def training_set(events_file: str, no_event_time_shift: int, table_name: str,
directory):
logging.info('start')
# stiahnutie dat
con = ConnectionUtil.create_con()
storage = Storage(events_file, no_event_time_shift, table_name)
d = storage.load_data(con, 0, 0, 'co2_in_ppm')
logging.info('downloaded events: %d' % len(d))
# aplikovanie filtrov na eventy
filtered = FilterUtil.only_valid_events(d)
# for travis
no_ev_records = no_events_records
if ConnectionUtil.is_testable_system():
filtered = filtered[:ConnectionUtil.MAX_TESTABLE_EVENTS]
no_ev_records = no_events_records[:ConnectionUtil.MAX_TESTABLE_EVENTS]
logging.info('events after applying the filter: %d' % len(filtered))
# selector pre data
row_selector = CachedDiffRowWithIntervalSelector(con, table_name, 0, 0)
interval_selector = None
# trenovacia mnozina
logging.info('start computing of training set')
training, tr_events = AttributeUtil.cached_training_data(
con, table_name, filtered, func, row_selector, interval_selector,
'open', '{0}/training_cached.csv'.format(directory))
count = len(training)
logging.info('training set contains %d events (%d records)' %
(count / 2, count))
GraphUtil.gen_duration_histogram(tr_events, 'save', ['png'],
'Histogram dlzok vetrania',
[x for x in range(5, 60, 5)], 1)
training2 = AttributeUtil.additional_training_set(con, table_name,
no_ev_records, func,
row_selector,
interval_selector)
count2 = len(training2)
logging.info('additional training set contains %d records' % count2)
logging.info('end computing of training set')
logging.info('start preparing file of training set')
balanced = AttributeUtil.balance_set(training, training2)
CSVUtil.create_csv_file(balanced, '{0}/training.csv'.format(directory))
logging.info('end preparing file of training set')
def training_set(events_file: str, no_event_time_shift: int, table_name: str):
logging.info('start')
# download data
con = ConnectionUtil.create_con()
storage = Storage(events_file, no_event_time_shift, table_name)
d = storage.load_data(con, 0, 0, 'rh_in2_specific_g_kg')
logging.info('downloaded events: %d' % len(d))
# apply filters to data
filtered = FilterUtil.only_valid_events(d)
# filtered = FilterUtil.temperature_diff(filtered, 5, 100)
# filtered = FilterUtil.temperature_out_max(filtered, 15)
# filtered = FilterUtil.humidity(filtered, 6, 1.6, 100)
# for travis
no_ev_records = no_events_records
if ConnectionUtil.is_testable_system():
filtered = filtered[:ConnectionUtil.MAX_TESTABLE_EVENTS]
no_ev_records = no_events_records[:ConnectionUtil.MAX_TESTABLE_EVENTS]
logging.info('events after applying the filter: %d' % len(filtered))
row_selector = CachedDiffRowWithIntervalSelector(con, table_name, 0, 0)
interval_selector = SimpleIntervalSelector(con, table_name)
logging.info('start computing of training set')
training, tr_events = AttributeUtil.training_data(con, table_name,
filtered, func,
row_selector,
interval_selector,
'open')
count = len(training)
logging.info('training set contains %d events (%d records)' %
(count / 2, count))
training2 = AttributeUtil.additional_training_set(con, table_name,
no_ev_records, func,
row_selector,
interval_selector)
count2 = len(training2)
logging.info('additional training set contains %d records' % count2)
logging.info('end computing of training set')
logging.info('start preparing file of training set')
balanced = AttributeUtil.balance_set(training, training2)
CSVUtil.create_csv_file(balanced, 'training.csv')
logging.info('end preparing file of training set')
def testing_set(table_name: str, start, end, filename):
logging.info('start')
con = ConnectionUtil.create_con()
logging.info('start computing of testing set')
length = AttributeUtil.testing_data_with_write(con, table_name, start, end, 30, func,
None, None, 'open', filename)
logging.info('testing set contains %d records' % length)
logging.info('end computing of testing set')
logging.info('end')
def training_set(events_file: str, no_event_time_shift: int, table_name: str):
logging.info('start')
# stiahnutie dat
con = ConnectionUtil.create_con()
storage = Storage(events_file, no_event_time_shift, table_name)
d = storage.load_data(con, 0, 0, 'co2_in_ppm')
logging.info('downloaded events: %d' % len(d))
# aplikovanie filtrov na eventy
filtered = FilterUtil.only_valid_events(d)
# for travis
if ConnectionUtil.is_testable_system():
filtered = filtered[:ConnectionUtil.MAX_TESTABLE_EVENTS]
logging.info('events after applying the filter: %d' % len(filtered))
# selector pre data
row_selector = CachedDiffRowWithIntervalSelector(con, table_name, 0, 0)
interval_selector = None
# datova mnozina
logging.info('start computing of data set')
data = AttributeUtil.training_data_without_opposite(
con, table_name, filtered, func, row_selector, interval_selector)
logging.info('data set contains %d events' % len(data))
logging.info('end computing of data set')
# generovanie suborov
logging.info('start preparing file of training and testing set')
random.seed(len(data) // 2)
random.shuffle(data)
CSVUtil.create_csv_file(data, 'data.csv')
logging.info('end preparing file of training and testing set')
logging.info('end')
def main(events_file: str, no_event_time_shift: int):
logging.info('start')
table_name = 'measured_klarka'
# download data
con = ConnectionUtil.create_con()
storage = Storage(events_file, no_event_time_shift, table_name)
d = storage.load_data(con, 0, 0, 'rh_in2_specific_g_kg')
logging.info('downloaded events: %d' % len(d))
# apply filters to data
filtered = FilterUtil.only_valid_events(d)
# filtered = FilterUtil.temperature_diff(filtered, 5, 17.5)
# filtered = FilterUtil.temperature_diff(filtered, 17.5, 30)
# filtered = FilterUtil.temperature_diff(filtered, 5, 13.3)
# filtered = FilterUtil.temperature_diff(filtered, 13.3, 21.6)
# filtered = FilterUtil.temperature_diff(filtered, 21.6, 30)
# filtered = FilterUtil.temperature_diff(filtered, 10, 15)
# filtered = FilterUtil.temperature_diff(filtered, 15, 20)
# filtered = FilterUtil.temperature_diff(filtered, 20, 25)
logging.info('events after applying the filter: %d' % len(filtered))
row_selector = CachedDiffRowWithIntervalSelector(con, table_name, 0, 0)
interval_selector = SimpleIntervalSelector(con, table_name)
# data set
logging.info('start computing of data set')
data = AttributeUtil.training_data_without_opposite(
con, table_name, filtered, func, row_selector, interval_selector)
logging.info('data set contains %d events' % len(data))
logging.info('end computing of data set')
# split data set into training and testing set
random.seed(len(data) // 2)
random.shuffle(data)
training, testing, minimum = training_testing_data(data, 0.7)
logging.info('training set contains %d records, each %d-krat' %
(len(training), minimum))
logging.info('testing set contains %d records' % len(testing))
training_testing_data_with_distance(copy.deepcopy(training),
copy.deepcopy(testing), 0,
CenterLineSlope(), "trendline_", False,
False, False, False)
training_testing_data_with_distance(copy.deepcopy(training),
copy.deepcopy(testing), 1,
PolyfitLineAvgSlope(), "polyfit_",
False, False, False, False)
training_testing_data_with_distance(copy.deepcopy(training),
copy.deepcopy(testing), 2,
CenterLineSlope(), "center_", False,
False, False, False)
training_testing_data_only_distance(copy.deepcopy(training),
copy.deepcopy(testing), 3,
CenterLineSlope(), "trendline_", False,
False, False, False)
training_testing_data_only_distance(copy.deepcopy(training),
copy.deepcopy(testing), 4,
PolyfitLineAvgSlope(), "polyfit_",
False, False, False, False)
training_testing_data_only_distance(copy.deepcopy(training),
copy.deepcopy(testing), 5,
CenterLineSlope(), "center_", False,
False, False, False)
training_testing_data_without_distance(copy.deepcopy(training),
copy.deepcopy(testing), 6,
CenterLineSlope(), "trendline_",
False, False, False, False)
logging.info('end')