Back ArrowExplore all kits

How to use gensim simple preprocess function

share link

by Abdul Rawoof A R dot icon Updated: Oct 9, 2023

technology logo
technology logo

Solution Kit Solution Kit  

A preprocessing function is in the context of data science and machine learning. They must process or change it before machine learning or data analysis can use raw data. The primary purpose of a preprocessing function is to clean, format, and prepare the data.   

Here's a brief overview of these tasks and the libraries used for them:   

  • Linear Regression: It is a supervised ML technique. We use one or more input features to predict a continuous target variable. Scikit-Learn is a popular library for performing linear regression in Python. To make and train linear regression models, use the LinearRegression class in Scikit-Learn.   
  • Clustering: It is an unsupervised machine-learning technique. Users utilize it to group similar data points. Scikit-Learn provides various clustering algorithms like K-Means, DBSCAN, and Hierarchical Clustering.   
  • Dimensionality Reduction: These techniques reduce the number of features in a dataset. Principal Component Analysis (PCA) is a used method for dimensionality reduction.  

  

Here is an example of how to use gensim simple preprocess function:   

Fig: Preview the output you will get on running this code from your IDE.

Code

In this solution, we are using the Gensim library.

Correct way of using Phrases and preprocess_string gensim

Lines of Code : 27dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
documents = ["the mayor of new york was there",
             "machine learning can be useful sometimes",
             "new york mayor was present"]

import gensim, pprint

# tokenize documents with gensim's tokenize() function
tokens = [list(gensim.utils.tokenize(doc, lower=True)) for doc in documents]

# build bigram model
bigram_mdl = gensim.models.phrases.Phrases(tokens, min_count=1, threshold=2)

# do more pre-processing on tokens (remove stopwords, stemming etc.)
# NOTE: this can be done better
from gensim.parsing.preprocessing import preprocess_string, remove_stopwords, stem_text
CUSTOM_FILTERS = [remove_stopwords, stem_text]
tokens = [preprocess_string(" ".join(doc), CUSTOM_FILTERS) for doc in tokens]

# apply bigram model on tokens
bigrams = bigram_mdl[tokens]

pprint.pprint(list(bigrams))

[['mayor', 'new_york'],
 ['machin', 'learn', 'us'],
 ['new_york', 'mayor', 'present']]

Instructions

Follow the steps carefully to get the output easily.

  1. Install PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Gensim - pip install gensim.
  4. Create a new Python file(e.g.: test.py).
  5. Copy the snippet using the 'copy' button and paste it into that file(remove output lines).
  6. Run the file using the run button.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for 'Correct way of using Phrases and preprocess_string gensim' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.3.
  2. The solution is tested on Python 3.9.7.
  3. Gensim version 4.3.0.


Using this solution, we are able to use the gensim simple preprocess function with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to use the gensim simple preprocess function.

gensimby RaRe-Technologies

Python doticonstar image 14417 doticonVersion:4.3.0doticon
License: Weak Copyleft (LGPL-2.1)

Topic Modelling for Humans

Support
    Quality
      Security
        License
          Reuse

            gensimby RaRe-Technologies

            Python doticon star image 14417 doticonVersion:4.3.0doticon License: Weak Copyleft (LGPL-2.1)

            Topic Modelling for Humans
            Support
              Quality
                Security
                  License
                    Reuse

                      Dependent Library

                      You can also search for any dependent libraries on kandi like 'Gensim'.

                      FAQ   

                      1. How can I use the gensim simple preprocess function for text data?   

                      Gensim's simple_preprocess function is a handy utility for preprocessing text data. Users tokenize and preprocess text documents. Here's how you can use it:   

                      • Import gensim   
                      • Use simple_preprocess to preprocess your text data. You can pass a single string or a list of strings as input.   


                      2. How does Natural Language Processing use the Gensim Simple Preprocess Function?   

                      NLP is a field of AI. It concentrates on the interaction between humans and computers through natural language. It encompasses various techniques and tools for processing and understanding human language. Gensim is a popular Python library for topic modeling and document similarity analysis. It includes a function called simple_preprocess that is often used in NLP pipelines.  

                        

                      3. Can we use the Natural Language Toolkit for unsupervised topic modeling? Can we also use the Gensim Simple Preprocess Function?   

                      The Natural Language Toolkit (NLTK) and Gensim are separate Python libraries. You can use it together for unsupervised topic modeling. NLTK provides various text processing and preprocessing capabilities. To preprocess text data, combine Gensim's SimplePreprocess function and NLTK. 

                        

                      4. Can I use a Word2Vec model with Gensim Simple Preprocess Function?   

                      You can use a Word2Vec model with the Gensim simple_preprocess function. However, it's crucial to know that we use simple methods to divide and prepare text data.  

                        

                      5. What is the word tokenization? How does it help with the Gensim Simple Preprocess Function?   

                      Word tokenization is when you break down text into separate words or tokens. Tokens are words but can also be subwords, phrases, or other meaningful units. It depends on the specific tokenization method used. Tokenization is fundamental in natural language processing (NLP) and text analysis. It enables you to work with and analyze text data more granularly.  

                      Support

                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.

                      See similar Kits and Libraries

                      Open Weaver – Develop Applications Faster with Open Source

                      kandi

                      Community and Support

                      Company

                      Follow

                      • © 2023 Open Weaver Inc.