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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/numeric_clustering commit bafd56379ff227fb81f8cd61d708ebc39814da54 |
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Untitled.ipynb foo.tab numeric_clustering.xml test-data/cluster_result01.txt test-data/cluster_result02.txt test-data/cluster_result03.txt test-data/cluster_result04.txt test-data/cluster_result05.txt test-data/cluster_result06.txt test-data/cluster_result07.txt test-data/cluster_result08.txt test-data/cluster_result09.txt test-data/cluster_result10.txt test-data/cluster_result11.txt test-data/cluster_result12.txt test-data/cluster_result13.txt test-data/cluster_result14.txt test-data/cluster_result15.txt test-data/cluster_result16.txt test-data/numeric_values.tabular tool_dependencies.xml |
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| diff -r 000000000000 -r a3fd214e7555 Untitled.ipynb --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Untitled.ipynb Fri Jan 01 18:37:54 2016 -0500 |
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| b'@@ -0,0 +1,744 @@\n+{\n+ "cells": [\n+ {\n+ "cell_type": "code",\n+ "execution_count": 29,\n+ "metadata": {\n+ "collapsed": false\n+ },\n+ "outputs": [\n+ {\n+ "data": {\n+ "text/plain": [\n+ "KMeans(copy_x=True, init=\'k-means++\', max_iter=300, n_clusters=8, n_init=10,\\n",\n+ " n_jobs=1, precompute_distances=\'auto\', random_state=None, tol=0.0001,\\n",\n+ " verbose=0)"\n+ ]\n+ },\n+ "execution_count": 29,\n+ "metadata": {},\n+ "output_type": "execute_result"\n+ }\n+ ],\n+ "source": [\n+ "import sys\\n",\n+ "import json\\n",\n+ "import numpy as np\\n",\n+ "import sklearn.cluster\\n",\n+ "import pandas\\n",\n+ "\\n",\n+ "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",\n+ "my_class = getattr(sklearn.cluster, \\"KMeans\\")\\n",\n+ "cluster_object = my_class()\\n",\n+ "\\n",\n+ "params = dict()\\n",\n+ "cluster_object.set_params(**params)\\n"\n+ ]\n+ },\n+ {\n+ "cell_type": "code",\n+ "execution_count": 32,\n+ "metadata": {\n+ "collapsed": false\n+ },\n+ "outputs": [\n+ {\n+ "name": "stdout",\n+ "output_type": "stream",\n+ "text": [\n+ "[0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1\\n",\n+ " 2 2 2 2 2 2 2 2 2 2 2] 48 48\\n",\n+ " 0\\n",\n+ "0 0\\n",\n+ "1 0\\n",\n+ "2 0\\n",\n+ "3 0\\n",\n+ "4 0\\n",\n+ "5 0\\n",\n+ "6 0\\n",\n+ "7 0\\n",\n+ "8 0\\n",\n+ "9 0\\n",\n+ "10 0\\n",\n+ "11 0\\n",\n+ "12 3\\n",\n+ "13 3\\n",\n+ "14 3\\n",\n+ "15 3\\n",\n+ "16 3\\n",\n+ "17 3\\n",\n+ "18 3\\n",\n+ "19 3\\n",\n+ "20 3\\n",\n+ "21 3\\n",\n+ "22 3\\n",\n+ "23 3\\n",\n+ "24 3\\n",\n+ "25 1\\n",\n+ "26 1\\n",\n+ "27 1\\n",\n+ "28 1\\n",\n+ "29 1\\n",\n+ "30 1\\n",\n+ "31 1\\n",\n+ "32 1\\n",\n+ "33 1\\n",\n+ "34 1\\n",\n+ "35 1\\n",\n+ "36 1\\n",\n+ "37 2\\n",\n+ "38 2\\n",\n+ "39 2\\n",\n+ "40 2\\n",\n+ "41 2\\n",\n+ "42 2\\n",\n+ "43 2\\n",\n+ "44 2\\n",\n+ "45 2\\n",\n+ "46 2\\n",\n+ "47 2\\n"\n+ ]\n+ }\n+ ],\n+ "source": [\n+ "\\n",\n+ "if 4 >= 4:\\n",\n+ " data_matrix = data.values[:, 1-1:1]\\n",\n+ " #print data_matrix\\n",\n+ "else:\\n",\n+ " data_matrix = data.values\\n",\n+ "\\n",\n+ "prediction = cluster_object.fit_predict( data_matrix )\\n",\n+ "print prediction, len(prediction), len(data_matrix)\\n",\n+ "\\n",\n+ "pred = pandas.DataFrame(prediction)\\n",\n+ "print pred\\n",\n+ "\\n",\n+ "#data[len(data.columns)] = prediction\\n",\n+ "#data.to_csv(path_or_buf = \\"foo.tab\\", sep=\\"\\\\t\\")\\n"\n+ ]\n+ },\n+ {\n+ "cell_type": "code",\n+ "execution_count": 34,\n+ "metadata": {\n+ "collapsed": false\n+ },\n+ "outputs": [\n+ {\n+ "data": {\n+ "text/html": [\n+ "<div style=\\"max-height:1000px;max-width:1500px;overflow:auto;\\">\\n",\n+ "<table border=\\"1\\" class=\\"dataframe\\">\\n",\n+ " <thead>\\n",\n+ " <tr style=\\"text-align: right;\\">\\n",\n+ " <th></th>\\n",\n+ " <th>0</th>\\n",\n+ " <th>-67</th>\\n",\n+ " <th>0</th>\\n",\n+ 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pred], axis=0)"\n+ ]\n+ },\n+ {\n+ "cell_type": "code",\n+ "execution_count": null,\n+ "metadata": {\n+ "collapsed": true\n+ },\n+ "outputs": [],\n+ "source": []\n+ }\n+ ],\n+ "metadata": {\n+ "kernelspec": {\n+ "display_name": "Python 2",\n+ "language": "python",\n+ "name": "python2"\n+ },\n+ "language_info": {\n+ "codemirror_mode": {\n+ "name": "ipython",\n+ "version": 2\n+ },\n+ "file_extension": ".py",\n+ "mimetype": "text/x-python",\n+ "name": "python",\n+ "nbconvert_exporter": "python",\n+ "pygments_lexer": "ipython2",\n+ "version": "2.7.10"\n+ }\n+ },\n+ "nbformat": 4,\n+ "nbformat_minor": 0\n+}\n' |
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| diff -r 000000000000 -r a3fd214e7555 foo.tab --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/foo.tab Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 numeric_clustering.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/numeric_clustering.xml Fri Jan 01 18:37:54 2016 -0500 |
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| b'@@ -0,0 +1,390 @@\n+<tool id="numeric_clustering" name="Numeric Clustering" version="@VERSION@">\n+ <description></description>\n+ <requirements>\n+ <requirement type="package" version="2.3.0">anaconda</requirement>\n+ </requirements>\n+ <stdio>\n+ <exit_code level="fatal" range="1:"/>\n+ </stdio>\n+ <macros>\n+ <token name="@VERSION@">0.9</token>\n+ <macro name="n_clusters" token_default_value="8">\n+ <param name="n_clusters" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of clusters"\n+ help="default value is @DEFAULT_VALUE@ (--n_clusters)"/>\n+ </macro>\n+ <macro name="n_init">\n+ <param name="n_init" type="integer" optional="true" value="" label="Number of runs with different centroid seeds"/>\n+ </macro>\n+ <macro name="max_iter">\n+ <param name="max_iter" type="integer" optional="true" value="" label="Maximum number of iterations per single run"/>\n+ </macro>\n+ <macro name="random_state">\n+ <param name="random_state" type="integer" optional="true" value="" label="Initialize centers"/>\n+ </macro>\n+ <macro name="affinity">\n+ <param name="affinity" type="text" optional="true" value="" label="Affinity"/>\n+ </macro>\n+ <macro name="tol">\n+ <param name="tol" type="float" optional="true" value="" label="Relative tolerance"/>\n+ </macro>\n+ <macro name="init">\n+ <param name="init" type="select" label="Select initialization method">\n+ <option value="k-means++">k-means++</option>\n+ <option value="random">random</option>\n+ </param>\n+ </macro>\n+ </macros>\n+ <version_command>echo "@VERSION@"</version_command>\n+ <command><![CDATA[\n+ cat "$cluster_script" >&2\n+ &&\n+ #import json\n+ #set $params = dict()\n+ #for $key, $value in $algorithm_options.items():\n+ #if not $key.startswith(\'__\') and $key.strip() != \'selected_algorithm\' and str($value).strip():\n+ #if str($value).strip() == \'false\':\n+ #set $value = False\n+ #elif str($value).strip() == \'true\':\n+ #set $value = True\n+ #else:\n+ #try:\n+ #set $val = float($value)\n+ #try:\n+ #set $value = int($value)\n+ #except:\n+ #set $value = float($value)\n+ #end try\n+ #except:\n+ #set $value = str($value)\n+ #end try\n+ #end if\n+ $params.update({str($key): $value})\n+ #end if\n+ #end for\n+ #set $json_string = json.dumps( $params )\n+\n+ python "$cluster_script" \'$json_string\'\n+\n+]]>\n+ </command>\n+ <configfiles>\n+ <configfile name="cluster_script">\n+<![CDATA[\n+import sys\n+import json\n+import numpy as np\n+import sklearn.cluster\n+import pandas\n+\n+data = pandas.read_csv("$infile", sep=\'\\t\', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )\n+my_class = getattr(sklearn.cluster, "$algorithm_options.selected_algorithm")\n+cluster_object = my_class()\n+\n+params = json.loads( sys.argv[1] )\n+cluster_object.set_params(**params)\n+#if $end_column and $start_column:\n+\n+if $end_column >= $start_column:\n+ data_matrix = data.values[:, $start_column-1:$end_column]\n+else:\n+ data_matrix = data.values\n+\n+#else:\n+data_matrix = data.values\n+#end if\n+prediction = cluster_object.fit_predict( data_matrix )\n+prediction_df = pandas.DataFrame(prediction)\n+res = pandas.concat([data, prediction_df], axis=1)\n+res.to_csv(path_or_buf = "$outfile", sep="\\t", index=False)\n+]]>\n+ </configfile>\n+ </configfiles>\n+ <inputs>\n+ <param name="infile" type="data" format="tabular" label="Data file with numeric values" />\n+ <param name="start_column" type="data_column" data_ref="infile" optional="True" label'..b'"start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="linkage" value="complete"/>\n+ <param name="n_clusters" value="4"/>\n+ <output name="outfile" file="cluster_result11.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="SpectralClustering"/>\n+ <param name="start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="eigen_solver" value="arpack"/>\n+ <param name="n_neighbors" value="12"/>\n+ <param name="n_clusters" value="4"/>\n+ <param name="assign_labels" value="discretize"/>\n+ <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result12.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="SpectralClustering"/>\n+ <param name="start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="assign_labels" value="discretize"/>\n+ <param name="random_state" value="100"/>\n+ <param name="degree" value="2"/>\n+ <output name="outfile" file="cluster_result13.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="MiniBatchKMeans"/>\n+ <param name="start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="tol" value="0.5"/>\n+ <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result14.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="MiniBatchKMeans"/>\n+ <param name="n_init" value="5"/>\n+ <param name="start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="batch_size" value="10"/>\n+ <param name="n_clusters" value="4"/>\n+ <param name="random_state" value="100"/>\n+ <param name="reassignment_ratio" value="1.0"/>\n+ <output name="outfile" file="cluster_result15.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="KMeans"/>\n+ <param name="start_column" value="1" />\n+ <param name="end_column" value="1" />\n+ <param name="n_clusters" value="4" />\n+ <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result16.txt"/>\n+ </test>\n+ </tests>\n+ <help><![CDATA[\n+**What it does**\n+\n+This clustering tool offers different clustering algorithms which are provided by\n+scikit-learn to find similarities among samples and cluster the samples based on these similarities.\n+\n+ ]]></help>\n+ <citations>\n+ <citation type="bibtex">\n+ @article{scikit-learn,\n+ title={Scikit-learn: Machine Learning in {P}ython},\n+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n+ and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n+ and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n+ Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n+ journal={Journal of Machine Learning Research},\n+ volume={12},\n+ pages={2825--2830},\n+ year={2011}\n+ url = {https://github.com/scikit-learn/scikit-learn}\n+ }\n+ </citation>\n+ </citations>\n+</tool>\n' |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result01.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result01.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result02.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result02.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result03.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result03.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result04.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result04.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result06.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result06.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result07.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result07.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result08.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result08.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result09.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result09.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result10.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result10.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result11.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result11.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result12.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result12.txt Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result13.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result13.txt Fri Jan 01 18:37:54 2016 -0500 |
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| b |
| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result14.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result14.txt Fri Jan 01 18:37:54 2016 -0500 |
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| b |
| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result15.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result15.txt Fri Jan 01 18:37:54 2016 -0500 |
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| b |
| diff -r 000000000000 -r a3fd214e7555 test-data/cluster_result16.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result16.txt Fri Jan 01 18:37:54 2016 -0500 |
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| b |
| diff -r 000000000000 -r a3fd214e7555 test-data/numeric_values.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/numeric_values.tabular Fri Jan 01 18:37:54 2016 -0500 |
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| diff -r 000000000000 -r a3fd214e7555 tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Fri Jan 01 18:37:54 2016 -0500 |
| b |
| @@ -0,0 +1,6 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="anaconda" version="2.3.0"> + <repository changeset_revision="94d978ebbfd4" name="package_anaconda_2_3_0" owner="iuc" toolshed="https://toolshed.g2.bx.psu.edu" /> + </package> +</tool_dependency> |