view pre_process.xml @ 46:761269451e98 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5

<tool id="sklearn_data_preprocess" name="Preprocess" version="@VERSION@" profile="@PROFILE@">
    <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

from scipy.io import mmread
from scipy.io import mmwrite
from sklearn import preprocessing
from galaxy_ml.model_persist import dump_model_to_h5
from galaxy_ml.utils import read_columns, SafeEval


safe_eval = SafeEval()

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

#if $input_type.selected_input_type == "sparse":
X = mmread("$infile")
#else:
header = 'infer' if params["input_type"]["header1"] else None
column_option = params["input_type"]["column_selector_options_1"]["selected_column_selector_option"]
if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
    c = params["input_type"]["column_selector_options_1"]["col1"]
else:
    c = None
X, input_df = read_columns(
        "$input_type.infile",
        c=c,
        c_option=column_option,
        return_df=True,
        sep='\t',
        header=header,
        parse_dates=True,
        encoding=None,
        index_col=None)
X = X.astype(float)
#end if

preprocessor = params["input_type"]["pre_processors"]["selected_pre_processor"]
options = params["input_type"]["pre_processors"]["options"]
if 'feature_range' in options:
    feature_range = safe_eval(options['feature_range'].strip())
    if not feature_range:
        feature_range = (0, 1)
    options['feature_range'] = feature_range

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

#if $input_type.selected_input_type == "sparse":
with open("$outfile_transform", "wb") as transform_handler:
    mmwrite(transform_handler, result)
#else:
columns = input_df.columns
if preprocessor == 'PolynomialFeatures':
    columns = None
    header = False
res = pandas.DataFrame(result, columns=columns)
res.to_csv(path_or_buf = "$outfile_transform", sep="\t",
           index=False, header=True if header else False)
#end if

#if $save:
dump_model_to_h5(estimator, "$outfile_fit")
#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:" />
                <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
                <conditional name="column_selector_options_1">
                    <expand macro="samples_column_selector_options" multiple="true" column_option="selected_column_selector_option" col_name="col1" infile="infile" />
                </conditional>
                <conditional name="pre_processors">
                    <expand macro="sparse_preprocessors_ext" />
                    <expand macro="sparse_preprocessor_options_ext" />
                </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="h5mlm" name="outfile_fit">
            <filter>save</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <param name="selected_input_type" value="tabular" />
            <param name="selected_pre_processor" value="QuantileTransformer" />
            <param name="save" value="true" />
            <param name="random_state" value="200" />
            <param name="n_quantiles" value="10" />
            <param name="subsample" value="100" />
            <output name="outfile_transform" file="prp_result01" ftype="tabular" />
            <output name="outfile_fit" file="prp_model01" ftype="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" 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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" value="train.tabular" ftype="tabular" />
            <param name="selected_input_type" value="tabular" />
            <param name="selected_column_selector_option" value="all_columns" />
            <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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" 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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" 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="h5mlm" compare="sim_size" delta="5" />
        </test>
        <test>
            <param name="infile" value="regression_X.tabular" ftype="tabular" />
            <param name="header1" value="true" />
            <param name="selected_column_selector_option" value="all_columns" />
            <param name="selected_input_type" value="tabular" />
            <param name="selected_pre_processor" value="MinMaxScaler" />
            <param name="feature_range" value="(-1, 1)" />
            <param name="save" value="false" />
            <output name="outfile_transform" file="prp_result10" ftype="tabular" />
        </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>