ExplainableBoostingRegressor#
Link to Algorithm description: EBM
- class interpret.glassbox.ExplainableBoostingRegressor(feature_names=None, feature_types=None, max_bins=256, max_interaction_bins=32, interactions=10, exclude=[], validation_size=0.15, outer_bags=8, inner_bags=0, learning_rate=0.01, greediness=0.0, smoothing_rounds=0, max_rounds=5000, early_stopping_rounds=50, early_stopping_tolerance=0.0001, min_samples_leaf=2, max_leaves=3, objective='rmse', n_jobs=- 2, random_state=42)#
An Explainable Boosting Regressor
- Parameters:
feature_names (list of str, default=None) – List of feature names.
feature_types (list of FeatureType, default=None) –
List of feature types. FeatureType can be:
None: Auto-detect
’quantile’: Continuous with equal density bins
’rounded_quantile’: Continuous with quantile bins, but the cut values are rounded when possible
’uniform’: Continuous with equal width bins
’winsorized’: Continuous with equal width bins, but the leftmost and rightmost cut are chosen by quantiles
’continuous’: Use the default binning for continuous features, which is ‘quantile’ currently
[List of float]: Continuous with specified cut values. Eg: [5.5, 8.75]
[List of str]: Ordinal categorical where the order has meaning. Eg: [“low”, “medium”, “high”]
’ordinal’: Ordinal categorical where the order is determined by sorting the feature strings
’nominal’: Categorical where the order has no meaning. Eg: country names
max_bins (int, default=256) – Max number of bins per feature for the main effects stage.
max_interaction_bins (int, default=32) – Max number of bins per feature for interaction terms.
interactions (int, float, or list of tuples of feature indices, default=10) –
Interaction terms to be included in the model. Options are:
Integer (1 <= interactions): Count of interactions to be automatically selected
Percentage (interactions < 1.0): Determine the integer count of interactions by multiplying the number of features by this percentage
List of tuples: The tuples contain the indices of the features within the additive term
exclude ('mains' or list of tuples of feature indices|names, default=[]) – Features or terms to be excluded.
validation_size (int or float, default=0.15) –
Validation set size. Used for early stopping during boosting, and is needed to create outer bags.
Integer (1 <= validation_size): Count of samples to put in the validation sets
Percentage (validation_size < 1.0): Percentage of the data to put in the validation sets
0: Turns off early stopping. Outer bags have no utility. Error bounds will be eliminated
outer_bags (int, default=8) – Number of outer bags. Outer bags are used to generate error bounds and help with smoothing the graphs.
inner_bags (int, default=0) – Number of inner bags. 0 turns off inner bagging.
learning_rate (float, default=0.01) – Learning rate for boosting.
greediness (float, default=0.0) – Percentage of rounds where boosting is greedy instead of round-robin. Greedy rounds are intermixed with cyclic rounds.
smoothing_rounds (int, default=0) – Number of initial highly regularized rounds to set the basic shape of the main effect feature graphs.
max_rounds (int, default=5000) – Total number of boosting rounds with n_terms boosting steps per round.
early_stopping_rounds (int, default=50) – Number of rounds with no improvement to trigger early stopping. 0 turns off early stopping and boosting will occur for exactly max_rounds.
early_stopping_tolerance (float, default=1e-4) – Tolerance that dictates the smallest delta required to be considered an improvement.
min_samples_leaf (int, default=2) – Minimum number of samples allowed in the leaves.
max_leaves (int, default=3) – Maximum number of leaves allowed in each tree.
objective (str, default="rmse") – The objective to optimize. Options include: “rmse”, “poisson_deviance”, “tweedie_deviance:variance_power=1.5”, “gamma_deviance”, “pseudo_huber:delta=1.0”, “rmse_log” (rmse with a log link function)
n_jobs (int, default=-2) – Number of jobs to run in parallel. Negative integers are interpreted as following joblib’s formula (n_cpus + 1 + n_jobs), just like scikit-learn. Eg: -2 means using all threads except 1.
random_state (int or None, default=42) – Random state. None uses device_random and generates non-repeatable sequences.
- Variables:
n_features_in_ (int) – Number of features.
feature_names_in_ (List of str) – Resolved feature names. Names can come from feature_names, X, or be auto-generated.
feature_types_in_ (List of str) – Resolved feature types. Can be: ‘continuous’, ‘nominal’, or ‘ordinal’.
bins_ (List[Union[List[Dict[str, int]], List[array of float with shape
(n_cuts,)
]]]) – Per-feature list that defines how to bin each feature. Each feature in the list contains a list of binning resolutions. The first item in the binning resolution list is for binning main effect features. If there are more items in the binning resolution list, they define the binning for successive levels of resolutions. The item at index 1, if it exists, defines the binning for pairs. The last binning resolution defines the bins for all successive interaction levels. If the binning resolution list contains dictionaries, then the feature is either a ‘nominal’ or ‘ordinal’ categorical. If the binning resolution list contains arrays, then the feature is ‘continuous’ and the arrays will contain float cut points that separate continuous values into bins.feature_bounds_ (array of float with shape
(n_features, 2)
) – min/max bounds for each feature. feature_bounds_[feature_index, 0] is the min value of the feature and feature_bounds_[feature_index, 1] is the max value of the feature. Categoricals have min & max values of NaN.histogram_edges_ (List of None or array of float with shape
(n_hist_edges,)
) – Per-feature list of the histogram edges. Categorical features contain None within the List at their feature index.histogram_weights_ (List of array of float with shape
(n_hist_bins,)
) – Per-feature list of the total sample weights within each feature’s histogram bins.unique_val_counts_ (array of int with shape
(n_features,)
) – Per-feature count of the number of unique feature values.term_features_ (List of tuples of feature indices) – Additive terms used in the model and their component feature indices.
term_names_ (List of str) – List of term names.
bin_weights_ (List of array of float with shape
(n_feature0_bins, ..., n_featureN_bins)
) – Per-term list of the total sample weights in each term’s tensor bins.bagged_scores_ (List of array of float with shape
(n_outer_bags, n_feature0_bins, ..., n_featureN_bins)
) – Per-term list of the bagged model scores.term_scores_ (List of array of float with shape
(n_feature0_bins, ..., n_featureN_bins)
) – Per-term list of the model scores.standard_deviations_ (List of array of float with shape
(n_feature0_bins, ..., n_featureN_bins)
) – Per-term list of the standard deviations of the bagged model scores.link_ (str) – Link function used to convert the predictions or targets into linear space additive scores and vice versa via the inverse link. Possible values include: “custom_regression”, “power”, “identity”, “log”, “inverse”, “inverse_square”, “sqrt”
link_param_ (float) – Float value that can be used by the link function. The primary use is for the power link.
bag_weights_ (array of float with shape
(n_outer_bags,)
) – Per-bag record of the total weight within each bag.breakpoint_iteration_ (array of int with shape
(n_stages, n_outer_bags)
) – The number of boosting rounds performed within each stage until either early stopping, or the max_rounds was reached. Normally, the count of main effects boosting rounds will be in breakpoint_iteration_[0], and the count of interaction boosting rounds will be in breakpoint_iteration_[1].intercept_ (float) – Intercept of the model.
min_target_ (float) – The minimum value found in ‘y’.
max_target_ (float) – The maximum value found in ‘y’.
- copy()#
Makes a deepcopy of the EBM.
Args:
- Returns:
The new copy.
- decision_function(X, init_score=None)#
Predict scores from model before calling the link function.
- Parameters:
X – Numpy array for samples.
init_score – Optional. Either a model that can generate scores or per-sample initialization score. If samples scores it should be the same length as X.
- Returns:
The sum of the additive term contributions.
- explain_global(name=None)#
Provides global explanation for model.
- Parameters:
name – User-defined explanation name.
- Returns:
An explanation object, visualizing feature-value pairs as horizontal bar chart.
- explain_local(X, y=None, name=None, init_score=None)#
Provides local explanations for provided samples.
- Parameters:
X – Numpy array for X to explain.
y – Numpy vector for y to explain.
name – User-defined explanation name.
init_score – Optional. Either a model that can generate scores or per-sample initialization score. If samples scores it should be the same length as X.
- Returns:
An explanation object, visualizing feature-value pairs for each sample as horizontal bar charts.
- fit(X, y, sample_weight=None, bags=None, init_score=None)#
Fits model to provided samples.
- Parameters:
X – Numpy array for training samples.
y – Numpy array as training labels.
sample_weight – Optional array of weights per sample. Should be same length as X and y.
bags – Optional bag definitions. The first dimension should have length equal to the number of outer_bags. The second dimension should have length equal to the number of samples. The contents should be +1 for training, -1 for validation, and 0 if not included in the bag. Numbers other than 1 indicate how many times to include the sample in the training or validation sets.
init_score – Optional. Either a model that can generate scores or per-sample initialization score. If samples scores it should be the same length as X.
- Returns:
Itself.
- monotonize(term, increasing='auto', passthrough=0.0)#
Adjusts a term to be monotone using isotonic regression. An important consideration is that this function only adjusts a single term and will not modify pairwise terms. When a feature needs to be globally monotonic, any pairwise terms that include the feature should be excluded from the model.
- Parameters:
term – Index or name of the term to monotonize
increasing – ‘auto’ or bool. ‘auto’ decides direction based on Spearman correlation estimate.
passthrough – the process of monotonization can result in a change to the mean response of the model. If passthrough is set to 0.0 then the model’s mean response to the training set will not change. If passthrough is set to 1.0 then any change to the mean response made by monotonization will be passed through to self.intercept_. Values between 0 and 1 will result in that percentage being passed through.
- Returns:
Itself.
- predict(X, init_score=None)#
Predicts on provided samples.
- Parameters:
X – Numpy array for samples.
init_score – Optional. Either a model that can generate scores or per-sample initialization score. If samples scores it should be the same length as X.
- Returns:
Predicted class label per sample.
- predict_and_contrib(X, init_score=None)#
Predicts on provided samples, returning predictions and explanations for each sample.
- Parameters:
X – Numpy array for samples.
init_score – Optional. Either a model that can generate scores or per-sample initialization score. If samples scores it should be the same length as X.
- Returns:
Predictions and local explanations for each sample.
- remove_features(features)#
Removes features (and their associated components) from a fitted EBM. Note that this will change the structure (i.e., by removing the specified indices) of the following components of
self
:histogram_edges_
,histogram_weights_
,unique_val_counts_
,bins_
,feature_names_in_
,feature_types_in_
, andfeature_bounds_
. Also, any terms that use the features being deleted will be deleted. The following attributes that the caller passed to the __init__ function are not modified:feature_names
, andfeature_types
.- Parameters:
features – A list or enumerable of feature names or indices or booleans indicating which features to remove.
- Returns:
Itself.
- remove_terms(terms)#
Removes terms (and their associated components) from a fitted EBM. Note that this will change the structure (i.e., by removing the specified indices) of the following components of
self
:term_features_
,term_names_
,term_scores_
,bagged_scores_
,standard_deviations_
, andbin_weights_
.- Parameters:
terms – A list (or other enumerable object) of term names or indices or booleans.
- Returns:
Itself.
- scale(term, factor)#
Scale the individual term contribution by a constant factor. For example, you can nullify the contribution of specific terms by setting their corresponding weights to zero; this would cause the associated global explanations (e.g., variable importance) to also be zero. A couple of things are worth noting: 1) this method has no affect on the fitted intercept and users will have to change that attribute directly (if desired), and 2) reweighting specific term contributions will also reweight their related components in a similar manner (e.g., variable importance scores, standard deviations, etc.).
- Parameters:
term – term index or name of the term to be scaled.
factor – The amount to scale the term by.
- Returns:
Itself.
- score(X, y, sample_weight=None)#
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares
((y_true - y_pred)** 2).sum()
and \(v\) is the total sum of squares((y_true - y_true.mean()) ** 2).sum()
. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns:
score – \(R^2\) of
self.predict(X)
w.r.t. y.- Return type:
float
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- sweep(terms=True, bins=True, features=False)#
Purges unused elements from a fitted EBM.
- Parameters:
terms – Boolean indicating if zeroed terms that do not affect the output should be purged from the model.
bins – Boolean indicating if unused bin levels that do not affect the output should be purged from the model.
features – Boolean indicating if features that are not used in any terms and therefore do not affect the output should be purged from the model.
- Returns:
Itself.
- term_importances(importance_type='avg_weight')#
Provides the term importances
- Parameters:
importance_type – the type of term importance requested (‘avg_weight’, ‘min_max’)
- Returns:
An array term importances with one importance per additive term
- to_json(file=None, indent=2, detail='all')#
Exports the model to a JSON representation.
- Parameters:
file – None to return a JSONable object, a path-like object (str or os.PathLike) to write a file, or a file-like object implementing .write().
indent – If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or “” will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as ” “), that string is used to indent each level.
detail – ‘minimal’, ‘interpretable’, ‘mergeable’, ‘all’
- Returns:
JSONable object if file=None, otherwise returns None.