Metadata-Version: 2.1
Name: lightgbm-embedding
Version: 0.1.1
Summary: Feature embeddings with LightGBM
License: MIT
Keywords: nlp,turkish
Author: Atilla Karaahmetoğlu
Author-email: atilla.karaahmetoglu@gmail.com
Requires-Python: >=3.7,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: lightgbm (>=3.0.0,<4.0.0)
Requires-Dist: pandas (>=1.0.0,<2.0.0)
Requires-Dist: scikit-learn (>=1.0.0,<2.0.0)
Description-Content-Type: text/markdown

# LightGBM Embeddings

Feature embeddings with LightGBM

## Installation

    pip install lightgbm-embedding

## Examples
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from lightgbm_embedding import LightgbmEmbedding

df = pd.read_csv(
    "https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv"
)
cols = df.columns[:-1]
target = df.columns[-1]
num_classes = df[target].nunique()

X_train, X_test = train_test_split(
    df, test_size=0.2, stratify=df[target], random_state=42
)

n_dim = 20
emb = LightgbmEmbedding(n_dim=n_dim)
emb.fit(X_train[cols], X_train[target])
X_train_embed = emb.transform(X_train[cols])
X_test_embed = emb.transform(X_test[cols])
```

