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TFIDFVectorizer
TFIDFVectorizer is a custom implementation of the TF-IDF transformation algorithm, using scikit-learn's TfidfVectorizer as a base. The implementation is written in Python, making use of numpy, scikit-learn and other commonly used packages.
The main aim of this implementation is to provide a simple and efficient way of transforming a collection of text documents into a matrix representation, which can then be used as input to various machine learning algorithms.
Installation
The package can be installed using pip:
pip install tfIdfInheritVectorizer
Usage
To use the TFIDFVectorizer, simply create an instance of the class and call its fit_transform method. The method takes a list of text documents as input, and returns a sparse matrix representation of the TF-IDF scores for each document.
from tfIdfInheritVectorizer.feature_extraction.vectorizer import TFIDFVectorizer
text_data = [ "This is the first document.", "This is the second document.", "And this is the third one.", "Is this the first document?"]
vectorizer = TFIDFVectorizer()
tfidf = vectorizer.fit_transform(text_data)
In addition to the fit_transform method, the TFIDFVectorizer also has a transform method that can be used to transform new text data into a matrix representation, given the model has already been fit to the training data.
new_text_data = [
"This is a new document.",
"Is this a new one?"
]
new_tfidf = vectorizer.transform(new_text_data)
Configuration
The TFIDFVectorizer has several parameters that can be configured to customize its behavior. Some of the most important parameters are:
- stop_words: a list of stop words that will be ignored during the tokenization process
vectorizer = TFIDFVectorizer(stop_words=["is", "the", "this"])
- max_features: the maximum number of features to keep, based on term frequency across the entire corpus.
vectorizer = TFIDFVectorizer(max_features=50)
- use_idf: a flag indicating whether to use the inverse document frequency (IDF) weighting.
vectorizer = TFIDFVectorizer(use_idf=False)
For a full list of parameters, see the scikit-learn documentation
Conclusion
TFIDFVectorizer is a simple and efficient implementation of the TF-IDF transformation algorithm, suitable for use in various machine learning applications. By using scikit-learn as a base, it provides a wide range of customization options and can be easily integrated into existing machine learning workflows.
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