For the package installation, first install all the requirements and then install the leaf_att_forest package.
$ pip install -r requirements.txt
$ python setup.py install
The model interface is scikit-learn like, except
it is extended with optimize_weights method which can be executed with the same training data as used for an underlying forest training (see example), or with a new data set (see example).
Code example for model instantiation:
from leaf_att_forest import (
GLAFParams,
GammaLeafAttentionForest,
ForestKind,
TaskType,
)
model = GammaLeafAttentionForest(
GLAFParams(
kind=ForestKind.EXTRA,
task=TaskType.REGRESSION,
# Gamma-Leaf Attention Forest Parameters
leaf_tau=1.0,
leaf_attention=True,
n_tau=5,
fit_tree_weights=True,
# Base forest parameters
forest=dict(
n_estimators=200,
max_depth=None,
min_samples_leaf=5,
random_state=12345,
),
)
)
After the underlying forest should be trained:
model.fit(X_train, y_train)
And then weights are optimized:
model.optimize_weights(X_train, y_train)
In order to estimate weights optimization impact scores for model.predict_original(X_val) and model.predict(X_val) could be compared.
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