def test_interestingness_deviation_nan():
import numpy as np
dataset = [
{"date": "2017-08-25 09:06:11+00:00", "category": "A", "value": 25.0},
{"date": "2017-08-25 09:06:11+00:00", "category": "B", "value": 1.2},
{"date": "2017-08-25 09:06:11+00:00", "category": "C", "value": 1.3},
{"date": "2017-08-25 09:06:11+00:00", "category": "D", "value": 1.4},
{"date": "2017-08-25 09:06:11+00:00", "category": "E", "value": 1.5},
{"date": "2017-08-25 09:06:11+00:00", "category": "F", "value": 0.1},
{"date": np.nan, "category": "C", "value": 0.2},
{"date": np.nan, "category": "B", "value": 0.2},
{"date": np.nan, "category": "F", "value": 0.3},
{"date": np.nan, "category": "E", "value": 0.3},
{"date": np.nan, "category": "D", "value": 0.4},
{"date": np.nan, "category": "A", "value": 10.4},
{"date": "2017-07-25 15:06:11+00:00", "category": "A", "value": 15.5},
{"date": "2017-07-25 15:06:11+00:00", "category": "F", "value": 1.0},
{"date": "2017-07-25 15:06:11+00:00", "category": "B", "value": 0.1},
]
test = pd.DataFrame(dataset)
from lux.vis.Vis import Vis
vis = Vis(["date", "value", "category=A"], test)
vis2 = Vis(["date", "value", "category=B"], test)
from lux.interestingness.interestingness import interestingness
smaller_diff_score = interestingness(vis, test)
bigger_diff_score = interestingness(vis2, test)
assert np.isclose(smaller_diff_score, 0.29, rtol=0.1)
assert np.isclose(bigger_diff_score, 0.94, rtol=0.1)
assert smaller_diff_score < bigger_diff_score
def test_interestingness_1_1_1(global_var):
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([
lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="Origin", filter_op="=", value="USA",
bin_size=20),
])
df._repr_html_()
# check that top recommended Enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Enhance"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Enhance"])):
if (str(df.recommendation["Enhance"][f]._inferred_intent[2].value)
== "USA"
and str(df.recommendation["Enhance"]
[f]._inferred_intent[1].attribute) == "Cylinders"):
rank1 = f
if (str(df.recommendation["Enhance"][f]._inferred_intent[2].value)
== "USA"
and str(df.recommendation["Enhance"]
[f]._inferred_intent[1].attribute) == "Weight"):
rank2 = f
if (str(df.recommendation["Enhance"][f]._inferred_intent[2].value)
== "USA"
and str(df.recommendation["Enhance"]
[f]._inferred_intent[1].attribute) == "Horsepower"):
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
# check for top recommended Filter graph score is not none
assert interestingness(df.recommendation["Filter"][0], df) != None
def test_interestingness_0_2_0(global_var):
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([
lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="Acceleration")
])
df._repr_html_()
# check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Enhance"][0], df) != None
rank1 = -1
rank2 = -1
for f in range(0, len(df.recommendation["Enhance"])):
if (str(df.recommendation["Enhance"][f]._inferred_intent[2].attribute)
== "Origin"
and str(df.recommendation["Enhance"][f].mark) == "scatter"):
rank1 = f
if (str(df.recommendation["Enhance"][f]._inferred_intent[2].attribute)
== "Displacement"
and str(df.recommendation["Enhance"][f].mark) == "scatter"):
rank2 = f
assert rank1 < rank2
# check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Filter"][0], df) != None
# check that top recommended Generalize graph score is not none
assert interestingness(df.recommendation["Generalize"][0], df) != None
df.clear_intent()
def test_interestingness_1_1_0():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([lux.Clause(attribute = "Horsepower"),lux.Clause(attribute = "Year")])
df._repr_html_()
#check that top recommended Enhance graph score is not none (all graphs here have same score)
assert interestingness(df.recommendation['Enhance'][0],df) != None
#check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Filter'][0],df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Filter'])):
vis = df.recommendation['Filter'][f]
if len(vis.get_attr_by_attr_name("Cylinders"))>0:
if int(vis._inferred_intent[2].value) == 6:
rank1 = f
if int(vis._inferred_intent[2].value) == 5:
rank3 = f
if len(vis.get_attr_by_attr_name("Origin"))>0:
if str(vis._inferred_intent[2].value) == "Europe":
rank2 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check that top recommended generalize graph score is not none
assert interestingness(df.recommendation['Filter'][0],df) != None
def test_interestingness_1_1_1():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([
lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="Origin", filter_op="=", value="USA", bin_size=20)
])
df.show_more()
#check that top recommended Enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Enhance'][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Enhance'])):
if str(df.recommendation['Enhance']
[f]._inferred_intent[2].value) == "USA" and str(
df.recommendation['Enhance']
[f]._inferred_intent[1].attribute) == 'Cylinders':
rank1 = f
if str(df.recommendation['Enhance'][f]._inferred_intent[2].value
) == "USA" and str(df.recommendation['Enhance'][f].
_inferred_intent[1].attribute) == 'Weight':
rank2 = f
if str(df.recommendation['Enhance']
[f]._inferred_intent[2].value) == "USA" and str(
df.recommendation['Enhance']
[f]._inferred_intent[1].attribute) == 'Horsepower':
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check for top recommended Filter graph score is not none
assert interestingness(df.recommendation['Filter'][0], df) != None
def test_interestingness_0_1_0():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([lux.Clause(attribute = "Horsepower")])
df._repr_html_()
#check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Enhance'][0],df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Enhance'])):
if df.recommendation['Enhance'][f].mark == 'scatter' and df.recommendation['Enhance'][f]._inferred_intent[1].attribute == 'Weight':
rank1 = f
if df.recommendation['Enhance'][f].mark == 'scatter' and df.recommendation['Enhance'][f]._inferred_intent[1].attribute == 'Acceleration':
rank2 = f
if df.recommendation['Enhance'][f].mark == 'line' and df.recommendation['Enhance'][f]._inferred_intent[0].attribute == 'Year':
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Filter'][0],df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Filter'])):
if df.recommendation['Filter'][f]._inferred_intent[2].value == 4:
rank1 = f
if str(df.recommendation['Filter'][f]._inferred_intent[2].value) == "Europe":
rank2 = f
if '1971' in str(df.recommendation['Filter'][f]._inferred_intent[2].value):
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
def test_interestingness_1_1_0(global_var):
lux.config.set_executor_type("Pandas")
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent(
[lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="Year")])
df._ipython_display_()
# check that top recommended Enhance graph score is not none (all graphs here have same score)
assert interestingness(df.recommendation["Enhance"][0], df) != None
# check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Filter"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Filter"])):
vis = df.recommendation["Filter"][f]
if len(vis.get_attr_by_attr_name("Cylinders")) > 0:
if int(vis._inferred_intent[2].value) == 6:
rank1 = f
if int(vis._inferred_intent[2].value) == 8:
rank2 = f
if len(vis.get_attr_by_attr_name("Origin")) > 0:
if str(vis._inferred_intent[2].value) == "Europe":
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
# check that top recommended generalize graph score is not none
assert interestingness(df.recommendation["Filter"][0], df) != None
df.clear_intent()
def filter(dobj):
result = lux.Result()
recommendation = {"action":"Filter",
"description":"Shows possible visualizations when filtered by categorical variables in the data object's dataset."}
filters = dobj.getObjByRowColType("Row")
filterValues = []
output = []
#if Row is specified, create visualizations where data is filtered by all values of the Row's categorical variable
if len(filters) > 0:
completedFilters = []
columnSpec = dobj.getObjByRowColType("Column")
#get unique values for all categorical values specified and creates corresponding filters
for row in filters:
if row.fAttribute not in completedFilters:
uniqueValues = dobj.dataset.df[row.fAttribute].unique()
filterValues.append(row.fVal)
#creates new data objects with new filters
for i in range(0, len(uniqueValues)):
if uniqueValues[i] not in filterValues:
#create new Data Object
newSpec = columnSpec.copy()
newFilter = lux.Row(fAttribute = row.fAttribute, fVal = uniqueValues[i])
newSpec.append(newFilter)
tempDataObj = lux.DataObj(dobj.dataset, newSpec)
tempDataObj.score = interestingness(tempDataObj)
#recompile the new Data Object before appending to output
tempDataObj.compile()
output.append(tempDataObj.compiled)
completedFilters.append(row.fAttribute)
#if Row is not specified, create filters using unique values from all categorical variables in the dataset
else:
categoricalVars = dobj.dataset.dataType['categorical']
columnSpec = dobj.getObjByRowColType("Column")
for cat in categoricalVars:
uniqueValues = dobj.dataset.df[cat].unique()
for i in range(0, len(uniqueValues)):
newSpec = columnSpec.copy()
newFilter = lux.Row(fAttribute = cat, fVal = uniqueValues[i])
newSpec.append(newFilter)
tempDataObj = lux.DataObj(dobj.dataset, newSpec)
tempDataObj.score = interestingness(tempDataObj)
tempDataObj.compile()
output.append(tempDataObj.compiled)
outputDataObjCol = lux.DataObjCollection(output)
outputDataObjCol = outputDataObjCol.topK(5)
recommendation["collection"] = outputDataObjCol
result.addResult(recommendation,dobj)
return result
def test_interestingness_0_2_0():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([
lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="Acceleration")
])
df.show_more()
#check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Enhance'][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Enhance'])):
if str(df.recommendation['Enhance']
[f]._inferred_intent[2].attribute) == "Origin" and str(
df.recommendation['Enhance'][f].mark) == 'scatter':
rank1 = f
if str(df.recommendation['Enhance']
[f]._inferred_intent[2].attribute) == "Displacement" and str(
df.recommendation['Enhance'][f].mark) == 'scatter':
rank2 = f
if str(df.recommendation['Enhance']
[f]._inferred_intent[2].attribute) == "Year" and str(
df.recommendation['Enhance'][f].mark) == 'scatter':
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Filter'][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Filter'])):
if '1973' in str(
df.recommendation['Filter'][f]._inferred_intent[2].value):
rank1 = f
if '1976' in str(
df.recommendation['Filter'][f]._inferred_intent[2].value):
rank2 = f
if str(df.recommendation['Filter']
[f]._inferred_intent[2].value) == "Europe":
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check that top recommended Generalize graph score is not none
assert interestingness(df.recommendation['Generalize'][0], df) != None
def user_defined(ldf):
'''
Generates user-defined views based on the context.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified context.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Distribution action.
'''
recommendation = {
"action": "Current Views",
"description": "Shows a view collection defined by the context"
}
recommendation["collection"] = ldf.current_view
vc = ldf.current_view
PandasExecutor.execute(vc, ldf)
for view in vc:
view.score = interestingness(view, ldf)
# ldf.clear_context()
vc.sort(remove_invalid=True)
return recommendation
def test_interestingness_1_1_1(global_var):
tbl = lux.LuxSQLTable()
tbl.set_SQL_table("cars")
tbl.set_intent(
[
lux.Clause(attribute="horsepower"),
lux.Clause(attribute="origin", filter_op="=", value="USA", bin_size=20),
]
)
tbl._repr_html_()
assert interestingness(tbl.recommendation["Enhance"][0], tbl) != None
# check for top recommended Filter graph score is not none
assert interestingness(tbl.recommendation["Filter"][0], tbl) != None
tbl.clear_intent()
def custom(ldf):
"""
Generates user-defined vis based on the intent.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Distribution action.
"""
recommendation = {
"action": "Current Vis",
"description": "Shows the list of visualizations generated based on user specified intent",
}
recommendation["collection"] = ldf.current_vis
vlist = ldf.current_vis
PandasExecutor.execute(vlist, ldf)
for vis in vlist:
vis.score = interestingness(vis, ldf)
# ldf.clear_intent()
vlist.sort(remove_invalid=True)
return recommendation
def enhance(ldf):
"""
Given a set of vis, generates possible visualizations when an additional attribute is added to the current vis.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Enhance action.
"""
filters = utils.get_filter_specs(ldf._intent)
# Collect variables that already exist in the intent
attr_specs = list(
filter(lambda x: x.value == "" and x.attribute != "Record",
ldf._intent))
fltr_str = [
fltr.attribute + fltr.filter_op + str(fltr.value) for fltr in filters
]
attr_str = [str(clause.attribute) for clause in attr_specs]
intended_attrs = f'<p class="highlight-intent">{", ".join(attr_str + fltr_str)}</p>'
if len(attr_specs) == 1:
recommendation = {
"action":
"Enhance",
"description":
f"Augmenting current {intended_attrs} intent with additional attribute.",
}
elif len(attr_specs) == 2:
recommendation = {
"action":
"Enhance",
"description":
f"Further breaking down current {intended_attrs} intent by additional attribute.",
}
# if there are too many column attributes, return don't generate Enhance recommendations
elif len(attr_specs) > 2:
recommendation = {"action": "Enhance"}
recommendation["collection"] = []
return recommendation
intent = ldf._intent.copy()
# Clear channel so that channel not enforced based on input vis intent
for clause in intent:
clause.channel = ""
intent = filters + attr_specs
intent.append("?")
vlist = lux.vis.VisList.VisList(intent, ldf)
# Then use the data populated in the vis list to compute score
for vis in vlist:
vis.score = interestingness(vis, ldf)
vlist.sort()
vlist = vlist.showK()
recommendation["collection"] = vlist
return recommendation
def test_interestingness_0_1_1():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([lux.Clause(attribute = "Origin", filter_op="=",value="?"),lux.Clause(attribute = "MilesPerGal")])
df._repr_html_()
assert interestingness(df.recommendation['Current Vis'][0],df) != None
assert str(df.recommendation['Current Vis'][0]._inferred_intent[2].value) == 'USA'
def test_interestingness_0_2_1():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([lux.Clause(attribute = "Horsepower"),lux.Clause(attribute = "MilesPerGal"),lux.Clause(attribute = "Acceleration", filter_op=">",value = 10)])
df._repr_html_()
#check that top recommended Generalize graph score is not none
assert interestingness(df.recommendation['Generalize'][0],df) != None
def enhance(dobj):
result = lux.Result()
recommendation = {
"action":
"Enhance",
"description":
"Shows possible visualizations when an additional attribute is added to the current view."
}
quantitativeVars = dobj.dataset.dataType['quantitative']
categoricalVars = dobj.dataset.dataType['categorical']
output = []
dobjVars = []
for i in range(0, len(dobj.spec)):
if dobj.spec[i].className == "Column":
dobjVars.append(dobj.spec[i].columnName)
elif dobj.spec[i].className == "Row":
dobjVars.append(dobj.spec[i].fAttribute)
#go through and add additional quantitative variable
for qVar in quantitativeVars:
if qVar not in dobjVars:
newSpec = dobj.spec.copy()
newSpec.append(Column(qVar))
tempDataObj = DataObj(dobj.dataset, newSpec)
tempDataObj.score = interestingness(tempDataObj)
tempDataObj.compile()
output.append(tempDataObj.compiled)
#go through and add additional categorical variable
for cVar in categoricalVars:
if cVar not in dobjVars:
newSpec = dobj.spec.copy()
newSpec.append(Column(cVar))
tempDataObj = DataObj(dobj.dataset, newSpec)
tempDataObj.score = interestingness(tempDataObj)
tempDataObj.compile()
output.append(tempDataObj.compiled)
recommendation["collection"] = DataObjCollection(output)
result.addResult(recommendation, dobj)
return result
def test_interestingness_0_1_0(global_var):
lux.config.set_executor_type("Pandas")
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([lux.Clause(attribute="Horsepower")])
df._ipython_display_()
# check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Enhance"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Enhance"])):
if (df.recommendation["Enhance"][f].mark == "scatter" and
df.recommendation["Enhance"][f]._inferred_intent[1].attribute
== "Weight"):
rank1 = f
if (df.recommendation["Enhance"][f].mark == "scatter" and
df.recommendation["Enhance"][f]._inferred_intent[1].attribute
== "Acceleration"):
rank2 = f
if (df.recommendation["Enhance"][f].mark == "line" and
df.recommendation["Enhance"][f]._inferred_intent[0].attribute
== "Year"):
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
# check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Filter"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Filter"])):
if df.recommendation["Filter"][f]._inferred_intent[2].value == 4:
rank1 = f
if str(df.recommendation["Filter"]
[f]._inferred_intent[2].value) == "Europe":
rank2 = f
if "1970" in str(
df.recommendation["Filter"][f]._inferred_intent[2].value):
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
df.clear_intent()
def test_interestingness_0_1_1(global_var):
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([
lux.Clause(attribute="Origin", filter_op="=", value="?"),
lux.Clause(attribute="MilesPerGal"),
])
df._repr_html_()
assert interestingness(df.recommendation["Current Vis"][0], df) != None
assert str(
df.recommendation["Current Vis"][0]._inferred_intent[2].value) == "USA"
def test_interestingness_0_2_1(global_var):
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([
lux.Clause(attribute="Horsepower"),
lux.Clause(attribute="MilesPerGal"),
lux.Clause(attribute="Acceleration", filter_op=">", value=10),
])
df._repr_html_()
# check that top recommended Generalize graph score is not none
assert interestingness(df.recommendation["Generalize"][0], df) != None
def test_interestingness_1_0_0(global_var):
df = pytest.car_df
df["Year"] = pd.to_datetime(df["Year"], format="%Y")
df.set_intent([lux.Clause(attribute="Origin")])
df._repr_html_()
# check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Enhance"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Enhance"])):
vis = df.recommendation["Enhance"][f]
if vis.get_attr_by_channel("x")[0].attribute == "Displacement":
rank1 = f
if vis.get_attr_by_channel("x")[0].attribute == "Weight":
rank2 = f
if vis.get_attr_by_channel("x")[0].attribute == "Acceleration":
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
# check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation["Filter"][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation["Filter"])):
vis = df.recommendation["Filter"][f]
if len(vis.get_attr_by_attr_name("Cylinders")) > 0:
if int(vis._inferred_intent[2].value) == 8:
rank1 = f
if int(vis._inferred_intent[2].value) == 6:
rank3 = f
if "ford" in str(
df.recommendation["Filter"][f]._inferred_intent[2].value):
rank2 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
df.clear_intent()
def test_interestingness_0_1_1(global_var):
tbl = lux.LuxSQLTable()
tbl.set_SQL_table("cars")
tbl.set_intent(
[
lux.Clause(attribute="origin", filter_op="=", value="?"),
lux.Clause(attribute="milespergal"),
]
)
tbl._repr_html_()
assert interestingness(tbl.recommendation["Current Vis"][0], tbl) != None
assert str(tbl.recommendation["Current Vis"][0]._inferred_intent[2].value) == "USA"
tbl.clear_intent()
def enhance(ldf):
#for benchmarking
if ldf.toggle_benchmarking == True:
tic = time.perf_counter()
'''
Given a set of views, generates possible visualizations when an additional attribute is added to the current view.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified context.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Enhance action.
'''
recommendation = {
"action":
"Enhance",
"description":
"Shows possible visualizations when an additional attribute is added to the current view."
}
filters = utils.get_filter_specs(ldf.context)
# Collect variables that already exist in the context
attr_specs = list(
filter(lambda x: x.value == "" and x.attribute != "Record",
ldf.context))
if (
len(attr_specs) > 2
): # if there are too many column attributes, return don't generate Enhance recommendations
recommendation["collection"] = []
return recommendation
query = ldf.context.copy()
query = filters + attr_specs
query.append("?")
vc = lux.view.ViewCollection.ViewCollection(query)
vc = vc.load(ldf)
# Then use the data populated in the view collection to compute score
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 enhance action in {toc - tic:0.4f} seconds")
return recommendation
def test_interestingness_1_0_0():
df = pd.read_csv("lux/data/car.csv")
df["Year"] = pd.to_datetime(df["Year"], format='%Y')
df.set_intent([lux.Clause(attribute="Origin")])
df.show_more()
#check that top recommended enhance graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Enhance'][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Enhance'])):
if df.recommendation['Enhance'][f]._inferred_intent[
0].attribute == 'Displacement':
rank1 = f
if df.recommendation['Enhance'][f]._inferred_intent[
0].attribute == 'Weight':
rank2 = f
if df.recommendation['Enhance'][f]._inferred_intent[
0].attribute == 'Acceleration':
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
#check that top recommended filter graph score is not none and that ordering makes intuitive sense
assert interestingness(df.recommendation['Filter'][0], df) != None
rank1 = -1
rank2 = -1
rank3 = -1
for f in range(0, len(df.recommendation['Filter'])):
if int(df.recommendation['Filter'][f]._inferred_intent[2].value) == 8:
rank1 = f
if int(df.recommendation['Filter'][f]._inferred_intent[2].value) == 6:
rank2 = f
if '1972' in str(
df.recommendation['Filter'][f]._inferred_intent[2].value):
rank3 = f
assert rank1 < rank2 and rank1 < rank3 and rank2 < rank3
def test_interestingness_1_2_0():
from lux.vis.Vis import Vis
from lux.vis.Vis import Clause
from lux.interestingness.interestingness import interestingness
df = pd.read_csv("lux/data/car.csv")
y_clause = Clause(attribute = "Name", channel = "y")
color_clause = Clause(attribute = 'Cylinders', channel = "color")
new_vis = Vis([y_clause, color_clause])
new_vis.refresh_source(df)
new_vis
#assert(len(new_vis.data)==color_cardinality*group_by_cardinality)
assert(interestingness(new_vis, df)<0.01)
def test_interestingness_1_2_0(global_var):
from lux.vis.Vis import Vis
from lux.vis.Vis import Clause
from lux.interestingness.interestingness import interestingness
df = pytest.car_df
y_clause = Clause(attribute="Name", channel="y")
color_clause = Clause(attribute="Cylinders", channel="color")
new_vis = Vis([y_clause, color_clause])
new_vis.refresh_source(df)
new_vis
# assert(len(new_vis.data)==color_cardinality*group_by_cardinality)
assert interestingness(new_vis, df) < 0.01
def distribution(ldf,dataTypeConstraint="quantitative"):
'''
Generates bar chart distributions of different attributes in the dataset.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified context.
dataTypeConstraint: str
The variable that 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.toggleBenchmarking == True:
tic = time.perf_counter()
if (dataTypeConstraint=="quantitative"):
query = [lux.Spec("?",dataType="quantitative")]
query.extend(ldf.filterSpecs)
recommendation = {"action":"Distribution",
"description":"Show univariate count distributions of different attributes in the dataset."}
elif (dataTypeConstraint=="nominal"):
query = [lux.Spec("?",dataType="nominal")]
query.extend(ldf.filterSpecs)
recommendation = {"action":"Category",
"description":"Show bar chart distributions of different attributes in the dataset."}
vc = ViewCollection(query)
vc = vc.load(ldf)
for view in vc:
view.score = interestingness(view,ldf)
vc = vc.topK(15)
recommendation["collection"] = vc
#for benchmarking
if ldf.toggleBenchmarking == True:
toc = time.perf_counter()
print(f"Performed distribution action in {toc - tic:0.4f} seconds")
return recommendation
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
def enhance(ldf):
#for benchmarking
if ldf.toggle_benchmarking == True:
tic = time.perf_counter()
'''
Given a set of views, generates possible visualizations when an additional attribute is added to the current vis.
Parameters
----------
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with underspecified intent.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Enhance action.
'''
filters = utils.get_filter_specs(ldf.intent)
# Collect variables that already exist in the intent
attr_specs = list(
filter(lambda x: x.value == "" and x.attribute != "Record",
ldf.intent))
fltr_str = [
fltr.attribute + fltr.filter_op + str(fltr.value) for fltr in filters
]
attr_str = [clause.attribute for clause in attr_specs]
intended_attrs = '<p class="highlight-intent">' + ', '.join(
attr_str + fltr_str) + '</p>'
if (len(attr_specs) == 1):
recommendation = {
"action":
"Enhance",
"description":
f"Augmenting current {intended_attrs} intent with additional attribute."
}
elif (len(attr_specs) == 2):
recommendation = {
"action":
"Enhance",
"description":
f"Further breaking down current {intended_attrs} intent by additional attribute."
}
elif (
len(attr_specs) > 2
): # if there are too many column attributes, return don't generate Enhance recommendations
recommendation = {"action": "Enhance"}
recommendation["collection"] = []
return recommendation
intent = ldf.intent.copy()
intent = filters + attr_specs
intent.append("?")
vc = lux.vis.VisList.VisList(intent, ldf)
# Then use the data populated in the vis list to compute score
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 enhance action in {toc - tic:0.4f} seconds")
return recommendation
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]._inferred_intent)
column_spec_attr = 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 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 i in range(0, len(unique_values)):
new_spec = column_spec.copy()
new_filter = lux.Clause(attribute=cat,
filter_op="=",
value=unique_values[i])
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
def univariate(ldf, data_type_constraint="quantitative"):
'''
Generates bar chart distributions of different attributes in the dataframe.
Parameters
----------
ldf : lux.core.frame
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 numpy as np
filter_specs = utils.get_filter_specs(ldf._intent)
ignore_rec_flag = False
if (data_type_constraint == "quantitative"):
intent = [
lux.Clause("?",
data_type="quantitative",
exclude="Number of Records")
]
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
vlist = VisList(intent, ldf)
for vis in vlist:
vis.score = interestingness(vis, ldf)
# vlist = vlist.topK(15) # Basic visualizations should not be capped
vlist.sort()
recommendation["collection"] = vlist
return recommendation
