Mercurial > repos > bgruening > sklearn_ensemble
annotate ensemble.xml @ 34:841a6cc5fc58 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"
author | bgruening |
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date | Thu, 01 Oct 2020 20:15:12 +0000 |
parents | af0523c606a7 |
children | 19d6c2745d34 |
rev | line source |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@"> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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2 <description>for classification and regression</description> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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3 <macros> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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4 <import>main_macros.xml</import> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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5 </macros> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
parents:
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6 <expand macro="python_requirements"/> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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7 <expand macro="macro_stdio"/> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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8 <version_command>echo "@VERSION@"</version_command> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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9 <command><![CDATA[ |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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10 python "$ensemble_script" '$inputs' |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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11 ]]> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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12 </command> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
parents:
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13 <configfiles> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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14 <inputs name="inputs"/> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
parents:
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15 <configfile name="ensemble_script"> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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16 <![CDATA[ |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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17 import json |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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18 import numpy as np |
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e94395c672bd
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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19 import pandas |
e94395c672bd
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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20 import pickle |
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dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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21 import sys |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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22 |
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e94395c672bd
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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23 from scipy.io import mmread |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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24 import sklearn.ensemble |
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dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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25 from galaxy_ml.utils import load_model, get_X_y |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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26 |
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e94395c672bd
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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27 |
e94395c672bd
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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28 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) |
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0431274c367d
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit cea052cf3b8dd4f3620253bd222e126de32e7466
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29 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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30 # Get inputs, outputs. |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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31 input_json_path = sys.argv[1] |
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f02eeabab5d1
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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32 with open(input_json_path, "r") as param_handler: |
f02eeabab5d1
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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33 params = json.load(param_handler) |
f02eeabab5d1
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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34 print(params) |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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35 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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36 # Put all cheetah up here to avoid confusion. |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
bgruening
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37 #if $selected_tasks.selected_task == "train": |
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84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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38 infile1 = "$selected_tasks.selected_algorithms.input_options.infile1" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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39 infile2 = "$selected_tasks.selected_algorithms.input_options.infile2" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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40 #else: |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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41 infile_model = "$selected_tasks.infile_model" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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42 infile_data = "$selected_tasks.infile_data" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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43 #end if |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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44 outfile_fit = "$outfile_fit" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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45 outfile_predict = "$outfile_predict" |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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46 |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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47 # All Python from here on out: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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48 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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49 if params["selected_tasks"]["selected_task"] == "train": |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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50 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"] |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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51 options = params["selected_tasks"]["selected_algorithms"]["options"] |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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52 if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']: |
4570575d060c
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
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53 options['n_jobs'] = N_JOBS |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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54 if "select_max_features" in options: |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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55 if options["select_max_features"]["max_features"] == "number_input": |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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56 options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"] |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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57 options["select_max_features"].pop("num_max_features") |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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58 options["max_features"] = options["select_max_features"]["max_features"] |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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59 options.pop("select_max_features") |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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60 if "presort" in options: |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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61 if options["presort"] == "true": |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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62 options["presort"] = True |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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63 if options["presort"] == "false": |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
parents:
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64 options["presort"] = False |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
parents:
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65 if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0: |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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66 options["min_samples_leaf"] = 1 |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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67 if "min_samples_split" in options and options["min_samples_split"] > 1.0: |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
bgruening
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68 options["min_samples_split"] = int(options["min_samples_split"]) |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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69 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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70 X, y = get_X_y(params, infile1, infile2) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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71 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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72 my_class = getattr(sklearn.ensemble, algorithm) |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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73 estimator = my_class(**options) |
84724d805bfa
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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74 estimator.fit(X,y) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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75 with open(outfile_fit, 'wb') as out_handler: |
f02eeabab5d1
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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76 pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL) |
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569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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77 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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78 else: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48
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79 with open(infile_model, 'rb') as model_handler: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
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80 classifier_object = load_model(model_handler) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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81 header = 'infer' if params["selected_tasks"]["header"] else None |
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af0523c606a7
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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82 data = pandas.read_csv(infile_data, sep='\t', header=header, index_col=None, parse_dates=True, encoding=None) |
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83 prediction = classifier_object.predict(data) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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84 prediction_df = pandas.DataFrame(prediction, columns=["predicted"]) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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85 res = pandas.concat([data, prediction_df], axis=1) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 7c2fd140e89605fe689c39e21d70a400545e38cf
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86 res.to_csv(path_or_buf = outfile_predict, sep="\t", index=False) |
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87 |
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88 ]]> |
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89 </configfile> |
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90 </configfiles> |
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91 <inputs> |
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92 <expand macro="sl_Conditional" model="zip"> |
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93 <param name="selected_algorithm" type="select" label="Select an ensemble method:"> |
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94 <option value="RandomForestClassifier" selected="true">Random forest classifier</option> |
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95 <option value="AdaBoostClassifier">Ada boost classifier</option> |
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96 <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option> |
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97 <option value="RandomForestRegressor">Random forest regressor</option> |
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98 <option value="AdaBoostRegressor">Ada boost regressor</option> |
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99 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option> |
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100 </param> |
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101 <when value="RandomForestClassifier"> |
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102 <expand macro="sl_mixed_input"/> |
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103 <section name="options" title="Advanced Options" expanded="False"> |
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104 <expand macro="n_estimators" default_value="100"/> |
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105 <expand macro="criterion"/> |
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106 <expand macro="max_features"/> |
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107 <expand macro="max_depth"/> |
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108 <expand macro="min_samples_split"/> |
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109 <expand macro="min_samples_leaf"/> |
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110 <expand macro="min_weight_fraction_leaf"/> |
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111 <expand macro="max_leaf_nodes"/> |
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112 <expand macro="bootstrap"/> |
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113 <expand macro="warm_start" checked="false"/> |
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114 <expand macro="random_state"/> |
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115 <expand macro="oob_score"/> |
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116 <!--class_weight=None--> |
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117 </section> |
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118 </when> |
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119 <when value="AdaBoostClassifier"> |
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120 <expand macro="sl_mixed_input"/> |
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121 <section name="options" title="Advanced Options" expanded="False"> |
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122 <!--base_estimator=None--> |
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123 <expand macro="n_estimators" default_value="50"/> |
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124 <expand macro="learning_rate"/> |
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125 <param argument="algorithm" type="select" label="Boosting algorithm" help=" "> |
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126 <option value="SAMME.R" selected="true">SAMME.R</option> |
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127 <option value="SAMME">SAMME</option> |
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128 </param> |
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129 <expand macro="random_state"/> |
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130 </section> |
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131 </when> |
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132 <when value="GradientBoostingClassifier"> |
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133 <expand macro="sl_mixed_input"/> |
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134 <section name="options" title="Advanced Options" expanded="False"> |
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135 <!--base_estimator=None--> |
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136 <param argument="loss" type="select" label="Loss function"> |
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137 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option> |
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138 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option> |
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139 </param> |
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140 <expand macro="learning_rate" default_value='0.1'/> |
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141 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/> |
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142 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/> |
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143 <expand macro="criterion2"> |
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144 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option> |
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145 </expand> |
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146 <expand macro="min_samples_split" type="float"/> |
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147 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/> |
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148 <expand macro="min_weight_fraction_leaf"/> |
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149 <expand macro="subsample"/> |
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150 <expand macro="max_features"/> |
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151 <expand macro="max_leaf_nodes"/> |
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152 <expand macro="min_impurity_decrease"/> |
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153 <expand macro="verbose"/> |
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154 <expand macro="warm_start" checked="false"/> |
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155 <expand macro="random_state"/> |
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156 <expand macro="presort"/> |
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157 </section> |
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158 </when> |
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159 <when value="RandomForestRegressor"> |
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160 <expand macro="sl_mixed_input"/> |
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161 <section name="options" title="Advanced Options" expanded="False"> |
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162 <expand macro="n_estimators" default_value="100"/> |
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163 <expand macro="criterion2"/> |
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164 <expand macro="max_features"/> |
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165 <expand macro="max_depth"/> |
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166 <expand macro="min_samples_split"/> |
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167 <expand macro="min_samples_leaf"/> |
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168 <expand macro="min_weight_fraction_leaf"/> |
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169 <expand macro="max_leaf_nodes"/> |
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170 <expand macro="min_impurity_decrease"/> |
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171 <expand macro="bootstrap"/> |
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172 <expand macro="oob_score"/> |
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173 <expand macro="random_state"/> |
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174 <expand macro="verbose"/> |
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175 <expand macro="warm_start" checked="false"/> |
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176 </section> |
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177 </when> |
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178 <when value="AdaBoostRegressor"> |
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179 <expand macro="sl_mixed_input"/> |
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180 <section name="options" title="Advanced Options" expanded="False"> |
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181 <!--base_estimator=None--> |
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182 <expand macro="n_estimators" default_value="50"/> |
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183 <expand macro="learning_rate"/> |
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184 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. "> |
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185 <option value="linear" selected="true">linear</option> |
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186 <option value="square">square</option> |
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187 <option value="exponential">exponential</option> |
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188 </param> |
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189 <expand macro="random_state"/> |
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190 </section> |
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191 </when> |
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192 <when value="GradientBoostingRegressor"> |
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193 <expand macro="sl_mixed_input"/> |
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194 <section name="options" title="Advanced Options" expanded="False"> |
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195 <param argument="loss" type="select" label="Loss function"> |
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196 <option value="ls" selected="true">ls - least squares regression</option> |
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197 <option value="lad">lad - least absolute deviation</option> |
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198 <option value="huber">huber - combination of least squares regression and least absolute deviation</option> |
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199 <option value="quantile">quantile - use alpha to specify the quantile</option> |
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200 </param> |
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201 <expand macro="learning_rate" default_value="0.1"/> |
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202 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/> |
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203 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/> |
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204 <expand macro="criterion2"> |
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205 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option> |
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206 </expand> |
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207 <expand macro="min_samples_split" type="float"/> |
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208 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/> |
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209 <expand macro="min_weight_fraction_leaf"/> |
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210 <expand macro="subsample"/> |
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211 <expand macro="max_features"/> |
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212 <expand macro="max_leaf_nodes"/> |
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213 <expand macro="min_impurity_decrease"/> |
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214 <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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215 <!--base_estimator=None--> |
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216 <expand macro="verbose"/> |
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217 <expand macro="warm_start" checked="false"/> |
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218 <expand macro="random_state"/> |
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219 <expand macro="presort"/> |
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220 </section> |
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221 </when> |
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222 </expand> |
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223 </inputs> |
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224 |
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225 <expand macro="output"/> |
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226 |
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227 <tests> |
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228 <test> |
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229 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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230 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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231 <param name="col1" value="1,2,3,4"/> |
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232 <param name="col2" value="5"/> |
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233 <param name="selected_task" value="train"/> |
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234 <param name="selected_algorithm" value="RandomForestClassifier"/> |
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235 <param name="random_state" value="10"/> |
23
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236 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5"/> |
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237 </test> |
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238 <test> |
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239 <param name="infile_model" value="rfc_model01" ftype="zip"/> |
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240 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
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241 <param name="selected_task" value="load"/> |
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242 <output name="outfile_predict" file="rfc_result01"/> |
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243 </test> |
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244 <test> |
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245 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> |
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246 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> |
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247 <param name="col1" value="1,2,3,4,5"/> |
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248 <param name="col2" value="6"/> |
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249 <param name="selected_task" value="train"/> |
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250 <param name="selected_algorithm" value="RandomForestRegressor"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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251 <param name="random_state" value="10"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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252 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5"/> |
0
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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253 </test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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254 <test> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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255 <param name="infile_model" value="rfr_model01" ftype="zip"/> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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256 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/> |
569eefee7ed8
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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257 <param name="selected_task" value="load"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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258 <output name="outfile_predict" file="rfr_result01"/> |
0
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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259 </test> |
5
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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260 <test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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261 <param name="infile1" value="regression_X.tabular" ftype="tabular"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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262 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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263 <param name="header1" value="True"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
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264 <param name="selected_column_selector_option" value="all_columns"/> |
5
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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265 <param name="header2" value="True"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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266 <param name="col2" value="1"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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267 <param name="selected_task" value="train"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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268 <param name="selected_algorithm" value="GradientBoostingRegressor"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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269 <param name="max_features" value="number_input"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
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270 <param name="num_max_features" value="0.5"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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271 <param name="random_state" value="42"/> |
23
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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272 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5"/> |
5
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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273 </test> |
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274 <test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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275 <param name="infile_model" value="gbr_model01" ftype="zip"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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276 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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277 <param name="selected_task" value="load"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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278 <param name="header" value="True"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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279 <output name="outfile_predict" file="gbr_prediction_result01.tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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280 </test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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281 <test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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282 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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283 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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284 <param name="col1" value="1,2,3,4"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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285 <param name="col2" value="5"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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286 <param name="selected_task" value="train"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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287 <param name="selected_algorithm" value="GradientBoostingClassifier"/> |
23
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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288 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="5"/> |
5
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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289 </test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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290 <test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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291 <param name="infile_model" value="gbc_model01" ftype="zip"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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292 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
f1761288587e
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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293 <param name="selected_task" value="load"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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294 <output name="outfile_predict" file="gbc_result01"/> |
5
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
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295 </test> |
22
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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296 <test> |
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297 <param name="infile1" value="train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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298 <param name="infile2" value="train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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299 <param name="col1" value="1,2,3,4"/> |
2e69c6ca6e91
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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300 <param name="col2" value="5"/> |
2e69c6ca6e91
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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301 <param name="selected_task" value="train"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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302 <param name="selected_algorithm" value="AdaBoostClassifier"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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303 <param name="random_state" value="10"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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304 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="5"/> |
22
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305 </test> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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306 <test> |
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307 <param name="infile_model" value="abc_model01" ftype="zip"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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308 <param name="infile_data" value="test.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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309 <param name="selected_task" value="load"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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310 <output name="outfile_predict" file="abc_result01"/> |
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311 </test> |
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312 <test> |
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313 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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314 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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315 <param name="col1" value="1,2,3,4,5"/> |
2e69c6ca6e91
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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316 <param name="col2" value="6"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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317 <param name="selected_task" value="train"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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318 <param name="selected_algorithm" value="AdaBoostRegressor"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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319 <param name="random_state" value="10"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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320 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="5"/> |
22
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321 </test> |
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322 <test> |
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323 <param name="infile_model" value="abr_model01" ftype="zip"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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324 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/> |
2e69c6ca6e91
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
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325 <param name="selected_task" value="load"/> |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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326 <output name="outfile_predict" file="abr_result01"/> |
22
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327 </test> |
0
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328 </tests> |
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329 <help><![CDATA[ |
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330 ***What it does*** |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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331 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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332 |
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333 .. _`Scikit-learn ensemble`: http://scikit-learn.org/stable/modules/ensemble.html |
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334 |
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335 **1 - Methods** |
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336 There are two groups of operations available: |
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337 |
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338 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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339 |
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340 2 - Load a model and predict : An existing model predicts the class labels (or regression values) for a new dataset. |
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341 |
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342 **2 - Trainig input** |
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343 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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344 |
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345 **Sample tabular features dataset** |
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346 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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347 |
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348 :: |
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349 |
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350 4.01163365529 -6.10797684314 8.29829894763 1 |
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351 10.0788438916 1.59539821454 10.0684278289 0 |
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352 -5.17607775503 -0.878286135332 6.92941850665 2 |
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353 4.00975406235 -7.11847496542 9.3802423585 1 |
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354 4.61204065139 -5.71217537352 9.12509610964 1 |
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355 |
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356 |
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357 **Sample sparse features dataset** |
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358 In this case you cannot specifiy a column range. |
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359 |
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360 :: |
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361 |
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362 4 1048577 8738 |
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363 1 271 0.02083333333333341 |
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364 1 1038 0.02461995616119806 |
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365 2 829017 0.01629088031127686 |
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366 2 829437 0.01209127083516686 |
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367 2 830752 0.02535100632816968 |
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368 3 1047487 0.01485722929945572 |
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369 3 1047980 0.02640566620767753 |
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370 3 1048475 0.01665869913262564 |
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371 4 608 0.01662975263094352 |
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372 4 1651 0.02519674277562741 |
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373 4 4053 0.04223659971350601 |
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374 |
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375 |
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376 **2 - Trainig output** |
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377 The trained model is generated and output in the form of a binary file. |
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378 |
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379 |
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380 **3 - Prediction input** |
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381 |
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382 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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383 |
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384 |
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385 .. class:: warningmark |
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386 |
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387 The number of feature columns must be the same in training and prediction datasets! |
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388 |
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389 |
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390 **3 - Prediction output** |
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391 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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392 |
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393 ]]></help> |
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394 <expand macro="sklearn_citation"/> |
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395 </tool> |