Mercurial > repos > bgruening > numeric_clustering
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/numeric_clustering commit bafd56379ff227fb81f8cd61d708ebc39814da54
author | bgruening |
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date | Fri, 01 Jan 2016 18:37:54 -0500 |
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1 { | |
2 "cells": [ | |
3 { | |
4 "cell_type": "code", | |
5 "execution_count": 29, | |
6 "metadata": { | |
7 "collapsed": false | |
8 }, | |
9 "outputs": [ | |
10 { | |
11 "data": { | |
12 "text/plain": [ | |
13 "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=8, n_init=10,\n", | |
14 " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", | |
15 " verbose=0)" | |
16 ] | |
17 }, | |
18 "execution_count": 29, | |
19 "metadata": {}, | |
20 "output_type": "execute_result" | |
21 } | |
22 ], | |
23 "source": [ | |
24 "import sys\n", | |
25 "import json\n", | |
26 "import numpy as np\n", | |
27 "import sklearn.cluster\n", | |
28 "import pandas\n", | |
29 "\n", | |
30 "data = pandas.read_csv(\"/home/bag/projects/code/galaxytools/tools/numeric_clustering/test-data/numeric_values.tabular\", sep='\\t', header=0, index_col=None, parse_dates=True, encoding=None )\n", | |
31 "my_class = getattr(sklearn.cluster, \"KMeans\")\n", | |
32 "cluster_object = my_class()\n", | |
33 "\n", | |
34 "params = dict()\n", | |
35 "cluster_object.set_params(**params)\n" | |
36 ] | |
37 }, | |
38 { | |
39 "cell_type": "code", | |
40 "execution_count": 32, | |
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102 ], | |
103 "source": [ | |
104 "\n", | |
105 "if 4 >= 4:\n", | |
106 " data_matrix = data.values[:, 1-1:1]\n", | |
107 " #print data_matrix\n", | |
108 "else:\n", | |
109 " data_matrix = data.values\n", | |
110 "\n", | |
111 "prediction = cluster_object.fit_predict( data_matrix )\n", | |
112 "print prediction, len(prediction), len(data_matrix)\n", | |
113 "\n", | |
114 "pred = pandas.DataFrame(prediction)\n", | |
115 "print pred\n", | |
116 "\n", | |
117 "#data[len(data.columns)] = prediction\n", | |
118 "#data.to_csv(path_or_buf = \"foo.tab\", sep=\"\\t\")\n" | |
119 ] | |
120 }, | |
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585 " <th>42</th>\n", | |
586 " <td> 2</td>\n", | |
587 " <td> NaN</td>\n", | |
588 " <td>NaN</td>\n", | |
589 " <td> NaN</td>\n", | |
590 " <td> NaN</td>\n", | |
591 " </tr>\n", | |
592 " <tr>\n", | |
593 " <th>43</th>\n", | |
594 " <td> 2</td>\n", | |
595 " <td> NaN</td>\n", | |
596 " <td>NaN</td>\n", | |
597 " <td> NaN</td>\n", | |
598 " <td> NaN</td>\n", | |
599 " </tr>\n", | |
600 " <tr>\n", | |
601 " <th>44</th>\n", | |
602 " <td> 2</td>\n", | |
603 " <td> NaN</td>\n", | |
604 " <td>NaN</td>\n", | |
605 " <td> NaN</td>\n", | |
606 " <td> NaN</td>\n", | |
607 " </tr>\n", | |
608 " <tr>\n", | |
609 " <th>45</th>\n", | |
610 " <td> 2</td>\n", | |
611 " <td> NaN</td>\n", | |
612 " <td>NaN</td>\n", | |
613 " <td> NaN</td>\n", | |
614 " <td> NaN</td>\n", | |
615 " </tr>\n", | |
616 " <tr>\n", | |
617 " <th>46</th>\n", | |
618 " <td> 2</td>\n", | |
619 " <td> NaN</td>\n", | |
620 " <td>NaN</td>\n", | |
621 " <td> NaN</td>\n", | |
622 " <td> NaN</td>\n", | |
623 " </tr>\n", | |
624 " <tr>\n", | |
625 " <th>47</th>\n", | |
626 " <td> 2</td>\n", | |
627 " <td> NaN</td>\n", | |
628 " <td>NaN</td>\n", | |
629 " <td> NaN</td>\n", | |
630 " <td> NaN</td>\n", | |
631 " </tr>\n", | |
632 " </tbody>\n", | |
633 "</table>\n", | |
634 "<p>96 rows × 5 columns</p>\n", | |
635 "</div>" | |
636 ], | |
637 "text/plain": [ | |
638 " 0 -67 0 56 58\n", | |
639 "0 NaN -76 0 64 44\n", | |
640 "1 NaN -73 0 48 51\n", | |
641 "2 NaN -49 0 65 58\n", | |
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666 "27 NaN -88 2 26 -85\n", | |
667 "28 NaN -114 2 33 -90\n", | |
668 "29 NaN -106 2 9 -63\n", | |
669 ".. .. ... .. ... ...\n", | |
670 "18 3 NaN NaN NaN NaN\n", | |
671 "19 3 NaN NaN NaN NaN\n", | |
672 "20 3 NaN NaN NaN NaN\n", | |
673 "21 3 NaN NaN NaN NaN\n", | |
674 "22 3 NaN NaN NaN NaN\n", | |
675 "23 3 NaN NaN NaN NaN\n", | |
676 "24 3 NaN NaN NaN NaN\n", | |
677 "25 1 NaN NaN NaN NaN\n", | |
678 "26 1 NaN NaN NaN NaN\n", | |
679 "27 1 NaN NaN NaN NaN\n", | |
680 "28 1 NaN NaN NaN NaN\n", | |
681 "29 1 NaN NaN NaN NaN\n", | |
682 "30 1 NaN NaN NaN NaN\n", | |
683 "31 1 NaN NaN NaN NaN\n", | |
684 "32 1 NaN NaN NaN NaN\n", | |
685 "33 1 NaN NaN NaN NaN\n", | |
686 "34 1 NaN NaN NaN NaN\n", | |
687 "35 1 NaN NaN NaN NaN\n", | |
688 "36 1 NaN NaN NaN NaN\n", | |
689 "37 2 NaN NaN NaN NaN\n", | |
690 "38 2 NaN NaN NaN NaN\n", | |
691 "39 2 NaN NaN NaN NaN\n", | |
692 "40 2 NaN NaN NaN NaN\n", | |
693 "41 2 NaN NaN NaN NaN\n", | |
694 "42 2 NaN NaN NaN NaN\n", | |
695 "43 2 NaN NaN NaN NaN\n", | |
696 "44 2 NaN NaN NaN NaN\n", | |
697 "45 2 NaN NaN NaN NaN\n", | |
698 "46 2 NaN NaN NaN NaN\n", | |
699 "47 2 NaN NaN NaN NaN\n", | |
700 "\n", | |
701 "[96 rows x 5 columns]" | |
702 ] | |
703 }, | |
704 "execution_count": 34, | |
705 "metadata": {}, | |
706 "output_type": "execute_result" | |
707 } | |
708 ], | |
709 "source": [ | |
710 "pandas.concat([data, pred], axis=0)" | |
711 ] | |
712 }, | |
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714 "cell_type": "code", | |
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720 "source": [] | |
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