semvec/pypi-packages huggingface dataset:
mixnet 0.0.3
A model to predict compressive strength of concrete based on it's composition
Requires Python: >=3.6

#  MixNet


## Installation
```bash
pip install mixnet
```

## Usage
```python
from mixnet import compressive_strength


compressive_strength(
    CEMENT,
    SLAG,
    FLY_ASH,
    WATER,
    SUPERPLASTICIZER,
    COARSE_AGGREGATE,
    FINE_AGGREGATE,
    AGE,
)
```
entity-resolution 0.1.0
Extensible entity resolution framework
Requires Python: >=3.6

# ResolvER

ResolvER is an extensible framework for building Entity Resolution pipelines in order to merge datasets around "things" based on complex join logic and transitive linking.

Entity Resolution is a complex and computationally expensive process. ResolvER seeks to provide tools that cover the majority of use cases, the ability to enhance those tools with machine learning, and leverage developers' experiential knowledge of data to provide a flexible and efficient solution to the Entity Resolution problem.

## Quick/Simple Example
The University of Leipzig provides [test datasets](https://dbs.uni-leipzig.de/research/projects/object_matching/benchmark_datasets_for_entity_resolution) for Entity Resolution, let's say you're working with the DBLP-ACM dataset.

The dataset provides two files, both describing published papers, with similar columns:
* A unique id (unique to that file only)
* A title
* A list of authors
* A venue
* A year

The titles vary slightly between files, and different authors may be listed for a given paper - in short, there's no clean or consistent way to deduplicate the data.

Using ResolvER, you can specify any number of complex operations to determine whether two given records are duplicates of each other in what are called "Strategies." One such strategy may be:
* `title` has Levenshtein ratio of at least 0.9 AND
* `authors` has Jaccard distance of at least 0.4 AND
* `year` is an exact match

## In Depth examples
For more in depth workflows and explanations of the methodology, reference the [notebooks](./notebooks) folder.

## Install
Install the latest version of ResolvER:
```shell
$ pip install entity-resolution
```

## Research
ResolvER is (most notably) inspired by the below publications:
* [Collective Entity Resolution in Relational Data](https://www.norc.org/pdfs/may%202011%20personal%20validation%20and%20entity%20resolution%20conference/collective%20entity%20resolution%20in%20relational%20data_pverconf_may2011.pdf)
* [Comparative Analysis of Approximate
Blocking Techniques for Entity Resolution](http://www.vldb.org/pvldb/vol9/p684-papadakis.pdf)


## License
Released under standard MIT license (see LICENSE.txt):
```
Copyright (c) 2021 ResolvER Developers
Carl Best
```
cryptounifier-python-sdk 0.0.4
CryptoUnifier PYTHON SDK
Requires Python: >=3.7

# CryptoUnifier PYTHON SDK

A simple PYTHON SDK for interacting with [Crypto Unifier](https://cryptounifier.io) API V1.

## Installation

You can install the package via pypi:

```bash
pip install cryptounifier_python_sdk
```

## Usage

### Using the Wallet API client

You can use the `WalletAPI` class for convenient access to API methods. Some are defined in the code:

```python
from  cryptounifier_python_sdk import MerchantAPI

client = new WalletAPI("WALLET_KEY", "SECRET_KEY", "btc")

balance = client.getBalance()
print(balance)

depositAddresses = client.getDepositAddresses()
print(depositAddresses)
```

### Using the Merchant API client

You can use the `MerchantAPI` class for convenient access to API methods. Some are defined in the code:

```python
from  cryptounifier_python_sdk import MerchantAPI

client = new MerchantAPI("MERCHANT_KEY", "SECRET_KEY")

invoice = client.createInvoice(["btc", "bch", "eth"])
print(invoice)
```

## License

The MIT License (MIT). Please see [License File](LICENSE.md) for more information.