Correlation and Matrix Plots. Linear Regression
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timing-analysis.ipynb
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231
timing-analysis.ipynb
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{
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"\n",
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"import pandas as pd\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": null,
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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('TimingData.csv')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Compute mean, median, min, max, and standard deviation for quantitative variables\n",
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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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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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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)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Correlation and Matrix Plot\n",
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"timing_corr = timing_df.corr().round(3)\n",
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"print(timing_corr)\n",
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"fig, ax = plt.subplots()\n",
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"fig.set_size_inches(11, 7)\n",
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"sns.heatmap(timing_corr, annot=True, fmt=\".1f\", cmap=\"RdBu\", center=0, ax=ax)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Overall\n",
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"predictors = [\"nodes\", \"exploits\", \"appl\", \"load\"]\n",
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"overall_outcome = [\"runtime\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[overall_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 0\n",
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"t0_outcome = [\"task0\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t0_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 1\n",
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"t1_outcome = [\"task1\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t1_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 2\n",
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"t2_outcome = [\"task2\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t2_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 3\n",
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"t3_outcome = [\"task3\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t3_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 4\n",
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"t4_outcome = [\"task4\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t4_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Linear Regression - Task 5\n",
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"t5_outcome = [\"task5\"]\n",
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"\n",
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"# partition data\n",
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"X = pd.get_dummies(timing_df[predictors], drop_first=True)\n",
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"overall_y = timing_df[t5_outcome]\n",
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"train_X, valid_X, train_y, valid_y = train_test_split(X, overall_y, test_size=0.4, random_state=1)\n",
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"housing_lm = LinearRegression()\n",
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"housing_lm.fit(train_X, train_y)\n",
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"# print coefficients\n",
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"print('intercept ', housing_lm.intercept_)\n",
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"print(pd.DataFrame({'Predictor': X.columns, 'coefficient': housing_lm.coef_}))\n",
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"# print performance measures\n",
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"regressionSummary(train_y, housing_lm.predict(train_X))"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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