comparison nn_classifier.xml @ 20:fa36c40c2990 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48

19:73195a06ff03

@COLUMNS_FUNCTION@
@GET_X_y_FUNCTION@

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


#if $selected_tasks.selected_task == "load":

classifier_object = pickle.load(open("$infile_model", 'r'))

header = 'infer' if params["selected_tasks"]["header"] else None
data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
prediction = classifier_object.predict(data)
prediction_df = pandas.DataFrame(prediction)

my_class = getattr(sklearn.neighbors, classifier)
classifier_object = my_class(**options)
classifier_object.fit(X, y)

pickle.dump(classifier_object,open("$outfile_fit", 'w+'))

#end if

]]>
        </configfile>

20:fa36c40c2990

@COLUMNS_FUNCTION@
@GET_X_y_FUNCTION@

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

#if $selected_tasks.selected_task == "load":

with open("$infile_model", 'rb') as model_handler:
    classifier_object = pickle.load(model_handler)

header = 'infer' if params["selected_tasks"]["header"] else None
data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
prediction = classifier_object.predict(data)
prediction_df = pandas.DataFrame(prediction)

my_class = getattr(sklearn.neighbors, classifier)
classifier_object = my_class(**options)
classifier_object.fit(X, y)

with open("$outfile_fit", 'wb') as out_handler:
    pickle.dump(classifier_object, out_handler)

#end if

]]>
        </configfile>