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)
if __name__ == "__main__":
config = Config("config.yaml")
_id = dt.now().strftime("%Y-%m-%d_%H.%M.%S")
helpers.make_dirs(_id)
logger = helpers.setup_logging(config, _id)
run(config, _id, logger)
import os
import numpy as np
import pandas as pd
from datetime import datetime as dt
import logging
from telemanom.helpers import Config
from telemanom.errors import Errors
import telemanom.helpers as helpers
from telemanom.channel import Channel
from telemanom.modeling import Model
logger = helpers.setup_logging()
class Detector:
def __init__(self,
labels_path=None,
result_path='results/',
config_path='config.yaml'):
"""
Top-level class for running anomaly detection over a group of channels
with values stored in .npy files. Also evaluates performance against a
set of labels if provided.
Args:
labels_path (str): path to .csv containing labeled anomaly ranges
for group of channels to be processed
result_path (str): directory indicating where to stick result .csv
config_path (str): path to config.yaml