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Distillation Decision Tree

class DDT(teacher_predict, attr_info:dict, label_info, data:np.ndarray)

__init__(teacher_predict, attr_info:dict, label_info, data:np.ndarray)

Construct a Distillation Decision Tree Model.

Parameters:
  • teacher_prdict - The teacher (trained complex) model's predict method.
  • attr_info (dict) - The name and range of all the attributes: Continuous: {attr1: {"min": x, "max": y}, attr2: ...}; Categorical: {attr1: [c1, c2, ...], attr2: ...}.
  • label_info (dict or list) - The name and range of the labels: Continuous: {"min": x, "max": y}; Categorical: [c1, c2, ...].
  • data (ndarray) - The data values without label, which is used to train the teacher model.
Methods
__init__ Construct a Distillation Decision Tree Model
fit Build a Distillation Decision Tree Model
predict Return the predicted value for each sample
save Save the model
load Load the model

fit(stopping_criteria, **kwargs)

Build a Distillation Decision Tree Model.

Parameters:
  • stop_criteria (int) - The depth of DDT of which each node samples the pseudo data.
  • **kwargs - Other parameters of the model:
    • split_criteria (str) - The spliting criteria used for classification. Accept "Gain ratio" or "Gini index".
    • num_of_iter (int) - The number of times sampling the data for each size. Default 100.
    • min_sample_each (int) - The minimum sample size for sampling each attribute. Default 2.
    • max_sample_size (int) - The maximum sample size. Default None. If specified, stable_sample_size function will stop when the sample size reaches the limit.
    • stability (tuple or list) - The first and second level stability. If specified, the stable_sample_size function will stop when the both stability are satisfied.
    • pca (bool) - Whether doing PCA on the samples. Default False.
    • criteria (float) - The precentage of variance explained by the first t new covariates. Default 0.9. Only work when pca is True.
    • train_test_split_para (dict) - The parameters passed to sklearn's train_test_split method.
    • lgb_para (dict) - The parameters passed to LightGBM Classifer or Regressor.
    • fit_para (dict) - The parameters passed to the fit method of LightGBM.
Returns:
  • root - The root node of DDT.

predict(root_node:Node, data:pd.DataFrame)

Return the predicted value for each sample.

Parameters:
  • root_node (Node) - The root of DDT.
  • data (DataFrame) - The data without labels.
Returns:
  • label (Series) - A Series of predicted values.

save(filepath:str)

Save the root node.

Parameters:
  • filepath (str) - The model path.

load(filepath:str)

Load the root node.

Parameters:
  • filepath (str) - The model path.
Returns:
  • root (Node) - The root node.