alibi_detect.od.prophet

Constants

logger

logger: logging.Logger = <Logger alibi_detect.od.prophet (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

OutlierProphet

Inherits from: BaseDetector, FitMixin, ABC

Constructor

OutlierProphet(self, threshold: float = 0.8, growth: str = 'linear', cap: float = None, holidays: pandas.core.frame.DataFrame = None, holidays_prior_scale: float = 10.0, country_holidays: str = None, changepoint_prior_scale: float = 0.05, changepoint_range: float = 0.8, seasonality_mode: str = 'additive', daily_seasonality: Union[str, bool, int] = 'auto', weekly_seasonality: Union[str, bool, int] = 'auto', yearly_seasonality: Union[str, bool, int] = 'auto', add_seasonality: List = None, seasonality_prior_scale: float = 10.0, uncertainty_samples: int = 1000, mcmc_samples: int = 0) -> None

Name	Type	Default	Description
threshold	float	0.8	Width of the uncertainty intervals of the forecast, used as outlier threshold. Equivalent to interval_width. If the instance lies outside of the uncertainty intervals, it is flagged as an outlier. If mcmc_samples equals 0, it is the uncertainty in the trend using the MAP estimate of the extrapolated model. If mcmc_samples >0, then uncertainty over all parameters is used.
growth	str	'linear'	'linear' or 'logistic' to specify a linear or logistic trend.
cap	Optional[float]	None	Growth cap in case growth equals 'logistic'.
holidays	Optional[pandas.core.frame.DataFrame]	None	pandas DataFrame with columns holiday (string) and ds (dates) and optionally columns lower_window and upper_window which specify a range of days around the date to be included as holidays.
holidays_prior_scale	float	10.0	Parameter controlling the strength of the holiday components model. Higher values imply a more flexible trend, more prone to more overfitting.
country_holidays	Optional[str]	None	Include country-specific holidays via country abbreviations. The holidays for each country are provided by the holidays package in Python. A list of available countries and the country name to use is available on: https://github.com/dr-prodigy/python-holidays. Additionally, Prophet includes holidays for: Brazil (BR), Indonesia (ID), India (IN), Malaysia (MY), Vietnam (VN), Thailand (TH), Philippines (PH), Turkey (TU), Pakistan (PK), Bangladesh (BD), Egypt (EG), China (CN) and Russian (RU).
changepoint_prior_scale	float	0.05	Parameter controlling the flexibility of the automatic changepoint selection. Large values will allow many changepoints, potentially leading to overfitting.
changepoint_range	float	0.8	Proportion of history in which trend changepoints will be estimated. Higher values means more changepoints, potentially leading to overfitting.
seasonality_mode	str	'additive'	Either 'additive' or 'multiplicative'.
daily_seasonality	Union[str, bool, int]	'auto'	Can be 'auto', True, False, or a number of Fourier terms to generate.
weekly_seasonality	Union[str, bool, int]	'auto'	Can be 'auto', True, False, or a number of Fourier terms to generate.
yearly_seasonality	Union[str, bool, int]	'auto'	Can be 'auto', True, False, or a number of Fourier terms to generate.
add_seasonality	Optional[List[Any]]	None	Manually add one or more seasonality components. Pass a list of dicts containing the keys name, period, fourier_order (obligatory), prior_scale and mode (optional).
seasonality_prior_scale	float	10.0	Parameter controlling the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, potentially leading to overfitting.
uncertainty_samples	int	1000	Number of simulated draws used to estimate uncertainty intervals.
mcmc_samples	int	0	If >0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation.

Methods

fit

fit(df: pandas.core.frame.DataFrame) -> None

Fit Prophet model on normal (inlier) data.

Name	Type	Default	Description
df	pandas.core.frame.DataFrame		Dataframe with columns ds with timestamps and y with target values.

Returns

• Type: None

predict

predict(df: pandas.core.frame.DataFrame, return_instance_score: bool = True, return_forecast: bool = True) -> Dict[Dict[str, str], Dict[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]]

Compute outlier scores and transform into outlier predictions.

Name	Type	Default	Description
df	pandas.core.frame.DataFrame		DataFrame with columns ds with timestamps and y with values which need to be flagged as outlier or not.
return_instance_score	bool	True	Whether to return instance level outlier scores.
return_forecast	bool	True	Whether to return the model forecast.

Returns

• Type: Dict[Dict[str, str], Dict[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]]

score

score(df: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame

Compute outlier scores.

Name	Type	Default	Description
df	pandas.core.frame.DataFrame		DataFrame with columns ds with timestamps and y with values which need to be flagged as outlier or not.

Returns

• Type: pandas.core.frame.DataFrame