{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "from pandas import DataFrame, Series\n", "import numpy as np\n", "import math\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import matplotlib.colors as colors\n", "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from scipy import stats\n", "import sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "matrixMalEX=\"data_GG.csv\"\n", "matrixMal=\"data_GM.csv\"\n", "matrixIt=\"data_L.csv\"\n", "matrixIt_Total=\"data_L_Total.csv\"\n", "n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n", "n_groups= 2\n", "repet = 10 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n", "\n", "p_value = 0.05\n", "values = [2, 10, 20, 40]\n", "# WORST BEST\n", "dist_names = ['null', 'BalancedFit', 'CompactFit']\n", "\n", "processes = [1,10,20,40,80,160]\n", "\n", "labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],\n", " ['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]\n", "labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',\n", " '(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']\n", "positions = [321, 322, 323, 324, 325]\n", "positions_small = [221, 222, 223, 224]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "dfG = pd.read_csv( matrixMalEX )\n", "\n", "dfG = dfG.drop(columns=dfG.columns[0])\n", "dfG['S'] = dfG['N']\n", "dfG['N'] = dfG['S'] + dfG['%Async']\n", "dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n", "dfG['%Async'] = dfG['%Async'].fillna(0)\n", "\n", "if(n_qty == 1):\n", " group = dfG.groupby(['%Async', 'Cst', 'Css', 'Groups'])['TE']\n", " group2 = dfG.groupby(['%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n", "else: \n", " group = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'Groups'])['TE']\n", " group2 = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n", "\n", "grouped_aggG = group.agg(['median'])\n", "grouped_aggG.rename(columns={'median':'TE'}, inplace=True)\n", "\n", "grouped_aggG2 = group2.agg(['median'])\n", "grouped_aggG2.rename(columns={'median':'TE'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "dfM = pd.read_csv( matrixMal )\n", "dfM = dfM.drop(columns=dfM.columns[0])\n", "\n", "dfM['S'] = dfM['N']\n", "dfM['N'] = dfM['S'] + dfM['%Async']\n", "dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TH\"] + dfM[\"TS\"] + dfM[\"TA\"]\n", "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n", "\n", "dfM['%Async'] = dfM['%Async'].fillna(0)\n", "\n", "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n", "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n", "else:\n", " groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n", "\n", "#group\n", "grouped_aggM = groupM.agg(['median'])\n", "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: FutureWarning: set_axis currently defaults to operating inplace.\n", "This will change in a future version of pandas, use inplace=True to avoid this warning.\n" ] } ], "source": [ "dfL = pd.read_csv( matrixIt )\n", "dfL = dfL.drop(columns=dfL.columns[0])\n", "\n", "dfL['%Async'] = dfL['%Async'].fillna(0)\n", "\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n", "else:\n", " groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n", "\n", "#group\n", "grouped_aggL = groupL.agg(['median', 'count'])\n", "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n", "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')\n", "\n", "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n", "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: FutureWarning: set_axis currently defaults to operating inplace.\n", "This will change in a future version of pandas, use inplace=True to avoid this warning.\n" ] } ], "source": [ "dfLT = pd.read_csv( matrixIt_Total )\n", "dfLT = dfLT.drop(columns=dfLT.columns[0])\n", "\n", "dfLT['%Async'] = dfLT['%Async'].fillna(0)\n", "\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupLT = dfLT[dfLT['NS'] != 0].groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n", "else:\n", " groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n", "\n", "#group\n", "grouped_aggLT = groupLT.agg(['median'])\n", "grouped_aggLT.columns = grouped_aggLT.columns.get_level_values(0)\n", "grouped_aggLT.set_axis(['Sum'], axis='columns')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "grouped_aggL.to_excel(\"resultL.xlsx\") \n", "grouped_aggLT.to_excel(\"resultLT.xlsx\")\n", "dfLT.to_excel(\"resultLT_all.xlsx\")\n", "grouped_aggM.to_excel(\"resultM.xlsx\") \n", "grouped_aggG2.to_excel(\"resultG.xlsx\") " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Dist | \n", "%Async | \n", "Cst | \n", "Css | \n", "Groups | \n", "\n", " |
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80,20 | \n", "19.728576 | \n", "||||
80,40 | \n", "10.049131 | \n", "
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---|---|---|---|---|---|---|---|---|---|---|
Dist | \n", "%Async | \n", "Cst | \n", "Css | \n", "NP | \n", "NS | \n", "\n", " | \n", " | \n", " | \n", " | \n", " |
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80 | \n", "0.919644 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.919644 | \n", "|||||
160 | \n", "1.176074 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.176074 | \n", "|||||
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "||
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80 | \n", "0.131250 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.131250 | \n", "
240 rows × 5 columns
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---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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240000 rows × 15 columns
\n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | Ti | \n", "Iters | \n", "To | \n", "Iters2 | \n", "
---|---|---|---|---|---|---|---|---|---|---|
Tt | \n", "Dist | \n", "%Async | \n", "Cst | \n", "Css | \n", "NP | \n", "NS | \n", "\n", " | \n", " | \n", " | \n", " |
0.0 | \n", "2 | \n", "0.0 | \n", "0 | \n", "0 | \n", "1 | \n", "10 | \n", "1.999120 | \n", "3.0 | \n", "2242.0 | \n", "3.0 | \n", "
20 | \n", "1.999183 | \n", "3.0 | \n", "2242.0 | \n", "3.0 | \n", "||||||
40 | \n", "1.999150 | \n", "3.0 | \n", "2242.0 | \n", "3.0 | \n", "||||||
80 | \n", "1.999131 | \n", "3.0 | \n", "2242.0 | \n", "3.0 | \n", "||||||
160 | \n", "1.999115 | \n", "3.0 | \n", "2242.0 | \n", "3.0 | \n", "||||||
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
1.0 | \n", "2 | \n", "0.0 | \n", "1 | \n", "3 | \n", "20 | \n", "80 | \n", "0.105980 | \n", "1.0 | \n", "112.0 | \n", "1.0 | \n", "
160 | \n", "0.105645 | \n", "1.0 | \n", "112.0 | \n", "1.0 | \n", "||||||
40 | \n", "80 | \n", "0.068359 | \n", "1.0 | \n", "56.0 | \n", "1.0 | \n", "|||||
160 | \n", "0.052386 | \n", "1.0 | \n", "56.0 | \n", "1.0 | \n", "||||||
80 | \n", "160 | \n", "0.026651 | \n", "2.0 | \n", "28.0 | \n", "2.0 | \n", "
330 rows × 4 columns
\n", "