# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import lux
from lux.interestingness.interestingness import interestingness
from lux.vis.Vis import Vis
from lux.vis.VisList import VisList
from lux.processor.Compiler import Compiler
from lux.utils import utils
[docs]def filter(ldf):
"""
Iterates over all possible values of a categorical variable and generates visualizations where each categorical value filters the data.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Filter action.
"""
filters = utils.get_filter_specs(ldf._intent)
filter_values = []
output = []
# if fltr is specified, create visualizations where data is filtered by all values of the fltr's categorical variable
column_spec = utils.get_attrs_specs(ldf.current_vis[0].intent)
column_spec_attr = list(map(lambda x: x.attribute, column_spec))
if len(filters) == 1:
# get unique values for all categorical values specified and creates corresponding filters
fltr = filters[0]
if ldf.data_type_lookup[fltr.attribute] == "nominal":
recommendation = {
"action": "Filter",
"description": f"Changing the <p class='highlight-intent'>{fltr.attribute}</p> filter to an alternative value.",
}
unique_values = ldf.unique_values[fltr.attribute]
filter_values.append(fltr.value)
# creates vis with new filters
for val in unique_values:
if val not in filter_values:
new_spec = column_spec.copy()
new_filter = lux.Clause(attribute=fltr.attribute, value=val)
new_spec.append(new_filter)
temp_vis = Vis(new_spec)
output.append(temp_vis)
elif ldf.data_type_lookup[fltr.attribute] == "quantitative":
recommendation = {
"action": "Filter",
"description": f"Changing the <p class='highlight-intent'>{fltr.attribute}</p> filter to an alternative inequality operation.",
}
def get_complementary_ops(fltr_op):
if fltr_op == ">":
return "<="
elif fltr_op == "<":
return ">="
elif fltr_op == ">=":
return "<"
elif fltr_op == "<=":
return ">"
# TODO: need to support case where fltr_op is "=" --> auto-binned ranges
# Create vis with complementary filter operations
new_spec = column_spec.copy()
new_filter = lux.Clause(
attribute=fltr.attribute,
filter_op=get_complementary_ops(fltr.filter_op),
value=fltr.value,
)
new_spec.append(new_filter)
temp_vis = Vis(new_spec, score=1)
output.append(temp_vis)
# if no existing filters, create filters using unique values from all categorical variables in the dataset
else:
intended_attrs = ", ".join(
[
clause.attribute
for clause in ldf._intent
if clause.value == "" and clause.attribute != "Record"
]
)
recommendation = {
"action": "Filter",
"description": f"Applying filters to the <p class='highlight-intent'>{intended_attrs}</p> intent.",
}
categorical_vars = []
for col in list(ldf.columns):
# if cardinality is not too high, and attribute is not one of the X,Y (specified) column
if 1 < ldf.cardinality[col] < 30 and col not in column_spec_attr:
categorical_vars.append(col)
for cat in categorical_vars:
unique_values = ldf.unique_values[cat]
for val in unique_values:
new_spec = column_spec.copy()
new_filter = lux.Clause(attribute=cat, filter_op="=", value=val)
new_spec.append(new_filter)
temp_vis = Vis(new_spec)
output.append(temp_vis)
vlist = lux.vis.VisList.VisList(output, ldf)
for vis in vlist:
vis.score = interestingness(vis, ldf)
vlist = vlist.topK(15)
recommendation["collection"] = vlist
return recommendation