| # Install on Terminal of MacOS # 1. pandas #pip3 install -U pandas # 2. NumPy #pip3 install -U numpy # 3. matplotlib #pip3 install -U matplotlib # 4. scikit-learn (sklearn) #pip3 install -U scikit-learn |
1_MacOS_Terminal.txt
| ########## Run Terminal on MacOS and execute ### TO UPDATE cd "YOUR_WORKING_DIRECTORY" python3 pca.py wine.csv #python3 pca.py wine4data.csv |
Input data files
wine.csv
| ,class,Alcohol,Malic acid,Ash,Alcalinity of ash,Magnesium,Total phenols,Flavanoids,Nonflavanoid phenols,Proanthocyanins,Color intensity,Hue,OD280/OD315 of diluted wines,Proline 0,1,14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065 1,1,13.2,1.78,2.14,11.2,100,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050 2,1,13.16,2.36,2.67,18.6,101,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185 3,1,14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480 4,1,13.24,2.59,2.87,21.0,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735 5,1,14.2,1.76,2.45,15.2,112,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450 6,1,14.39,1.87,2.45,14.6,96,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290 7,1,14.06,2.15,2.61,17.6,121,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295 8,1,14.83,1.64,2.17,14.0,97,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045 9,1,13.86,1.35,2.27,16.0,98,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045 10,1,14.1,2.16,2.3,18.0,105,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510 11,1,14.12,1.48,2.32,16.8,95,2.2,2.43,0.26,1.57,5.0,1.17,2.82,1280 12,1,13.75,1.73,2.41,16.0,89,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320 13,1,14.75,1.73,2.39,11.4,91,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150 14,1,14.38,1.87,2.38,12.0,102,3.3,3.64,0.29,2.96,7.5,1.2,3.0,1547 15,1,13.63,1.81,2.7,17.2,112,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310 16,1,14.3,1.92,2.72,20.0,120,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280 17,1,13.83,1.57,2.62,20.0,115,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130 18,1,14.19,1.59,2.48,16.5,108,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680 19,1,13.64,3.1,2.56,15.2,116,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845 20,1,14.06,1.63,2.28,16.0,126,3.0,3.17,0.24,2.1,5.65,1.09,3.71,780 21,1,12.93,3.8,2.65,18.6,102,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770 22,1,13.71,1.86,2.36,16.6,101,2.61,2.88,0.27,1.69,3.8,1.11,4.0,1035 23,1,12.85,1.6,2.52,17.8,95,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015 24,1,13.5,1.81,2.61,20.0,96,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845 25,1,13.05,2.05,3.22,25.0,124,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830 26,1,13.39,1.77,2.62,16.1,93,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195 27,1,13.3,1.72,2.14,17.0,94,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285 28,1,13.87,1.9,2.8,19.4,107,2.95,2.97,0.37,1.76,4.5,1.25,3.4,915 29,1,14.02,1.68,2.21,16.0,96,2.65,2.33,0.26,1.98,4.7,1.04,3.59,1035 30,1,13.73,1.5,2.7,22.5,101,3.0,3.25,0.29,2.38,5.7,1.19,2.71,1285 31,1,13.58,1.66,2.36,19.1,106,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515 32,1,13.68,1.83,2.36,17.2,104,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990 33,1,13.76,1.53,2.7,19.5,132,2.95,2.74,0.5,1.35,5.4,1.25,3.0,1235 34,1,13.51,1.8,2.65,19.0,110,2.35,2.53,0.29,1.54,4.2,1.1,2.87,1095 35,1,13.48,1.81,2.41,20.5,100,2.7,2.98,0.26,1.86,5.1,1.04,3.47,920 36,1,13.28,1.64,2.84,15.5,110,2.6,2.68,0.34,1.36,4.6,1.09,2.78,880 37,1,13.05,1.65,2.55,18.0,98,2.45,2.43,0.29,1.44,4.25,1.12,2.51,1105 38,1,13.07,1.5,2.1,15.5,98,2.4,2.64,0.28,1.37,3.7,1.18,2.69,1020 39,1,14.22,3.99,2.51,13.2,128,3.0,3.04,0.2,2.08,5.1,0.89,3.53,760 40,1,13.56,1.71,2.31,16.2,117,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795 41,1,13.41,3.84,2.12,18.8,90,2.45,2.68,0.27,1.48,4.28,0.91,3.0,1035 42,1,13.88,1.89,2.59,15.0,101,3.25,3.56,0.17,1.7,5.43,0.88,3.56,1095 43,1,13.24,3.98,2.29,17.5,103,2.64,2.63,0.32,1.66,4.36,0.82,3.0,680 44,1,13.05,1.77,2.1,17.0,107,3.0,3.0,0.28,2.03,5.04,0.88,3.35,885 45,1,14.21,4.04,2.44,18.9,111,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080 46,1,14.38,3.59,2.28,16.0,102,3.25,3.17,0.27,2.19,4.9,1.04,3.44,1065 47,1,13.9,1.68,2.12,16.0,101,3.1,3.39,0.21,2.14,6.1,0.91,3.33,985 48,1,14.1,2.02,2.4,18.8,103,2.75,2.92,0.32,2.38,6.2,1.07,2.75,1060 49,1,13.94,1.73,2.27,17.4,108,2.88,3.54,0.32,2.08,8.9,1.12,3.1,1260 50,1,13.05,1.73,2.04,12.4,92,2.72,3.27,0.17,2.91,7.2,1.12,2.91,1150 51,1,13.83,1.65,2.6,17.2,94,2.45,2.99,0.22,2.29,5.6,1.24,3.37,1265 52,1,13.82,1.75,2.42,14.0,111,3.88,3.74,0.32,1.87,7.05,1.01,3.26,1190 53,1,13.77,1.9,2.68,17.1,115,3.0,2.79,0.39,1.68,6.3,1.13,2.93,1375 54,1,13.74,1.67,2.25,16.4,118,2.6,2.9,0.21,1.62,5.85,0.92,3.2,1060 55,1,13.56,1.73,2.46,20.5,116,2.96,2.78,0.2,2.45,6.25,0.98,3.03,1120 56,1,14.22,1.7,2.3,16.3,118,3.2,3.0,0.26,2.03,6.38,0.94,3.31,970 57,1,13.29,1.97,2.68,16.8,102,3.0,3.23,0.31,1.66,6.0,1.07,2.84,1270 58,1,13.72,1.43,2.5,16.7,108,3.4,3.67,0.19,2.04,6.8,0.89,2.87,1285 59,2,12.37,0.94,1.36,10.6,88,1.98,0.57,0.28,0.42,1.95,1.05,1.82,520 60,2,12.33,1.1,2.28,16.0,101,2.05,1.09,0.63,0.41,3.27,1.25,1.67,680 61,2,12.64,1.36,2.02,16.8,100,2.02,1.41,0.53,0.62,5.75,0.98,1.59,450 62,2,13.67,1.25,1.92,18.0,94,2.1,1.79,0.32,0.73,3.8,1.23,2.46,630 63,2,12.37,1.13,2.16,19.0,87,3.5,3.1,0.19,1.87,4.45,1.22,2.87,420 64,2,12.17,1.45,2.53,19.0,104,1.89,1.75,0.45,1.03,2.95,1.45,2.23,355 65,2,12.37,1.21,2.56,18.1,98,2.42,2.65,0.37,2.08,4.6,1.19,2.3,678 66,2,13.11,1.01,1.7,15.0,78,2.98,3.18,0.26,2.28,5.3,1.12,3.18,502 67,2,12.37,1.17,1.92,19.6,78,2.11,2.0,0.27,1.04,4.68,1.12,3.48,510 68,2,13.34,0.94,2.36,17.0,110,2.53,1.3,0.55,0.42,3.17,1.02,1.93,750 69,2,12.21,1.19,1.75,16.8,151,1.85,1.28,0.14,2.5,2.85,1.28,3.07,718 70,2,12.29,1.61,2.21,20.4,103,1.1,1.02,0.37,1.46,3.05,0.9060000000000000,1.82,870 71,2,13.86,1.51,2.67,25.0,86,2.95,2.86,0.21,1.87,3.38,1.36,3.16,410 72,2,13.49,1.66,2.24,24.0,87,1.88,1.84,0.27,1.03,3.74,0.98,2.78,472 73,2,12.99,1.67,2.6,30.0,139,3.3,2.89,0.21,1.96,3.35,1.31,3.5,985 74,2,11.96,1.09,2.3,21.0,101,3.38,2.14,0.13,1.65,3.21,0.99,3.13,886 75,2,11.66,1.88,1.92,16.0,97,1.61,1.57,0.34,1.15,3.8,1.23,2.14,428 76,2,13.03,0.9,1.71,16.0,86,1.95,2.03,0.24,1.46,4.6,1.19,2.48,392 77,2,11.84,2.89,2.23,18.0,112,1.72,1.32,0.43,0.95,2.65,0.96,2.52,500 78,2,12.33,0.99,1.95,14.8,136,1.9,1.85,0.35,2.76,3.4,1.06,2.31,750 79,2,12.7,3.87,2.4,23.0,101,2.83,2.55,0.43,1.95,2.57,1.19,3.13,463 80,2,12.0,0.92,2.0,19.0,86,2.42,2.26,0.3,1.43,2.5,1.38,3.12,278 81,2,12.72,1.81,2.2,18.8,86,2.2,2.53,0.26,1.77,3.9,1.16,3.14,714 82,2,12.08,1.13,2.51,24.0,78,2.0,1.58,0.4,1.4,2.2,1.31,2.72,630 83,2,13.05,3.86,2.32,22.5,85,1.65,1.59,0.61,1.62,4.8,0.84,2.01,515 84,2,11.84,0.89,2.58,18.0,94,2.2,2.21,0.22,2.35,3.05,0.79,3.08,520 85,2,12.67,0.98,2.24,18.0,99,2.2,1.94,0.3,1.46,2.62,1.23,3.16,450 86,2,12.16,1.61,2.31,22.8,90,1.78,1.69,0.43,1.56,2.45,1.33,2.26,495 87,2,11.65,1.67,2.62,26.0,88,1.92,1.61,0.4,1.34,2.6,1.36,3.21,562 88,2,11.64,2.06,2.46,21.6,84,1.95,1.69,0.48,1.35,2.8,1.0,2.75,680 89,2,12.08,1.33,2.3,23.6,70,2.2,1.59,0.42,1.38,1.74,1.07,3.21,625 90,2,12.08,1.83,2.32,18.5,81,1.6,1.5,0.52,1.64,2.4,1.08,2.27,480 91,2,12.0,1.51,2.42,22.0,86,1.45,1.25,0.5,1.63,3.6,1.05,2.65,450 92,2,12.69,1.53,2.26,20.7,80,1.38,1.46,0.58,1.62,3.05,0.96,2.06,495 93,2,12.29,2.83,2.22,18.0,88,2.45,2.25,0.25,1.99,2.15,1.15,3.3,290 94,2,11.62,1.99,2.28,18.0,98,3.02,2.26,0.17,1.35,3.25,1.16,2.96,345 95,2,12.47,1.52,2.2,19.0,162,2.5,2.27,0.32,3.28,2.6,1.16,2.63,937 96,2,11.81,2.12,2.74,21.5,134,1.6,0.99,0.14,1.56,2.5,0.95,2.26,625 97,2,12.29,1.41,1.98,16.0,85,2.55,2.5,0.29,1.77,2.9,1.23,2.74,428 98,2,12.37,1.07,2.1,18.5,88,3.52,3.75,0.24,1.95,4.5,1.04,2.77,660 99,2,12.29,3.17,2.21,18.0,88,2.85,2.99,0.45,2.81,2.3,1.42,2.83,406 100,2,12.08,2.08,1.7,17.5,97,2.23,2.17,0.26,1.4,3.3,1.27,2.96,710 101,2,12.6,1.34,1.9,18.5,88,1.45,1.36,0.29,1.35,2.45,1.04,2.77,562 102,2,12.34,2.45,2.46,21.0,98,2.56,2.11,0.34,1.31,2.8,0.8,3.38,438 103,2,11.82,1.72,1.88,19.5,86,2.5,1.64,0.37,1.42,2.06,0.94,2.44,415 104,2,12.51,1.73,1.98,20.5,85,2.2,1.92,0.32,1.48,2.94,1.04,3.57,672 105,2,12.42,2.55,2.27,22.0,90,1.68,1.84,0.66,1.42,2.7,0.86,3.3,315 106,2,12.25,1.73,2.12,19.0,80,1.65,2.03,0.37,1.63,3.4,1.0,3.17,510 107,2,12.72,1.75,2.28,22.5,84,1.38,1.76,0.48,1.63,3.3,0.88,2.42,488 108,2,12.22,1.29,1.94,19.0,92,2.36,2.04,0.39,2.08,2.7,0.86,3.02,312 109,2,11.61,1.35,2.7,20.0,94,2.74,2.92,0.29,2.49,2.65,0.96,3.26,680 110,2,11.46,3.74,1.82,19.5,107,3.18,2.58,0.24,3.58,2.9,0.75,2.81,562 111,2,12.52,2.43,2.17,21.0,88,2.55,2.27,0.26,1.22,2.0,0.9,2.78,325 112,2,11.76,2.68,2.92,20.0,103,1.75,2.03,0.6,1.05,3.8,1.23,2.5,607 113,2,11.41,0.74,2.5,21.0,88,2.48,2.01,0.42,1.44,3.08,1.1,2.31,434 114,2,12.08,1.39,2.5,22.5,84,2.56,2.29,0.43,1.04,2.9,0.93,3.19,385 115,2,11.03,1.51,2.2,21.5,85,2.46,2.17,0.52,2.01,1.9,1.71,2.87,407 116,2,11.82,1.47,1.99,20.8,86,1.98,1.6,0.3,1.53,1.95,0.95,3.33,495 117,2,12.42,1.61,2.19,22.5,108,2.0,2.09,0.34,1.61,2.06,1.06,2.96,345 118,2,12.77,3.43,1.98,16.0,80,1.63,1.25,0.43,0.83,3.4,0.7,2.12,372 119,2,12.0,3.43,2.0,19.0,87,2.0,1.64,0.37,1.87,1.28,0.93,3.05,564 120,2,11.45,2.4,2.42,20.0,96,2.9,2.79,0.32,1.83,3.25,0.8,3.39,625 121,2,11.56,2.05,3.23,28.5,119,3.18,5.08,0.47,1.87,6.0,0.93,3.69,465 122,2,12.42,4.43,2.73,26.5,102,2.2,2.13,0.43,1.71,2.08,0.92,3.12,365 123,2,13.05,5.8,2.13,21.5,86,2.62,2.65,0.3,2.01,2.6,0.73,3.1,380 124,2,11.87,4.31,2.39,21.0,82,2.86,3.03,0.21,2.91,2.8,0.75,3.64,380 125,2,12.07,2.16,2.17,21.0,85,2.6,2.65,0.37,1.35,2.76,0.86,3.28,378 126,2,12.43,1.53,2.29,21.5,86,2.74,3.15,0.39,1.77,3.94,0.69,2.84,352 127,2,11.79,2.13,2.78,28.5,92,2.13,2.24,0.58,1.76,3.0,0.97,2.44,466 128,2,12.37,1.63,2.3,24.5,88,2.22,2.45,0.4,1.9,2.12,0.89,2.78,342 129,2,12.04,4.3,2.38,22.0,80,2.1,1.75,0.42,1.35,2.6,0.79,2.57,580 130,3,12.86,1.35,2.32,18.0,122,1.51,1.25,0.21,0.94,4.1,0.76,1.29,630 131,3,12.88,2.99,2.4,20.0,104,1.3,1.22,0.24,0.83,5.4,0.74,1.42,530 132,3,12.81,2.31,2.4,24.0,98,1.15,1.09,0.27,0.83,5.7,0.66,1.36,560 133,3,12.7,3.55,2.36,21.5,106,1.7,1.2,0.17,0.84,5.0,0.78,1.29,600 134,3,12.51,1.24,2.25,17.5,85,2.0,0.58,0.6,1.25,5.45,0.75,1.51,650 135,3,12.6,2.46,2.2,18.5,94,1.62,0.66,0.63,0.94,7.1,0.73,1.58,695 136,3,12.25,4.72,2.54,21.0,89,1.38,0.47,0.53,0.8,3.85,0.75,1.27,720 137,3,12.53,5.51,2.64,25.0,96,1.79,0.6,0.63,1.1,5.0,0.82,1.69,515 138,3,13.49,3.59,2.19,19.5,88,1.62,0.48,0.58,0.88,5.7,0.81,1.82,580 139,3,12.84,2.96,2.61,24.0,101,2.32,0.6,0.53,0.81,4.92,0.89,2.15,590 140,3,12.93,2.81,2.7,21.0,96,1.54,0.5,0.53,0.75,4.6,0.77,2.31,600 141,3,13.36,2.56,2.35,20.0,89,1.4,0.5,0.37,0.64,5.6,0.7,2.47,780 142,3,13.52,3.17,2.72,23.5,97,1.55,0.52,0.5,0.55,4.35,0.89,2.06,520 143,3,13.62,4.95,2.35,20.0,92,2.0,0.8,0.47,1.02,4.4,0.91,2.05,550 144,3,12.25,3.88,2.2,18.5,112,1.38,0.78,0.29,1.14,8.21,0.65,2.0,855 145,3,13.16,3.57,2.15,21.0,102,1.5,0.55,0.43,1.3,4.0,0.6,1.68,830 146,3,13.88,5.04,2.23,20.0,80,0.98,0.34,0.4,0.68,4.9,0.58,1.33,415 147,3,12.87,4.61,2.48,21.5,86,1.7,0.65,0.47,0.86,7.65,0.54,1.86,625 148,3,13.32,3.24,2.38,21.5,92,1.93,0.76,0.45,1.25,8.42,0.55,1.62,650 149,3,13.08,3.9,2.36,21.5,113,1.41,1.39,0.34,1.14,9.4,0.57,1.33,550 150,3,13.5,3.12,2.62,24.0,123,1.4,1.57,0.22,1.25,8.6,0.59,1.3,500 151,3,12.79,2.67,2.48,22.0,112,1.48,1.36,0.24,1.26,10.8,0.48,1.47,480 152,3,13.11,1.9,2.75,25.5,116,2.2,1.28,0.26,1.56,7.1,0.61,1.33,425 153,3,13.23,3.3,2.28,18.5,98,1.8,0.83,0.61,1.87,10.52,0.56,1.51,675 154,3,12.58,1.29,2.1,20.0,103,1.48,0.58,0.53,1.4,7.6,0.58,1.55,640 155,3,13.17,5.19,2.32,22.0,93,1.74,0.63,0.61,1.55,7.9,0.6,1.48,725 156,3,13.84,4.12,2.38,19.5,89,1.8,0.83,0.48,1.56,9.01,0.57,1.64,480 157,3,12.45,3.03,2.64,27.0,97,1.9,0.58,0.63,1.14,7.5,0.67,1.73,880 158,3,14.34,1.68,2.7,25.0,98,2.8,1.31,0.53,2.7,13.0,0.57,1.96,660 159,3,13.48,1.67,2.64,22.5,89,2.6,1.1,0.52,2.29,11.75,0.57,1.78,620 160,3,12.36,3.83,2.38,21.0,88,2.3,0.92,0.5,1.04,7.65,0.56,1.58,520 161,3,13.69,3.26,2.54,20.0,107,1.83,0.56,0.5,0.8,5.88,0.96,1.82,680 162,3,12.85,3.27,2.58,22.0,106,1.65,0.6,0.6,0.96,5.58,0.87,2.11,570 163,3,12.96,3.45,2.35,18.5,106,1.39,0.7,0.4,0.94,5.28,0.68,1.75,675 164,3,13.78,2.76,2.3,22.0,90,1.35,0.68,0.41,1.03,9.58,0.7,1.68,615 165,3,13.73,4.36,2.26,22.5,88,1.28,0.47,0.52,1.15,6.62,0.78,1.75,520 166,3,13.45,3.7,2.6,23.0,111,1.7,0.92,0.43,1.46,10.68,0.85,1.56,695 167,3,12.82,3.37,2.3,19.5,88,1.48,0.66,0.4,0.97,10.26,0.72,1.75,685 168,3,13.58,2.58,2.69,24.5,105,1.55,0.84,0.39,1.54,8.66,0.74,1.8,750 169,3,13.4,4.6,2.86,25.0,112,1.98,0.96,0.27,1.11,8.5,0.67,1.92,630 170,3,12.2,3.03,2.32,19.0,96,1.25,0.49,0.4,0.73,5.5,0.66,1.83,510 171,3,12.77,2.39,2.28,19.5,86,1.39,0.51,0.48,0.64,9.899999000000000,0.57,1.63,470 172,3,14.16,2.51,2.48,20.0,91,1.68,0.7,0.44,1.24,9.7,0.62,1.71,660 173,3,13.71,5.65,2.45,20.5,95,1.68,0.61,0.52,1.06,7.7,0.64,1.74,740 174,3,13.4,3.91,2.48,23.0,102,1.8,0.75,0.43,1.41,7.3,0.7,1.56,750 175,3,13.27,4.28,2.26,20.0,120,1.59,0.69,0.43,1.35,10.2,0.59,1.56,835 176,3,13.17,2.59,2.37,20.0,120,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840 177,3,14.13,4.1,2.74,24.5,96,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560 |
| ,class,Alcohol,Flavanoids,Color intensity,Proline 0,1,14.23,3.06,5.64,1065 1,1,13.2,2.76,4.38,1050 2,1,13.16,3.24,5.68,1185 3,1,14.37,3.49,7.8,1480 4,1,13.24,2.69,4.32,735 5,1,14.2,3.39,6.75,1450 6,1,14.39,2.52,5.25,1290 7,1,14.06,2.51,5.05,1295 8,1,14.83,2.98,5.2,1045 9,1,13.86,3.15,7.22,1045 10,1,14.1,3.32,5.75,1510 11,1,14.12,2.43,5.0,1280 12,1,13.75,2.76,5.6,1320 13,1,14.75,3.69,5.4,1150 14,1,14.38,3.64,7.5,1547 15,1,13.63,2.91,7.3,1310 16,1,14.3,3.14,6.2,1280 17,1,13.83,3.4,6.6,1130 18,1,14.19,3.93,8.7,1680 19,1,13.64,3.03,5.1,845 20,1,14.06,3.17,5.65,780 21,1,12.93,2.41,4.5,770 22,1,13.71,2.88,3.8,1035 23,1,12.85,2.37,3.93,1015 24,1,13.5,2.61,3.52,845 25,1,13.05,2.68,3.58,830 26,1,13.39,2.94,4.8,1195 27,1,13.3,2.19,3.95,1285 28,1,13.87,2.97,4.5,915 29,1,14.02,2.33,4.7,1035 30,1,13.73,3.25,5.7,1285 31,1,13.58,3.19,6.9,1515 32,1,13.68,2.69,3.84,990 33,1,13.76,2.74,5.4,1235 34,1,13.51,2.53,4.2,1095 35,1,13.48,2.98,5.1,920 36,1,13.28,2.68,4.6,880 37,1,13.05,2.43,4.25,1105 38,1,13.07,2.64,3.7,1020 39,1,14.22,3.04,5.1,760 40,1,13.56,3.29,6.13,795 41,1,13.41,2.68,4.28,1035 42,1,13.88,3.56,5.43,1095 43,1,13.24,2.63,4.36,680 44,1,13.05,3.0,5.04,885 45,1,14.21,2.65,5.24,1080 46,1,14.38,3.17,4.9,1065 47,1,13.9,3.39,6.1,985 48,1,14.1,2.92,6.2,1060 49,1,13.94,3.54,8.9,1260 50,1,13.05,3.27,7.2,1150 51,1,13.83,2.99,5.6,1265 52,1,13.82,3.74,7.05,1190 53,1,13.77,2.79,6.3,1375 54,1,13.74,2.9,5.85,1060 55,1,13.56,2.78,6.25,1120 56,1,14.22,3.0,6.38,970 57,1,13.29,3.23,6.0,1270 58,1,13.72,3.67,6.8,1285 59,2,12.37,0.57,1.95,520 60,2,12.33,1.09,3.27,680 61,2,12.64,1.41,5.75,450 62,2,13.67,1.79,3.8,630 63,2,12.37,3.1,4.45,420 64,2,12.17,1.75,2.95,355 65,2,12.37,2.65,4.6,678 66,2,13.11,3.18,5.3,502 67,2,12.37,2.0,4.68,510 68,2,13.34,1.3,3.17,750 69,2,12.21,1.28,2.85,718 70,2,12.29,1.02,3.05,870 71,2,13.86,2.86,3.38,410 72,2,13.49,1.84,3.74,472 73,2,12.99,2.89,3.35,985 74,2,11.96,2.14,3.21,886 75,2,11.66,1.57,3.8,428 76,2,13.03,2.03,4.6,392 77,2,11.84,1.32,2.65,500 78,2,12.33,1.85,3.4,750 79,2,12.7,2.55,2.57,463 80,2,12.0,2.26,2.5,278 81,2,12.72,2.53,3.9,714 82,2,12.08,1.58,2.2,630 83,2,13.05,1.59,4.8,515 84,2,11.84,2.21,3.05,520 85,2,12.67,1.94,2.62,450 86,2,12.16,1.69,2.45,495 87,2,11.65,1.61,2.6,562 88,2,11.64,1.69,2.8,680 89,2,12.08,1.59,1.74,625 90,2,12.08,1.5,2.4,480 91,2,12.0,1.25,3.6,450 92,2,12.69,1.46,3.05,495 93,2,12.29,2.25,2.15,290 94,2,11.62,2.26,3.25,345 95,2,12.47,2.27,2.6,937 96,2,11.81,0.99,2.5,625 97,2,12.29,2.5,2.9,428 98,2,12.37,3.75,4.5,660 99,2,12.29,2.99,2.3,406 100,2,12.08,2.17,3.3,710 101,2,12.6,1.36,2.45,562 102,2,12.34,2.11,2.8,438 103,2,11.82,1.64,2.06,415 104,2,12.51,1.92,2.94,672 105,2,12.42,1.84,2.7,315 106,2,12.25,2.03,3.4,510 107,2,12.72,1.76,3.3,488 108,2,12.22,2.04,2.7,312 109,2,11.61,2.92,2.65,680 110,2,11.46,2.58,2.9,562 111,2,12.52,2.27,2.0,325 112,2,11.76,2.03,3.8,607 113,2,11.41,2.01,3.08,434 114,2,12.08,2.29,2.9,385 115,2,11.03,2.17,1.9,407 116,2,11.82,1.6,1.95,495 117,2,12.42,2.09,2.06,345 118,2,12.77,1.25,3.4,372 119,2,12.0,1.64,1.28,564 120,2,11.45,2.79,3.25,625 121,2,11.56,5.08,6.0,465 122,2,12.42,2.13,2.08,365 123,2,13.05,2.65,2.6,380 124,2,11.87,3.03,2.8,380 125,2,12.07,2.65,2.76,378 126,2,12.43,3.15,3.94,352 127,2,11.79,2.24,3.0,466 128,2,12.37,2.45,2.12,342 129,2,12.04,1.75,2.6,580 130,3,12.86,1.25,4.1,630 131,3,12.88,1.22,5.4,530 132,3,12.81,1.09,5.7,560 133,3,12.7,1.2,5.0,600 134,3,12.51,0.58,5.45,650 135,3,12.6,0.66,7.1,695 136,3,12.25,0.47,3.85,720 137,3,12.53,0.6,5.0,515 138,3,13.49,0.48,5.7,580 139,3,12.84,0.6,4.92,590 140,3,12.93,0.5,4.6,600 141,3,13.36,0.5,5.6,780 142,3,13.52,0.52,4.35,520 143,3,13.62,0.8,4.4,550 144,3,12.25,0.78,8.21,855 145,3,13.16,0.55,4.0,830 146,3,13.88,0.34,4.9,415 147,3,12.87,0.65,7.65,625 148,3,13.32,0.76,8.42,650 149,3,13.08,1.39,9.4,550 150,3,13.5,1.57,8.6,500 151,3,12.79,1.36,10.8,480 152,3,13.11,1.28,7.1,425 153,3,13.23,0.83,10.52,675 154,3,12.58,0.58,7.6,640 155,3,13.17,0.63,7.9,725 156,3,13.84,0.83,9.01,480 157,3,12.45,0.58,7.5,880 158,3,14.34,1.31,13.0,660 159,3,13.48,1.1,11.75,620 160,3,12.36,0.92,7.65,520 161,3,13.69,0.56,5.88,680 162,3,12.85,0.6,5.58,570 163,3,12.96,0.7,5.28,675 164,3,13.78,0.68,9.58,615 165,3,13.73,0.47,6.62,520 166,3,13.45,0.92,10.68,695 167,3,12.82,0.66,10.26,685 168,3,13.58,0.84,8.66,750 169,3,13.4,0.96,8.5,630 170,3,12.2,0.49,5.5,510 171,3,12.77,0.51,9.899999000000000,470 172,3,14.16,0.7,9.7,660 173,3,13.71,0.61,7.7,740 174,3,13.4,0.75,7.3,750 175,3,13.27,0.69,10.2,835 176,3,13.17,0.68,9.3,840 177,3,14.13,0.76,9.2,560 |
Output files
| ,class,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC12,PC13 0,1,3.3074209742892218,-1.4394022531822968,-0.1652728297819703,-0.21502462886791013,0.6910933491309144,-0.22325036575091023,0.5947488306815286,0.06495586200824276,0.6396383626544324,1.0180839601389415,-0.4502931724404516,0.5392891438984798,-0.06605230525465954 1,1,2.203249813420225,0.3324550711941807,-2.020757060479493,-0.2905387432289206,-0.25692986617101377,-0.9245123057914416,0.05362434498856938,1.0215343244190644,-0.3079779836123777,0.15925214078798267,-0.1422560187907293,0.3871456498926666,0.0036262731908069533 2,1,2.5096606947618634,-1.0282507242941965,0.9800540552553032,0.7228631990496799,-0.25032697580580315,0.5477309633081178,0.4230121843493222,-0.34324787002566237,-1.1745212875879927,0.11304197887868297,-0.28586645250265047,0.000581931623216335,0.021655423052880457 3,1,3.746497190498001,-2.748618390858541,-0.17569622422135825,0.5663856019167641,-0.31096439791833425,0.11410911207014857,-0.38225899027920607,0.6417831052414534,0.05239661692354329,0.2387391504379256,0.7574476431290621,-0.24133877573986928,-0.3684441939604296 4,1,1.0060704895977026,-0.8673840350754072,2.0209872565518503,-0.4086131396933835,0.29761795846868305,-0.4053760832003349,0.4428253058240363,0.41552831458168393,0.32589984041222253,-0.07814604136464899,-0.5244656285832899,-0.21605469342071176,-0.07914032015451253 5,1,3.0416737256112696,-2.116430916831081,-0.6276253711399857,-0.5141870345752622,-0.6302408997615397,0.1230833540519225,0.40052392817214855,0.3937826075026363,-0.15171809772553935,-0.10170890759159312,0.4044444267393124,-0.37836536058787584,0.14474701715254223 6,1,2.4422005148251396,-1.171545342735355,-0.9743463764348986,-0.06564532699526031,-1.0248708712061014,-0.618376376956238,0.05274194975480953,-0.370887632301127,-0.4557302943391878,1.0137039217067525,-0.4411888701049246,0.14083257227219292,-0.27101368729615755 7,1,2.053643788593804,-1.6044371435937752,0.14587040015624617,-1.1892532694404772,0.0766871685043793,-1.4357561211356402,0.03228452156232402,0.23232359673626163,0.12302328241458269,0.73353084461641,0.29272910497317384,0.37859505482382616,-0.10985390195718889 8,1,2.5038113458622306,-0.9154884741383152,-1.7659873885920068,0.05611207579344695,-0.8897471068353372,-0.12881766870639613,0.12493265131002868,-0.49817261903938187,0.6048828958113518,0.17361686082084915,-0.5075012931685446,-0.6334624142350219,0.14168386288991672 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Python files
pca.py
| #################### Principal Component Analysis (PCA) in Python # # ########## Run this script on Terminal of MacOS as follows: # #python3 pca.py (csv data file) (target: classification column name) # #For instance, #python3 pca.py wine.csv #python3 pca.py wine4data.csv # # ########## Reference #https://qiita.com/maskot1977/items/082557fcda78c4cdb41f # # ########## Data Source #https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/wine.txt ########## import import numpy as np import pandas as pd from pandas import plotting import matplotlib.pyplot as plt import sklearn from sklearn.decomposition import PCA #from sklearn.preprocessing import StandardScaler import matplotlib.ticker as ticker import sys ########## arguments dfname = str(sys.argv[1]) # e.g., wine.csv #tgname = str(sys.argv[2]) # e.g., target (classification name columns) ########## load data #df = pd.read_csv("wine.txt", sep="\t", index_col=0) df = pd.read_csv(dfname, sep=",", index_col=0) #print(df.head()) ''' class Alcohol Malic acid Ash ... Color intensity Hue OD280/OD315 of diluted wines Proline 0 1 14.23 1.71 2.43 ... 5.64 1.04 3.92 1065 1 1 13.20 1.78 2.14 ... 4.38 1.05 3.40 1050 2 1 13.16 2.36 2.67 ... 5.68 1.03 3.17 1185 3 1 14.37 1.95 2.50 ... 7.80 0.86 3.45 1480 4 1 13.24 2.59 2.87 ... 4.32 1.04 2.93 735 ''' ########## show data #print(df.iloc[:, 1:].head()) ''' Alcohol Malic acid Ash ... Hue OD280/OD315 of diluted wines Proline 0 14.23 1.71 2.43 ... 1.04 3.92 1065 1 13.20 1.78 2.14 ... 1.05 3.40 1050 2 13.16 2.36 2.67 ... 1.03 3.17 1185 3 14.37 1.95 2.50 ... 0.86 3.45 1480 4 13.24 2.59 2.87 ... 1.04 2.93 735 [5 rows x 13 columns] ''' # #print(df.iloc[:, 0:].head()) ''' class Alcohol Malic acid Ash ... Color intensity Hue OD280/OD315 of diluted wines Proline 0 1 14.23 1.71 2.43 ... 5.64 1.04 3.92 1065 1 1 13.20 1.78 2.14 ... 4.38 1.05 3.40 1050 2 1 13.16 2.36 2.67 ... 5.68 1.03 3.17 1185 3 1 14.37 1.95 2.50 ... 7.80 0.86 3.45 1480 4 1 13.24 2.59 2.87 ... 4.32 1.04 2.93 735 [5 rows x 14 columns] ''' #class is a column for classifications (1, 2, and 3) plotting.scatter_matrix(df.iloc[:, 1:], figsize=(8, 8), c=list(df.iloc[:, 0]), alpha=0.5) plt.savefig("Figure_1_scatter_matrix_raw_data.png") plt.show() ########## Standardization (every column excluding class has mean = 0 & standard deviation = 1) dfs = df.iloc[:, 1:].apply(lambda x: (x-x.mean())/x.std(), axis=0) #dfs = StandardScaler().fit_transform(df) #dfs.head() #print(dfs.head()) ''' Alcohol Malic acid Ash ... Hue OD280/OD315 of diluted wines Proline 0 1.514341 -0.560668 0.231400 ... 0.361158 1.842721 1.010159 1 0.245597 -0.498009 -0.825667 ... 0.404908 1.110317 0.962526 2 0.196325 0.021172 1.106214 ... 0.317409 0.786369 1.391224 3 1.686791 -0.345835 0.486554 ... -0.426341 1.180741 2.328007 4 0.294868 0.227053 1.835226 ... 0.361158 0.448336 -0.037767 [5 rows x 13 columns] ''' ########## PCA pca = PCA() pca.fit(dfs) feature = pca.transform(dfs) ########## PC score #print(pd.DataFrame(feature, columns=["PC{}".format(x + 1) for x in range(len(dfs.columns))]).head()) ''' PC1 PC2 PC3 PC4 PC5 ... PC9 PC10 PC11 PC12 PC13 0 3.307421 -1.439402 -0.165273 -0.215025 0.691093 ... 0.639638 1.018084 -0.450293 0.539289 -0.066052 1 2.203250 0.332455 -2.020757 -0.290539 -0.256930 ... -0.307978 0.159252 -0.142256 0.387146 0.003626 2 2.509661 -1.028251 0.980054 0.722863 -0.250327 ... -1.174521 0.113042 -0.285866 0.000582 0.021655 3 3.746497 -2.748618 -0.175696 0.566386 -0.310964 ... 0.052397 0.238739 0.757448 -0.241339 -0.368444 4 1.006070 -0.867384 2.020987 -0.408613 0.297618 ... 0.325900 -0.078146 -0.524466 -0.216055 -0.079140 [5 rows x 13 columns] ''' dfcls = df.iloc[:, 0] dfspc = pd.DataFrame(feature, columns=["PC{}".format(x + 1) for x in range(len(dfs.columns))]) dfclsspc = pd.concat([dfcls, dfspc], axis=1) #print(dfclsspc.head()) ''' class PC1 PC2 PC3 PC4 PC5 ... PC8 PC9 PC10 PC11 PC12 PC13 0 1 3.307421 -1.439402 -0.165273 -0.215025 0.691093 ... 0.064956 0.639638 1.018084 -0.450293 0.539289 -0.066052 1 1 2.203250 0.332455 -2.020757 -0.290539 -0.256930 ... 1.021534 -0.307978 0.159252 -0.142256 0.387146 0.003626 2 1 2.509661 -1.028251 0.980054 0.722863 -0.250327 ... -0.343248 -1.174521 0.113042 -0.285866 0.000582 0.021655 3 1 3.746497 -2.748618 -0.175696 0.566386 -0.310964 ... 0.641783 0.052397 0.238739 0.757448 -0.241339 -0.368444 4 1 1.006070 -0.867384 2.020987 -0.408613 0.297618 ... 0.415528 0.325900 -0.078146 -0.524466 -0.216055 -0.079140 [5 rows x 14 columns] ''' dfclsspc.to_csv('dfclsspc.csv', header=True, index=True) ########## PCA plot (PC1 and PC2) plt.figure(figsize=(6, 6)) #plt.scatter(feature[:, 0], feature[:, 1], alpha=0.8, c=list(df.iloc[:, 0])) plt.scatter(dfclsspc.iloc[:, 1], dfclsspc.iloc[:, 2], alpha=0.8, c=list(dfclsspc.iloc[:, 0])) plt.grid() plt.xlabel("PC1") plt.ylabel("PC2") #plt.legend() #plt.legend(list(dfclsspc.iloc[:, 0])) #print(dfclsspc.iloc[:, 0]) # ### comment out if you want to delete numbers for each data point for x, y, name in zip(dfclsspc.iloc[:, 1], dfclsspc.iloc[:, 2], dfclsspc.iloc[:, 0]): plt.text(x, y, name) plt.savefig("Figure_2_PCA_plot_PC1_and_PC2.png") plt.show() ########## show all PCs plotting.scatter_matrix(pd.DataFrame(feature, columns=["PC{}".format(x + 1) for x in range(len(dfs.columns))]), figsize=(8, 8), c=list(df.iloc[:, 0]), alpha=0.5) plt.savefig("Figure_3_PCA_plot_all_PCs.png") plt.show() ########## Cumulative contributions # calculate cumulative contribution #pd.DataFrame(pca.explained_variance_ratio_, index=["PC{}".format(x + 1) for x in range(len(dfs.columns))]) #print(pd.DataFrame(pca.explained_variance_ratio_, index=["PC{}".format(x + 1) for x in range(len(dfs.columns))])) ''' 0 PC1 0.361988 PC2 0.192075 PC3 0.111236 PC4 0.070690 PC5 0.065633 PC6 0.049358 PC7 0.042387 PC8 0.026807 PC9 0.022222 PC10 0.019300 PC11 0.017368 PC12 0.012982 PC13 0.007952 ''' ##### show cumulative contribution plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True)) plt.plot([0] + list( np.cumsum(pca.explained_variance_ratio_)), "-o") plt.xlabel("Number of principal components") plt.ylabel("Cumulative contribution rate") plt.grid() plt.savefig("Figure_4_PCA_plot_cum_contb.png") plt.show() ########## PCA eigenvalues #pd.DataFrame(pca.explained_variance_, index=["PC{}".format(x + 1) for x in range(len(dfs.columns))]) #print(pd.DataFrame(pca.explained_variance_, index=["PC{}".format(x + 1) for x in range(len(dfs.columns))])) ''' 0 PC1 4.705850 PC2 2.496974 PC3 1.446072 PC4 0.918974 PC5 0.853228 PC6 0.641657 PC7 0.551028 PC8 0.348497 PC9 0.288880 PC10 0.250902 PC11 0.225789 PC12 0.168770 PC13 0.103378 ''' ########## PCA eigenvectors #pd.DataFrame(pca.components_, columns=df.columns[1:], index=["PC{}".format(x + 1) for x in range(len(dfs.columns))]) #print(pd.DataFrame(pca.components_, columns=df.columns[1:], index=["PC{}".format(x + 1) for x in range(len(dfs.columns))])) ''' Alcohol Malic acid Ash Alcalinity of ash ... Color intensity Hue OD280/OD315 of diluted wines Proline PC1 0.144329 -0.245188 -0.002051 -0.239320 ... -0.088617 0.296715 0.376167 0.286752 PC2 -0.483652 -0.224931 -0.316069 0.010591 ... -0.529996 0.279235 0.164496 -0.364903 PC3 -0.207383 0.089013 0.626224 0.612080 ... -0.137306 0.085222 0.166005 -0.126746 PC4 -0.017856 0.536890 -0.214176 0.060859 ... 0.065926 -0.427771 0.184121 -0.232071 PC5 -0.265664 0.035214 -0.143025 0.066103 ... -0.076437 -0.173615 -0.101161 -0.157869 PC6 -0.213539 -0.536814 -0.154475 0.100825 ... 0.418644 -0.105983 -0.265851 -0.119726 PC7 -0.056396 0.420524 -0.149171 -0.286969 ... -0.227712 0.232076 -0.044764 0.076805 PC8 -0.396139 -0.065827 0.170260 -0.427970 ... 0.033797 -0.436624 0.078108 -0.120023 PC9 0.508619 -0.075283 -0.307694 0.200449 ... 0.056218 0.085828 0.137227 -0.575786 PC10 0.211605 -0.309080 -0.027125 0.052799 ... -0.290775 -0.522399 0.523706 0.162116 PC11 -0.225917 0.076486 -0.498691 0.479314 ... 0.031839 -0.048212 0.046423 0.539270 PC12 -0.266286 0.121696 -0.049622 -0.055743 ... 0.604222 0.259214 0.600959 -0.079402 PC13 0.014970 0.025964 -0.141218 0.091683 ... -0.011993 -0.089889 -0.156718 0.014447 [13 rows x 13 columns] ''' ########## PCA PC1 & PC2 and observations plt.figure(figsize=(6, 6)) for x, y, name in zip(pca.components_[0], pca.components_[1], df.columns[1:]): plt.text(x, y, name) plt.scatter(pca.components_[0], pca.components_[1], alpha=0.8) plt.grid() plt.xlabel("PC1") plt.ylabel("PC2") plt.savefig("Figure_5_PCA_plot_PC1_PC2_and_observations.png") plt.show() |
Figures
Figure_1_scatter_matrix_raw_data.png
Figure_2_PCA_plot_PC1_and_PC2.png
Figure_3_PCA_plot_all_PCs.png
Figure_4_PCA_plot_cum_contb.png
Figure_5_PCA_plot_PC1_PC2_and_observations.png
