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Data time check #189

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merged 2 commits into from
Nov 15, 2021
Merged

Data time check #189

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8bcd606
Updated data time check to handle hourly granularity
ankitkpandey Oct 18, 2021
9a9ba97
Today selection modified
ankitkpandey Oct 27, 2021
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6 changes: 4 additions & 2 deletions api/ops/tasks/detection/core/detectionTypes/lifetime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,11 +32,13 @@ def lifetimeDetect(df, granularity):
"""
Method to perform anomaly detection on given dataframe
"""
today = dt.datetime.now()
today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
df["ds"] = pd.to_datetime(df["ds"])
df = df.sort_values("ds")
df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
if granularity == "day":
today = today.replace(hour=0)
todayISO = today.isoformat()[:19]
df = df[df["ds"] < todayISO]
maxVal = df.y.max()
minVal = df.y.min()
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6 changes: 4 additions & 2 deletions api/ops/tasks/detection/core/detectionTypes/percentageChange.py
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Expand Up @@ -27,11 +27,13 @@ def percentChangeDetect(df, granularity, threshold):
Method to perform anomaly detection on given dataframe using fbProphet
"""
threshold = float(threshold)
today = dt.datetime.now()
today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
df["ds"] = pd.to_datetime(df["ds"])
df = df.sort_values("ds")
df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
if granularity == "day":
today = today.replace(hour=0)
todayISO = today.isoformat()[:19]
df = df[df["ds"] < todayISO]
df["percentageChange"] = 100 * (df.y - df.y.shift(1)) / df.y.shift(1)
df["anomaly"] = (abs(df["percentageChange"]) > threshold) * 14 + 1
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8 changes: 4 additions & 4 deletions api/ops/tasks/detection/core/detectionTypes/prophet.py
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Expand Up @@ -40,12 +40,12 @@ def prophetDetect(df, granularity, iterations=None):
"""
Method to perform anomaly detection on given dataframe using fbProphet
"""
today = dt.datetime.now()
today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
df["ds"] = pd.to_datetime(df["ds"])
df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
todayISO = today.replace(
hour=0, minute=0, second=0, microsecond=0, tzinfo=None
).isoformat()[:19]
if granularity == "day":
today = today.replace(hour=0)
todayISO = today.isoformat()[:19]
df = df[df["ds"] < todayISO]
lastActualRow = df[-1:]
lastISO = df.iloc[-1]["ds"]
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6 changes: 4 additions & 2 deletions api/ops/tasks/detection/core/detectionTypes/valueThreshold.py
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Expand Up @@ -49,11 +49,13 @@ def valueThresholdDetect(df, granularity, operator, value1, value2):
"between": '((df["y"] >= lowerVal) & (df["y"] <= upperVal)) * 14 + 1',
"!between": '((df["y"] < lowerVal) | (df["y"] > upperVal)) * 14 + 1'
}
today = dt.datetime.now()
today = dt.datetime.now().replace(minute=0, second=0, microsecond=0, tzinfo=None)
df["ds"] = pd.to_datetime(df["ds"])
df = df.sort_values("ds")
df["ds"] = df["ds"].apply(lambda date: date.isoformat()[:19])
todayISO = today.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None).isoformat()[:19]
if granularity == "day":
today = today.replace(hour=0)
todayISO = today.isoformat()[:19]
df = df[df["ds"] < todayISO]
df["anomaly"] = eval(operationDict[operator])
anomalyLatest = checkLatestAnomaly(df, operationStrDict[operator])
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