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comparison nn_classifier.xml @ 0:b7d0fc23bfe4 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cbb681224f23fa95783514f949c97d6c2c60966
author bgruening
date Sat, 04 Aug 2018 12:49:47 -0400
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children e8362e64026d
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-1:000000000000 0:b7d0fc23bfe4
1 <tool id="sklearn_nn_classifier" name="Nearest Neighbors Classification" version="@VERSION@">
2 <description></description>
3 <macros>
4 <import>main_macros.xml</import>
5 </macros>
6 <expand macro="python_requirements"/>
7 <expand macro="macro_stdio"/>
8 <version_command>echo "@VERSION@"</version_command>
9 <command><![CDATA[
10 python '$nnc_script' '$inputs'
11 ]]>
12 </command>
13 <configfiles>
14 <inputs name="inputs"/>
15 <configfile name="nnc_script">
16 <![CDATA[
17 import sys
18 import json
19 import numpy as np
20 import sklearn.neighbors
21 import pandas
22 import pickle
23
24 @COLUMNS_FUNCTION@
25 @GET_X_y_FUNCTION@
26
27 input_json_path = sys.argv[1]
28 with open(input_json_path, "r") as param_handler:
29 params = json.load(param_handler)
30
31 #if $selected_tasks.selected_task == "load":
32
33 with open("$infile_model", 'rb') as model_handler:
34 classifier_object = pickle.load(model_handler)
35
36 header = 'infer' if params["selected_tasks"]["header"] else None
37 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
38 prediction = classifier_object.predict(data)
39 prediction_df = pandas.DataFrame(prediction)
40 res = pandas.concat([data, prediction_df], axis=1)
41 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
42
43 #else:
44
45 X, y = get_X_y(params, "$selected_tasks.selected_algorithms.input_options.infile1" ,"$selected_tasks.selected_algorithms.input_options.infile2")
46
47 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
48
49 if selected_algorithm == "nneighbors":
50 classifier = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["sampling_method"]
51 sys.stdout.write(classifier)
52 options = params["selected_tasks"]["selected_algorithms"]["sampling_methods"]["options"]
53 sys.stdout.write(str(options))
54 elif selected_algorithm == "ncentroid":
55 options = params["selected_tasks"]["selected_algorithms"]["options"]
56 classifier = "NearestCentroid"
57
58 my_class = getattr(sklearn.neighbors, classifier)
59 classifier_object = my_class(**options)
60 classifier_object.fit(X, y)
61
62 with open("$outfile_fit", 'wb') as out_handler:
63 pickle.dump(classifier_object, out_handler)
64
65 #end if
66
67 ]]>
68 </configfile>
69 </configfiles>
70 <inputs>
71 <expand macro="sl_Conditional" model="zip"><!--Todo: add sparse to targets-->
72 <param name="selected_algorithm" type="select" label="Classifier type">
73 <option value="nneighbors">Nearest Neighbors</option>
74 <option value="ncentroid">Nearest Centroid</option>
75 </param>
76 <when value="nneighbors">
77 <expand macro="sl_mixed_input"/>
78 <conditional name="sampling_methods">
79 <param name="sampling_method" type="select" label="Neighbor selection method">
80 <option value="KNeighborsClassifier" selected="true">K-nearest neighbors</option>
81 <option value="RadiusNeighborsClassifier">Radius-based</option>
82 </param>
83 <when value="KNeighborsClassifier">
84 <expand macro="nn_advanced_options">
85 <param argument="n_neighbors" type="integer" optional="true" value="5" label="Number of neighbors" help=" "/>
86 </expand>
87 </when>
88 <when value="RadiusNeighborsClassifier">
89 <expand macro="nn_advanced_options">
90 <param argument="radius" type="float" optional="true" value="1.0" label="Radius"
91 help="Range of parameter space to use by default for :meth ''radius_neighbors'' queries."/>
92 </expand>
93 </when>
94 </conditional>
95 </when>
96 <when value="ncentroid">
97 <expand macro="sl_mixed_input"/>
98 <section name="options" title="Advanced Options" expanded="False">
99 <param argument="metric" type="text" optional="true" value="euclidean" label="Metric"
100 help="The metric to use when calculating distance between instances in a feature array."/>
101 <param argument="shrink_threshold" type="float" optional="true" value="" label="Shrink threshold"
102 help="Floating point number for shrinking centroids to remove features."/>
103 </section>
104 </when>
105 </expand>
106 </inputs>
107
108 <expand macro="output"/>
109
110 <tests>
111 <test>
112 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
113 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
114 <param name="header1" value="True"/>
115 <param name="header2" value="True"/>
116 <param name="col1" value="1,2,3,4"/>
117 <param name="col2" value="5"/>
118 <param name="selected_task" value="train"/>
119 <param name="selected_algorithm" value="nneighbors"/>
120 <param name="sampling_method" value="KNeighborsClassifier" />
121 <param name="algorithm" value="brute" />
122 <output name="outfile_fit" file="nn_model01.txt"/>
123 </test>
124 <test>
125 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
126 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
127 <param name="header1" value="True"/>
128 <param name="header2" value="True"/>
129 <param name="col1" value="1,2,3,4"/>
130 <param name="col2" value="5"/>
131 <param name="selected_task" value="train"/>
132 <param name="selected_algorithm" value=""/>
133 <param name="selected_algorithm" value="nneighbors"/>
134 <param name="sampling_method" value="RadiusNeighborsClassifier" />
135 <output name="outfile_fit" file="nn_model02.txt"/>
136 </test>
137 <test>
138 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
139 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
140 <param name="header1" value="True"/>
141 <param name="header2" value="True"/>
142 <param name="col1" value="1,2,3,4"/>
143 <param name="col2" value="5"/>
144 <param name="selected_task" value="train"/>
145 <param name="selected_algorithm" value="ncentroid"/>
146 <output name="outfile_fit" file="nn_model03.txt"/>
147 </test>
148 <test>
149 <param name="infile_model" value="nn_model01.txt" ftype="txt"/>
150 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
151 <param name="header" value="True"/>
152 <param name="selected_task" value="load"/>
153 <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
154 </test>
155 <test>
156 <param name="infile_model" value="nn_model02.txt" ftype="txt"/>
157 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
158 <param name="header" value="True"/>
159 <param name="selected_task" value="load"/>
160 <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
161 </test>
162 <test>
163 <param name="infile_model" value="nn_model03.txt" ftype="txt"/>
164 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
165 <param name="header" value="True"/>
166 <param name="selected_task" value="load"/>
167 <output name="outfile_predict" file="nn_prediction_result03.tabular"/>
168 </test>
169 </tests>
170 <help><![CDATA[
171 **What it does**
172 This module implements the k-nearest neighbors classification algorithms.
173 For more information check http://scikit-learn.org/stable/modules/neighbors.html
174 ]]></help>
175 <expand macro="sklearn_citation"/>
176 </tool>