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The aim of this PR is to solve the issue related to the period parameter and to add some additional steps that will solve some issues related the code #41

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The aim of this PR is to solve the issue related to the period parameter and to add some additional steps that will solve some issues related the code #41

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13 changes: 9 additions & 4 deletions tad/anomaly_detect_ts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -409,7 +409,7 @@ def _get_decomposed_data_tuple(data, num_obs_per_period):
the number of observations in each period
"""
decomposed = sm.tsa.seasonal_decompose(
data, freq=num_obs_per_period, two_sided=False)
data, period=num_obs_per_period, two_sided=False)
smoothed = data - decomposed.resid.fillna(0)
data = data - decomposed.seasonal - data.mean()
return (data, smoothed)
Expand Down Expand Up @@ -439,7 +439,7 @@ def anomaly_detect_ts(x, max_anoms=0.1, direction="pos", alpha=0.05, only_last=N
logger.warning('alpha is the statistical significance and is usually between 0.01 and 0.1')

data, period, granularity = _get_data_tuple(x, period_override, resampling)
if granularity is 'day':
if granularity == 'day':
num_days_per_line = 7
only_last = 'day' if only_last == 'hr' else only_last

Expand Down Expand Up @@ -470,7 +470,7 @@ def anomaly_detect_ts(x, max_anoms=0.1, direction="pos", alpha=0.05, only_last=N
seasonal_plus_trend = seasonal_plus_trend.append(shesd_stl)

# De-dupe
all_anoms.drop_duplicates(inplace=True)
all_anoms[~all_anoms.index.duplicated(keep='first')]
seasonal_plus_trend.drop_duplicates(inplace=True)

# If only_last is specified, create a subset of the data corresponding to the most recent day or hour
Expand Down Expand Up @@ -566,7 +566,12 @@ def _detect_anoms(data, k=0.49, alpha=0.05, num_obs_per_period=None,
ares = ares / data.mad()

tmp_anom_index = ares[ares.values == ares.max()].index
cand = pd.Series(data.loc[tmp_anom_index], index=tmp_anom_index)
try:
cand = pd.Series(data.loc[tmp_anom_index], index=tmp_anom_index)
except ValueError:
# drop the index duplicates but keep first and try again
cand = pd.Series(data.loc[tmp_anom_index][~data.loc[tmp_anom_index].index.duplicated(keep='first')],
index=tmp_anom_index)

data.drop(tmp_anom_index, inplace=True)

Expand Down