semvec/pypi-packages huggingface dataset:
deep-toggl 0.5
Simple tool for toggl summary
Requires Python: >=3.6

[![Build
Status](https://travis-ci.com/gianscarpe/toggl_personal_evaluation.svg?branch=master)](https://travis-ci.com/gianscarpe/toggl_personal_evaluation)
[![Coverage
Status](https://coveralls.io/repos/github/gianscarpe/toggl_personal_evaluation/badge.svg?branch=developing)](https://coveralls.io/github/gianscarpe/toggl_personal_evaluation?branch=developing)
[![PyPI version](https://badge.fury.io/py/deep-toggl.svg)](https://badge.fury.io/py/deep-toggl)


# DEEP TOGGL
Open source project based on `toggl_cli` apis to get summaries from your `toggl`
profile

# INSTALL
1. Clone repositoy at https://github.com/gianscarpe/toggl_personal_evaluation
2. Install using pip
   `python -m pip install .`

# USE
From CLI, call `deep_toggl`

# LICENSE
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.f
TOPSIS-Shivam-101803158 0.0.3
TOPSIS Implementation

# TOPSIS-ShivamPundir-101803158
Submitted By: Shivam Pundir(101803158)

## What is TOPSIS?
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.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install **TOPSIS**.
Dependencies and devDependencies will be installed automatically.

```bash
pip install TOPSIS-Shivam-101803158
```

## Usage
##### 1) As a Library:
Import in your python File:
```python
from TOPSIS import topsis
topsis()
```
Run the python file by typing in terminal/cmd:
```sh
python nameOfFile.py nameOfDataFile.csv "weights" "impacts" nameOfOutputFile.csv
```
##### 2) Using Command Promt:

Command line args:
- name of input File(csv format)
- weights(as a string)
- impacts(as a string)
- name of output file(csv format)
Eg. 
```bash
topsis data.csv "1,1,1,1" "+,+,-,+" output.csv
```

# Input file (data.csv)
The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

|Model|Corr|Rseq|RMSE|Accuracy|
|---|----|----|----|--------|
|M1   |0.79|0.62|1.25|60.89   |
|M2   |0.66|0.44|2.89|63.07   |
|M3   |0.56|0.31|1.57|62.87   |
|M4   |0.82|0.67|2.68|70.19   |
|M5   |0.75|0.56|1.3 |80.39   |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.
# Output file (output.csv)

|Model|Corr|Rseq|RMSE|Accuracy|Topsis_score       |Rank|
|---|----|----|----|--------|-------------------|----|
|M1   |0.79|0.62|1.25|60.89   |0.7722097345612788 |2   |
|M2   |0.66|0.44|2.89|63.07   |0.22559875426413367|5   |
|M3   |0.56|0.31|1.57|62.87   |0.43889731728018605|4   |
|M4   |0.82|0.67|2.68|70.19   |0.5238778712729114 |3   |
|M5   |0.75|0.56|1.3 |80.39   |0.8113887082429979 |1   |

The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
## License
[MIT](https://choosealicense.com/licenses/mit/)
TOPSIS-ShivamPundir-101803158 0.0.5
TOPSIS Implementation

# TOPSIS-ShivamPundir-101803158
Submitted By: Shivam Pundir(101803158)

## What is TOPSIS?
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.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install **TOPSIS**.
Dependencies and devDependencies will be installed automatically.

```bash
pip install TOPSIS-ShivamPundir-101803158
```

## Usage
##### 1) As a Library:
Import in your python File:
```python
from TOPSIS import topsis
topsis()
```
Run the python file by typing in terminal/cmd:
```sh
python nameOfFile.py nameOfDataFile.csv "weights" "impacts" nameOfOutputFile.csv
```
##### 2) Using Command Promt:

Command line args:
- name of input File(csv format)
- weights(as a string)
- impacts(as a string)
- name of output file(csv format)
Eg. 
```bash
topsis data.csv "1,1,1,1" "+,+,-,+" output.csv
```

# Input file (data.csv)
The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

|Model|Corr|Rseq|RMSE|Accuracy|
|---|----|----|----|--------|
|M1   |0.79|0.62|1.25|60.89   |
|M2   |0.66|0.44|2.89|63.07   |
|M3   |0.56|0.31|1.57|62.87   |
|M4   |0.82|0.67|2.68|70.19   |
|M5   |0.75|0.56|1.3 |80.39   |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.
# Output file (output.csv)

|Model|Corr|Rseq|RMSE|Accuracy|Topsis_score       |Rank|
|---|----|----|----|--------|-------------------|----|
|M1   |0.79|0.62|1.25|60.89   |0.7722097345612788 |2   |
|M2   |0.66|0.44|2.89|63.07   |0.22559875426413367|5   |
|M3   |0.56|0.31|1.57|62.87   |0.43889731728018605|4   |
|M4   |0.82|0.67|2.68|70.19   |0.5238778712729114 |3   |
|M5   |0.75|0.56|1.3 |80.39   |0.8113887082429979 |1   |

The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
## License
[MIT](https://choosealicense.com/licenses/mit/)