From 4a08ae63a4534e846cce4ec508d3a1f15e2db656 Mon Sep 17 00:00:00 2001
From: Stefan Schneider <stefan.schneider@upb.de>
Date: Tue, 4 Aug 2020 08:50:43 +0200
Subject: [PATCH] analyze ratio

---
 eval/research_q_2_distance_plots.ipynb | 95 ++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)

diff --git a/eval/research_q_2_distance_plots.ipynb b/eval/research_q_2_distance_plots.ipynb
index 8fc9085..187c7dd 100644
--- a/eval/research_q_2_distance_plots.ipynb
+++ b/eval/research_q_2_distance_plots.ipynb
@@ -42,6 +42,35 @@
     "    return [label_mapping[l] for l in original_labels]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# analyze difference between k=1,2,3\n",
+    "def analyze_ratio(metric, k1_by_k3=False):\n",
+    "    df_k1 = df_wo_inf[df_wo_inf['hierarchy'] == '[1]']\n",
+    "    df_k3 = df_wo_inf[df_wo_inf['hierarchy'] == '[2, 4]']\n",
+    "\n",
+    "    np.mean(df_k3[metric].to_numpy() / df_k1[metric].to_numpy())\n",
+    "\n",
+    "    # how much do the means per request deviate?\n",
+    "    ratios = []\n",
+    "    for r in range(1, 6):\n",
+    "        mean_k1 = df_k1[df_k1['request_count'] == r][metric].mean()\n",
+    "        mean_k3 = df_k3[df_k3['request_count'] == r][metric].mean()\n",
+    "        if k1_by_k3:\n",
+    "            ratio = mean_k1 / mean_k3\n",
+    "            print(f'Ratio k1/k3 for {r} requests: {ratio}')\n",
+    "        else:\n",
+    "            ratio = mean_k3 / mean_k1\n",
+    "            print(f'Ratio k3/k1 for {r} requests: {ratio}')\n",
+    "        ratios.append(ratio)\n",
+    "        \n",
+    "    print(f'Min: {min(ratios)}, Max: {max(ratios)}, Mean: {np.mean(ratios)}')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 60,
@@ -97,6 +126,28 @@
     "plot_runtime(df_wo_inf)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio k1/k3 for 1 requests: 14.039137608004829\n",
+      "Ratio k1/k3 for 2 requests: 3.490641264692949\n",
+      "Ratio k1/k3 for 3 requests: 4.047355549885894\n",
+      "Ratio k1/k3 for 4 requests: 1.3483874411788275\n",
+      "Ratio k1/k3 for 5 requests: 0.9387512659005236\n",
+      "Min: 0.9387512659005236, Max: 14.039137608004829, Mean: 4.772854625932604\n"
+     ]
+    }
+   ],
+   "source": [
+    "analyze_ratio('runtime', k1_by_k3=True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 54,
@@ -146,6 +197,28 @@
     "plot_delay(df_wo_inf)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio k3/k1 for 1 requests: 1.2680510324454204\n",
+      "Ratio k3/k1 for 2 requests: 1.176557213089767\n",
+      "Ratio k3/k1 for 3 requests: 1.07766541555919\n",
+      "Ratio k3/k1 for 4 requests: 1.1598862750934744\n",
+      "Ratio k3/k1 for 5 requests: 1.1741791440663432\n",
+      "Min: 1.07766541555919, Max: 1.2680510324454204, Mean: 1.1712678160508392\n"
+     ]
+    }
+   ],
+   "source": [
+    "analyze_ratio('sum_total_delay')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 61,
@@ -195,6 +268,28 @@
     "plot_vnfs(df_wo_inf)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio k3/k1 for 1 requests: 1.0\n",
+      "Ratio k3/k1 for 2 requests: 1.0666666666666667\n",
+      "Ratio k3/k1 for 3 requests: 1.1463414634146343\n",
+      "Ratio k3/k1 for 4 requests: 1.1132075471698115\n",
+      "Ratio k3/k1 for 5 requests: 1.079734219269103\n",
+      "Min: 1.0, Max: 1.1463414634146343, Mean: 1.0811899793040431\n"
+     ]
+    }
+   ],
+   "source": [
+    "analyze_ratio('placements')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,