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newplot.py
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newplot.py
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import csv
import sys
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import json
from os import listdir
from os.path import isfile, join
import re
monnomdistances={'C':0,'I':0,'D':1,'J':1,'K':2,'L':1,'M':2,'S':1,'T':2}
markersize=8
linewidth=3
markerstyles = {'MonNom':{'color':'#000000', 'symbol':'x','size':markersize+2},
'Nom':{'color':'#00B050', 'symbol':'cross','size':markersize},
'Proxied Grift':{'color':'#ED7D31', 'symbol':'arrow-up','size':markersize},
'Monotonic Grift':{'color':'#ED7D31', 'symbol':'diamond-open','size':markersize},
'Racket':{'color':'#4472C4', 'symbol':'circle-open','size':markersize},
'C#':{'color':'#264478', 'symbol':'diamond','size':markersize},
'Java':{'color':'#7030A0', 'symbol':'diamond-wide','size':markersize+3},
'NodeJS':{'color':'#9E480E', 'symbol':'circle','size':markersize},
'HiggsCheck':{'color':'#C00000', 'symbol':'arrow-up','size':markersize},
'Reticulated':{'color':'#B21E6F', 'symbol':'circle-open','size':markersize}}
linestyles = {'MonNom':{'color':'#000000', 'width':linewidth},
'Nom':{'color':'#00aa00', 'dash':'dash', 'width':linewidth},
'Proxied Grift':{'color':'#ED7D31', 'dash':'longdash', 'width':linewidth},
'Monotonic Grift':{'color':'#ED7D31', 'dash':'dashdot', 'width':linewidth},
'Racket':{'color':'#4472C4', 'dash':'dot', 'width':linewidth},
'C#':{'color':'#264478', 'dash':'dot', 'width':linewidth},
'Java':{'color':'#7030A0', 'dash':'dot', 'width':linewidth},
'NodeJS':{'color':'#9E480E', 'dash':'dot', 'width':linewidth},
'HiggsCheck':{'color':'#C00000', 'dash':'dot', 'width':linewidth},
'Reticulated':{'color':'#B21E6F', 'dash':'dot', 'width':linewidth}}
def distance_to_fully_typed(config):
ret=0
for c in config:
ret+=monnomdistances[c]
return ret
def combine_funcs(f1,f2):
return lambda x: f2(f1(x))
def cut_dotbm(str):
return str[4:]
def fetch_key(key,data):
try:
if(key.isdigit()):
return data.loc[int(key)][0]
else:
return data.loc[key][0]
except KeyError:
return "REMOVE"
def load_converter(path):
data=pd.read_csv(path,index_col=0)
return lambda k: fetch_key(str(k),data)
def check_key(key,data):
try:
if(key.isdigit()):
return data.loc[int(key)][0] is None
else:
return data.loc[key][0] is None
except KeyError:
return True
def load_skipper(path,results):
data=pd.read_csv(path,index_col=0)
actualdata=pd.read_csv(results,header=None)
return lambda i: check_key(cut_dotbm(actualdata.iat[i,0]),data)
def load_benchmark(path):
config=json.load(open(path+"/plotconfig.json","rt"))
if(config.get("version")!=None):
if(config.get("version")=="v2"):
return load_newbenchmark(path,config)
if(config.get("version")=="v3"):
return load_benchmarkv3(path,config)
if(config.get("version")=="v1"):
return load_oldbenchmark(path,config)
return load_newbenchmark(path,config)
def load_newbenchmark(path,config):
converter=cut_dotbm
skipper=lambda x : False
if(config.get("mapping")!=None):
converter=combine_funcs(cut_dotbm,load_converter(path+"/"+config["mapping"]))
skipper=load_skipper(path+"/"+config["mapping"],path+"/results.csv")
data=pd.read_csv(path+"/results.csv",header=None,converters={0:converter},skiprows=skipper,index_col=0)
datacolumns=len(data.columns)
linesperprog=config["lines"]
resultcolumns=[[] for i in range(0,linesperprog-1)]
timescolumns=[]
if(datacolumns%linesperprog!=0):
raise Exception("Invalid number of columns: "+path)
rightvalues=[]
for i in range(0,linesperprog):
if i!=config["time"]:
rightvalues.append(data.iat[0,i])
for i in range(0,datacolumns):
if i%linesperprog==config["time"]:
timescolumns.append(i)
else:
if i%linesperprog<config["time"]:
resultcolumns[i%linesperprog].append(i)
else:
resultcolumns[(i-1)%linesperprog].append(i)
for i in range(0,linesperprog-1):
if not data.take(resultcolumns[i],axis=1).applymap(lambda x : x==rightvalues[i]).all(axis=None):
print(data.take(resultcolumns[i],axis=1))
raise Exception("not all result values match!")
times=data.take(timescolumns,axis=1)
times=times.rename(columns={0:'Configuration'})
dists=pd.Series(times.index.map(distance_to_fully_typed), name='Distance to Fully Typed/Nominal')
means=times.mean(axis=1,numeric_only=True).rename("Running Time in Seconds")
stdevs=times.std(axis=1,numeric_only=True).rename("Running Time Standard Deviation")
extended=pd.concat([pd.Series(times.index),dists],join="inner",axis=1)
extended=extended.set_index([0])
dtable=pd.DataFrame(extended).join(means).join(stdevs)
return dtable
def load_benchmarkv3(path,config):
fullresultsfiles=[(x.string,x.group()[(x.string.find("-")+1):-9]) for x in [re.search("[a-zA-Z]+\-([0-9\-]+)_finished.csv",f) for f in listdir(path) if re.search("[a-zA-Z]+\-([0-9\-]+)_finished.csv",f)!=None]]
fullresultsfiles.sort(key=lambda x:x[1],reverse=True)
data=pd.read_csv(path+"/"+fullresultsfiles[0][0],header=None,index_col=0)
datacolumns=len(data.columns)
timescolumns=[]
for i in range(0,datacolumns):
timescolumns.append(i)
times=data.take(timescolumns,axis=1)
times=times.rename(columns={0:'Configuration'})
dists=pd.Series(times.index.map(distance_to_fully_typed), name='Distance to Fully Typed/Nominal')
means=times.mean(axis=1,numeric_only=True).rename("Running Time in Seconds")
stdevs=times.std(axis=1,numeric_only=True).rename("Running Time Standard Deviation")
extended=pd.concat([pd.Series(times.index),dists],join="inner",axis=1)
extended=extended.set_index([0])
dtable=pd.DataFrame(extended).join(means).join(stdevs)
return dtable
def load_oldbenchmark(path, config):
fullresultsfiles=[(x.string,x.group()[8:-9]) for x in [re.search("results_([0-9\-]+)_full.csv",f) for f in listdir(path+"/benchmark") if re.search("results_([0-9\-]+)_full.csv",f)!=None]]
fullresultsfiles.sort(key=lambda x:x[1],reverse=True)
converter=load_converter(path+"/"+config["mapping"])
data=pd.read_csv(path+"/benchmark/"+fullresultsfiles[0][0],header=0,converters={"Folder":converter}).pivot(index="Folder",columns="Run",values="Seconds")
means=data.mean(axis=1,numeric_only=True).rename("Running Time in Seconds")
stdevs=data.std(axis=1,numeric_only=True).rename("Running Time Standard Deviation")
dists=pd.Series(data.index.map(distance_to_fully_typed), name='Distance to Fully Typed/Nominal')
extended=pd.concat([pd.Series(data.index),dists],join="inner",axis=1)
extended=extended.set_index(["Folder"])
dtable=pd.DataFrame(extended).join(means).join(stdevs)
return dtable
def load_monnom(fig, path, multilang):
bmd=load_benchmark(path)
baserow=1
if multilang:
baserow=2
fig.append_trace(go.Scatter(None, mode='markers', x=bmd['Distance to Fully Typed/Nominal'],y=bmd["Running Time in Seconds"],marker=markerstyles["MonNom"],name="MonNom",legendgroup="runningtimes"),row=1,col=1)
fig.append_trace(go.Scatter(None, mode='markers', x=bmd['Distance to Fully Typed/Nominal'],y=bmd["Running Time in Seconds"],marker=markerstyles["MonNom"],name="MonNom",showlegend=not multilang),row=baserow,col=1)
mindistancetime = bmd["Distance to Fully Typed/Nominal"].idxmin()
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
mindistentry=bmd.loc[mindistancetime]
maxdistentry=bmd.loc[maxdistancetime]
mindisttime=mindistentry["Running Time in Seconds"]
maxdisttime=maxdistentry["Running Time in Seconds"]
mindist=int(mindistentry['Distance to Fully Typed/Nominal'])
maxdist=int(maxdistentry['Distance to Fully Typed/Nominal'])
fig.add_shape(type="line", x0=maxdist,y0=maxdisttime,x1=mindist,y1=mindisttime, row=baserow,col=1,line={'color':"Green",'width':linewidth,'dash':'dot'})
fig.add_shape(type="line", x0=maxdist,y0=maxdisttime,x1=mindist,y1=maxdisttime, row=baserow,col=1,line={'color':"RoyalBlue",'width':linewidth,'dash':'dash'})
fig.add_shape(type="line", x0=0,y0=0,x1=0,y1=1, row=baserow,col=2,line={'color':"Black",'width':linewidth,'dash':'dash'})
expectedTimes=[mindisttime+(((maxdisttime-mindisttime)/(maxdist-mindist))*i) for i in range(0,maxdist-mindist+1)]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd["Compared to Linear Baseline"] = (bmd["Running Time in Seconds"]/bmd["Distance to Fully Typed/Nominal"].map(lambda x:expectedTimes[x]))-1.0
bmd=bmd.sort_values(by=["Compared to Linear Baseline"])
linearOverheads = bmd["Compared to Linear Baseline"].drop_duplicates()
linearCumulatives = [bmd[bmd["Compared to Linear Baseline"] <= x]["Compared to Linear Baseline"].count()/(bmd["Compared to Linear Baseline"].count()*1.0) for x in linearOverheads]
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=linearOverheads, y=linearCumulatives,marker={'color':'Green', 'symbol':'circle','size':markersize},name="Compared to Linear Baseline",line={'color':"Green",'width':linewidth,'dash':'dot'},legendgroup="baselines"),row=baserow,col=2)
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker={'color':'RoyalBlue', 'symbol':'circle','size':markersize},name="Compared to Untyped Baseline",line={'color':"RoyalBlue",'width':linewidth,'dash':'dash'},legendgroup="baselines"),row=baserow,col=2)
if multilang:
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["MonNom"],showlegend=True,line=linestyles["MonNom"],legendgroup="cumulatives",name="MonNom"),row=1,col=2)
def load_grift_version(fig, path, versionname, offset, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x+offset for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles[versionname],name=versionname,legendgroup="runningtimes"),row=1,col=1)
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
maxdistentry=bmd.loc[maxdistancetime]
maxdisttime=maxdistentry["Running Time in Seconds"]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles[versionname],showlegend=True,line=linestyles[versionname],legendgroup="cumulatives",name=versionname),row=1,col=2)
def load_grift(fig, path, multilang):
load_grift_version(fig, path+"/proxies", "Proxied Grift", 0.15, multilang)
load_grift_version(fig, path+"/monotonic", "Monotonic Grift", -0.15, multilang)
def load_griftnew(fig, path, multilang):
load_grift_version(fig, path, "Proxied Grift", 0.15, multilang)
load_grift_version(fig, path+"mono", "Monotonic Grift", -0.15, multilang)
def load_racket(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x-0.3 for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["Racket"],name="Racket",legendgroup="runningtimes"),row=1,col=1)
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
maxdistentry=bmd.loc[maxdistancetime]
maxdisttime=maxdistentry["Running Time in Seconds"]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["Racket"],showlegend=True,line=linestyles["Racket"],legendgroup="cumulatives",name="Racket"),row=1,col=2)
def load_nom(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x+0.3 for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["Nom"],name="Nom",legendgroup="runningtimes"),row=1,col=1)
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
maxdistentry=bmd.loc[maxdistancetime]
maxdisttime=maxdistentry["Running Time in Seconds"]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["Nom"],showlegend=True,line=linestyles["Nom"],legendgroup="cumulatives",name="Nom"),row=1,col=2)
def load_java(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["Java"],name="Java",legendgroup="runningtimes"),row=1,col=1)
#maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
#maxdistentry=bmd.loc[maxdistancetime]
#maxdisttime=maxdistentry["Running Time in Seconds"]
#bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
#bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
#untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
#untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
#fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["Java"],showlegend=True,line=linestyles["Java"],legendgroup="cumulatives",name="Java"),row=1,col=2)
def load_csharp(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["C#"],name="C#",legendgroup="runningtimes"),row=1,col=1)
#maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
#maxdistentry=bmd.loc[maxdistancetime]
#maxdisttime=maxdistentry["Running Time in Seconds"]
#bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
#bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
#untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
#untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
#fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["C#"],showlegend=True,line=linestyles["C#"],legendgroup="cumulatives",name="C#"),row=1,col=2)
def load_node(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["NodeJS"],name="NodeJS",legendgroup="runningtimes"),row=1,col=1)
#maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
#maxdistentry=bmd.loc[maxdistancetime]
#maxdisttime=maxdistentry["Running Time in Seconds"]
#bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
#bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
#untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
#untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
#fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["NodeJS"],showlegend=True,line=linestyles["NodeJS"],legendgroup="cumulatives",name="NodeJS"),row=1,col=2)
def load_higgs(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["HiggsCheck"],name="HiggsCheck",legendgroup="runningtimes"),row=1,col=1)
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
maxdistentry=bmd.loc[maxdistancetime]
maxdisttime=maxdistentry["Running Time in Seconds"]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["HiggsCheck"],showlegend=True,line=linestyles["HiggsCheck"],legendgroup="cumulatives",name="HiggsCheck"),row=1,col=2)
def load_reticulated(fig, path, multilang):
bmd=load_benchmark(path)
fig.append_trace(go.Scatter(None, mode='markers', x=[x for x in bmd['Distance to Fully Typed/Nominal']],y=bmd["Running Time in Seconds"],marker=markerstyles["Reticulated"],name="Reticulated",legendgroup="runningtimes"),row=1,col=1)
maxdistancetime = bmd["Distance to Fully Typed/Nominal"].idxmax()
maxdistentry=bmd.loc[maxdistancetime]
maxdisttime=maxdistentry["Running Time in Seconds"]
bmd["Compared to Untyped Baseline"] = (bmd["Running Time in Seconds"]/maxdisttime)-1.0
bmd=bmd.sort_values(by=["Compared to Untyped Baseline"])
untypedOverheads = bmd["Compared to Untyped Baseline"].drop_duplicates()
untypedCumulatives = [bmd[bmd["Compared to Untyped Baseline"] <= x]["Compared to Untyped Baseline"].count()/(bmd["Compared to Untyped Baseline"].count()*1.0) for x in untypedOverheads]
fig.append_trace(go.Scatter(None, x=untypedOverheads, y=untypedCumulatives,marker=markerstyles["Reticulated"],showlegend=True,line=linestyles["Reticulated"],legendgroup="cumulatives",name="Reticulated"),row=1,col=2)
langhandlers={}
langhandlers['monnom'] = load_monnom
langhandlers['grift'] = load_grift
langhandlers['griftnew'] = load_griftnew
langhandlers['racket'] = load_racket
langhandlers['nom'] = load_nom
langhandlers['csharp'] = load_csharp
langhandlers['java'] = load_java
langhandlers['node'] = load_node
langhandlers['higgs'] = load_higgs
langhandlers['retic'] = load_reticulated
if(len(sys.argv)>2):
folders=[]
hasMonnom=False
for i in range(2,len(sys.argv),2):
folders.append({'path':sys.argv[i],'kind':sys.argv[i+1]})
hasMonnom=hasMonnom or (sys.argv[i+1]=="monnom")
multilang = hasMonnom and len(folders)>1
figrows=1
if multilang:
figrows=2
fig = make_subplots(rows=figrows, cols=2)
for f in folders:
langhandlers[f['kind']](fig, f['path'],multilang)
fig.layout.xaxis.autorange="reversed"
fig.layout.yaxis.rangemode="tozero"
fig.layout.yaxis.title="Running Time in Seconds"
fig.layout.xaxis.title="Number of Steps to Fully Typed"
fig.layout.yaxis2.tickformat=",.0%"
fig.layout.yaxis2.title="Configurations Below"
if multilang:
fig.layout.xaxis3.title="Number of Steps to Fully Typed"
fig.layout.xaxis3.autorange="reversed"
fig.layout.yaxis3.title="Running Time in Seconds"
fig.layout.yaxis3.rangemode="tozero"
fig.layout.yaxis4.tickformat=",.0%"
fig.layout.xaxis4.tickformat=",.0%"
fig.layout.xaxis2.ticksuffix="x"
fig.layout.yaxis4.title="Configurations Below"
else:
fig.layout.xaxis2.tickformat=",.0%"
figtitle=sys.argv[1]
renderimg=False
if(sys.argv[1][-4:]==".png"):
renderimg=True
figtitle=figtitle[0:-4]
fig.layout.title.text=sys.argv[1]
fig.layout.title.font.size=30
if(renderimg):
fig.write_image(sys.argv[1])
else:
fig.show()