embedding-tool 0.1

An embedding toolkit that can perform multiple embedding process which are low-dimensional embedding (dimension reduction), categorical variable embedding, and financial time-series embedding.

Dimension Reduction

The function performs dimensionality reduction, pre-processing the data and comparing the reconstruction error via PCA and autoencoder.

Install

pip install embedding-tools

How to use

from short_text_analyzer.core import *

Input data: The input matrix has a size of 863 $\times$ 768.

print ("Data's size: ", testing_data.shape)
print ("Dimension:   ", testing_data.shape[1])

Data's size:  (863, 768)
Dimension:    768

Performing dimension reduction: we will reduce the number of dimension from 768 to 2.

dim_reducer = dimensionReducer(analyzer.embeddingRaw, 2, 0.002)
dim_reducer.fit()

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-5-6ee2cf251bab> in <module>
----> 1 dim_reducer = dimensionReducer(analyzer.embeddingRaw, 2, 0.002)
      2 dim_reducer.fit()


NameError: name 'dimensionReducer' is not defined

Calculating the MSE of the reconstructed vectors

dim_reducer.rmse_result