comparison ensemble.xml @ 19:4570575d060c draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7

18:0b3144c0b4ee

    <configfiles>
        <inputs name="inputs"/>
        <configfile name="ensemble_script">
<![CDATA[
import sys
import json
import numpy as np
import sklearn.ensemble
import pandas
import pickle
from scipy.io import mmread

@COLUMNS_FUNCTION@
@GET_X_y_FUNCTION@

# Get inputs, outputs.
input_json_path = sys.argv[1]
with open(input_json_path, "r") as param_handler:
    params = json.load(param_handler)

# All Python from here on out:

if params["selected_tasks"]["selected_task"] == "train":
    algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
    options = params["selected_tasks"]["selected_algorithms"]["options"]
    if "select_max_features" in options:
        if options["select_max_features"]["max_features"] == "number_input":
            options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
            options["select_max_features"].pop("num_max_features")
        options["max_features"] = options["select_max_features"]["max_features"]

                    <expand macro="min_samples_leaf"/>
                    <expand macro="min_weight_fraction_leaf"/>
                    <expand macro="max_leaf_nodes"/>
                    <expand macro="bootstrap"/>
                    <expand macro="warm_start" checked="false"/>
                    <expand macro="n_jobs"/>
                    <expand macro="random_state"/>
                    <expand macro="oob_score"/>
                    <!--class_weight=None-->
                </section>
            </when>

                    <expand macro="min_weight_fraction_leaf"/>
                    <expand macro="max_leaf_nodes"/>
                    <expand macro="min_impurity_decrease"/>
                    <expand macro="bootstrap"/>
                    <expand macro="oob_score"/>
                    <expand macro="n_jobs"/>
                    <expand macro="random_state"/>
                    <expand macro="verbose"/>
                    <expand macro="warm_start" checked="false"/>
                </section>
            </when>

19:4570575d060c

    <configfiles>
        <inputs name="inputs"/>
        <configfile name="ensemble_script">
<![CDATA[
import sys
import os
import json
import numpy as np
import sklearn.ensemble
import pandas
import pickle
from scipy.io import mmread

execfile("$__tool_directory__/utils.py")

# Get inputs, outputs.
input_json_path = sys.argv[1]
with open(input_json_path, "r") as param_handler:
    params = json.load(param_handler)

# All Python from here on out:

if params["selected_tasks"]["selected_task"] == "train":
    algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
    options = params["selected_tasks"]["selected_algorithms"]["options"]
    if algorithm in ['RandomForestClassifier', 'RandomForestRegressor']:
        options['n_jobs'] = N_JOBS
    if "select_max_features" in options:
        if options["select_max_features"]["max_features"] == "number_input":
            options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
            options["select_max_features"].pop("num_max_features")
        options["max_features"] = options["select_max_features"]["max_features"]

                    <expand macro="min_samples_leaf"/>
                    <expand macro="min_weight_fraction_leaf"/>
                    <expand macro="max_leaf_nodes"/>
                    <expand macro="bootstrap"/>
                    <expand macro="warm_start" checked="false"/>
                    <expand macro="random_state"/>
                    <expand macro="oob_score"/>
                    <!--class_weight=None-->
                </section>
            </when>

                    <expand macro="min_weight_fraction_leaf"/>
                    <expand macro="max_leaf_nodes"/>
                    <expand macro="min_impurity_decrease"/>
                    <expand macro="bootstrap"/>
                    <expand macro="oob_score"/>
                    <expand macro="random_state"/>
                    <expand macro="verbose"/>
                    <expand macro="warm_start" checked="false"/>
                </section>
            </when>