def compute_scores(self, dataframe: pd.DataFrame, classes: np.array): bin_dataframe = dataframe._binarize_categorical_values() self.clf = TDMetric() self.values = self.clf.findLeafDepthWithoutPrunning( bin_dataframe, classes) print("TD without prunning done sucessfully!") return self
def compute_scores(self, dataframe: pd.DataFrame, classes: np.array): if "min_impurity_split" in self.settings: minimum_impurity_split = float(self.settings["min_impurity_split"]) bin_dataframe = dataframe._binarize_categorical_values() self.clf = TDMetric() self.values = self.clf.findLeafDepthWithPrunning( bin_dataframe, classes, minimum_impurity_split) print("TD with prunning done sucessfully!") return self
class TDWithPrunning(AbstractDetector): name = "TDWithPrunning" data_type = "REAL" def compute_scores(self, dataframe: pd.DataFrame, classes: np.array): min_impurity_split = float(self.settings.get("min_impurity_split", 0.5)) bin_dataframe = dataframe._binarize_categorical_values() self.clf = TDMetric() self.values = self.clf.findLeafDepthWithPrunning( bin_dataframe, classes, min_impurity_split) # print("TD with prunning done sucessfully!") return self