default_predictor.py 文件源码

def get_feature_stats(self):

        # #get input feature
        feature_input=self.feature_input.currentText()
        try:
            if feature_input[0]=='X':
                try:
                    feature_index=int("".join(feature_input[1:]))
                    feature_index-=1

                except:
                    QtWidgets.QMessageBox.information(self, "Wrong Format","Please enter a feature name in the format: X%d.")
                    return
            elif "".join(feature_input[0]+feature_input[1])=='LD' or "".join(feature_input[0]+feature_input[1])=='PC':
                try:
                    feature_index=int("".join(feature_input[2:]))
                    feature_index-=1

                except:
                    QtWidgets.QMessageBox.information(self, "Wrong Format","Please enter a feature name in the format: X||LD||PC%d.")
                    return
            else:
                QtWidgets.QMessageBox.information(self, "Wrong Format","Feature names must be in the format: X%d.")
                return
        except:
            QtWidgets.QMessageBox.information(self, "Data Not Found","Please load a dataset first.")
            return


        try:
            max_value=self.X[:,feature_index].max()
            min_value=self.X[:,feature_index].min()
            mean_value=self.X[:,feature_index].mean()
            std_value=self.X[:,feature_index].std()
            var_value=self.X[:,feature_index].var()
            skewness=stats.skew(self.X[:,feature_index])
            kurtosis=stats.kurtosis(self.X[:,feature_index],fisher=True)
            chi2,chi_p_val=chi2_feature_test(self.X,self.y,int(feature_index))
            H_kw,kw_p_val=kw_feature_test(self.X,self.y,int(feature_index))
            info_gain=information_gain(self.X,self.y,int(feature_index))
            gain_rt=gain_ratio(self.X,self.y,int(feature_index))

        except:
            QtWidgets.QMessageBox.information(self, "Wrong Index","Feature Index Out Of Bounds.")
            return

        feature_stats="""Statistics:\n\nMinimum Value: """+str(min_value)\
                      +"""\n\nMaximum Value: """+str(max_value)\
                      +"""\n\nMean: """+str(mean_value)\
                      +"""\n\nStandard Deviation: """+str(std_value)\
                      +"""\n\nVariance: """+str(var_value)\
                      +"""\n\nSkewness: """+str(skewness)\
                      +"""\n\nKurtosis: """+str(kurtosis)\
                      +"""\n\nChi Squared Test: """+str(chi2[0])\
                      +"""\n\nKruskal-Wallis Test:  """+str(H_kw)\
                      +"""\n\nInformation Gain: """+str(info_gain)\
                      +"""\n\nGain Ratio: """+str(gain_rt)

        self.feature_stats.setText(feature_stats)