# Source code for darts.ad.detectors.quantile_detector

```
"""
Quantile Detector
-----------------
Flags anomalies that are beyond some quantiles of historical data.
This is similar to a threshold-based detector, where the thresholds are
computed as quantiles of historical data when the detector is fitted.
"""
from typing import Sequence, Union
import numpy as np
from darts.ad.detectors.detectors import FittableDetector
from darts.ad.detectors.threshold_detector import ThresholdDetector
from darts.logging import raise_if, raise_if_not
from darts.timeseries import TimeSeries
[docs]class QuantileDetector(FittableDetector):
def __init__(
self,
low_quantile: Union[Sequence[float], float, None] = None,
high_quantile: Union[Sequence[float], float, None] = None,
) -> None:
"""
Flags values that are either
below or above the `low_quantile` and `high_quantile`
quantiles of historical data, respectively.
If a single value is provided for `low_quantile` or `high_quantile`, this same
value will be used across all components of the series.
If sequences of values are given for the parameters `low_quantile` and/or `high_quantile`,
they must be of the same length, matching the dimensionality of the series passed
to ``fit()``, or have a length of 1. In the latter case, this single value will be used
across all components of the series.
If either `low_quantile` or `high_quantile` is None, the corresponding bound will not be used.
However, at least one of the two must be set.
Parameters
----------
low_quantile
(Sequence of) quantile of historical data below which a value is regarded as anomaly.
Must be between 0 and 1. If a sequence, must match the dimensionality of the series
this detector is applied to.
high_quantile
(Sequence of) quantile of historical data above which a value is regarded as anomaly.
Must be between 0 and 1. If a sequence, must match the dimensionality of the series
this detector is applied to.
Attributes
----------
low_threshold
The (sequence of) lower quantile values.
high_threshold
The (sequence of) upper quantile values.
"""
super().__init__()
raise_if(
low_quantile is None and high_quantile is None,
"At least one parameter must be not None (`low` and `high` are both None).",
)
def _prep_quantile(q):
return (
q.tolist()
if isinstance(q, np.ndarray)
else [q] if not isinstance(q, Sequence) else q
)
low = _prep_quantile(low_quantile)
high = _prep_quantile(high_quantile)
for q in (low, high):
raise_if_not(
all([x is None or 0 <= x <= 1 for x in q]),
"Quantiles must be between 0 and 1, or None.",
)
self.low_quantile = low * len(high) if len(low) == 1 else low
self.high_quantile = high * len(low) if len(high) == 1 else high
# the quantiles parameters are now sequences of the same length,
# possibly containing some None values, but at least one non-None value
# We'll use an inner Threshold detector once the quantiles are fitted
self.detector = None
# A few more checks:
raise_if_not(
len(self.low_quantile) == len(self.high_quantile),
"Parameters `low_quantile` and `high_quantile` must be of the same length,"
+ f" found `low`: {len(self.low_quantile)} and `high`: {len(self.high_quantile)}.",
)
raise_if(
all([lo is None for lo in self.low_quantile])
and all([hi is None for hi in self.high_quantile]),
"All provided quantile values are None.",
)
raise_if_not(
all(
low <= high
for (low, high) in zip(self.low_quantile, self.high_quantile)
if ((low is not None) and (high is not None))
),
"all values in `low_quantile` must be lower than or equal"
+ "to their corresponding value in `high_quantile`.",
)
def _fit_core(self, list_series: Sequence[TimeSeries]) -> None:
# if len(low) > 1 and len(high) > 1, then check it matches input width:
raise_if(
len(self.low_quantile) > 1
and len(self.low_quantile) != list_series[0].width,
"The number of components of input must be equal to the number"
+ " of values given for `high_quantile` or/and `low_quantile`. Found number of "
+ f"components equal to {list_series[0].width} and expected {len(self.low_quantile)}.",
)
# otherwise, make them the right length
self.low_quantile = (
self.low_quantile * list_series[0].width
if len(self.low_quantile) == 1
else self.low_quantile
)
self.high_quantile = (
self.high_quantile * list_series[0].width
if len(self.high_quantile) == 1
else self.high_quantile
)
# concatenate everything along time axis
np_series = np.concatenate(
[series.all_values(copy=False) for series in list_series], axis=0
)
# move sample dimension to position 1
np_series = np.moveaxis(np_series, 2, 1)
# flatten it in order to obtain an array of shape (time * samples, components)
# where all samples of a given component are concatenated along time
np_series = np_series.reshape(np_series.shape[0] * np_series.shape[1], -1)
# Compute 2 thresholds (low, high) for each component:
# TODO: we could make this more efficient when low_quantile or high_quantile contain a single value
self.low_threshold = [
np.quantile(np_series[:, i], q=lo, axis=0) if lo is not None else None
for i, lo in enumerate(self.low_quantile)
]
self.high_threshold = [
np.quantile(np_series[:, i], q=hi, axis=0) if hi is not None else None
for i, hi in enumerate(self.high_quantile)
]
self.detector = ThresholdDetector(
low_threshold=self.low_threshold, high_threshold=self.high_threshold
)
return self
def _detect_core(self, series: TimeSeries) -> TimeSeries:
return self.detector.detect(series)
```