def test_remove():
from lux.view.View import View
df = pd.read_csv("lux/data/car.csv")
view = View(["Horsepower", "Horsepower"])
view.load(df)
view.remove_column_from_spec_new("Horsepower", remove_first=False)
assert (view.spec_lst == []), "Remove all instances of Horsepower"
df = pd.read_csv("lux/data/car.csv")
view = View(["Horsepower", "Horsepower"])
view.load(df)
view.remove_column_from_spec_new("Horsepower", remove_first=True)
assert (len(view.spec_lst) == 1), "Remove only 1 instances of Horsepower"
assert (view.spec_lst[0].attribute == "Horsepower"
), "Remove only 1 instances of Horsepower"
def generalize(ldf):
#for benchmarking
if ldf.toggle_benchmarking == True:
tic = time.perf_counter()
'''
Generates all possible visualizations when one attribute or filter from the current view is removed.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified context.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Generalize action.
'''
# takes in a dataObject and generates a list of new dataObjects, each with a single measure from the original object removed
# --> return list of dataObjects with corresponding interestingness scores
recommendation = {
"action":
"Generalize",
"description":
"Remove one attribute or filter to observe a more general trend."
}
output = []
excluded_columns = []
column_spec = list(
filter(lambda x: x.value == "" and x.attribute != "Record",
ldf.context))
row_specs = utils.get_filter_specs(ldf.context)
# if we do no have enough column attributes or too many, return no views.
if (len(column_spec) < 2 or len(column_spec) > 4):
recommendation["collection"] = []
return recommendation
for spec in column_spec:
columns = spec.attribute
if type(columns) == list:
for column in columns:
if column not in excluded_columns:
temp_view = View(ldf.context)
temp_view.remove_column_from_spec_new(column,
remove_first=True)
excluded_columns.append(column)
output.append(temp_view)
elif type(columns) == str:
if columns not in excluded_columns:
temp_view = View(ldf.context)
temp_view.remove_column_from_spec_new(columns,
remove_first=True)
excluded_columns.append(columns)
output.append(temp_view)
for i, spec in enumerate(row_specs):
new_spec = ldf.context.copy()
new_spec.pop(i)
temp_view = View(new_spec)
output.append(temp_view)
vc = lux.view.ViewCollection.ViewCollection(output)
vc = vc.load(ldf)
recommendation["collection"] = vc
for view in vc:
view.score = interestingness(view, ldf)
vc.sort(remove_invalid=True)
vc.remove_duplicates()
#for benchmarking
if ldf.toggle_benchmarking == True:
toc = time.perf_counter()
print(f"Performed generalize action in {toc - tic:0.4f} seconds")
return recommendation