Distributed-Matrix-Factorization
A Machine Learning project: Distributed Stochastic Gradient Descent method for Matrix Factorization in pySpark by Sudev Bohra
How to Run
- Run
$ $(SPARK) dsgd_mf.py $(NUM_FACTOR) $(NUM_WORKER) $(NUM_ITER) $(BETA) $(LAMBDA) $(TRAINV) $(OUTPUTW) $(OUTPUTH)
Example
$ spark-submit dsgd_mf.py 20 5 100 0.9 1.0 test.csv w.csv h.csv
Input
Takes input of matrix V in sparse format. One row, col, value per line eg. the 2x2 identity matrix would look like
0,0,1
1,1,1
Output
Outputs file w.csv and h.csv in dense matrix format