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.
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Release files for ent-embedding 0.0.1
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ent_embedding-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Release files / ent_embedding-0.0.1-py3-none-any.whl
| Download URL | ent_embedding-0.0.1-py3-none-any.whl |
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| Size | 2.5 kB |
| Tags | Python 3 |
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