t-SNE visualization | Python Unsupervised Learning -4

t-SNE visualization of grain dataset

I will make a short example about t-SNE in this article.

from sklearn.manifold import TSNE
import pandas as pd
import numpy
samples =[[15.26 , 14.84 , 0.871 , 5.763 , 3.312 , 2.221 , 5.22 ],
[14.88 , 14.57 , 0.8811, 5.554 , 3.333 , 1.018 , 4.956 ],
[14.29 , 14.09 , 0.905 , 5.291 , 3.337 , 2.699 , 4.825 ],
[13.84 , 13.94 , 0.8955, 5.324 , 3.379 , 2.259 , 4.805 ],
[16.14 , 14.99 , 0.9034, 5.658 , 3.562 , 1.355 , 5.175 ],
[14.38 , 14.21 , 0.8951, 5.386 , 3.312 , 2.462 , 4.956 ],
[14.69 , 14.49 , 0.8799, 5.563 , 3.259 , 3.586 , 5.219 ],
[14.11 , 14.1 , 0.8911, 5.42 , 3.302 , 2.7 , 5. ],
[16.63 , 15.46 , 0.8747, 6.053 , 3.465 , 2.04 , 5.877 ],
[16.44 , 15.25 , 0.888 , 5.884 , 3.505 , 1.969 , 5.533 ],
[15.26 , 14.85 , 0.8696, 5.714 , 3.242 , 4.543 , 5.314 ],
[14.03 , 14.16 , 0.8796, 5.438 , 3.201 , 1.717 , 5.001 ],
[13.89 , 14.02 , 0.888 , 5.439 , 3.199 , 3.986 , 4.738 ],
[13.78 , 14.06 , 0.8759, 5.479 , 3.156 , 3.136 , 4.872 ],
[13.74 , 14.05 , 0.8744, 5.482 , 3.114 , 2.932 , 4.825 ],
[14.59 , 14.28 , 0.8993, 5.351 , 3.333 , 4.185 , 4.781 ],
[13.99 , 13.83 , 0.9183, 5.119 , 3.383 , 5.234 , 4.781 ],
[15.69 , 14.75 , 0.9058, 5.527 , 3.514 , 1.599 , 5.046 ],
[14.7 , 14.21 , 0.9153, 5.205 , 3.466 , 1.767 , 4.649 ],
[12.72 , 13.57 , 0.8686, 5.226 , 3.049 , 4.102 , 4.914 ],
[14.16 , 14.4 , 0.8584, 5.658 , 3.129 , 3.072 , 5.176 ],
[14.11 , 14.26 , 0.8722, 5.52 , 3.168 , 2.688 , 5.219 ],
[15.88 , 14.9 , 0.8988, 5.618 , 3.507 , 0.7651, 5.091 ],
[12.08 , 13.23 , 0.8664, 5.099 , 2.936 , 1.415 , 4.961 ],
[15.01 , 14.76 , 0.8657, 5.789 , 3.245 , 1.791 , 5.001 ],
[16.19 , 15.16 , 0.8849, 5.833 , 3.421 , 0.903 , 5.307 ],
[13.02 , 13.76 , 0.8641, 5.395 , 3.026 , 3.373 , 4.825 ],
[12.74 , 13.67 , 0.8564, 5.395 , 2.956 , 2.504 , 4.869 ],
[14.11 , 14.18 , 0.882 , 5.541 , 3.221 , 2.754 , 5.038 ],
[13.45 , 14.02 , 0.8604, 5.516 , 3.065 , 3.531 , 5.097 ],
[13.16 , 13.82 , 0.8662, 5.454 , 2.975 , 0.8551, 5.056 ],
[15.49 , 14.94 , 0.8724, 5.757 , 3.371 , 3.412 , 5.228 ],
[14.09 , 14.41 , 0.8529, 5.717 , 3.186 , 3.92 , 5.299 ],
[13.94 , 14.17 , 0.8728, 5.585 , 3.15 , 2.124 , 5.012 ],
[15.05 , 14.68 , 0.8779, 5.712 , 3.328 , 2.129 , 5.36 ],
[16.12 , 15. , 0.9 , 5.709 , 3.485 , 2.27 , 5.443 ],
[16.2 , 15.27 , 0.8734, 5.826 , 3.464 , 2.823 , 5.527 ],
[17.08 , 15.38 , 0.9079, 5.832 , 3.683 , 2.956 , 5.484 ],
[14.8 , 14.52 , 0.8823, 5.656 , 3.288 , 3.112 , 5.309 ],
[14.28 , 14.17 , 0.8944, 5.397 , 3.298 , 6.685 , 5.001 ],
[13.54 , 13.85 , 0.8871, 5.348 , 3.156 , 2.587 , 5.178 ],
[13.5 , 13.85 , 0.8852, 5.351 , 3.158 , 2.249 , 5.176 ],
[13.16 , 13.55 , 0.9009, 5.138 , 3.201 , 2.461 , 4.783 ],
[15.5 , 14.86 , 0.882 , 5.877 , 3.396 , 4.711 , 5.528 ],
[15.11 , 14.54 , 0.8986, 5.579 , 3.462 , 3.128 , 5.18 ],
[13.8 , 14.04 , 0.8794, 5.376 , 3.155 , 1.56 , 4.961 ],
[15.36 , 14.76 , 0.8861, 5.701 , 3.393 , 1.367 , 5.132 ],
[14.99 , 14.56 , 0.8883, 5.57 , 3.377 , 2.958 , 5.175 ],
[14.79 , 14.52 , 0.8819, 5.545 , 3.291 , 2.704 , 5.111 ],
[14.86 , 14.67 , 0.8676, 5.678 , 3.258 , 2.129 , 5.351 ],
[14.43 , 14.4 , 0.8751, 5.585 , 3.272 , 3.975 , 5.144 ],
[15.78 , 14.91 , 0.8923, 5.674 , 3.434 , 5.593 , 5.136 ],
[14.49 , 14.61 , 0.8538, 5.715 , 3.113 , 4.116 , 5.396 ],
[14.33 , 14.28 , 0.8831, 5.504 , 3.199 , 3.328 , 5.224 ],
[14.52 , 14.6 , 0.8557, 5.741 , 3.113 , 1.481 , 5.487 ],
[15.03 , 14.77 , 0.8658, 5.702 , 3.212 , 1.933 , 5.439 ],
[14.46 , 14.35 , 0.8818, 5.388 , 3.377 , 2.802 , 5.044 ],
[14.92 , 14.43 , 0.9006, 5.384 , 3.412 , 1.142 , 5.088 ],
[15.38 , 14.77 , 0.8857, 5.662 , 3.419 , 1.999 , 5.222 ],
[12.11 , 13.47 , 0.8392, 5.159 , 3.032 , 1.502 , 4.519 ],
[11.42 , 12.86 , 0.8683, 5.008 , 2.85 , 2.7 , 4.607 ],
[11.23 , 12.63 , 0.884 , 4.902 , 2.879 , 2.269 , 4.703 ],
[12.36 , 13.19 , 0.8923, 5.076 , 3.042 , 3.22 , 4.605 ],
[13.22 , 13.84 , 0.868 , 5.395 , 3.07 , 4.157 , 5.088 ],
[12.78 , 13.57 , 0.8716, 5.262 , 3.026 , 1.176 , 4.782 ],
[12.88 , 13.5 , 0.8879, 5.139 , 3.119 , 2.352 , 4.607 ],
[14.34 , 14.37 , 0.8726, 5.63 , 3.19 , 1.313 , 5.15 ],
[14.01 , 14.29 , 0.8625, 5.609 , 3.158 , 2.217 , 5.132 ],
[14.37 , 14.39 , 0.8726, 5.569 , 3.153 , 1.464 , 5.3 ],
[12.73 , 13.75 , 0.8458, 5.412 , 2.882 , 3.533 , 5.067 ],
[17.63 , 15.98 , 0.8673, 6.191 , 3.561 , 4.076 , 6.06 ],
[16.84 , 15.67 , 0.8623, 5.998 , 3.484 , 4.675 , 5.877 ],
[17.26 , 15.73 , 0.8763, 5.978 , 3.594 , 4.539 , 5.791 ],
[19.11 , 16.26 , 0.9081, 6.154 , 3.93 , 2.936 , 6.079 ],
[16.82 , 15.51 , 0.8786, 6.017 , 3.486 , 4.004 , 5.841 ],
[16.77 , 15.62 , 0.8638, 5.927 , 3.438 , 4.92 , 5.795 ],
[17.32 , 15.91 , 0.8599, 6.064 , 3.403 , 3.824 , 5.922 ],
[20.71 , 17.23 , 0.8763, 6.579 , 3.814 , 4.451 , 6.451 ],
[18.94 , 16.49 , 0.875 , 6.445 , 3.639 , 5.064 , 6.362 ],
[17.12 , 15.55 , 0.8892, 5.85 , 3.566 , 2.858 , 5.746 ],
[16.53 , 15.34 , 0.8823, 5.875 , 3.467 , 5.532 , 5.88 ],
[18.72 , 16.19 , 0.8977, 6.006 , 3.857 , 5.324 , 5.879 ],
[20.2 , 16.89 , 0.8894, 6.285 , 3.864 , 5.173 , 6.187 ],
[19.57 , 16.74 , 0.8779, 6.384 , 3.772 , 1.472 , 6.273 ],
[19.51 , 16.71 , 0.878 , 6.366 , 3.801 , 2.962 , 6.185 ],
[18.27 , 16.09 , 0.887 , 6.173 , 3.651 , 2.443 , 6.197 ],
[18.88 , 16.26 , 0.8969, 6.084 , 3.764 , 1.649 , 6.109 ],
[18.98 , 16.66 , 0.859 , 6.549 , 3.67 , 3.691 , 6.498 ],
[21.18 , 17.21 , 0.8989, 6.573 , 4.033 , 5.78 , 6.231 ],
[20.88 , 17.05 , 0.9031, 6.45 , 4.032 , 5.016 , 6.321 ],
[20.1 , 16.99 , 0.8746, 6.581 , 3.785 , 1.955 , 6.449 ],
[18.76 , 16.2 , 0.8984, 6.172 , 3.796 , 3.12 , 6.053 ],
[18.81 , 16.29 , 0.8906, 6.272 , 3.693 , 3.237 , 6.053 ],
[18.59 , 16.05 , 0.9066, 6.037 , 3.86 , 6.001 , 5.877 ],
[18.36 , 16.52 , 0.8452, 6.666 , 3.485 , 4.933 , 6.448 ],
[16.87 , 15.65 , 0.8648, 6.139 , 3.463 , 3.696 , 5.967 ],
[19.31 , 16.59 , 0.8815, 6.341 , 3.81 , 3.477 , 6.238 ],
[18.98 , 16.57 , 0.8687, 6.449 , 3.552 , 2.144 , 6.453 ],
[18.17 , 16.26 , 0.8637, 6.271 , 3.512 , 2.853 , 6.273 ],
[18.72 , 16.34 , 0.881 , 6.219 , 3.684 , 2.188 , 6.097 ],
[16.41 , 15.25 , 0.8866, 5.718 , 3.525 , 4.217 , 5.618 ],
[17.99 , 15.86 , 0.8992, 5.89 , 3.694 , 2.068 , 5.837 ],
[19.46 , 16.5 , 0.8985, 6.113 , 3.892 , 4.308 , 6.009 ],
[19.18 , 16.63 , 0.8717, 6.369 , 3.681 , 3.357 , 6.229 ],
[18.95 , 16.42 , 0.8829, 6.248 , 3.755 , 3.368 , 6.148 ],
[18.83 , 16.29 , 0.8917, 6.037 , 3.786 , 2.553 , 5.879 ],
[18.85 , 16.17 , 0.9056, 6.152 , 3.806 , 2.843 , 6.2 ],
[17.63 , 15.86 , 0.88 , 6.033 , 3.573 , 3.747 , 5.929 ],
[19.94 , 16.92 , 0.8752, 6.675 , 3.763 , 3.252 , 6.55 ],
[18.55 , 16.22 , 0.8865, 6.153 , 3.674 , 1.738 , 5.894 ],
[18.45 , 16.12 , 0.8921, 6.107 , 3.769 , 2.235 , 5.794 ],
[19.38 , 16.72 , 0.8716, 6.303 , 3.791 , 3.678 , 5.965 ],
[19.13 , 16.31 , 0.9035, 6.183 , 3.902 , 2.109 , 5.924 ],
[19.14 , 16.61 , 0.8722, 6.259 , 3.737 , 6.682 , 6.053 ],
[20.97 , 17.25 , 0.8859, 6.563 , 3.991 , 4.677 , 6.316 ],
[19.06 , 16.45 , 0.8854, 6.416 , 3.719 , 2.248 , 6.163 ],
[18.96 , 16.2 , 0.9077, 6.051 , 3.897 , 4.334 , 5.75 ],
[19.15 , 16.45 , 0.889 , 6.245 , 3.815 , 3.084 , 6.185 ],
[18.89 , 16.23 , 0.9008, 6.227 , 3.769 , 3.639 , 5.966 ],
[20.03 , 16.9 , 0.8811, 6.493 , 3.857 , 3.063 , 6.32 ],
[20.24 , 16.91 , 0.8897, 6.315 , 3.962 , 5.901 , 6.188 ],
[18.14 , 16.12 , 0.8772, 6.059 , 3.563 , 3.619 , 6.011 ],
[16.17 , 15.38 , 0.8588, 5.762 , 3.387 , 4.286 , 5.703 ],
[18.43 , 15.97 , 0.9077, 5.98 , 3.771 , 2.984 , 5.905 ],
[15.99 , 14.89 , 0.9064, 5.363 , 3.582 , 3.336 , 5.144 ],
[18.75 , 16.18 , 0.8999, 6.111 , 3.869 , 4.188 , 5.992 ],
[18.65 , 16.41 , 0.8698, 6.285 , 3.594 , 4.391 , 6.102 ],
[17.98 , 15.85 , 0.8993, 5.979 , 3.687 , 2.257 , 5.919 ],
[20.16 , 17.03 , 0.8735, 6.513 , 3.773 , 1.91 , 6.185 ],
[17.55 , 15.66 , 0.8991, 5.791 , 3.69 , 5.366 , 5.661 ],
[18.3 , 15.89 , 0.9108, 5.979 , 3.755 , 2.837 , 5.962 ],
[18.94 , 16.32 , 0.8942, 6.144 , 3.825 , 2.908 , 5.949 ],
[15.38 , 14.9 , 0.8706, 5.884 , 3.268 , 4.462 , 5.795 ],
[16.16 , 15.33 , 0.8644, 5.845 , 3.395 , 4.266 , 5.795 ],
[15.56 , 14.89 , 0.8823, 5.776 , 3.408 , 4.972 , 5.847 ],
[15.38 , 14.66 , 0.899 , 5.477 , 3.465 , 3.6 , 5.439 ],
[17.36 , 15.76 , 0.8785, 6.145 , 3.574 , 3.526 , 5.971 ],
[15.57 , 15.15 , 0.8527, 5.92 , 3.231 , 2.64 , 5.879 ],
[15.6 , 15.11 , 0.858 , 5.832 , 3.286 , 2.725 , 5.752 ],
[16.23 , 15.18 , 0.885 , 5.872 , 3.472 , 3.769 , 5.922 ],
[13.07 , 13.92 , 0.848 , 5.472 , 2.994 , 5.304 , 5.395 ],
[13.32 , 13.94 , 0.8613, 5.541 , 3.073 , 7.035 , 5.44 ],
[13.34 , 13.95 , 0.862 , 5.389 , 3.074 , 5.995 , 5.307 ],
[12.22 , 13.32 , 0.8652, 5.224 , 2.967 , 5.469 , 5.221 ],
[11.82 , 13.4 , 0.8274, 5.314 , 2.777 , 4.471 , 5.178 ],
[11.21 , 13.13 , 0.8167, 5.279 , 2.687 , 6.169 , 5.275 ],
[11.43 , 13.13 , 0.8335, 5.176 , 2.719 , 2.221 , 5.132 ],
[12.49 , 13.46 , 0.8658, 5.267 , 2.967 , 4.421 , 5.002 ],
[12.7 , 13.71 , 0.8491, 5.386 , 2.911 , 3.26 , 5.316 ],
[10.79 , 12.93 , 0.8107, 5.317 , 2.648 , 5.462 , 5.194 ],
[11.83 , 13.23 , 0.8496, 5.263 , 2.84 , 5.195 , 5.307 ],
[12.01 , 13.52 , 0.8249, 5.405 , 2.776 , 6.992 , 5.27 ],
[12.26 , 13.6 , 0.8333, 5.408 , 2.833 , 4.756 , 5.36 ],
[11.18 , 13.04 , 0.8266, 5.22 , 2.693 , 3.332 , 5.001 ],
[11.36 , 13.05 , 0.8382, 5.175 , 2.755 , 4.048 , 5.263 ],
[11.19 , 13.05 , 0.8253, 5.25 , 2.675 , 5.813 , 5.219 ],
[11.34 , 12.87 , 0.8596, 5.053 , 2.849 , 3.347 , 5.003 ],
[12.13 , 13.73 , 0.8081, 5.394 , 2.745 , 4.825 , 5.22 ],
[11.75 , 13.52 , 0.8082, 5.444 , 2.678 , 4.378 , 5.31 ],
[11.49 , 13.22 , 0.8263, 5.304 , 2.695 , 5.388 , 5.31 ],
[12.54 , 13.67 , 0.8425, 5.451 , 2.879 , 3.082 , 5.491 ],
[12.02 , 13.33 , 0.8503, 5.35 , 2.81 , 4.271 , 5.308 ],
[12.05 , 13.41 , 0.8416, 5.267 , 2.847 , 4.988 , 5.046 ],
[12.55 , 13.57 , 0.8558, 5.333 , 2.968 , 4.419 , 5.176 ],
[11.14 , 12.79 , 0.8558, 5.011 , 2.794 , 6.388 , 5.049 ],
[12.1 , 13.15 , 0.8793, 5.105 , 2.941 , 2.201 , 5.056 ],
[12.44 , 13.59 , 0.8462, 5.319 , 2.897 , 4.924 , 5.27 ],
[12.15 , 13.45 , 0.8443, 5.417 , 2.837 , 3.638 , 5.338 ],
[11.35 , 13.12 , 0.8291, 5.176 , 2.668 , 4.337 , 5.132 ],
[11.24 , 13. , 0.8359, 5.09 , 2.715 , 3.521 , 5.088 ],
[11.02 , 13. , 0.8189, 5.325 , 2.701 , 6.735 , 5.163 ],
[11.55 , 13.1 , 0.8455, 5.167 , 2.845 , 6.715 , 4.956 ],
[11.27 , 12.97 , 0.8419, 5.088 , 2.763 , 4.309 , 5. ],
[11.4 , 13.08 , 0.8375, 5.136 , 2.763 , 5.588 , 5.089 ],
[10.83 , 12.96 , 0.8099, 5.278 , 2.641 , 5.182 , 5.185 ],
[10.8 , 12.57 , 0.859 , 4.981 , 2.821 , 4.773 , 5.063 ],
[11.26 , 13.01 , 0.8355, 5.186 , 2.71 , 5.335 , 5.092 ],
[10.74 , 12.73 , 0.8329, 5.145 , 2.642 , 4.702 , 4.963 ],
[11.48 , 13.05 , 0.8473, 5.18 , 2.758 , 5.876 , 5.002 ],
[12.21 , 13.47 , 0.8453, 5.357 , 2.893 , 1.661 , 5.178 ],
[11.41 , 12.95 , 0.856 , 5.09 , 2.775 , 4.957 , 4.825 ],
[12.46 , 13.41 , 0.8706, 5.236 , 3.017 , 4.987 , 5.147 ],
[12.19 , 13.36 , 0.8579, 5.24 , 2.909 , 4.857 , 5.158 ],
[11.65 , 13.07 , 0.8575, 5.108 , 2.85 , 5.209 , 5.135 ],
[12.89 , 13.77 , 0.8541, 5.495 , 3.026 , 6.185 , 5.316 ],
[11.56 , 13.31 , 0.8198, 5.363 , 2.683 , 4.062 , 5.182 ],
[11.81 , 13.45 , 0.8198, 5.413 , 2.716 , 4.898 , 5.352 ],
[10.91 , 12.8 , 0.8372, 5.088 , 2.675 , 4.179 , 4.956 ],
[11.23 , 12.82 , 0.8594, 5.089 , 2.821 , 7.524 , 4.957 ],
[10.59 , 12.41 , 0.8648, 4.899 , 2.787 , 4.975 , 4.794 ],
[10.93 , 12.8 , 0.839 , 5.046 , 2.717 , 5.398 , 5.045 ],
[11.27 , 12.86 , 0.8563, 5.091 , 2.804 , 3.985 , 5.001 ],
[11.87 , 13.02 , 0.8795, 5.132 , 2.953 , 3.597 , 5.132 ],
[10.82 , 12.83 , 0.8256, 5.18 , 2.63 , 4.853 , 5.089 ],
[12.11 , 13.27 , 0.8639, 5.236 , 2.975 , 4.132 , 5.012 ],
[12.8 , 13.47 , 0.886 , 5.16 , 3.126 , 4.873 , 4.914 ],
[12.79 , 13.53 , 0.8786, 5.224 , 3.054 , 5.483 , 4.958 ],
[13.37 , 13.78 , 0.8849, 5.32 , 3.128 , 4.67 , 5.091 ],
[12.62 , 13.67 , 0.8481, 5.41 , 2.911 , 3.306 , 5.231 ],
[12.76 , 13.38 , 0.8964, 5.073 , 3.155 , 2.828 , 4.83 ],
[12.38 , 13.44 , 0.8609, 5.219 , 2.989 , 5.472 , 5.045 ],
[12.67 , 13.32 , 0.8977, 4.984 , 3.135 , 2.3 , 4.745 ],
[11.18 , 12.72 , 0.868 , 5.009 , 2.81 , 4.051 , 4.828 ],
[12.7 , 13.41 , 0.8874, 5.183 , 3.091 , 8.456 , 5. ],
[12.37 , 13.47 , 0.8567, 5.204 , 2.96 , 3.919 , 5.001 ],
[12.19 , 13.2 , 0.8783, 5.137 , 2.981 , 3.631 , 4.87 ],
[11.23 , 12.88 , 0.8511, 5.14 , 2.795 , 4.325 , 5.003 ],
[13.2 , 13.66 , 0.8883, 5.236 , 3.232 , 8.315 , 5.056 ],
[11.84 , 13.21 , 0.8521, 5.175 , 2.836 , 3.598 , 5.044 ],
[12.3 , 13.34 , 0.8684, 5.243 , 2.974 , 5.637 , 5.063 ]]
variety_numbers = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]

model = TSNE(learning_rate=200)
# Apply fit_transform to samples: tsne_features
tsne_features = model.fit_transform(samples)
# Select the 0th feature: xs
xs = tsne_features[:,0]
# Select the 1st feature: ys
ys = tsne_features[:,1]
# Scatter plot, coloring by variety_numbers
plt.scatter(xs,ys,c=variety_numbers)
plt.show()

 

 

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About Deniz Parlak

Hi, i’m Security Data Scientist & Data Engineer at My Security Analytics. I have experienced Advance Python, Machine Learning and Big Data tools. Also i worked Oracle Database Administration, Migration and upgrade projects. For your questions [email protected]

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