comparison numeric_clustering.xml @ 30:60d80322e1e9 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7

29:c156b85a6389

        <inputs name="inputs"/>
        <configfile name="cluster_script">
<![CDATA[
import sys
import json
import sklearn.cluster
import pandas
from sklearn import metrics
from scipy.io import mmread

exec(open("$__tool_directory__/utils.py").read(), globals())

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

    c_option = column_option,
    sep='\t',
    header=header,
    parse_dates=True,
    encoding=None,
    tupleize_cols=False
)
#end if

prediction = cluster_object.fit_predict( data_matrix )

if len(np.unique(prediction)) > 1:

30:60d80322e1e9

        <inputs name="inputs"/>
        <configfile name="cluster_script">
<![CDATA[
import sys
import json
import numpy as np
import sklearn.cluster
import pandas
from sklearn import metrics
from scipy.io import mmread

sys.path.insert(0, '$__tool_directory__')
from utils import read_columns

N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))

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

    c_option = column_option,
    sep='\t',
    header=header,
    parse_dates=True,
    encoding=None,
    tupleize_cols=False)
#end if

prediction = cluster_object.fit_predict( data_matrix )

if len(np.unique(prediction)) > 1: