updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
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MassiveAtoms
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.vscode/settings.json
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.vscode/settings.json
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{
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"python.pythonPath": "C:\\python3.8\\python.exe"
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}
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14038
last_attempt.html
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14038
last_attempt.html
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last_attempt.py
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last_attempt.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import pandas as pd
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from pandas import DataFrame, Series
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from numpy import nan
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import matplotlib.pyplot as plt
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plt.rcParams["figure.figsize"] = (24,5)
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import seaborn as sns
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sns.set()
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sns.set(font_scale=1.75)
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# In[2]:
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cols = [
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"TEST","MAP","SIZE", "TIME"
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]
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onmodded = pd.read_csv("new_repr.csv", quotechar="'", header=None)
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onmodded = onmodded.drop([0],1)
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onmodded.columns= cols
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onmodded = onmodded.drop([0],0)
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onmodded.iloc[:,2:] = onmodded.iloc[:,2:].astype("int32")
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styles = {'absl::flat_hash_map': ["#0000cc"], # blue
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"absl::node_hash_map'": ["#3366ff"],
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'absl::node_hash_map': ["#99ccff"],
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'google::dense_hash_map': ["#ff0000"], # reds
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'google::sparse_hash_map': ["#ff6666"],
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'phmap::parallel_flat_hash_map': ["#ff0066"],
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'ska::bytell_hash_map': ["#009933"], # greens
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'ska::flat_hash_map': ["#33cc33"],
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'ska::unordered_map': ["#99ff66"],
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'tsl::hopscotch_map': ["#9900cc"], # purples
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'tsl::robin_map': ["#cc33ff"],
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'tsl::sparse_map': ["#cc99ff"],
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'robin_hood::unordered_flat_map': ["#ffcc99"],
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'robin_hood::unordered_node_map': ["#ccff66"],
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'boost::unordered::unordered_map': ["#663300"], # brown
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'emilib::HashMap': ["#9900cc"], # purple
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# weird orange
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'phmap::parallel_node_hash_map': ["#ffcc66", "solid"],
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'std::unordered_map': ["#000000", "solid"], # black
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}
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ticks = [50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 500000,
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600000, 700000, 800000, 900000, 1000000,
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2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000,
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15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000]
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ticklabels = ['50 K', '100 K',
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'150 K', '200 K', '250 K', '300 K',
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'350 K', '400 K', '0.5 M', '0.6 M',
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'0.7 M', '0.8 M', '0.9 M', '1 M',
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'2 M', '3 M', '4 M', '5 M',
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'6 M', '7 M', '8 M', '9 M',
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'10 M', '15 M', '20 M', '25 M',
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'30 M', '35 M', '40 M', '45 M', '50 M']
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labels = {
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'int_delete' : ["mean int deletion time", "deletion time (ns)"],
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'int_insert' : ["mean int insertion time", "insertion time(ns)"],
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'int_nosucc_lookup' : ["mean int unsucessful lookup time", "unsucessful lookup time (ns)"],
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'int_succ_lookup' : ["mean int succesful lookup time", "succesful lookup time (ns)"],
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'string_delete' : ["mean string deletion time", "deletion time (ns)"],
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'string_insert' : ["mean string insertion time", "insertion time(ns)"],
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'string_nosucc_lookup' : ["mean string unsucessful lookup time", "unsucessful lookup time (ns)"],
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'string_succ_lookup' : ["mean string succesful lookup time", "succesful lookup time (ns)"]
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}
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# In[3]:
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# outlier testing functions
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def remove_with_modified_z_score(data, treshold=3.5):
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# https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
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data = data.astype(int)
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stats = data.describe()
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median_absolute_deviation = abs(data - data.median()).median()
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if not median_absolute_deviation:
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return data
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modified_z_scores = abs(0.6745 * (data - data.median()) / median_absolute_deviation)
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cutoff = modified_z_scores <= treshold
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data = data * cutoff
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data = data.replace(0, nan)
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return data
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#function that takes one of the outlier testers and data, and removes outliers
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def remove_outlier(data, function):
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new_data = data.copy(True)
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new_data["TIME"] = new_data["TIME"].astype(int)
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new_data["SIZE"] = new_data["SIZE"].astype(int)
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new_data
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for i in range(4216):
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start = i * 30
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end = start+30
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new_data.loc[start:end, "TIME"] = function(data.loc[start:end, "TIME"])
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if not i % 420:
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print(i / 42 , "% done")
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return new_data
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# helpers for plot functions
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def sort_maps(test):
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maps = data[data["TEST"]== test]["MAP"].unique()
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new = [(gr_max.loc[test, i]["TIME"], i) for i in maps]
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new.sort()
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new = [i[1] for i in new]
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return new
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def divider(df, maplist):
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filters = df['MAP'].isin(maplist)
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return df[filters]
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def plotter2(test, data):
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mydata = data[data["TEST"] == test]
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maps = sort_maps(test)
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set1 = divider(mydata, maps[:5])
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set2 = divider(mydata, maps[5:11])
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set3 = divider(mydata, maps[11:])
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plot = sns.lineplot(x="SIZE", y="TIME", hue="MAP", data=set1)
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plt.xscale("log")
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plt.xticks(ticks, ticklabels)
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plot.set_xticklabels(
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plot.get_xticklabels(),
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rotation=55,
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horizontalalignment='center',
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fontweight='light',
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)
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plt.ylabel(labels[test][1])
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plt.legend()
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plt.title(labels[test][0])
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plt.savefig("./plots/{}/{}.png".format(test,1))
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plt.clf()
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plot = sns.lineplot(x="SIZE", y="TIME", hue="MAP", data=set2)
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plt.xscale("log")
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plt.xticks(ticks, ticklabels)
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plot.set_xticklabels(
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plot.get_xticklabels(),
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rotation=55,
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horizontalalignment='center',
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fontweight='light',
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)
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plt.ylabel(labels[test][1])
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plt.legend()
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plt.title(labels[test][0])
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plt.savefig("./plots/{}/{}.png".format(test,2))
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plt.clf()
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plot = sns.lineplot(x="SIZE", y="TIME", hue="MAP", data=set2)
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plt.xscale("log")
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plt.xticks(ticks, ticklabels)
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plot.set_xticklabels(
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plot.get_xticklabels(),
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rotation=55,
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horizontalalignment='center',
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fontweight='light',
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)
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plt.ylabel(labels[test][1])
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plt.legend()
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plt.title(labels[test][0])
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plt.savefig("./plots/{}/{}.png".format(test,3))
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plt.clf()
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# In[4]:
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data = remove_outlier(onmodded, remove_with_modified_z_score)
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groups = data.groupby(["TEST", "MAP"])
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gr_max = groups.max()
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gr_mean = groups.mean()
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# In[5]:
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tests = data["TEST"].unique()
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for i in tests:
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plotter2(i, data)
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# In[6]:
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# In[ ]:
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# In[ ]:
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# In[ ]:
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@ -1,90 +0,0 @@
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import seaborn as sns
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import pandas as pd
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from pandas import DataFrame, Series
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import matplotlib.pyplot as plt
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import pathlib
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import os
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plt.rcParams["figure.figsize"] = (40, 5)
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sns.set()
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## new cell
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cols = [
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"TEST", "MAP", '50000', '100000', '150000', '200000', '250000', '300000', '350000', '400000',
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'500000', '600000', '700000', '800000', '900000', '1000000', '2000000', '3000000',
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'4000000', '5000000', '6000000', '7000000', '8000000', '9000000', '10000000', '15000000',
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'20000000', '25000000', '30000000', '35000000', '40000000', '45000000', '50000000'
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]
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data = pd.read_csv("results.csv", quotechar="'", header=None)
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data.columns = cols
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# data.head()
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## new cell
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groups = data.groupby(["TEST", "MAP"])
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groups_mean = groups.mean()
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groups_std = groups.std()
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## new cell
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def max_val(hmap, test):
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return groups_mean.loc[test, hmap].max()
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def sort_maps(test):
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maps = list(groups_mean.loc[test].index)
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new = [(max_val(i, test), i) for i in maps]
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new.sort()
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new = [i[1] for i in new]
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return new
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def plot_test(test, include_error=True, log=False):
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sizes = [50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 500000,
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600000, 700000, 800000, 900000, 1000000,
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2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000,
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15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000]
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maps = sort_maps(test)
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# print(maps)
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count = 16
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repeats = [0, 5, 11]
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while count > -1:
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if not count and count not in repeats:
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break
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mp = maps[count]
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if include_error:
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plt.errorbar(groups_mean.columns,groups_mean.loc[test, mp], yerr=groups_std.loc[test, mp], label=mp)
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else:
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plt.plot(groups_mean.columns, groups_mean.loc[test, mp], label=mp)
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if count in repeats:
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if log:
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plt.xscale("log")
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plt.ylabel("{} time (ns)".format(test))
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plt.legend()
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plt.title(test)
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plt.savefig("./plots/{}/{}.png".format(test,count))
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plt.clf()
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# plt.show()
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repeats.pop(repeats.index(count))
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else:
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count -=1
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## new cell
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tests = data["TEST"].unique()
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p = pathlib.Path("./plots")
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if not p.is_dir():
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p.mkdir()
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for i in tests:
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path = pathlib.Path("./plots/{}/".format(i))
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if not path.is_dir():
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path.mkdir()
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print(i)
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plot_test(i, False)
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# break
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12
readme.md
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readme.md
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# Files and what they contain
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# HTML export for those who don't have a data science stack + python installed
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[last_attempt.html](./last_attempt.html)
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## results.csv
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[Results.csv](./results.csv) has the raw data as outputed by the benchmark code
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@ -17,12 +20,13 @@ to
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## new_repr_no_outlier.csv
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[New_repr.csv](./new_repr_no_outlier.csv) has the data from no_repr but with outliers removed with the modified z score test
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[new_repr_no_outlier.csv](./new_repr_no_outlier.csv) has the data from no_repr but with outliers removed with the modified z score test
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# Notebooks
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## Better_plotting
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better plotting is an incomplete and now broken notebook where we attempted to plot on the raw results
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## Last_attempt
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Last attempt has our last attempt at plotting, plus some random stuff at the end
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File diff suppressed because it is too large
Load Diff
28
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28
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styles = {'absl::flat_hash_map': ["#0000cc", "--"], # blue
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"absl::node_hash_map'": ["#3366ff", "--"],
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'absl::node_hash_map': ["#99ccff", "--"],
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'google::dense_hash_map': ["#ff0000", "-."], # reds
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'google::sparse_hash_map': ["#ff6666", "-,"],
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'phmap::parallel_flat_hash_map': ["#ff0066", "-."],
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'ska::bytell_hash_map': ["#009933", "- "], # greens
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'ska::flat_hash_map': ["#33cc33", "- "],
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'ska::unordered_map': ["#99ff66", "- "],
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'tsl::hopscotch_map': ["#9900cc", ":"], # purples
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'tsl::robin_map': ["#cc33ff", ":"],
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'tsl::sparse_map': ["#cc99ff", ":"],
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'robin_hood::unordered_flat_map': ["#ffcc99", ".."],
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'robin_hood::unordered_node_map': ["#ccff66", ".."],
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'boost::unordered::unordered_map': ["#663300", "solid"], # brown
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'emilib::HashMap': ["#9900cc", "solid"], # purple
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# weird orange
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'phmap::parallel_node_hash_map': ["#ffcc66", "solid"],
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'std::unordered_map': ["#000000", "solid"], # black
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}
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Reference in New Issue
Block a user