DPExplainableBoostingClassifier#

Link to Algorithm description: DPEBM

class interpret.privacy.DPExplainableBoostingClassifier(feature_names=None, feature_types=None, max_bins=32, exclude=[], validation_size=0, outer_bags=1, learning_rate=0.01, max_rounds=300, max_leaves=3, n_jobs=- 2, random_state=None, epsilon=1.0, delta=1e-05, composition='gdp', bin_budget_frac=0.1, privacy_bounds=None)#

Differentially Private Explainable Boosting Classifier. Note that many arguments are defaulted differently than regular EBMs.

Parameters:
  • feature_names (list of str, default=None) – List of feature names.

  • feature_types (list of FeatureType, default=None) –

    List of feature types. For DP-EBMs, feature_types should be fully specified. The auto-detector, if used, examines the data and is not included in the privacy budget. If auto-detection is used, a privacy warning will be issued. FeatureType can be:

    • None: Auto-detect (privacy budget is not respected!).

    • ’continuous’: Use private continuous binning.

    • [List of str]: Ordinal categorical where the order has meaning. Eg: [“low”, “medium”, “high”]. Uses private categorical binning.

    • ’ordinal’: Ordinal categorical where the order is determined by sorting the feature strings. Uses private categorical binning.

    • ’nominal’: Categorical where the order has no meaning. Eg: country names. Uses private categorical binning.

  • max_bins (int, default=32) – Max number of bins per feature.

  • exclude (list of tuples of feature indices|names, default=[]) – Features to be excluded.

  • validation_size (int or float, default=0) –

    Validation set size. A validation set is needed if outer bags or error bars are desired.

    • 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: Outer bags have no utility and error bounds will be eliminated

  • outer_bags (int, default=1) – Number of outer bags. Outer bags are used to generate error bounds and help with smoothing the graphs.

  • learning_rate (float, default=0.01) – Learning rate for boosting.

  • max_rounds (int, default=300) – Total number of boosting rounds with n_terms boosting steps per round.

  • max_leaves (int, default=3) – Maximum number of leaves allowed in each tree.

  • 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=None) – Random state. None uses device_random and generates non-repeatable sequences. Should be set to ‘None’ for privacy, but can be set to an integer for testing and repeatability.

  • epsilon (float, default=1.0) – Total privacy budget to be spent.

  • delta (float, default=1e-5) – Additive component of differential privacy guarantee. Should be smaller than 1/n_training_samples.

  • composition ({'gdp', 'classic'}, default='gdp') – Method of tracking noise aggregation.

  • bin_budget_frac (float, default=0.1) – Percentage of total epsilon budget to use for private binning.

  • privacy_bounds (Union[np.ndarray, Mapping[Union[int, str], Tuple[float, float]]], default=None) – Specifies known min/max values for each feature. If None, DP-EBM shows a warning and uses the data to determine these values.

Variables:
  • classes_ (array of bool, int, or unicode with shape (2,)) – The class labels. DPExplainableBoostingClassifier only supports binary classification, so there are 2 classes.

  • 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.

  • 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_bins)) – Per-term list of the total sample weights in each term’s bins.

  • bagged_scores_ (List of array of float with shape (n_outer_bags, n_bins)) – Per-term list of the bagged model scores.

  • term_scores_ (List of array of float with shape (n_bins)) – Per-term list of the model scores.

  • standard_deviations_ (List of array of float with shape (n_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: “monoclassification”, “custom_binary”, “custom_ovr”, “custom_multinomial”, “mlogit”, “vlogit”, “logit”, “probit”, “cloglog”, “loglog”, “cauchit”

  • link_param_ (float) – Float value that can be used by the link function. For classification it is only used by “custom_classification”.

  • 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. Normally, the count of main effects boosting rounds will be in breakpoint_iteration_[0].

  • intercept_ (array of float with shape (1,)) – Intercept of the model.

  • bagged_intercept_ (array of float with shape (n_outer_bags,)) – Bagged intercept of the model.

  • noise_scale_binning_ (float) – The noise scale during binning.

  • noise_scale_boosting_ (float) – The noise scale during boosting.

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.

eval_terms(X)#

The term scores returned will be identical to the local explanation values obtained by calling ebm.explain_local(X). Calling interpret.utils.inv_link(ebm.eval_terms(X).sum(axis=1) + ebm.intercept_, ebm.link_) is equivalent to calling ebm.predict(X) for regression or ebm.predict_proba(X) for classification.

Parameters:

X – Numpy array for samples.

Returns:

local explanation scores for each term of each sample.

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_proba(X, init_score=None)#

Probability estimates 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:

Probability estimate of sample for each class.

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_, and feature_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, and feature_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_, and bin_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 mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:
  • X (array-like of shape (n_samples, n_features)) – Test samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

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, detail='all', indent=2)#

Exports the model to a JSON text file.

Parameters:
  • file – a path-like object (str or os.PathLike), or a file-like object implementing .write().

  • detail – ‘minimal’, ‘interpretable’, ‘mergeable’, ‘all’

  • 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.

to_jsonable(detail='all')#

Converts the model to a JSONable representation.

Parameters:

detail – ‘minimal’, ‘interpretable’, ‘mergeable’, ‘all’

Returns:

JSONable object