rubixml / sentiment Goto Github PK

An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

Home Page: https://rubixml.com

License: MIT License

PHP 100.00%

sentiment-analysis sentiment-classification neural-network natural-language-processing machine-learning text-classification deep-learning tf-idf tutorial example-project

sentiment's Introduction

Rubix ML

A high-level machine learning and deep learning library for the PHP language.

Developer-friendly API is delightful to use
40+ supervised and unsupervised learning algorithms
Support for ETL, preprocessing, and cross-validation
Open source and free to use commercially

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

PHP 7.4 or above

Optional

GD extension for image support
Mbstring extension for fast multibyte string manipulation
SVM extension for Support Vector Machine engine (libsvm)
PDO extension for relational database support
GraphViz for graph visualization

Documentation

Read the latest docs here.

What is Rubix ML?

Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.

Getting Started

If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

Interact With The Community

Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.

sentiment's People

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brbart dovidezra beniyke vsotirasqmplus andrewdalpino antx42 everaldofilho dralghazali drdub rohini2611 textualization gogomann sitedata

sentiment's Issues

hebrew support

hello,
thanks for the great work, can i train on hebrew language and the get predictions based on that training for hebrew texts?
cheers

Is there anyway to report back if the prediction was wrong or right

Hi 👋
I was asking if there is any way to report back if the prediction was wrong or right in a way that the library uses the feedback for learning and future predictions?
Like that #5 (comment)

Error trying to train, WordCountVectorizer missing parameter $maxDocumentFrequency

I am getting this error, when I am trying to train using your train.php (https://github.com/RubixML/Sentiment/blob/master/train.php) example:
Fatal error: Uncaught TypeError: Argument 3 passed to Rubix\ML\Transformers\WordCountVectorizer::__construct() must be of the type int, object given....

In your example on Line 44 you have:
new WordCountVectorizer(10000, 3, new NGram(1, 2)),

But the constuctor for WordCountVectorizer expects this:
public function __construct(
int $maxVocabulary = PHP_INT_MAX,
int $minDocumentFrequency = 1,
int $maxDocumentFrequency = PHP_INT_MAX,
?Tokenizer $tokenizer = null
)
What would be your recommended parameters for WordCountVectorizer for your example to work best?

How to optimize memory (RAM) usage

I'm running a sentiment type training environment with about 400,000 samples. The text is short, at most 255 characters each, but most are much less.

I'm running 32G on Linux running without a GUI and few other processes running. PHP is set to memory_limit -1.

Linux routinely kills my PHP process due to lack of memory. Following this guide I've set my overcommit_memory value to 2 which allows the script to run a little longer but it eventually runs out of memory and stops.

I've ordered 32G of more RAM hoping this will help with my problem, but, are there any other techniques I could use to reduce memory usage? Or, is there a way to see/calculate how much memory is needed before even attempting to run the script?

Neural network crash when using AdaMax optimizer with Tensor extension

The program work find before installing tensor extension, but after I install the extension I get this error.
Can someone help? What do I miss ?

zephir_call_class_method_aparams: *return_value must be IS_NULL or IS_UNDEF
#0 0x7f4afdead288 [/usr/lib/php/20190902/tensor.so(zephir_print_backtrace+0x38) [0x7f4afdead288]]
#1 0x7f4afdeb7215 [/usr/lib/php/20190902/tensor.so(zephir_call_class_method_aparams+0x285) [0x7f4afdeb7215]]
#2 0x7f4afdeb1469 [/usr/lib/php/20190902/tensor.so(zephir_array_fetch+0x2c9) [0x7f4afdeb1469]]
#3 0x7f4afdf4f165 [/usr/lib/php/20190902/tensor.so(zim_Tensor_Matrix_maximum+0xe15) [0x7f4afdf4f165]]
#4 0x55bf0acf5af5 [php(execute_ex+0x8635) [0x55bf0acf5af5]]
#5 0x55bf0acf714b [php(zend_execute+0x12b) [0x55bf0acf714b]]
#6 0x55bf0ac6e1ec [php(zend_execute_scripts+0xcc) [0x55bf0ac6e1ec]]
#7 0x55bf0ac0ded0 [php(php_execute_script+0x2d0) [0x55bf0ac0ded0]]
#8 0x55bf0acf9282 [php(+0x331282) [0x55bf0acf9282]]
#9 0x55bf0aad5938 [php(+0x10d938) [0x55bf0aad5938]]
#10 0x7f4b00f4b0b3 [/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7f4b00f4b0b3]]
#11 0x55bf0aad5ade [php(_start+0x2e) [0x55bf0aad5ade]]

Aborted (core dumped)

harry@ubuntu-server:~/sentiment$ php74 predict.php
Enter some text to analyze:
rubixml is great
The sentiment is: negative

harry@ubuntu-server:~/sentiment$ php74 predict.php
Enter some text to analyze:
Rubix ML is really great
The sentiment is: positive