├── LICENSE ├── README.md ├── data-cg ├── 100Homophily-0Heterophily.matrix ├── 20Homophily-80Heterophily.matrix ├── 80Homophily-20Heterophily.matrix ├── 80ToBlue-20ToRed.matrix ├── 80ToRed-20ToBlue.matrix ├── 90Homophily-10Heterophily.matrix ├── ToBlueOnly.matrix ├── ToRedOnly.matrix ├── data.matrix ├── noise-1.0.matrix ├── noise-10.0.matrix ├── noise-100.0.matrix ├── noise-1000.0.matrix ├── noise-5.0.matrix └── uniform-0.02.matrix ├── data-rg ├── Barabassi-m49.matrix ├── Erdos-Renyi-p0.5.matrix ├── Erdos-Renyi-p1.0.matrix ├── Geometric-r1.0.matrix ├── data.matrix ├── noise-1.0.matrix ├── noise-10.0.matrix ├── noise-100.0.matrix ├── noise-1000.0.matrix ├── noise-5.0.matrix └── uniform-0.02.matrix ├── data ├── advice.dat ├── crudematerials.dat ├── diplomatic.dat ├── foods.dat ├── friendship.dat ├── info.dat ├── manufacturedgoods.dat ├── minerals.dat └── money.dat ├── example_biasedwalker.py ├── example_countries.py ├── example_countries_timing_permutations.py ├── example_diplomaticexchange.py ├── example_friendadvice.py ├── example_infomoney.py ├── example_materialsgoods.py ├── example_randomgraphs.py ├── example_synthetic_timing_netsize_nodes_ernos_renyi.py └── libs ├── __init__.py ├── mrqap.py ├── profiling.py ├── qap.py └── utils.py /LICENSE: -------------------------------------------------------------------------------- 1 | CC0 1.0 Universal 2 | 3 | Statement of Purpose 4 | 5 | The laws of most jurisdictions throughout the world automatically confer 6 | exclusive Copyright and Related Rights (defined below) upon the creator and 7 | subsequent owner(s) (each and all, an "owner") of an original work of 8 | authorship and/or a database (each, a "Work"). 9 | 10 | Certain owners wish to permanently relinquish those rights to a Work for the 11 | purpose of contributing to a commons of creative, cultural and scientific 12 | works ("Commons") that the public can reliably and without fear of later 13 | claims of infringement build upon, modify, incorporate in other works, reuse 14 | and redistribute as freely as possible in any form whatsoever and for any 15 | purposes, including without limitation commercial purposes. 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Affirmer understands and acknowledges that Creative Commons is not a 112 | party to this document and has no duty or obligation with respect to this 113 | CC0 or use of the Work. 114 | 115 | For more information, please see 116 | 117 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MRQAP Implementation in Python 2 2 | Multiple Regression Quadratic Assignment Procedure 3 | 4 | - This project contains 5 examples: 5 | - 2 MRQAP (more than 1 dependent variables): 6 | - example_countries.py 7 | - example_diplomaticexchange.py 8 | - 3 QAP (only 1 dependent variable) 9 | - example_friendadvice.py 10 | - example_infomoney.py 11 | - example_materialsgoods.py 12 | - All 14 different datasets are in folder "data". 13 | - 9 of them (.dat) are already in a matrix text format 14 | - 5 of them (.txt) contain relationship values (e.g., nodeA nodeB valueAB) 15 | 16 | 17 | ### Other versions: 18 | - Python3 [here](https://github.com/lisette-espin/mrqap-python/tree/p3). 19 | - R [here](https://github.com/lisette-espin/mrqap-r "MRQAP in R"). 20 | -------------------------------------------------------------------------------- /data-cg/ToBlueOnly.matrix: -------------------------------------------------------------------------------- 1 | 0.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000 2 | 1.000,0.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000 3 | 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11.000,7.000,5.000,9.000,2.000,6.000,8.000,9.000,0.000,1.000,4.000,2.000,6.000,8.000,10.000,3.000,6.000,3.000,8.000,3.000,3.000,9.000,10.000,3.000,8.000,6.000,3.000,4.000,2.000,3.000,0.000,1.000,3.000,3.000,1.000,2.000,8.000,3.000,0.000,6.000,1.000,1.000,1.000,10.000,5.000,4.000,3.000,1.000,3.000,3.000 10 | 0.000,2.000,9.000,5.000,10.000,2.000,2.000,1.000,0.000,0.000,2.000,8.000,2.000,2.000,2.000,8.000,0.000,5.000,1.000,8.000,9.000,1.000,3.000,10.000,3.000,1.000,5.000,1.000,7.000,8.000,6.000,5.000,8.000,7.000,8.000,7.000,1.000,8.000,8.000,3.000,8.000,8.000,9.000,1.000,1.000,8.000,8.000,5.000,10.000,8.000 11 | 9.000,9.000,2.000,10.000,3.000,8.000,9.000,9.000,10.000,2.000,0.000,3.000,6.000,9.000,6.000,0.000,9.000,0.000,9.000,3.000,3.000,10.000,8.000,3.000,5.000,7.000,4.000,8.000,2.000,1.000,2.000,2.000,6.000,2.000,4.000,2.000,6.000,2.000,2.000,7.000,2.000,0.000,0.000,7.000,9.000,0.000,2.000,3.000,2.000,1.000 12 | 2.000,3.000,7.000,4.000,7.000,4.000,4.000,2.000,2.000,9.000,4.000,0.000,3.000,1.000,2.000,10.000,1.000,10.000,6.000,9.000,9.000,0.000,3.000,6.000,1.000,0.000,10.000,2.000,9.000,9.000,7.000,4.000,8.000,9.000,8.000,6.000,2.000,9.000,9.000,0.000,11.000,8.000,7.000,0.000,0.000,7.000,8.000,9.000,9.000,8.000 13 | 6.000,7.000,1.000,8.000,2.000,9.000,9.000,9.000,9.000,3.000,9.000,4.000,0.000,10.000,6.000,3.000,10.000,2.000,9.000,2.000,3.000,8.000,7.000,2.000,8.000,7.000,0.000,10.000,1.000,0.000,0.000,1.000,1.000,1.000,2.000,1.000,7.000,2.000,3.000,9.000,1.000,2.000,0.000,8.000,9.000,3.000,0.000,2.000,3.000,1.000 14 | 8.000,7.000,2.000,7.000,0.000,9.000,8.000,8.000,8.000,1.000,7.000,5.000,8.000,0.000,9.000,0.000,8.000,1.000,7.000,1.000,2.000,9.000,7.000,3.000,8.000,5.000,0.000,7.000,3.000,2.000,3.000,2.000,0.000,0.000,2.000,0.000,8.000,5.000,2.000,9.000,1.000,1.000,2.000,10.000,9.000,4.000,0.000,0.000,4.000,0.000 15 | 6.000,8.000,1.000,8.000,2.000,7.000,10.000,7.000,7.000,2.000,10.000,1.000,7.000,6.000,0.000,5.000,7.000,2.000,9.000,1.000,2.000,10.000,8.000,1.000,9.000,9.000,4.000,9.000,3.000,4.000,1.000,3.000,2.000,2.000,3.000,1.000,9.000,4.000,1.000,7.000,1.000,0.000,3.000,10.000,10.000,1.000,1.000,2.000,3.000,2.000 16 | 2.000,4.000,10.000,3.000,10.000,4.000,5.000,2.000,3.000,9.000,0.000,8.000,0.000,2.000,0.000,0.000,3.000,10.000,3.000,8.000,6.000,5.000,4.000,7.000,3.000,5.000,10.000,2.000,11.000,9.000,9.000,5.000,7.000,9.000,9.000,8.000,1.000,8.000,10.000,0.000,8.000,10.000,8.000,0.000,2.000,7.000,9.000,9.000,9.000,10.000 17 | 8.000,6.000,7.000,10.000,2.000,10.000,6.000,8.000,6.000,5.000,8.000,3.000,8.000,11.000,9.000,1.000,0.000,2.000,7.000,1.000,4.000,9.000,8.000,3.000,8.000,4.000,2.000,8.000,0.000,2.000,0.000,0.000,2.000,4.000,0.000,2.000,8.000,2.000,0.000,9.000,0.000,2.000,2.000,9.000,9.000,2.000,3.000,1.000,2.000,0.000 18 | 0.000,0.000,7.000,3.000,6.000,2.000,3.000,2.000,1.000,6.000,2.000,10.000,1.000,2.000,2.000,9.000,3.000,0.000,0.000,10.000,8.000,1.000,1.000,9.000,5.000,2.000,9.000,3.000,10.000,7.000,9.000,11.000,9.000,6.000,8.000,5.000,2.000,9.000,9.000,2.000,8.000,9.000,7.000,3.000,1.000,10.000,7.000,10.000,9.000,7.000 19 | 9.000,6.000,3.000,7.000,3.000,5.000,8.000,10.000,8.000,3.000,7.000,1.000,7.000,7.000,9.000,3.000,9.000,3.000,0.000,2.000,3.000,6.000,8.000,2.000,7.000,9.000,1.000,8.000,2.000,5.000,4.000,3.000,3.000,5.000,1.000,1.000,9.000,0.000,4.000,9.000,1.000,3.000,4.000,7.000,6.000,2.000,0.000,0.000,1.000,0.000 20 | 4.000,1.000,9.000,2.000,9.000,0.000,2.000,3.000,3.000,9.000,3.000,7.000,4.000,4.000,4.000,10.000,2.000,8.000,3.000,0.000,8.000,0.000,3.000,5.000,0.000,1.000,8.000,2.000,8.000,6.000,8.000,10.000,8.000,8.000,11.000,9.000,1.000,11.000,9.000,2.000,8.000,9.000,10.000,5.000,1.000,7.000,10.000,9.000,6.000,7.000 21 | 2.000,2.000,7.000,3.000,10.000,0.000,3.000,2.000,1.000,8.000,2.000,9.000,0.000,0.000,1.000,9.000,1.000,10.000,6.000,9.000,0.000,2.000,1.000,7.000,2.000,0.000,9.000,3.000,10.000,9.000,8.000,7.000,9.000,9.000,8.000,7.000,2.000,6.000,8.000,4.000,8.000,8.000,9.000,4.000,1.000,9.000,7.000,7.000,7.000,11.000 22 | 8.000,11.000,1.000,7.000,1.000,8.000,11.000,8.000,8.000,0.000,5.000,3.000,7.000,8.000,9.000,6.000,6.000,4.000,5.000,0.000,2.000,0.000,8.000,3.000,7.000,9.000,4.000,9.000,2.000,0.000,1.000,2.000,3.000,5.000,0.000,4.000,6.000,1.000,1.000,9.000,4.000,1.000,1.000,7.000,8.000,3.000,0.000,3.000,2.000,3.000 23 | 6.000,8.000,3.000,5.000,1.000,9.000,11.000,8.000,7.000,5.000,10.000,3.000,10.000,7.000,7.000,2.000,9.000,4.000,9.000,5.000,4.000,5.000,0.000,1.000,9.000,8.000,2.000,5.000,2.000,0.000,2.000,3.000,2.000,4.000,1.000,4.000,8.000,2.000,1.000,8.000,4.000,0.000,4.000,8.000,7.000,1.000,0.000,2.000,3.000,2.000 24 | 0.000,2.000,8.000,0.000,7.000,0.000,1.000,4.000,1.000,4.000,0.000,9.000,1.000,1.000,1.000,10.000,3.000,9.000,1.000,8.000,5.000,3.000,2.000,0.000,0.000,2.000,9.000,3.000,8.000,8.000,9.000,9.000,5.000,7.000,8.000,6.000,0.000,6.000,8.000,4.000,6.000,7.000,7.000,3.000,2.000,8.000,10.000,6.000,9.000,6.000 25 | 9.000,7.000,4.000,9.000,2.000,7.000,7.000,8.000,10.000,2.000,7.000,0.000,8.000,9.000,7.000,2.000,8.000,3.000,7.000,1.000,1.000,9.000,8.000,1.000,0.000,7.000,0.000,9.000,4.000,1.000,4.000,3.000,2.000,0.000,3.000,3.000,9.000,4.000,4.000,9.000,2.000,0.000,1.000,7.000,9.000,3.000,2.000,2.000,5.000,0.000 26 | 6.000,7.000,4.000,9.000,4.000,7.000,6.000,9.000,9.000,2.000,7.000,3.000,9.000,8.000,10.000,2.000,6.000,1.000,8.000,1.000,0.000,8.000,10.000,1.000,5.000,0.000,0.000,9.000,2.000,2.000,2.000,2.000,3.000,1.000,2.000,5.000,6.000,0.000,3.000,8.000,0.000,1.000,2.000,10.000,10.000,1.000,3.000,3.000,3.000,2.000 27 | 3.000,3.000,8.000,2.000,10.000,3.000,1.000,1.000,1.000,7.000,3.000,8.000,1.000,1.000,1.000,9.000,2.000,5.000,2.000,7.000,8.000,4.000,2.000,10.000,0.000,2.000,0.000,1.000,7.000,9.000,8.000,9.000,9.000,9.000,6.000,5.000,1.000,10.000,5.000,0.000,8.000,10.000,9.000,5.000,1.000,9.000,8.000,8.000,9.000,9.000 28 | 11.000,8.000,2.000,7.000,2.000,8.000,9.000,8.000,7.000,5.000,9.000,0.000,10.000,5.000,9.000,5.000,7.000,1.000,7.000,0.000,1.000,11.000,8.000,0.000,8.000,7.000,4.000,0.000,1.000,4.000,2.000,1.000,5.000,3.000,0.000,3.000,9.000,2.000,1.000,9.000,1.000,1.000,3.000,8.000,6.000,3.000,2.000,0.000,2.000,2.000 29 | 2.000,4.000,8.000,3.000,7.000,3.000,3.000,1.000,2.000,9.000,4.000,8.000,0.000,1.000,2.000,7.000,3.000,10.000,0.000,8.000,9.000,1.000,3.000,8.000,0.000,2.000,8.000,1.000,0.000,10.000,7.000,8.000,11.000,8.000,8.000,6.000,1.000,10.000,9.000,0.000,8.000,8.000,9.000,0.000,2.000,8.000,7.000,7.000,9.000,8.000 30 | 2.000,2.000,9.000,0.000,10.000,0.000,2.000,0.000,2.000,7.000,2.000,5.000,1.000,5.000,7.000,9.000,2.000,8.000,2.000,5.000,8.000,0.000,2.000,4.000,0.000,0.000,10.000,3.000,10.000,0.000,6.000,6.000,11.000,10.000,8.000,8.000,0.000,8.000,7.000,2.000,10.000,6.000,7.000,1.000,1.000,10.000,8.000,9.000,6.000,9.000 31 | 2.000,3.000,10.000,3.000,4.000,1.000,2.000,0.000,3.000,8.000,2.000,8.000,4.000,3.000,2.000,9.000,3.000,10.000,0.000,10.000,8.000,1.000,2.000,8.000,0.000,0.000,11.000,3.000,9.000,9.000,0.000,10.000,6.000,8.000,8.000,5.000,1.000,10.000,7.000,2.000,5.000,7.000,9.000,0.000,0.000,8.000,6.000,7.000,9.000,11.000 32 | 0.000,3.000,8.000,0.000,9.000,0.000,4.000,1.000,3.000,9.000,1.000,9.000,1.000,3.000,3.000,8.000,5.000,7.000,0.000,9.000,10.000,5.000,2.000,10.000,3.000,1.000,10.000,0.000,8.000,6.000,3.000,0.000,9.000,5.000,9.000,7.000,1.000,9.000,8.000,2.000,10.000,8.000,9.000,2.000,1.000,10.000,7.000,8.000,5.000,7.000 33 | 0.000,2.000,8.000,1.000,8.000,2.000,1.000,2.000,1.000,8.000,2.000,9.000,4.000,3.000,4.000,6.000,3.000,8.000,2.000,7.000,10.000,1.000,1.000,8.000,1.000,4.000,8.000,1.000,10.000,4.000,7.000,6.000,0.000,8.000,7.000,10.000,0.000,9.000,7.000,1.000,9.000,7.000,8.000,1.000,2.000,8.000,10.000,7.000,7.000,6.000 34 | 0.000,1.000,9.000,0.000,8.000,0.000,2.000,1.000,2.000,9.000,2.000,5.000,3.000,2.000,1.000,9.000,0.000,7.000,3.000,8.000,9.000,3.000,2.000,11.000,1.000,1.000,8.000,4.000,10.000,8.000,5.000,9.000,7.000,0.000,8.000,8.000,4.000,9.000,9.000,2.000,9.000,10.000,7.000,4.000,2.000,7.000,6.000,7.000,7.000,8.000 35 | 0.000,1.000,7.000,3.000,6.000,2.000,2.000,0.000,0.000,9.000,3.000,6.000,4.000,0.000,0.000,9.000,3.000,5.000,3.000,9.000,6.000,2.000,1.000,8.000,2.000,2.000,6.000,1.000,8.000,8.000,7.000,9.000,5.000,8.000,0.000,10.000,0.000,7.000,7.000,4.000,7.000,5.000,8.000,2.000,3.000,9.000,8.000,7.000,10.000,10.000 36 | 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1 1 1 1 1 1 1 1 1 1 0 1 24 | 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 -------------------------------------------------------------------------------- /data/money.dat: -------------------------------------------------------------------------------- 1 | 0 0 1 0 1 0 0 1 1 1 2 | 0 0 1 0 0 0 0 0 0 0 3 | 0 0 0 0 0 0 0 1 0 0 4 | 0 1 1 0 0 0 1 1 1 0 5 | 0 1 1 0 0 0 0 1 1 0 6 | 0 0 0 0 0 0 0 0 0 0 7 | 0 1 0 0 0 0 0 1 0 0 8 | 0 0 0 0 0 0 0 0 1 1 9 | 0 0 1 0 0 0 0 1 0 0 10 | 0 0 0 0 0 0 0 0 0 0 -------------------------------------------------------------------------------- /example_biasedwalker.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from libs.mrqap import MRQAP 8 | import time 9 | from pandas.core.series import Series 10 | 11 | ####################################################################### 12 | # Constants 13 | ####################################################################### 14 | N = 50 15 | DIRECTED = True 16 | NPERMUTATIONS = 2000 17 | 18 | ####################################################################### 19 | # Data Matrices 20 | ####################################################################### 21 | Y = np.loadtxt('data-cg/data.matrix',delimiter=',') 22 | X1 = np.loadtxt('data-cg/noise-1.0.matrix',delimiter=',') 23 | X2 = np.loadtxt('data-cg/noise-5.0.matrix',delimiter=',') 24 | X3 = np.loadtxt('data-cg/noise-10.0.matrix',delimiter=',') 25 | X4 = np.loadtxt('data-cg/noise-100.0.matrix',delimiter=',') 26 | X5 = np.loadtxt('data-cg/noise-1000.0.matrix',delimiter=',') 27 | X6 = np.loadtxt('data-cg/20Homophily-80Heterophily.matrix',delimiter=',') 28 | X7 = np.loadtxt('data-cg/80Homophily-20Heterophily.matrix',delimiter=',') 29 | X8 = np.loadtxt('data-cg/80ToBlue-20ToRed.matrix',delimiter=',') 30 | X9 = np.loadtxt('data-cg/80ToRed-20ToBlue.matrix',delimiter=',') 31 | X10 = np.loadtxt('data-cg/90Homophily-10Heterophily.matrix',delimiter=',') 32 | X11 = np.loadtxt('data-cg/100Homophily-0Heterophily.matrix',delimiter=',') 33 | X12 = np.loadtxt('data-cg/ToBlueOnly.matrix',delimiter=',') 34 | X13 = np.loadtxt('data-cg/ToRedOnly.matrix',delimiter=',') 35 | X14 = np.loadtxt('data-cg/uniform-0.02.matrix',delimiter=',') 36 | 37 | X = {'NOISE1':X1, 'NOISE5':X2, 'NOISE10':X3, 'NOISE100':X4, 'NOISE1000':X5, 38 | 'Hom20Het80':X6, 'Hom80Het20':X7, 'Blue80Red20':X8, 'Red80Blue20':X9, 39 | 'Hom90Het10':X10,'Hom100Het0':X11, 'ToBlue':X12, 'ToRed':X13, 'UNIFORM':X14} 40 | Y = {'DATA':Y} 41 | np.random.seed(1) 42 | 43 | ####################################################################### 44 | # QAP 45 | ####################################################################### 46 | start_time = time.time() 47 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=DIRECTED, standarized=True) 48 | mrqap.mrqap() 49 | mrqap.summary() 50 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time)) 51 | mrqap.plot('betas','results-cg/betas.pdf') 52 | mrqap.plot('tvalues','results-cg/tvalues.pdf') 53 | -------------------------------------------------------------------------------- /example_countries.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from libs.mrqap import MRQAP 8 | import time 9 | 10 | ####################################################################### 11 | # Constants 12 | ####################################################################### 13 | NCOUNTRIES = 249 14 | DIRECTED = True 15 | NPERMUTATIONS = 2000 16 | 17 | ####################################################################### 18 | # Functions 19 | ####################################################################### 20 | def getMatrix(path, directed=False, log1p=False): 21 | matrix = np.zeros(shape=(NCOUNTRIES,NCOUNTRIES)) 22 | with open(path, 'rb') as f: 23 | for line in f: 24 | data = line.split(' ') 25 | c1 = int(data[0])-1 26 | c2 = int(data[1])-1 27 | v = np.log1p(float(data[2])) if log1p else float(data[2]) 28 | matrix[c1][c2] = v # real data from file 29 | if not DIRECTED: 30 | matrix[c2][c1] = v # symmetry 31 | print '{} loaded as a matrix!'.format(path) 32 | return matrix 33 | 34 | ####################################################################### 35 | # Data Matrices 36 | ####################################################################### 37 | X1 = getMatrix('data/country_trade_index.txt',DIRECTED,True) 38 | X2 = getMatrix('data/country_distance_index.txt',DIRECTED,True) 39 | X3 = getMatrix('data/country_colonial_index.txt',DIRECTED) 40 | Y = getMatrix('data/country_lang_index.txt',DIRECTED) 41 | X = {'TRADE':X1, 'DISTANCE':X2, 'COLONIAL':X3} 42 | Y = {'LANG':Y} 43 | np.random.seed(1) 44 | 45 | ####################################################################### 46 | # QAP 47 | ####################################################################### 48 | start_time = time.time() 49 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=True) 50 | mrqap.mrqap() 51 | mrqap.summary() 52 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time)) 53 | mrqap.plot('betas') 54 | mrqap.plot('tvalues') 55 | -------------------------------------------------------------------------------- /example_countries_timing_permutations.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from libs.mrqap import MRQAP 8 | import time 9 | from libs import utils 10 | from libs.profiling import Profiling 11 | import sys 12 | 13 | 14 | ####################################################################### 15 | # Functions 16 | ####################################################################### 17 | NCOUNTRIES = 249 18 | def getMatrix(path, directed=False, log1p=False): 19 | matrix = np.zeros(shape=(NCOUNTRIES,NCOUNTRIES)) 20 | with open(path, 'rb') as f: 21 | for line in f: 22 | data = line.split(' ') 23 | c1 = int(data[0])-1 24 | c2 = int(data[1])-1 25 | v = np.log1p(float(data[2])) if log1p else float(data[2]) 26 | matrix[c1][c2] = v # real data from file 27 | if not directed: 28 | matrix[c2][c1] = v # symmetry 29 | print '{} loaded as a matrix!'.format(path) 30 | return matrix 31 | 32 | ####################################################################### 33 | # Main 34 | ###################################################################### 35 | @profile 36 | def main(directed): 37 | 38 | logfile = 'results-permutations/timigs-{}.txt'.format('directed' if directed else 'undirected') 39 | memory = Profiling('Permutations {}'.format('directed' if directed else 'undirected'), 'results-permutations/python-profiling-nperm-nodes{}-{}.png'.format(NCOUNTRIES,'directed' if directed else 'undirected'), True) 40 | memory.check_memory('init-{}'.format('d' if directed else 'i')) 41 | 42 | ####################################################################### 43 | # Data Matrices 44 | ####################################################################### 45 | X1 = getMatrix('data-permutations/country_trade_index.txt',directed,True) 46 | memory.check_memory('X1-{}'.format('d' if directed else 'i')) 47 | X2 = getMatrix('data-permutations/country_distance_index.txt',directed,True) 48 | memory.check_memory('X2-{}'.format('d' if directed else 'i')) 49 | X3 = getMatrix('data-permutations/country_colonial_index.txt',directed) 50 | Y = getMatrix('data-permutations/country_lang_index.txt',directed) 51 | memory.check_memory('Y-{}'.format('d' if directed else 'i')) 52 | X = {'TRADE':X1, 'DISTANCE':X2, 'COLONIAL':X3} 53 | Y = {'LANG':Y} 54 | np.random.seed(1) 55 | 56 | ####################################################################### 57 | # QAP 58 | ####################################################################### 59 | perms = np.logspace(1,7,num=7-1, endpoint=False) 60 | for nperm in perms: 61 | start_time = time.time() 62 | mrqap = MRQAP(Y=Y, X=X, npermutations=int(nperm), diagonal=False, directed=directed, logfile=logfile, memory=memory) 63 | mrqap.mrqap() 64 | 65 | utils.printf("--- {}, nperm {}: {} seconds ---".format('directed' if directed else 'undirected', nperm, time.time() - start_time), logfile) 66 | mrqap.summary() 67 | 68 | fn = 'results-permutations/python-nperm{}-{}-.png'.format(nperm,'directed' if directed else 'undirected') 69 | mrqap.plot('betas', fn.replace('','betas')) 70 | mrqap.plot('tvalues', fn.replace('','tvalues')) 71 | 72 | utils.printf('******************************************************************************\n\n', logfile) 73 | del(mrqap) 74 | return 75 | 76 | 77 | if __name__ == '__main__': 78 | directed = sys.argv[1] == '1' 79 | main(directed) 80 | sys.exit(0) 81 | -------------------------------------------------------------------------------- /example_diplomaticexchange.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # References 5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf 6 | ####################################################################### 7 | 8 | ####################################################################### 9 | # Dependencies 10 | ####################################################################### 11 | import numpy as np 12 | from libs.mrqap import MRQAP 13 | 14 | ####################################################################### 15 | # Data 16 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm 17 | ####################################################################### 18 | X1 = np.loadtxt('data/crudematerials.dat') 19 | X2 = np.loadtxt('data/foods.dat') 20 | X3 = np.loadtxt('data/manufacturedgoods.dat') 21 | X4 = np.loadtxt('data/minerals.dat') 22 | Y = np.loadtxt('data/diplomatic.dat') 23 | X = {'CRUDEMATERIALS':X1, 'FOODS':X2, 'MANUFACTUREDGOODS':X3, 'MINERALS':X4} 24 | Y = {'DIPLOMATIC':Y} 25 | np.random.seed(473) 26 | 27 | ####################################################################### 28 | # QAP 29 | ####################################################################### 30 | mrqap = MRQAP(Y=Y, X=X, npermutations=2000, diagonal=False, directed=True) 31 | mrqap.mrqap() 32 | mrqap.summary() 33 | mrqap.plot('betas') 34 | mrqap.plot('tvalues') 35 | 36 | -------------------------------------------------------------------------------- /example_friendadvice.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # References 5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf 6 | ####################################################################### 7 | 8 | ####################################################################### 9 | # Dependencies 10 | ####################################################################### 11 | import numpy as np 12 | 13 | from libs import utils 14 | from libs.qap import QAP 15 | 16 | 17 | ####################################################################### 18 | # Data 19 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm 20 | ####################################################################### 21 | X = minfo = np.loadtxt('data/friendship.dat') 22 | Y = np.loadtxt('data/advice.dat') 23 | utils.printf('Friendship: \n{}'.format(X)) 24 | utils.printf('Advise: \n{}'.format(Y)) 25 | np.random.seed(831) 26 | 27 | ####################################################################### 28 | # QAP 29 | ####################################################################### 30 | qap = QAP(Y, X, 2000) 31 | qap.qap() 32 | qap.summary() 33 | qap.plot() 34 | -------------------------------------------------------------------------------- /example_infomoney.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # References 5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf 6 | ####################################################################### 7 | 8 | ####################################################################### 9 | # Dependencies 10 | ####################################################################### 11 | import numpy as np 12 | from libs import utils 13 | from libs.qap import QAP 14 | 15 | ####################################################################### 16 | # Data 17 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm 18 | ####################################################################### 19 | minfo = minfo = np.loadtxt('data/info.dat') 20 | mmoney = np.loadtxt('data/money.dat') 21 | utils.printf('Information: \n{}'.format(minfo)) 22 | utils.printf('Money Exchange: \n{}'.format(mmoney)) 23 | 24 | ####################################################################### 25 | # QAP 26 | ####################################################################### 27 | qap = QAP(minfo, mmoney, 5000) 28 | qap.qap() 29 | qap.summary() 30 | -------------------------------------------------------------------------------- /example_materialsgoods.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # References 5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf 6 | ####################################################################### 7 | 8 | ####################################################################### 9 | # Dependencies 10 | ####################################################################### 11 | import numpy as np 12 | from libs import utils 13 | from libs.qap import QAP 14 | 15 | ####################################################################### 16 | # Data 17 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm 18 | ####################################################################### 19 | X = np.loadtxt('data/crudematerials.dat') 20 | Y = np.loadtxt('data/manufacturedgoods.dat') 21 | utils.printf('Crude Materials: \n{}'.format(X)) 22 | utils.printf('Manufactured Goods: \n{}'.format(Y)) 23 | np.random.seed(15843) 24 | 25 | ####################################################################### 26 | # QAP 27 | ####################################################################### 28 | qap = QAP(Y, X, 5000) 29 | qap.qap() 30 | qap.summary() 31 | qap.plot() -------------------------------------------------------------------------------- /example_randomgraphs.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from libs.mrqap import MRQAP 8 | import time 9 | from pandas.core.series import Series 10 | 11 | ####################################################################### 12 | # Constants 13 | ####################################################################### 14 | N = 50 15 | DIRECTED = True 16 | NPERMUTATIONS = 2000 17 | 18 | ####################################################################### 19 | # Data Matrices 20 | ####################################################################### 21 | Y = np.loadtxt('data-rg/data.matrix',delimiter=',') 22 | X1 = np.loadtxt('data-rg/noise-1.0.matrix',delimiter=',') 23 | X2 = np.loadtxt('data-rg/noise-5.0.matrix',delimiter=',') 24 | X3 = np.loadtxt('data-rg/noise-10.0.matrix',delimiter=',') 25 | X4 = np.loadtxt('data-rg/noise-100.0.matrix',delimiter=',') 26 | X5 = np.loadtxt('data-rg/noise-1000.0.matrix',delimiter=',') 27 | X6 = np.loadtxt('data-rg/Erdos-Renyi-p0.5.matrix',delimiter=',') 28 | X7 = np.loadtxt('data-rg/Erdos-Renyi-p1.0.matrix',delimiter=',') 29 | X8 = np.loadtxt('data-rg/Geometric-r1.0.matrix',delimiter=',') 30 | X9 = np.loadtxt('data-rg/Barabassi-m49.matrix',delimiter=',') 31 | X10 = np.loadtxt('data-rg/uniform-0.02.matrix',delimiter=',') 32 | 33 | X = {'NOISE1':X1, 'NOISE5':X2, 'NOISE10':X3, 'NOISE100':X4, 'NOISE1000':X5,'ERDOS05':X6, 'ERDOS1':X7, 'GEOMETRIC1':X8, 'BARABASI49':X9, 'UNIFORM':X10} 34 | Y = {'DATA':Y} 35 | np.random.seed(1) 36 | 37 | ####################################################################### 38 | # QAP 39 | ####################################################################### 40 | start_time = time.time() 41 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=DIRECTED, standarized=False) 42 | mrqap.mrqap() 43 | mrqap.summary() 44 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time)) 45 | mrqap.plot('betas','results-rg/betas.pdf') 46 | mrqap.plot('tvalues','results-rg/tvalues.pdf') 47 | -------------------------------------------------------------------------------- /example_synthetic_timing_netsize_nodes_ernos_renyi.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from libs.mrqap import MRQAP 8 | import time 9 | import networkx as nx 10 | from libs import utils 11 | from libs.profiling import Profiling 12 | import threading 13 | import time 14 | import sys 15 | from libs.mtTkinter import * 16 | import os 17 | 18 | ####################################################################### 19 | # Constants 20 | ####################################################################### 21 | NPERMUTATIONS = 1000 22 | EDGEPROB = 0.1 23 | SEED = 1 24 | np.random.seed(SEED) 25 | 26 | ####################################################################### 27 | # Global 28 | ####################################################################### 29 | 30 | 31 | ####################################################################### 32 | # Functions 33 | ####################################################################### 34 | def generateGraph(nnodes, edgeprob, directed, pathtosave): 35 | if os.path.exists(pathtosave): 36 | matrix = np.loadtxt(pathtosave) 37 | else: 38 | shape = (nnodes,nnodes) 39 | G = nx.fast_gnp_random_graph(n=nnodes, p=edgeprob, directed=directed) 40 | matrix = nx.adjacency_matrix(G) 41 | 42 | if pathtosave is not None: 43 | np.savetxt(pathtosave, matrix.toarray(), fmt='%d',) 44 | 45 | print nx.info(G) 46 | matrix = matrix.toarray() 47 | 48 | return matrix 49 | 50 | ####################################################################### 51 | # Main 52 | ####################################################################### 53 | @profile 54 | def main(directed): 55 | 56 | logfile = 'results-synthetic-ernos-renyi/timigs-{}.txt'.format('directed' if directed else 'undirected') 57 | memory = Profiling('Nodes {}'.format('directed' if directed else 'undirected'), 'results-synthetic-ernos-renyi/python-profiling-netsize-edgeprob{}-nperm{}-{}.png'.format(EDGEPROB, NPERMUTATIONS,'directed' if directed else 'undirected'), False) 58 | memory.check_memory('init-{}'.format('d' if directed else 'i')) 59 | 60 | ####################################################################### 61 | # Data Matrices 62 | ####################################################################### 63 | #nnodes = np.logspace(1,7,num=7-1, endpoint=False) 64 | nnodes = np.logspace(1,5,num=5-1, endpoint=False) 65 | for n in nnodes: 66 | n = int(n) 67 | fn = 'data-synthetic-ernos-renyi/nodes{}_edgeprob{}_.dat'.format(n,EDGEPROB) 68 | memory.check_memory('nodes-{}'.format(n)) 69 | X1 = generateGraph(n,EDGEPROB,directed, fn.replace('','X1')) 70 | memory.check_memory('X1-{}'.format(n)) 71 | X2 = generateGraph(n,EDGEPROB,directed, fn.replace('','X2')) 72 | memory.check_memory('X2-{}'.format(n)) 73 | X3 = generateGraph(n,EDGEPROB,directed, fn.replace('','X3')) 74 | memory.check_memory('X3-{}'.format(n)) 75 | Y = generateGraph(n,EDGEPROB,directed, fn.replace('','Y')) 76 | memory.check_memory('Y-{}'.format(n)) 77 | X = {'X1':X1, 'X2':X2, 'X3':X3} 78 | Y = {'Y':Y} 79 | 80 | ####################################################################### 81 | # QAP 82 | ####################################################################### 83 | start_time = time.time() 84 | mrqap = MRQAP(Y=Y, X=X, npermutations=int(NPERMUTATIONS), diagonal=False, directed=directed, logfile=logfile, memory=memory) 85 | mrqap.mrqap() 86 | 87 | utils.printf("\n--- {}, nodes {}: {} seconds ---".format('directed' if directed else 'undirected', n, time.time() - start_time), logfile) 88 | mrqap.summary() 89 | 90 | fn = 'results-synthetic-ernos-renyi/python-nodes{}-edgeprob{}-nperm{}-{}-.png'.format(n, EDGEPROB, NPERMUTATIONS,'directed' if directed else 'undirected') 91 | mrqap.plot('betas',fn.replace('','betas')) 92 | mrqap.plot('tvalues',fn.replace('','tvalues')) 93 | 94 | utils.printf('******************************************************************************\n\n', logfile) 95 | del(mrqap) 96 | return 97 | 98 | 99 | if __name__ == '__main__': 100 | directed = sys.argv[1] == '1' 101 | main(directed) 102 | sys.exit(0) 103 | -------------------------------------------------------------------------------- /libs/__init__.py: -------------------------------------------------------------------------------- 1 | __author__ = 'espin' 2 | -------------------------------------------------------------------------------- /libs/mrqap.py: -------------------------------------------------------------------------------- 1 | __author__ = 'espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import sys 7 | import collections 8 | import numpy as np 9 | import pandas 10 | import matplotlib 11 | matplotlib.use('Agg') 12 | import matplotlib.pyplot as plt 13 | from statsmodels.formula.api import ols 14 | from libs import utils 15 | from libs.profiling import Profiling 16 | import time 17 | import gc 18 | from scipy import stats 19 | from scipy.stats.mstats import zscore 20 | 21 | ####################################################################### 22 | # MRQAP 23 | ####################################################################### 24 | INTERCEPT = 'Intercept' 25 | 26 | class MRQAP(): 27 | 28 | ##################################################################################### 29 | # Constructor and Init 30 | ##################################################################################### 31 | 32 | def __init__(self, Y=None, X=None, npermutations=-1, diagonal=False, directed=False, logfile=None, memory=None, standarized=False): 33 | ''' 34 | Initialization of variables 35 | :param Y: numpy array depended variable 36 | :param X: dictionary of numpy array independed variables 37 | :param npermutations: int number of permutations 38 | :param diagonal: boolean, False to delete diagonal from the OLS model 39 | :return: 40 | ''' 41 | self.X = X # independent variables: dictionary of numpy.array 42 | self.Y = Y # dependent variable: dictionary numpy.array 43 | self.n = Y.values()[0].shape[0] # number of nodes 44 | self.npermutations = npermutations # number of permutations 45 | self.diagonal = diagonal # False then diagonal is removed 46 | self.directed = directed # directed True, undirected False 47 | self.data = None # Pandas DataFrame 48 | self.model = None # OLS Model y ~ x1 + x2 + x3 (original) 49 | self.v = collections.OrderedDict() # vectorized matrices, flatten variables with no diagonal 50 | self.betas = collections.OrderedDict() # betas distribution 51 | self.tvalues = collections.OrderedDict() # t-test values 52 | self.logfile = logfile # logfile path name 53 | self.standarized = standarized 54 | self.memory = memory if memory is not None else Profiling() # to track memory usage 55 | 56 | def init(self): 57 | ''' 58 | Generating the original OLS model. Y and Xs are flattened. 59 | Also, the betas and tvalues dictionaries are initialized (key:independent variables, value:[]) 60 | :return: 61 | ''' 62 | self.v[self.Y.keys()[0]] = self._getFlatten(self.Y.values()[0]) 63 | self._initCoefficients(INTERCEPT) 64 | for k,x in self.X.items(): 65 | if k == self.Y.keys()[0]: 66 | utils.printf('ERROR: Idependent variable cannot be named \'[}\''.format(self.Y.keys()[0]), self.logfile) 67 | sys.exit(0) 68 | self.v[k] = self._getFlatten(x) 69 | self._initCoefficients(k) 70 | self.data = pandas.DataFrame(self.v) 71 | self.model = self._fit(self.v.keys(), self.data) 72 | del(self.X) 73 | 74 | def profiling(self, key): 75 | self.memory.check_memory(key) 76 | 77 | ##################################################################################### 78 | # Core QAP methods 79 | ##################################################################################### 80 | 81 | def mrqap(self): 82 | ''' 83 | MultipleRegression Quadratic Assignment Procedure 84 | :return: 85 | ''' 86 | directed = 'd' if self.directed else 'i' 87 | key = self.npermutations if self.memory.perm else self.n 88 | self.profiling('init-{}-{}'.format(directed, key)) 89 | self.init() 90 | self.profiling('shuffle-{}-{}'.format(directed, key)) 91 | self._shuffle() 92 | self.profiling('end-{}-{}'.format(directed, key)) 93 | 94 | def _shuffle(self): 95 | ''' 96 | Shuffling rows and columns npermutations times. 97 | beta coefficients and tvalues are stored. 98 | :return: 99 | ''' 100 | for p in range(self.npermutations): 101 | self.Ymod = self.Y.values()[0].copy() 102 | self._rmperm() 103 | model = self._newfit() 104 | self._update_betas(model._results.params) 105 | self._update_tvalues(model.tvalues) 106 | self.Ymod = None 107 | gc.collect() 108 | 109 | 110 | def _newfit(self): 111 | ''' 112 | Generates a new OLS fit model 113 | :return: 114 | ''' 115 | newv = collections.OrderedDict() 116 | newv[self.Y.keys()[0]] = self._getFlatten(self.Ymod) 117 | for k,x in self.v.items(): 118 | if k != self.Y.keys()[0]: 119 | newv[k] = x 120 | newdata = pandas.DataFrame(newv) 121 | newfit = self._fit(newv.keys(), newdata) 122 | del(newdata) 123 | del(newv) 124 | return newfit 125 | 126 | 127 | ##################################################################################### 128 | # Handlers 129 | ##################################################################################### 130 | 131 | def _fit(self, keys, data): 132 | ''' 133 | Fitting OLS model 134 | v a dictionary with all variables. 135 | :return: 136 | ''' 137 | if self.standarized: 138 | data = data.apply(lambda x: (x - np.mean(x)) / (np.std(x)), axis=0) #axis: 0 to each column, 1 to each row 139 | 140 | formula = '{} ~ {}'.format(self.Y.keys()[0], ' + '.join([k for k in keys if k != self.Y.keys()[0]])) 141 | return ols(formula, data).fit() 142 | 143 | def _initCoefficients(self, key): 144 | self.betas[key] = [] 145 | self.tvalues[key] = [] 146 | 147 | def _rmperm(self, duplicates=True): 148 | shuffle = np.random.permutation(self.Ymod.shape[0]) 149 | np.take(self.Ymod,shuffle,axis=0,out=self.Ymod) 150 | np.take(self.Ymod,shuffle,axis=1,out=self.Ymod) 151 | del(shuffle) 152 | 153 | def _update_betas(self, betas): 154 | for idx,k in enumerate(self.betas.keys()): 155 | self.betas[k].append(round(betas[idx],6)) 156 | 157 | def _update_tvalues(self, tvalues): 158 | for k in self.tvalues.keys(): 159 | self.tvalues[k].append(round(tvalues[k],6)) 160 | 161 | def _getFlatten(self, original): 162 | return self._deleteDiagonalFlatten(original) 163 | 164 | 165 | def _deleteDiagonalFlatten(self, original): 166 | tmp = original.flatten() 167 | if not self.diagonal: 168 | tmp = np.delete(tmp, [i*(original.shape[0]+1)for i in range(original.shape[0])]) 169 | return tmp 170 | 171 | def _zeroDiagonalFlatten(self, original): 172 | tmp = original.copy() 173 | if not self.diagonal: 174 | np.fill_diagonal(tmp,0) 175 | f = tmp.flatten() 176 | del(tmp) 177 | return f 178 | 179 | 180 | ##################################################################################### 181 | # Prints 182 | ##################################################################################### 183 | 184 | def summary(self): 185 | ''' 186 | Prints the OLS original summary and beta and tvalue summary. 187 | :return: 188 | ''' 189 | self._summary_ols() 190 | self._summary_betas() 191 | self._summary_tvalues() 192 | self._ttest() 193 | 194 | def _summary_ols(self): 195 | ''' 196 | Print the OLS summary 197 | :return: 198 | ''' 199 | utils.printf('', self.logfile) 200 | utils.printf('=== Summary OLS (original) ===\n{}'.format(self.model.summary()), self.logfile) 201 | utils.printf('', self.logfile) 202 | utils.printf('# of Permutations: {}'.format(self.npermutations), self.logfile) 203 | 204 | def _summary_betas(self): 205 | ''' 206 | Summary of beta coefficients 207 | :return: 208 | ''' 209 | utils.printf('', self.logfile) 210 | utils.printf('=== Summary beta coefficients ===', self.logfile) 211 | utils.printf('{:20s}{:>10s}{:>10s}{:>10s}{:>10s}{:>12s}{:>12s}{:>12s}{:>12s}{:>12s}'.format('INDEPENDENT VAR.','MIN','MEDIAN','MEAN','MAX','STD. DEV.','B.COEFF.','As Large', 'As Small', 'P-VALUE'), self.logfile) 212 | for k,v in self.betas.items(): 213 | beta = self.model.params[k] 214 | pstats = self.model.pvalues[k] 215 | aslarge = sum([1 for c in v if c >= beta]) / float(len(v)) 216 | assmall = sum([1 for c in v if c <= beta]) / float(len(v)) 217 | utils.printf('{:20s}{:10f}{:10f}{:10f}{:10f}{:12f}{:12f}{:12f}{:12f}{:12f}'.format(k,min(v),sorted(v)[len(v)/2],sum(v)/len(v),max(v),round(np.std(v),6),beta,aslarge,assmall,round(float(pstats),2)), self.logfile) 218 | 219 | def _summary_tvalues(self): 220 | ''' 221 | Summary t-values 222 | :return: 223 | ''' 224 | utils.printf('', self.logfile) 225 | utils.printf('=== Summary T-Values ===', self.logfile) 226 | utils.printf('{:20s}{:>10s}{:>10s}{:>10s}{:>10s}{:>12s}{:>12s}{:>12s}{:>12s}'.format('INDEPENDENT VAR.','MIN','MEDIAN','MEAN','MAX','STD. DEV.','T-TEST','As Large', 'As Small'), self.logfile) 227 | for k,v in self.tvalues.items(): 228 | tstats = self.model.tvalues[k] 229 | aslarge = sum([1 for c in v if c >= tstats]) / float(len(v)) 230 | assmall = sum([1 for c in v if c <= tstats]) / float(len(v)) 231 | utils.printf('{:20s}{:10f}{:10f}{:10f}{:10f}{:12f}{:12f}{:12f}{:12f}'.format(k,min(v),sorted(v)[len(v)/2],sum(v)/len(v),max(v),round(np.std(v),6),round(float(tstats),2),aslarge,assmall), self.logfile) 232 | 233 | def _ttest(self): 234 | utils.printf('') 235 | utils.printf('========== T-TEST ==========') 236 | utils.printf('{:25s} {:25s} {:25s} {:25s}'.format('IND. VAR.','COEF.','T-STAT','P-VALUE')) 237 | 238 | ts = {} 239 | lines = {} 240 | for k,vlist in self.betas.items(): 241 | t = stats.ttest_1samp(vlist,self.model.params[k]) 242 | ts[k] = abs(round(float(t[0]),6)) 243 | lines[k] = '{:20s} {:25f} {:25f} {:25f}'.format(k,self.model.params[k],round(float(t[0]),6),round(float(t[1]),6)) 244 | 245 | ts = utils.sortDictByValue(ts,True) 246 | for t in ts: 247 | utils.printf(lines[t[0]]) 248 | 249 | 250 | ##################################################################################### 251 | # Plots 252 | ##################################################################################### 253 | 254 | def plot(self,coef='betas',fn=None): 255 | ''' 256 | Plots frequency of pearson's correlation values 257 | :param coef: string \in {betas, tvalues} 258 | :return: 259 | ''' 260 | ncols = 3 261 | m = len(self.betas.keys()) 262 | ranges = range(ncols, m, ncols) 263 | i = np.searchsorted(ranges, m, 'left') 264 | nrows = len(ranges) 265 | 266 | if i == nrows: 267 | ranges.append((i+1)*ncols) 268 | nrows += 1 269 | 270 | fig = plt.figure(figsize=(8,3*i)) 271 | for idx,k in enumerate(self.betas.keys()): 272 | plt.subplot(nrows,ncols,idx+1) 273 | 274 | if coef == 'betas': 275 | plt.hist(self.betas[k]) 276 | elif coef == 'tvalues': 277 | plt.hist(self.tvalues[k]) 278 | 279 | plt.xlabel('regression coefficients', fontsize=8) 280 | plt.ylabel('frequency', fontsize=8) 281 | plt.title(k) 282 | plt.grid(True) 283 | 284 | for ax in fig.get_axes(): 285 | ax.tick_params(axis='x', labelsize=5) 286 | ax.tick_params(axis='y', labelsize=5) 287 | 288 | plt.tight_layout() 289 | plt.savefig(fn) 290 | plt.close() 291 | 292 | 293 | 294 | 295 | 296 | 297 | -------------------------------------------------------------------------------- /libs/profiling.py: -------------------------------------------------------------------------------- 1 | __author__ = 'espin' 2 | 3 | ####################################################################################### 4 | ### Dependences 5 | ### Reference: 6 | ### http://fa.bianp.net/blog/2013/different-ways-to-get-memory-consumption-or-lessons-learned-from-memory_profiler/ 7 | ####################################################################################### 8 | import resource 9 | import psutil 10 | import sys 11 | import os 12 | import matplotlib 13 | matplotlib.use('Agg') 14 | import matplotlib.pyplot as plt 15 | from libs import utils 16 | import copy 17 | from threading import Thread 18 | import time 19 | from collections import OrderedDict 20 | 21 | ####################################################################################### 22 | # FUNCTIONS 23 | ####################################################################################### 24 | 25 | class Profiling(): 26 | 27 | def __init__(self, title=None, fn=None, perm=False): 28 | self.mem_resource = OrderedDict() 29 | self.mem_psutil = OrderedDict() 30 | self.cpu_usage = OrderedDict() 31 | self.virtual_memory_usage = OrderedDict() 32 | self.swap_memory_usage = OrderedDict() 33 | self.title = title 34 | self.fn = fn 35 | self.perm = perm 36 | 37 | def check_memory(self, key): 38 | self.memory_usage_resource(key) 39 | self.memory_usage_psutil(key) 40 | self.memory_cpu_usage(key) 41 | self.plot() 42 | 43 | def kill_if_necessary(self, vm, sm): 44 | if vm.percent >= 85. or sm.percent >= 85.: 45 | print('FULL MEMORY: \n- Virtual Memory: {}\n- Swap Memory: {}'.format(vm, sm)) 46 | 47 | def memory_cpu_usage(self, key): 48 | cpu = psutil.cpu_percent(interval=None) 49 | vm = psutil.virtual_memory() 50 | sm = psutil.swap_memory() 51 | 52 | self.cpu_usage[key] = int(255 * cpu / 100) 53 | self.virtual_memory_usage[key] = vm.percent 54 | self.swap_memory_usage[key] = sm.percent 55 | self.kill_if_necessary(vm, sm) 56 | 57 | 58 | def memory_usage_psutil(self, key): 59 | # return the memory usage in MB 60 | process = psutil.Process(os.getpid()) 61 | self.mem_psutil[key] = process.memory_info()[0] / float(2 ** 20) 62 | 63 | def memory_usage_resource(self, key): 64 | rusage_denom = 1024. 65 | if sys.platform == 'darwin': 66 | # ... it seems that in OSX the output is different units ... 67 | rusage_denom = rusage_denom * rusage_denom 68 | self.mem_resource[key] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / rusage_denom 69 | 70 | def copy(self, mem): 71 | self.mem_resource = mem.mem_resource.deepcopy() 72 | self.mem_psutil = mem.mem_psutil.deepcopy() 73 | self.cpu_usage = mem.cpu_usage.deepcopy() 74 | self.virtual_memory_usage = mem.virtual_memory_usage.deepcopy() 75 | self.swap_memory_usage = mem.swap_memory_usage.deepcopy() 76 | 77 | def plot(self): 78 | ''' 79 | Plots the Memory usage in MB 80 | :return: 81 | ''' 82 | if self.fn is not None and self.title is not None: 83 | labels = self.mem_resource.keys() 84 | x = range(len(labels)) 85 | 86 | plt.figure(1) 87 | ax1 = plt.subplot(211) 88 | ax1.plot(x, self.mem_resource.values(), color='red', marker='o', label='mem_resource') 89 | ax1.plot(x, self.mem_psutil.values(), color='blue', marker='o', label='mem_psutil') 90 | ax1.set_ylabel('Memory usage in MB') 91 | ax1.grid(True) 92 | ax1.set_xticks(x) 93 | ax1.set_xticklabels(labels, rotation=20, fontsize=7) 94 | box = ax1.get_position() 95 | ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height]) 96 | ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size':10}) 97 | 98 | ax2 = plt.subplot(212) 99 | ax2.plot(x, self.cpu_usage.values(), color='black', marker='o', label='cpu_usage') 100 | ax2.plot(x, self.virtual_memory_usage.values(), color='orange', marker='o', label='virtual_memory') 101 | ax2.plot(x, self.swap_memory_usage.values(), color='green', marker='o', label='swap_memory') 102 | #ax2.set_xlabel(self.xlabel) 103 | ax2.set_ylabel('Percentage (usage)') 104 | ax2.grid(True) 105 | ax2.set_xticks(x) 106 | ax2.set_xticklabels(labels, rotation=20, fontsize=7) 107 | box = ax2.get_position() 108 | ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height]) 109 | ax2.legend(loc='center left', prop={'size':10}) # bbox_to_anchor=(1, 0.5), 110 | 111 | plt.suptitle('Profiling - {}'.format(self.title)) 112 | plt.tight_layout() 113 | plt.savefig(self.fn) 114 | plt.close() 115 | -------------------------------------------------------------------------------- /libs/qap.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ####################################################################### 4 | # Dependencies 5 | ####################################################################### 6 | import numpy as np 7 | from scipy.stats.stats import pearsonr 8 | import statsmodels.api as sm 9 | from statsmodels.stats.weightstats import ztest 10 | import matplotlib.pyplot as plt 11 | from scipy.stats import ttest_ind 12 | from libs import utils 13 | 14 | 15 | ####################################################################### 16 | # QAP 17 | ####################################################################### 18 | class QAP(): 19 | 20 | ##################################################################################### 21 | # Constructor and Init 22 | ##################################################################################### 23 | 24 | def __init__(self, Y=None, X=None, npermutations=-1, diagonal=False): 25 | ''' 26 | Initialization of variables 27 | :param Y: numpy array depended variable 28 | :param X: numpy array independed variable 29 | :return: 30 | ''' 31 | self.Y = Y 32 | self.X = X 33 | self.npermutations = npermutations 34 | self.diagonal = diagonal 35 | self.beta = None 36 | self.Ymod = None 37 | self.betas = [] 38 | 39 | def init(self): 40 | ''' 41 | Shows the correlation of the initial/original variables (no shuffeling) 42 | :return: 43 | ''' 44 | self.beta = self.correlation(self.X, self.Y) 45 | self.stats(self.X, self.Y) 46 | 47 | ##################################################################################### 48 | # Core QAP methods 49 | ##################################################################################### 50 | 51 | def qap(self): 52 | ''' 53 | Quadratic Assignment Procedure 54 | :param npermutations: 55 | :return: 56 | ''' 57 | self.init() 58 | self._shuffle() 59 | 60 | def _shuffle(self): 61 | self.Ymod = self.Y.copy() 62 | for t in range(self.npermutations): 63 | self._rmperm() 64 | self._addBeta(self.correlation(self.X, self.Ymod, False)) 65 | 66 | def correlation(self, x, y, show=True): 67 | ''' 68 | Computes Pearson's correlation value of variables x and y. 69 | Diagonal values are removed. 70 | :param x: numpy array independent variable 71 | :param y: numpu array dependent variable 72 | :param show: if True then shows pearson's correlation and p-value. 73 | :return: 74 | ''' 75 | if not self.diagonal: 76 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])]) 77 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])]) 78 | pc = pearsonr(xflatten, yflatten) 79 | else: 80 | pc = pearsonr(x.flatten(), y.flatten()) 81 | if show: 82 | utils.printf('Pearson Correlation: {}'.format(pc[0])) 83 | utils.printf('p-value: {}'.format(pc[1])) 84 | return pc 85 | 86 | ##################################################################################### 87 | # Handlers 88 | ##################################################################################### 89 | 90 | def _addBeta(self, p): 91 | ''' 92 | frequency dictionary of pearson's correlation values 93 | :param p: person's correlation value 94 | :return: 95 | ''' 96 | p = round(p[0],6) 97 | self.betas.append(p) 98 | 99 | def _rmperm(self): 100 | shuffle = np.random.permutation(self.Ymod.shape[0]) 101 | np.take(self.Ymod,shuffle,axis=0,out=self.Ymod) 102 | np.take(self.Ymod,shuffle,axis=1,out=self.Ymod) 103 | 104 | 105 | ##################################################################################### 106 | # Plots & Prints 107 | ##################################################################################### 108 | 109 | def summary(self): 110 | utils.printf('') 111 | utils.printf('# Permutations: {}'.format(self.npermutations)) 112 | utils.printf('Correlation coefficients: Obs. Value({}), Significance({})'.format(self.beta[0], self.beta[1])) 113 | utils.printf('') 114 | utils.printf('- Sum all betas: {}'.format(sum(self.betas))) 115 | utils.printf('- Min betas: {}'.format(min(self.betas))) 116 | utils.printf('- Max betas: {}'.format(max(self.betas))) 117 | utils.printf('- Average betas: {}'.format(np.average(self.betas))) 118 | utils.printf('- Std. Dev. betas: {}'.format(np.std(self.betas))) 119 | utils.printf('') 120 | utils.printf('prop >= {}: {}'.format(self.beta[0], sum([1 for b in self.betas if b >= self.beta[0] ])/float(len(self.betas)))) 121 | utils.printf('prop <= {}: {} (proportion of randomly generated correlations that were as {} as the observed)'.format(self.beta[0], sum([1 for b in self.betas if b <= self.beta[0] ])/float(len(self.betas)), 'large' if self.beta[0] >= 0 else 'small')) 122 | utils.printf('') 123 | 124 | def plot(self): 125 | ''' 126 | Plots frequency of pearson's correlation values 127 | :return: 128 | ''' 129 | plt.hist(self.betas) 130 | plt.xlabel('regression coefficients') 131 | plt.ylabel('frequency') 132 | plt.title('QAP') 133 | plt.grid(True) 134 | plt.show() 135 | plt.close() 136 | 137 | ##################################################################################### 138 | # Others 139 | ##################################################################################### 140 | 141 | def stats(self, x, y): 142 | if not self.diagonal: 143 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])]) 144 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])]) 145 | p = np.corrcoef(xflatten,yflatten) 146 | utils.printf('Pearson\'s correlation:\n{}'.format(p)) 147 | utils.printf('Z-Test:{}'.format(ztest(xflatten, yflatten))) 148 | utils.printf('T-Test:{}'.format(ttest_ind(xflatten, yflatten))) 149 | else: 150 | p = np.corrcoef(x, y) 151 | utils.printf('Pearson\'s correlation:\n{}'.format(p)) 152 | utils.printf('Z-Test:{}'.format(ztest(x, y))) 153 | utils.printf('T-Test:{}'.format(ttest_ind(x, y))) 154 | 155 | def ols(self, x, y): 156 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])]) 157 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])]) 158 | xflatten = sm.add_constant(xflatten) 159 | model = sm.OLS(yflatten,xflatten) 160 | results = model.fit() 161 | print results.summary() 162 | -------------------------------------------------------------------------------- /libs/utils.py: -------------------------------------------------------------------------------- 1 | __author__ = 'lisette.espin' 2 | 3 | ###################################################################################################################### 4 | # SYSTEM DEPENDENCES 5 | ###################################################################################################################### 6 | from datetime import datetime 7 | import operator 8 | import sys, os 9 | 10 | ###################################################################################################################### 11 | # FUNCTIONS 12 | ###################################################################################################################### 13 | def printf(msg, logfile=None): 14 | strtowrite = "[{}] {}".format(datetime.now(), msg) 15 | print(strtowrite) 16 | if logfile is not None: 17 | with open(logfile, 'a') as f: 18 | f.write('{}\n'.format(strtowrite)) 19 | 20 | def sortDictByValue(x,desc): 21 | sorted_x = sorted(x.items(), key=operator.itemgetter(1),reverse=desc) 22 | return sorted_x 23 | 24 | def sortDictByKey(x,desc): 25 | sorted_x = sorted(x.items(), key=operator.itemgetter(0),reverse=desc) 26 | return sorted_x 27 | 28 | def _swap_cols(arr, frm, to): 29 | arr[:,[frm, to]] = arr[:,[to, frm]] 30 | 31 | def _swap_rows(arr, frm, to): 32 | arr[[frm, to],:] = arr[[to, frm],:] 33 | 34 | def appendToFile(str, path): 35 | with open(path,'a') as f: 36 | f.write(str) --------------------------------------------------------------------------------