LogisticGAM

class pygam.pygam.LogisticGAM(lam=0.6, max_iter=100, n_splines=25, spline_order=3, penalties='auto', dtype='auto', tol=0.0001, callbacks=['deviance', 'diffs', 'accuracy'], fit_intercept=True, fit_linear=False, fit_splines=True, constraints=None, verbose=False)

Bases: pygam.pygam.GAM

Logistic GAM

This is a GAM with a Binomial error distribution, and a logit link.

Parameters:
  • callbacks (list of strings or list of CallBack objects,) – default: [‘deviance’, ‘diffs’] Names of callback objects to call during the optimization loop.
  • constraints (str or callable, or iterable of str or callable,) –

    default: None Names of constraint functions to call during the optimization loop.

    Must be in {‘convex’, ‘concave’, ‘monotonic_inc’, ‘monotonic_dec’,
    ’circular’, ‘none’}

    If None, then the model will apply no constraints.

    If only one str or callable is specified, then is it copied for all features.

  • dtype (str in {'auto', 'numerical', 'categorical'},) –

    or list of str, default: ‘auto’ String describing the data-type of each feature.

    ’numerical’ is used for continuous-valued data-types,
    like in regression.
    ’categorical’ is used for discrete-valued data-types,
    like in classification.

    If only one str is specified, then is is copied for all features.

  • lam (float or iterable of floats > 0, default: 0.6) –

    Smoothing strength; must be a positive float, or one positive float per feature.

    Larger values enforce stronger smoothing.

    If only one float is specified, then it is copied for all features.

  • fit_intercept (bool, default: True) –

    Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.

    NOTE: the intercept receives no smoothing penalty.

  • fit_linear (bool or iterable of bools, default: False) –

    Specifies if a linear term should be added to any of the feature functions. Useful for including pre-defined feature transformations in the model.

    If only one bool is specified, then it is copied for all features.

    NOTE: Many constraints are incompatible with an additional linear fit.
    eg. if a non-zero linear function is added to a periodic spline function, it will cease to be periodic.

    this is also possible for a monotonic spline function.

  • fit_splines (bool or iterable of bools, default: True) –

    Specifies if a smoother should be added to any of the feature functions. Useful for defining feature transformations a-priori that should not have splines fitted to them.

    If only one bool is specified, then it is copied for all features.

    NOTE: fit_splines supercedes n_splines. ie. if n_splines > 0 and fit_splines = False, no splines will be fitted.

  • max_iter (int, default: 100) – Maximum number of iterations allowed for the solver to converge.
  • penalties (str or callable, or iterable of str or callable,) –

    default: ‘auto’ Type of penalty to use for each feature.

    penalty should be in {‘auto’, ‘none’, ‘derivative’, ‘l2’, }

    If ‘auto’, then the model will use 2nd derivative smoothing for features of dtype ‘numerical’, and L2 smoothing for features of dtype ‘categorical’.

    If only one str or callable is specified, then is it copied for all features.

  • n_splines (int, or iterable of ints, default: 25) –

    Number of splines to use in each feature function; must be non-negative. If only one int is specified, then it is copied for all features.

    Note: this value is set to 0 if fit_splines is False

  • spline_order (int, or iterable of ints, default: 3) –

    Order of spline to use in each feature function; must be non-negative. If only one int is specified, then it is copied for all features

    Note: if a feature is of type categorical, spline_order will be set to 0.

  • tol (float, default: 1e-4) – Tolerance for stopping criteria.
  • verbose (bool, default: False) – whether to show pyGAM warnings
coef_

array, shape (n_classes, m_features) – Coefficient of the features in the decision function. If fit_intercept is True, then self.coef_[0] will contain the bias.

statistics_

dict – Dictionary containing model statistics like GCV/UBRE scores, AIC/c, parameter covariances, estimated degrees of freedom, etc.

logs_

dict – Dictionary containing the outputs of any callbacks at each optimization loop.

The logs are structured as {callback: […]}

References

Simon N. Wood, 2006 Generalized Additive Models: an introduction with R

Hastie, Tibshirani, Friedman The Elements of Statistical Learning http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

Paul Eilers & Brian Marx, 2015 International Biometric Society: A Crash Course on P-splines http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf

accuracy(X=None, y=None, mu=None)

computes the accuracy of the LogisticGAM

Parameters:
  • note (X or mu must be defined. defaults to mu) –
  • X (array-like of shape (n_samples, m_features), default: None) – containing input data
  • y (array-like of shape (n,)) – containing target data
  • mu (array-like of shape (n_samples,), default: None) – expected value of the targets given the model and inputs
Returns:
Return type:

float in [0, 1]
predict(X)

preduct binary targets given model and input X

Parameters:X (array-like of shape (n_samples, m_features), default: None) – containing the input dataset
Returns:y – containing binary targets under the model
Return type:np.array of shape (n_samples,)
predict_proba(X)

preduct targets given model and input X

Parameters:X (array-like of shape (n_samples, m_features), default: None) – containing the input dataset
Returns:y – containing expected values under the model
Return type:np.array of shape (n_samples,)