def csv2dict(raw_data):
'''
This method converts the supplied csv file-object to a python dictionary.
@raw_data, generally a file (or json string) containing the raw dataset(s),
to be used when computing a corresponding model. If this argument is a
file, it needs to be closed.
Note: we use the 'Universal Newline Support' with the 'U' parameter when
opening 'raw_data'. This allows newlines to be understood regardless,
if the newline character was created in osx, windows, or linux.
Note: since 'row' is a list, with one comma-delimited string element, the
following line is required in this method:
row = row[0].split(',')
'''
# local variables:
dataset = []
validate = Validator()
# local variable: open temporary 'csvfile' reader object
dataset_reader = csv.reader(
raw_data,
delimiter=' ',
quotechar='|'
)
# first row of csvfile: get all columns, except first
for row in islice(dataset_reader, 0, 1):
indep_labels_list = row[0].split(',')[1:]
# all rows of csvfile: except first row
for dep_index, row in enumerate(islice(dataset_reader, 0, None)):
row_arr = row[0].split(',')
features_list = row_arr[1:]
# merge lists into dict if each independent variable validates
if all(validate.validate_value(item) for item in features_list):
features_dict = {k: v for k, v in zip(indep_labels_list, features_list)}
error = None
else:
error = 'csv conversion failed: ' + validate.get_error()
observation = {
'dependent-variable': row_arr[:1][0],
'independent-variables': [features_dict],
'error': error
}
dataset.append(observation)
# close file, return dataset
raw_data.close()
return dataset
def csv2dict(raw_data):
'''
This method converts the supplied csv file-object to a python dictionary.
@raw_data, generally a file (or json string) containing the raw dataset(s),
to be used when computing a corresponding model. If this argument is a
file, it needs to be closed.
Note: we use the 'Universal Newline Support' with the 'U' parameter when
opening 'raw_data'. This allows newlines to be understood regardless,
if the newline character was created in osx, windows, or linux.
Note: since 'row' is a list, with one comma-delimited string element, the
following line is required in this method:
row = row[0].split(',')
'''
# local variables:
dataset = []
validate = Validator()
# local variable: open temporary 'csvfile' reader object
dataset_reader = csv.reader(raw_data, delimiter=' ', quotechar='|')
# first row of csvfile: get all columns, except first
for row in islice(dataset_reader, 0, 1):
indep_labels_list = row[0].split(',')[1:]
# all rows of csvfile: except first row
for dep_index, row in enumerate(islice(dataset_reader, 0, None)):
row_arr = row[0].split(',')
features_list = row_arr[1:]
# merge lists into dict if each independent variable validates
if all(validate.validate_value(item) for item in features_list):
features_dict = {
k: v
for k, v in zip(indep_labels_list, features_list)
}
error = None
else:
error = 'csv conversion failed: ' + validate.get_error()
observation = {
'dependent-variable': row_arr[:1][0],
'independent-variables': [features_dict],
'error': error
}
dataset.append(observation)
# close file, return dataset
raw_data.close()
return dataset
def xml2dict(raw_data):
'''
This method converts the supplied xml file-object to a python dictionary.
@raw_data, generally a file (or json string) containing the raw dataset(s),
to be used when computing a corresponding model. If this argument is a
file, it needs to be closed.
'''
# local variables
dataset = []
validate = Validator()
# local variable: open temporary 'xmltodict' object
dataset_reader = xmltodict.parse(raw_data)
# build dataset
for observation in dataset_reader['dataset']['observation']:
features_dict = {}
dependent_variable = observation['dependent-variable']
# define features set if independent variable validates
for feature in observation['independent-variable']:
if validate.validate_value(feature['value']):
features_dict[feature['label']] = feature['value']
error = None
else:
error = 'xml conversion failed: ' + validate.get_error()
adjusted = {
'dependent-variable': dependent_variable,
'independent-variables': [features_dict],
'error': error
}
dataset.append(adjusted)
# save observation labels, and return
raw_data.close()
return dataset
def xml2dict(raw_data):
'''
This method converts the supplied xml file-object to a python dictionary.
@raw_data, generally a file (or json string) containing the raw dataset(s),
to be used when computing a corresponding model. If this argument is a
file, it needs to be closed.
'''
# local variables
dataset = []
validate = Validator()
# local variable: open temporary 'xmltodict' object
dataset_reader = xmltodict.parse(raw_data)
# build dataset
for observation in dataset_reader['dataset']['observation']:
features_dict = {}
dependent_variable = observation['dependent-variable']
# define features set if independent variable validates
for feature in observation['independent-variable']:
if validate.validate_value(feature['value']):
features_dict[feature['label']] = feature['value']
error = None
else:
error = 'xml conversion failed: ' + validate.get_error()
adjusted = {
'dependent-variable': dependent_variable,
'independent-variables': [features_dict],
'error': error
}
dataset.append(adjusted)
# save observation labels, and return
raw_data.close()
return dataset