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- from typing import List
- import pandas as pd
- from sklearn.base import ClassifierMixin, RegressorMixin
- from imodels.rule_set.rule_fit import RuleFit
- from imodels.util.convert import itemsets_to_rules
- from imodels.util.extract import extract_fpgrowth
- class FPLasso(RuleFit):
- def __init__(self,
- minsupport=0.1,
- maxcardinality=2,
- verbose=False,
- n_estimators=100,
- tree_size=4,
- sample_fract='default',
- max_rules=2000,
- memory_par=0.01,
- tree_generator=None,
- lin_trim_quantile=0.025,
- lin_standardise=True,
- exp_rand_tree_size=True,
- include_linear=True,
- alpha=None,
- random_state=None):
- super().__init__(n_estimators,
- tree_size,
- sample_fract,
- max_rules,
- memory_par,
- tree_generator,
- lin_trim_quantile,
- lin_standardise,
- exp_rand_tree_size,
- include_linear,
- alpha,
- random_state)
- self.minsupport = minsupport
- self.maxcardinality = maxcardinality
- self.verbose = verbose
- def fit(self, X, y=None, feature_names=None, undiscretized_features=[]):
- self.undiscretized_features = undiscretized_features
- super().fit(X, y, feature_names=feature_names)
- return self
- def _extract_rules(self, X, y) -> List[str]:
- X = pd.DataFrame(X, columns=self.feature_placeholders)
- itemsets = extract_fpgrowth(X, minsupport=self.minsupport,
- maxcardinality=self.maxcardinality,
- verbose=self.verbose)
- return itemsets_to_rules(itemsets)
- class FPLassoRegressor(FPLasso, RegressorMixin):
- def _init_prediction_task(self):
- self.prediction_task = 'regression'
- class FPLassoClassifier(FPLasso, ClassifierMixin):
- def _init_prediction_task(self):
- self.prediction_task = 'classification'
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