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annotate generalized_linear.xml @ 1:8e02b727dd74 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 69ddd1c2c64661f6ac6d18848188330c679ce894
author bgruening
date Fri, 16 Feb 2018 14:57:09 -0500
parents 32a88b3bea94
children 3326dd4f1e8d
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32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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1 <tool id="sklearn_generalized_linear" name="Generalized linear models" version="@VERSION@">
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2 <description>for classification and regression</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><![CDATA[
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10 python "$glm_script" '$inputs'
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11 ]]>
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12 </command>
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13 <configfiles>
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14 <inputs name="inputs"/>
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15 <configfile name="glm_script">
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16 <![CDATA[
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17 import sys
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18 import json
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19 import numpy as np
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20 import sklearn.linear_model
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21 import pandas
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22 import pickle
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23 from scipy.io import mmread
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24
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25 @COLUMNS_FUNCTION@
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26
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27 input_json_path = sys.argv[1]
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28 params = json.load(open(input_json_path, "r"))
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29
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30 #if $selected_tasks.selected_task == "train":
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31
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32 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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33 options = params["selected_tasks"]["selected_algorithms"]["options"]
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34
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35 #if $selected_tasks.selected_algorithms.input_options.selected_input=="tabular":
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36 X = columns("$selected_tasks.selected_algorithms.input_options.infile1","$selected_tasks.selected_algorithms.input_options.col1")
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37 #else:
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38 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r'))
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39 #end if
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40
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41 y = columns("$selected_tasks.selected_algorithms.input_options.infile2","$selected_tasks.selected_algorithms.input_options.col2")
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42
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43 my_class = getattr(sklearn.linear_model, algorithm)
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44 estimator = my_class(**options)
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45 estimator.fit(X,y)
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46 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
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47
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48 #else:
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49 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
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50 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
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51 prediction = classifier_object.predict(data)
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52 prediction_df = pandas.DataFrame(prediction)
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53 res = pandas.concat([data, prediction_df], axis=1)
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54 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False, header=None)
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55 #end if
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56
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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 <expand macro="sl_Conditional" model="zip">
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62 <param name="selected_algorithm" type="select" label="Select a linear model:">
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63 <option value="SGDClassifier" selected="true">Stochastic Gradient Descent (SGD) classifier</option>
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64 <option value="SGDRegressor">Stochastic Gradient Descent (SGD) regressor</option>
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65 <option value="LinearRegression">Linear Regression model</option>
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66 <option value="RidgeClassifier">Ridge classifier</option>
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67 <option value="Ridge">Ridge regressor</option>
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68 <option value="LogisticRegression">Logistic Regression</option>
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69 <option value="LogisticRegressionCV">Logitic Regression with Cross Validation</option>
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70 <option value="Perceptron">Perceptron</option>
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71 </param>
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72 <when value="SGDClassifier">
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73 <expand macro="sl_mixed_input"/>
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74 <section name="options" title="Advanced Options" expanded="False">
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75 <expand macro="loss">
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76 <option value="hinge" selected="true">hinge</option>
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77 <option value="log">log</option>
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78 <option value="modified_huber">modified huber</option>
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79 <option value="squared_hinge">squared hinge</option>
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80 <option value="perceptron">perceptron</option>
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81 </expand>
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82 <expand macro="penalty"/>
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83 <expand macro="alpha"/>
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84 <expand macro="l1_ratio"/>
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85 <expand macro="fit_intercept"/>
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86 <expand macro="n_iter" />
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87 <expand macro="shuffle"/>
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88 <expand macro="epsilon"/>
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89 <expand macro="learning_rate_s" selected1="true"/>
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90 <expand macro="eta0"/>
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91 <expand macro="power_t"/>
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diff changeset
92 <!--class_weight-->
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diff changeset
93 <expand macro="warm_start" checked="false"/>
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diff changeset
94 <expand macro="random_state"/>
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parents:
diff changeset
95 <!--average-->
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96 </section>
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97 </when>
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98 <when value="SGDRegressor">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
99 <expand macro="sl_mixed_input"/>
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100 <section name="options" title="Advanced Options" expanded="False">
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101 <expand macro="loss" select="true"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
102 <expand macro="penalty"/>
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parents:
diff changeset
103 <expand macro="alpha"/>
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diff changeset
104 <expand macro="l1_ratio"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
105 <expand macro="fit_intercept"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
106 <expand macro="n_iter" />
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diff changeset
107 <expand macro="shuffle"/>
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diff changeset
108 <expand macro="epsilon"/>
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109 <expand macro="learning_rate_s" selected2="true"/>
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110 <expand macro="eta0" default_value="0.01"/>
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111 <expand macro="power_t" default_value="0.25"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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112 <expand macro="warm_start" checked="false"/>
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113 <expand macro="random_state"/>
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114 <!--average-->
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115 </section>
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116 </when>
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117 <when value="LinearRegression">
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118 <expand macro="sl_mixed_input"/>
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119 <section name="options" title="Advanced Options" expanded="False">
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120 <expand macro="fit_intercept"/>
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diff changeset
121 <expand macro="normalize"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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parents:
diff changeset
122 <expand macro="copy_X"/>
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123 </section>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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124 </when>
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125 <when value="RidgeClassifier">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
126 <expand macro="sl_mixed_input"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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127 <section name="options" title="Advanced Options" expanded="False">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
128 <expand macro="ridge_params"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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129 </section>
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130 </when>
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diff changeset
131 <when value="Ridge">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
132 <expand macro="sl_mixed_input"/>
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133 <section name="options" title="Advanced Options" expanded="False">
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diff changeset
134 <expand macro="ridge_params"/>
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135 </section>
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136 </when>
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137 <when value="LogisticRegression">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
138 <expand macro="sl_mixed_input"/>
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139 <section name="options" title="Advanced Options" expanded="False">
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diff changeset
140 <expand macro="penalty"/>
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diff changeset
141 <param argument="dual" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Use dual formulation" help=" "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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142 <expand macro="tol" default_value="0.0001" help_text="Tolerance for stopping criteria. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
143 <expand macro="C"/>
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144 <expand macro="fit_intercept"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
145 <expand macro="max_iter" default_value="100"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
146 <expand macro="warm_start" checked="false"/>
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diff changeset
147 <param argument="solver" type="select" label="Optimization algorithm" help=" ">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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148 <option value="liblinear" selected="true">liblinear</option>
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149 <option value="sag">sag</option>
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150 <option value="lbfgs">lbfgs</option>
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151 <option value="newton-cg">newton-cg</option>
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152 </param>
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diff changeset
153 <param argument="intercept_scaling" type="float" value="1" label="Intercept scaling factor" help="Useful only if solver is liblinear. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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154 <param argument="multi_class" type="select" label="Multiclass option" help="Works only for lbfgs solver. ">
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155 <option value="ovr" selected="true">ovr</option>
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156 <option value="multinomial">multinomial</option>
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diff changeset
157 </param>
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158 <!--class_weight-->
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159 <expand macro="random_state"/>
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160 </section>
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161 </when>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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162 <when value="LogisticRegressionCV">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
163 <expand macro="sl_mixed_input"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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164 <section name="options" title="Advanced Options" expanded="False">
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165 <param argument="Cs" type="integer" value="10" label="Inverse of regularization strength" help="A grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. Like in support vector machines, smaller values specify stronger regularization. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
166 <param argument="dual" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Use dual formulation" help=" "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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167 <param argument="cv" type="integer" optional="true" value="" label="Number of folds used in cross validation" help="If not set, the default cross-validation generator (Stratified K-Folds) is used. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
168 <expand macro="penalty"/>
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169 <expand macro="tol" default_value="0.0001" help_text="Tolerance for stopping criteria. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
170 <expand macro="fit_intercept"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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171 <expand macro="max_iter" default_value="100"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
172 <param argument="solver" type="select" label="Optimization algorithm" help=" ">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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173 <option value="liblinear" selected="true">liblinear</option>
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174 <option value="sag">sag</option>
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175 <option value="lbfgs">lbfgs</option>
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176 <option value="newton-cg">newton-cg</option>
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177 </param>
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178 <param argument="intercept_scaling" type="float" value="1" label="Intercept scaling factor" help="Useful only if solver is liblinear. "/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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179 <param argument="multi_class" type="select" label="Multiclass option" help="Works only for lbfgs solver. ">
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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180 <option value="ovr" selected="true">ovr</option>
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181 <option value="multinomial">multinomial</option>
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182 </param>
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183 <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Average scores across all folds" help=" "/>
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184 <expand macro="random_state"/>
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185 <!--scoring=None>
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186 <class_weight=None-->
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187 </section>
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188 </when>
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189 <when value="Perceptron">
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190 <expand macro="sl_mixed_input"/>
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191 <section name="options" title="Advanced Options" expanded="False">
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192 <expand macro="penalty" default_value="none"/>
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193 <expand macro="alpha"/>
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194 <expand macro="fit_intercept"/>
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195 <expand macro="n_iter" />
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196 <expand macro="shuffle"/>
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197 <expand macro="eta0" default_value="1"/>
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198 <expand macro="warm_start" checked="false"/>
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199 <expand macro="random_state" default_value="0"/>
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200 <!--class_weight=None-->
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201 </section>
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202 </when>
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203 </expand>
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204 </inputs>
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205 <outputs>
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206 <data format="tabular" name="outfile_predict">
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207 <filter>selected_tasks['selected_task'] == 'load'</filter>
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208 </data>
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209 <data format="zip" name="outfile_fit">
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210 <filter>selected_tasks['selected_task'] == 'train'</filter>
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211 </data>
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212 </outputs>
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213 <tests>
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214 <test>
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215 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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216 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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217 <param name="col1" value="1,2,3,4,5"/>
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218 <param name="col2" value="6"/>
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219 <param name="selected_task" value="train"/>
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220 <param name="selected_algorithm" value="SGDRegressor"/>
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221 <param name="random_state" value="10"/>
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222 <output name="outfile_fit" file="glm_model01" compare="sim_size" delta="500"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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223 </test>
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224 <test>
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225 <param name="infile_model" value="glm_model01" ftype="zip"/>
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226 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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227 <param name="selected_task" value="load"/>
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228 <output name="outfile_predict" file="glm_result01" compare="sim_size" delta="500"/>
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229 </test>
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230 <test>
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231 <param name="infile1" value="train.tabular" ftype="tabular"/>
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232 <param name="infile2" value="train.tabular" ftype="tabular"/>
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233 <param name="col1" value="1,2,3,4"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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234 <param name="col2" value="5"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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235 <param name="selected_task" value="train"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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236 <param name="selected_algorithm" value="SGDClassifier"/>
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237 <param name="random_state" value="10"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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238 <output name="outfile_fit" file="glm_model02" compare="sim_size" delta="500"/>
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239 </test>
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240 <test>
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241 <param name="infile_model" value="glm_model02" ftype="zip"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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242 <param name="infile_data" value="test.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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243 <param name="selected_task" value="load"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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244 <output name="outfile_predict" file="glm_result02"/>
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245 </test>
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246 <test>
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247 <param name="infile1" value="train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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248 <param name="infile2" value="train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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249 <param name="col1" value="1,2,3,4"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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250 <param name="col2" value="5"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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251 <param name="selected_task" value="train"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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252 <param name="selected_algorithm" value="RidgeClassifier"/>
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253 <param name="random_state" value="10"/>
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254 <output name="outfile_fit" file="glm_model03" compare="sim_size" delta="500"/>
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255 </test>
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256 <test>
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257 <param name="infile_model" value="glm_model03" ftype="zip"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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258 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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diff changeset
259 <param name="selected_task" value="load"/>
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260 <output name="outfile_predict" file="glm_result03"/>
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261 </test>
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262 <test>
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263 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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264 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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265 <param name="col1" value="1,2,3,4,5"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
266 <param name="col2" value="6"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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diff changeset
267 <param name="selected_task" value="train"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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268 <param name="selected_algorithm" value="LinearRegression"/>
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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269 <param name="random_state" value="10"/>
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270 <output name="outfile_fit" file="glm_model04" compare="sim_size" delta="500"/>
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271 </test>
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272 <test>
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273 <param name="infile_model" value="glm_model04" ftype="zip"/>
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274 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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275 <param name="selected_task" value="load"/>
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276 <output name="outfile_predict" file="glm_result04" compare="sim_size"/>
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277 </test>
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278 <test>
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279 <param name="infile1" value="train.tabular" ftype="tabular"/>
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280 <param name="infile2" value="train.tabular" ftype="tabular"/>
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281 <param name="col1" value="1,2,3,4"/>
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282 <param name="col2" value="5"/>
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283 <param name="selected_task" value="train"/>
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284 <param name="selected_algorithm" value="LogisticRegression"/>
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285 <param name="random_state" value="10"/>
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286 <output name="outfile_fit" file="glm_model05" compare="sim_size" delta="500"/>
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287 </test>
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288 <test>
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289 <param name="infile_model" value="glm_model05" ftype="zip"/>
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290 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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291 <param name="selected_task" value="load"/>
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292 <output name="outfile_predict" file="glm_result05"/>
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293 </test>
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294 <test>
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295 <param name="infile1" value="train.tabular" ftype="tabular"/>
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296 <param name="infile2" value="train.tabular" ftype="tabular"/>
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297 <param name="col1" value="1,2,3,4"/>
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298 <param name="col2" value="5"/>
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299 <param name="selected_task" value="train"/>
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300 <param name="selected_algorithm" value="LogisticRegressionCV"/>
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301 <param name="random_state" value="10"/>
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302 <output name="outfile_fit" file="glm_model06" compare="sim_size" delta="500"/>
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303 </test>
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304 <test>
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305 <param name="infile_model" value="glm_model06" ftype="zip"/>
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306 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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307 <param name="selected_task" value="load"/>
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308 <output name="outfile_predict" file="glm_result06"/>
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309 </test>
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310 <test>
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311 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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312 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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313 <param name="col1" value="1,2,3,4,5"/>
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314 <param name="col2" value="6"/>
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315 <param name="selected_task" value="train"/>
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316 <param name="selected_algorithm" value="Ridge"/>
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317 <param name="random_state" value="10"/>
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318 <output name="outfile_fit" file="glm_model07" compare="sim_size" delta="500"/>
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319 </test>
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320 <test>
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321 <param name="infile_model" value="glm_model07" ftype="zip"/>
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322 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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323 <param name="selected_task" value="load"/>
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324 <output name="outfile_predict" file="glm_result07" compare="sim_size"/>
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325 </test>
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326 <test>
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327 <param name="infile1" value="train.tabular" ftype="tabular"/>
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328 <param name="infile2" value="train.tabular" ftype="tabular"/>
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329 <param name="col1" value="1,2,3,4"/>
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330 <param name="col2" value="5"/>
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331 <param name="selected_task" value="train"/>
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332 <param name="selected_algorithm" value="LogisticRegressionCV"/>
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333 <param name="random_state" value="10"/>
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334 <output name="outfile_fit" file="glm_model08" compare="sim_size" delta="500"/>
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335 </test>
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336 <test>
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337 <param name="infile_model" value="glm_model08" ftype="zip"/>
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338 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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339 <param name="selected_task" value="load"/>
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340 <output name="outfile_predict" file="glm_result08"/>
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341 </test>
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342 </tests>
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343 <help><![CDATA[
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344 ***What it does***
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345 This module implements a set of linear models for classification and regression such as: SGD classification and regression, Linear and Ridge regression and classification. This wrapper is using sklearn.linear_model module at its core. For information about linear models and their parameter settings please refer to `Scikit-learn generalized linear models`_.
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346
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347 .. _`Scikit-learn generalized linear models`: http://scikit-learn.org/stable/modules/linear_model.html
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348
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349 **1 - Methods**
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350 There are two groups of operations available:
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351
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352 1 - Train a model : A training set containing samples and their respective labels (or predicted values) are input. Based on the selected algorithm and options, an estimator object is fit to the data and is returned.
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353
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354 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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355
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356 **2 - Trainig input**
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357 When you choose to train a model, you need a features dataset X and a labels set y. This tool expects tabular or sparse data for X and a single column for y (tabular). You can select a subset of columns in a tabular dataset as your features dataset or labels column. Below you find some examples:
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358
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359 **Sample tabular features dataset**
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360 The following training dataset contains 3 feature columns and a column containing class labels. You can simply select the first 3 columns as features and the last column as labels:
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361
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362 ::
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363
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364 4.01163365529 -6.10797684314 8.29829894763 1
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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365 10.0788438916 1.59539821454 10.0684278289 0
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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366 -5.17607775503 -0.878286135332 6.92941850665 2
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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367 4.00975406235 -7.11847496542 9.3802423585 1
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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368 4.61204065139 -5.71217537352 9.12509610964 1
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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369
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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370
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371 **Sample sparse features dataset**
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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372 In this case you cannot specifiy a column range.
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373
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374 ::
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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375
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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376 4 1048577 8738
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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377 1 271 0.020833
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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378 1 1038 0.02461
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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379 2 829017 0.016
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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380 2 829437 0.012
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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381 2 830752 0.025
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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382 3 1047487 0.01
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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383 3 1047980 0.02
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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384 3 1048475 0.01
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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385 4 608 0.016629
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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386 4 1651 0.02519
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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387 4 4053 0.04223
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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388
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389
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390 **2 - Trainig output**
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391 The trained model is generated and output in the form of a binary file.
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392
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393
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394 **3 - Prediction input**
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395
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396 When you choose to load a model and do prediction, the tool expects an already trained estimator and a tabular dataset as input. The dataset contains new samples which you want to classify or predict regression values for.
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397
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398
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399 .. class:: warningmark
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400
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401 The number of feature columns must be the same in training and prediction datasets!
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402
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403
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404 **3 - Prediction output**
32a88b3bea94 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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405 The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset.
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406
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407 ]]></help>
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408 <expand macro="sklearn_citation"/>
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409 </tool>