pygam#
Module: pygam#
Helper functions for GAMs#
This module contains functions used for the GAM lab of ISLP.
Functions#
- ISLP.pygam.anova(*models, scale=None, useF=True)#
Compute an ANOVA table for a sequence of GAM models.
- Parameters:
- modelsGAMs
Sequence of fitted GAM models.
- scalefloat
Estimate of noise level, defaults to None.
- useFbool
If True use an F distribution for p-value computation based on degrees of freedom of largest model. Otherwise, use chi-squared.
- Returns:
- anova_table: pd.DataFrame
Notes
Implicitly assumes models are nested, fit on the same (X,y) with the same sample weights.
- ISLP.pygam.approx_lam(X, term, df, W=None, CUTOFF=1000000000000.0)#
For a given term, try to find multiplier of penalty to achieve a specified degrees of freedom.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input dataset
- termTerm
Term for which we which to scale penalty
- dffloat
Desired degrees of freedom
- Warray-like (optional)
Diagonal weight matrix.
- CUTOFFfloat
Search for solution in interval [0,CUTOFF].
- Returns:
- lamarray-like
Rescaled lam values.
Notes
The term must be part of a GAM that has already been fit.
- ISLP.pygam.degrees_of_freedom(X, term, lam=None, W=None)#
For a given term, try to find multiplier of penalty to achieve a specified degrees of freedom.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input dataset
- termTerm
Term for which we which to scale penalty.
- lamarray-like (optional)
Values at which to approximate degrees of freedom. If None, defaults to term.lam
- Warray-like (optional)
Diagonal weight matrix.
- Returns:
- dffloat
Degrees of freedom as computed by the trace of the smoother matrix.
Notes
The term must be part of a GAM that has already been fit.
- ISLP.pygam.plot(gam, term_idx, quantiles=[0.025, 0.975], ax=None, levels=None, partial_kwargs={'c': 'b', 'linewidth': 4}, err_kwargs={'c': 'r', 'linewidth': 4, 'ls': '--'}, bar_kwargs={'capsize': 10})#
Plot the fitted function of a term in a GAM model.
- Parameters:
- gamGAM
A fitted GAM model.
- term_idxint
Which term in the GAM to plot?
- quantiles[float, float], default=[0.025, 0.0975]
Which quantiles for pointwise confidence bands?
- axmatplotlib axes, optional
- levelssequence
For categorical features, which indices to include in plot. Defaults to all levels.
- partial_kwargsdict
Keyword arguments for partial dependence plot for continuous variables.
- err_kwargsdict
Keyword arguments for pointwise confidence bands for continuous variables.
- bar_kwargsdict
Keyword arguments for barplot for for categorical variables.
- Returns:
- axmatplotlib axes
Axes with partial dependence plot added.