Fitting and prediction: estimator basics#
ruptures has an object-oriented modelling approach (largely inspired by scikit-learn): change point detection algorithms are broken down into two conceptual objects that inherits from base classes:
Initializing a new estimator#
Each change point detection algorithm inherits from the base class
When a class that inherits from the base estimator is created, the
.__init__() method initializes
an estimator with the following arguments:
model: "l1", "l2", "normal", "rbf", "linear", etc. Cost function to use to compute the approximation error.
cost: a custom cost function to the detection algorithm. Should be a
jump: reduce the set of possible change point indexes; predicted change points can only be a multiple of
min_size: minimum number of samples between two change points.
Making a prediction#
The main methods are
.fit(): generally takes a signal as input and fit the algorithm to the data.
.predict(): performs the change point detection. This method returns a list of indexes corresponding to the end of each regimes. By design, the last element of this list is the number of samples.
.fit_predict(): helper method which calls