Source code for lux.action.enhance

#  Copyright 2019-2020 The Lux Authors.
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  See the License for the specific language governing permissions and
#  limitations under the License.

import lux
from lux.interestingness.interestingness import interestingness
from lux.processor.Compiler import Compiler
from lux.utils import utils

[docs]def enhance(ldf): """ Given a set of vis, generates possible visualizations when an additional attribute is added to the current vis. Parameters ---------- ldf : lux.core.frame LuxDataFrame with underspecified intent. Returns ------- recommendations : Dict[str,obj] object with a collection of visualizations that result from the Enhance action. """ filters = utils.get_filter_specs(ldf._intent) # Collect variables that already exist in the intent attr_specs = list(filter(lambda x: x.value == "" and x.attribute != "Record", ldf._intent)) fltr_str = [fltr.attribute + fltr.filter_op + str(fltr.value) for fltr in filters] attr_str = [str(clause.attribute) for clause in attr_specs] intended_attrs = f'<p class="highlight-intent">{", ".join(attr_str + fltr_str)}</p>' if len(attr_specs) == 1: recommendation = { "action": "Enhance", "description": f"Augmenting current {intended_attrs} intent with additional attribute.", "long_description": f"Enhance adds an additional attribute displaying how {intended_attrs} changes with respect to other attributes. Visualizations are ranked based on interestingness. The top 15 visualizations are displayed.", } elif len(attr_specs) == 2: recommendation = { "action": "Enhance", "description": f"Further breaking down current {intended_attrs} intent by additional attribute.", "long_description": f"Enhance adds an additional attribute as the color to break down the {intended_attrs} distribution", } # if there are too many column attributes, return don't generate Enhance recommendations else: recommendation = {"action": "Enhance"} recommendation["collection"] = [] return recommendation intent = ldf._intent.copy() # Clear channel so that channel not enforced based on input vis intent for clause in intent: = "" intent = filters + attr_specs intent.append("?") vlist = lux.vis.VisList.VisList(intent, ldf) # Then use the data populated in the vis list to compute score for vis in vlist: vis.score = interestingness(vis, ldf) vlist.sort() vlist = vlist.showK() recommendation["collection"] = vlist return recommendation