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Commit e1b97b65 authored by iker_martin's avatar iker_martin
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Merge branch 'RMA-Distributions' of... 

Merge branch 'RMA-Distributions' of http://lorca.act.uji.es/gitlab/martini/malleability_benchmark into RMA-Distributions
parents 64edabe3 181646e0
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Analysis/CreateResizeDataframe.py
View file @ e1b97b65
......@@ -36,8 +36,8 @@ class G_enum(Enum):
NC = 1
#columnsG = ["Total_Resizes", "Total_Groups", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redistribution_Method", \
"Redistribution_Strategy", "Spawn_Method", "Spawn_Strategy", "Groups", "FactorS", "Dist", "Stage_Types", "Stage_Times", \
"Stage_Bytes", "Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR", "T_total"] #26
# "Redistribution_Strategy", "Spawn_Method", "Spawn_Strategy", "Groups", "FactorS", "Dist", "Stage_Types", "Stage_Times", \
# "Stage_Bytes", "Iters", "Asynch_Iters", "T_iter", "T_stages", "T_spawn", "T_spawn_real", "T_SR", "T_AR", "T_total"] #26
columnsM = ["NP", "NC", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redistribution_Method", \
"Redistribution_Strategy", "Spawn_Method", "Spawn_Strategy", "FactorS", "Dist", "Stage_Type", "Stage_Time", \
......@@ -45,8 +45,8 @@ columnsM = ["NP", "NC", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redi
def copy_resize(row, dataM_it, resize):
basic_indexes = [G_enum.TOTAL_STAGES.value, G_enum.GRANULARITY.value, G_enum.SDR.value, \
G_enum.ADR.value, G_enum.DR.value, G_enum.STAGE_TYPES.value, \
G_enum.STAGE_TIMES.value, G_enum.STAGE_BYTES.value]
G_enum.ADR.value, G_enum.DR.value]
basic_group = [G_enum.STAGE_TYPES.value, G_enum.STAGE_TIMES.value, G_enum.STAGE_BYTES.value]
array_actual_group = [G_enum.FACTOR_S.value, G_enum.ITERS.value, G_enum.ASYNCH_ITERS.value, \
G_enum.T_SPAWN.value, G_enum.T_SPAWN_REAL.value, G_enum.T_SR.value, \
G_enum.T_AR.value, G_enum.T_ITER.value, G_enum.T_STAGES.value]
......@@ -55,12 +55,15 @@ def copy_resize(row, dataM_it, resize):
dataM_it[G_enum.NP.value] = row[G_enum.GROUPS.value][resize]
dataM_it[G_enum.NC.value] = row[G_enum.GROUPS.value][resize+1]
dataM_it[G_enum.DIST.value] = [None, None]
dataM_it[G_enum.DIST.value][0] = row[G_enum.DIST.value][resize]
dataM_it[G_enum.DIST.value][1] = row[G_enum.DIST.value][resize+1]
dataM_it[G_enum.DIST.value-1] = [None, None]
dataM_it[G_enum.DIST.value-1][0] = row[G_enum.DIST.value][resize]
dataM_it[G_enum.DIST.value-1][1] = row[G_enum.DIST.value][resize+1]
for index in basic_indexes:
dataM_it[index] = row[index]
for index in basic_group:
dataM_it[index-1] = row[index]
for index in array_actual_group:
dataM_it[index-1] = row[index][resize]
......@@ -73,11 +76,13 @@ def copy_resize(row, dataM_it, resize):
def create_resize_dataframe(dfG, dataM):
it = -1
for row in dfG.itertuples(index=False, name=None):
for row_index in range(len(dfG)):
row = dfG.iloc[row_index]
resizes = row[G_enum.TOTAL_RESIZES.value]
for resize in range(resizes):
it += 1
dataM[it].append( [None] * len(columnsM) )
dataM.append( [None] * len(columnsM) )
copy_resize(row, dataM[it], resize)
#-----------------------------------------------
......@@ -90,16 +95,13 @@ if len(sys.argv) > 2:
name = sys.argv[2]
else:
name = "dataM"
print("Csv name will be: " + name + ".csv")
print("Csv name will be: " + name + ".pkl")
dfG = pd.read_csv(input_name)
dfG = pd.read_pickle(input_name)
dataM = []
create_resize_dataframe(dfG, dataM)
#dfM = pd.DataFrame(dataM, columns=columnsM)
#Poner en TC el valor real y en TH el necesario para la app
#cond = dfM.TH != 0
#dfM.loc[cond, ['TC', 'TH']] = dfM.loc[cond, ['TH', 'TC']].values
#dfM.to_csv(name + 'M.csv')
dfM = pd.DataFrame(dataM, columns=columnsM)
dfM.to_pickle(name + '.pkl')
dfM.to_excel(name + '.xlsx')
Analysis/MallTimes.py
View file @ e1b97b65
......@@ -34,7 +34,6 @@ class G_enum(Enum):
#Malleability specific
NP = 0
NC = 1
BAR = 11 # Extract 1 from index
columnsG = ["Total_Resizes", "Total_Groups", "Total_Stages", "Granularity", "SDR", "ADR", "DR", "Redistribution_Method", \
......@@ -254,8 +253,7 @@ for elem in lista:
dfG = pd.DataFrame(dataG, columns=columnsG)
dfG.to_csv(name + 'G.csv')
dfG.to_excel(name + 'G.xlsx')
dfG.to_pickle(name + 'G.pkl')
#dfM = pd.DataFrame(dataM, columns=columnsM)
......
Analysis/analyser.ipynb
View file @ e1b97b65
......@@ -3374,7 +3374,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
......@@ -3388,7 +3388,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.7.4"
}
},
"nbformat": 4,
Analysis/new_analyser.ipynb 0 → 100644
View file @ e1b97b65
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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 scikit_posthocs as sp\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"AllName=\"dataG.pkl\"\n",
"ResizesName=\"dataM.pkl\"\n",
"matrixIt=\"dataL.pkl\"\n",
"matrixIt_Total=\"data_L_Total.csv\"\n",
"n_qty=8 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
"n_groups= 2\n",
"repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
"time_constant = False # Cambiar segun el speedUp usado\n",
"speedup = 0.66 # Porcentaje del speedup ideal\n",
"\n",
"p_value = 0.05\n",
"values = [2, 10, 20, 40, 80, 120, 160, 180]\n",
"# WORST BEST\n",
"dist_names = ['null', 'BalancedFit', 'CompactFit']\n",
"\n",
"processes = [2,10,20,40,80,120,160,180]\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]\n",
"\n",
"labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
" '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
" '(20,1)', '(20,10)','(20,40)','(20,80)','(20,120)',\n",
" '(40,1)', '(40,10)', '(40,20)','(40,80)','(40,120)',\n",
" '(80,1)', '(80,10)', '(80,20)', '(80,40)','(80,120)',\n",
" '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
"\n",
"labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
" '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
" '(20,40)','(20,80)','(20,120)',\n",
" '(40,80)','(40,120)',\n",
" '(80,120)']\n",
"labelsShrink = ['(10,1)', \n",
" '(20,1)', '(20,10)', \n",
" '(40,1)', '(40,10)', '(40,20)',\n",
" '(80,1)', '(80,10)', '(80,20)', '(80,40)',\n",
" '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
"\n",
"labelsExpandOrdered = ['(1,10)', '(1,20)', '(10,20)',\n",
" '(1,40)','(10,40)','(20,40)',\n",
" '(1,80)','(10,80)','(20,80)','(40,80)',\n",
" '(1,120)', '(10,120)', '(20,120)','(40,120)','(80,120)']\n",
"labelsShrinkOrdered = ['(10,1)', '(20,1)', '(40,1)', '(80,1)', '(120,1)',\n",
" '(20,10)', '(40,10)', '(80,10)', '(120,10)', \n",
" '(40,20)', '(80,20)', '(120,20)',\n",
" '(80,40)','(120,40)',\n",
" '(120,80)']\n",
"\n",
"labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n",
"labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n",
"labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',\n",
" '(10,40)','(10,80)', '(10,160)',\n",
" '(20,40)','(20,80)', '(20,160)',\n",
" '(40,80)', '(40,160)',\n",
" '(80,160)']\n",
"labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n",
" '(80,1)', '(80,10)', '(80,20)','(80,40)',\n",
" '(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']\n",
"\n",
" #0 #1 #2 #3\n",
"labelsMethods = ['Baseline', 'Baseline single','Baseline - Asynchronous','Baseline single - Asynchronous',\n",
" 'Merge','Merge single','Merge - Asynchronous','Merge single - Asynchronous']\n",
" #4 #5 #6 #7\n",
"colors_spawn = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive','violet']\n",
"linestyle_spawn = ['-', '--', '-.', ':']\n",
"markers_spawn = ['.','v','s','p', 'h','d','X','P','^']\n",
"\n",
"OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n",
"OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n",
"OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Asyncrhonous')\n",
"OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n",
"MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
"MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
"MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n",
"MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')\n",
"\n",
"handles_spawn = [OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unhashable type: 'list'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmedian\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1230\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1232\u001b[0m )\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m 880\u001b[0m result, names = self.grouper.aggregate(\n\u001b[0;32m--> 881\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 882\u001b[0m )\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, values, how, axis, min_count)\u001b[0m\n\u001b[1;32m 595\u001b[0m return self._cython_operation(\n\u001b[0;32m--> 596\u001b[0;31m \u001b[0;34m\"aggregate\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 597\u001b[0m )\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m \u001b[0mout_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 486\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mngroups\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mngroups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mresult_index\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m \u001b[0mcodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecons_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 336\u001b[0m \u001b[0mlevels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_index\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mrecons_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrecons_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m \u001b[0mcomp_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 327\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mgroup_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgroup_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mcomp_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs_group_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_compressed_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mall_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mall_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mlabels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 398\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_make_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 422\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mfactorize\u001b[0;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[1;32m 671\u001b[0m labels, uniques = _factorize_array(\n\u001b[0;32m--> 672\u001b[0;31m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_hint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize_hint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 673\u001b[0m )\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_factorize_array\u001b[0;34m(values, na_sentinel, size_hint, na_value)\u001b[0m\n\u001b[1;32m 507\u001b[0m uniques, labels = table.factorize(\n\u001b[0;32m--> 508\u001b[0;31m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 509\u001b[0m )\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.factorize\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable._unique\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_splitter\u001b[0;34m(self, data, axis)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_splitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0mcomp_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mngroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mget_splitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomp_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mngroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mgroup_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgroup_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mcomp_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs_group_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0mngroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs_group_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_compressed_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mall_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mgroup_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mall_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mgroup_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mlabels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 398\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36m_make_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 422\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mfactorize\u001b[0;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m labels, uniques = _factorize_array(\n\u001b[0;32m--> 672\u001b[0;31m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_hint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize_hint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 673\u001b[0m )\n\u001b[1;32m 674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_factorize_array\u001b[0;34m(values, na_sentinel, size_hint, na_value)\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhash_klass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_hint\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m uniques, labels = table.factorize(\n\u001b[0;32m--> 508\u001b[0;31m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 509\u001b[0m )\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.factorize\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable._unique\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
]
}
],
"source": [
"dfG = pd.read_pickle( AllName )\n",
"\n",
"dfG['ADR'] = (dfG['ADR'] / dfG['DR']) * 100\n",
" \n",
"group = dfG.groupby(['Redistribution_Method', 'Redistribution_Strategy', 'Groups'])['T_total']\n",
"\n",
"grouped_aggG = group.agg(['median'])\n",
"grouped_aggG.rename(columns={'median':'T_total'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"24 0.0\n",
"25 0.0\n",
"26 0.0\n",
"27 0.0\n",
"28 0.0\n",
"29 0.0\n",
"30 0.0\n",
"31 0.0\n",
"32 0.0\n",
"33 0.0\n",
"34 0.0\n",
"35 0.0\n",
"36 0.0\n",
"37 0.0\n",
"38 0.0\n",
"39 0.0\n",
"40 0.0\n",
"41 0.0\n",
"42 0.0\n",
"43 0.0\n",
"44 0.0\n",
"45 0.0\n",
"46 0.0\n",
"47 0.0\n",
"48 0.0\n",
"49 0.0\n",
"50 0.0\n",
"51 0.0\n",
"52 0.0\n",
"53 0.0\n",
"dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfG"
]
},
{
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
``` python
%matplotlib inline
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.colors as colors
from matplotlib.legend_handler import HandlerLine2D, HandlerTuple
from matplotlib.colors import LinearSegmentedColormap
from scipy import stats
#import scikit_posthocs as sp
import sys
```
%% Cell type:code id: tags:
``` python
AllName="dataG.pkl"
ResizesName="dataM.pkl"
matrixIt="dataL.pkl"
matrixIt_Total="data_L_Total.csv"
n_qty=8 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS
n_groups= 2
repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG
time_constant = False # Cambiar segun el speedUp usado
speedup = 0.66 # Porcentaje del speedup ideal
p_value = 0.05
values = [2, 10, 20, 40, 80, 120, 160, 180]
# WORST BEST
dist_names = ['null', 'BalancedFit', 'CompactFit']
processes = [2,10,20,40,80,120,160,180]
labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],
['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]
labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',
'(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']
positions = [321, 322, 323, 324, 325]
positions_small = [221, 222, 223, 224]
labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',
'(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)',
'(20,1)', '(20,10)','(20,40)','(20,80)','(20,120)',
'(40,1)', '(40,10)', '(40,20)','(40,80)','(40,120)',
'(80,1)', '(80,10)', '(80,20)', '(80,40)','(80,120)',
'(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']
labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',
'(10,20)', '(10,40)','(10,80)','(10,120)',
'(20,40)','(20,80)','(20,120)',
'(40,80)','(40,120)',
'(80,120)']
labelsShrink = ['(10,1)',
'(20,1)', '(20,10)',
'(40,1)', '(40,10)', '(40,20)',
'(80,1)', '(80,10)', '(80,20)', '(80,40)',
'(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']
labelsExpandOrdered = ['(1,10)', '(1,20)', '(10,20)',
'(1,40)','(10,40)','(20,40)',
'(1,80)','(10,80)','(20,80)','(40,80)',
'(1,120)', '(10,120)', '(20,120)','(40,120)','(80,120)']
labelsShrinkOrdered = ['(10,1)', '(20,1)', '(40,1)', '(80,1)', '(120,1)',
'(20,10)', '(40,10)', '(80,10)', '(120,10)',
'(40,20)', '(80,20)', '(120,20)',
'(80,40)','(120,40)',
'(120,80)']
labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']
labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']
labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',
'(10,40)','(10,80)', '(10,160)',
'(20,40)','(20,80)', '(20,160)',
'(40,80)', '(40,160)',
'(80,160)']
labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',
'(80,1)', '(80,10)', '(80,20)','(80,40)',
'(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']
#0 #1 #2 #3
labelsMethods = ['Baseline', 'Baseline single','Baseline - Asynchronous','Baseline single - Asynchronous',
'Merge','Merge single','Merge - Asynchronous','Merge single - Asynchronous']
#4 #5 #6 #7
colors_spawn = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive','violet']
linestyle_spawn = ['-', '--', '-.', ':']
markers_spawn = ['.','v','s','p', 'h','d','X','P','^']
OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')
OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')
OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Asyncrhonous')
OrPthSing_patch = mpatches.Patch(hatch='\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')
MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')
MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')
MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')
MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')
handles_spawn = [OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]
```
%% Cell type:code id: tags:
``` python
dfG = pd.read_pickle( AllName )
dfG['ADR'] = (dfG['ADR'] / dfG['DR']) * 100
group = dfG.groupby(['Redistribution_Method', 'Redistribution_Strategy', 'Groups'])['T_total']
grouped_aggG = group.agg(['median'])
grouped_aggG.rename(columns={'median':'T_total'}, inplace=True)
```
%%%% Output: error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in median(self, **kwargs)
1230 alt=lambda x, axis: Series(x).median(axis=axis, **kwargs),
-> 1231 **kwargs
1232 )
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
880 result, names = self.grouper.aggregate(
--> 881 obj.values, how, min_count=min_count
882 )
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in aggregate(self, values, how, axis, min_count)
595 return self._cython_operation(
--> 596 "aggregate", values, how, axis, min_count=min_count
597 )
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in _cython_operation(self, kind, values, how, axis, min_count, **kwargs)
484 values = values[:, None]
--> 485 out_shape = (self.ngroups, arity)
486 else:
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in ngroups(self)
321 def ngroups(self):
--> 322 return len(self.result_index)
323
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in result_index(self)
334
--> 335 codes = self.recons_labels
336 levels = [ping.result_index for ping in self.groupings]
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in recons_labels(self)
325 def recons_labels(self):
--> 326 comp_ids, obs_ids, _ = self.group_info
327 labels = (ping.labels for ping in self.groupings)
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in group_info(self)
295 def group_info(self):
--> 296 comp_ids, obs_group_ids = self._get_compressed_labels()
297
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in _get_compressed_labels(self)
311 def _get_compressed_labels(self):
--> 312 all_labels = [ping.labels for ping in self.groupings]
313 if len(all_labels) > 1:
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in <listcomp>(.0)
311 def _get_compressed_labels(self):
--> 312 all_labels = [ping.labels for ping in self.groupings]
313 if len(all_labels) > 1:
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in labels(self)
396 if self._labels is None:
--> 397 self._make_labels()
398 return self._labels
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in _make_labels(self)
420 else:
--> 421 labels, uniques = algorithms.factorize(self.grouper, sort=self.sort)
422 uniques = Index(uniques, name=self.name)
~/anaconda3/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
671 labels, uniques = _factorize_array(
--> 672 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
673 )
~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)
507 uniques, labels = table.factorize(
--> 508 values, na_sentinel=na_sentinel, na_value=na_value
509 )
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()
TypeError: unhashable type: 'list'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-14-8f3f554db108> in <module>
5 group = dfG.groupby(['Redistribution_Method', 'Redistribution_Strategy', 'Groups'])['T_total']
6
----> 7 grouped_aggG = group.agg(['median'])
8 grouped_aggG.rename(columns={'median':'T_total'}, inplace=True)
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in aggregate(self, func_or_funcs, *args, **kwargs)
849 # but not the class list / tuple itself.
850 func_or_funcs = _maybe_mangle_lambdas(func_or_funcs)
--> 851 ret = self._aggregate_multiple_funcs(func_or_funcs, (_level or 0) + 1)
852 if relabeling:
853 ret.columns = columns
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _aggregate_multiple_funcs(self, arg, _level)
928 obj._reset_cache()
929 obj._selection = name
--> 930 results[name] = obj.aggregate(func)
931
932 if any(isinstance(x, DataFrame) for x in results.values()):
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in aggregate(self, func_or_funcs, *args, **kwargs)
843
844 if isinstance(func_or_funcs, str):
--> 845 return getattr(self, func_or_funcs)(*args, **kwargs)
846
847 if isinstance(func_or_funcs, abc.Iterable):
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in median(self, **kwargs)
1241
1242 with _group_selection_context(self):
-> 1243 return self._python_agg_general(f)
1244
1245 @Substitution(name="groupby")
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_agg_general(self, func, *args, **kwargs)
904
905 if len(output) == 0:
--> 906 return self._python_apply_general(f)
907
908 if self.grouper._filter_empty_groups:
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
740
741 def _python_apply_general(self, f):
--> 742 keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
743
744 return self._wrap_applied_output(
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis)
189 def apply(self, f, data, axis=0):
190 mutated = self.mutated
--> 191 splitter = self._get_splitter(data, axis=axis)
192 group_keys = self._get_group_keys()
193 result_values = None
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in _get_splitter(self, data, axis)
166
167 def _get_splitter(self, data, axis=0):
--> 168 comp_ids, _, ngroups = self.group_info
169 return get_splitter(data, comp_ids, ngroups, axis=axis)
170
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in group_info(self)
294 @cache_readonly
295 def group_info(self):
--> 296 comp_ids, obs_group_ids = self._get_compressed_labels()
297
298 ngroups = len(obs_group_ids)
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in _get_compressed_labels(self)
310
311 def _get_compressed_labels(self):
--> 312 all_labels = [ping.labels for ping in self.groupings]
313 if len(all_labels) > 1:
314 group_index = get_group_index(all_labels, self.shape, sort=True, xnull=True)
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/ops.py in <listcomp>(.0)
310
311 def _get_compressed_labels(self):
--> 312 all_labels = [ping.labels for ping in self.groupings]
313 if len(all_labels) > 1:
314 group_index = get_group_index(all_labels, self.shape, sort=True, xnull=True)
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in labels(self)
395 def labels(self):
396 if self._labels is None:
--> 397 self._make_labels()
398 return self._labels
399
~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/grouper.py in _make_labels(self)
419 uniques = self.grouper.result_index
420 else:
--> 421 labels, uniques = algorithms.factorize(self.grouper, sort=self.sort)
422 uniques = Index(uniques, name=self.name)
423 self._labels = labels
~/anaconda3/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
206 else:
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
210 return wrapper
~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
670
671 labels, uniques = _factorize_array(
--> 672 values, na_sentinel=na_sentinel, size_hint=size_hint, na_value=na_value
673 )
674
~/anaconda3/lib/python3.7/site-packages/pandas/core/algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)
506 table = hash_klass(size_hint or len(values))
507 uniques, labels = table.factorize(
--> 508 values, na_sentinel=na_sentinel, na_value=na_value
509 )
510
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.factorize()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable._unique()
TypeError: unhashable type: 'list'
%% Cell type:code id: tags:
``` python
dfG
```
%%%% Output: execute_result
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
6 0.0
7 0.0
8 0.0
9 0.0
10 0.0
11 0.0
12 0.0
13 0.0
14 0.0
15 0.0
16 0.0
17 0.0
18 0.0
19 0.0
20 0.0
21 0.0
22 0.0
23 0.0
24 0.0
25 0.0
26 0.0
27 0.0
28 0.0
29 0.0
30 0.0
31 0.0
32 0.0
33 0.0
34 0.0
35 0.0
36 0.0
37 0.0
38 0.0
39 0.0
40 0.0
41 0.0
42 0.0
43 0.0
44 0.0
45 0.0
46 0.0
47 0.0
48 0.0
49 0.0
50 0.0
51 0.0
52 0.0
53 0.0
dtype: float64
%% Cell type:code id: tags:
``` python
```
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