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 across all splits the feature is used in.
        * 'cover': the average coverage across all splits the feature is used in.
        * 'total_gain': the total gain across all splits the feature is used in.
        * 'total_cover': the total coverage across all splits the feature is used in.

        .. note:: Feature importance is defined only for tree boosters

            Feature importance is only defined when the decision tree model is chosen as base
            learner (`booster=gbtree`). It is not defined for other base learner types, such
            as linear learners (`booster=gblinear`).

        Parameters
        ----------
        fmap: str (optional)
           The name of feature map file.
        importance_type: str, default 'weight'
            One of the importance types defined above.
        """
        if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
            raise ValueError('Feature importance is not defined for Booster type {}'
                             .format(self.booster))

        allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
        if importance_type not in allowed_importance_types:
            msg = ("importance_type mismatch, got '{}', expected one of " +
                   repr(allowed_importance_types))
            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

        average_over_splits = True
        if importance_type == 'total_gain':
            importance_type = 'gain'
            average_over_splits = False
        elif importance_type == 'total_cover':
            importance_type = 'cover'
            average_over_splits = False

        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
        if average_over_splits:
            for fid in gmap:
                gmap[fid] = gmap[fid] / fmap[fid]

        return gmap
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 across all splits the feature is used in.
        * 'cover': the average coverage across all splits the feature is used in.
        * 'total_gain': the total gain across all splits the feature is used in.
        * 'total_cover': the total coverage across all splits the feature is used in.

        .. note:: Feature importance is defined only for tree boosters

            Feature importance is only defined when the decision tree model is chosen as base
            learner (`booster=gbtree`). It is not defined for other base learner types, such
            as linear learners (`booster=gblinear`).

        Parameters
        ----------
        fmap: str (optional)
           The name of feature map file.
        importance_type: str, default 'weight'
            One of the importance types defined above.