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.
- Python 2.7 (not tested with Python 3)
- numpy
- scipy
- sparsesvd
- sklearn
- svmlight-loader
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