mirror of
https://github.com/soconnor0919/f1-race-prediction.git
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1250 lines
392 KiB
Plaintext
1250 lines
392 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Formula One Project: Modeling\n",
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"\n",
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"DUE: December 4th, 2024 (Wed) \n",
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"Name(s): Sean O'Connor, Connor Coles \n",
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"Class: CSCI 349 - Intro to Data Mining \n",
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"Semester: Fall 2024 \n",
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"Instructor: Brian King "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Assignment Description\n",
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"\n",
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"Copy over the important cells from the previous step that read in and cleaned your data to this new notebook file. You do not need to copy over all your EDA and plots describing your data, only the code that prepares your data for modeling. This notebook is about exploring the development of predictive models. Some initial preliminary work on applying some modeling techniques should be completed.\n",
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"Be sure to commit and push all supporting code that you've completed in this file. Include in this notebook a summary cell at the top that details your accomplishments, challenges, and what you expect to accomplish for your final steps. Be sure to update your readme.md in your repository."
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importing Libraries\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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"import os\n",
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"\n",
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"import fastf1\n",
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"import fastf1.plotting\n",
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"from fastf1.ergast.structure import FastestLap\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.ensemble import RandomForestRegressor\n",
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"from sklearn.metrics import mean_squared_error, r2_score\n",
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"from sklearn.tree import DecisionTreeRegressor\n",
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"from sklearn.ensemble import GradientBoostingRegressor\n",
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"from sklearn.svm import SVR\n",
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"import xgboost as xgb\n",
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"from sklearn.model_selection import cross_val_score"
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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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"source": [
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"# FastF1 general setup\n",
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"cache_dir = '../data/cache'\n",
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"if not os.path.exists(cache_dir):\n",
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" os.makedirs(cache_dir)\n",
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"\n",
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"fastf1.Cache.enable_cache(cache_dir)\n",
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"fastf1.plotting.setup_mpl(misc_mpl_mods=False, color_scheme=None)"
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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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"source": [
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"# Define years, sessions, and events of interest\n",
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"years = [2021, 2022, 2023, 2024]\n",
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"sessions = ['Race']\n",
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"events = ['Bahrain Grand Prix', 'British Grand Prix', 'United States Grand Prix', 'Mexico City Grand Prix', 'São Paulo Grand Prix'] "
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2021 Bahrain Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"core INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n",
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['33', '44', '77', '16', '10', '3', '4', '55', '14', '18', '11', '99', '22', '7', '63', '31', '6', '47', '9', '5']\n",
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"core INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2021 British Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['33', '44', '77', '16', '4', '3', '14', '5', '31', '55', '10', '63', '7', '18', '99', '22', '6', '47', '9', '11']\n",
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"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2021 United States Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"core WARNING \tDriver 7: Lap timing integrity check failed for 1 lap(s)\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['33', '44', '11', '16', '55', '3', '4', '10', '77', '22', '31', '99', '18', '6', '7', '47', '9', '5', '14', '63']\n",
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"core INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2021 Mexico City Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['77', '44', '33', '11', '10', '55', '3', '16', '5', '7', '99', '14', '6', '47', '9', '63', '22', '4', '31', '18']\n",
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"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2021 São Paulo Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['77', '33', '55', '11', '4', '16', '10', '31', '5', '44', '3', '14', '99', '18', '22', '6', '63', '47', '9', '7']\n",
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"core INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2022 Bahrain Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['16', '1', '55', '11', '44', '77', '20', '14', '63', '10', '31', '47', '4', '23', '24', '22', '27', '3', '18', '6']\n",
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"core INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2022 British Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['55', '1', '16', '11', '44', '4', '14', '63', '24', '6', '10', '77', '22', '3', '31', '23', '20', '5', '47', '18']\n",
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"events WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
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"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2022 United States Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
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"req INFO \tUsing cached data for timing_app_data\n",
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['55', '1', '44', '63', '18', '4', '77', '23', '11', '5', '10', '16', '20', '14', '3', '47', '6', '24', '22', '31']\n",
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"core INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2022 Mexico City Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
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"req INFO \tUsing cached data for session_status_data\n",
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"req INFO \tUsing cached data for lap_count\n",
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"req INFO \tUsing cached data for track_status_data\n",
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"req INFO \tUsing cached data for _extended_timing_data\n",
|
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"req INFO \tUsing cached data for timing_app_data\n",
|
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['1', '63', '44', '11', '55', '77', '16', '4', '14', '31', '3', '24', '22', '10', '47', '5', '23', '6', '20', '18']\n",
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"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing 2022 São Paulo Grand Prix - Race\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"logger WARNING \tFailed to load result data from Ergast!\n",
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"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
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"req INFO \tUsing cached data for session_status_data\n",
|
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"req INFO \tUsing cached data for lap_count\n",
|
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"req INFO \tUsing cached data for track_status_data\n",
|
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"req INFO \tUsing cached data for _extended_timing_data\n",
|
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"req INFO \tUsing cached data for timing_app_data\n",
|
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"core INFO \tProcessing timing data...\n",
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"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
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"req INFO \tUsing cached data for car_data\n",
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"req INFO \tUsing cached data for position_data\n",
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"req INFO \tUsing cached data for weather_data\n",
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"req INFO \tUsing cached data for race_control_messages\n",
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"core INFO \tFinished loading data for 20 drivers: ['63', '44', '1', '11', '16', '4', '55', '20', '5', '10', '3', '47', '24', '77', '18', '31', '14', '6', '23', '22']\n",
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"core INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
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"req INFO \tUsing cached data for session_info\n",
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"req INFO \tUsing cached data for driver_info\n"
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]
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},
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2023 Bahrain Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['1', '11', '16', '55', '14', '63', '44', '18', '31', '27', '4', '77', '24', '22', '23', '2', '20', '81', '21', '10']\n",
|
|
"core INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2023 British Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['1', '4', '81', '16', '55', '63', '44', '23', '14', '10', '27', '18', '31', '2', '11', '22', '24', '21', '20', '77']\n",
|
|
"events WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
|
|
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2023 United States Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['16', '4', '44', '55', '63', '1', '10', '31', '11', '81', '22', '24', '77', '3', '23', '2', '20', '27', '14', '18']\n",
|
|
"core INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2023 Mexico City Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['16', '55', '1', '3', '11', '44', '81', '63', '77', '24', '10', '27', '14', '23', '31', '20', '4', '22', '2', '18']\n",
|
|
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2023 São Paulo Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['1', '16', '18', '14', '44', '4', '55', '63', '11', '81', '27', '20', '23', '31', '10', '22', '3', '77', '2', '24']\n",
|
|
"core INFO \tLoading data for Bahrain Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2024 Bahrain Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['1', '16', '63', '55', '11', '14', '4', '81', '44', '27', '22', '18', '23', '3', '20', '77', '24', '2', '31', '10']\n",
|
|
"core INFO \tLoading data for British Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2024 British Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['63', '44', '4', '1', '81', '27', '55', '18', '23', '14', '16', '2', '22', '24', '3', '77', '20', '31', '10', '11']\n",
|
|
"events WARNING \tCorrecting user input 'United States Grand Prix' to 'United States Grand Prix'\n",
|
|
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2024 United States Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['4', '1', '55', '16', '81', '10', '14', '20', '11', '22', '27', '31', '18', '23', '43', '77', '44', '24', '30', '63']\n",
|
|
"core INFO \tLoading data for Mexico City Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2024 Mexico City Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['55', '1', '4', '16', '63', '44', '20', '10', '23', '27', '22', '30', '14', '18', '77', '43', '81', '11', '24', '31']\n",
|
|
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
|
|
"req INFO \tUsing cached data for session_info\n",
|
|
"req INFO \tUsing cached data for driver_info\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing 2024 São Paulo Grand Prix - Race\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"logger WARNING \tFailed to load result data from Ergast!\n",
|
|
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
|
|
"req INFO \tUsing cached data for session_status_data\n",
|
|
"req INFO \tUsing cached data for lap_count\n",
|
|
"req INFO \tUsing cached data for track_status_data\n",
|
|
"req INFO \tUsing cached data for _extended_timing_data\n",
|
|
"req INFO \tUsing cached data for timing_app_data\n",
|
|
"core INFO \tProcessing timing data...\n",
|
|
"core WARNING \tNo lap data for driver 23\n",
|
|
"core WARNING \tFailed to perform lap accuracy check - all laps marked as inaccurate (driver 23)\n",
|
|
"logger WARNING \tFailed to add first lap time from Ergast!\n",
|
|
"req INFO \tUsing cached data for car_data\n",
|
|
"req INFO \tUsing cached data for position_data\n",
|
|
"req INFO \tUsing cached data for weather_data\n",
|
|
"req INFO \tUsing cached data for race_control_messages\n",
|
|
"core INFO \tFinished loading data for 20 drivers: ['4', '63', '22', '31', '30', '16', '23', '81', '14', '18', '77', '11', '10', '44', '50', '43', '1', '27', '24', '55']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get data from FastF1 API\n",
|
|
"\n",
|
|
"# Data containers\n",
|
|
"weather_data_list = []\n",
|
|
"lap_data_list = []\n",
|
|
"\n",
|
|
"# Loop through years and sessions\n",
|
|
"for year in years:\n",
|
|
" for event_name in events: \n",
|
|
" for session_name in sessions:\n",
|
|
" try:\n",
|
|
" print(f\"Processing {year} {event_name} - {session_name}\")\n",
|
|
" \n",
|
|
" # Load the session\n",
|
|
" session = fastf1.get_session(year, event_name, session_name, backend='fastf1')\n",
|
|
" session.load()\n",
|
|
" \n",
|
|
" # Process weather data\n",
|
|
" weather_data = session.weather_data\n",
|
|
" if weather_data is not None:\n",
|
|
" weather_df = pd.DataFrame(weather_data)\n",
|
|
" # Add context columns\n",
|
|
" weather_df['Year'] = year\n",
|
|
" weather_df['Event'] = event_name\n",
|
|
" weather_df['Session'] = session_name\n",
|
|
" weather_data_list.append(weather_df)\n",
|
|
"\n",
|
|
" # Process lap data\n",
|
|
" lap_data = session.laps\n",
|
|
" if lap_data is not None:\n",
|
|
" lap_df = pd.DataFrame(lap_data)\n",
|
|
" # Add context columns\n",
|
|
" lap_df['Year'] = year\n",
|
|
" lap_df['Event'] = event_name\n",
|
|
" lap_df['Session'] = session_name\n",
|
|
" # Ensure driver information is included\n",
|
|
" if 'Driver' not in lap_df.columns:\n",
|
|
" lap_df['Driver'] = lap_df['DriverNumber'].map(session.drivers)\n",
|
|
" # Add team information if available\n",
|
|
" if 'Team' not in lap_df.columns:\n",
|
|
" lap_df['Team'] = lap_df['Driver'].map(session.drivers_info['TeamName'])\n",
|
|
" lap_data_list.append(lap_df)\n",
|
|
" \n",
|
|
" except Exception as e:\n",
|
|
" print(f\"Error with {event_name} {session_name} ({year}): {e}\")\n",
|
|
"\n",
|
|
"# Combine data into DataFrames\n",
|
|
"if weather_data_list:\n",
|
|
" weather_data_combined = pd.concat(weather_data_list, ignore_index=True)\n",
|
|
" # Ensure consistent column ordering\n",
|
|
" weather_cols = ['Time', 'Year', 'Event', 'Session', \n",
|
|
" 'AirTemp', 'Humidity', 'Pressure', 'Rainfall', \n",
|
|
" 'TrackTemp', 'WindDirection', 'WindSpeed']\n",
|
|
" weather_data_combined = weather_data_combined[weather_cols]\n",
|
|
" \n",
|
|
"if lap_data_list:\n",
|
|
" lap_data_combined = pd.concat(lap_data_list, ignore_index=True)\n",
|
|
" # Ensure consistent column ordering\n",
|
|
" lap_cols = ['Time', 'Year', 'Event', 'Session', \n",
|
|
" 'Driver', 'Team', 'LapNumber', 'LapTime',\n",
|
|
" 'Sector1Time', 'Sector2Time', 'Sector3Time',\n",
|
|
" 'Compound', 'TyreLife', 'FreshTyre',\n",
|
|
" 'SpeedI1', 'SpeedI2', 'SpeedFL', 'SpeedST']\n",
|
|
" # Only include columns that exist\n",
|
|
" existing_cols = [col for col in lap_cols if col in lap_data_combined.columns]\n",
|
|
" lap_data_combined = lap_data_combined[existing_cols]\n",
|
|
" \n",
|
|
"# Time conversion\n",
|
|
"# Function to convert timedelta to datetime\n",
|
|
"def convert_timedelta_to_datetime(df, base_date='2021-01-01'):\n",
|
|
" if 'Time' in df.columns:\n",
|
|
" # Create a base datetime and add the timedelta\n",
|
|
" base = pd.Timestamp(base_date)\n",
|
|
" if df['Time'].dtype == 'timedelta64[ns]':\n",
|
|
" df['Time'] = base + df['Time']\n",
|
|
" return df\n",
|
|
"\n",
|
|
"# Apply conversion to both dataframes\n",
|
|
"weather_data_combined = convert_timedelta_to_datetime(weather_data_combined)\n",
|
|
"lap_data_combined = convert_timedelta_to_datetime(lap_data_combined)\n",
|
|
"\n",
|
|
"# Remove missing values\n",
|
|
"weather_data_combined = weather_data_combined.dropna()\n",
|
|
"lap_data_combined = lap_data_combined.dropna()\n",
|
|
"\n",
|
|
"# Create a new column for lap time in seconds\n",
|
|
"lap_data_combined['LapTime_seconds'] = lap_data_combined['LapTime'].dt.total_seconds()\n",
|
|
"\n",
|
|
"# Merge the data\n",
|
|
"merged_data = pd.merge_asof(\n",
|
|
" lap_data_combined.sort_values('Time'),\n",
|
|
" weather_data_combined.sort_values('Time'),\n",
|
|
" on='Time',\n",
|
|
" by=['Event', 'Year'], # Match within same event and year\n",
|
|
" direction='nearest',\n",
|
|
" tolerance=pd.Timedelta('1 min') # Allow matching within 1 minute\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Feature Engineering (per event)\n",
|
|
"def engineer_features(df):\n",
|
|
" \"\"\"\n",
|
|
" Create F1-specific features that affect lap times:\n",
|
|
" - Tire age effect (exponential decay)\n",
|
|
" - Track evolution (improving grip over session)\n",
|
|
" - Weather impact (combined temperature effects)\n",
|
|
" \"\"\"\n",
|
|
" # Tire performance degradation (exponential)\n",
|
|
" df['TyreAgeFactor'] = np.exp(-0.02 * df['TyreLife'])\n",
|
|
" \n",
|
|
" # Track evolution (grip improvement)\n",
|
|
" df['TrackEvolution'] = df.groupby(['Event', 'Year'])['LapNumber'].transform(\n",
|
|
" lambda x: (x - x.min()) / (x.max() - x.min())\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Combined temperature effect (track and air)\n",
|
|
" df['TempDelta'] = df['TrackTemp'] - df['AirTemp']\n",
|
|
" \n",
|
|
" # Fuel load effect (decreasing over race)\n",
|
|
" df['FuelEffect'] = 1 - (df['LapNumber'] / df.groupby(['Event', 'Year'])['LapNumber'].transform('max'))\n",
|
|
" \n",
|
|
" return df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Prepare data for modeling (per event)\n",
|
|
"def prepare_modeling_data(df):\n",
|
|
" # Engineer features\n",
|
|
" data = engineer_features(df)\n",
|
|
" \n",
|
|
" # Select features for modeling\n",
|
|
" feature_columns = [\n",
|
|
" 'TrackTemp', 'AirTemp', 'Humidity', 'WindSpeed',\n",
|
|
" 'TyreLife', 'TyreAgeFactor', 'TrackEvolution',\n",
|
|
" 'TempDelta', 'FuelEffect', 'SpeedI1', 'SpeedI2'\n",
|
|
" ]\n",
|
|
" \n",
|
|
" # Create dummy variables for categorical features (excluding Event)\n",
|
|
" data = pd.get_dummies(data, columns=['Compound', 'Team'])\n",
|
|
" \n",
|
|
" # Add categorical columns to features\n",
|
|
" feature_columns.extend([col for col in data.columns if col.startswith(('Compound_', 'Team_'))])\n",
|
|
" \n",
|
|
" return data, feature_columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Split and scale data\n",
|
|
"def prepare_train_test(data, feature_columns, target_column='LapTime_seconds'):\n",
|
|
" X = data[feature_columns]\n",
|
|
" y = data[target_column]\n",
|
|
" \n",
|
|
" X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X, y, test_size=0.2, random_state=42\n",
|
|
" )\n",
|
|
" \n",
|
|
" scaler = StandardScaler()\n",
|
|
" X_train_scaled = scaler.fit_transform(X_train)\n",
|
|
" X_test_scaled = scaler.transform(X_test)\n",
|
|
" \n",
|
|
" return X_train_scaled, X_test_scaled, y_train, y_test, scaler"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Model training and evaluation\n",
|
|
"def train_and_evaluate_models(X_train, X_test, y_train, y_test):\n",
|
|
" models = {\n",
|
|
" 'Linear Regression': LinearRegression(),\n",
|
|
" 'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42),\n",
|
|
" 'XGBoost': xgb.XGBRegressor(n_estimators=100, random_state=42),\n",
|
|
" 'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, random_state=42)\n",
|
|
" }\n",
|
|
" \n",
|
|
" results = {}\n",
|
|
" for name, model in models.items():\n",
|
|
" # Train model\n",
|
|
" model.fit(X_train, y_train)\n",
|
|
" \n",
|
|
" # Make predictions\n",
|
|
" y_pred = model.predict(X_test)\n",
|
|
" \n",
|
|
" # Calculate metrics\n",
|
|
" mse = mean_squared_error(y_test, y_pred)\n",
|
|
" rmse = np.sqrt(mse)\n",
|
|
" r2 = r2_score(y_test, y_pred)\n",
|
|
" \n",
|
|
" # Cross-validation score\n",
|
|
" cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')\n",
|
|
" \n",
|
|
" results[name] = {\n",
|
|
" 'RMSE': rmse,\n",
|
|
" 'R2': r2,\n",
|
|
" 'CV_R2_mean': cv_scores.mean(),\n",
|
|
" 'CV_R2_std': cv_scores.std()\n",
|
|
" }\n",
|
|
" \n",
|
|
" return results, models"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Train models for each event\n",
|
|
"def train_event_models(merged_data):\n",
|
|
" event_models = {}\n",
|
|
" event_results = {}\n",
|
|
" \n",
|
|
" for event in merged_data['Event'].unique():\n",
|
|
" print(f\"\\nTraining models for {event}\")\n",
|
|
" \n",
|
|
" # Filter data for this event\n",
|
|
" event_data = merged_data[merged_data['Event'] == event].copy()\n",
|
|
" \n",
|
|
" # Prepare data\n",
|
|
" data, feature_columns = prepare_modeling_data(event_data)\n",
|
|
" X_train_scaled, X_test_scaled, y_train, y_test, scaler = prepare_train_test(\n",
|
|
" data, feature_columns\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Train and evaluate models\n",
|
|
" results, models = train_and_evaluate_models(X_train_scaled, X_test_scaled, y_train, y_test)\n",
|
|
" \n",
|
|
" # Store results\n",
|
|
" event_models[event] = models\n",
|
|
" event_results[event] = results\n",
|
|
" \n",
|
|
" # Print results for this event\n",
|
|
" print(f\"\\nModel Performance for {event}:\")\n",
|
|
" for name, metrics in results.items():\n",
|
|
" print(f\"\\n{name}:\")\n",
|
|
" print(f\"RMSE: {metrics['RMSE']:.2f} seconds\")\n",
|
|
" print(f\"R2 Score: {metrics['R2']:.3f}\")\n",
|
|
" print(f\"Cross-validation R2: {metrics['CV_R2_mean']:.3f} (±{metrics['CV_R2_std']:.3f})\")\n",
|
|
" \n",
|
|
" # Feature importance for best model\n",
|
|
" best_model_name = max(results.items(), key=lambda x: x[1]['R2'])[0]\n",
|
|
" if best_model_name in ['Random Forest', 'XGBoost', 'Gradient Boosting']:\n",
|
|
" best_model = models[best_model_name]\n",
|
|
" feature_importance = pd.DataFrame({\n",
|
|
" 'feature': feature_columns,\n",
|
|
" 'importance': best_model.feature_importances_\n",
|
|
" }).sort_values('importance', ascending=False)\n",
|
|
" \n",
|
|
" plt.figure(figsize=(12, 6))\n",
|
|
" sns.barplot(x='importance', y='feature', data=feature_importance.head(15))\n",
|
|
" plt.title(f'Top 15 Most Important Features for {event} ({best_model_name})')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
" \n",
|
|
" return event_models, event_results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Training models for Bahrain Grand Prix\n",
|
|
"\n",
|
|
"Model Performance for Bahrain Grand Prix:\n",
|
|
"\n",
|
|
"Linear Regression:\n",
|
|
"RMSE: 5.63 seconds\n",
|
|
"R2 Score: 0.490\n",
|
|
"Cross-validation R2: 0.621 (±0.031)\n",
|
|
"\n",
|
|
"Random Forest:\n",
|
|
"RMSE: 2.71 seconds\n",
|
|
"R2 Score: 0.882\n",
|
|
"Cross-validation R2: 0.886 (±0.023)\n",
|
|
"\n",
|
|
"XGBoost:\n",
|
|
"RMSE: 2.99 seconds\n",
|
|
"R2 Score: 0.857\n",
|
|
"Cross-validation R2: 0.868 (±0.023)\n",
|
|
"\n",
|
|
"Gradient Boosting:\n",
|
|
"RMSE: 2.91 seconds\n",
|
|
"R2 Score: 0.864\n",
|
|
"Cross-validation R2: 0.876 (±0.021)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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",
|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"\n",
|
|
"Training models for Mexico City Grand Prix\n",
|
|
"\n",
|
|
"Model Performance for Mexico City Grand Prix:\n",
|
|
"\n",
|
|
"Linear Regression:\n",
|
|
"RMSE: 5.77 seconds\n",
|
|
"R2 Score: 0.681\n",
|
|
"Cross-validation R2: 0.686 (±0.028)\n",
|
|
"\n",
|
|
"Random Forest:\n",
|
|
"RMSE: 1.96 seconds\n",
|
|
"R2 Score: 0.963\n",
|
|
"Cross-validation R2: 0.945 (±0.010)\n",
|
|
"\n",
|
|
"XGBoost:\n",
|
|
"RMSE: 2.39 seconds\n",
|
|
"R2 Score: 0.945\n",
|
|
"Cross-validation R2: 0.941 (±0.008)\n",
|
|
"\n",
|
|
"Gradient Boosting:\n",
|
|
"RMSE: 2.26 seconds\n",
|
|
"R2 Score: 0.951\n",
|
|
"Cross-validation R2: 0.939 (±0.014)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"\n",
|
|
"Training models for United States Grand Prix\n",
|
|
"\n",
|
|
"Model Performance for United States Grand Prix:\n",
|
|
"\n",
|
|
"Linear Regression:\n",
|
|
"RMSE: 4.67 seconds\n",
|
|
"R2 Score: 0.520\n",
|
|
"Cross-validation R2: 0.496 (±0.059)\n",
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|
"\n",
|
|
"Random Forest:\n",
|
|
"RMSE: 2.49 seconds\n",
|
|
"R2 Score: 0.863\n",
|
|
"Cross-validation R2: 0.852 (±0.022)\n",
|
|
"\n",
|
|
"XGBoost:\n",
|
|
"RMSE: 2.45 seconds\n",
|
|
"R2 Score: 0.868\n",
|
|
"Cross-validation R2: 0.852 (±0.015)\n",
|
|
"\n",
|
|
"Gradient Boosting:\n",
|
|
"RMSE: 2.71 seconds\n",
|
|
"R2 Score: 0.838\n",
|
|
"Cross-validation R2: 0.860 (±0.020)\n"
|
|
]
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Training models for British Grand Prix\n",
|
|
"\n",
|
|
"Model Performance for British Grand Prix:\n",
|
|
"\n",
|
|
"Linear Regression:\n",
|
|
"RMSE: 6.47 seconds\n",
|
|
"R2 Score: 0.680\n",
|
|
"Cross-validation R2: 0.717 (±0.030)\n",
|
|
"\n",
|
|
"Random Forest:\n",
|
|
"RMSE: 3.66 seconds\n",
|
|
"R2 Score: 0.898\n",
|
|
"Cross-validation R2: 0.939 (±0.020)\n",
|
|
"\n",
|
|
"XGBoost:\n",
|
|
"RMSE: 3.64 seconds\n",
|
|
"R2 Score: 0.899\n",
|
|
"Cross-validation R2: 0.933 (±0.022)\n",
|
|
"\n",
|
|
"Gradient Boosting:\n",
|
|
"RMSE: 3.66 seconds\n",
|
|
"R2 Score: 0.898\n",
|
|
"Cross-validation R2: 0.925 (±0.020)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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|
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"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Training models for São Paulo Grand Prix\n",
|
|
"\n",
|
|
"Model Performance for São Paulo Grand Prix:\n",
|
|
"\n",
|
|
"Linear Regression:\n",
|
|
"RMSE: 4.66 seconds\n",
|
|
"R2 Score: 0.837\n",
|
|
"Cross-validation R2: 0.843 (±0.026)\n",
|
|
"\n",
|
|
"Random Forest:\n",
|
|
"RMSE: 1.91 seconds\n",
|
|
"R2 Score: 0.973\n",
|
|
"Cross-validation R2: 0.956 (±0.010)\n",
|
|
"\n",
|
|
"XGBoost:\n",
|
|
"RMSE: 2.01 seconds\n",
|
|
"R2 Score: 0.970\n",
|
|
"Cross-validation R2: 0.957 (±0.010)\n",
|
|
"\n",
|
|
"Gradient Boosting:\n",
|
|
"RMSE: 2.35 seconds\n",
|
|
"R2 Score: 0.959\n",
|
|
"Cross-validation R2: 0.951 (±0.011)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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D0rX5wgsvWPW1bds2Q5Ixfvx4wzAM499//zWcnJyMpk2bWtU7ffq0YTabjQ4dOqSLKTw8PN01ZPU7vVVKSoqRlJRkjB071ihUqJCRmppqOVa6dGnD0dHR+P333y1l169fNwoWLGj06dPHUna39zejLSkpyTh9+rTh4OBgDBgwwOq8y5cvG97e3kbbtm0zbTs5Odm4cuWK4eLiYrz//vvZirF06dJWr7009evXN+rXr2/Z/+GHHwxJRr169azqXb161ShYsKDx3HPPWZWnpKQYVapUMWrVqmUpc3V1NQYOHJhp/JlJ63vVqlVW7RcvXtwIDAw0UlJSLOWXL182vLy8jDp16ljK0t6HI0eOzFZ/CQkJRsuWLS2/F3t7e6NatWrGO++8Y5w7dy7T81JTU42kpCTj999/NyQZX3zxheXYk08+aXh5eRmXL1+2lCUnJxuPP/64UbJkSavXXkYkGf369TOSkpKMGzduGDExMUaTJk0MScbHH3+co1i6du2a4fsj7T7d6vbXR3Y/mzLy7rvvGpIy/OxMSkqy2m6/dg8PD+PixYuZtm0YufN+bteuXaZ/WyQZJ0+ezDKGtHt49uxZIykpyfj333+NxYsXG05OToaPj49x/fp1wzBuvr8kGZs2bUrXxu3vPcMwjH/++ccoWbKkUatWLWP//v2Gs7Oz0alTpyxjSePs7Gz07ds3W7GuXLnScHFxMdq3b59lm8nJyUZSUpLRsGFDq8/1kydPGpKMwMBAIzk52VK+e/duQ5KxbNkywzD+7/1bvXp1q9/TqVOnjHz58lm9PnP6bwNvb2/jypUrlrI1a9YYkoyqVata9TVz5kxDknHo0KEsr/VOn9m58Vm0Y8cOQ5Ixbdo0q/IzZ84YTk5OxtChQw3DMIy9e/cakow1a9ZkGfPt/6a43TvvvGPY2dlZ3SfgYcBIKQDAPfn+++/l4uKiF1980ao8bWrR7VOBGjZsqKJFi1r27e3t1a5dO/3666+WYez3qlq1apo5c6Zatmypp59+Wt27d9f27dtVrFgxDR06NEdttWnTRvnz59eSJUv01Vdf6ezZs5k+1er7779XpUqVVKtWLavybt26yTAMy/9JrlWrli5duqT27dvriy++uK9PrTKZTGratKll38HBQX5+fipWrJiqVatmKS9YsKC8vLysppCl6dixo9V+nTp1VLp0af3www+Sbv4f3uvXr6e7Lz4+PmrQoEG614AktW7dOkfX8f333+uZZ56Rh4eH7O3tlS9fPo0cOVIXLlzQuXPnrOpWrVpVpUqVsuw7OjqqQoUKGV5bTi1cuFB79uyx2hwcHPTNN98oOTlZXbp0sfo/8o6Ojqpfv77VqIQrV65o2LBh8vPzk4ODgxwcHOTq6qqrV6+mm+6YW26/39u3b9fFixfVtWtXq3hTU1PVuHFj7dmzxzJyr1atWoqMjNT48eO1c+fOexq9c/z4cf3111/q3Lmz1XRcV1dXtW7dWjt37tS1a9eyjD0zZrNZn3/+uY4dO6YZM2bopZde0vnz5zVhwgQFBATo+PHjlrrnzp1T37595ePjIwcHB+XLl0+lS5eWJMvv4OrVq9q1a5defPFFqymB9vb26ty5s/744w+rNjMza9Ys5cuXT/nz51dAQIC2b9+usWPHWkbYZCeWe5Xdz6acSFtQ+9bt9s+yBg0aqECBAhnGk5vv5x9++CHTvy054e3trXz58qlAgQLq1KmTqlevrg0bNsjR0dFSp0CBAmrQoEG22itUqJBWrFih/fv3q06dOipVqlSWo/3SXLp0SdeuXZOXl1e2Ym3btq1q1KiR4SLYc+bMUfXq1eXo6Gh5fW3atCnD11azZs1kb29v2a9cubKk/5u+mPb+7dChg9WU0dKlS6d7SEBO/20QEhIiFxcXy35AQIAkqUmTJlZ9pZVn9/M8s8/s3PgsWr9+vUwmkzp16mT1Oert7a0qVapYPvf9/PxUoEABDRs2THPmzLEaOZsTXl5eSk1NzdOnLAL3AwudAwDuyYULF+Tt7Z1uTRMvLy85ODikm+Lj7e2dro20sgsXLqhkyZL3JU5PT081b95cc+bM0fXr17O92K6Li4vatWun8PBwlS5dWs8884zlC+PtLly4kOGaL2lP+km7F507d1ZycrI+/fRTtW7dWqmpqXriiSc0fvx4NWrU6O4uMBPOzs5WX6ikm9MAChYsmK5u/vz5lZCQkK48s99Z2vWk/bdYsWLp6hUvXtxqTa60mNKegpYdu3fv1rPPPqvg4GB9+umnKlmypPLnz681a9ZowoQJ6aZpFSpUKF0bZrM5y+lc2RUQEJDhorlp0zSeeOKJDM+79UtPhw4dtGnTJo0YMUJPPPGE3N3dLcnD3IgxI7f/btLivf0L460uXrwoFxcXrVixQuPHj9e8efM0YsQIubq66oUXXtCUKVMyfG1k5U6vldTUVP37779WCwhnVDcrAQEBli+uhmFo5syZGjRokEaMGKGVK1cqNTVVzz77rP766y+NGDFCgYGBcnFxUWpqqp588knL7+Dff/+VYRiZxnrr9WSlbdu2evPNN2UymeTm5qZy5cpZvvhnN5Z7ld3PpoykJYRuTwL4+/trz549kqRPPvkkw7VuMrp39+P9nPZ36HY5fX1u3LhRHh4eypcvn0qWLJlh3zl9PdauXVuPPfaYDh48qFdeecUq8ZKZtGu7/bM7o1gvXryoTz75RJ999pkGDBhglfSaPn26Bg8erL59+2rcuHEqXLiw7O3tNWLEiAyTUrdfr9lstoon7XWS2b0+deqUZT+n/za4/W9S/vz5syzP6G9VRjL7zM6Nz6K///5bhmFYJUNv5evrK0ny8PDQ5s2bNWHCBL399tv6999/VaxYMfXu3VvDhw/P9tMS014P9+vvBJBXSEoBAO5JoUKFtGvXLhmGYfWPz3Pnzik5OdnydKA0Gf0fvrSyjL4A5Cbj/69BkZ1FgW/Vo0cPzZs3T4cOHcryCVOFChVSbGxsuvK//vpLkqzuRffu3dW9e3ddvXpVW7Zs0ahRo9S8eXP9/PPPmSa98kpmvzM/Pz9J//d7y+zab38N5PT+L1++XPny5dP69eutvqStWbMmR+3cT2nXmLZOWmbi4uK0fv16jRo1Sm+99ZalPDEx0bL+S3Y4OjqmWyxYkv75559091tKf8/T6nz44YeZPqEy7YtW4cKFNXPmTM2cOVOnT5/W2rVr9dZbb+ncuXPasGFDtmOW7vxasbOzSzeyJqevl9vPfeONNzR27FjL0zqPHDmigwcPKjIyUl27drXUvX3x5AIFCsjOzi7b7+nMFClSJNOnf2U3Finr3/md5OSz6XbBwcFycHDQ2rVr9fLLL1vK0xajlm6OGMlIRr+7+/F+LlSoUJZ/W7KrSpUqd/yd5vT1OGrUKB0+fFg1atTQyJEj1bx5c0uyIjNp75OsPhNujbVRo0YKDQ3VJ598op49e1qS44sXL1ZwcLBmz55tdW7aOkY5lRZXdu51Tv9tYGu58VlUuHBhmUwm/fjjj5YE3q1uLQsMDNTy5ctlGIYOHTqkyMhIjR07Vk5OTlZ/C7KS9nrI63sH5Dam7wEA7knDhg115cqVdF8o0p7Gc/uC4Js2bbJ6OlBKSopWrFihcuXK3bdRUtLNUQ/r169X1apVs/y/zxkJCgpSjx499MILL+iFF17ItF7Dhg117Ngx7d+/36p84cKFMplMGT6m28XFRU2aNNE777yjGzdu6OjRo5LS/x/qvHR7Im779u36/fffLU8GCgoKkpOTkxYvXmxV748//tD333+f4aLwGclsNJPJZJKDg4PVtJLr169r0aJFObwS677S2skNoaGhcnBw0IkTJ1SzZs0MN+nmtRiGke4LzLx585SSkpLtGMuUKaNDhw5Zlf3888/Zmk4mSXXr1pWnp6eOHTuWabxpIxJuVapUKfXv31+NGjVK9zrPDn9/f5UoUUJLly61Wqj46tWr+uyzzyxPwbobGX25lG5+wYyPj7eMCkr7Ynn772Du3LlW+y4uLqpdu7ZWr15t9TtITU3V4sWLVbJkSatFpe9GdmORbv7Oz507Z/X5eePGDX3zzTd37OduPpvSFCtWTD169NCXX36Z7SelZuV+vJ9DQkIy/duSl7777jtNmjRJw4cP13fffScPDw+1a9dON27cyPK8tCfVnThxIlv9mEwmffzxx7K3t9fw4cOtym9/bR06dCjd0+Wyy9/fX8WKFdOyZcus3r+///67tm/fblU3p/82sLXc+Cxq3ry5DMPQn3/+meFnaEaL2ZtMJlWpUkUzZsyQp6en1XvyTiN6f/vtNxUqVCjTkVnAfxUjpQAA96RLly76+OOP1bVrV506dUqBgYHaunWrJk6cqKZNm+qZZ56xql+4cGE1aNBAI0aMkIuLi2bNmqWffvopW192jh07ZlmL4ezZs7p27ZrlqX2VKlVSpUqVJN2cHlWqVCnVrFlThQsX1i+//KJp06bp77//VmRk5F1d5/z58+9Y54033tDChQvVrFkzjR07VqVLl9aXX36pWbNm6ZVXXrF8ge3du7ecnJxUt25dFStWTGfPntWkSZPk4eFh+T/cjz/+uKSb02Lc3Nzk6OiosmXL3vfRZBnZu3evevXqpTZt2ujMmTN65513VKJECcuaOJ6enhoxYoTefvttdenSRe3bt9eFCxc0ZswYOTo6atSoUdnqJzAwUKtXr9bs2bNVo0YN2dnZqWbNmmrWrJmmT5+uDh066OWXX9aFCxf03nvvZfh/prMrt+9vmTJlNHbsWL3zzjv67bff1LhxYxUoUEB///23du/eLRcXF40ZM0bu7u6qV6+epk6dqsKFC6tMmTLavHmz5s+fn+4pW1nF2LlzZ3Xq1En9+vVT69at9fvvv2vKlClWTwnLiqurqz788EN17dpVFy9e1IsvvigvLy+dP39eBw8e1Pnz5zV79mzFxcUpJCREHTp0UMWKFeXm5qY9e/Zow4YNGT5B607s7Ow0ZcoUdezYUc2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|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Execute the modeling pipeline\n",
|
|
"event_models, event_results = train_event_models(merged_data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 0 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Compare performance across events\n",
|
|
"comparison_df = pd.DataFrame({\n",
|
|
" event: {model: results[model]['RMSE'] \n",
|
|
" for model in event_results[event].keys()}\n",
|
|
" for event in event_results.keys()\n",
|
|
"})\n",
|
|
"\n",
|
|
"# Visualize performance comparison\n",
|
|
"plt.figure(figsize=(12, 6))\n",
|
|
"comparison_df.plot(kind='bar')\n",
|
|
"plt.title('Model Performance Comparison Across Events')\n",
|
|
"plt.xlabel('Model')\n",
|
|
"plt.ylabel('RMSE (seconds)')\n",
|
|
"plt.xticks(rotation=45)\n",
|
|
"plt.legend(title='Event', bbox_to_anchor=(1.05, 1))\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "csci349",
|
|
"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": "3.10.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|