def get_fscore(self, fmap=‘‘):        """Get feature importance of each feature.        Parameters        ----------        fmap: str (optional)           The name of feature map file        """        return self.get_score(fmap, importance_type=‘weight‘)    def get_score(self, fmap=‘‘, importance_type=‘weight‘):        """Get feature importance of each feature.        Importance type can be defined as:            ‘weight‘ - the number of times a feature is used to split the data across all trees.            ‘gain‘ - the average gain of the feature when it is used in trees            ‘cover‘ - the average coverage of the feature when it is used in trees        Parameters        ----------        fmap: str (optional)           The name of feature map file        """        if importance_type not in [‘weight‘, ‘gain‘, ‘cover‘]:            msg = "importance_type mismatch, got ‘{}‘, expected ‘weight‘, ‘gain‘, or ‘cover‘"            raise ValueError(msg.format(importance_type))        # if it‘s weight, then omap stores the number of missing values        if importance_type == ‘weight‘:            # do a simpler tree dump to save time            trees = self.get_dump(fmap, with_stats=False)            fmap = {}            for tree in trees:                for line in tree.split(‘\n‘):                    # look for the opening square bracket                    arr = line.split(‘[‘)                    # if no opening bracket (leaf node), ignore this line                    if len(arr) == 1:                        continue                    # extract feature name from string between []                    fid = arr[1].split(‘]‘)[0].split(‘<‘)[0]                    if fid not in fmap:                        # if the feature hasn‘t been seen yet                        fmap[fid] = 1                    else:                        fmap[fid] += 1            return fmap        else:            trees = self.get_dump(fmap, with_stats=True)            importance_type += ‘=‘            fmap = {}            gmap = {}            for tree in trees:                for line in tree.split(‘\n‘):                    # look for the opening square bracket                    arr = line.split(‘[‘)                    # if no opening bracket (leaf node), ignore this line                    if len(arr) == 1:                        continue                    # look for the closing bracket, extract only info within that bracket                    fid = arr[1].split(‘]‘)                    # extract gain or cover from string after closing bracket                    g = float(fid[1].split(importance_type)[1].split(‘,‘)[0])                    # extract feature name from string before closing bracket                    fid = fid[0].split(‘<‘)[0]                    if fid not in fmap:                        # if the feature hasn‘t been seen yet                        fmap[fid] = 1                        gmap[fid] = g                    else:                        fmap[fid] += 1                        gmap[fid] += g            # calculate average value (gain/cover) for each feature            for fid in gmap:                gmap[fid] = gmap[fid] / fmap[fid]            return gmap