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annotate ensemble.xml @ 14:923ecece9e9c draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
author bgruening
date Tue, 05 Jun 2018 06:40:49 -0400
parents e4fcbbc81083
children 706149106031
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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2 <description>for classification and regression</description>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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3 <macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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4 <import>main_macros.xml</import>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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5 </macros>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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6 <expand macro="python_requirements"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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7 <expand macro="macro_stdio"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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8 <version_command>echo "@VERSION@"</version_command>
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9 <command><![CDATA[
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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10 python "$ensemble_script" '$inputs'
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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11 ]]>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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12 </command>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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13 <configfiles>
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14 <inputs name="inputs"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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15 <configfile name="ensemble_script">
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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16 <![CDATA[
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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17 import sys
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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18 import json
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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19 import numpy as np
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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20 import sklearn.ensemble
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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21 import pandas
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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22 import pickle
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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23 from scipy.io import mmread
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25 @COLUMNS_FUNCTION@
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26
0
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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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"]
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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33 options = params["selected_tasks"]["selected_algorithms"]["options"]
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34 if "select_max_features" in options:
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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35 if options["select_max_features"]["max_features"] == "number_input":
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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36 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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37 options["select_max_features"].pop("num_max_features")
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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38 options["max_features"] = options["select_max_features"]["max_features"]
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39 options.pop("select_max_features")
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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40 if "presort" in options:
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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41 if options["presort"] == "true":
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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42 options["presort"] = True
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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43 if options["presort"] == "false":
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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44 options["presort"] = False
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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45 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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46 options["min_samples_leaf"] = 1
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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47 if "min_samples_split" in options and options["min_samples_split"] > 1.0:
e4fcbbc81083 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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48 options["min_samples_split"] = int(options["min_samples_split"])
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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49 input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
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50 if input_type=="tabular":
8
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51 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
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52 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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53 if column_option == "by_index_number":
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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54 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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55 else:
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56 c = None
6
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57 X = read_columns(
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58 "$selected_tasks.selected_algorithms.input_options.infile1",
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59 c = c,
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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60 c_option = column_option,
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61 sep='\t',
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62 header=header,
6
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63 parse_dates=True
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64 )
0
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65 else:
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66 X = mmread(open("$selected_tasks.selected_algorithms.input_options.infile1", 'r'))
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67
8
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68 header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
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69 column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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70 if column_option == "by_index_number":
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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71 c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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72 else:
923ecece9e9c planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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73 c = None
6
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74 y = read_columns(
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75 "$selected_tasks.selected_algorithms.input_options.infile2",
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76 c = c,
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77 c_option = column_option,
6
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78 sep='\t',
8
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79 header=header,
6
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80 parse_dates=True
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81 )
9
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82 y=y.ravel()
0
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83
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84 my_class = getattr(sklearn.ensemble, algorithm)
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85 estimator = my_class(**options)
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86 estimator.fit(X,y)
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87 pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
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88
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89 #else:
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90 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
9
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91 header = 'infer' if params["selected_tasks"]["header"] else None
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92 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
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93 prediction = classifier_object.predict(data)
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94 prediction_df = pandas.DataFrame(prediction)
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95 res = pandas.concat([data, prediction_df], axis=1)
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96 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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97 #end if
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98
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99 ]]>
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100 </configfile>
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101 </configfiles>
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102 <inputs>
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103 <expand macro="sl_Conditional" model="zip">
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104 <param name="selected_algorithm" type="select" label="Select an ensemble method:">
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105 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
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106 <option value="AdaBoostClassifier">Ada boost classifier</option>
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107 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
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108 <option value="RandomForestRegressor">Random forest regressor</option>
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109 <option value="AdaBoostRegressor">Ada boost regressor</option>
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110 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
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111 </param>
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112 <when value="RandomForestClassifier">
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113 <expand macro="sl_mixed_input"/>
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114 <section name="options" title="Advanced Options" expanded="False">
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115 <expand macro="n_estimators"/>
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116 <expand macro="criterion"/>
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117 <expand macro="max_features"/>
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118 <expand macro="max_depth"/>
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119 <expand macro="min_samples_split"/>
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120 <expand macro="min_samples_leaf"/>
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121 <expand macro="min_weight_fraction_leaf"/>
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122 <expand macro="max_leaf_nodes"/>
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123 <expand macro="bootstrap"/>
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124 <expand macro="warm_start" checked="false"/>
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125 <expand macro="n_jobs"/>
0
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126 <expand macro="random_state"/>
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127 <expand macro="oob_score"/>
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128 <!--class_weight=None-->
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129 </section>
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130 </when>
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131 <when value="AdaBoostClassifier">
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132 <expand macro="sl_mixed_input"/>
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133 <section name="options" title="Advanced Options" expanded="False">
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134 <!--base_estimator=None-->
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135 <expand macro="n_estimators" default_value="50"/>
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136 <expand macro="learning_rate"/>
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137 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
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138 <option value="SAMME.R" selected="true">SAMME.R</option>
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139 <option value="SAMME">SAMME</option>
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140 </param>
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141 <expand macro="random_state"/>
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142 </section>
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143 </when>
9
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144 <when value="GradientBoostingClassifier">
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145 <expand macro="sl_mixed_input"/>
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146 <section name="options" title="Advanced Options" expanded="False">
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147 <!--base_estimator=None-->
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148 <param argument="loss" type="select" label="Loss function">
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149 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
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150 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
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151 </param>
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152 <expand macro="learning_rate" default_value='0.1'/>
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153 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
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154 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
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155 <expand macro="criterion2">
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156 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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157 </expand>
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158 <expand macro="min_samples_split" type="float"/>
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159 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
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160 <expand macro="min_weight_fraction_leaf"/>
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161 <expand macro="subsample"/>
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162 <expand macro="max_features"/>
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163 <expand macro="max_leaf_nodes"/>
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164 <expand macro="min_impurity_decrease"/>
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165 <expand macro="verbose"/>
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166 <expand macro="warm_start" checked="false"/>
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167 <expand macro="random_state"/>
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168 <expand macro="presort"/>
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169 </section>
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170 </when>
0
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171 <when value="RandomForestRegressor">
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172 <expand macro="sl_mixed_input"/>
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173 <section name="options" title="Advanced Options" expanded="False">
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174 <expand macro="n_estimators"/>
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175 <expand macro="criterion2"/>
0
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176 <expand macro="max_features"/>
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177 <expand macro="max_depth"/>
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178 <expand macro="min_samples_split"/>
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179 <expand macro="min_samples_leaf"/>
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180 <expand macro="min_weight_fraction_leaf"/>
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181 <expand macro="max_leaf_nodes"/>
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182 <expand macro="min_impurity_decrease"/>
0
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183 <expand macro="bootstrap"/>
9
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184 <expand macro="oob_score"/>
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185 <expand macro="n_jobs"/>
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186 <expand macro="random_state"/>
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187 <expand macro="verbose"/>
0
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188 <expand macro="warm_start" checked="false"/>
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189 </section>
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190 </when>
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191 <when value="AdaBoostRegressor">
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192 <expand macro="sl_mixed_input"/>
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193 <section name="options" title="Advanced Options" expanded="False">
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194 <!--base_estimator=None-->
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195 <expand macro="n_estimators" default_value="50"/>
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196 <expand macro="learning_rate"/>
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197 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
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198 <option value="linear" selected="true">linear</option>
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199 <option value="square">square</option>
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200 <option value="exponential">exponential</option>
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201 </param>
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202 <expand macro="random_state"/>
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203 </section>
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204 </when>
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205 <when value="GradientBoostingRegressor">
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206 <expand macro="sl_mixed_input"/>
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207 <section name="options" title="Advanced Options" expanded="False">
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208 <param argument="loss" type="select" label="Loss function">
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209 <option value="ls" selected="true">ls - least squares regression</option>
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210 <option value="lad">lad - least absolute deviation</option>
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211 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
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212 <option value="quantile">quantile - use alpha to specify the quantile</option>
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213 </param>
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214 <expand macro="learning_rate" default_value="0.1"/>
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215 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
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216 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
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217 <expand macro="criterion2">
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218 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
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219 </expand>
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220 <expand macro="min_samples_split" type="float"/>
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221 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
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222 <expand macro="min_weight_fraction_leaf"/>
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223 <expand macro="subsample"/>
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224 <expand macro="max_features"/>
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225 <expand macro="max_leaf_nodes"/>
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226 <expand macro="min_impurity_decrease"/>
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227 <param argument="alpha" type="float" value="0.9" label="alpha" help="The alpha-quantile of the huber loss function and the quantile loss function" />
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228 <!--base_estimator=None-->
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229 <expand macro="verbose"/>
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230 <expand macro="warm_start" checked="false"/>
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231 <expand macro="random_state"/>
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232 <expand macro="presort"/>
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233 </section>
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234 </when>
0
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235 </expand>
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236 </inputs>
4
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237
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238 <expand macro="output"/>
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239
0
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240 <tests>
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241 <test>
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242 <param name="infile1" value="train.tabular" ftype="tabular"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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243 <param name="infile2" value="train.tabular" ftype="tabular"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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244 <param name="col1" value="1,2,3,4"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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245 <param name="col2" value="5"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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246 <param name="selected_task" value="train"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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247 <param name="selected_algorithm" value="RandomForestClassifier"/>
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248 <param name="random_state" value="10"/>
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249 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="500"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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250 </test>
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251 <test>
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252 <param name="infile_model" value="rfc_model01" ftype="zip"/>
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253 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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254 <param name="selected_task" value="load"/>
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255 <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
0
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256 </test>
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257 <test>
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258 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
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259 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
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260 <param name="col1" value="1,2,3,4,5"/>
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261 <param name="col2" value="6"/>
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262 <param name="selected_task" value="train"/>
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263 <param name="selected_algorithm" value="RandomForestRegressor"/>
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264 <param name="random_state" value="10"/>
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265 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="500"/>
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266 </test>
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267 <test>
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268 <param name="infile_model" value="rfr_model01" ftype="zip"/>
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269 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
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270 <param name="selected_task" value="load"/>
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271 <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
0
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272 </test>
9
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273 <test>
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274 <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
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275 <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
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276 <param name="header1" value="True"/>
14
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277 <param name="selected_column_selector_option" value="all_columns"/>
9
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278 <param name="header2" value="True"/>
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279 <param name="col2" value="1"/>
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280 <param name="selected_task" value="train"/>
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281 <param name="selected_algorithm" value="GradientBoostingRegressor"/>
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282 <param name="max_features" value="number_input"/>
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283 <param name="num_max_features" value=""/>
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284 <param name="random_state" value="42"/>
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285 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="500"/>
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286 </test>
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287 <test>
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288 <param name="infile_model" value="gbr_model01" ftype="zip"/>
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289 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/>
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290 <param name="selected_task" value="load"/>
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291 <param name="header" value="True"/>
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292 <output name="outfile_predict" file="gbr_prediction_result01.tabular" compare="sim_size" delta="500"/>
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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="GradientBoostingClassifier"/>
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301 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="500"/>
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302 </test>
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303 <test>
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304 <param name="infile_model" value="gbc_model01" ftype="zip"/>
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305 <param name="infile_data" value="test.tabular" ftype="tabular"/>
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306 <param name="selected_task" value="load"/>
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307 <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/>
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308 </test>
0
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309 </tests>
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310 <help><![CDATA[
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311 ***What it does***
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312 The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. This tool offers two sets of ensemble algorithms for classification and regression: random forests and ADA boosting which are based on sklearn.ensemble library from Scikit-learn. Here you can find out about the input, output and methods presented in the tools. For information about ensemble methods and parameters settings please refer to `Scikit-learn ensemble`_.
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313
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314 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html
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315
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316 **1 - Methods**
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317 There are two groups of operations available:
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318
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319 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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320
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321 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset.
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322
0
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323 **2 - Trainig input**
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324 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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325
0
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326 **Sample tabular features dataset**
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327 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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328
0
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329 ::
4
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330
0
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331 4.01163365529 -6.10797684314 8.29829894763 1
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332 10.0788438916 1.59539821454 10.0684278289 0
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333 -5.17607775503 -0.878286135332 6.92941850665 2
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334 4.00975406235 -7.11847496542 9.3802423585 1
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335 4.61204065139 -5.71217537352 9.12509610964 1
4
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336
0
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337
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338 **Sample sparse features dataset**
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339 In this case you cannot specifiy a column range.
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340
0
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341 ::
4
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342
0
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343 4 1048577 8738
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344 1 271 0.02083333333333341
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345 1 1038 0.02461995616119806
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346 2 829017 0.01629088031127686
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347 2 829437 0.01209127083516686
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348 2 830752 0.02535100632816968
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349 3 1047487 0.01485722929945572
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350 3 1047980 0.02640566620767753
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351 3 1048475 0.01665869913262564
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352 4 608 0.01662975263094352
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353 4 1651 0.02519674277562741
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354 4 4053 0.04223659971350601
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355
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356
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357 **2 - Trainig output**
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358 The trained model is generated and output in the form of a binary file.
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359
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360
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361 **3 - Prediction input**
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362
0
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363 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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364
0
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365
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366 .. class:: warningmark
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367
0
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368 The number of feature columns must be the same in training and prediction datasets!
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369
3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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370
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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371 **3 - Prediction output**
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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372 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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3bc536788043 planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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373
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31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
bgruening
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374 ]]></help>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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375 <expand macro="sklearn_citation"/>
31fd07e0acdb planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 6c002ea2995c85f5f16adb2ef1c6be82dfbc5417
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376 </tool>