Metadata-Version: 2.1
Name: hyperopt-prophet
Version: 0.1.1
Summary: Integration of prophet forecasting with hyperopt, mlflow
License: MIT
Author: Carlos D. Escobar-Valbuena
Author-email: carlosdavidescobar@gmail.com
Requires-Python: >=3.8,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: cloudpickle (>=2.2.1,<3.0.0)
Requires-Dist: cython (>=3.0.0,<4.0.0)
Requires-Dist: hyperopt (>=0.2.7,<0.3.0)
Requires-Dist: mlflow (>=2.5.0,<3.0.0)
Requires-Dist: mlflowops (>=0.1.2,<0.2.0)
Requires-Dist: pandas (>=2.0.3,<3.0.0)
Requires-Dist: prophet (>=1.0.0,<2.0.0)
Requires-Dist: pydantic (>=2.1.0,<3.0.0)
Requires-Dist: pydantic-settings (>=2.0.2,<3.0.0)
Requires-Dist: pystan (>=2.19.1.1,<3.0.0.0)
Requires-Dist: python-dotenv (>=1.0.0,<2.0.0)
Description-Content-Type: text/markdown

# Hyperopt Prophet

**Integration of prophet forecasting with hyperopt, mlflow**
This implementation is based on the [Databricks AutoML](https://github.com/databricks/automl) repository.

## Setup

### Quick Install

```shell
python -m pip install hyperopt_prophet
```

### Build from source

Clone the repository

```shell
git clone https://github.com/Broomva/hyperopt_prophet.git
```

Install the package

``` shell
cd hyperopt_prophet && make install
```

### Build manually

After cloning, create a virtual environment

```shell
conda create -n hyperopt_prophet python=3.9
conda activate hyperopt_prophet
```

Install the requirements

```shell
pip install -r requirements.txt
```

Run the python installation

```shell
python setup.py install
```

## Usage

```python
import hyperopt_prophet 
```

## Attribution

Hyperopt Prophet builds upon the hard work of others. Here are the original leveraged repositories:

- [Databricks AutoML](https://github.com/databricks/automl)

