signal_extractor.py 文件源码

def basic_quality(self, target, feature_vector):
        assert (len(target) == len(feature_vector))
        if self.quality == 'NWP':
            sort_data_p = np.array([x for (y,x) in sorted(zip(feature_vector, target), key=lambda x: x[0])])
            sort_data_n = np.array([x for (y,x) in sorted(zip(-1.0 * feature_vector, target), key=lambda x: x[0])])
            p_nwp = QualityMeasure.calc_nwp(sort_data_p)
            n_nwp = QualityMeasure.calc_nwp(sort_data_n)
            return min(n_nwp, p_nwp)
        if self.quality == 'corrcoef':
            return 1 - abs(np.corrcoef(target, feature_vector)[0][1])
        if self.quality == 'mutual_info':
            m = MINE()
            m.compute_score(target, feature_vector)
            return 1.0 - m.mic()
        if self.quality == 'chi2':
            return 1 - chi2(abs(feature_vector.reshape(len(feature_vector), 1)), target)[0][0]
        if self.quality == 'distcorr':
            return 1 - distcorr(target, feature_vector)
        if self.quality == 'distree':
            data = np.column_stack((feature_vector, self.random_feature))
            clf = DecisionTreeClassifier(max_depth=5,  random_state=0)
            clf.fit(data, target)
            return 1.0 - clf.feature_importances_[0]
        if self.quality == 'knnscore':
            errors = []
            clf = KNeighborsClassifier()
            data = np.array([feature_vector]).transpose()
            loo = LeaveOneOut()
            for train, test in loo.split(data):
                clf = KNeighborsClassifier()
                clf.fit(data[train], target[train])
                errors.append(accuracy_score(target[test], clf.predict(data[test])))
            return 1.0 - np.mean(errors)
        return 'WRONG QUALITY NAME'