crowdtruth.load.load() - Code Metrics - Inspection of "added metrics documentation" - CrowdTruth/CrowdTruth-core - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( a2486a...0c98d4 )

by Oana

created 2018-08-18 11:20 UTC

crowdtruth.load.load() B

↳ Parent: crowdtruth.load

Complexity

Conditions

Size

Total Lines	44
Code Lines	28

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	16
CRAP Score	7.1929

Importance

Changes

Metric	Value
eloc	28
dl	0
loc	44
ccs	16
cts	19
cp	0.8421
rs	7.808
c	0
b	0
f	0
cc	7
nop	1
crap	7.1929

#pylint: disable=W0223

"""
Module used to process and load the input files to be evaluated with the CrowdTruth metrics.
"""

import os

import logging
import datetime

from collections import Counter, OrderedDict

import pandas as pd

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):
    """ Instantiates an ordered counter. """
    pass

def create_ordered_counter(ordered_counter, annotation_vector):
    """ Instantiates an ordered counter from a given annotation vector. """
    for relation in annotation_vector:
        if relation not in ordered_counter:
            ordered_counter.update({relation: 0})
    return ordered_counter


class Found(Exception):
    """ Exception. """
    pass

def validate_timestamp_field(date_string, date_format):
    """ Validates the time columns (started time and submitted time) in input files. """

    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):
    """ List the files in the directry given as argument. """
    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)
            sublist_length = len(sublist)
            if sublist_length:
                filelist.append(sublist)

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

def list_files(kwargs):
    """ Creates a list of files to be processed. """
    files = []
    directory = ""
    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')
    return files, directory

def load(**kwargs):
    """ Loads the input files. """

    # 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']

    files, directory = list_files(kwargs)

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

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


    # 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 remove_empty_rows(config, judgments):
    """ 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.")
    return judgments

def remove_single_judgment_units(judgments):
    """ 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.")
    return judgments

def make_output_cols_safe_keys(config, judgments):
    """ make output values safe keys """
    for col in config.output.values():
        if isinstance(judgments[col].iloc[0], 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))
    return judgments

def process_file(filename, config):
    """ process input files with the given configuration"""

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

    platform = get_platform(judgments)

    if platform is 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)

    judgments = remove_empty_rows(config, judgments)
    # 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'] = filename.split('.csv')[0]

    # make output values safe keys
    judgments = make_output_cols_safe_keys(config, judgments)

    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
    judgments = remove_single_judgment_units(judgments)

    #
    # 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 configure_amt_columns(dframe, config):
    """ Configures AMT input and output columns. """
    config.input = {}
    config.output = {}

    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.input, config.output

def configure_platform_columns(dframe, config):
    """ Configures FigureEight and custom platforms input and output columns. """
    config.input = {}
    config.output = {}

    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}
    return config.input, config.output


def get_column_types(dframe, config):
    """ return input and output columns """
    # 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
        config.input, config.output = configure_amt_columns(dframe, config)

        return config

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

        # if a config is specified, use those columns
        config.input, config.output = configure_platform_columns(dframe, config)
        # 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 _, 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
        config.input, config.output = configure_platform_columns(dframe, config)
        # if there is a config for both input and output columns, we can return those
        if config.inputColumns and config.outputColumns:
            return config


1		#pylint: disable=W0223
2
3	1	"""
4		Module used to process and load the input files to be evaluated with the CrowdTruth metrics.
5		"""
6
7	1	import os
8
9	1	import logging
10	1	import datetime
11
12	1	from collections import Counter, OrderedDict
13
14	1	import pandas as pd
15
16	1	from crowdtruth.models.worker import Worker
17	1	from crowdtruth.models.unit import Unit
18	1	from crowdtruth.models.job import Job
19	1	from crowdtruth.configuration import DefaultConfig
20
21
22
23
24		# create an ordered counter so that we can maintain
25		# the position of tags in the order they were annotated
26	1	class OrderedCounter(Counter, OrderedDict):
27		""" Instantiates an ordered counter. """
28	1	pass
29
30	1	def create_ordered_counter(ordered_counter, annotation_vector):
31		""" Instantiates an ordered counter from a given annotation vector. """
32	1	for relation in annotation_vector:
33	1	if relation not in ordered_counter:
34	1	ordered_counter.update({relation: 0})
35	1	return ordered_counter
36
37
38	1	class Found(Exception):
39		""" Exception. """
40	1	pass
41
42	1	def validate_timestamp_field(date_string, date_format):
43		""" Validates the time columns (started time and submitted time) in input files. """
44
45		try:
46		date_obj = datetime.datetime.strptime(date_string, date_format)
47		print(date_obj)
48		except ValueError:
49		raise ValueError('Incorrect date format')
50
51	1	def get_file_list(directory):
52		""" List the files in the directry given as argument. """
53		filelist = []
54
55		# go through all files in this folder
56		for file in os.listdir(directory):
57		# if it is a folder scan it
58		if os.path.isdir(directory+'/'+file):
59		sublist = get_file_list(directory+'/'+file)
60		sublist_length = len(sublist)
61		if sublist_length:
62		filelist.append(sublist)
63
64		# if it is a csv file open it
65		if file.endswith('.csv') and file != 'groundtruth.csv':
66		filelist.append(file)
67		return filelist
68
69	1	def list_files(kwargs):
70		""" Creates a list of files to be processed. """
71	1	files = []
72	1	directory = ""
73	1	if 'file' in kwargs and kwargs['file'].endswith('.csv'):
74	1	files = [kwargs['file']]
75		elif 'directory' in kwargs:
76		directory = kwargs['directory']
77		files = get_file_list(directory)
78		logging.info('Found ' + str(len(files)) + ' files')
79		else:
80		raise ValueError('No input was provided')
81	1	return files, directory
82
83	1	def load(**kwargs):
84		""" Loads the input files. """
85
86		# placeholder for aggregated results
87	1	results = {
88		'jobs' : [],
89		'units' : [],
90		'workers' : [],
91		'judgments' : [],
92		'annotations' : []
93		}
94
95	1	if 'config' not in kwargs:
96		config = DefaultConfig()
97		else:
98	1	logging.info('Config loaded')
99	1	config = kwargs['config']
100
101	1	files, directory = list_files(kwargs)
102
103	1	for file in files:
104	1	if 'directory' in locals() and directory != "":
105		logging.info("Processing " + file)
106		file = directory + "/" + file
107	1	res, config = process_file(file, config)
108	1	for value in res:
109	1	results[value].append(res[value])
110
111	1	for value in results:
112	1	results[value] = pd.concat(results[value])
113
114
115		# workers and annotations can appear across jobs, so we have to aggregate those extra
116	1	results['workers'] = results['workers'].groupby(results['workers'].index).agg({
117		'unit' : 'sum',
118		'judgment' : 'sum',
119		'job' : 'count',
120		'duration' : 'mean'
121		})
122
123		# aggregate annotations
124	1	results['annotations'] = results['annotations'].groupby(results['annotations'].index).sum()
125
126	1	return results, config
127
128	1	def remove_empty_rows(config, judgments):
129		""" remove rows where the worker did not give an answer (AMT issue) """
130	1	empty_rows = set()
131	1	for col in config.outputColumns:
132	1	empty_rows = empty_rows.union(judgments[pd.isnull(judgments[col]) == True].index)
133	1	for col in config.outputColumns:
134	1	judgments = judgments[pd.isnull(judgments[col]) == False]
135	1	judgments = judgments.reset_index(drop=True)
136	1	count_empty_rows = len(empty_rows)
137	1	if count_empty_rows > 0:
138		if count_empty_rows == 1:
139		logging.warning(str(count_empty_rows) + " row with incomplete information in "
140		"output columns was removed.")
141		else:
142		logging.warning(str(count_empty_rows) + " rows with incomplete information in "
143		"output columns were removed.")
144	1	return judgments
145
146	1	def remove_single_judgment_units(judgments):
147		""" remove units with just 1 judgment """
148	1	units_1work = judgments.groupby('unit').filter(lambda x: len(x) == 1)["unit"]
149	1	judgments = judgments[~judgments['unit'].isin(units_1work)]
150	1	judgments = judgments.reset_index(drop=True)
151	1	no_units_1work = len(units_1work)
152	1	if no_units_1work > 0:
153		if no_units_1work == 1:
154		logging.warning(str(no_units_1work) + " Media Unit that was annotated by only"
155		" 1 Worker was omitted, since agreement cannot be calculated.")
156		else:
157		logging.warning(str(no_units_1work) + " Media Units that were annotated by only"
158		" 1 Worker were omitted, since agreement cannot be calculated.")
159	1	return judgments
160
161	1	def make_output_cols_safe_keys(config, judgments):
162		""" make output values safe keys """
163	1	for col in config.output.values():
164	1	if isinstance(judgments[col].iloc[0], dict):
165		logging.info("Values stored as dictionary")
166		if config.open_ended_task:
167		judgments[col] = judgments[col].apply(lambda x: OrderedCounter(x))
168		else:
169		judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
170		OrderedCounter(x), config.annotation_vector))
171		else:
172	1	logging.info("Values not stored as dictionary")
173	1	if config.open_ended_task:
174	1	judgments[col] = judgments[col].apply(lambda x: OrderedCounter( \
175		x.split(config.annotation_separator)))
176		else:
177	1	judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
178		OrderedCounter(x.split(config.annotation_separator)), \
179		config.annotation_vector))
180	1	return judgments
181
182	1	def process_file(filename, config):
183		""" process input files with the given configuration"""
184
185	1	judgments = pd.read_csv(filename)#, encoding=result['encoding'])
186
187	1	platform = get_platform(judgments)
188
189	1	if platform is False:
190	1	logging.info("Custom crowdsourcing platform!")
191	1	no_of_columns = len(config.customPlatformColumns)
192	1	if no_of_columns != 5:
193		logging.warning("The following column names are required: judgment id, "
194		"unit id, worker id, start time, submit time")
195		raise ValueError('No custom platform configuration was provided')
196		else:
197
198	1	platform = {
199		#'id' : 'custom',
200		config.customPlatformColumns[0] : 'judgment',
201		config.customPlatformColumns[1] : 'unit',
202		config.customPlatformColumns[2] : 'worker',
203		config.customPlatformColumns[3] : 'started',
204		config.customPlatformColumns[4] : 'submitted'
205		}
206
207
208		# we must establish which fields were part of the input data and which are output judgments
209		# if there is a config, check if there is a definition of which fields to use
210		#config = []
211		# else use the default and select them automatically
212	1	config = get_column_types(judgments, config)
213
214	1	judgments = remove_empty_rows(config, judgments)
215		# allow customization of the judgments
216	1	judgments = config.processJudgments(judgments)
217
218		# update the config after the preprocessing of judgments
219	1	config = get_column_types(judgments, config)
220
221	1	all_columns = dict(list(config.input.items()) + list(config.output.items()) \
222		+ list(platform.items()))
223		# allColumns = dict(config.input.items() \| config.output.items() \| platform.items())
224	1	judgments = judgments.rename(columns=all_columns)
225
226		# remove columns we don't care about
227	1	judgments = judgments[list(all_columns.values())]
228
229	1	judgments['job'] = filename.split('.csv')[0]
230
231		# make output values safe keys
232	1	judgments = make_output_cols_safe_keys(config, judgments)
233
234	1	judgments['started'] = judgments['started'].apply(lambda x: pd.to_datetime(str(x)))
235	1	judgments['submitted'] = judgments['submitted'].apply(lambda x: pd.to_datetime(str(x)))
236	1	judgments['duration'] = judgments.apply(lambda row: (row['submitted'] - row['started']).seconds,
237		axis=1)
238
239		# remove units with just 1 judgment
240	1	judgments = remove_single_judgment_units(judgments)
241
242		#
243		# aggregate units
244		#
245	1	units = Unit.aggregate(judgments, config)
246
247	1	for col in config.output.values():
248	1	judgments[col+'.count'] = judgments[col].apply(lambda x: sum(x.values()))
249	1	judgments[col+'.unique'] = judgments[col].apply(lambda x: len(x))
250
251
252		#
253		# aggregate workers
254		#
255	1	workers = Worker.aggregate(judgments, config)
256
257
258		#
259		# aggregate annotations
260		# i.e. output columns
261		#
262	1	annotations = pd.DataFrame()
263	1	for col in config.output.values():
264	1	res = pd.DataFrame(judgments[col].apply(lambda x: \
265		pd.Series(list(x.keys())).value_counts()).sum(), columns=[col])
266	1	annotations = pd.concat([annotations, res], axis=0)
267
268		#
269		# aggregate job
270		#
271	1	job = Job.aggregate(units, judgments, config)
272
273		# Clean up judgments
274		# remove input columns from judgments
275	1	output_cols = [col for col in judgments.columns.values \
276		if col.startswith('output') or col.startswith('metric')]
277	1	judgments = judgments[output_cols + list(platform.values()) + ['duration', 'job']]
278
279		# set judgment id as index
280	1	judgments.set_index('judgment', inplace=True)
281
282		# add missing vector values if closed task
283	1	for col in config.output.values():
284	1	try:
285		# openended = config.open_ended_task
286	1	for idx in list(units.index):
287	1	for relation in config.annotation_vector:
288	1	if relation not in units[col][idx]:
289	1	units[col][idx].update({relation : 0})
290		except AttributeError:
291		continue
292
293	1	return {
294		'jobs' : job,
295		'units' : units,
296		'workers' : workers,
297		'judgments' : judgments,
298		'annotations' : annotations,
299		}, config
300
301
302	1	def get_platform(dframe):
303		""" Get the crowdsourcing platform this file originates to """
304
305	1	if dframe.columns.values[0] == '_unit_id':
306		# CrowdFlower
307	1	return {
308		#'_platform' : 'cf',
309		'_id' : 'judgment',
310		'_unit_id' : 'unit',
311		'_worker_id' : 'worker',
312		'_started_at' : 'started',
313		'_created_at' : 'submitted'
314		}
315	1	elif dframe.columns.values[0] == 'HITId':
316		# Mturk
317		return {
318		#'id' : 'amt',
319		'AssignmentId' : 'judgment',
320		'HITId' : 'unit',
321		'WorkerId' : 'worker',
322		'AcceptTime' : 'started',
323		'SubmitTime' : 'submitted'
324		}
325	1	return False
326
327	1	def configure_amt_columns(dframe, config):
328		""" Configures AMT input and output columns. """
329		config.input = {}
330		config.output = {}
331
332		if config.inputColumns:
333		config.input = {c: 'input.'+c.replace('Input.', '') \
334		for c in dframe.columns.values if c in config.inputColumns}
335		else:
336		config.input = {c: 'input.'+c.replace('Input.', '') \
337		for c in dframe.columns.values if c.startswith('Input.')}
338
339		# if config is specified, use those columns
340		if config.outputColumns:
341		config.output = {c: 'output.'+c.replace('Answer.', '') \
342		for c in dframe.columns.values if c in config.outputColumns}
343		else:
344		config.output = {c: 'output.'+c.replace('Answer.', '') \
345		for c in dframe.columns.values if c.startswith('Answer.')}
346		return config.input, config.output
347
348	1	def configure_platform_columns(dframe, config):
349		""" Configures FigureEight and custom platforms input and output columns. """
350	1	config.input = {}
351	1	config.output = {}
352
353	1	if config.inputColumns:
354	1	config.input = {c: 'input.'+c for c in dframe.columns.values \
355		if c in config.inputColumns}
356	1	if config.outputColumns:
357	1	config.output = {c: 'output.'+c for c in dframe.columns.values \
358		if c in config.outputColumns}
359	1	return config.input, config.output
360
361
362	1	def get_column_types(dframe, config):
363		""" return input and output columns """
364		# returns a list of columns that contain are input content
365	1	config.input = {}
366	1	config.output = {}
367
368		# get a dict of the columns with input content and the columns with output judgments
369		# each entry matches [original column name]:[safestring column name]
370	1	if dframe.columns.values[0] == 'HITId':
371		# Mturk
372		# if config is specified, use those columns
373		config.input, config.output = configure_amt_columns(dframe, config)
374
375		return config
376
377	1	elif dframe.columns.values[0] == '_unit_id':
378
379		# if a config is specified, use those columns
380	1	config.input, config.output = configure_platform_columns(dframe, config)
381		# if there is a config for both input and output columns, we can return those
382	1	if config.inputColumns and config.outputColumns:
383	1	return config
384
385		# try to identify the input and output columns
386		# this is the case if all the values in the column are identical
387		# this is not failsafe but should give decent results without settings
388		# it is best to make a settings.py file for a collection
389
390		units = dframe.groupby('_unit_id')
391		columns = [c for c in dframe.columns.values if c != 'clustering' and not c.startswith('_')
392		and not c.startswith('e_') and not c.endswith('_gold')
393		and not c.endswith('_reason') and not c.endswith('browser')]
394		for colname in columns:
395		try:
396		for _, unit in units:
397		unique = unit[colname].nunique()
398		if unique != 1 and unique != 0:
399		raise Found
400		if not config.inputColumns:
401		config.input[colname] = 'input.'+colname
402
403		except Found:
404		if not config.outputColumns:
405		config.output[colname] = 'output.'+colname
406
407		return config
408		else:
409		# unknown platform type
410
411		# if a config is specified, use those columns
412	1	config.input, config.output = configure_platform_columns(dframe, config)
413		# if there is a config for both input and output columns, we can return those
414	1	if config.inputColumns and config.outputColumns:
415		return config
416

CrowdTruth / CrowdTruth-core

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