semvec/pypi-packages huggingface dataset:
FastMlOps 0.0.2
a package that use for esay way for deploy ml model with fastapi
Requires Python: >=3.6

# FastMlOps
esay to deploy your ml model 

## example
```python
from FastMlOps import FastMlOps

app = FastMlOps()

config = {
    "method" : "POST" ,
    "path" : "/getclass" ,
    "inputModel" : {
        "x" : int,
        "y" : int
    } , 
    "responseModel" : {
        "catagory" : str ,
        "confidence" : float
    },
    "model" : "model1.h5"
}

app.createAPI(config)
```
- **method** : use for http method ex. **GET** , **POST**
- **path** : url path for test server
- **inputModel** : input of your ml model
- **responseModel** : output of your ml model
- **model** : path to your ml model ex. **"/path/ml.h5"**

connect to 
```
http://localhost:8000/docs#/
```
![Test Image 1](image.png)

enjoy for ez API
SpecialModel 0.0.22
Dictionary Model
Requires Python: >=3.6

# SpecialModel
Special Model

![alt_text](https://raw.githubusercontent.com/DanielBNY/DictionaryModel/dev/extra/Logo.png)

The library returns an object with defined attributes in types from an input dictionary, 
with optional rules about types and more.

The declaration for the object variables and their types uses annotations:

        number: int
        question: bool

The mapping between variable and path inside the input dictionary are inside the source variables, in this format:
a string separated by '-' for each inner jump inside the dictionary
For example:

        NUMBER_SOURCE = "data-number"           # == dict_input["data"]["number"]
        QUESTION_SOURCE = "data-question"       # == dict_input["data"]["question"]

The optional parameter are:
    
    DISABLE_TYPE_EXCEPTIONS = True
    DISABLE_PATH_EXCEPTIONS = True

Type exception indicate to stop the object creation if theirs an error in matching types.
The second variable indicate to stop the object creation if a value don't exist in the source path.
When disabled variables are true the problematic variables are set to None. 
The default is DISABLE_TYPE_EXCEPTIONS and DISABLE_PATH_EXCEPTIONS as False.

Full example:

    class TestObj:
        number: int
        question: bool
        NUMBER_SOURCE = "data-number"
        QUESTION_SOURCE = "data-question"
        DISABLE_TYPE_EXCEPTIONS = True
        DISABLE_PATH_EXCEPTIONS = True


##Example:

>pip install SpecialModel


    from special_model.SpecialModelFactory import SpecialModelFactory
    
    
    class TestObj:
        number: int
        question: bool
    
        NUMBER_SOURCE = "data-number"
        QUESTION_SOURCE = "data-question"
        DISABLE_TYPE_EXCEPTIONS = True
        DISABLE_PATH_EXCEPTIONS = True
    
    
    dict_input_test = {
        "data": {
            "number": 123,
            "question": True
        }
    }
    
    factory = SpecialModelFactory(TestObj, dict_input_test)
    test_obj: TestObj = factory.run()
    print(test_obj.number, test_obj.question)
    print(type(test_obj.number), type(test_obj.question))
dsd 0.0.3
Dense subgraph discovery
Requires Python: >=3.0

# dsd_pip

This package contains three algorithms that solve dense subgraph problem exactly or approximately, including Goldberg's max flow based algorithm [1], charikar's greedy peeling algorithm [2], and state-of-the-art flowless (greedy++) algorithm [3].

We also leverage all algorithms to find k-clique dense subgraph, given list of k-cliques in a certain graph. Remind this can be done with algorithms like KClist [5].
We follow [4] when contructing clique based max flow graph.

To install the package, run

    pip install dsd

Check https://github.com/c752334430/dsd_pip/blob/main/dsd_package_demo.ipynb to see how to use the package.

[1] A. V. Goldberg. Finding a maximum density subgraph. University of California Berkeley, CA, 1984.

[2] M. Charikar. Greedy approximation algorithms for finding dense components in a graph. In
International Workshop on Approximation Algorithms for Combinatorial Optimization, pages
84–95. Springer, 2000

[3] Boob D., Gao Y., Peng R., Sawlani S., Tsourakakis C.~E., Wang D., Wang J., Flowless: Extracting Densest Subgraphs Without Flow Computations, <i>arXiv e-prints</i>, 2019.

[4] Shuguang Hu, Xiaowei Wu, and T-H. Hubert Chan. 2017. Maintaining Densest Subsets Efficiently in Evolving Hypergraphs. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 929–938. DOI:https://doi.org/10.1145/3132847.3132907

[5] Maximilien Danisch, Oana Balalau, and Mauro Sozio. 2018. Listing k-cliques in Sparse Real-World Graphs*. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 589–598. DOI:https://doi.org/10.1145/3178876.3186125