comparison nn_classifier.xml @ 7:5072ac474cd5 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1

6:e972a913e61a

import json
import numpy as np
import sklearn.neighbors
import pandas

execfile("$__tool_directory__/sk_whitelist.py")
execfile("$__tool_directory__/utils.py", globals())

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 = SafePickler.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)

            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value="nneighbors"/>
            <param name="sampling_method" value="KNeighborsClassifier" />
            <param name="algorithm" value="brute" />
            <output name="outfile_fit" file="nn_model01.txt"/>
        </test>
        <test>
            <param name="infile1" value="train_set.tabular" ftype="tabular"/>
            <param name="infile2" value="train_set.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="header2" value="True"/>
            <param name="col1" value="1,2,3,4"/>
            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value=""/>
            <param name="selected_algorithm" value="nneighbors"/>
            <param name="sampling_method" value="RadiusNeighborsClassifier" />
            <output name="outfile_fit" file="nn_model02.txt"/>
        </test>
        <test>
            <param name="infile1" value="train_set.tabular" ftype="tabular"/>
            <param name="infile2" value="train_set.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="header2" value="True"/>
            <param name="col1" value="1,2,3,4"/>
            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value="ncentroid"/>
            <output name="outfile_fit" file="nn_model03.txt"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model01.txt" ftype="txt"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model02.txt" ftype="txt"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model03.txt" ftype="txt"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result03.tabular"/>
        </test>

7:5072ac474cd5

import json
import numpy as np
import sklearn.neighbors
import pandas

with open("$__tool_directory__/sk_whitelist.json", "r") as f:
    sk_whitelist = json.load(f)
exec(open("$__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)

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

with open("$infile_model", 'rb') as model_handler:
    classifier_object = load_model(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)

            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value="nneighbors"/>
            <param name="sampling_method" value="KNeighborsClassifier" />
            <param name="algorithm" value="brute" />
            <output name="outfile_fit" file="nn_model01"/>
        </test>
        <test>
            <param name="infile1" value="train_set.tabular" ftype="tabular"/>
            <param name="infile2" value="train_set.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="header2" value="True"/>
            <param name="col1" value="1,2,3,4"/>
            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value="nneighbors"/>
            <param name="sampling_method" value="RadiusNeighborsClassifier" />
            <output name="outfile_fit" file="nn_model02"/>
        </test>
        <test>
            <param name="infile1" value="train_set.tabular" ftype="tabular"/>
            <param name="infile2" value="train_set.tabular" ftype="tabular"/>
            <param name="header1" value="True"/>
            <param name="header2" value="True"/>
            <param name="col1" value="1,2,3,4"/>
            <param name="col2" value="5"/>
            <param name="selected_task" value="train"/>
            <param name="selected_algorithm" value="ncentroid"/>
            <output name="outfile_fit" file="nn_model03"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model01" ftype="zip"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result01.tabular"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model02" ftype="zip"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result02.tabular"/>
        </test>
        <test>
            <param name="infile_model" value="nn_model03" ftype="zip"/>
            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
            <param name="header" value="True"/>
            <param name="selected_task" value="load"/>
            <output name="outfile_predict" file="nn_prediction_result03.tabular"/>
        </test>