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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from tqdm import tqdm\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.linear_model import LinearRegression\n",
"import numpy as np\n",
"import pickle\n",
"import joblib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the `prediction_tests.ipynb` file, I found that I want to use the following features:\n",
" - Temperature\n",
" - Altimeter_Pressure\n",
" - Visibility\n",
" - Wind_Speed\n",
" - Precipitation\n",
"\n",
"The dataset is heavily imbalanced, as most flights are on time. I tried altering the weights of the classes in random forest and `SMOTE`. I found that altering the weights produced a higher recall for the minority class. This is my most important metric, so I will use this. \n",
"\n",
"We will create three models for each airport:\n",
" - Flight Delay Boolean Classification\n",
" - Flight Cancellation Boolean Classification\n",
" - Flight Delay Regression\n",
"\n",
"The first two models will be random forests, and the third will be a linear regression.\n",
"\n",
"The models will then be saved to `joblib`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"origins_folder = os.path.join(\"..\", \"data\", \"origins\")\n",
"\n",
"models = [\n",
" \"Flight Cancellation Boolean Classification\",\n",
" \"Flight Delay Boolean Classification\",\n",
" \"Flight Delay Regression\",]\n",
"\n",
"seed_value = 0\n",
"os.environ['PYTHONHASHSEED'] = str(seed_value)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def weather_cancellation_classification(data_frame_copy, origin_name):\n",
" # Split X and y\n",
" X = data_frame_copy.drop(columns=['WeatherCancellation', 'WeatherOrNasDelay', 'WeatherDelay', 'NASDelay'])\n",
" y = data_frame_copy['WeatherCancellation']\n",
"\n",
" # Split data into training and testing sets\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"\n",
" # Compute class weights\n",
" class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
"\n",
" # Train model\n",
" model = RandomForestClassifier(class_weight={0: class_weights[0], 1: class_weights[1]})\n",
" model.fit(X_train, y_train)\n",
"\n",
" model_path = os.path.join(os.path.splitext(origin_name)[0], \"weather_cancellation_classification.pkl\")\n",
"\n",
" joblib.dump(model, model_path)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def weather_delay_classification(data_frame_copy, origin_name):\n",
" # Split X and y\n",
" X = data_frame_copy.drop(columns=['WeatherCancellation', 'WeatherOrNasDelay', 'WeatherDelay', 'NASDelay'])\n",
" y = data_frame_copy['WeatherOrNasDelay']\n",
"\n",
" # Split data into training and testing sets\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"\n",
" # Compute class weights\n",
" class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)\n",
"\n",
" # Train model\n",
" model = RandomForestClassifier(class_weight={0: class_weights[0], 1: class_weights[1]})\n",
" model.fit(X_train, y_train)\n",
"\n",
" model_path = os.path.join(os.path.splitext(origin_name)[0], \"weather_delay_classification.pkl\")\n",
"\n",
" joblib.dump(model, model_path)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def weather_delay_regression(data_frame_copy, origin_name):\n",
" # Split X and y\n",
" X = data_frame_copy.drop(columns=['WeatherAndNasDelay', 'WeatherCancellation', 'WeatherOrNasDelay', 'WeatherDelay', 'NASDelay'])\n",
" y = data_frame_copy['WeatherAndNasDelay']\n",
"\n",
" # Split the data into training and testing sets\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
" # Train model\n",
" model = LinearRegression()\n",
" model.fit(X_train, y_train)\n",
"\n",
" model_path = os.path.join(os.path.splitext(origin_name)[0], \"weather_delay_regression.pkl\")\n",
"\n",
" joblib.dump(model, model_path)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_data_frame(origin):\n",
" data_frame = pd.read_csv(os.path.join(origins_folder, origin))\n",
"\n",
" data_frame['WeatherCancellation'] = data_frame['CancellationReason'] == 'Weather'\n",
" data_frame['WeatherOrNasDelay'] = (data_frame['WeatherDelay'] > 0) | (data_frame['NASDelay'] > 0)\n",
"\n",
" data_frame.drop(columns=['Time','Origin','Dest','Carrier','Cancelled','CancellationReason','CarrierDelay', 'Sea_Level_Pressure','SecurityDelay',\n",
" 'LateAircraftDelay', 'Feels_Like_Temperature', 'Wind_Gust', 'Delayed', 'Ice_Accretion_3hr', 'DepDelayMinutes'], inplace=True)\n",
" \n",
" return data_frame"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WeatherDelay float64\n",
"NASDelay float64\n",
"Temperature float64\n",
"Altimeter_Pressure float64\n",
"Visibility float64\n",
"Wind_Speed float64\n",
"Precipitation float64\n",
"WeatherCancellation bool\n",
"WeatherOrNasDelay bool\n",
"dtype: object\n"
]
}
],
"source": [
"print(get_data_frame(\"ATL.csv\").dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Origin: JFK Flight Delay Regression: 50%|█████ | 45/90 [00:16<00:13, 3.38it/s]C:\\Users\\wipar\\AppData\\Local\\Temp\\ipykernel_3440\\2046165627.py:2: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data_frame = pd.read_csv(os.path.join(origins_folder, origin))\n",
"Origin: TPA Flight Delay Regression: 100%|██████████| 90/90 [00:30<00:00, 2.93it/s]\n"
]
}
],
"source": [
"total_iterations = len(os.listdir(origins_folder) * 3)\n",
"progress_bar = tqdm(total=total_iterations, position=0)\n",
"\n",
"for origin in os.listdir(origins_folder):\n",
"\n",
" if not os.path.exists(os.path.splitext(origin)[0]):\n",
" os.makedirs(os.path.splitext(origin)[0])\n",
" \n",
" # progress_bar.set_description(f\"Origin: {os.path.splitext(origin)[0]} Loading Data...\")\n",
" # data_frame = pd.read_csv(os.path.join(origins_folder, origin))\n",
" # progress_bar.update(1)\n",
"\n",
" # data_frame['WeatherCancellation'] = data_frame['CancellationReason'] == 'Weather'\n",
" # data_frame['WeatherOrNasDelay'] = (data_frame['WeatherDelay'] > 0) | (data_frame['NASDelay'] > 0)\n",
"\n",
" # data_frame.drop(columns=['Time','Origin','Dest','Carrier','Cancelled','CancellationReason','CarrierDelay', 'Sea_Level_Pressure','SecurityDelay',\n",
" # 'LateAircraftDelay', 'Feels_Like_Temperature', 'Wind_Gust', 'Delayed', 'Ice_Accretion_3hr', 'DepDelayMinutes', 'WeatherDelay', 'NASDelay'], inplace=True)\n",
"\n",
"\n",
" if not os.path.exists(os.path.join(os.path.splitext(origin)[0], \"weather_cancellation_classification.pkl\")):\n",
" progress_bar.set_description(f\"Origin: {os.path.splitext(origin)[0]} Flight Cancellation Boolean Classification\")\n",
" try:\n",
" weather_cancellation_classification(get_data_frame(origin), origin)\n",
" except:\n",
" print(f\"Error in {os.path.splitext(origin)[0]} weather_cancellation_classification\")\n",
" progress_bar.update(1)\n",
"\n",
"\n",
" if not os.path.exists(os.path.join(os.path.splitext(origin)[0], \"weather_delay_classification.pkl\")):\n",
" progress_bar.set_description(f\"Origin: {os.path.splitext(origin)[0]} Flight Delay Boolean Classification\")\n",
" try:\n",
" weather_delay_classification(get_data_frame(origin), origin)\n",
" except:\n",
" print(f\"Error in {os.path.splitext(origin)[0]} weather_delay_classification\")\n",
" progress_bar.update(1)\n",
"\n",
" # if not os.path.exists(os.path.join(os.path.splitext(origin)[0], \"weather_delay_regression.pkl\")):\n",
" try:\n",
" data_frame = get_data_frame(origin)\n",
"\n",
" data_frame['WeatherDelay'] = data_frame['WeatherDelay'].fillna(0)\n",
" data_frame['NASDelay'] = data_frame['NASDelay'].fillna(0)\n",
" data_frame = data_frame[(data_frame['WeatherDelay'] != 0) & (data_frame['NASDelay'] != 0)]\n",
"\n",
" data_frame['WeatherAndNasDelay'] = data_frame['WeatherDelay'] + data_frame['NASDelay'] \n",
"\n",
" progress_bar.set_description(f\"Origin: {os.path.splitext(origin)[0]} Flight Delay Regression\")\n",
" weather_delay_regression(data_frame, origin)\n",
" except:\n",
" print(f\"Error in {os.path.splitext(origin)[0]} weather_delay_regression\")\n",
" progress_bar.update(1)\n",
"\n",
"\n",
"progress_bar.close()"
]
}
],
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"file_extension": ".py",
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