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
# for benchmarking
if ldf.toggle_benchmarking == True:
tic = time.perf_counter()
filter_specs = utils.get_filter_specs(ldf.intent)
intent = [
lux.Clause("?", data_model="measure"),
lux.Clause("?", data_model="measure")
]
intent.extend(filter_specs)
vc = VisList(intent, ldf)
recommendation = {
"action":
"Correlation",
"description":
"Show relationships between two <p class='highlight-descriptor'>quantitative</p> attributes."
}
ignore_rec_flag = False
if (
len(ldf) < 5
): # Doesn't make sense to compute correlation if less than 4 data values
ignore_rec_flag = True
# Then use the data populated in the vis list to compute score
for view in vc:
measures = view.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(vc, msr1, msr2)
else:
check_transpose = True
if (check_transpose):
view.score = interestingness(view, ldf)
else:
view.score = -1
if (ignore_rec_flag):
recommendation["collection"] = []
return recommendation
vc = vc.topK(15)
recommendation["collection"] = vc
# for benchmarking
if ldf.toggle_benchmarking == True:
toc = time.perf_counter()
print(f"Performed correlation action in {toc - tic:0.4f} seconds")
return recommendation
def univariate(ldf, data_type_constraint="quantitative"):
'''
Generates bar chart distributions of different attributes in the dataframe.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified intent.
data_type_constraint: str
Controls the type of distribution chart that will be rendered.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Distribution action.
'''
import scipy.stats
import numpy as np
#for benchmarking
if ldf.toggle_benchmarking == True:
tic = time.perf_counter()
filter_specs = utils.get_filter_specs(ldf.intent)
ignore_rec_flag = False
if (data_type_constraint == "quantitative"):
intent = [lux.Clause("?", data_type="quantitative")]
intent.extend(filter_specs)
recommendation = {
"action":
"Distribution",
"description":
"Show univariate histograms of <p class='highlight-descriptor'>quantitative</p> attributes."
}
if (
len(ldf) < 5
): # Doesn't make sense to generate a histogram if there is less than 5 datapoints (pre-aggregated)
ignore_rec_flag = True
elif (data_type_constraint == "nominal"):
intent = [lux.Clause("?", data_type="nominal")]
intent.extend(filter_specs)
recommendation = {
"action":
"Occurrence",
"description":
"Show frequency of occurrence for <p class='highlight-descriptor'>categorical</p> attributes."
}
elif (data_type_constraint == "temporal"):
intent = [lux.Clause("?", data_type="temporal")]
intent.extend(filter_specs)
recommendation = {
"action":
"Temporal",
"description":
"Show trends over <p class='highlight-descriptor'>time-related</p> attributes."
}
if (
len(ldf) < 3
): # Doesn't make sense to generate a line chart if there is less than 3 datapoints (pre-aggregated)
ignore_rec_flag = True
if (ignore_rec_flag):
recommendation["collection"] = []
return recommendation
vc = VisList(intent, ldf)
for view in vc:
view.score = interestingness(view, ldf)
vc = vc.topK(15)
recommendation["collection"] = vc
#for benchmarking
if ldf.toggle_benchmarking == True:
toc = time.perf_counter()
print(f"Performed distribution action in {toc - tic:0.4f} seconds")
return recommendation