crowdtruth.load.load() - Code Metrics - Inspection of "small changes for code readability" - CrowdTruth/CrowdTruth-core - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 4984d7...2915fd )

by Oana

created 2018-08-15 19:10 UTC

crowdtruth.load.load() C

↳ Parent: crowdtruth.load

Complexity

Conditions

Size

Total Lines	53
Code Lines	34

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	17
CRAP Score	11.6542

Importance

Changes

Metric	Value
eloc	34
dl	0
loc	53
rs	6.6666
c	0
b	0
f	0
ccs	17
cts	25
cp	0.68
cc	9
nop	1
crap	11.6542

How to fix Long Method

import os
import logging

import re
import string
import datetime

from collections import Counter
from collections import OrderedDict

import chardet

import pandas as pd
import numpy as np


from crowdtruth.models.worker import Worker
from crowdtruth.models.unit import Unit
from crowdtruth.models.job import Job
from crowdtruth.configuration import DefaultConfig




# create an ordered counter so that we can maintain the position
# of tags in the order they were annotated
class OrderedCounter(Counter, OrderedDict):
    pass

def create_ordered_counter(ordered_counter, annotation_vector):
    for relation in annotation_vector:
        if relation not in ordered_counter:
            ordered_counter.update({relation: 0})
    return ordered_counter


class Found(Exception):
    pass

def validate_timestamp_field(date_string, date_format):
    try:
        date_obj = datetime.datetime.strptime(date_string, date_format)
        print(date_obj)
    except ValueError:
        raise ValueError('Incorrect date format')

def get_file_list(directory):
    filelist = []

    # go through all files in this folder
    for file in os.listdir(directory):
        # if it is a folder scan it
        if os.path.isdir(directory+'/'+file):
            sublist = get_file_list(directory+'/'+file)
            len_sublist = len(sublist)
            if len_sublist:
                filelist.append(sublist)

        # if it is a csv file open it
        elif file.endswith('.csv') and file != 'groundtruth.csv':
            filelist.append(file)
    return filelist

def load(**kwargs):

    # placeholder for aggregated results
    results = {
        'jobs' : [],
        'units' : [],
        'workers' : [],
        'judgments' : [],
        'annotations' : []
        }


    if 'config' not in kwargs:
        config = DefaultConfig()
    else:
        logging.info('Config loaded')
        config = kwargs['config']

    # check if files is a single file or folder
    if 'file' in kwargs and kwargs['file'].endswith('.csv'):
        files = [kwargs['file']]
    elif 'directory' in kwargs:
        directory = kwargs['directory']
        files = get_file_list(directory)
        logging.info('Found ' + str(len(files)) + ' files')
    else:
        raise ValueError('No input was provided')


    for file in files:
        if 'directory' in locals():
            logging.info("Processing " + file)
            file = directory + "/" + file
        res, config = process_file(file, config)
        for resource in res:
            results[resource].append(res[resource])

    for resource in results:
        results[resource] = pd.concat(results[resource])


    # workers and annotations can appear across jobs, so we have to aggregate those extra
    results['workers'] = results['workers'].groupby(results['workers'].index).agg({
        'unit' : 'sum',
        'judgment' : 'sum',
        'job' : 'count',
        'duration' : 'mean'
        })

    # aggregate annotations
    results['annotations'] = results['annotations'].groupby(results['annotations'].index).sum()

    return results, config


def process_file(filename, config):

    job = filename.split('.csv')[0]

    judgments = pd.read_csv(filename)#, encoding=result['encoding'])

    platform = get_platform(judgments)

    if platform == False:
        logging.info("Custom crowdsourcing platform!")
        no_of_columns = len(config.customPlatformColumns)
        if no_of_columns != 5:
            logging.warning("The following column names are required: judgment id, "
                            "unit id, worker id, start time, submit time")
            raise ValueError('No custom platform configuration was provided')
        else:

            platform = {
                #'id'       : 'custom',
                config.customPlatformColumns[0] : 'judgment',
                config.customPlatformColumns[1] : 'unit',
                config.customPlatformColumns[2] : 'worker',
                config.customPlatformColumns[3] : 'started',
                config.customPlatformColumns[4] : 'submitted'
            }


    # we must establish which fields were part of the input data and which are output judgments
    # if there is a config, check if there is a definition of which fields to use
    #config = []
    # else use the default and select them automatically
    config = get_column_types(judgments, config)

    # remove rows where the worker did not give an answer (AMT issue)
    empty_rows = set()
    for col in config.outputColumns:
        empty_rows = empty_rows.union(judgments[pd.isnull(judgments[col]) == True].index)
    for col in config.outputColumns:
        judgments = judgments[pd.isnull(judgments[col]) == False]
    judgments = judgments.reset_index(drop=True)
    count_empty_rows = len(empty_rows)
    if count_empty_rows > 0:
        if count_empty_rows == 1:
            logging.warning(str(count_empty_rows) + " row with incomplete information in "
                            "output columns was removed.")
        else:
            logging.warning(str(count_empty_rows) + " rows with incomplete information in "
                            "output columns were removed.")

    # allow customization of the judgments
    judgments = config.processJudgments(judgments)

    # update the config after the preprocessing of judgments
    config = get_column_types(judgments, config)

    all_columns = dict(list(config.input.items()) + list(config.output.items()) \
                       + list(platform.items()))
    # allColumns = dict(config.input.items() | config.output.items() | platform.items())
    judgments = judgments.rename(columns=all_columns)

    # remove columns we don't care about
    judgments = judgments[list(all_columns.values())]

    judgments['job'] = job

    # make output values safe keys
    for col in config.output.values():
        if type(judgments[col].iloc[0]) is dict:
            logging.info("Values stored as dictionary")
            if config.open_ended_task:
                judgments[col] = judgments[col].apply(lambda x: OrderedCounter(x))
            else:
                judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
                                 OrderedCounter(x), config.annotation_vector))
        else:
            logging.info("Values not stored as dictionary")
            if config.open_ended_task:
                judgments[col] = judgments[col].apply(lambda x: OrderedCounter( \
                                                      x.split(config.annotation_separator)))
            else:
                judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
                                 OrderedCounter(x.split(config.annotation_separator)), \
                                 config.annotation_vector))

    judgments['started'] = judgments['started'].apply(lambda x: pd.to_datetime(str(x)))
    judgments['submitted'] = judgments['submitted'].apply(lambda x: pd.to_datetime(str(x)))
    judgments['duration'] = judgments.apply(lambda row: (row['submitted'] - row['started']).seconds,
                                            axis=1)

    # remove units with just 1 judgment
    units_1work = judgments.groupby('unit').filter(lambda x: len(x) == 1)["unit"]
    judgments = judgments[~judgments['unit'].isin(units_1work)]
    judgments = judgments.reset_index(drop=True)
    no_units_1work = len(units_1work)
    if no_units_1work > 0:
        if no_units_1work == 1:
            logging.warning(str(no_units_1work) + " Media Unit that was annotated by only"
                            " 1 Worker was omitted, since agreement cannot be calculated.")
        else:
            logging.warning(str(no_units_1work) + " Media Units that were annotated by only"
                            " 1 Worker were omitted, since agreement cannot be calculated.")

    #
    # aggregate units
    #
    units = Unit.aggregate(judgments, config)

    for col in config.output.values():
        judgments[col+'.count'] = judgments[col].apply(lambda x: sum(x.values()))
        judgments[col+'.unique'] = judgments[col].apply(lambda x: len(x))


    #
    # aggregate workers
    #
    workers = Worker.aggregate(judgments, config)


    #
    # aggregate annotations
    # i.e. output columns
    #
    annotations = pd.DataFrame()
    for col in config.output.values():
        res = pd.DataFrame(judgments[col].apply(lambda x: \
              pd.Series(list(x.keys())).value_counts()).sum(), columns=[col])
        annotations = pd.concat([annotations, res], axis=0)

    #
    # aggregate job
    #
    job = Job.aggregate(units, judgments, config)

    # Clean up judgments
    # remove input columns from judgments
    output_cols = [col for col in judgments.columns.values \
                    if col.startswith('output') or col.startswith('metric')]
    judgments = judgments[output_cols + list(platform.values()) + ['duration', 'job']]

    # set judgment id as index
    judgments.set_index('judgment', inplace=True)

    # add missing vector values if closed task
    for col in config.output.values():
        try:
            # openended = config.open_ended_task
            for idx in list(units.index):
                for relation in config.annotation_vector:
                    if relation not in units[col][idx]:
                        units[col][idx].update({relation : 0})
        except AttributeError:
            continue

    return {
        'jobs' : job,
        'units' : units,
        'workers' : workers,
        'judgments' : judgments,
        'annotations' : annotations,
        }, config


def get_platform(dframe):
    # Get the crowdsourcing platform this file originates to

    if dframe.columns.values[0] == '_unit_id':
        # CrowdFlower
        return {
            #'_platform'        : 'cf',
            '_id'           : 'judgment',
            '_unit_id'      : 'unit',
            '_worker_id'    : 'worker',
            '_started_at'   : 'started',
            '_created_at'   : 'submitted'
        }
    elif dframe.columns.values[0] == 'HITId':
        # Mturk
        return {
            #'id'       : 'amt',
            'AssignmentId'  : 'judgment',
            'HITId'         : 'unit',
            'WorkerId'      : 'worker',
            'AcceptTime'    : 'started',
            'SubmitTime'    : 'submitted'
        }
    return False


def get_column_types(dframe, config):

    # returns a list of columns that contain are input content
    config.input = {}
    config.output = {}

    # get a dict of the columns with input content and the columns with output judgments
    # each entry matches [original column name]:[safestring column name]
    if dframe.columns.values[0] == 'HITId':
        # Mturk
        # if config is specified, use those columns
        if config.inputColumns:
            config.input = {c: 'input.'+c.replace('Input.', '')
                            for c in dframe.columns.values if c in config.inputColumns}
        else:
            config.input = {c: 'input.'+c.replace('Input.', '')
                            for c in dframe.columns.values if c.startswith('Input.')}

        # if config is specified, use those columns
        if config.outputColumns:
            config.output = {c: 'output.'+c.replace('Answer.', '')
                             for c in dframe.columns.values if c in config.outputColumns}
        else:
            config.output = {c: 'output.'+c.replace('Answer.', '')
                             for c in dframe.columns.values if c.startswith('Answer.')}
        return config

    elif dframe.columns.values[0] == '_unit_id':

        # if a config is specified, use those columns
        if config.inputColumns:
            config.input = {c: 'input.'+c for c in dframe.columns.values \
                            if c in config.inputColumns}
        if config.outputColumns:
            config.output = {c: 'output.'+c for c in dframe.columns.values \
                             if c in config.outputColumns}
        # if there is a config for both input and output columns, we can return those
        if config.inputColumns and config.outputColumns:
            return config

        # try to identify the input and output columns
        # this is the case if all the values in the column are identical
        # this is not failsafe but should give decent results without settings
        # it is best to make a settings.py file for a collection

        units = dframe.groupby('_unit_id')
        columns = [c for c in dframe.columns.values if c != 'clustering' and not c.startswith('_')
                   and not c.startswith('e_') and not c.endswith('_gold')
                   and not c.endswith('_reason') and not c.endswith('browser')]
        for colname in columns:
            try:
                for i, unit in units:
                    unique = unit[colname].nunique()
                    if unique != 1 and unique != 0:
                        raise Found
                if not config.inputColumns:
                    config.input[colname] = 'input.'+colname

            except Found:
                if not config.outputColumns:
                    config.output[colname] = 'output.'+colname

        return config
    else:
        # unknown platform type

        # if a config is specified, use those columns
        if config.inputColumns:
            config.input = {c: 'input.'+c for c in dframe.columns.values \
                            if c in config.inputColumns}
        if config.outputColumns:
            config.output = {c: 'output.'+c for c in dframe.columns.values \
                             if c in config.outputColumns}
        # if there is a config for both input and output columns, we can return those
        if config.inputColumns and config.outputColumns:
            return config


1	1	import os
2	1	import logging
3
4	1	import re
5	1	import string
6	1	import datetime
7
8	1	from collections import Counter
9	1	from collections import OrderedDict
10
11	1	import chardet
12
13	1	import pandas as pd
14	1	import numpy as np
15
16
17	1	from crowdtruth.models.worker import Worker
18	1	from crowdtruth.models.unit import Unit
19	1	from crowdtruth.models.job import Job
20	1	from crowdtruth.configuration import DefaultConfig
21
22
23
24
25		# create an ordered counter so that we can maintain the position
26		# of tags in the order they were annotated
27	1	class OrderedCounter(Counter, OrderedDict):
28	1	pass
29
30	1	def create_ordered_counter(ordered_counter, annotation_vector):
31	1	for relation in annotation_vector:
32	1	if relation not in ordered_counter:
33	1	ordered_counter.update({relation: 0})
34	1	return ordered_counter
35
36
37	1	class Found(Exception):
38	1	pass
39
40	1	def validate_timestamp_field(date_string, date_format):
41		try:
42		date_obj = datetime.datetime.strptime(date_string, date_format)
43		print(date_obj)
44		except ValueError:
45		raise ValueError('Incorrect date format')
46
47	1	def get_file_list(directory):
48		filelist = []
49
50		# go through all files in this folder
51		for file in os.listdir(directory):
52		# if it is a folder scan it
53		if os.path.isdir(directory+'/'+file):
54		sublist = get_file_list(directory+'/'+file)
55		len_sublist = len(sublist)
56		if len_sublist:
57		filelist.append(sublist)
58
59		# if it is a csv file open it
60		elif file.endswith('.csv') and file != 'groundtruth.csv':
61		filelist.append(file)
62		return filelist
63
64	1	def load(**kwargs):
65
66		# placeholder for aggregated results
67	1	results = {
68		'jobs' : [],
69		'units' : [],
70		'workers' : [],
71		'judgments' : [],
72		'annotations' : []
73		}
74
75
76	1	if 'config' not in kwargs:
77		config = DefaultConfig()
78		else:
79	1	logging.info('Config loaded')
80	1	config = kwargs['config']
81
82		# check if files is a single file or folder
83	1	if 'file' in kwargs and kwargs['file'].endswith('.csv'):
84	1	files = [kwargs['file']]
85		elif 'directory' in kwargs:
86		directory = kwargs['directory']
87		files = get_file_list(directory)
88		logging.info('Found ' + str(len(files)) + ' files')
89		else:
90		raise ValueError('No input was provided')
91
92
93	1	for file in files:
94	1	if 'directory' in locals():
95		logging.info("Processing " + file)
96		file = directory + "/" + file
97	1	res, config = process_file(file, config)
98	1	for resource in res:
99	1	results[resource].append(res[resource])
100
101	1	for resource in results:
102	1	results[resource] = pd.concat(results[resource])
103
104
105		# workers and annotations can appear across jobs, so we have to aggregate those extra
106	1	results['workers'] = results['workers'].groupby(results['workers'].index).agg({
107		'unit' : 'sum',
108		'judgment' : 'sum',
109		'job' : 'count',
110		'duration' : 'mean'
111		})
112
113		# aggregate annotations
114	1	results['annotations'] = results['annotations'].groupby(results['annotations'].index).sum()
115
116	1	return results, config
117
118
119	1	def process_file(filename, config):
120
121	1	job = filename.split('.csv')[0]
122
123	1	judgments = pd.read_csv(filename)#, encoding=result['encoding'])
124
125	1	platform = get_platform(judgments)
126
127	1	if platform == False:
128		logging.info("Custom crowdsourcing platform!")
129		no_of_columns = len(config.customPlatformColumns)
130		if no_of_columns != 5:
131		logging.warning("The following column names are required: judgment id, "
132		"unit id, worker id, start time, submit time")
133		raise ValueError('No custom platform configuration was provided')
134		else:
135
136		platform = {
137		#'id' : 'custom',
138		config.customPlatformColumns[0] : 'judgment',
139		config.customPlatformColumns[1] : 'unit',
140		config.customPlatformColumns[2] : 'worker',
141		config.customPlatformColumns[3] : 'started',
142		config.customPlatformColumns[4] : 'submitted'
143		}
144
145
146		# we must establish which fields were part of the input data and which are output judgments
147		# if there is a config, check if there is a definition of which fields to use
148		#config = []
149		# else use the default and select them automatically
150	1	config = get_column_types(judgments, config)
151
152		# remove rows where the worker did not give an answer (AMT issue)
153	1	empty_rows = set()
154	1	for col in config.outputColumns:
155	1	empty_rows = empty_rows.union(judgments[pd.isnull(judgments[col]) == True].index)
156	1	for col in config.outputColumns:
157	1	judgments = judgments[pd.isnull(judgments[col]) == False]
158	1	judgments = judgments.reset_index(drop=True)
159	1	count_empty_rows = len(empty_rows)
160	1	if count_empty_rows > 0:
161		if count_empty_rows == 1:
162		logging.warning(str(count_empty_rows) + " row with incomplete information in "
163		"output columns was removed.")
164		else:
165		logging.warning(str(count_empty_rows) + " rows with incomplete information in "
166		"output columns were removed.")
167
168		# allow customization of the judgments
169	1	judgments = config.processJudgments(judgments)
170
171		# update the config after the preprocessing of judgments
172	1	config = get_column_types(judgments, config)
173
174	1	all_columns = dict(list(config.input.items()) + list(config.output.items()) \
175		+ list(platform.items()))
176		# allColumns = dict(config.input.items() \| config.output.items() \| platform.items())
177	1	judgments = judgments.rename(columns=all_columns)
178
179		# remove columns we don't care about
180	1	judgments = judgments[list(all_columns.values())]
181
182	1	judgments['job'] = job
183
184		# make output values safe keys
185	1	for col in config.output.values():
186	1	if type(judgments[col].iloc[0]) is dict:
187		logging.info("Values stored as dictionary")
188		if config.open_ended_task:
189		judgments[col] = judgments[col].apply(lambda x: OrderedCounter(x))
190		else:
191		judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
192		OrderedCounter(x), config.annotation_vector))
193		else:
194	1	logging.info("Values not stored as dictionary")
195	1	if config.open_ended_task:
196	1	judgments[col] = judgments[col].apply(lambda x: OrderedCounter( \
197		x.split(config.annotation_separator)))
198		else:
199	1	judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
200		OrderedCounter(x.split(config.annotation_separator)), \
201		config.annotation_vector))
202
203	1	judgments['started'] = judgments['started'].apply(lambda x: pd.to_datetime(str(x)))
204	1	judgments['submitted'] = judgments['submitted'].apply(lambda x: pd.to_datetime(str(x)))
205	1	judgments['duration'] = judgments.apply(lambda row: (row['submitted'] - row['started']).seconds,
206		axis=1)
207
208		# remove units with just 1 judgment
209	1	units_1work = judgments.groupby('unit').filter(lambda x: len(x) == 1)["unit"]
210	1	judgments = judgments[~judgments['unit'].isin(units_1work)]
211	1	judgments = judgments.reset_index(drop=True)
212	1	no_units_1work = len(units_1work)
213	1	if no_units_1work > 0:
214		if no_units_1work == 1:
215		logging.warning(str(no_units_1work) + " Media Unit that was annotated by only"
216		" 1 Worker was omitted, since agreement cannot be calculated.")
217		else:
218		logging.warning(str(no_units_1work) + " Media Units that were annotated by only"
219		" 1 Worker were omitted, since agreement cannot be calculated.")
220
221		#
222		# aggregate units
223		#
224	1	units = Unit.aggregate(judgments, config)
225
226	1	for col in config.output.values():
227	1	judgments[col+'.count'] = judgments[col].apply(lambda x: sum(x.values()))
228	1	judgments[col+'.unique'] = judgments[col].apply(lambda x: len(x))
229
230
231		#
232		# aggregate workers
233		#
234	1	workers = Worker.aggregate(judgments, config)
235
236
237		#
238		# aggregate annotations
239		# i.e. output columns
240		#
241	1	annotations = pd.DataFrame()
242	1	for col in config.output.values():
243	1	res = pd.DataFrame(judgments[col].apply(lambda x: \
244		pd.Series(list(x.keys())).value_counts()).sum(), columns=[col])
245	1	annotations = pd.concat([annotations, res], axis=0)
246
247		#
248		# aggregate job
249		#
250	1	job = Job.aggregate(units, judgments, config)
251
252		# Clean up judgments
253		# remove input columns from judgments
254	1	output_cols = [col for col in judgments.columns.values \
255		if col.startswith('output') or col.startswith('metric')]
256	1	judgments = judgments[output_cols + list(platform.values()) + ['duration', 'job']]
257
258		# set judgment id as index
259	1	judgments.set_index('judgment', inplace=True)
260
261		# add missing vector values if closed task
262	1	for col in config.output.values():
263	1	try:
264		# openended = config.open_ended_task
265	1	for idx in list(units.index):
266	1	for relation in config.annotation_vector:
267	1	if relation not in units[col][idx]:
268	1	units[col][idx].update({relation : 0})
269		except AttributeError:
270		continue
271
272	1	return {
273		'jobs' : job,
274		'units' : units,
275		'workers' : workers,
276		'judgments' : judgments,
277		'annotations' : annotations,
278		}, config
279
280
281	1	def get_platform(dframe):
282		# Get the crowdsourcing platform this file originates to
283
284	1	if dframe.columns.values[0] == '_unit_id':
285		# CrowdFlower
286	1	return {
287		#'_platform' : 'cf',
288		'_id' : 'judgment',
289		'_unit_id' : 'unit',
290		'_worker_id' : 'worker',
291		'_started_at' : 'started',
292		'_created_at' : 'submitted'
293		}
294		elif dframe.columns.values[0] == 'HITId':
295		# Mturk
296		return {
297		#'id' : 'amt',
298		'AssignmentId' : 'judgment',
299		'HITId' : 'unit',
300		'WorkerId' : 'worker',
301		'AcceptTime' : 'started',
302		'SubmitTime' : 'submitted'
303		}
304		return False
305
306
307	1	def get_column_types(dframe, config):
308
309		# returns a list of columns that contain are input content
310	1	config.input = {}
311	1	config.output = {}
312
313		# get a dict of the columns with input content and the columns with output judgments
314		# each entry matches [original column name]:[safestring column name]
315	1	if dframe.columns.values[0] == 'HITId':
316		# Mturk
317		# if config is specified, use those columns
318		if config.inputColumns:
319		config.input = {c: 'input.'+c.replace('Input.', '')
320		for c in dframe.columns.values if c in config.inputColumns}
321		else:
322		config.input = {c: 'input.'+c.replace('Input.', '')
323		for c in dframe.columns.values if c.startswith('Input.')}
324
325		# if config is specified, use those columns
326		if config.outputColumns:
327		config.output = {c: 'output.'+c.replace('Answer.', '')
328		for c in dframe.columns.values if c in config.outputColumns}
329		else:
330		config.output = {c: 'output.'+c.replace('Answer.', '')
331		for c in dframe.columns.values if c.startswith('Answer.')}
332		return config
333
334	1	elif dframe.columns.values[0] == '_unit_id':
335
336		# if a config is specified, use those columns
337	1	if config.inputColumns:
338	1	config.input = {c: 'input.'+c for c in dframe.columns.values \
339		if c in config.inputColumns}
340	1	if config.outputColumns:
341	1	config.output = {c: 'output.'+c for c in dframe.columns.values \
342		if c in config.outputColumns}
343		# if there is a config for both input and output columns, we can return those
344	1	if config.inputColumns and config.outputColumns:
345	1	return config
346
347		# try to identify the input and output columns
348		# this is the case if all the values in the column are identical
349		# this is not failsafe but should give decent results without settings
350		# it is best to make a settings.py file for a collection
351
352		units = dframe.groupby('_unit_id')
353		columns = [c for c in dframe.columns.values if c != 'clustering' and not c.startswith('_')
354		and not c.startswith('e_') and not c.endswith('_gold')
355		and not c.endswith('_reason') and not c.endswith('browser')]
356		for colname in columns:
357		try:
358		for i, unit in units:
359		unique = unit[colname].nunique()
360		if unique != 1 and unique != 0:
361		raise Found
362		if not config.inputColumns:
363		config.input[colname] = 'input.'+colname
364
365		except Found:
366		if not config.outputColumns:
367		config.output[colname] = 'output.'+colname
368
369		return config
370		else:
371		# unknown platform type
372
373		# if a config is specified, use those columns
374		if config.inputColumns:
375		config.input = {c: 'input.'+c for c in dframe.columns.values \
376		if c in config.inputColumns}
377		if config.outputColumns:
378		config.output = {c: 'output.'+c for c in dframe.columns.values \
379		if c in config.outputColumns}
380		# if there is a config for both input and output columns, we can return those
381		if config.inputColumns and config.outputColumns:
382		return config
383

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