Entity Embedding Encoder

A tool to numerically encode categorical variables in datasets, based on the work 'Entity Embeddings of Categorical Variables' by C. Guo and F. Berkhahr, 2016.

Description and usage

Transforms a dataset with categorical and numerical variables into a purely numerical dataset. The dataset is required to have a dependent variable.

The encoding is performed as follows:

1. Numerical variables, if any, are scaled to [0,1]
2. Each categorical variable is encoded with the LabelEncoder from Scikit-Learn
3. A neural network N is built:
   a) for each categorical variable a Keras Embedding layer is added, which we call an EE-layer.
   b) all the numerical variables are concatenated to the outputs of all the EE-layers into a single numerical vector, which is then fed to a dense layer.
   c) the output from b) is then passed to a smaller dense layer.
   d) the output from c) is fed to a final layer appropriate for the dependent variable.
4. N is trained with 70% of the data and validated with the remaining 30%.
5. Once N is trained, the encoding of the dataset are the outputs from all the EE-layers with the originally numerical variables appended.

Initialisation:

encoder = EntityEmbeddingEncoder(epochs=100, dense_layers_sizes=(1000,500), dropout=True)

Main fit method:

encoder.fit(X, y, cat_cols=[], ee_sizes={}, verbose=True, test=None)
encoder.fit_transform(X, y, cat_cols=[], ee_sizes={}, verbose=True, test=None)

Where:

• X is a pandas dataframe with all the independet variables
• y is the dependent variable
• cat_cols is a list with the column numbers of all the categorical variables to encode. If empty, all categorical variables detected will be encoded
• ee_sizes is a dictionary with the sizes for EE-layers. If empty, a categorical variable with k distinct values will be assigned an EE-layer of size min(30, k/3)
• verbose is a boolean for whether to print details on the training of the network
• test is a pandas dataframe containing all the independet variables of the test data (use in case there is a suspicion that the test set contains categories not present in X)

For further theoretical details see Section 2.3.6 of 'On the encoding of categorical variables for machine learning applications'.

Requirements (developed and tested under)

• Python 3.6
• Tensorflow 2.0
• Pandas 1.1.4
• Scikit-Learn 0.22.2
• Category Encoders 2.1.0 (site)

Authors

• Erick GR - erickgrm

License

This project is licensed under the GNU GPLv3 License - see the LICENSE file for details