tfIdfInheritVectorizer 0.1

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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.

License

MIT