def run(config, _id, logger):
''' Top-level function for running experiment.
Args:
config (dict): Parameters for modeling, execution levels, and error calculations loaded from config.yaml
_id (str): Unique id for each processing run generated from current time
logger (obj): Logger obj from logging module
Returns:
None
'''
with open("results/%s.csv" % _id, "a") as out:
writer = csv.DictWriter(
out, config.header) # line by line results written to csv
writer.writeheader()
path_to_chan_data = 'data/train/' # MOVE TO CONFIG
chans = [
x[:-4] for x in os.listdir(path_to_chan_data) if x[-4:] == '.npy'
]
print("Chans being evaluated: %s" % chans)
for i, chan in enumerate(chans):
if i >= 0:
anom = {}
anom['chan_id'] = chan
anom['run_id'] = _id
logger.info("Chan: %s (%s of %s)" % (chan, i, len(chans)))
X_train, y_train, X_test, y_test = helpers.load_data(anom)
# Generate or load predictions
# ===============================
y_hat = []
if config.predict:
model = models.get_model(anom,
X_train,
y_train,
logger,
train=config.train)
y_hat = models.predict_in_batches(y_test, X_test, model,
anom)
else:
y_hat = [
float(x) for x in list(
np.load(
os.path.join("data", config.use_id, "y_hat",
anom["chan_id"] + ".npy")))
]
# Error calculations
# ====================================================================================================
e = err.get_errors(y_test, y_hat, anom, smoothed=False)
e_s = err.get_errors(y_test, y_hat, anom, smoothed=True)
anom["normalized_error"] = np.mean(e) / np.ptp(y_test)
logger.info("normalized prediction error: %s" %
anom["normalized_error"])
# Error processing (batch)
# =========================
E_seq, E_seq_scores = err.process_errors(
y_test, y_hat, e_s, anom, logger)
anom['anomaly_sequences'] = E_seq
anom['scores'] = E_seq_scores
anom['num_anoms'] = len(anom['anomaly_sequences'])
anom["num_values"] = y_test.shape[0]
writer.writerow(anom)
def run(config, _id, logger):
''' Top-level function for running experiment.
Args:
config (dict): Parameters for modeling, execution levels, and error calculations loaded from config.yaml
_id (str): Unique id for each processing run generated from current time
logger (obj): Logger obj from logging module
Returns:
None
'''
stats = {"true_positives": 0, "false_positives": 0, "false_negatives": 0}
with open("labeled_anomalies.csv", "rU") as f:
reader = csv.DictReader(f)
with open("results/%s.csv" % _id, "a") as out:
writer = csv.DictWriter(
out, config.header) # line by line results written to csv
writer.writeheader()
for i, anom in enumerate(reader):
if reader.line_num >= 1:
anom['run_id'] = _id
logger.info("Stream # %s: %s" %
(reader.line_num - 1, anom['chan_id']))
model = None
X_train, y_train, X_test, y_test = helpers.load_data(anom)
# Generate or load predictions
# ===============================
y_hat = []
if config.predict:
model = models.get_model(anom,
X_train,
y_train,
logger,
train=config.train)
y_hat = models.predict_in_batches(
y_test, X_test, model, anom)
else:
y_hat = [
float(x) for x in list(
np.load(
os.path.join("data", config.use_id,
"y_hat", anom["chan_id"] +
".npy")))
]
# Error calculations
# ====================================================================================================
e = err.get_errors(y_test, y_hat, anom, smoothed=False)
e_s = err.get_errors(y_test, y_hat, anom, smoothed=True)
anom["normalized_error"] = np.mean(e) / np.ptp(y_test)
logger.info("normalized prediction error: %s" %
anom["normalized_error"])
# Error processing (batch)
# =========================
E_seq, E_seq_scores = err.process_errors(
y_test, y_hat, e_s, anom, logger)
anom['scores'] = E_seq_scores
anom = err.evaluate_sequences(E_seq, anom)
anom["num_values"] = y_test.shape[
0] + config.l_s + config.n_predictions
for key, value in stats.items():
stats[key] += anom[key]
helpers.anom_stats(stats, anom, logger)
writer.writerow(anom)
helpers.final_stats(stats, logger)
def run(config, _id, logger):
stats = {"true_positives": 0, "false_positives": 0, "false_negatives": 0}
with open("labeled_anomalies.csv", "rU") as f:
reader = csv.DictReader(f)
with open("results/%s.csv" % _id, "a") as out:
writer = csv.DictWriter(out, config.header)
writer.writeheader()
for i, anom in enumerate(reader):
if reader.line_num >= 1:
anom['run_id'] = _id
logger.info("Поток # %s: %s" %
(reader.line_num - 1, anom['chan_id']))
model = None
X_train, y_train, X_test, y_test = helpers.load_data(anom)
#
# ===============================
y_hat = []
if config.predict:
model = models.get_model(anom,
X_train,
y_train,
logger,
train=config.train)
y_hat = models.predict_in_batches(
y_test, X_test, model, anom)
else:
y_hat = [
float(x) for x in list(
np.load(
os.path.join("data", config.use_id,
"y_hat", anom["chan_id"] +
".npy")))
]
#
# ====================================================================================================
e = err.get_errors(y_test, y_hat, anom, smoothed=False)
e_s = err.get_errors(y_test, y_hat, anom, smoothed=True)
anom["normalized_error"] = np.mean(e) / np.ptp(y_test)
logger.info("нормализованная ошибка предсказания: %s" %
anom["normalized_error"])
#
# =========================
E_seq, E_seq_scores = err.process_errors(
y_test, y_hat, e_s, anom, logger)
anom['scores'] = E_seq_scores
anom = err.evaluate_sequences(E_seq, anom)
anom["num_values"] = y_test.shape[0]
for key, value in stats.items():
stats[key] += anom[key]
helpers.anom_stats(stats, anom, logger)
writer.writerow(anom)
helpers.final_stats(stats, logger)