5fe98231a26e08d81f9684a681af43bc2fb2b847,pyinterpret/data/dataset.py,DataSet,generate_grid,#DataSet#Any#Any#Any#,60

Before Change

        assert isinstance(grid_resolution, int) and grid_resolution > 0, grid_resolution_warning

        feature_id_warning = "Must pass in feature ids contained in DataSet.feature_ids"
        assert all(feature_id in self.feature_ids for feature_id in feature_ids), feature_id_warning

        grid_range = [x * 100 for x in grid_range]
        bins = np.linspace(*grid_range, num=grid_resolution)
        grid = []

After Change

                                There are as many columns as specified by grid_resolution
        

        if not all(i >= 0 and i <= 1 for i in grid_range):
            err_msg = "Grid range values must be between 0 and 1 but got:" \
                                 "{}".format(grid_range)
            raise exceptions.MalformedGridRangeError(err_msg)

        if not isinstance(grid_resolution, int) and grid_resolution > 0:
            err_msg = "Grid resolution {} is not a positive integer".format(grid_resolution)
            raise exceptions.MalformedGridRangeError(err_msg)

        if not all(feature_id in self.feature_ids for feature_id in feature_ids):
            missing_features = []
            for feature_id in feature_ids:
                if feature_id not in self.feature_ids:
                    missing_features.append(feature_id)
            err_msg = "Feature ids {} not found in DataSet.feature_ids".format(missing_features)
            raise KeyError(err_msg)

        grid_range = [x * 100 for x in grid_range]
        bins = np.linspace(*grid_range, num=grid_resolution)
        grid = []
        for feature_id in feature_ids:
            vals = np.percentile(self[feature_id], bins)
            grid.append(vals)
        grid = np.array(grid)
        self.logger.info("Generated grid of shape {}".format(grid.shape))
        return grid

    def _build_metastore(self, bin_count):

Instances