Common pre-processing in NLP such as PPMI computation, SVD-based dimensionality reduction, and PLSR-based distribution prediction.
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__init__.py

README.md

svdmi

This Python script contains several common pre-processing in NLP such as PPMI computation, SVD-based dimensionality reduction, and PLSR-based distribution prediction.

Dependencies

The following packages are required.

Installation

There is no specific installation for svdmi. Once you have all the dependencies installed, you can run svmi as described in the usage section.

Usage

PPMI

Positive Pointwise Mutual Information

$ python svdmi.py -m PPMI -i raw_co-occurrences_matrix_file_name -o ppmi_matrix_file_name

SVD

Singular Value Decomposition-based dimensionality reduction (SVD1) and matrix smoothing (SVD2).

For SVD1 mode

$ python svdmi.py -m SVD1 -i matrix_file_name -o dimensionality_reduced_matrix_file_name -n svd_dimensions -p power_to_raise_singular_values

For SVD2 mode

$ python svdmi.py -m SVD2 -i raw_co-occurrences_matrix_file_name -o smoothed_matrix_file_name -n svd_dimensions -p power_to_raise_singular_values

Use -v option to print the reproduction error (Frobenious norm)

PLSR

Partial Least Square Regression-based distribution prediction.

Training a PLSR model

$ python svdmi.py -m  -m PLSR.train -x x_matrix_file_name -y y_matrix_file_name -n PLSR_components -i model_file_name

Use -v option to print the reproduction error (Frobenious norm)

Predicting using the trained PLSR model.

$ python svdmi.py -m PLSR.pred -x x_matrix_file_name -y predicted_y_matrix_file_name -i model_file_name

License

Simple BSD

Author

Danushka Bollegala