def svr_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.
@list_observation_label, is a list containing dependent variable
labels.
'''
feature_count = None
list_dataset = []
list_observation_label = []
logger = Logger(__name__, 'error', 'error')
# convert xml file to python 'dict'
dataset = xmltodict.parse(raw_data)
# build 'list_dataset'
for observation in dataset['dataset']['observation']:
for key in observation:
if key == 'criterion':
observation_label = observation['criterion']
list_observation_label.append(observation[key])
elif key == 'predictor':
for predictor in observation[key]:
predictor_label = predictor['label']
predictor_value = predictor['value']
validate_value = Validator(predictor_value)
validate_value.validate_value()
list_error_value = validate_value.get_errors()
if list_error_value:
logger.log(list_error_value)
return None
else:
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(predictor_label),
'indep_variable_value':
predictor_value
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(observation['predictor'])
# save observation labels, and return
raw_data.close()
return {
'dataset': list_dataset,
'observation_labels': list_observation_label,
'feature_count': feature_count
}
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
def svm_csv2dict(raw_data):
'''
This method converts the supplied csv file-object, intended for an svm
model, 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.
@list_observation_label, is a list containing dependent variable labels.
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(',')
'''
feature_count = None
list_dataset = []
list_observation_label = []
list_feature_label = []
logger = Logger(__name__, 'error', 'error')
# open temporary 'csvfile' reader object
dataset_reader = csv.reader(
raw_data,
delimiter=' ',
quotechar='|'
)
# iterate first row of csvfile
for row in islice(dataset_reader, 0, 1):
# iterate each column in a given row
row_indep_label = row[0].split(',')
for value in islice(row_indep_label, 1, None):
list_feature_label.append(str(value))
# iterate all rows of csvfile
for dep_index, row in enumerate(islice(dataset_reader, 0, None)):
# iterate first column of each row (except first)
row_dep_label = row[0].split(',')
for value in row_dep_label[:1]:
list_observation_label.append(str(value))
# generalized feature count in an observation
row_indep_variable = row[0].split(',')
if not feature_count:
feature_count = len(row_indep_variable) - 1
# iterate each column in a given row
for indep_index, value in enumerate(
islice(row_indep_variable, 1, None)
):
try:
validate = Validator(value)
validate.validate_value()
list_error = validate.get_errors()
if list_error:
logger.log(list_error)
return None
else:
value = float(value)
except Exception as error:
logger.log(error)
return False
list_dataset.append({
'dep_variable_label': list_observation_label[dep_index],
'indep_variable_label': list_feature_label[indep_index],
'indep_variable_value': value
})
# close file, save observation labels, and return
raw_data.close()
return {
'dataset': list_dataset,
'observation_labels': list_observation_label,
'feature_count': feature_count
}
def svm_json2dict(raw_data, is_json):
'''
This method converts the supplied json 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.
@is_json, flag indicating 'raw_data' is a json string.
@observation_labels, is a list containing dependent variable labels.
'''
# local variables
feature_count = None
list_dataset = []
observation_labels = []
logger = Logger(__name__, 'error', 'error')
# web-interface
if not is_json:
dataset = json.load(raw_data)
for observation_label in dataset:
# variables
observations = dataset[observation_label]
# dependent variable with single observation
if type(observations) == dict:
for feature_label, feature_value in observations.items():
# validation
validate_fvalue = Validator(feature_value)
validate_fvalue.validate_value()
if validate_fvalue.get_errors():
logger.log(validate_fvalue.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(feature_label),
'indep_variable_value':
feature_value
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(observations)
# dependent variable with multiple observations
elif type(observations) == list:
for observation in observations:
for feature_label, feature_value in observation.items():
# validation
validate_fvalue = Validator(feature_value)
validate_fvalue.validate_value()
if validate_fvalue.get_errors():
logger.log(validate_fvalue.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(feature_label),
'indep_variable_value':
feature_value
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(observation)
# list of observation label
observation_labels.append(observation_label)
# programmatic-interface
else:
dataset = raw_data
observation_label = raw_data[0]
# list of observation label
observation_labels.append(observation_label)
# dependent variable with single observation
if type(raw_data[1]) == dict:
for label, feature in raw_data[1].items():
# validation
validate_fvalue = Validator(feature)
validate_fvalue.validate_value()
if validate_fvalue.get_errors():
logger.log(validate_fvalue.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(label),
'indep_variable_value':
feature
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(raw_data[1])
# dependent variable with multiple observations
if type(raw_data[1]) == list:
for feature_set in raw_data[1]:
for feature_label, feature_value in feature_set.items():
# validation
validate_fvalue = Validator(feature_value)
validate_fvalue.validate_value()
if validate_fvalue.get_errors():
logger.log(validate_fvalue.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(feature_label),
'indep_variable_value':
feature_value
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(feature_set)
# close file
if not is_json:
raw_data.close()
# save observation labels, and return
return {
'dataset': list_dataset,
'observation_labels': observation_labels,
'feature_count': feature_count
}
def svr_json2dict(raw_data, is_json):
'''
This method converts the supplied json 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.
@is_json, flag indicating 'raw_data' is a json string.
@observation_labels, is a list containing dependent variable labels.
'''
# local variables
feature_count = None
list_dataset = []
observation_labels = []
logger = Logger(__name__, 'error', 'error')
# web-interface
if not is_json:
dataset = json.load(raw_data)
for criterion, predictors in dataset.items():
observation_label = criterion
# list of observation label
observation_labels.append(criterion)
# criterion with single observation
if type(predictors) == dict:
for label, predictor in predictors.items():
# validation (part 1)
validate_predictor = Validator(predictor)
validate_predictor.validate_value()
if validate_predictor.get_errors():
logger.log(validate_predictor.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(label),
'indep_variable_value':
predictor
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(predictors)
# criterion with multiple observation
if type(predictors) == list:
for criterion in predictors:
for label, predictor in criterion.items():
# validation (part 1)
validate_predictor = Validator(predictor)
validate_predictor.validate_value()
if validate_predictor.get_errors():
logger.log(validate_predictor.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(observation_label),
'indep_variable_label':
str(label),
'indep_variable_value':
predictor
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(criterion.items())
# programmatic-interface
else:
dataset = raw_data
for criterion, predictors in dataset.items():
# list of observation label
observation_labels.append(criterion)
# criterion with single observation
if type(predictors) == dict:
for label, predictor in predictors.items():
# validation (part 1)
validate_predictor = Validator(predictor)
validate_predictor.validate_value()
if validate_predictor.get_errors():
logger.log(validate_predictor.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(criterion),
'indep_variable_label':
str(label),
'indep_variable_value':
predictor
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(predictors.items())
# criterion with multiple observation
if type(predictors) == list:
for single_predictors in predictors:
for label, predictor in single_predictors.items():
# validation (part 1)
validate_predictor = Validator(predictor)
validate_predictor.validate_value()
if validate_predictor.get_errors():
logger.log(validate_predictor.get_errors())
else:
# restructured data
list_dataset.append({
'dep_variable_label':
str(criterion),
'indep_variable_label':
str(label),
'indep_variable_value':
predictor
})
# generalized feature count in an observation
if not feature_count:
feature_count = len(single_predictors.items())
# close file
if not is_json:
raw_data.close()
# save observation labels, and return
return {
'dataset': list_dataset,
'observation_labels': observation_labels,
'feature_count': feature_count
}