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