def confusion_matrix(self, pos_label=None, cutoff: float = 0.5):
pos_label = self.classes[1] if (
pos_label == None and len(self.classes) == 2) else pos_label
if (pos_label in self.classes and cutoff < 1 and cutoff > 0):
input_relation = self.deploySQL(
) + " WHERE predict_knc = '{}'".format(pos_label)
y_score = "(CASE WHEN proba_predict > {} THEN 1 ELSE 0 END)".format(
cutoff)
y_true = "DECODE({}, '{}', 1, 0)".format(self.y, pos_label)
result = confusion_matrix(y_true, y_score, input_relation,
self.cursor)
if pos_label == 1:
return (result)
else:
return (tablesample(values={
"index":
["Non-{}".format(pos_label), "{}".format(pos_label)],
"Non-{}".format(pos_label):
result.values[0],
"{}".format(pos_label):
result.values[1]
},
table_info=False))
else:
input_relation = "(SELECT *, ROW_NUMBER() OVER(PARTITION BY {}, row_id ORDER BY proba_predict DESC) AS pos FROM {}) knc_table_predict WHERE pos = 1".format(
", ".join(self.X), self.deploySQL())
return (multilabel_confusion_matrix(self.y, "predict_knc",
input_relation, self.cursor,
self.classes))
def confusion_matrix(self, pos_label=None, cutoff: float = 0.5):
pos_label = self.classes[1] if (
pos_label == None and len(self.classes) == 2) else pos_label
if (pos_label in self.classes and cutoff < 1 and cutoff > 0):
input_relation = self.deploySQL(
) + " WHERE predict_nc = '{}'".format(pos_label)
y_score = "(CASE WHEN proba_predict > {} THEN 1 ELSE 0 END)".format(
cutoff)
y_true = "DECODE({}, '{}', 1, 0)".format(self.y, pos_label)
result = confusion_matrix(y_true, y_score, input_relation,
self.cursor)
if pos_label == 1:
return (result)
else:
return (tablesample(values={
"index":
["Non-{}".format(pos_label), "{}".format(pos_label)],
"Non-{}".format(pos_label):
result.values[0],
"{}".format(pos_label):
result.values[1]
},
table_info=False))
else:
return (multilabel_confusion_matrix(self.y, "predict_nc",
self.deploySQL(predict=True),
self.cursor, self.classes))
def confusion_matrix(self, pos_label=None, cutoff: float = 0.5):
pos_label = self.classes[1] if (
pos_label == None and len(self.classes) == 2) else pos_label
if (pos_label in self.classes and cutoff < 1 and cutoff > 0):
return (confusion_matrix(self.y,
self.deploySQL(pos_label, cutoff),
self.test_relation,
self.cursor,
pos_label=pos_label))
else:
return (multilabel_confusion_matrix(self.y, self.deploySQL(),
self.test_relation,
self.cursor, self.classes))
def confusion_matrix(self, cutoff: float = 0.5):
return (confusion_matrix(self.y, self.deploySQL(cutoff),
self.test_relation, self.cursor))