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 vis is removed.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified intent.
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
output = []
excluded_columns = []
attributes = list(filter(lambda x: x.value=="" and x.attribute!="Record", ldf.intent))
filters = utils.get_filter_specs(ldf.intent)
fltr_str = [fltr.attribute+fltr.filter_op+str(fltr.value) for fltr in filters]
attr_str = [clause.attribute for clause in attributes]
intended_attrs = '<p class="highlight-intent">'+', '.join(attr_str+fltr_str)+'</p>'
recommendation = {"action":"Generalize",
"description":f"Remove an attribute or filter from {intended_attrs}."}
# to observe a more general trend
# if we do no have enough column attributes or too many, return no views.
if(len(attributes)<1 or len(attributes)>4):
recommendation["collection"] = []
return recommendation
#for each column specification, create a copy of the ldf's view and remove the column specification
#then append the view to the output
if (len(attributes)>1):
for clause in attributes:
columns = clause.attribute
if type(columns) == list:
for column in columns:
if column not in excluded_columns:
temp_view = Vis(ldf.copy_intent(),score=1)
temp_view.remove_column_from_spec(column, remove_first = True)
excluded_columns.append(column)
output.append(temp_view)
elif type(columns) == str:
if columns not in excluded_columns:
temp_view = Vis(ldf.copy_intent(),score=1)
temp_view.remove_column_from_spec(columns, remove_first = True)
excluded_columns.append(columns)
output.append(temp_view)
#for each filter specification, create a copy of the ldf's current vis and remove the filter specification,
#then append the view to the output
for clause in filters:
#new_spec = ldf.intent.copy()
#new_spec.remove_column_from_spec(new_spec.attribute)
temp_view = Vis(ldf.current_vis[0]._inferred_intent.copy(),source = ldf,title="Overall",score=0)
temp_view.remove_filter_from_spec(clause.value)
output.append(temp_view)
vc = lux.vis.VisList.VisList(output,source=ldf)
# Ignore interestingness sorting since Generalize yields very few vis (preserve order of remove attribute, then remove filters)
# for view in vc:
# view.score = interestingness(view,ldf)
vc.remove_duplicates()
vc.sort(remove_invalid=True)
recommendation["collection"] = vc
#for benchmarking
if ldf.toggle_benchmarking == True:
toc = time.perf_counter()
print(f"Performed generalize action in {toc - tic:0.4f} seconds")
return recommendation
def determine_encoding(ldf: LuxDataFrame, vis: Vis):
"""
Populates Vis with the appropriate mark type and channel information based on ShowMe logic
Currently support up to 3 dimensions or measures
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent
vis : lux.vis.Vis
Returns
-------
None
Notes
-----
Implementing automatic encoding from Tableau's VizQL
Mackinlay, J. D., Hanrahan, P., & Stolte, C. (2007).
Show Me: Automatic presentation for visual analysis.
IEEE Transactions on Visualization and Computer Graphics, 13(6), 1137–1144.
https://doi.org/10.1109/TVCG.2007.70594
"""
# Count number of measures and dimensions
ndim = vis._ndim
nmsr = vis._nmsr
# preserve to add back to _inferred_intent later
filters = utils.get_filter_specs(vis._inferred_intent)
# Helper function (TODO: Move this into utils)
def line_or_bar_or_geo(ldf, dimension: Clause, measure: Clause):
dim_type = dimension.data_type
# If no aggregation function is specified, then default as average
if measure.aggregation == "":
measure.set_aggregation("mean")
if dim_type == "temporal" or dim_type == "oridinal":
return "line", {"x": dimension, "y": measure}
else: # unordered categorical
# if cardinality large than 5 then sort bars
if ldf.cardinality[dimension.attribute] > 5:
dimension.sort = "ascending"
if utils.like_geo(dimension.get_attr()):
return "geographical", {"x": dimension, "y": measure}
return "bar", {"x": measure, "y": dimension}
# ShowMe logic + additional heuristics
# count_col = Clause( attribute="count()", data_model="measure")
count_col = Clause(
attribute="Record",
aggregation="count",
data_model="measure",
data_type="quantitative",
)
auto_channel = {}
if ndim == 0 and nmsr == 1:
# Histogram with Count
measure = vis.get_attr_by_data_model("measure",
exclude_record=True)[0]
if len(vis.get_attr_by_attr_name("Record")) < 0:
vis._inferred_intent.append(count_col)
# If no bin specified, then default as 10
if measure.bin_size == 0:
measure.bin_size = 10
auto_channel = {"x": measure, "y": count_col}
vis._mark = "histogram"
elif ndim == 1 and (nmsr == 0 or nmsr == 1):
# Line or Bar Chart
if nmsr == 0:
vis._inferred_intent.append(count_col)
dimension = vis.get_attr_by_data_model("dimension")[0]
measure = vis.get_attr_by_data_model("measure")[0]
vis._mark, auto_channel = line_or_bar_or_geo(
ldf, dimension, measure)
elif ndim == 2 and (nmsr == 0 or nmsr == 1):
# Line or Bar chart broken down by the dimension
dimensions = vis.get_attr_by_data_model("dimension")
d1 = dimensions[0]
d2 = dimensions[1]
if ldf.cardinality[d1.attribute] < ldf.cardinality[d2.attribute]:
# d1.channel = "color"
vis.remove_column_from_spec(d1.attribute)
dimension = d2
color_attr = d1
else:
# if same attribute then remove_column_from_spec will remove both dims, we only want to remove one
if d1.attribute == d2.attribute:
vis._inferred_intent.pop(0)
else:
vis.remove_column_from_spec(d2.attribute)
dimension = d1
color_attr = d2
# Colored Bar/Line chart with Count as default measure
if not ldf.pre_aggregated:
if nmsr == 0 and not ldf.pre_aggregated:
vis._inferred_intent.append(count_col)
measure = vis.get_attr_by_data_model("measure")[0]
vis._mark, auto_channel = line_or_bar_or_geo(
ldf, dimension, measure)
auto_channel["color"] = color_attr
elif ndim == 0 and nmsr == 2:
# Scatterplot
vis._mark = "scatter"
vis._inferred_intent[0].set_aggregation(None)
vis._inferred_intent[1].set_aggregation(None)
auto_channel = {
"x": vis._inferred_intent[0],
"y": vis._inferred_intent[1]
}
elif ndim == 1 and nmsr == 2:
# Scatterplot broken down by the dimension
measure = vis.get_attr_by_data_model("measure")
m1 = measure[0]
m2 = measure[1]
vis._inferred_intent[0].set_aggregation(None)
vis._inferred_intent[1].set_aggregation(None)
color_attr = vis.get_attr_by_data_model("dimension")[0]
vis.remove_column_from_spec(color_attr)
vis._mark = "scatter"
auto_channel = {"x": m1, "y": m2, "color": color_attr}
elif ndim == 0 and nmsr == 3:
# Scatterplot with color
vis._mark = "scatter"
auto_channel = {
"x": vis._inferred_intent[0],
"y": vis._inferred_intent[1],
"color": vis._inferred_intent[2],
}
relevant_attributes = [
auto_channel[channel].attribute for channel in auto_channel
]
relevant_min_max = dict((attr, ldf._min_max[attr])
for attr in relevant_attributes
if attr != "Record" and attr in ldf._min_max)
vis._min_max = relevant_min_max
if auto_channel != {}:
vis = Compiler.enforce_specified_channel(vis, auto_channel)
vis._inferred_intent.extend(
filters) # add back the preserved filters
def test_remove(global_var):
df = pytest.car_df
vis = Vis([lux.Clause("Horsepower"), lux.Clause("Acceleration")], df)
vis.remove_column_from_spec("Horsepower", remove_first=False)
assert vis._inferred_intent[0].attribute == "Acceleration"
def test_remove():
from lux.vis.Vis import Vis
df = pd.read_csv("lux/data/car.csv")
vis = Vis([lux.Clause("Horsepower"), lux.Clause("Acceleration")], df)
vis.remove_column_from_spec("Horsepower", remove_first=False)
assert vis._inferred_intent[0].attribute == "Acceleration"
def generalize(ldf):
"""
Generates all possible visualizations when one attribute or filter from the current vis is removed.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
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
output = []
excluded_columns = []
attributes = list(filter(lambda x: x.value == "" and x.attribute != "Record", ldf._intent))
filters = utils.get_filter_specs(ldf._intent)
fltr_str = [fltr.attribute + fltr.filter_op + str(fltr.value) for fltr in filters]
attr_str = [str(clause.attribute) for clause in attributes]
intended_attrs = f'<p class="highlight-intent">{", ".join(attr_str + fltr_str)}</p>'
recommendation = {
"action": "Generalize",
"description": f"Remove an attribute or filter from {intended_attrs}.",
"long_description": f"Remove one aspect of the Current Vis. We can either remove an attribute or filter from {intended_attrs}.",
}
# to observe a more general trend
# if we do no have enough column attributes or too many, return no vis.
if len(attributes) < 1 or len(attributes) > 4:
recommendation["collection"] = []
return recommendation
# for each column specification, create a copy of the ldf's vis and remove the column specification
# then append the vis to the output
if len(attributes) > 1:
for clause in attributes:
columns = clause.attribute
if type(columns) == list:
for column in columns:
if column not in excluded_columns:
temp_vis = Vis(ldf.copy_intent(), score=1)
temp_vis.remove_column_from_spec(column, remove_first=True)
excluded_columns.append(column)
output.append(temp_vis)
else:
if columns not in excluded_columns:
temp_vis = Vis(ldf.copy_intent(), score=1)
temp_vis.remove_column_from_spec(columns, remove_first=True)
excluded_columns.append(columns)
output.append(temp_vis)
# for each filter specification, create a copy of the ldf's current vis and remove the filter specification,
# then append the vis to the output
for clause in filters:
# new_spec = ldf._intent.copy()
# new_spec.remove_column_from_spec(new_spec.attribute)
temp_vis = Vis(
ldf.current_vis[0]._inferred_intent.copy(),
source=ldf,
title="Overall",
score=0,
)
temp_vis.remove_filter_from_spec(clause.value)
output.append(temp_vis)
vlist = lux.vis.VisList.VisList(output, source=ldf)
# Ignore interestingness sorting since Generalize yields very few vis (preserve order of remove attribute, then remove filters)
# for vis in vlist:
# vis.score = interestingness(vis,ldf)
vlist.remove_duplicates()
vlist.sort(remove_invalid=True)
vlist._collection = list(filter(lambda x: x.score != -1, vlist._collection))
recommendation["collection"] = vlist
return recommendation
