comparison feature_selection.xml @ 10:96f9b73327f2 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 76583c1fcd9d06a4679cc46ffaee44117b9e22cd

9:537c6763c018

            <![CDATA[
import sys
import json
import pandas
import pickle
import numpy as np
import sklearn.feature_selection
from sklearn import svm, linear_model, ensemble

@COLUMNS_FUNCTION@

@FEATURE_SELECTOR_FUNCTION@

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

## Read features
features_has_header = params["input_options"]["header1"]
input_type = params["input_options"]["selected_input"]
if input_type=="tabular":
    header = 'infer' if features_has_header else None
    column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]

            parse_dates=True
    )
else:
    X = mmread("$input_options.infile1")

## Read labels
header = 'infer' if params["input_options"]["header2"] else None
column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
    c = params["input_options"]["column_selector_options_2"]["col2"]
else:

        header=header,
        parse_dates=True
)
y=y.ravel()

## Create feature selector
new_selector = feature_selector(params['feature_selection_algorithms'])
if params['feature_selection_algorithms']['selected_algorithm'] != 'SelectFromModel' or \
        'extra_estimator' not in params['feature_selection_algorithms'] or \
        params['feature_selection_algorithms']['extra_estimator']['has_estimator'] != 'no_load' :
    new_selector.fit(X, y)

## Transform to select features
selected_names = None
if "$select_methods.selected_method" == "fit_transform":
    res = new_selector.transform(X)
    if features_has_header:
        selected_names = input_df.columns[new_selector.get_support(indices=True)]
else:
    res = new_selector.get_support(params["select_methods"]["indices"])

res = pandas.DataFrame(res, columns = selected_names)
res.to_csv(path_or_buf="$outfile", sep='\t', index=False)


            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <expand macro="feature_selection_all" />
        <expand macro="feature_selection_methods" />
        <expand macro="sl_mixed_input"/>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile"/>
    </outputs>
    <tests>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result01"/>
        </test>
        <test>
            <param name="selected_algorithm" value="GenericUnivariateSelect"/>
            <param name="param" value="20"/>

            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result07"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFE"/>
            <param name="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result08"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFECV"/>
            <param name="has_estimator" value="no"/>
            <param name="new_estimator" value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result09"/>
        </test>
        <test>
            <param name="selected_algorithm" value="VarianceThreshold"/>
            <param name="threshold" value="0.1"/>

            <param name="infile2" value="test3.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="selected_column_selector_option2" value="by_header_name"/>
            <param name="col2" value="target"/>
            <output name="outfile" file="feature_selection_result11"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

10:96f9b73327f2

            <![CDATA[
import sys
import json
import pandas
import pickle
import ast
import numpy as np
import xgboost
import sklearn.feature_selection
from sklearn import svm, linear_model, ensemble, naive_bayes, tree, neighbors

@COLUMNS_FUNCTION@
@GET_ESTIMATOR_FUNCTION@
@FEATURE_SELECTOR_FUNCTION@

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

#handle cheetah
#if $fs_algorithm_selector.selected_algorithm == "SelectFromModel"\
        and $fs_algorithm_selector.model_inputter.input_mode == "prefitted":
params['fs_algorithm_selector']['model_inputter']['fitted_estimator'] =\
        "$fs_algorithm_selector.model_inputter.fitted_estimator"
#end if

# Read features
features_has_header = params["input_options"]["header1"]
input_type = params["input_options"]["selected_input"]
if input_type=="tabular":
    header = 'infer' if features_has_header else None
    column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]

            parse_dates=True
    )
else:
    X = mmread("$input_options.infile1")

# Read labels
header = 'infer' if params["input_options"]["header2"] else None
column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
    c = params["input_options"]["column_selector_options_2"]["col2"]
else:

        header=header,
        parse_dates=True
)
y=y.ravel()

# Create feature selector
new_selector = feature_selector(params['fs_algorithm_selector'])
if params['fs_algorithm_selector']['selected_algorithm'] != 'SelectFromModel'\
        or params['fs_algorithm_selector']['model_inputter']['input_mode'] != 'prefitted' :
    new_selector.fit(X, y)

## Transform to select features
selected_names = None
if "$output_method_selector.selected_method" == "fit_transform":
    res = new_selector.transform(X)
    if features_has_header:
        selected_names = input_df.columns[new_selector.get_support(indices=True)]
else:
    res = new_selector.get_support(params["output_method_selector"]["indices"])

res = pandas.DataFrame(res, columns = selected_names)
res.to_csv(path_or_buf="$outfile", sep='\t', index=False)


            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <expand macro="feature_selection_all">
            <expand macro="fs_selectfrommodel_prefitted"/>
        </expand>
        <expand macro="feature_selection_output_mothods" />
        <expand macro="sl_mixed_input"/>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile"/>
    </outputs>
    <tests>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="'n_estimators': 10, 'random_state': 10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result01"/>
        </test>
        <test>
            <param name="selected_algorithm" value="GenericUnivariateSelect"/>
            <param name="param" value="20"/>

            <param name="header2" value="True"/>
            <output name="outfile" file="feature_selection_result07"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFE"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="'n_estimators': 10, 'random_state':10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result08"/>
        </test>
        <test>
            <param name="selected_algorithm" value="RFECV"/>
            <param name="input_mode" value="new"/>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="'n_estimators': 10, 'random_state':10"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="6"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result09"/>
        </test>
        <test>
            <param name="selected_algorithm" value="VarianceThreshold"/>
            <param name="threshold" value="0.1"/>

            <param name="infile2" value="test3.tabular" ftype="tabular"/>
            <param name="header2" value="True"/>
            <param name="selected_column_selector_option2" value="by_header_name"/>
            <param name="col2" value="target"/>
            <output name="outfile" file="feature_selection_result11"/>
        </test>
        <test>
            <param name="selected_algorithm" value="SelectFromModel"/>
            <param name="input_mode" value="prefitted"/>
            <param name="fitted_estimator" value="rfr_model01" ftype="zip"/>
            <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
            <param name="header1" value="false"/>
            <param name="col1" value="1,2,3,4,5"/>
            <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
            <param name="col2" value="1"/>
            <param name="header2" value="false"/>
            <output name="outfile" file="feature_selection_result12"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**