def random_categorical(ldf):
intent = [lux.Clause("?", data_type="nominal")]
vlist = VisList(intent, ldf)
for vis in vlist:
vis.score = 10
vlist.sort()
vlist = vlist.showK()
return {
"action": "bars",
"description": "Random list of Bar charts",
"collection": vlist,
}
def correlation(ldf: LuxDataFrame, ignore_transpose: bool = True):
"""
Generates bivariate visualizations that represent all pairwise relationships in the data.
Parameters
----------
ldf : LuxDataFrame
LuxDataFrame with underspecified intent.
ignore_transpose: bool
Boolean flag to ignore pairs of attributes whose transpose are already computed (i.e., {X,Y} will be ignored if {Y,X} is already computed)
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Correlation action.
"""
import numpy as np
filter_specs = utils.get_filter_specs(ldf._intent)
intent = [
lux.Clause("?", data_model="measure"),
lux.Clause("?", data_model="measure"),
]
intent.extend(filter_specs)
vlist = VisList(intent, ldf)
recommendation = {
"action":
"Correlation",
"description":
"Show relationships between two <p class='highlight-descriptor'>quantitative</p> attributes.",
}
ignore_rec_flag = False
# Doesn't make sense to compute correlation if less than 4 data values
if len(ldf) < 5:
ignore_rec_flag = True
# Then use the data populated in the vis list to compute score
for vis in vlist:
measures = vis.get_attr_by_data_model("measure")
if len(measures) < 2:
raise ValueError(
f"Can not compute correlation between {[x.attribute for x in ldf.columns]} since less than 2 measure values present."
)
msr1 = measures[0].attribute
msr2 = measures[1].attribute
if ignore_transpose:
check_transpose = check_transpose_not_computed(vlist, msr1, msr2)
else:
check_transpose = True
if check_transpose:
vis.score = interestingness(vis, ldf)
else:
vis.score = -1
if ignore_rec_flag:
recommendation["collection"] = []
return recommendation
vlist.sort()
vlist = vlist.showK()
recommendation["collection"] = vlist
return recommendation