semvec/pypi-packages huggingface dataset:
Energy-Models 0.0.2
Prediction models in timeseries
Requires Python: >=3.7

# Energy Models Package

                             THIS IS A PACKAGE OF MODELS OF PREDICT IN TIMESERIES FORECASTING                
             this package helps any developer in univariate and multivariate-multi-step time series forcasting in house-power-consumption dataset lets take a look about each type 
             Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables,the requirement 
             to predict multiple time steps,nd the need to a perform the same type of prediction for multiple physical sites.

# Installation

````
pip install Energy_Models
````

# Models list
  
  * LSTM 
  * BILSTM
  * GRU
  * BIGRU
  * TimeDistributer
  * CNN
  * TCN
  All models take 3 parameters except TCN :
    
    * must take value 
      -1 : n_steps
      -2 : n_features 
    * default value = 1 
      -3 : n_outputs  
      
  TCN Model you can build it by just give it data to build function
      
  
# Package Folders 
 
 * Data
 * models
 
# how to use the package

 first you must read the data set you want to use the models on it 
 and then import preprocess_data from Data folder :
 
 ````
 from Data import preprocess_data as pr
 
 df = pd.read_csv('Data.txt',sep=';', 
                  parse_dates={'date_time' : ['Date', 'Time']}, infer_datetime_format=True, 
                  low_memory=False, na_values=['nan','?'], index_col='date_time')

 pr.fill_missing(df.values)
 df.to_csv('new_data.csv')
 
 df = pd.read_csv('new_data.csv',parse_dates=['date_time'], index_col= 'date_time')
 ````
 
 next step you can use functions on preprocess_data to split and scale the data . 
 
   ````
   X_train, X_test = pr.train_test_split(df)
   X_train, X_test, scaler = pr.scale_data(X_train, X_test)
   ```` 
   
 After that converting the data to supervised.
 now you can build model by import it from models folder :
 
  ````
  from models import Models as m
  model=m.lstm(21, 7 , 7 ).getModel()
  21 ==> n_steps
  7 ==> n_features
  7==>n_outputs
  ````
  
 After that you will able to predict and evaluate your models used. 
 
  ````
  y=model.predict(X)
  X==>input
  ````
 
 Models have evaluation function you can give it  the model and (actual , predicted) values 
 
  ````
  m.evaluate(model,actual,pred)
  ````
   
 Else you can calculate loss using metrics function for train and test both :
 
  ````
  m.print_metrics(model,Y_train,Y_pred_train,Y_test,Y_pred_test)
  ````
doc2data 0.2.0
Integrated document processing with machine learning.
Requires Python: >=3.7
Keywords: deep learning,document processing,machine learning,pdf parsing

# doc2data
[![PyPI - Version](https://img.shields.io/pypi/v/doc2data.svg)](https://pypi.org/project/doc2data)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/doc2data.svg)](https://pypi.org/project/doc2data)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)

-----

## About doc2data
doc2data is a Python library that provides functionality to train deep learning models for various document processing tasks.

Currently, models can be trained for four tasks:

1. Page rotation
2. Page cropping
3. Document (multi-page) classification
4. Token classification

Please note that doc2data is currently in a prototype stage.

## Installation
```console
pip install doc2data
```

## Documentation
The documentation can be found [here](https://doc2data.readthedocs.io/en/latest/).

## License
`doc2data` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.

## Credits
![alt text](https://raw.githubusercontent.com/serge724/d2d_sample_datasets/2575ad957bf407e676acdd71e8cffe7fe2fae2ee/PrototypeFund-P-Logo.svg)
![alt text](https://raw.githubusercontent.com/serge724/d2d_sample_datasets/823b78f99e01493f43023e8ad67008c4d1eaf4cf/BMBF_CMYK_Gef_L_e.svg)