768 lines
195 KiB
Plaintext
768 lines
195 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Group 7 - Noah L. Schrick"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from dmba import regressionSummary"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import data\n",
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"timing_df = pd.read_csv('timing.csv')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exploratory Analysis"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute mean, median, min, max, and standard deviation for quantitative variables"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>median</th>\n",
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" <th>min</th>\n",
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" <th>max</th>\n",
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" <th>sd</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>load</th>\n",
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" <td>316.437553</td>\n",
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" <td>395.000</td>\n",
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" <td>1.0000</td>\n",
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" <td>395.000</td>\n",
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" <td>117.362235</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>exploit</th>\n",
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" <td>2672.502974</td>\n",
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" <td>96.000</td>\n",
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" <td>6.0000</td>\n",
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" <td>49152.000</td>\n",
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" <td>7980.309163</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>appl</th>\n",
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" <td>49.596432</td>\n",
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" <td>50.000</td>\n",
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" <td>0.0000</td>\n",
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" <td>100.000</td>\n",
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" <td>35.285320</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>nodes</th>\n",
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" <td>5.343246</td>\n",
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" <td>5.000</td>\n",
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" <td>1.0000</td>\n",
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" <td>12.000</td>\n",
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" <td>3.801330</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>runtime</th>\n",
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" <td>123485.810836</td>\n",
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" <td>6939.100</td>\n",
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" <td>534.1570</td>\n",
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" <td>4172158.645</td>\n",
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" <td>451077.472306</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task0</th>\n",
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" <td>0.000000</td>\n",
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" <td>0.000</td>\n",
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" <td>0.0000</td>\n",
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" <td>0.000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task1</th>\n",
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" <td>44869.445570</td>\n",
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" <td>2201.240</td>\n",
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" <td>141.7060</td>\n",
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" <td>1021260.000</td>\n",
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" <td>152181.284667</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task2</th>\n",
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" <td>62773.283784</td>\n",
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" <td>954.697</td>\n",
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" <td>8.2555</td>\n",
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" <td>2891390.000</td>\n",
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" <td>289238.546817</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task3</th>\n",
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" <td>234.804263</td>\n",
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" <td>288.196</td>\n",
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" <td>20.4200</td>\n",
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" <td>469.373</td>\n",
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" <td>141.938703</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task4</th>\n",
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" <td>114.152247</td>\n",
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" <td>0.000</td>\n",
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" <td>0.0000</td>\n",
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" <td>840.559</td>\n",
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" <td>177.298509</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>task5</th>\n",
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" <td>1.317881</td>\n",
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" <td>0.000</td>\n",
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" <td>0.0000</td>\n",
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" <td>6.964</td>\n",
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" <td>2.428805</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" mean median min max sd\n",
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"load 316.437553 395.000 1.0000 395.000 117.362235\n",
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"exploit 2672.502974 96.000 6.0000 49152.000 7980.309163\n",
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"appl 49.596432 50.000 0.0000 100.000 35.285320\n",
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"nodes 5.343246 5.000 1.0000 12.000 3.801330\n",
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"runtime 123485.810836 6939.100 534.1570 4172158.645 451077.472306\n",
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"task0 0.000000 0.000 0.0000 0.000 0.000000\n",
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"task1 44869.445570 2201.240 141.7060 1021260.000 152181.284667\n",
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"task2 62773.283784 954.697 8.2555 2891390.000 289238.546817\n",
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"task3 234.804263 288.196 20.4200 469.373 141.938703\n",
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"task4 114.152247 0.000 0.0000 840.559 177.298509\n",
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"task5 1.317881 0.000 0.0000 6.964 2.428805"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pd.DataFrame({'mean': timing_df.mean(),\n",
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"'median': timing_df.median(),\n",
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"'min': timing_df.min(),\n",
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"'max': timing_df.max(),\n",
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"'sd': timing_df.std()\n",
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"})"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Show avg timing data for each var step (EX: Avg time for 12 nodes, 11...1, Avg time for 6 exploits...49152)\n"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"<AxesSubplot: xlabel='load'>"
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]
|
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"node_pivot = timing_df.pivot_table(index=[\"nodes\"], values=[\"runtime\"], aggfunc='mean')\n",
|
|
"exploit_pivot = timing_df.pivot_table(index=[\"exploit\"], values=[\"runtime\"], aggfunc='mean')\n",
|
|
"appl_pivot = timing_df.pivot_table(index=[\"appl\"], values=[\"runtime\"], aggfunc='mean')\n",
|
|
"load_pivot = timing_df.pivot_table(index=[\"load\"], values=[\"runtime\"], aggfunc='mean')\n",
|
|
"\n",
|
|
"node_pivot.plot(kind='bar')\n",
|
|
"exploit_pivot.plot(kind='bar')\n",
|
|
"appl_pivot.plot(kind='bar')\n",
|
|
"load_pivot.plot(kind='bar')\n"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Correlation and Matrix Plot"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<AxesSubplot: >"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1100x700 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"timing_corr = timing_df.corr().round(3)\n",
|
|
"# print(timing_corr)\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"fig.set_size_inches(11, 7)\n",
|
|
"sns.heatmap(timing_corr, annot=True, fmt=\".1f\", cmap=\"RdBu\", center=0, ax=ax)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Linear Regression"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Indiv Vars"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## All Vars"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Overall Runtime"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept -121736.6271124178\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes -1679.735263\n",
|
|
"1 exploit 52.744604\n",
|
|
"2 appl 1830.947327\n",
|
|
"3 load 82.707386\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : 0.0000\n",
|
|
" Root Mean Squared Error (RMSE) : 207583.0527\n",
|
|
" Mean Absolute Error (MAE) : 92386.4892\n",
|
|
" Mean Percentage Error (MPE) : 575.6888\n",
|
|
"Mean Absolute Percentage Error (MAPE) : 1325.9592\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"predictors = ['nodes', 'exploit', 'appl', 'load']\n",
|
|
"overall_outcome = 'runtime'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[overall_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"runtime_lm = LinearRegression()\n",
|
|
"runtime_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', runtime_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': runtime_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, runtime_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept 0.0\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes 0.0\n",
|
|
"1 exploit 0.0\n",
|
|
"2 appl 0.0\n",
|
|
"3 load 0.0\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : 0.0000\n",
|
|
"Root Mean Squared Error (RMSE) : 0.0000\n",
|
|
" Mean Absolute Error (MAE) : 0.0000\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 0\n",
|
|
"t0_outcome = 'task0'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t0_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t0_lm = LinearRegression()\n",
|
|
"t0_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t0_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t0_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t0_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept -11885.738854304313\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes -191.295808\n",
|
|
"1 exploit 18.988550\n",
|
|
"2 appl 168.021933\n",
|
|
"3 load -3.516380\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : -0.0000\n",
|
|
" Root Mean Squared Error (RMSE) : 20302.8215\n",
|
|
" Mean Absolute Error (MAE) : 12528.3260\n",
|
|
" Mean Percentage Error (MPE) : 813.6684\n",
|
|
"Mean Absolute Percentage Error (MAPE) : 955.8818\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 1\n",
|
|
"t1_outcome = 'task1'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t1_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t1_lm = LinearRegression()\n",
|
|
"t1_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t1_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t1_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t1_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept -87124.78314049005\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes -626.010138\n",
|
|
"1 exploit 30.801199\n",
|
|
"2 appl 1384.702778\n",
|
|
"3 load 17.010740\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : 0.0000\n",
|
|
" Root Mean Squared Error (RMSE) : 180975.0953\n",
|
|
" Mean Absolute Error (MAE) : 76437.9774\n",
|
|
" Mean Percentage Error (MPE) : 15760.8385\n",
|
|
"Mean Absolute Percentage Error (MAPE) : 46261.7955\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 2\n",
|
|
"t2_outcome = 'task2'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t2_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t2_lm = LinearRegression()\n",
|
|
"t2_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t2_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t2_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t2_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept 59.578798554050366\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes 20.955291\n",
|
|
"1 exploit -0.002769\n",
|
|
"2 appl -0.019094\n",
|
|
"3 load 0.233217\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : -0.0000\n",
|
|
" Root Mean Squared Error (RMSE) : 95.4037\n",
|
|
" Mean Absolute Error (MAE) : 79.9728\n",
|
|
" Mean Percentage Error (MPE) : -50.0297\n",
|
|
"Mean Absolute Percentage Error (MAPE) : 78.9078\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 3\n",
|
|
"t3_outcome = 'task3'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t3_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t3_lm = LinearRegression()\n",
|
|
"t3_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t3_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t3_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t3_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 4"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept 346.34017133766497\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes -3.956753\n",
|
|
"1 exploit 0.001025\n",
|
|
"2 appl 0.022288\n",
|
|
"3 load -0.679112\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : 0.0000\n",
|
|
"Root Mean Squared Error (RMSE) : 153.7768\n",
|
|
" Mean Absolute Error (MAE) : 115.1342\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 4\n",
|
|
"t4_outcome = 'task4'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t4_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t4_lm = LinearRegression()\n",
|
|
"t4_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t4_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t4_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t4_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Linear Regression - Task 5"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"intercept 3.3756590690238775\n",
|
|
" Predictor coefficient\n",
|
|
"0 nodes -0.347252\n",
|
|
"1 exploit 0.000053\n",
|
|
"2 appl 0.000413\n",
|
|
"3 load -0.001227\n",
|
|
"\n",
|
|
"Regression statistics\n",
|
|
"\n",
|
|
" Mean Error (ME) : -0.0000\n",
|
|
"Root Mean Squared Error (RMSE) : 1.8589\n",
|
|
" Mean Absolute Error (MAE) : 1.4836\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Linear Regression - Task 5\n",
|
|
"t5_outcome = 'task5'\n",
|
|
"\n",
|
|
"# partition data\n",
|
|
"X = timing_df[predictors]\n",
|
|
"overall_y = timing_df[t5_outcome]\n",
|
|
"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
|
|
"t5_lm = LinearRegression()\n",
|
|
"t5_lm.fit(train_X, train_y)\n",
|
|
"# print coefficients\n",
|
|
"print('intercept ', t5_lm.intercept_)\n",
|
|
"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': t5_lm.coef_}))\n",
|
|
"# print performance measures\n",
|
|
"regressionSummary(train_y, t5_lm.predict(train_X))"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Nonlinear Regression"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Random Forest"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Random Forest"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## SVR"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# SVR"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.10.10"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|