def open_csv(path,
delimiter=',',
header=True,
col_names=None,
parse_datetimes=[]):
"""Creates a structured array from a local .csv file
Parameters
----------
path : str
path of the csv file
delimiter : str
Character used to delimit csv fields
header : bool
If True, assumes the first line of the csv has column names
col_names : list of str or None
If header is False, this list will be used for column names
parse_datetimes : list of col names
Columns that should be interpreted as datetimes
Returns
-------
numpy.ndarray
structured array corresponding to the csv
If header is False and col_names is None, diogenes will assign
arbitrary column names
"""
with open(path, 'rU') as fin:
return open_csv_as_sa(fin,
delimiter,
header,
col_names,
parse_datetimes=parse_datetimes)
def open_csv_url(url, delimiter=',', header=True, col_names=None, parse_datetimes=[]):
"""Creates a structured array from a url
Parameters
----------
url : str
url of the csv file
delimiter : str
Character used to delimit csv fields
header : bool
If True, assumes the first line of the csv has column names
col_names : list of str or None
If header is False, this list will be used for column names
parse_datetimes : list of col names
Columns that should be interpreted as datetimes
Returns
-------
numpy.ndarray
structured array corresponding to the csv
If header is False and col_names is None, diogenes will assign
arbitrary column names
"""
fin = urllib2.urlopen(url)
sa = open_csv_as_sa(fin, delimiter, header, col_names,
parse_datetimes=parse_datetimes)
fin.close()
return sa
def open_csv(path, delimiter=',', header=True, col_names=None, parse_datetimes=[]):
"""Creates a structured array from a local .csv file
Parameters
----------
path : str
path of the csv file
delimiter : str
Character used to delimit csv fields
header : bool
If True, assumes the first line of the csv has column names
col_names : list of str or None
If header is False, this list will be used for column names
parse_datetimes : list of col names
Columns that should be interpreted as datetimes
Returns
-------
numpy.ndarray
structured array corresponding to the csv
If header is False and col_names is None, diogenes will assign
arbitrary column names
"""
with open(path, 'rU') as fin:
return open_csv_as_sa(fin, delimiter, header, col_names,
parse_datetimes=parse_datetimes)
def run_csv(fin, uid_feature, label_feature, clfs=DBG_std_clfs):
""" Turn a CSV into an Experiment then turn the Experiment into models"""
sa = open_csv_as_sa(fin)
labels = sa[label_feature]
M = remove_cols(sa, label_feature)
exp = Experiment(M, labels, clfs=clfs)
register_exp(exp, uid_feature)
def run_csv(fin, uid_feature, label_feature, clfs=DBG_std_clfs):
""" Turn a CSV into an Experiment then turn the Experiment into models"""
sa = open_csv_as_sa(fin)
labels = sa[label_feature]
M = remove_cols(sa, label_feature)
exp = Experiment(M, labels, clfs=clfs)
register_exp(exp, uid_feature)
def run_csv(fin, uid_feature, label_feature):
sa = open_csv_as_sa(fin)
labels = sa[label_feature]
M = remove_cols(sa, label_feature)
exp = Experiment(M, labels, clfs=DBG_std_clfs)
exp.run()
last_experiments[current_user.id] = exp
clear_models(current_user.id)
for trial in exp.trials:
for subset in trial.runs:
for run in subset:
register_model(current_user.id, run.clf, dt.now(),
run.M[run.train_indices],
run.M[run.test_indices],
run.labels[run.train_indices],
run.labels[run.test_indices], run.col_names,
uid_feature)
def run_csv(fin, uid_feature, label_feature):
sa = open_csv_as_sa(fin)
labels = sa[label_feature]
M = remove_cols(sa, label_feature)
exp = Experiment(M, labels, clfs=DBG_std_clfs)
exp.run()
last_experiments[current_user.id] = exp
clear_models(current_user.id)
for trial in exp.trials:
for subset in trial.runs:
for run in subset:
register_model(
current_user.id,
run.clf,
dt.now(),
run.M[run.train_indices],
run.M[run.test_indices],
run.labels[run.train_indices],
run.labels[run.test_indices],
run.col_names,
uid_feature)
def open_csv_url(url,
delimiter=',',
header=True,
col_names=None,
parse_datetimes=[]):
"""Creates a structured array from a url
Parameters
----------
url : str
url of the csv file
delimiter : str
Character used to delimit csv fields
header : bool
If True, assumes the first line of the csv has column names
col_names : list of str or None
If header is False, this list will be used for column names
parse_datetimes : list of col names
Columns that should be interpreted as datetimes
Returns
-------
numpy.ndarray
structured array corresponding to the csv
If header is False and col_names is None, diogenes will assign
arbitrary column names
"""
fin = urllib2.urlopen(url)
sa = open_csv_as_sa(fin,
delimiter,
header,
col_names,
parse_datetimes=parse_datetimes)
fin.close()
return sa
