Skip to main content

No project description provided

Project description

PyPI version Build Status Coverage Status GitHub

Overview

This project is aimed to serve as an utility tool for the preprocessing, training and extraction of entity embeddings through Neural Networks using the Keras framework. It's still under construction, so please use it carefully.

Installation

The installation is pretty simple if you have a virtualenv already installed on your machine. If you don't please rely to VirtualEnv official documentation.

pip install entity-embeddings-categorical

Usage

The usage of this utility library is provided in two modes: default and custom. In the default configuration, you can perform the following operations: Regression, Binary Classification and Multiclass Classification.

If your data type differs from any of these, you can feel free to use the custom mode, where you can define most of the configurations related to the target processing and output from the neural network.

Default mode

The usage of the default mode is pretty straightforward, you just need to provide a few parameters to the Config object:

So for creating a simple embedding network that reads from file sales_last_semester.csv, where the target name is total_sales, with the desired output being a binary classification and with a training ratio of 0.9, our Python script would look like this:

    config = Config.make_default_config(csv_path='sales_last_semester.csv',
                                        target_name='total_sales',
                                        target_type=TargetType.BINARY_CLASSIFICATION,
                                        train_ratio=0.9)


    embedder = Embedder(config)
    embedder.perform_embedding()

Pretty simple, huh?

A working example of default mode can be found here as a Python script.

Custom mode

If you intend to customize the output of the Neural Network or even the way that the target variables are processed, you need to specify these when creating the configuration object. This can be done by creating a class that extend from TargetProcessor and ModelAssembler.

A working example of custom configuration mode can be found here.

Visualization

To-DO;

Documentation

Testing

This project is inteded to suit most of the existent needs, so for this reason, testability is a major concern. Most of the code is heavily tested, along with Travis as Continuous Integration tool to run all the unit tests once there is a new commit.

To-Do:

  • Notebok sample;
  • Visualization;
  • Draw CNN;

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

entity_embeddings_categorical-0.6.tar.gz (14.7 kB view details)

Uploaded Source

Built Distribution

entity_embeddings_categorical-0.6-py3-none-any.whl (23.2 kB view details)

Uploaded Python 3

File details

Details for the file entity_embeddings_categorical-0.6.tar.gz.

File metadata

  • Download URL: entity_embeddings_categorical-0.6.tar.gz
  • Upload date:
  • Size: 14.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.3

File hashes

Hashes for entity_embeddings_categorical-0.6.tar.gz
Algorithm Hash digest
SHA256 69cf80c434919752a0b394768120464bbcf836e5335beff387fa564cd672bc9a
MD5 f948a1551f2d697d7fb0e28847ef3f92
BLAKE2b-256 a85fea7260720dff857842c1c5dbcea0be3d37992eb70dd276392c745827600d

See more details on using hashes here.

File details

Details for the file entity_embeddings_categorical-0.6-py3-none-any.whl.

File metadata

  • Download URL: entity_embeddings_categorical-0.6-py3-none-any.whl
  • Upload date:
  • Size: 23.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.3

File hashes

Hashes for entity_embeddings_categorical-0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 3a9d59de720b3200afd235de0d2949b14fb070fc2e09e376d5145512ed43cd82
MD5 75e4d15d0a7bb88b1ca4e0dfe196a66f
BLAKE2b-256 9b23adabb0445c7fdd7eaa0b349804c6112de6e1d69b0832c5ad23676c9e3ebf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page