mirror of
https://github.com/logos-storage/das-research.git
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281 lines
9.0 KiB
Plaintext
281 lines
9.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"from IPython.display import display\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"from plots import make_folder, SingleMetrics, CombinedMetrics\n",
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"import plots\n",
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"\n",
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"# Necessary folders to start\n",
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"CSV_FOLDER = \"./csvs/retrieval_3\"\n",
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"IMG_FOLDER = \"./imgs/retrieval_3\"\n",
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"\n",
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"# make sure that the output folder exists\n",
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"make_folder(IMG_FOLDER, \"keeping track of the generated images\")\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Read all the available csv files in the given folder\n",
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"def read_files_with(target: str):\n",
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" files = []\n",
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" for dir, _, files in os.walk(CSV_FOLDER):\n",
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" for file in files:\n",
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" if target in file:\n",
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" files.append(dir+\"/\"+file)\n",
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" else:\n",
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" continue\n",
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" print(f\"found {len(files)} with {target} files in {CSV_FOLDER}\")\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Analysis of the data\n",
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"#### Individual metrics"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Individual metrics\n",
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"def print_avg_lookup(df):\n",
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" print(f\"lookup_wallclock_time\\t\\t\\t {df.lookup_wallclock_time.mean()}\")\n",
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" print(f\"attempted_nodes\\t\\t\\t\\t\\t {df.attempted_nodes.mean()}\")\n",
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" print(f\"finished_connection_attempts\\t {df.finished_connection_attempts.mean()}\")\n",
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" print(f\"successful_connections\\t\\t\\t {df.successful_connections.mean()}\")\n",
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" print(f\"failed_connections\\t\\t\\t\\t {df.failed_connections.mean()}\")\n",
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" print(f\"total_discovered_nodes\\t\\t\\t {df.total_discovered_nodes.mean()}\")\n",
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" print(f\"retrievable\\t\\t\\t\\t\\t\\t {df.retrievable.mean()}\")\n",
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" print(f\"accuracy\\t\\t\\t\\t\\t\\t {df.accuracy.mean()}\")\n",
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"\n",
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"\n",
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"# Display the sigle metrics of the test individually\n",
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"files = read_files_with(\"retrieval_lookup_nn\")\n",
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"for file in files:\n",
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" df = pd.read_csv(file)\n",
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" print(\"\\nmax simulated lookup delay\")\n",
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" display(df.loc[df['lookup_aggregated_delay'].idxmax()])\n",
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"\n",
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" print(\"\\nmin simulated lookup delay\")\n",
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" display(df.loc[df['lookup_aggregated_delay'].idxmin()])\n",
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"\n",
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" print(\"\\navg simulated lookup delay\")\n",
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" print_avg_lookup(df)\n",
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" metrics = SingleMetrics(file, IMG_FOLDER, \"Retrievals\", {\n",
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" \"retrievable\": {\n",
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" \"title_tag\": \"retriebable\",\n",
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" \"xlabel_tag\": \"retriebable\",\n",
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" \"ylabel_tag\": \"\",\n",
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" },})"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"#### Aggregated accross samples"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# aggregate metrics across runs\n",
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"files = read_files_with(\"lookup_nn\")\n",
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"unifiedMetrics = CombinedMetrics(\n",
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" files=files, aggregator=\"fast_delay_range\",\n",
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" operation=\"retrieval\",\n",
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" filters=[\"y0.125\", \"cdr50-75\"], output_image_folder=IMG_FOLDER,\n",
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" metrics={\n",
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" \"retrievable\": {\n",
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" \"title_tag\": \"retriebable\",\n",
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" \"xlabel_tag\": \"retriebable\",\n",
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" \"ylabel_tag\": \"\",\n",
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" },\n",
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" },\n",
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" legend=[\n",
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" plots.RETRIEVAL_NODES,\n",
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" plots.CONCURRENT_SAMPLES,\n",
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" plots.FAST_ERROR_RATE,\n",
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" plots.CONNECTION_DELAYS,\n",
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" plots.GAMMA,\n",
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" ])"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# example to reproduce the network details\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"file = CSV_FOLDER+\"/retrieval_lookup_network_nn12000_rn100_sampl100_fer10_ser0_cdr50-75_fdr50-100_sdr0_k20_a3_b20_y0.125_steps3.csv\"\n",
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"\n",
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"df = pd.read_csv(file)\n",
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"data = df.groupby([\"from\", \"to\"]).count()\n",
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"data = data.reset_index()\n",
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"data = data.rename(columns={\"Unnamed: 0\": \"total_connections\"})\n",
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"data = data.sort_values(by=\"total_connections\", ascending=False)\n",
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"pivoted_data = data.pivot(index=\"from\", columns=\"to\", values=\"total_connections\").fillna(0)\n",
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"display(pivoted_data)\n",
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"\n",
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"# plot heatmap of connections\n",
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"cmap = sns.cm.rocket_r\n",
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"\n",
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"sns.set()\n",
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"plt.show()\n",
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"g = sns.heatmap(data=pivoted_data, xticklabels=\"to\", yticklabels=\"from\", cmap = cmap)\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# example to reproduce the network details\n",
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"import networkx as nx\n",
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"import plotly.graph_objects as go\n",
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"\n",
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"\n",
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"file = CSV_FOLDER+\"/retrieval_lookup_network_nn12000_rn100_sampl100_fer10_ser0_cdr50-75_fdr50-100_sdr0_k20_a3_b20_y0.125_steps3.csv\"\n",
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"\n",
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"df = pd.read_csv(file)\n",
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"df = df.groupby([\"from\", \"to\"]).size().reset_index(name=\"count\")\n",
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"top_interactions = df.sort_values('count', ascending=False).head(10000) # top 10000 interactions\n",
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"display(top_interactions)\n",
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"\n",
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"G = nx.from_pandas_edgelist(top_interactions, 'to', 'from', ['count'])\n",
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"pos = nx.spring_layout(G)\n",
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"\n",
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"for node in G.nodes():\n",
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" G.nodes[node]['pos'] = list(pos[node])\n",
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"\n",
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"edge_x = []\n",
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"edge_y = []\n",
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"for edge in G.edges():\n",
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" x0, y0 = G.nodes[edge[0]]['pos']\n",
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" x1, y1 = G.nodes[edge[1]]['pos']\n",
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" edge_x.extend([x0, x1, None])\n",
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" edge_y.extend([y0, y1, None])\n",
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"\n",
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"node_x = [pos[node][0] for node in G.nodes()]\n",
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"node_y = [pos[node][1] for node in G.nodes()]\n",
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"\n",
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"edge_trace = go.Scatter(\n",
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" x=edge_x, y=edge_y,\n",
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" line=dict(width=0.5, color='#888'),\n",
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" hoverinfo='none',\n",
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" mode='lines')\n",
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"\n",
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"node_trace = go.Scatter(\n",
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" x=node_x, y=node_y,\n",
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" mode='markers',\n",
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" hoverinfo='text',\n",
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" marker=dict(\n",
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" showscale=True,\n",
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" colorscale='YlGnBu',\n",
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" size=10,\n",
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" colorbar=dict(\n",
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" thickness=15,\n",
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" title='Node Connections',\n",
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" xanchor='left',\n",
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" titleside='right'\n",
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" ),\n",
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" line_width=2))\n",
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"\n",
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"node_adjacencies = []\n",
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"node_text = []\n",
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"for node in G.nodes():\n",
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" adjacencies = list(G.adj[node]) # List of nodes adjacent to the current node\n",
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" num_connections = len(adjacencies)\n",
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"\n",
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" node_adjacencies.append(num_connections)\n",
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" node_text.append(f'Node id: {node}<br># of connections: {num_connections}')\n",
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"\n",
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"node_trace.marker.color = node_adjacencies\n",
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"node_trace.text = node_text\n",
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"\n",
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"fig = go.Figure(data=[edge_trace, node_trace],\n",
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" layout=go.Layout(\n",
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" title='Network of Top Address Interactions',\n",
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" titlefont_size=16,\n",
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" showlegend=False,\n",
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" hovermode='closest',\n",
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" margin=dict(b=0, l=0, r=0, t=0),\n",
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" annotations=[dict(\n",
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" text=\"Based on top interactions\",\n",
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" showarrow=False,\n",
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" xref=\"paper\", yref=\"paper\",\n",
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" x=0.005, y=-0.002)],\n",
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" xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
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" yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n",
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" )\n",
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"fig.update_layout(title_text=\"DHT network's interactions\")\n",
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"fig.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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