srlearn.rdn.BoostedRDNRegressor

class srlearn.rdn.BoostedRDNRegressor(background=None, target='None', n_estimators=10, node_size=2, max_tree_depth=3, neg_pos_ratio=2, solver='BoostSRL')

Relational Dependency Networks Regressor

Wrappers around BoostSRL for learning and inference of RDNs for regression task.

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

Examples

>>> from srlearn.rdn import BoostedRDNRegressor
>>> from srlearn import Background
>>> from srlearn import Database
>>> train = Database.from_files(
...     pos="../datasets/Boston/train/pos.pl",
...     neg="../datasets/Boston/train/neg.pl",
...     facts="../datasets/Boston/train/facts.pl",
...     lazy_load=False,
... )
>>> test = Database.from_files(
...     pos="../datasets/Boston/test/pos.pl",
...     neg="../datasets/Boston/test/neg.pl",
...     facts="../datasets/Boston/test/facts.pl",
...     lazy_load=False,
... )
>>> train.modes = ["crim(+id,#varsrim).",
...     "zn(+id,#varzn).",
...     "indus(+id,#varindus).",
...     "chas(+id,#varchas).",
...     "nox(+id,#varnox).",
...     "rm(+id,#varrm).",
...     "age(+id,#varage).",
...     "dis(+id,#vardis).",
...     "rad(+id,#varrad).",
...     "tax(+id,#vartax).",
...     "ptratio(+id,#varptrat).",
...     "b(+id,#varb).",
...     "lstat(+id,#varlstat).",
...     "medv(+id)."]
>>> bk = Background(modes=train.modes)
>>> reg = BoostedRDNRegressor(background=bk, target="medv", n_estimators=5)
>>> reg.fit(train)
BoostedRDNRegressor(background=setParam: numOfClauses=100.
setParam: numOfCycles=100.
usePrologVariables: true.
setParam: nodeSize=2.
setParam: maxTreeDepth=3.
mode: crim(+id,#varsrim).
mode: zn(+id,#varzn).
mode: indus(+id,#varindus).
mode: chas(+id,#varchas).
mode: nox(+id,#varnox).
mode: rm(+id,#varrm).
mode: age(+id,#varage).
mode: dis(+id,#vardis).
mode: rad(+id,#varrad).
mode: tax(+id,#vartax).
mode: ptratio(+id,#varptrat).
mode: b(+id,#varb).
mode: lstat(+id,#varlstat).
mode: medv(+id).
, n_estimators=5, neg_pos_ratio=2, solver='BoostSRL', target='medv')
>>> reg.predict(test)   
array([10.04313307 13.55804603 20.549378   18.14681934 23.9393469  10.01292162
     29.83298024 20.34668817 27.81642572 32.04067867  9.41342835 20.975001
     19.21966845])

__init__(background=None, target='None', n_estimators=10, node_size=2, max_tree_depth=3, neg_pos_ratio=2, solver='BoostSRL')

Initialize a BoostedRDN

Parameters

backgroundsrlearn.background.Background (default: None)

Background knowledge with respect to the database

targetstr (default: “None”)

Target predicate to learn

n_estimatorsint, optional (default: 10)

Number of trees to fit

node_sizeint, optional (default: 2)

Maximum number of literals in each node.

max_tree_depthint, optional (default: 3)

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

neg_pos_ratioint 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)