crowdtruth.load.remove_single_judgment_units() - Code Metrics - CrowdTruth/CrowdTruth-core - Measure and Improve Code Quality continuously with Scrutinizer

crowdtruth.load.remove_single_judgment_units() A
last analyzed 2019-05-10 09:08 UTC

↳ Parent: crowdtruth.load

Complexity

Conditions

Size

Total Lines	14
Code Lines	10

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	7
CRAP Score	4.432

Importance

Changes

Metric	Value
eloc	10
dl	0
loc	14
rs	9.9
c	0
b	0
f	0
ccs	7
cts	10
cp	0.7
cc	4
nop	1
crap	4.432

#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 dateparser

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
from crowdtruth.crowd_platform import *




# 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

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, results, config):
    """ Creates a list of files to be processed. """
    files = []
    directory = ""
    if 'data_frame' in kwargs:
        res, config = process_file(kwargs['data_frame'], config)
        for value in res:
            results[value].append(res[value])
        return results
    elif '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() and directory != "":
            logging.info("Processing " + file)
            file = directory + "/" + file
        if  config.csv_file_separator:
            judgments = pd.read_csv(file, sep=config.csv_file_separator)#, encoding=result['encoding'])
        else:
            judgments = pd.read_csv(file)#, encoding=result['encoding'])
        res, config = process_file(judgments, config, filename=file)
        for value in res:
            results[value].append(res[value])

    return results

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']

    results = list_files(kwargs, results, config)
    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):
    """ handle rows where the worker did not give an answer (AMT issue) """

    # if config keeps empty rows, add NONE placehoder token
    if not config.remove_empty_rows:
        for col in config.outputColumns:
            for idx in range(len(judgments[col])):
                if (pd.isnull(judgments[col][idx]) or
                        judgments[col][idx] is None or
                        judgments[col][idx] == '' or
                        judgments[col][idx] == 'nan'):
                    logging.info('judgments[' + str(idx) + '][' + col + '] is None')
                    judgments.at[idx, col] = config.none_token
    # remove empty rows
    else:
        empty_rows = set()
        for col in config.outputColumns:
            empty_rows = empty_rows.union(judgments[pd.isnull(judgments[col]) == True].index)
            empty_rows = empty_rows.union(judgments[judgments[col] == 'nan'].index)
        for col in config.outputColumns:
            judgments = judgments[pd.isnull(judgments[col]) == False]
            judgments = judgments[judgments[col] != 'nan']
        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 add_missing_values(config, units):
    """ Adds missing vector values if is a 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})
            return units
        except AttributeError:
            continue
def aggregate_annotations(config, judgments):
    """ Aggregates annotations and adds judgments stats. """
    annotations = pd.DataFrame()
    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))
        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)
    return annotations, judgments

def process_file(judgments, config, filename=""):
    """ Processes input files with the given configuration """

    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)

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

    # handle empty rows
    judgments = remove_empty_rows(config, 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())]

    if filename != "":
        judgments['job'] = filename.split('.csv')[0]
    else:
        judgments['job'] = "pd.DataFrame"

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

    # remove units with just 1 judgment
    judgments = remove_single_judgment_units(judgments)

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

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

    #
    # aggregate annotations
    # i.e. output columns
    #
    annotations, judgments = aggregate_annotations(config, judgments)

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

    #
    # 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
    units = add_missing_values(config, units)

    return {
        'jobs' : job,
        'units' : units,
        'workers' : workers,
        'judgments' : judgments,
        'annotations' : annotations,
        }, 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	1	from collections import Counter, OrderedDict
12	1	import dateparser
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	1	from crowdtruth.crowd_platform import *
21
22
23
24
25		# create an ordered counter so that we can maintain
26		# the position of tags in the order they were annotated
27	1	class OrderedCounter(Counter, OrderedDict):
28		""" Instantiates an ordered counter. """
29	1	pass
30
31	1	def create_ordered_counter(ordered_counter, annotation_vector):
32		""" Instantiates an ordered counter from a given annotation vector. """
33	1	for relation in annotation_vector:
34	1	if relation not in ordered_counter:
35	1	ordered_counter.update({relation: 0})
36	1	return ordered_counter
37
38	1	def validate_timestamp_field(date_string, date_format):
39		""" Validates the time columns (started time and submitted time) in input files. """
40
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		""" List the files in the directry given as argument. """
49	1	filelist = []
50
51		# go through all files in this folder
52	1	for file in os.listdir(directory):
53		# if it is a folder scan it
54	1	if os.path.isdir(directory+'/'+file):
55		sublist = get_file_list(directory+'/'+file)
56		sublist_length = len(sublist)
57		if sublist_length:
58		filelist.append(sublist)
59
60		# if it is a csv file open it
61	1	if file.endswith('.csv') and file != 'groundtruth.csv':
62	1	filelist.append(file)
63	1	return filelist
64
65	1	def list_files(kwargs, results, config):
66		""" Creates a list of files to be processed. """
67	1	files = []
68	1	directory = ""
69	1	if 'data_frame' in kwargs:
70	1	res, config = process_file(kwargs['data_frame'], config)
71	1	for value in res:
72	1	results[value].append(res[value])
73	1	return results
74	1	elif 'file' in kwargs and kwargs['file'].endswith('.csv'):
75	1	files = [kwargs['file']]
76	1	elif 'directory' in kwargs:
77	1	directory = kwargs['directory']
78	1	files = get_file_list(directory)
79	1	logging.info('Found ' + str(len(files)) + ' files')
80		else:
81		raise ValueError('No input was provided')
82
83	1	for file in files:
84	1	if 'directory' in locals() and directory != "":
85	1	logging.info("Processing " + file)
86	1	file = directory + "/" + file
87	1	if config.csv_file_separator:
88	1	judgments = pd.read_csv(file, sep=config.csv_file_separator)#, encoding=result['encoding'])
89		else:
90		judgments = pd.read_csv(file)#, encoding=result['encoding'])
91	1	res, config = process_file(judgments, config, filename=file)
92	1	for value in res:
93	1	results[value].append(res[value])
94
95	1	return results
96
97	1	def load(**kwargs):
98		""" Loads the input files. """
99
100		# placeholder for aggregated results
101	1	results = {
102		'jobs' : [],
103		'units' : [],
104		'workers' : [],
105		'judgments' : [],
106		'annotations' : []
107		}
108
109	1	if 'config' not in kwargs:
110		config = DefaultConfig()
111		else:
112	1	logging.info('Config loaded')
113	1	config = kwargs['config']
114
115	1	results = list_files(kwargs, results, config)
116	1	for value in results:
117	1	results[value] = pd.concat(results[value])
118
119
120		# workers and annotations can appear across jobs, so we have to aggregate those extra
121	1	results['workers'] = results['workers'].groupby(results['workers'].index).agg({
122		'unit' : 'sum',
123		'judgment' : 'sum',
124		'job' : 'count',
125		'duration' : 'mean'
126		})
127
128		# aggregate annotations
129	1	results['annotations'] = results['annotations'].groupby(results['annotations'].index).sum()
130
131	1	return results, config
132
133	1	def remove_empty_rows(config, judgments):
134		""" handle rows where the worker did not give an answer (AMT issue) """
135
136		# if config keeps empty rows, add NONE placehoder token
137	1	if not config.remove_empty_rows:
138	1	for col in config.outputColumns:
139	1	for idx in range(len(judgments[col])):
140	1	if (pd.isnull(judgments[col][idx]) or
141		judgments[col][idx] is None or
142		judgments[col][idx] == '' or
143		judgments[col][idx] == 'nan'):
144	1	logging.info('judgments[' + str(idx) + '][' + col + '] is None')
145	1	judgments.at[idx, col] = config.none_token
146		# remove empty rows
147		else:
148	1	empty_rows = set()
149	1	for col in config.outputColumns:
150	1	empty_rows = empty_rows.union(judgments[pd.isnull(judgments[col]) == True].index)
151	1	empty_rows = empty_rows.union(judgments[judgments[col] == 'nan'].index)
152	1	for col in config.outputColumns:
153	1	judgments = judgments[pd.isnull(judgments[col]) == False]
154	1	judgments = judgments[judgments[col] != 'nan']
155	1	judgments = judgments.reset_index(drop=True)
156	1	count_empty_rows = len(empty_rows)
157	1	if count_empty_rows > 0:
158	1	if count_empty_rows == 1:
159		logging.warning(str(count_empty_rows) + " row with incomplete information in "
160		"output columns was removed.")
161		else:
162	1	logging.warning(str(count_empty_rows) + " rows with incomplete information in "
163		"output columns were removed.")
164	1	return judgments
165
166	1	def remove_single_judgment_units(judgments):
167		""" remove units with just 1 judgment """
168	1	units_1work = judgments.groupby('unit').filter(lambda x: len(x) == 1)["unit"]
169	1	judgments = judgments[~judgments['unit'].isin(units_1work)]
170	1	judgments = judgments.reset_index(drop=True)
171	1	no_units_1work = len(units_1work)
172	1	if no_units_1work > 0:
173		if no_units_1work == 1:
174		logging.warning(str(no_units_1work) + " Media Unit that was annotated by only"
175		" 1 Worker was omitted, since agreement cannot be calculated.")
176		else:
177		logging.warning(str(no_units_1work) + " Media Units that were annotated by only"
178		" 1 Worker were omitted, since agreement cannot be calculated.")
179	1	return judgments
180
181	1	def make_output_cols_safe_keys(config, judgments):
182		""" make output values safe keys """
183	1	for col in config.output.values():
184	1	if isinstance(judgments[col].iloc[0], dict):
185		logging.info("Values stored as dictionary")
186		if config.open_ended_task:
187		judgments[col] = judgments[col].apply(lambda x: OrderedCounter(x))
188		else:
189		judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
190		OrderedCounter(x), config.annotation_vector))
191		else:
192	1	logging.info("Values not stored as dictionary")
193	1	if config.open_ended_task:
194	1	judgments[col] = judgments[col].apply(lambda x: OrderedCounter( \
195		x.split(config.annotation_separator)))
196		else:
197	1	judgments[col] = judgments[col].apply(lambda x: create_ordered_counter( \
198		OrderedCounter(x.split(config.annotation_separator)), \
199		config.annotation_vector))
200	1	return judgments
201
202
203	1	def add_missing_values(config, units):
204		""" Adds missing vector values if is a closed task """
205	1	for col in config.output.values():
206	1	try:
207		# openended = config.open_ended_task
208	1	for idx in list(units.index):
209	1	for relation in config.annotation_vector:
210	1	if relation not in units[col][idx]:
211	1	units[col][idx].update({relation : 0})
212	1	return units
213		except AttributeError:
214		continue
215	1	def aggregate_annotations(config, judgments):
216		""" Aggregates annotations and adds judgments stats. """
217	1	annotations = pd.DataFrame()
218	1	for col in config.output.values():
219	1	judgments[col+'.count'] = judgments[col].apply(lambda x: sum(x.values()))
220	1	judgments[col+'.unique'] = judgments[col].apply(lambda x: len(x))
221	1	res = pd.DataFrame(judgments[col].apply(lambda x: \
222		pd.Series(list(x.keys())).value_counts()).sum(), columns=[col])
223	1	annotations = pd.concat([annotations, res], axis=0)
224	1	return annotations, judgments
225
226	1	def process_file(judgments, config, filename=""):
227		""" Processes input files with the given configuration """
228
229	1	platform = get_platform(judgments)
230
231	1	if platform is False:
232	1	logging.info("Custom crowdsourcing platform!")
233	1	no_of_columns = len(config.customPlatformColumns)
234	1	if no_of_columns != 5:
235		logging.warning("The following column names are required: judgment id, "
236		"unit id, worker id, start time, submit time")
237		raise ValueError('No custom platform configuration was provided')
238		else:
239
240	1	platform = {
241		#'id' : 'custom',
242		config.customPlatformColumns[0] : 'judgment',
243		config.customPlatformColumns[1] : 'unit',
244		config.customPlatformColumns[2] : 'worker',
245		config.customPlatformColumns[3] : 'started',
246		config.customPlatformColumns[4] : 'submitted'
247		}
248
249
250		# we must establish which fields were part of the input data and which are output judgments
251		# if there is a config, check if there is a definition of which fields to use
252		#config = []
253		# else use the default and select them automatically
254	1	config = get_column_types(judgments, config)
255
256		# allow customization of the judgments
257	1	judgments = config.processJudgments(judgments)
258
259		# handle empty rows
260	1	judgments = remove_empty_rows(config, judgments)
261
262		# update the config after the preprocessing of judgments
263	1	config = get_column_types(judgments, config)
264
265	1	all_columns = dict(list(config.input.items()) + list(config.output.items()) \
266		+ list(platform.items()))
267		# allColumns = dict(config.input.items() \| config.output.items() \| platform.items())
268	1	judgments = judgments.rename(columns=all_columns)
269
270		# remove columns we don't care about
271	1	judgments = judgments[list(all_columns.values())]
272
273	1	if filename != "":
274	1	judgments['job'] = filename.split('.csv')[0]
275		else:
276	1	judgments['job'] = "pd.DataFrame"
277
278		# make output values safe keys
279	1	judgments = make_output_cols_safe_keys(config, judgments)
280
281		# remove units with just 1 judgment
282	1	judgments = remove_single_judgment_units(judgments)
283
284	1	judgments['started'] = judgments['started'].apply(lambda x: dateparser.parse(str(x)))
285	1	judgments['submitted'] = judgments['submitted'].apply(lambda x: dateparser.parse(str(x)))
286	1	judgments['duration'] = judgments.apply(lambda row: (row['submitted'] - row['started']).seconds,
287		axis=1)
288
289		#
290		# aggregate units
291		#
292	1	units = Unit.aggregate(judgments, config)
293
294		#
295		# aggregate annotations
296		# i.e. output columns
297		#
298	1	annotations, judgments = aggregate_annotations(config, judgments)
299
300		#
301		# aggregate workers
302		#
303	1	workers = Worker.aggregate(judgments, config)
304
305		#
306		# aggregate job
307		#
308	1	job = Job.aggregate(units, judgments, config)
309
310		# Clean up judgments
311		# remove input columns from judgments
312	1	output_cols = [col for col in judgments.columns.values \
313		if col.startswith('output') or col.startswith('metric')]
314	1	judgments = judgments[output_cols + list(platform.values()) + ['duration', 'job']]
315
316		# set judgment id as index
317	1	judgments.set_index('judgment', inplace=True)
318
319		# add missing vector values if closed task
320	1	units = add_missing_values(config, units)
321
322	1	return {
323		'jobs' : job,
324		'units' : units,
325		'workers' : workers,
326		'judgments' : judgments,
327		'annotations' : annotations,
328		}, config
329

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crowdtruth.load.remove_single_judgment_units() A last analyzed 2019-05-10 09:08 UTC

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crowdtruth.load.remove_single_judgment_units() A
last analyzed 2019-05-10 09:08 UTC