tick.linear_model.PoissonRegression¶

class tick.linear_model.PoissonRegression(step=0.001, fit_intercept=True, penalty='l2', C=1000.0, tol=1e-05, max_iter=100, solver='svrg', verbose=False, warm_start=False, print_every=10, record_every=1, elastic_net_ratio=0.95, random_state=None, blocks_start=None, blocks_length=None)[source]¶

Poisson regression learner, with exponential link function. It supports several solvers and several penalizations. Note that for this model, there is no way to tune automatically the step of the solver. Thus, the default for step might work, or not, so that several values should be tried out.

Parameters:

step : float, default=1e-3

Step-size to be used for the solver. For Poisson regression there is no way to tune it automatically. The default tuning might work, or not…

C : float, default=1e3

Level of penalization

penalty : {‘l1’, ‘l2’, ‘elasticnet’, ‘tv’}, default=’l2’

The penalization to use. Default is ridge penalization

solver : {‘gd’, ‘agd’, ‘bfgs’, ‘svrg’, ‘sgd’}, default=’svrg’

The name of the solver to use

fit_intercept : bool, default=True

If True, include an intercept in the model

warm_start : bool, default=False

If true, learning will start from the last reached solution

tol : float, default=1e-6

The tolerance of the solver (iterations stop when the stopping criterion is below it). By default the solver does max_iter iterations

max_iter : int, default=100

Maximum number of iterations of the solver

verbose : bool, default=False

If True, we verbose things, otherwise the solver does not print anything (but records information in history anyway)

print_every : int, default=10

Print history information when n_iter (iteration number) is a multiple of print_every

record_every : int, default=1

Record history information when n_iter (iteration number) is a multiple of record_every

Attributes:

weights : numpy.array, shape=(n_features,)

The learned weights of the model (not including the intercept)

intercept : float or None

The intercept, if fit_intercept=True, otherwise None

Examples using tick.linear_model.PoissonRegression¶