semvec/pypi-packages huggingface dataset:
Topsis-Paramvir-102117147 1.0.2
It gives the ranking to models as per the TOPSIS score.Please view the instructions so as to run the package smoothly in your terminal.
License: MIT

# TOPSIS PACKAGE - Assignment 1
I have developed a command line python program to implement the TOPSIS.
TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world
## Installation
```pip install topsis-Paramvir-102117147```

## Usage
Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

```py -m topsis.__main__ [input_file_name.csv] [weight as string] [impact as string] [result_file_name.csv]```

## Example Usage
The below example is for the data have 5 columns.
```py -m topsis.__main__ "C:\User\...." "1,1,2,0.5,0.75" "+,+,-,-,-" "C:\User\....."```

## Example Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37

## Output Dataset
Fund Name | P1 | P2 | P3 | P4 | P5 | TOPSIS Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2 

<br>

## Important Points
1) There should be only numeric columns except the first column i.e. Fund Name.
2) Input file must contain atleast three columns.

<br>
TOPSIS-Rajneesh-102283007 0.4
A Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Rajneesh Bansal**

***

## What is TOPSIS?

Technique for Order of 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. 

<br>

## Key Features:

### TOPSIS Algorithm Implementation:
This pacakge incorporates a robust and efficient implementation of the TOPSIS algorithm. It considers both positive and negative ideal solutions, calculating the relative closeness of alternatives to the ideal solution.

### The package is designed with a user-friendly interface, allowing users to input their decision matrices easily. The library handles the complexity of the TOPSIS method while providing a simple API for users.

### Customizable Weights:
This pacakge enables users to assign different weights to each criterion, allowing for flexibility in reflecting the relative importance of criteria in decision-making scenarios.

### Sensitivity Analysis:
Conduct sensitivity analyses to understand the impact of changes in criteria weights on the final decision. This pacakge provides tools for exploring various scenarios and making informed decisions.

### Result Interpretation:
The package provides intuitive result interpretation, presenting the ranked alternatives based on their closeness to the ideal solution. Detailed reports and visualizations aid in understanding the decision-making process.

### Compatibility:
This pacakge is compatible with Python 3.x and integrates seamlessly into various data science and analytics workflows. It can be easily incorporated into Jupyter notebooks, scripts, or larger applications.

## How to install this package:
```
>> pip install TOPSIS-Rajneesh-102283007
```


### In Command Prompt
```
>> topsis data.csv "1,1,2,1" "+,+,-,+" result.csv
```
flying-delta-tools-graphql 0.1.0
llama-index tools graphql integration
License: MIT
Requires Python: >=3.8.1,<3.12

# GraphQL Tool

This tool provides agents the ability to easily execute GraphQL queries against a server. The tool can be initialized with the server url and any required headers and thereafter perform queries against the server

## Usage

This tool has a more extensive example usage documented in a Jupyter notebook [here](https://github.com/emptycrown/llama-hub/tree/main/llama_hub/tools/notebooks/graphql.ipynb)

Here's an example usage of the GraphQLToolSpec.

This tool works best when the Agent has access to the GraphQL schema for the server. See [here](https://github.com/emptycrown/llama-hub/tree/main/llama_hub/tools/notebooks/shopify.ipynb) for an example of using a tool with a file loader to create even more powerful Agents.

```python
from llama_index.tools.graphql import GraphQLToolSpec
from llama_index.agent import OpenAIAgent

tool_spec = GraphQLToolSpec(
    url="https://spacex-production.up.railway.app/",
    headers={
        "content-type": "application/json",
    },
)

agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

agent.chat(
    "get the id, model, name and type of the Ships from the graphql endpoint"
)
```

`graphql_request`: Runs a GraphQL query against the configured server

This loader is designed to be used as a way to load data as a Tool in a Agent. See [here](https://github.com/emptycrown/llama-hub/tree/main) for examples.