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{
"cells": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
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"# Importing libs and setting some defaults like the plot size and forcing matplotlib to use seaborn color schemes with softer, more palatable colors and setting a good font scale"
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]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"from numpy import nan\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"import numpy as np\n",
"plt.rcParams[\"figure.figsize\"] = (24,5)\n",
"import seaborn as sns\n",
"sns.set()\n",
"sns.set(font_scale=1.75)\n"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some default styles that will be used for some functions such as tickmarks, labels, etc"
]
},
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{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
"cols = [\n",
" \"TEST\",\"MAP\",\"SIZE\", \"TIME\"\n",
"]\n",
"onmodded = pd.read_csv(\"new_repr.csv\", quotechar=\"'\", header=None)\n",
"\n",
"onmodded = onmodded.drop([0],1)\n",
"onmodded.columns= cols\n",
"onmodded = onmodded.drop([0],0)\n",
"onmodded.iloc[:,2:] = onmodded.iloc[:,2:].astype(\"int32\")\n",
"\n",
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"# used for plotter and plotter3, but we can't choose nice looking and distinct enough colors to plot 17 different lines.\n",
"# ultimately abandoned in favor of the default styles from seaborn\n",
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"styles = {'absl::flat_hash_map': [\"#0000cc\"], # blue\n",
" \"absl::node_hash_map'\": [\"#3366ff\"],\n",
" 'absl::node_hash_map': [\"#99ccff\"],\n",
"\n",
" 'google::dense_hash_map': [\"#ff0000\"], # reds\n",
" 'google::sparse_hash_map': [\"#ff6666\"],\n",
" 'phmap::parallel_flat_hash_map': [\"#ff0066\"],\n",
"\n",
" 'ska::bytell_hash_map': [\"#009933\"], # greens\n",
" 'ska::flat_hash_map': [\"#33cc33\"],\n",
" 'ska::unordered_map': [\"#99ff66\"],\n",
"\n",
" 'tsl::hopscotch_map': [\"#9900cc\"], # purples\n",
" 'tsl::robin_map': [\"#cc33ff\"],\n",
" 'tsl::sparse_map': [\"#cc99ff\"],\n",
"\n",
" 'robin_hood::unordered_flat_map': [\"#ffcc99\"],\n",
" 'robin_hood::unordered_node_map': [\"#ccff66\"],\n",
"\n",
" 'boost::unordered::unordered_map': [\"#663300\"], # brown\n",
"\n",
" 'emilib::HashMap': [\"#9900cc\"], # purple\n",
"\n",
" # weird orange\n",
" 'phmap::parallel_node_hash_map': [\"#ffcc66\", \"solid\"],\n",
"\n",
" 'std::unordered_map': [\"#000000\", \"solid\"], # black\n",
" }\n",
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"# where to place the x tickmarks and how to label them\n",
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"ticks = [50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 500000,\n",
"600000, 700000, 800000, 900000, 1000000,\n",
"2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000,\n",
"15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000]\n",
"ticklabels = ['50 K', '100 K',\n",
" '150 K', '200 K', '250 K', '300 K',\n",
" '350 K', '400 K', '0.5 M', '0.6 M',\n",
" '0.7 M', '0.8 M', '0.9 M', '1 M',\n",
" '2 M', '3 M', '4 M', '5 M',\n",
" '6 M', '7 M', '8 M', '9 M',\n",
" '10 M', '15 M', '20 M', '25 M',\n",
" '30 M', '35 M', '40 M', '45 M', '50 M']\n",
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"# convenient way to label the plots for later\n",
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"labels = {\n",
" 'int_delete' : [\"mean int deletion time\", \"deletion time (ns)\"],\n",
" 'int_insert' : [\"mean int insertion time\", \"insertion time(ns)\"],\n",
" 'int_nosucc_lookup' : [\"mean int unsucessful lookup time\", \"unsucessful lookup time (ns)\"],\n",
" 'int_succ_lookup' : [\"mean int succesful lookup time\", \"succesful lookup time (ns)\"],\n",
" 'string_delete' : [\"mean string deletion time\", \"deletion time (ns)\"],\n",
" 'string_insert' : [\"mean string insertion time\", \"insertion time(ns)\"], \n",
" 'string_nosucc_lookup' : [\"mean string unsucessful lookup time\", \"unsucessful lookup time (ns)\"],\n",
" 'string_succ_lookup' : [\"mean string succesful lookup time\", \"succesful lookup time (ns)\"]\n",
"}"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function definitions\n",
"Some functions that accept and delete outliers in a range \n",
"A function that removes outliers with the functions mentioned above \n",
"Some helper functions \n",
"Some plotter functions "
]
},
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{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# outlier testing functions\n",
"def remove_with_modified_z_score(data, treshold=3.5):\n",
" # https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm\n",
" data = data.astype(int)\n",
" stats = data.describe()\n",
" median_absolute_deviation = abs(data - data.median()).median()\n",
" if not median_absolute_deviation:\n",
" return data\n",
" modified_z_scores = abs(0.6745 * (data - data.median()) / median_absolute_deviation)\n",
" cutoff = modified_z_scores <= treshold\n",
" data = data * cutoff\n",
" data = data.replace(0, nan)\n",
" return data\n",
"\n",
"#function that takes one of the outlier testers and data, and removes outliers\n",
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"# originally, we had multiple outlier tests we tried out and this made it convenient to check the differences\n",
"# now it's just the modified_z_score, which we chose\n",
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"def remove_outlier(data, function):\n",
" new_data = data.copy(True)\n",
" new_data[\"TIME\"] = new_data[\"TIME\"].astype(int)\n",
" new_data[\"SIZE\"] = new_data[\"SIZE\"].astype(int)\n",
" new_data\n",
" for i in range(4216):\n",
" start = i * 30\n",
" end = start+30\n",
" new_data.loc[start:end, \"TIME\"] = function(data.loc[start:end, \"TIME\"])\n",
" if not i % 420:\n",
" print(i / 42 , \"% done\")\n",
" return new_data\n",
"\n",
"\n",
"# helpers for plot functions\n",
"def sort_maps(test):\n",
" maps = data[data[\"TEST\"]== test][\"MAP\"].unique()\n",
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" new = [(gr_max.loc[test, i][\"TIME\"], i) for i in maps]\n",
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" new.sort()\n",
" new = [i[1] for i in new]\n",
" return new\n",
"\n",
"def divider(df, maplist):\n",
" filters = df['MAP'].isin(maplist)\n",
" return df[filters]\n",
"\n",
"\n",
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"\n",
"\n",
"# These are in no way optimized\n",
"# way too slow for normal use\n",
"# And not particularly reusable\n",
"\n",
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"def plotter(test, groups):\n",
" maps = sort_maps(test)\n",
" plot_now = [5,11,16]\n",
"# plot_now = [2,5,8,11,14,16]\n",
"\n",
" for mp in range(0,17):\n",
" map_data = groups.get_group(('int_insert', maps[mp])) \n",
" sns.lineplot(x=\"SIZE\", y=\"TIME\", data=map_data, err_style=\"bars\", color=styles[maps[mp]][0], label=maps[mp], linestyle=\"--\")\n",
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"# sns.lmplot(x=map_data[\"SIZE\"], y=map_data[\"TIME\"], color=styles[maps[mp]][0], label=maps[mp])\n",
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" if mp in plot_now:\n",
" plt.xscale(\"log\")\n",
" plt.xticks(ticks, ticklabels)\n",
" plt.set_xticklabels(rotation=45)\n",
" plt.ylabel(labels[test][1])\n",
" plt.legend()\n",
" plt.title(labels[test][0])\n",
" plt.show()\n",
"\n",
"def plotter2(test, data):\n",
" mydata = data[data[\"TEST\"] == test]\n",
" maps = sort_maps(test)\n",
" set1 = divider(mydata, maps[:5])\n",
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" set3 = divider(mydata, maps[12:])\n",
" # ^ updated this to only do the top 5 and bottom 5\n",
" plot = sns.lineplot(x=\"SIZE\", y=\"TIME\", hue=\"MAP\", data=set1, ci=95) # < updated to use 95% CI\n",
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" plt.xscale(\"log\")\n",
" plt.xticks(ticks, ticklabels)\n",
" plot.set_xticklabels(\n",
" plot.get_xticklabels(), \n",
" rotation=55, \n",
" horizontalalignment='center',\n",
" fontweight='light',\n",
" )\n",
" plt.ylabel(labels[test][1])\n",
" plt.legend()\n",
" plt.title(labels[test][0])\n",
" plt.show()\n",
" \n",
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" plot = sns.lineplot(x=\"SIZE\", y=\"TIME\", hue=\"MAP\", data=set3, ci=95) # < updated to use 95% CI\n",
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" plt.xscale(\"log\")\n",
" plt.xticks(ticks, ticklabels)\n",
" plot.set_xticklabels(\n",
" plot.get_xticklabels(), \n",
" rotation=55, \n",
" horizontalalignment='center',\n",
" fontweight='light',\n",
" )\n",
" plt.ylabel(labels[test][1])\n",
" plt.legend()\n",
" plt.title(labels[test][0])\n",
" plt.show()\n",
"\n",
" \n",
"def plotter3(test, groups):\n",
" maps = sort_maps(test)\n",
" plot_now = [5,11,16]\n",
"# plot_now = [2,5,8,11,14,16]\n",
" for mp in range(0,17):\n",
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" map_data = groups.get_group(('int_insert', maps[mp])) \n",
" sns.lineplot(x=\"SIZE\", y=\"TIME\", data=map_data, err_style=\"band\", color=styles[maps[mp]][0], label=maps[mp], linestyle=\"--\")\n",
"# sns.regplot(x=\"SIZE\", y=\"TIME\", color=styles[maps[mp]][0],label=maps[mp], data=map_data)\n",
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" if mp in plot_now:\n",
" plt.xscale(\"log\")\n",
" plt.xticks(ticks, ticklabels)\n",
" plt.ylabel(labels[test][1])\n",
" plt.legend()\n",
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"# plt.set_xticklabels(rotation=45)\n",
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" plt.title(labels[test][0])\n",
" plt.show()\n"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Remove the outliers\n",
"* group the data by test and map, in case plotter or plotter3 is used\n",
"* compute means and max of the map"
]
},
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{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0 % done\n",
"10.0 % done\n",
"20.0 % done\n",
"30.0 % done\n",
"40.0 % done\n",
"50.0 % done\n",
"60.0 % done\n",
"70.0 % done\n",
"80.0 % done\n",
"90.0 % done\n",
"100.0 % done\n"
]
}
],
"source": [
"data = remove_outlier(onmodded, remove_with_modified_z_score)\n",
"groups = data.groupby([\"TEST\", \"MAP\"])\n",
"gr_max = groups.max()\n",
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"gr_mean = groups.mean()\n",
"tests = data[\"TEST\"].unique()"
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]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[0], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 6,
work done
2020-04-20 11:37:08 +00:00
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[1], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 7,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[2], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 8,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[3], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 9,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAABYoAAAGHCAYAAAAeOWZMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3zN1//A8dfdWTdTZIkZib33FpSaraJGS5WilSq+Vdp+i+5q+0Uppa1NKbU3DTGrRoLQ2jNGkCU7ufd+fn/kl1tXEkFDgvfz8eij7vmczznvc85Nwjvnno9KURQFIYQQQgghhBBCCCGEEM8sdWEHIIQQQgghhBBCCCGEEKJwSaJYCCGEEEIIIYQQQgghnnGSKBZCCCGEEEIIIYQQQohnnCSKhRBCCCGEEEIIIYQQ4hkniWIhhBBCCCGEEEIIIYR4xkmiWAghhBBCCCGEEEIIIZ5xkigWQgghhBBPjYsXL7Jq1ar7rh8cHExQUBAmk+kRRvXvNGvWjKCgoIe+f+rUqQQFBbFs2bKHbsNisbBkyRJu3bplLVuxYgVBQUFMmjTpodstKBkZGcycOdOmrCDGLYQQQgjxLJFEsRBCCCGEeCocP36cDh06sG/fvvu+p2/fvoSEhKBWy1+L72X48OGMGzeOtLQ0a1nFihUJCQmhQYMGhRhZlp49ezJx4kSbsnr16hESEkKlSpUKKSohhBBCiCeLtrADEEIIIYQQoiAkJCSQmZn5QPe89tprjyaYp8zNmzdzlFWsWJGKFSsWQjQ53bnTOVv9+vWpX79+IUQjhBBCCPFkkq0TQgghhBBCCCGEEEII8YyTRLEQQgghxBNozJgxBAUFcfXqVb799ltatGhB1apV6dChg/WM3v3799OnTx9q1KhB8+bN+fjjj0lKSsrR1tGjRxk6dCj169enSpUqtG3blsmTJ5OcnJyj7q1bt/j666/p2LEjNWvWpEqVKgQHB/Pf//6X69ev29R99dVXqVmzJgkJCXz66ac0a9bM2v6MGTMwm833NdZr164xZswYnnvuOapWrUqDBg0YMmQIf/75p8189O/fH4CVK1cSFBTEihUrgKxziDt27Mj27dsJDg6matWqdO/eHbPZnOOM4qioKIKCgvjkk08IDw+nf//+1KpVi5o1a9K3b1+bPrNlZGTwww8/0K5dO6pVq0ZwcDBTp07ljz/+ICgoiKlTp97XOE+cOMHbb79Nw4YNqVmzJgMGDODkyZN51n+QdctNQkIC33zzDc899xxVqlShQYMGDBs2jBMnTljrZM9HeHg4AK1atSI4OBjI+4zi9PR0ZsyYQadOnahWrRq1atWiT58+rF+/PkcM2Wtz/fp13nvvPRo2bEjVqlXp3LkzS5YsyXcMf/75J0FBQURHRwMQFBTEq6++CuR+RnH2ezIuLo6xY8fSuHFjqlevTteuXdmxYwcAW7ZsoWvXrlSvXp1WrVoxefJkMjIycvS9e/duXn/9derWrUu1atXo1KkTs2bNeuBd7UIIIYQQRYUcPSGEEEII8QQLCQnh5s2btG3bltTUVFavXs3o0aM5deoU8+fPp1mzZvTu3Ztt27bxyy+/kJaWxpdffmm9f8OGDYwaNQqdTkebNm3w8vIiPDycH374ge3bt7No0SKcnJyArOMHXnrpJW7dukXz5s1p0aIFycnJ7Nmzh2XLlvHHH3+wYcMGDAaDtX2TycSrr75KfHw8LVu2RKPRsG7dOiZNmkRqaiojRoy45/ji4uLo27cv0dHRtGnThnbt2nH9+nU2b97Mzp07+emnn2jcuDGtW7fGYrGwevVqKlSoQOvWrW2ORYiOjmb48OG0atUKd3d37O3t0Wg0efYbHh7O0qVLqVGjBi+//DIXLlxg27ZthIeHs2rVKgICAgAwm80MHjyYvXv3EhAQQM+ePYmLi2PmzJmsXbv2vtfx0KFDDBw4kPT0dFq3bo2Xlxd//PEHffr0yTWh/iDrlptbt27Ru3dvLl68SL169WjVqhWxsbFs2rSJsLAwpk+fTpMmTXB2diYkJITly5dz7do1+vbti5+fX57tJiUl0bdvX44fP07ZsmXp3r07ycnJhIWFMXLkSA4ePMi4ceNs7klISODll1/Gzs6ODh06kJKSwvr16xk3bhxqtZoePXrk2Z+fnx8hISHMmTOH5ORkQkJC7hkfZL0nX3nlFTIzM+ncuTPR0dFs2LCBt956i379+jF//nzatm1L/fr1Wb9+PT/88ANarZaQkBBrG7Nnz2bChAm4urrSpk0bXF1d2bNnD19//TV79+5l5syZaLXyTy0hhBBCPGEUIYQQQgjxxBk9erQSGBiotGzZUomNjbWWz507VwkMDFQCAwOVZcuWWctv376t1KxZU6lcubJiNpsVRVGUmzdvKtWrV1caNWqkXLp0yab9qVOnKoGBgcr48eOtZZ999pkSGBioLFmyxKauyWRSunbtqgQGBiq7du2ylr/yyitKYGCg0qNHDyUpKclafvr0aaVixYpKvXr1FJPJdM9xLly4UAkMDFSWLl1qU75r1y4lMDBQGThwoLVsz549SmBgoDJ69Gibui1btlQCAwOVr776Kkf72dcyMzMVRVGUy5cvW+dv1qxZNnW//fZbJTAwUPn0009zxDd06FAlPT3dWn7w4EGlYsWKSmBgoDJlypR7jtFisSjPP/+8UrFiRWXnzp3W8vT0dOWdd96xxpPtQddtypQpOeYwJCRECQwMVBYuXGhz/6lTp5QaNWooDRo0UFJSUqzlPXv2VAIDA5XLly9by5YvX64EBgYqEydOtJaNHz9eCQwMVMaMGWMzH9HR0Ur79u2VwMBAZePGjdby7PkPCQlRMjIyrOU7d+5UAgMDlU6dOt1z7rI1bdrUZo7yGnf2e7Jbt25Kamqqtfzzzz+3zvPevXut5ZcuXVICAwOV1q1bW8v+/vtvpUKFCkr79u2VmJgYa7nFYlHef/99JTAwUPnpp5/uK24hhBBCiKJEjp4QQgghhHiC9ezZEzc3N+vrevXqAeDh4cFLL71kLTcajZQrV47MzEzrx/RXrVpFamoqgwcPxt/f36bdIUOG4O7uzqpVq6wfpX/++ef5+OOPbdoF0Gg01n5jYmJyxNi/f38cHR2trwMCAihTpgzx8fHExsbec3wWiwWAyMhIm4/0N2nShC1btjBt2rR73n+njh073nddo9FI3759bcratGkDwPnz561ly5cvR61W88EHH6DX663ltWvXpnPnzvfVV2RkJGfPniU4OJimTZtay/V6PWPHjs2xM/VB1+1ut27dYuvWrVSqVIk+ffrYXCtfvjzdu3cnNjaW0NDQ+4o/W2ZmJqtWrcJoNDJ27Fib+ShevDgffPABQK5HSgwaNAidTmd93aRJExwcHGzmuiC99tpr2NnZWV9nv3+rVq1Kw4YNreX+/v54eHhw9epVFEUB4Ndff8VisTBy5Ejc3d2tdVUqFaNHj0atVtscdyGEEEII8aSQz0MJIYQQQjzBypYta/PawcEByEpwqVQqm2vZibHs81YjIyOt/8/tHF17e3tiY2O5cOEC5cuXp1atWtSqVYuUlBSOHj3KpUuXiIqK4uTJk+zbtw/4J7F7rxghKxF7Zyx5ad++PTNmzODXX39l48aNNGjQgIYNG9K0aVNKlSp1z3vv9iD1S5YsmSNB6+zsbBNzRkYGf/31Fz4+Pvj6+uZoo27duqxcuTLfvo4fPw5A9erVc1xzd3enbNmynDp1ylr2oOuWW3+KomAymXK9PyoqylrvQZLr58+fJyUlhSZNmmBvb5/jet26dVGr1fz11185rt39HlGpVDg5OXHjxo377v9B5PV1U7JkyRx17ezsiImJwWw2o9VqrfO/d+/eXMfi6OjIhQsXSE5OtvkFiRBCCCFEUSeJYiGEEEKIJ1h2gutud+7mzMvt27cBWLNmzT3rJSQkAJCcnMzEiRNZvnw5qampQFbytFq1agQEBBAeHm7ddXmnO88szpadxM6t/p08PDxYsWIFP/74I1u3bmXLli1s2bIFgJo1azJ27FgqVaqUz0iz3LmDND/3ijl
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[4], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 10,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[5], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 11,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[6], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 12,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"image/png": "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
work done
2020-04-20 11:37:08 +00:00
"text/plain": [
"<Figure size 1728x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotter2(tests[7], data)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 13,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"array(['absl::flat_hash_map', 'absl::node_hash_map',\n",
work done
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" 'boost::unordered::unordered_map', 'emilib::HashMap',\n",
" 'google::dense_hash_map', 'google::sparse_hash_map',\n",
" 'phmap::parallel_flat_hash_map', 'phmap::parallel_node_hash_map',\n",
" 'robin_hood::unordered_flat_map', 'robin_hood::unordered_node_map',\n",
" 'ska::bytell_hash_map', 'ska::flat_hash_map', 'ska::unordered_map',\n",
" 'std::unordered_map', 'tsl::hopscotch_map', 'tsl::robin_map',\n",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
" 'tsl::sparse_map'], dtype=object)"
work done
2020-04-20 11:37:08 +00:00
]
},
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 13,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"MAP\"].unique()"
]
},
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quick way of getting the mean time of each operation and map (for all sizes) \n",
"This number is totally meaningless on it's own, and the absolute values (in ns) should just not be mentioned\n",
"I think it's meaningless to mention that the mean insertion time of a map is 50ns between sizes 50K and 50M.\n",
"It \"loses\" so much info. \n",
"But it is a decent use to rank the maps on. \n",
"Eventhough it is somewhat biased towards maps that perform decently at large sizes. Or in other words, this ranking makes the fact that a function is blazing fast for small sizes matter way less. \n",
"This is due to the following:\n",
"lets say there are 5 measuring points below 1M, and 20 measuring points between 1M and 50M\n",
"when that is averaged, way more points will be >1M, so they will weight more for the mean.\n",
"\n",
"But the graphs are pretty hard to get a quick overview of after a quick glance, so this is my best attempt to create a summary of the relative rankings.\n",
"And I don't have the slightest idea how you would rank them besides a mean value. A max value would be a good idea, except that there's random spiky behavour which have higher values in the middle than at 50M. And it's even less representative of the times as a whole compared to the mean. So we have this. "
]
},
work done
2020-04-20 11:37:08 +00:00
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 14,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11\n",
"9\n",
"13\n",
"4\n",
"6\n",
"17\n",
"12\n",
"14\n",
"7\n",
"1\n",
"8\n",
"3\n",
"10\n",
"15\n",
"5\n",
"2\n",
"16\n"
]
}
],
"source": [
"test = \"string_insert\"\n",
"alpha_maps = gr_mean.loc[test][\"TIME\"].index\n",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"rank = list(gr_mean.loc[test][\"TIME\"].sort_values().index) # sort by mean\n",
"rank = [rank.index(i) for i in alpha_maps] # get the index for each map in alphamaps\n",
work done
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"for i in rank:\n",
" print(i + 1)"
]
},
{
"cell_type": "code",
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 15,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"Index(['absl::flat_hash_map', 'absl::node_hash_map',\n",
" 'boost::unordered::unordered_map', 'emilib::HashMap',\n",
" 'google::dense_hash_map', 'google::sparse_hash_map',\n",
" 'phmap::parallel_flat_hash_map', 'phmap::parallel_node_hash_map',\n",
" 'robin_hood::unordered_flat_map', 'robin_hood::unordered_node_map',\n",
" 'ska::bytell_hash_map', 'ska::flat_hash_map', 'ska::unordered_map',\n",
" 'std::unordered_map', 'tsl::hopscotch_map', 'tsl::robin_map',\n",
" 'tsl::sparse_map'],\n",
" dtype='object', name='MAP')"
work done
2020-04-20 11:37:08 +00:00
]
},
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"execution_count": 15,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
updated to do 95% CI instead of 1 std error bars. As if a difference is even noticable
2020-05-12 00:28:43 +00:00
"source": [
"alpha_maps"
]
work done
2020-04-20 11:37:08 +00:00
},
{
"cell_type": "code",
all data
2020-04-20 16:57:40 +00:00
"execution_count": null,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
all data
2020-04-20 16:57:40 +00:00
"outputs": [],
"source": []
work done
2020-04-20 11:37:08 +00:00
},
{
"cell_type": "code",
all data
2020-04-20 16:57:40 +00:00
"execution_count": null,
work done
2020-04-20 11:37:08 +00:00
"metadata": {},
"outputs": [],
all data
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"source": []
work done
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},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
"source": []
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},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
},
{
"cell_type": "code",
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"metadata": {},
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
changed readme
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}
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