piml.models.GAMClassifier¶
- class piml.models.GAMClassifier(feature_names=None, feature_types=None, spline_order=3, n_splines=20, lam=0.6, max_iter=100)¶
A wrapper of generalized additive model classifier in pygam.
- Parameters:
- feature_nameslist or None, default=None
The list of feature names.
- feature_typeslist or None, default=None
The list of feature types. Available types include “numerical” and “categorical”.
- n_splines: int, default=3
Number of splines to use for the feature function. Must be non-negative.
- spline_order: int, default=20
Order of spline to use for the feature function. Must be non-negative.
- lam: float or iterable of floats, default=0.6
Strength of smoothing penalty. Must be a positive float. Larger values enforce stronger smoothing. If single value is passed, it will be repeated for every penalty. If iterable is passed, the length of lam must be equal to the length of penalties.
- max_iter: int, default=100
Maximum number of iterations allowed for the solver to converge.
- Attributes:
- feature_names_list of str
The feature name list of all input features.
- feature_types_list of str
The feature type list of all input features.
Methods
Returns numpy array of raw predicted value before softmax.
fit(X, y[, sample_weight])Fit the gam model
Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
Interpret the model using functional ANOVA.
partial_dependence(fidx, X)Partial dependence of given effect index.
predict(X)Predict function.
Predict probability function.
score(X, y[, sample_weight])Returns accuracy metric for the given data.
set_params(**params)Set the parameters of this estimator.
- decision_function(X)¶
Returns numpy array of raw predicted value before softmax.
- Parameters:
- Xnp.ndarray of shape (n_samples, n_features)
Data features.
- Returns:
- prednp.ndarray of shape (n_samples, )
numpy array of predicted class values.
- fit(X, y, sample_weight=None)¶
Fit the gam model
- Parameters:
- Xnp.ndarray of shape (n_samples, n_features)
Data features.
- ynp.ndarray of shape (n_samples, )
Target response.
- sample_weightnp.ndarray of shape (n_samples, )
Sample weight.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequestencapsulating routing information.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- parse_model()¶
Interpret the model using functional ANOVA.
- Returns:
- An instance of FANOVAINterpreter
The interpretation results.
- partial_dependence(fidx, X)¶
Partial dependence of given effect index.
- Parameters:
- fidxtuple of int
The main effect or pairwise interaction feature index.
- Xnp.ndarray of shape (n_samples, n_features)
Data features.
- Returns:
- prednp.ndarray of shape (n_samples, )
numpy array of predicted class values.
- predict(X)¶
Predict function.
- Parameters:
- Xnp.ndarray
Data features.
- Returns:
- np.ndarray
The predicted label.
- predict_proba(X)¶
Predict probability function.
- Parameters:
- Xnp.ndarray
Data features.
- Returns:
- np.ndarray
The predicted probability.
- score(X, y, sample_weight=None)¶
Returns accuracy metric for the given data.
- Parameters:
- Xnp.ndarray of shape (n_samples, n_features)
Data features.
- ynp.ndarray of shape (n_samples, )
Target response.
- sample_weightnp.ndarray of shape (n_samples, )
Sample weight.
- Returns:
- scorefloat
Accuracy value.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
