def enforceSpecifiedChannel(view: View, autoChannel: Dict[str, str]):
"""
Enforces that the channels specified in the View by users overrides the showMe autoChannels.
Parameters
----------
view : lux.view.View
Input View without channel specification.
autoChannel : Dict[str,str]
Key-value pair in the form [channel: attributeName] specifying the showMe recommended channel location.
Returns
-------
view : lux.view.View
View with channel specification combining both original and autoChannel specification.
Raises
------
ValueError
Ensures no more than one attribute is placed in the same channel.
"""
resultDict = {
} # result of enforcing specified channel will be stored in resultDict
specifiedDict = {
} # specifiedDict={"x":[],"y":[list of Dobj with y specified as channel]}
# create a dictionary of specified channels in the given dobj
for val in autoChannel.keys():
specifiedDict[val] = view.getAttrByChannel(val)
resultDict[val] = ""
# for every element, replace with what's in specifiedDict if specified
for sVal, sAttr in specifiedDict.items():
if (len(sAttr) == 1): # if specified in dobj
# remove the specified channel from autoChannel (matching by value, since channel key may not be same)
for i in list(autoChannel.keys()):
if (
(autoChannel[i].attribute == sAttr[0].attribute)
and (autoChannel[i].channel == sVal)
): # need to ensure that the channel is the same (edge case when duplicate Cols with same attribute name)
autoChannel.pop(i)
break
sAttr[0].channel = sVal
resultDict[sVal] = sAttr[0]
elif (len(sAttr) > 1):
raise ValueError(
"There should not be more than one attribute specified in the same channel."
)
# For the leftover channels that are still unspecified in resultDict,
# and the leftovers in the autoChannel specification,
# step through them together and fill it automatically.
leftover_channels = list(
filter(lambda x: resultDict[x] == '', resultDict))
for leftover_channel, leftover_encoding in zip(leftover_channels,
autoChannel.values()):
leftover_encoding.channel = leftover_channel
resultDict[leftover_channel] = leftover_encoding
view.specLst = list(resultDict.values())
return view
def executeAggregate(view: View):
'''
Aggregate data points on an axis for bar or line charts
Parameters
----------
view: lux.View
lux.View object that represents a visualization
ldf : lux.luxDataFrame.LuxDataFrame
LuxDataFrame with specified context.
Returns
-------
None
'''
import numpy as np
xAttr = view.getAttrByChannel("x")[0]
yAttr = view.getAttrByChannel("y")[0]
groupbyAttr = ""
measureAttr = ""
if (yAttr.aggregation != ""):
groupbyAttr = xAttr
measureAttr = yAttr
aggFunc = yAttr.aggregation
if (xAttr.aggregation != ""):
groupbyAttr = yAttr
measureAttr = xAttr
aggFunc = xAttr.aggregation
allAttrVals = view.data.uniqueValues[groupbyAttr.attribute]
if (measureAttr != ""):
if (measureAttr.attribute == "Record"):
view.data = view.data.reset_index()
view.data = view.data.groupby(
groupbyAttr.attribute).count().reset_index()
view.data = view.data.rename(columns={"index": "Record"})
view.data = view.data[[groupbyAttr.attribute, "Record"]]
else:
groupbyResult = view.data.groupby(groupbyAttr.attribute)
view.data = groupbyResult.agg(aggFunc).reset_index()
resultVals = list(view.data[groupbyAttr.attribute])
if (len(resultVals) != len(allAttrVals)):
# For filtered aggregation that have missing groupby-attribute values, set these aggregated value as 0, since no datapoints
for vals in allAttrVals:
if (vals not in resultVals):
view.data.loc[len(view.data)] = [vals, 0]
assert len(list(view.data[groupbyAttr.attribute])) == len(
allAttrVals
), f"Aggregated data missing values compared to original range of values of `{groupbyAttr.attribute}`."
view.data = view.data.sort_values(by=groupbyAttr.attribute,
ascending=True)
view.data = view.data.reset_index()
view.data = view.data.drop(columns="index")
def executeAggregate(view:View, ldf:LuxDataFrame):
import pandas as pd
xAttr = view.getAttrByChannel("x")[0]
yAttr = view.getAttrByChannel("y")[0]
groupbyAttr =""
measureAttr =""
if (yAttr.aggregation!=""):
groupbyAttr = xAttr
measureAttr = yAttr
aggFunc = yAttr.aggregation
if (xAttr.aggregation!=""):
groupbyAttr = yAttr
measureAttr = xAttr
aggFunc = xAttr.aggregation
if (measureAttr!=""):
#barchart case, need count data for each group
if (measureAttr.attribute=="Record"):
whereClause, filterVars = SQLExecutor.executeFilter(view)
countQuery = "SELECT {}, COUNT({}) FROM {} {} GROUP BY {}".format(groupbyAttr.attribute, groupbyAttr.attribute, ldf.table_name, whereClause, groupbyAttr.attribute)
view.data = pd.read_sql(countQuery, ldf.SQLconnection)
view.data = view.data.rename(columns={"count":"Record"})
view.data = utils.pandasToLux(view.data)
else:
whereClause, filterVars = SQLExecutor.executeFilter(view)
if aggFunc == "mean":
meanQuery = "SELECT {}, AVG({}) as {} FROM {} {} GROUP BY {}".format(groupbyAttr.attribute, measureAttr.attribute, measureAttr.attribute, ldf.table_name, whereClause, groupbyAttr.attribute)
view.data = pd.read_sql(meanQuery, ldf.SQLconnection)
view.data = utils.pandasToLux(view.data)
if aggFunc == "sum":
meanQuery = "SELECT {}, SUM({}) as {} FROM {} {} GROUP BY {}".format(groupbyAttr.attribute, measureAttr.attribute, measureAttr.attribute, ldf.table_name, whereClause, groupbyAttr.attribute)
view.data = pd.read_sql(meanQuery, ldf.SQLconnection)
view.data = utils.pandasToLux(view.data)
if aggFunc == "max":
meanQuery = "SELECT {}, MAX({}) as {} FROM {} {} GROUP BY {}".format(groupbyAttr.attribute, measureAttr.attribute, measureAttr.attribute, ldf.table_name, whereClause, groupbyAttr.attribute)
view.data = pd.read_sql(meanQuery, ldf.SQLconnection)
view.data = utils.pandasToLux(view.data)
def interestingness(view:View ,ldf:LuxDataFrame) -> int:
"""
Compute the interestingness score of the view.
The interestingness metric is dependent on the view type.
Parameters
----------
view : View
ldf : LuxDataFrame
Returns
-------
int
Interestingness Score
"""
if view.data is None:
raise Exception("View.data needs to be populated before interestingness can be computed. Run Executor.execute(view,ldf).")
n_dim = 0
n_msr = 0
filterSpecs = utils.getFilterSpecs(view.specLst)
viewAttrsSpecs = utils.getAttrsSpecs(view.specLst)
for spec in viewAttrsSpecs:
if (spec.attribute!="Record"):
if (spec.dataModel == 'dimension'):
n_dim += 1
if (spec.dataModel == 'measure'):
n_msr += 1
n_filter = len(filterSpecs)
attr_specs = [spec for spec in viewAttrsSpecs if spec.attribute != "Record"]
dimensionLst = view.getAttrByDataModel("dimension")
measureLst = view.getAttrByDataModel("measure")
# Bar Chart
if (n_dim == 1 and (n_msr == 0 or n_msr==1)):
if (n_filter == 0):
return unevenness(view, ldf, measureLst, dimensionLst)
elif(n_filter==1):
return deviationFromOverall(view,ldf,filterSpecs,measureLst[0].attribute)
# Histogram
elif (n_dim == 0 and n_msr == 1):
if (n_filter == 0):
v = view.data["Count of Records"]
return skewness(v)
elif (n_filter == 1):
return deviationFromOverall(view,ldf,filterSpecs,"Count of Records")
# Scatter Plot
elif (n_dim == 0 and n_msr == 2):
if (n_filter==1):
v_filter_size = getFilteredSize(filterSpecs,view.data)
v_size = len(view.data)
sig = v_filter_size/v_size
else:
sig = 1
return sig * monotonicity(view,attr_specs)
# Scatterplot colored by Dimension
elif (n_dim == 1 and n_msr == 2):
colorAttr = view.getAttrByChannel("color")[0].attribute
C = ldf.cardinality[colorAttr]
if (C<40):
return 1/C
else:
return -1
# Scatterplot colored by dimension
elif (n_dim== 1 and n_msr == 2):
return 0.2
# Scatterplot colored by measure
elif (n_msr == 3):
return 0.1
# Default
else:
return -1
