diff --git a/execution/scalability-graph.ipynb b/execution/scalability-graph.ipynb
index 9de4b2011462e6c8281147520652db6caf3e8833..e0ad808cf0aec1b1242e7655c445b951497c2a9d 100644
--- a/execution/scalability-graph.ipynb
+++ b/execution/scalability-graph.ipynb
@@ -2,16 +2,22 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 114,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": "hello\n"
+ }
+ ],
"source": [
"print(\"hello\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
@@ -25,27 +31,37 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 116,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": "'/home/soeren/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/execution-new/results-final'"
+ },
+ "metadata": {},
+ "execution_count": 116
+ }
+ ],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
- "exp_id = 159\n",
+ "exp_id = 1005\n",
"warmup_sec = 60\n",
+ "warmup_partitions_sec = 120\n",
"threshold = 2000 #slope\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 118,
"metadata": {
"tags": [
"outputPrepend",
@@ -56,7 +72,7 @@
"source": [
"#exp_id = 35\n",
"\n",
- "#os.chdir(\"./results-new\")\n",
+ "#os.chdir(\"./results-final\")\n",
"\n",
"raw_runs = []\n",
"\n",
@@ -93,23 +109,183 @@
" #print(row)\n",
" raw_runs.append(row)\n",
"\n",
- "runs = pd.DataFrame(raw_runs)\n"
+ "lags = pd.DataFrame(raw_runs)\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 119,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": " dim_value instances trend_slope partitions obs_instances failed \\\n0 125000 1 112774.390209 363.951220 1.0 False \n1 75000 2 2642.445854 400.000000 2.0 False \n2 75000 1 59674.561013 400.000000 1.0 False \n3 150000 1 148989.656842 340.769231 1.0 False \n4 25000 4 -5.497205 400.000000 4.0 False \n\n suitable \n0 False \n1 False \n2 False \n3 False \n4 True ",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>dim_value</th>\n <th>instances</th>\n <th>trend_slope</th>\n <th>partitions</th>\n <th>obs_instances</th>\n <th>failed</th>\n <th>suitable</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>125000</td>\n <td>1</td>\n <td>112774.390209</td>\n <td>363.951220</td>\n <td>1.0</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>75000</td>\n <td>2</td>\n <td>2642.445854</td>\n <td>400.000000</td>\n <td>2.0</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>75000</td>\n <td>1</td>\n <td>59674.561013</td>\n <td>400.000000</td>\n <td>1.0</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>150000</td>\n <td>1</td>\n <td>148989.656842</td>\n <td>340.769231</td>\n <td>1.0</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>25000</td>\n <td>4</td>\n <td>-5.497205</td>\n <td>400.000000</td>\n <td>4.0</td>\n <td>False</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ },
+ "metadata": {},
+ "execution_count": 119
+ }
+ ],
"source": [
"runs.head()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 120,
"metadata": {},
"outputs": [],
+ "source": [
+ "\n",
+ "raw_partitions = []\n",
+ "\n",
+ "filenames = [filename for filename in os.listdir('.') if filename.startswith(f\"exp{exp_id}\") and filename.endswith(\"partitions.csv\")]\n",
+ "for filename in filenames:\n",
+ " #print(filename)\n",
+ " run_params = filename[:-4].split(\"_\")\n",
+ " dim_value = run_params[2]\n",
+ " instances = run_params[3]\n",
+ "\n",
+ " df = pd.read_csv(filename)\n",
+ " #input = df.loc[df['topic'] == \"input\"]\n",
+ " input = df\n",
+ " #print(input)\n",
+ " input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']\n",
+ " #print(input)\n",
+ " #print(input.iloc[0, 'timestamp'])\n",
+ " input = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up\n",
+ " #regress = input\n",
+ "\n",
+ " input = input.loc[input['topic'] >= 'input']\n",
+ " mean = input['value'].mean()\n",
+ "\n",
+ " #input.plot(kind='line',x='timestamp',y='value',color='red')\n",
+ " #plt.show()\n",
+ "\n",
+ "\n",
+ " row = {'dim_value': int(dim_value), 'instances': int(instances), 'partitions': mean}\n",
+ " #print(row)\n",
+ " raw_partitions.append(row)\n",
+ "\n",
+ "\n",
+ "partitions = pd.DataFrame(raw_partitions)\n",
+ "\n",
+ "runs = lags.join(partitions.set_index(['dim_value', 'instances']), on=['dim_value', 'instances'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "error",
+ "ename": "KeyError",
+ "evalue": "'timestamp'",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2645\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2647\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'timestamp'",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-121-89f718d4ccad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m#print(input)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0minput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sec_start'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\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'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\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'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;31m#print(input)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;31m#print(input.iloc[0, 'timestamp'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1759\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1760\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1761\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1762\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1763\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 1269\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1271\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1272\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 1386\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1387\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1388\u001b[0;31m \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1963\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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[0m\n\u001b[0m\u001b[1;32m 1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\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[0m\n\u001b[0m\u001b[1;32m 625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m 3527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3528\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3529\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3531\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2798\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2799\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2801\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2802\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Dokumente/Titan/Documents Repository/Publications/BDR-2020/Replication Package/.venv/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2646\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2647\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2649\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2650\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'timestamp'"
+ ]
+ }
+ ],
+ "source": [
+ "raw_obs_instances = []\n",
+ "\n",
+ "filenames = [filename for filename in os.listdir('.') if filename.startswith(f\"exp{exp_id}\") and filename.endswith(\"instances.csv\")]\n",
+ "for filename in filenames:\n",
+ " #print(filename)\n",
+ " run_params = filename[:-4].split(\"_\")\n",
+ " dim_value = run_params[2]\n",
+ " instances = run_params[3]\n",
+ "\n",
+ " df = pd.read_csv(filename)\n",
+ " #input = df.loc[df['topic'] == \"input\"]\n",
+ " input = df\n",
+ " #print(input)\n",
+ " input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']\n",
+ " #print(input)\n",
+ " #print(input.iloc[0, 'timestamp'])\n",
+ " input = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up\n",
+ " #regress = input\n",
+ "\n",
+ " #input = input.loc[input['topic'] >= 'input']\n",
+ " mean = input['value'].mean()\n",
+ "\n",
+ " #input.plot(kind='line',x='timestamp',y='value',color='red')\n",
+ " #plt.show()\n",
+ "\n",
+ "\n",
+ " row = {'dim_value': int(dim_value), 'instances': int(instances), 'obs_instances': mean}\n",
+ " #print(row)\n",
+ " raw_obs_instances.append(row)\n",
+ "\n",
+ "\n",
+ "obs_instances = pd.DataFrame(raw_obs_instances)\n",
+ "\n",
+ "obs_instances.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": "Empty DataFrame\nColumns: [dim_value, instances, trend_slope, partitions, obs_instances, failed]\nIndex: []",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>dim_value</th>\n <th>instances</th>\n <th>trend_slope</th>\n <th>partitions</th>\n <th>obs_instances</th>\n <th>failed</th>\n </tr>\n </thead>\n <tbody>\n </tbody>\n</table>\n</div>"
+ },
+ "metadata": {},
+ "execution_count": 122
+ }
+ ],
+ "source": [
+ "runs = lags.join(partitions.set_index(['dim_value', 'instances']), on=['dim_value', 'instances']).join(obs_instances.set_index(['dim_value', 'instances']), on=['dim_value', 'instances'])\n",
+ "\n",
+ "runs[\"failed\"] = runs.apply(lambda row: (abs(row['instances'] - row['obs_instances']) / row['instances']) > 0.1, axis=1)\n",
+ "\n",
+ "runs.loc[runs['failed']==True]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": " dim_value instances trend_slope partitions obs_instances failed \\\n45 6 1 5561.487307 317.500000 NaN False \n29 6 2 13.596355 400.000000 NaN False \n18 6 3 96.702473 379.201342 NaN False \n24 6 4 6.537204 391.483871 NaN False \n3 6 6 0.767027 400.000000 NaN False \n33 6 8 -105.817580 395.090909 NaN False \n41 6 10 4.667027 395.200000 NaN False \n36 6 12 0.735857 395.200000 NaN False \n6 6 14 2.476956 395.200000 NaN False \n46 6 16 -4.115907 395.183333 NaN False \n5 6 18 -9.234028 395.200000 NaN False \n20 6 20 5.313371 395.194595 NaN False \n1 7 1 6446.191125 83.000000 NaN False \n12 7 2 1945.020525 350.574468 NaN False \n11 7 3 -72.335081 400.000000 NaN False \n14 7 4 945.547288 382.038710 NaN False \n21 7 6 100.764034 398.481250 NaN False \n10 7 8 -220.212437 399.943750 NaN False \n34 7 10 -157.550502 400.000000 NaN False \n15 7 12 -819.659104 399.931429 NaN False \n19 7 14 -283.043101 400.000000 NaN False \n8 7 16 -174.236131 400.000000 NaN False \n26 7 18 -20.189412 400.000000 NaN False \n40 7 20 -16.065915 400.000000 NaN False \n9 8 1 7658.672143 21.800000 NaN False \n22 8 2 84143.711600 231.777778 NaN False \n44 8 3 -6167.223871 400.000000 NaN False \n17 8 4 -389.081253 400.000000 NaN False \n27 8 6 2049.951866 388.820000 NaN False \n0 8 8 1930.956834 399.933333 NaN False \n42 8 10 719.394310 399.682353 NaN False \n43 8 12 -210.856680 400.000000 NaN False \n13 8 14 -1107.783893 400.000000 NaN False \n32 8 16 -821.759267 400.000000 NaN False \n7 8 18 -508.689616 400.000000 NaN False \n16 8 20 -1438.296303 400.000000 NaN False \n25 9 1 19516.493571 18.400000 NaN False \n31 9 2 140367.213231 171.357143 NaN False \n4 9 3 120084.340788 NaN NaN False \n2 9 4 147670.147984 344.061538 NaN False \n37 9 6 -11325.508726 400.000000 NaN False \n47 9 8 59326.267156 380.655556 NaN False \n35 9 10 9300.347516 398.316667 NaN False \n38 9 12 5658.676216 400.000000 NaN False \n39 9 14 1702.122420 399.991667 NaN False \n28 9 16 -2212.967994 400.000000 NaN False \n30 9 18 -3130.724479 400.000000 NaN False \n23 9 20 -270.561345 400.000000 NaN False \n\n suitable \n45 False \n29 True \n18 True \n24 True \n3 True \n33 True \n41 True \n36 True \n6 True \n46 True \n5 True \n20 True \n1 False \n12 True \n11 True \n14 True \n21 True \n10 True \n34 True \n15 True \n19 True \n8 True \n26 True \n40 True \n9 False \n22 False \n44 True \n17 True \n27 False \n0 True \n42 True \n43 True \n13 True \n32 True \n7 True \n16 True \n25 False \n31 False \n4 False \n2 False \n37 True \n47 False \n35 False \n38 False \n39 True \n28 True \n30 True \n23 True ",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>dim_value</th>\n <th>instances</th>\n <th>trend_slope</th>\n <th>partitions</th>\n <th>obs_instances</th>\n <th>failed</th>\n <th>suitable</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>45</th>\n <td>6</td>\n <td>1</td>\n <td>5561.487307</td>\n <td>317.500000</td>\n <td>NaN</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>29</th>\n <td>6</td>\n <td>2</td>\n <td>13.596355</td>\n <td>400.000000</td>\n <td>NaN</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>18</th>\n <td>6</td>\n <td>3</td>\n <td>96.702473</td>\n <td>379.201342</td>\n <td>NaN</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>24</th>\n <td>6</td>\n <td>4</td>\n 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+ },
+ "metadata": {},
+ "execution_count": 123
+ }
+ ],
"source": [
"#threshold = 1000\n",
"\n",
@@ -122,9 +298,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 124,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": " dim_value instances\n0 6 2\n1 7 2\n2 8 3\n3 9 6",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>dim_value</th>\n <th>instances</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>6</td>\n <td>2</td>\n </tr>\n <tr>\n <th>1</th>\n <td>7</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>8</td>\n <td>3</td>\n </tr>\n <tr>\n <th>3</th>\n <td>9</td>\n <td>6</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ },
+ "metadata": {},
+ "execution_count": 124
+ }
+ ],
"source": [
"filtered = runs[runs.apply(lambda x: x['suitable'], axis=1)]\n",
"\n",
@@ -136,11 +322,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 125,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
"source": [
"min_suitable_instances.plot(kind='line',x='dim_value',y='instances')\n",
+ "# min_suitable_instances.plot(kind='line',x='dim_value',y='instances', logy=True)\n",
"\n",
"plt.show()"
]