srlearn.rdn.BoostedRDNClassifier¶

class srlearn.rdn.BoostedRDNClassifier(background=None, target='None', n_estimators=10, node_size=2, max_tree_depth=3, neg_pos_ratio=2, solver=None)[source]¶

Relational Dependency Networks Estimator

Wrappers around BoostSRL for learning and inference with Relational Dependency Networks written with a scikit-learn-style interface derived from sklearn.base.BaseEstimator

Similar to sklearn.ensemble.GradientBoostingClassifier, this builds a model by fitting a series of regression trees.

Examples

>>> from srlearn.rdn import BoostedRDNClassifier
>>> from srlearn import Background
>>> from srlearn.datasets import load_toy_cancer
>>> train, test = load_toy_cancer()
>>> bk = Background(modes=train.modes)
>>> dn = BoostedRDNClassifier(background=bk, target="cancer")
>>> dn.fit(train)
BoostedRDNClassifier(background=setParam: numOfClauses=100.
setParam: numOfCycles=100.
usePrologVariables: true.
setParam: nodeSize=2.
setParam: maxTreeDepth=3.
mode: friends(+Person,-Person).
mode: friends(-Person,+Person).
mode: smokes(+Person).
mode: cancer(+Person).
, n_estimators=10, neg_pos_ratio=2, solver='BoostSRL', target='cancer')
>>> dn.predict(test)
array([ True,  True,  True, False, False])
__init__(background=None, target='None', n_estimators=10, node_size=2, max_tree_depth=3, neg_pos_ratio=2, solver=None)[source]¶

Initialize a BoostedRDN

Parameters:
background : srlearn.background.Background (default: None)

Background knowledge with respect to the database

target : str (default: “None”)

Target predicate to learn

n_estimators : int, optional (default: 10)

Number of trees to fit

node_size : int, optional (default: 2)

Maximum number of literals in each node.

max_tree_depth : int, optional (default: 3)

Maximum number of nodes from root to leaf (height) in the tree.

neg_pos_ratio : int or float, optional (default: 2)

Ratio of negative to positive examples used during learning.
Attributes:
estimators_ : array, shape (n_estimators)

Return the boosted regression trees

feature_importances_ : array, shape (n_features)

Return the feature importances (based on how often each feature appears)
feature_importances_¶

Return the features contained in a tree.

Parameters:
tree_number: int

Index of the tree to read.
fit(database)[source]¶

Learn structure and parameters.

Fit the structure and parameters of a Relational Dependency Network using a database of positive examples, negative examples, facts, and any relevant background knowledge.

Parameters:
database : srlearn.database.Database

Database containing examples and facts.
Returns:
self : object

Returns self.

Notes

The underlying algorithm is based on the “Relational Functional Gradient Boosting” as described in [1].

This fit function is based on subprocess calling the BoostSRL jar files. This will require a Java runtime to also be available. See [2].

[1]Sriraam Natarajan, Tushar Khot, Kristian Kersting, and Jude Shavlik, “Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine”. SpringerBriefs in Computer Science, ISBN: 978-3-319-13643-1, 2015
[2]https://starling.utdallas.edu/software/boostsrl/
from_json(file_name)¶

Load a learned model from json.

Parameters:
file_name : str (or pathlike)

Path to a saved json file.
predict(database)[source]¶

Use the learned model to predict on new data.

Parameters:
database : srlearn.Database

Database containing examples and facts.
Returns:
results : ndarray

Positive or negative class.
predict_proba(database)[source]¶

Return class probabilities.

Parameters:
database : srlearn.Database

Database containing examples and facts.
Returns:
results : ndarray

Probability of belonging to the positive class
to_json(file_name) → None¶

Serialize a learned model to json.

Parameters:
file_name : str (or pathlike)

Path to a saved json file.