semvec/pypi-packages huggingface dataset:
queuery-client 1.1.1
Queuery Redshift HTTP API Client for Python
License: MIT
Requires Python: <4.0,>=3.8

# queuery_client_python

Queuery Redshift HTTP API Client for Python

## Installation

`pip install queuery-client`

## Usage

### Prerequisites

Set the following envronment variables to connect Queuery server:

- `QUEUERY_TOKEN`: Specify Queuery access token
- `QUEUERY_TOKEN_SECRET`:  Specify Queuery secret access token
- `QUEUERY_ENDPOINT`: Specify a Queuery endpoint URL via environment variables if you don't set the `endpoint` argument of `QueueryClient` in you code

### Basic Usage

```python
from queuery_client import QueueryClient

client = QueueryClient(endpoint="https://queuery.example.com")
response = client.run("select column_a, column_b from the_great_table")

# (a) iterate `response` naively
for elems in response:
    print(response)

# (b) invoke `read()` to fetch all records
print(response.read())

# (c) invoke `read()` with `use_pandas=True` (returns `pandas.DataFrame`)
print(response.read(use_pandas=True))
```

### Type Cast

By default, `QueueryClient` returns all values as `str` regardless of their definitions on Redshift.
You can use the `enable_cast` option to automatically convert types of the returned values into appropreate ones based on their definitions.

```python
from queuery_client import QueueryClient

client = QueueryClient(
    endpoint="https://queuery.example.com",
    enable_cast=True,   # Cast types of the returned values automatically!
)

sql = "select 1, 1.0, 'hoge', true, date '2021-01-01', timestamp '2021-01-01', null"
response = client.run(sql)
response.read() # => [[1, 1.0, 'hoge', True, datetime.date(2021, 1, 1), datetime.datetime(2021, 1, 1, 0, 0), None]]
```
embedding-disruptiveness 1.0.0
A Python package to calculate the disruption index and embedding disruptiveness measure using a citation network.
License: MIT
Requires Python: >=3.8
Keywords: science of science, disruption index, citation, innovation, edm, embedding disruptiveness measure

# embedding-disruptiveness

`embedding-disruptiveness` is a Python package for calculating the Embedding Disruptiveness Index of papers or patents using a citation network. This measure helps to identify how disruptive or consolidating a publication or patent is within its respective field.

The package builds upon the original `node2vec` code by @skojaku, which implements the directional skip-gram algorithm. You can find the original `node2vec` implementation [here](https://github.com/skojaku/node2vec). `embedding-disruptiveness` modifies and extends this code to specifically calculate the disruption index and an embedding-based disruptiveness measure.


## Installation

To install the latest version of `embedding-disruptiveness`, run:

```bash
pip install --upgrade embedding-disruptiveness
```


## Requirements
This code requires at least two gpus

## Usage
Here is a basic example of how to use embedding-disruptiveness:

```python
import embedding_disruptiveness

# Initialize the model with required parameters
trainer = embedding_disruptiveness.EmbeddingTrainer(net_input = NETWORK_FILE_LOCATION , #(npz file type)
                                                   dim = 128, # dimension of embedding vectors
                                                   window_size=5, # windowsize
                                                   device_in = '6', # cuda device where in-vectors will be 
                                                   device_out = '7',# cuda device where out-vectors will be 
                                                   q_value = 1, # q value in the randomwalk
                                                   epochs =1,  # epochsize
                                                    batch_size = 1024,# batchsize
                                                   save_dir = SAVE_LOCATION)

trainer.train()
```

## Model Parallelism

This package uses model parallelism to speed up the calculations, especially when dealing with large citation networks. Note: You need at least two GPUs to run the code. Ensure your environment is set up for multi-GPU usage, and the necessary CUDA drivers are installed.