ChangePoint API Reference
- class changepoint.ArgpCpd(scale=0.5, length_scale=10.0, noise_level=0.01, max_lag=3, alpha0=2.0, beta0=1.0, logistic_hazard_h=..., logistic_hazard_a=1.0, logistic_hazard_b=1.0)
Autoregressive Gaussian Process Change Point detection
Based on Ryan Turner’s [thesis](https://www.repository.cam.ac.uk/bitstream/handle/1810/242181/thesis.pdf?sequence=1&isAllowed=y).
- Parameters:
- scale: float
Scale of the ConstantKernel
- length_scale:float
Length Scale of RBFKernel
- noise_level: float
Noise standard deviation for the WhiteKernel
- max_lag: int > 0
Maximum Autoregressive lag
- alpha0float
Scale Gamma distribution alpha parameter
- beta0: float
Scale Gamma distribution beta parameter
- logistic_hazard_h: float
Hazard scale in logit units.
- logistic_hazard_a: float
Roughly the slope of the logistic hazard function
- logistic_hazard_b: float
The offset of the logistic hazard function.
Methods
reset()Reset the argpcpd to starting state
step(datum)Observe a new datum and return the run length probabilities
- reset()
Reset the argpcpd to starting state
- step(datum)
Observe a new datum and return the run length probabilities
- Parameters:
- datum: float
Next datum in the data stream.
- Returns:
- List[float]
A list of change point probabilities.
- changepoint.BetaBernoulli(alpha=0.5, beta=0.5)
Beta prior on a Bernoulli random variable.
- Parameters:
- alpha: float > 0
alpha parameter of beta distribution
- beta: float > 0
beta parameter of beta distribution
- Returns:
- Beta prior on Bernoulli distribution
- Raises:
- ValueError: alpha or beta is <= 0, infinite, or NaN
- class changepoint.Bocpd(prior, lam)
Online Bayesian Change Point Detection state container Create a new BOCPD
- Parameters:
- prior: Prior
The (conjugate) prior, which also describes the likelihood distribution for the stream.
- lam: float
Expected mean run length. A smaller value means changepoints are believed to occur at shorter intervals.
- Raises:
- ValueError: lam <= 0.0
Methods
reset()Reset the Bocpd to the new state
step(datum)Observe a new datum.
- reset()
Reset the Bocpd to the new state
- step(datum)
Observe a new datum. Returns the run length probabilities for each step.
- changepoint.NormalGamma(m=0.0, r=1.0, s=1.0, v=1.0)
Normal Gamma prior on univariate Normal random variable.
- Parameters:
- m: float
Prior mean
- r: float > 0.0
- s: float > 0.0
- v: float > 0.0
- Returns:
- Normal Gamma-Square prior
- Raises:
- ValueError:
m, r, s, v is infinite or NaN,
r, s, v <= 0.0
- changepoint.NormalInvChiSquared(m=0.0, k=1.0, v=1.0, s2=1.0)
Normal Inverse-Chi-Squared prior on univariate Normal random variable.
- Parameters:
- m: float
Prior mean
- k: float > 0.0
Amount of confidence in the prior mean; measured in pseudo observations.
- v: float > 0.0
Amount of confidence in the prior variance; measured in pseudo observations.
- s2: float > 0.0
Prior variance
- Returns:
- Normal Inverse-Chi-Square prior
- Raises:
- ValueError:
m, k, v, s2 is infinite or NaN,
k, v, s2 <= 0.0
- changepoint.NormalInvGamma(m=0.0, v=1.0, a=1.0, b=1.0)
Normal Inverse-Gamma prior on univariate Normal random variable.
- Parameters:
- m: float
Prior mean
- v: float > 0.0
- a: float > 0.0
- b: float > 0.0
- Returns:
- Normal Inverse-Gamma prior
- Raises:
- ValueError:
m, v, a, b is infinite or NaN,
v, a, b <= 0.0
- changepoint.NormalInvWishart(mu, k, df, scale)
Normal Inverse-Wishart prior on multivariate Normal random variable.
- changepoint.PoissonGamma(shape=1.0, rate=1.0)
Gamma prior on a Poisson random variable.
- Parameters:
- shape: float > 0
The Gamma distribution shape parameter
- rate: float > 0
The Gamma distribution rate parameter
- Returns:
- Gamma prior on Poisson
- Raises:
- ValueError: shape or rate is <= 0, infinite, or NaN
- class changepoint.Prior(kind, *args)
Prior distribution, which also describes the liklihood distribution of the change point detector.
Methods
beta_bernoulli
normal_gamma
normal_inv_chi_squared
normal_inv_gamma
normal_inv_wishart
poisson_gamma
- static beta_bernoulli(alpha=0.5, beta=0.5)
- static normal_gamma(m=0.0, r=1.0, s=1.0, v=1.0)
- static normal_inv_chi_squared(m=0.0, k=1.0, v=1.0, s2=1.0)
- static normal_inv_gamma(m=0.0, v=1.0, a=1.0, b=1.0)
- static normal_inv_wishart(mu, k, df, scale)
- static poisson_gamma(shape=1.0, rate=1.0)
- changepoint.infer_changepoints(history, n_samples)
Given the runlength probabilities vector for each step, and a Monte Carlo step size, return the probability that a change point occurred on a given step.
- Parameters:
- history: List[List[float]]
Run length probabilities for a sequence of observations.
- n_samples: int
Number of samples to draw from run-length process.
- changepoint.infer_pseudo_cmf_changepoints(history, n_samples)
Creates a pseudo cmf distribution for change-point locations.
This calculates the cumulative sum of the infer_changepoints return value mod 1.0.
- Parameters:
- history: List[List[float]]
Run length probabilities for a sequence of observations.
- n_samples: int
Number of samples to draw from run-length process.
- changepoint.map_changepoints(history)
Returns the most likely index (step) that a changepoint occurred. Each point corresponds to an individual changepoint.
- Parameters:
- history: List[List[float]]
Run length probabilities for a sequence of observations.