Mercurial > repos > bgruening > sklearn_pairwise_metrics

annotate pairwise_metrics.xml @ 22:bb6290994f98 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author bgruening
date Thu, 11 Oct 2018 03:32:00 -0400
parents 0dfaead1d284
children 86020dbc8ef7
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1 <tool id="sklearn_pairwise_metrics" name="Evaluate pairwise distances" version="@VERSION@">
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2 <description>or compute affinity or kernel for sets of samples</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 </macros>
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6 <expand macro="python_requirements"/>
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7 <expand macro="macro_stdio"/>
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command>
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10 <![CDATA[
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11 python "$pairwise_script" '$inputs'
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12 ]]>
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13 </command>
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14 <configfiles>
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15 <inputs name="inputs" />
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16 <configfile name="pairwise_script">
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17 <![CDATA[
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18 import sys
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19 import json
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20 import pandas
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21 import numpy as np
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22 from sklearn.metrics import pairwise
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23 from sklearn.metrics import pairwise_distances_argmin
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24 from scipy.io import mmread
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26 input_json_path = sys.argv[1]
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27 with open(input_json_path, "r") as param_handler:
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28 params = json.load(param_handler)
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29
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31 options = params["input_type"]["metric_functions"]["options"]
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32 metric_function = params["input_type"]["metric_functions"]["selected_metric_function"]
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34 input_iter = []
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35 #for $i, $s in enumerate( $input_type.input_files )
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36 input_index=$i
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37 input_path="${s.input.file_name}"
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38 #if $input_type.selected_input_type == "sparse":
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39 input_iter.append(mmread(input_path))
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40 #else:
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41 input_iter.append(pandas.read_csv(input_path, sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ).values)
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42 #end if
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43 #end for
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44
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45 if len(input_iter)>1:
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46 X = input_iter[0]
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47 Y = input_iter[1]
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48 else: X = Y = input_iter[0]
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49
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50 if metric_function=="pairwise_distances_argmin":
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51 metric_res = pairwise_distances_argmin(X,Y,**options)
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52 else:
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53 my_function = getattr(pairwise, metric_function)
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54 metric_res = my_function(X,Y,**options)
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55
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56 pandas.DataFrame(metric_res).to_csv(path_or_buf = "$outfile", sep="\t", index=False, header=False)
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57 ]]>
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58 </configfile>
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59 </configfiles>
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60 <inputs>
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61 <conditional name="input_type">
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62 <param name="selected_input_type" type="select" label="Select the type of your input data:">
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63 <option value="tabular" selected="true">Tabular data (.tabular, .txt)</option>
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64 <option value="sparse">Sparse matrix (.mtx)</option>
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65 </param>
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66 <when value="tabular">
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67 <expand macro="multiple_input" max_num="2" format="tabular"/>
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68 <conditional name="metric_functions">
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69 <expand macro="sparse_pairwise_metric_functions">
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70 <expand macro="pairwise_metric_functions"/>
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71 </expand>
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72 <when value="additive_chi2_kernel">
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73 </when>
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74 <when value="chi2_kernel">
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75 <section name="options" title="Advanced Options" expanded="False">
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76 <expand macro="gamma" help_text="Floating point scaling parameter of the chi2 kernel. "/>
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77 </section>
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78 </when>
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79 <when value="linear_kernel">
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80 </when>
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81 <when value="manhattan_distances">
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82 <section name="options" title="Advanced Options" expanded="False">
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83 <param argument="sum_over_features" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Sum over features" help="If True, return the pairwise distance matrix, else return the componentwise L1 pairwise-distances. "/>
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84 </section>
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85 </when>
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86 <when value="polynomial_kernel">
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87 <section name="options" title="Advanced Options" expanded="False">
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88 <expand macro="gamma" default_value=""/>
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89 <expand macro="degree"/>
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90 <expand macro="coef0"/>
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91 </section>
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92 </when>
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93 <when value="rbf_kernel">
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94 <section name="options" title="Advanced Options" expanded="False">
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95 <expand macro="gamma" default_value=""/>
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96 </section>
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97 </when>
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98 <when value="laplacian_kernel">
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99 <section name="options" title="Advanced Options" expanded="False">
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100 <expand macro="gamma" default_value=""/>
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101 </section>
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102 </when>
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103 <when value="pairwise_kernels">
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104 <section name="options" title="Advanced Options" expanded="False">
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105 <expand macro="pairwise_kernel_metrics"/>
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106 </section>
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107 </when>
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108 <expand macro="sparse_pairwise_condition">
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109 <expand macro="distance_nonsparse_metrics"/>
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110 </expand>
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111 <expand macro="argmin_distance_condition">
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112 <expand macro="distance_nonsparse_metrics"/>
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113 </expand>
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114 </conditional>
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115 </when>
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116 <when value="sparse">
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117 <expand macro="multiple_input" max_num="2"/>
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118 <conditional name="metric_functions">
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119 <expand macro="sparse_pairwise_metric_functions"/>
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120 <expand macro="sparse_pairwise_condition"/>
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121 <expand macro="argmin_distance_condition"/>
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122 </conditional>
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123 </when>
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124 </conditional>
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125 </inputs>
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126 <outputs>
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127 <data format="tabular" name="outfile"/>
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128 </outputs>
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129 <tests>
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130 <test>
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131 <param name="selected_input_type" value="tabular"/>
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132 <param name="selected_metric_function" value="rbf_kernel"/>
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133 <param name="input_files_0|input" value="test.tabular" ftype="tabular"/>
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134 <param name="input_files_1|input" value="test2.tabular" ftype="tabular"/>
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135 <param name="gamma" value="0.5"/>
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136 <output name="outfile" file="pw_metric01.tabular" compare="sim_size" />
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137 </test>
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138 <test>
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139 <param name="selected_input_type" value="tabular"/>
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140 <param name="selected_metric_function" value="pairwise_distances"/>
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141 <param name="metric" value="manhattan"/>
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142 <param name="input_files_0|input" value="test.tabular" ftype="tabular"/>
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143 <output name="outfile" file="pw_metric02.tabular"/>
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144 </test>
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145 <test>
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146 <param name="selected_input_type" value="sparse"/>
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147 <param name="selected_metric_function" value="pairwise_distances"/>
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148 <param name="metric" value="cosine"/>
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149 <param name="input_files_0|input" value="sparse.mtx" ftype="txt"/>
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150 <output name="outfile" file="pw_metric03.tabular"/>
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151 </test>
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152 </tests>
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153 <help>
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154 <![CDATA[
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155 **What it does**
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156
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157 This tool consists of utilities to evaluate pairwise distances or affinity of sets of samples.
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158 The base utilities are contained in Scikit-learn python library in sklearn.metrics package.
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159 This module contains both distance metrics and kernels. For a brief summary, please refer to:
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160 http://scikit-learn.org/stable/modules/metrics.html#metrics
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161 ]]>
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162 </help>
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163 <expand macro="sklearn_citation"/>
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164 </tool>