comparison ensemble.xml @ 20:038cecaa9e7c draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1

19:4570575d060c

import os
import json
import numpy as np
import sklearn.ensemble
import pandas
import pickle
from scipy.io import mmread

execfile("$__tool_directory__/utils.py")

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

    with open(outfile_fit, 'wb') as out_handler:
        pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL)

else:
    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(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, columns=["predicted"])
    res = pandas.concat([data, prediction_df], axis=1)

20:038cecaa9e7c

import os
import json
import numpy as np
import sklearn.ensemble
import pandas
from scipy.io import mmread

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

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

    with open(outfile_fit, 'wb') as out_handler:
        pickle.dump(estimator, out_handler, pickle.HIGHEST_PROTOCOL)

else:
    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(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, columns=["predicted"])
    res = pandas.concat([data, prediction_df], axis=1)