semvec/pypi-packages huggingface dataset:
EASIER-net 0.0.8
EASIER-net
Requires Python: >=3.6

# EASIER-net

Feng, Jean, and Noah Simon. 2022. “Ensembled Sparse‐input Hierarchical Networks for High‐dimensional Datasets.” Statistical Analysis and Data Mining, March. https://doi.org/10.1002/sam.11579.

Python code for fitting EASIER-nets and reproducing all results from the paper.
The python code uses [PyTorch](https://pytorch.org/).

R code for fitting EASIER-net is available at https://github.com/jjfeng/easier_net_R.

## Quick-start

Setup a python virtual environment (code runs for python 3.6) with the appropriate packages from `requirements.txt`.

Simulate data using by following the tutorial notebook or load your own into a `npz` format with `x` and `y` attributes. You may also perform GridSearchCV by following the tutorial.

To fit an EASIER-net, run
```
python fit_easier_net.py --n-estimators <N_ESTIMATORS> --input-filter-layer <INPUT_FILTER_LAYER> --n-layers <N_LAYERS> --n-hidden <N_HIDDEN> --input-pen <INPUT_PEN> --full-tree-pen <FULL_TREE_PEN> --batch-size <BATCH_SIZE> --num-classes <NUM_CLASSES>  --weight <WEIGHT> --max-iters <MAX_ITERS> --max-prox-iters <MAX_PROX_ITERS> --model-fit-params-file <MODEL_FIT_PARAMS_FILE>
```
where:
* `N_ESTIMATORS` should be size of ensemble; the number of SIER-nets being ensembled.
* `INPUT_FILTER_LAYER` is whether to scale the inputs by parameter β
* `N_LAYERS` is the number of hidden layers
* `N_HIDDEN` is the number of hidden nodes per layer
* `INPUT_PEN` specifies $\lambda_1$ in the paper; controls the input sparsity
* `FULL_TREE_PEN` specifies $\lambda_2$ in the paper; controls the number of active layers and hidden nodes
* `BATCH_SIZE` specifies the size of the mini-batches for Adam
* `NUM_CLASSES` should be 0 if doing regression and `NUM_CLASSES` should be the number of classes if doing binary/multi-classification
* `WEIGHT` is a list of weights for the classes
* `MAX_ITERS` is the number of epochs to run Adam
* `MAX_PROX_ITERS` is the number of epochs to run batch proximal gradient descent
* `MODEL_FIT_PARAMS_FILE` is a json file that specifies what the hyperparameters are. If given, this will override the arguments passed in.

To perform cross-validation, one should run separate `fit_easier_net.py` scripts for each candidate penalty parameter values.
Then select the best penalty parameter values using `collate_best_param.py`.
streamn 1.0.2
a python library to use Streamn API's
Requires Python: >=3.7
Keywords: streamn,ASR,ASR API,Speech Recognition API,Audio Labs

# Streamn Python SDK

This [Streamn](https://www.streamn.ai) Python SDK provides convenient access to the Streamn API's.

## Documentation

See the [API docs](https://docs.streamn.ai/)

### Requirements

- Python 3+

## Installation

First make sure that Python is installed in your system.

```sh
pip install --upgrade streamn
```

<!-- > install using github repo:

```
1. clone this repo
2. move the repo ( cd alignment )
3. create virtual env(python3 -m venv virtualenvName)
4. activate the virtualenv ( source virtualenvName/bin/activate)
5. install required libraries ( pip3 install -r requirements.txt )
6. call the required fucntion from the utils file and add it in your code 
``` -->

## Configuration

The library needs to be configured with your account's API Key

save credentials by saving a file named streamn_config.json in your working directory in the following format.

Example of 'streamn_cofig.json' file

```conf
{
    "api_key":"asaisdniasnasnfjasdfn_asjhnadjsdsa"
}
```
## A Transcript generation Example


```python
import streamnai

# Process audio file
status , transcipt = streamnai.process_audio.transcribe_audio( audio_file_path = <audio file path to transcibe> , is_medical = <bool, True or Flase> , vocabulary_name = <string, vocab name>  , LanguageModelName = <string,language model name >  )

```
## A Transcript with Audiolab scores generation Example

```python
import streamnai

# Process audio file
status , transcipt = streamnai.process_audio.get_audiolab_transcript(audio_file_path = <audio file path to transcibe> , is_medical = <bool, True or Flase> , vocabulary_name = <string, vocab name>  , LanguageModelName = <string,language model name >  )

```

## A Improved Transcript generation Example

```python
import streamnai

# Process audio file
status , transcipt = streamnai.process_audio.get_improved_transcript( audio_file_path = <audio file path to transcibe> , is_medical = <bool, True or Flase> , vocabulary_name = <string, vocab name>  , LanguageModelName = <string,language model name >  )

```

To know more about these functions and parameters, click [here][extended-readme-functions-object]



## Need support

If the above doesn't help, do let us know at contact@streamn.ai

[symbl-docs]: https://docs.streamn.ai/
[extended-readme-functions-object]: https://github.com/saireddy12/streamn_sdk/blob/main/src/streamnai/README.md