Ability to choose between 2 quality metrics (Lift and Certainty Factor)
- Ability to choose between 2 output formats (list-like and tree-like)
- Ability to choose between 2 similarity methods for unseen libraries (Latent Dirichlet Allocation and PyPI search engine)

### Getting started

To get a recommendation for a library simply type:

`piprec <library_name>`

To show the list of available flags type:

`piprec -h`

### Quality metrics

By default, PipRec uses Lift to indicate the quality of a recommendation.

To switch to a Certainty Factor metric simply add the -cf flag to your command:

`piprec <library_name> -cf`

### Output formats

By default, PipRec outputs the recommendations in a simple list-like format.

To enable the tree-like output add the flag -t to your command:

`piprec <library_name> -t`

### Similarity methods

By default, PipRec uses a trained LDA (Latent Dirichlet Allocation) model to find a similar library for which we have data about. We assume that we have a large enough dataset that the most similar library found will have relevant recommendations for your library.

Alternatively, you can use PyPI to find a similar library. The way this works is that we query search function on the PyPI website and take the top result which we know of. To enable this add the flag -pse to your command:

`piprec <library_name> -pse`

### Evaluation

We would be very pleased if you spent a little bit of your time to tell us how well we did. We do not collect any personal information and your feedback would help us in our future endeavours. Just click the link below to complete a survey.

https://docs.google.com/forms/d/1BzGa8oBFSSmS9NctdtDVNFhOiDJO-S5sRXm6NNm0oWc
outlier-naveen 1.1
Package for outlier removal
Requires Python: >=3.6

# Package for removing outliers from a dataset

```
Naveen Budhwal  
COE-16
Roll number: 101703362
```
## Description
Package for removing outliers from a given dataset using the IQR method.

## Installation
`pip install outlier_naveen`

## Command Line Arguments
`outlier_naveen <input_filename>.csv <output_filename>.csv`

First argument is the input csv filename containing the dataset. 
The second argument is the name of the output csv file that will store the modified dataset.

## Use via py script
```
from outlier_naveen import remove_outliers
remove_outliers('<input_filename>.csv', '<output_filename>.csv')
```
job-scheduler 0.3.4
Simple Scheduler for Python 3.X

Scheduler
=======================

Simple scheduler module
----

### Installation

Only tested on Python 3.X

From source:

```sn
$ python setup.py install
```

For production:

```sn
$ pip install job-scheduler
```

### Usage

```python
from datetime import datetime
from datetime import timedelta
from haydn.scheduler import S
from haydn.event_emitter import EventEmitter
import logging

logging.basicConfig(level=logging.DEBUG)


class Example(EventEmitter):

    def __init__(self):
        self.schedule_manager = Scheduler(timer_interval=1)

    def main(self):
        schedule_time = ScheduleTime(start_datetime=datetime.utcnow() + timedelta(seconds=2), end_datetime=datetime.utcnow() + timedelta(seconds=100), repeat=ScheduleTime.REPEAT_SECOND, repeat_interval=5, repeat_limit=5)
        schedule = Schedule(schedule_time, self.do_something, task_params=[1], reference_id=1)

        self.schedule_manager.add_schedule(schedule)
        self.schedule_manager.start()

    def do_something(self, p1):
        print("doing task")
        print("task parameter: ", p1)


example = Example()
example.main()

```