<tool id="sklearn_data_preprocess" name="Preprocess" version="@VERSION@">
    <description>raw feature vectors into standardized datasets</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$pre_processor_script" '$inputs'
        ]]>
    </command>
    <configfiles>
        <inputs name="inputs" />
        <configfile name="pre_processor_script">
            <![CDATA[
import sys
import json
import pandas
import pickle
import numpy as np
from scipy.io import mmread
from scipy.io import mmwrite
from sklearn import preprocessing

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

#if $input_type.selected_input_type == "sparse":
X = mmread(open("$infile", 'r'))
#else:
X = pandas.read_csv("$infile", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
#end if

#if $input_type.pre_processors.infile_transform.ext == 'txt':
y = mmread(open("$infile", 'r'))
#else:
y = pandas.read_csv("$infile", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
#end if

preprocessor = params["input_type"]["pre_processors"]["selected_pre_processor"]
options = params["input_type"]["pre_processors"]["options"]

my_class = getattr(preprocessing, preprocessor)
estimator = my_class(**options)
estimator.fit(X)
result = estimator.transform(y)

#if $input_type.pre_processors.infile_transform.ext == 'txt':
mmwrite(open("$outfile_transform" , 'w+'), result)
#else:
res = pandas.DataFrame(result)
res.to_csv(path_or_buf = "$outfile_transform", sep="\t", index=False, header=None)
#end if

#if $save:
pickle.dump(estimator,open("$outfile_fit", 'w+'), pickle.HIGHEST_PROTOCOL)
#end if
        ]]>
        </configfile>
    </configfiles>
    <inputs>
        <conditional name="input_type">
            <param name="selected_input_type" type="select" label="Select the type of your input data:">
                <option value="tabular" selected="true">Tabular</option>
                <option value="sparse">Sparse</option>
            </param>
            <when value="tabular">
                <param name="infile" type="data" format="tabular" label="Select a tabular file you want to train your preprocessor on its data:"/>
                <conditional name="pre_processors">
                    <expand macro="sparse_preprocessors">
                        <option value="KernelCenterer">Kernel Centerer (Centers a kernel matrix)</option>
                        <option value="MinMaxScaler">Minmax Scaler (Scales features to a range)</option>
                        <option value="PolynomialFeatures">Polynomial Features (Generates polynomial and interaction features)</option>
                        <option value="RobustScaler">Robust Scaler (Scales features using outlier-invariance statistics)</option>
                    </expand>
                    <expand macro="sparse_preprocessor_options">
                        <when value="KernelCenterer">
                            <expand macro="multitype_input"/>
                            <section name="options" title="Advanced Options" expanded="False">
                            </section>
                        </when>
                        <when value="MinMaxScaler">
                            <expand macro="multitype_input"/>
                            <section name="options" title="Advanced Options" expanded="False">
                                <!--feature_range-->
                                <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                                    label="Use a copy of data for precomputing normalization" help=" "/>
                            </section>
                        </when>            
                        <when value="PolynomialFeatures">
                            <expand macro="multitype_input"/>
                            <section name="options" title="Advanced Options" expanded="False">
                                <param argument="degree" type="integer" optional="true" value="2" label="The degree of the polynomial features " help=""/>
                                <param argument="interaction_only" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="false" label="Produce interaction features only" help="(Features that are products of at most degree distinct input features) "/>
                                <param argument="include_bias" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Include a bias column" help="Feature in which all polynomial powers are zero "/>
                            </section>
                        </when>
                        <when value="RobustScaler">
                            <expand macro="multitype_input"/>
                            <section name="options" title="Advanced Options" expanded="False">
                                <!--=True, =True, copy=True-->
                                <param argument="with_centering" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                                    label="Center the data before scaling" help=" "/>
                                <param argument="with_scaling" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                                    label="Scale the data to interquartile range" help=" "/>
                                <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true"
                                    label="Use a copy of data for inplace scaling" help=" "/>
                            </section>
                        </when>
                    </expand>
                </conditional>
            </when>
            <when value="sparse">
                <param name="infile" type="data" format="txt" label="Select a sparse representation you want to train your preprocessor on its data:"/>
                <conditional name="pre_processors">
                    <expand macro="sparse_preprocessors"/>
                    <expand macro="sparse_preprocessor_options"/>
                </conditional>
            </when>
        </conditional>
        <param name="save" type="boolean" truevalue="booltrue" falsevalue="boolflase" checked="false"
            label="Save the preprocessor"
            help="Saves the preprocessor after fitting to the data. The preprocessor can then be passed to other tools and used in later operations."/>
    </inputs>
    <outputs>
        <data format="tabular" name="outfile_transform" from_work_dir="./output"/>
        <data format="zip" name="outfile_fit">
            <filter>save</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular"/>
            <param name="infile_transform" value="train.tabular" ftype="tabular"/>
            <param name="selected_input_type" value="tabular"/>
            <param name="selected_pre_processor" value="KernelCenterer"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result01" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model01" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular"/>
            <param name="infile_transform" value="train.tabular" ftype="tabular"/>
            <param name="selected_input_type" value="tabular"/>
            <param name="selected_pre_processor" value="MinMaxScaler"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result02" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model02" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular"/>
            <param name="infile_transform" value="train.tabular" ftype="tabular"/>
            <param name="selected_input_type" value="tabular"/>
            <param name="selected_pre_processor" value="PolynomialFeatures"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result03" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model03" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular"/>
            <param name="infile_transform" value="train.tabular" ftype="tabular"/>
            <param name="selected_input_type" value="tabular"/>
            <param name="selected_pre_processor" value="RobustScaler"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result04" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model04" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="infile_transform" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="selected_input_type" value="sparse"/>
            <param name="selected_pre_processor" value="Binarizer"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result05" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model05" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="infile_transform" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="selected_input_type" value="sparse"/>
            <param name="selected_pre_processor" value="Imputer"/>
            <param name="save" value="true"/>
            <param name="axis" value="true"/>
            <output name="outfile_transform" file="prp_result06" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model06" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular"/>
            <param name="infile_transform" value="train.tabular" ftype="tabular"/>
            <param name="selected_input_type" value="tabular"/>
            <param name="selected_pre_processor" value="StandardScaler"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result07" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model07" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="infile_transform" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="selected_input_type" value="sparse"/>
            <param name="selected_pre_processor" value="MaxAbsScaler"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result08" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model08" ftype="zip" compare="sim_size" delta="500"/>
        </test>
        <test>
            <param name="infile" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="infile_transform" value="csr_sparse2.mtx" ftype="txt"/>
            <param name="selected_input_type" value="sparse"/>
            <param name="selected_pre_processor" value="Normalizer"/>
            <param name="save" value="true"/>
            <output name="outfile_transform" file="prp_result09" ftype="tabular"/>
            <output name="outfile_fit" file="prp_model09" ftype="zip" compare="sim_size" delta="500"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**

This tool provides several transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. The library is provided by sklearn.preprocessing package.

For information about preprocessing classes and parameter settings please refer to `Scikit-learn preprocessing`_.

.. _`Scikit-learn preprocessing`: http://scikit-learn.org/stable/modules/preprocessing.html
        ]]>
    </help>
    <expand macro="sklearn_citation"/>
</tool>