semvec/pypi-packages huggingface dataset:
ent-emb-avtocado16 0.0.1
Transforms categorical features into embedded vectors
Requires Python: >=3.6

Package transforms your categorical variables into embedded vectors. You should have tensorflow, pandas, numpy, keras and sklearn installed.

Attributes:
model = EntityEmbedding(dataframe, features from the copy of the df, target column, column you want a vector for)

Hyperparameters you can optimize:
model.train_fit(activation1='relu', activation2='relu', activation3='relu', loss='mean_squared_error', metrics='mape', dense_size_num=128, dense_size_conc_1=300, dense_size_conc_2=300, alpha=1e-3, epochs=1000, batch_size=512, verbose=1, patience=5)

Inside model.transform(), always provide embedded vector you want to use: model.transform(model.ent_emb)

model.visualize() returns 2 d visualization of your column categories.
znipy 0.1.1
Easy imports from Jupyter Notebooks
Requires Python: >=3.8
Keywords: IPython,Jupyter,ZnTrack

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# ZnIPy - Easy imports from Jupyter Notebooks

See [Importing Jupyter Notebooks as Modules](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Importing%20Notebooks.html) for more information.

```python
from znipy import NotebookLoader

module = NotebookLoader().load_module("JupyterNotebook.ipnyb")

hello_world = module.HelloWorld()
```
or with direct imports
```python
import znipy
znipy.register()

from JupyterNotebook import HelloWorld

hello_world = HelloWorld()
```