What are Entity Embeddings?
Entity Embeddings are vector representations of categorical variables or entities in a dataset. These embeddings are learned by training a neural network to capture the relationships between different entities in a high-dimensional space. Entity embeddings can be used to convert categorical data into continuous numerical data, enabling the use of machine learning algorithms that require numerical inputs. They can also be used for tasks such as clustering, visualization, and similarity search.
Example of training Entity Embeddings using Keras in Python:
import numpy as np
from keras.layers import Input, Embedding, Flatten, Dense
from keras.models import Model
# Generate sample categorical data
num_entities = 100
entity_data = np.random.randint(0, num_entities, size=(1000, 1))
# Define the embedding layer
input_layer = Input(shape=(1,))
embedding_layer = Embedding(input_dim=num_entities, output_dim=50)(input_layer)
embedding_output = Flatten()(embedding_layer)
# Add a dense layer and output layer
dense_layer = Dense(units=20, activation='relu')(embedding_output)
output_layer = Dense(units=1, activation='linear')(dense_layer)
# Create the model and train it
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer='adam', loss='mse')
model.fit(entity_data, np.random.rand(1000), epochs=10)
In this example, we generate some sample categorical data with 100 unique entities, define an embedding layer with an output dimension of 50, and train a simple neural network to learn the entity embeddings.
