示例#1
0
	def score(self, cutoff: float = 0.5, method: str = "accuracy"):
		if (method in ("accuracy", "acc")):
			return (accuracy_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method == "auc"):
			return auc(self.y, self.deploySQL(), self.test_relation, self.cursor)
		elif (method == "prc_auc"):
			return prc_auc(self.y, self.deploySQL(), self.test_relation, self.cursor)
		elif (method in ("best_cutoff", "best_threshold")):
			return (roc_curve(self.y, self.deploySQL(), self.test_relation, self.cursor, best_threshold = True))
		elif (method in ("recall", "tpr")):
			return (recall_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("precision", "ppv")):
			return (precision_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("specificity", "tnr")):
			return (specificity_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("negative_predictive_value", "npv")):
			return (precision_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("log_loss", "logloss")):
			return (log_loss(self.y, self.deploySQL(), self.test_relation, self.cursor))
		elif (method == "f1"):
			return (f1_score(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method == "mcc"):
			return (matthews_corrcoef(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("bm", "informedness")):
			return (informedness(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("mk", "markedness")):
			return (markedness(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		elif (method in ("csi", "critical_success_index")):
			return (critical_success_index(self.y, self.deploySQL(cutoff), self.test_relation, self.cursor))
		else:
			raise ValueError("The parameter 'method' must be in accuracy|auc|prc_auc|best_cutoff|recall|precision|log_loss|negative_predictive_value|specificity|mcc|informedness|markedness|critical_success_index")
示例#2
0
 def score(self,
           pos_label=None,
           cutoff: float = 0.5,
           method: str = "accuracy"):
     pos_label = self.classes[1] if (
         pos_label == None and len(self.classes) == 2) else pos_label
     input_relation = self.deploySQL() + " WHERE predict_knc = '{}'".format(
         pos_label)
     y_score = "(CASE WHEN proba_predict > {} THEN 1 ELSE 0 END)".format(
         cutoff)
     y_proba = "proba_predict"
     y_true = "DECODE({}, '{}', 1, 0)".format(self.y, pos_label)
     if (pos_label not in self.classes):
         raise ValueError(
             "'pos_label' must be one of the response column classes")
     elif (cutoff >= 1 or cutoff <= 0):
         raise ValueError("'cutoff' must be in ]0;1[")
     if (method in ("accuracy", "acc")):
         return (accuracy_score(y_true, y_score, input_relation,
                                self.cursor))
     elif (method == "auc"):
         return (auc(y_true, y_proba, input_relation, self.cursor))
     elif (method == "prc_auc"):
         return (prc_auc(y_true, y_proba, input_relation, self.cursor))
     elif (method in ("best_cutoff", "best_threshold")):
         return (roc_curve(y_true,
                           y_proba,
                           input_relation,
                           self.cursor,
                           best_threshold=True))
     elif (method in ("recall", "tpr")):
         return (recall_score(y_true, y_score, input_relation, self.cursor))
     elif (method in ("precision", "ppv")):
         return (precision_score(y_true, y_score, input_relation,
                                 self.cursor))
     elif (method in ("specificity", "tnr")):
         return (specificity_score(y_true, y_score, input_relation,
                                   self.cursor))
     elif (method in ("negative_predictive_value", "npv")):
         return (precision_score(y_true, y_score, input_relation,
                                 self.cursor))
     elif (method in ("log_loss", "logloss")):
         return (log_loss(y_true, y_proba, input_relation, self.cursor))
     elif (method == "f1"):
         return (f1_score(y_true, y_score, input_relation, self.cursor))
     elif (method == "mcc"):
         return (matthews_corrcoef(y_true, y_score, input_relation,
                                   self.cursor))
     elif (method in ("bm", "informedness")):
         return (informedness(y_true, y_score, input_relation, self.cursor))
     elif (method in ("mk", "markedness")):
         return (markedness(y_true, y_score, input_relation, self.cursor))
     elif (method in ("csi", "critical_success_index")):
         return (critical_success_index(y_true, y_score, input_relation,
                                        self.cursor))
     else:
         raise ValueError(
             "The parameter 'method' must be in accuracy|auc|prc_auc|best_cutoff|recall|precision|log_loss|negative_predictive_value|specificity|mcc|informedness|markedness|critical_success_index"
         )