Metadata-Version: 2.1
Name: deepneighbor
Version: 0.2.2
Summary: embedding-based item nearest neighborhoods extraction
Home-page: https://github.com/LouisBIGDATA/deepneighbor
Author: Yufeng Wang
Author-email: louiswang524@gmail.com
License: UNKNOWN
Keywords: embedding,information retrieval,deep learning,torch,tensor,pytorch,nearest neighbor
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.4
Description-Content-Type: text/markdown
Requires-Dist: h5py
Requires-Dist: requests
Requires-Dist: gensim (==3.7.0)
Requires-Dist: joblib (==0.13.0)
Requires-Dist: fastdtw (==0.3.2)
Requires-Dist: tqdm
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: annoy
Requires-Dist: dgl
Requires-Dist: torch (>=1.1.0)

# DeepNeighbor

[![Python Versions](https://img.shields.io/pypi/pyversions/deepneighbor.svg)](https://pypi.org/project/deepneighbor)
[![PyPI Version](https://img.shields.io/pypi/v/deepneighbor.svg)](https://pypi.org/project/deepneighbor)

---

DeepNeighbor is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based models along with lots of core components layers which can be used to easily build custom models.You can use any complex model with
<br>`model.train()`，which generates embeddings for users and items (Deep),
<br> and `model.search()`, which looks for Approximate nearest neighbor for seed user/item (Neighbor) .

```python
from deepneighbor.embed import Embed

model = Embed(data)
model.train()
model.search(seed = 'Louis', k=10)
```


