Put model development pipeline in a .py file. Rewrote README. Made a config folder to store hyperparameter space searches. Co-authored with Chris and Csenge. Several TODOs in the README.

README.md

@@ -6,7 +6,7 @@
 
 Air pollution is a significant environmental concern, especially in urban areas, where the high levels of nitrogen dioxide and ozone can have a negative impact on human health, the ecosystem and on the overall quality of life. Given these risks, monitoring and forecasting the level of air pollution is an important task in order to allow for timely actions to reduce the harmful effects. 
 
-In the Netherlands, cities like Utrecht experience challenges concerning air quality due to urbanization, transportation, and industrial activities. Developing a system that can provide accurate and robust real-time air quality monitoring and reliable forecasts for future pollution levels would allow authorities and residents to take preventive measures and adjust their future activities based on expected air quality. This project focuses on the time-series forecasting of air pollution levels, specifically NO$_2$ and O$_3$ concentrations, for the next three days. This task can be framed as a regression problem, where the goal is to predict continuous values based on historical environmental data. Moreover, it provides infrastructure for real-time prediction, based on recent measurements.
+In the Netherlands, cities like Utrecht experience challenges concerning air quality due to urbanization, transportation, and industrial activities. Developing a system that can provide accurate and robust real-time air quality monitoring and reliable forecasts for future pollution levels would allow authorities and residents to take preventive measures and adjust their future activities based on expected air quality. This project focuses on the time-series forecasting of air pollution levels, specifically NO<sub>2</sub> and O<sub>3</sub> concentrations, for the next three days. This task can be framed as a regression problem, where the goal is to predict continuous values based on historical environmental data. Moreover, it provides infrastructure for real-time prediction, based on recent measurements.
 
 ## How To Run This Code
 
@@ -21,49 +21,47 @@ The notebooks in this project were used as scratch for analysis and data merge a
 ├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
 ├── README.md          <- The top-level README for developers using this project.
 ├── data
-│   ├── external       <- Data from third party sources.
-│   ├── interim        <- Intermediate data that has been transformed.
-│   ├── processed      <- The final, canonical data sets for modeling.
+│   ├── processed      <- The final, canonical data sets for modeling. Contains the train-test split.
 │   └── raw            <- The original, immutable data dump.
 │
-├── docs               <- A default mkdocs project; see www.mkdocs.org for details
+├──.github             <- Contains automated workflows for reproducibility and flake8 checks. 
 │
-├── models             <- Trained and serialized models, model predictions, or model summaries
+├── docs               <- TODO: A default mkdocs project; see www.mkdocs.org for details
 │
-├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
-│                         the creator's initials, and a short `-` delimited description, e.g.
-│                         `1.0-jqp-initial-data-exploration`.
+├───mlruns             <- Contains all the experiments ran using mlflow.
+│
+├───mlartifacts        <- Contains the artifacts generated by mlflow experiments.
+│
+├── notebooks          <- Jupyter notebooks (not to be evaluated, source code is in air-quality-forecast)
 │
 ├── pyproject.toml     <- Project configuration file with package metadata for 
 │                         air-quality-forecast and configuration for tools like black
 │
-├── references         <- Data dictionaries, manuals, and all other explanatory materials.
+├── references         <- TODO: Data dictionaries, manuals, and all other explanatory materials.
 │
-├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
-│   └── figures        <- Generated graphics and figures to be used in reporting
+├── reports            <- TODO: Generated analysis as HTML, PDF, LaTeX, etc.
+│   └── figures        <- TODO: Generated graphics and figures to be used in reporting
 │
 ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
 │                         generated with `pip freeze > requirements.txt`
 │
 ├── setup.cfg          <- Configuration file for flake8
 │
+├── configs            <- Configuration folder for the hyperparameter search space (for now)
+│
+├── extra_scripts      <- Some extra scripts in R and .tex to generate figures
+│
 └── air-quality-forecast   <- Source code for use in this project.
     │
     ├── __init__.py             <- Makes air-quality-forecast a Python module
     │
-    ├── config.py               <- Store useful variables and configuration
-    │
-    ├── dataset.py              <- Scripts to download or generate data
+    ├── data_pipeline.py        <- Loads, extracts, and preprocesses the data. Final result is the train-test under data/processed
     │
-    ├── features.py             <- Code to create features for modeling
+    ├── model_development.py    <- Trains the three models using k-fold CV and Bayesian hyperparameter tuning
     │
-    ├── modeling                
-    │   ├── __init__.py 
-    │   ├── predict.py          <- Code to run model inference with trained models          
-    │   └── train.py            <- Code to train models
+    ├── utils.py                <- Utility functions, e.g. validation
     │
-    └── plots.py                <- Code to create visualizations
-```
+    └── main.py                 <- To execute and start the project
 
 --------
 

air-quality-forecast/model_development.py

@@ -0,0 +1,266 @@
+import mlflow
+import mlflow.sklearn
+import subprocess
+import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.metrics import mean_squared_error, root_mean_squared_error
+from sklearn.model_selection import TimeSeriesSplit
+from skopt import BayesSearchCV
+from typing import Dict, Any
+import socket
+import pandas as pd
+import warnings
+from sklearn.tree import DecisionTreeRegressor
+from xgboost import XGBRegressor
+from sklearn.ensemble import RandomForestRegressor
+import yaml
+import os
+import sys
+
+warnings.filterwarnings("ignore")
+
+RANDOM_SEED = 4242
+
+
+class RegressorTrainer:
+    def __init__(
+        self,
+        experiment_name: str,
+        regressor: BaseEstimator,
+        param_space: Dict[str, Any],
+        cv_splits: int = 5,
+        n_iter: int = 50,
+    ):
+        """
+        Initialize the RegressorTrainer.
+
+        Parameters:
+            experiment_name (str): The name of the MLflow experiment.
+            regressor (sklearn model): The regressor to optimize.
+            param_space (dict): The parameter space for Bayesian optimization.
+            cv_splits (int): Number of splits for cross-validation (default is 5).
+        """
+        self._experiment_name = experiment_name
+        self._regressor = regressor
+        self._param_space = param_space
+        self._cv_splits = cv_splits
+        self._bayes_search: BayesSearchCV | None = (
+            None  # Will hold the BayesSearchCV object
+        )
+        self._x_train: np.ndarray | None = None
+        self._y_train: np.ndarray | None = None
+        self._x_test: np.ndarray | None = None
+        self._y_test: np.ndarray | None = None
+        self._n_iter = n_iter
+
+    def _port_in_use(self, port: int) -> bool:
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            # This actually returns an int and not a bool
+            return s.connect_ex(("localhost", port)) == 0
+
+    def _launch_mlflow_server(self) -> None:
+        port = 5000
+        if not self._port_in_use(port):
+            try:
+                subprocess.Popen(
+                    ["py", "-m", "mlflow", "ui", "--port", str(port)],
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.PIPE,
+                )
+                print(f"MLflow server launched at http://127.0.0.1:{port}")
+            except Exception as e:
+                print("Error launching MLflow server:", e)
+        else:
+            print(f"MLflow server is running at http://127.0.0.1:{port}")
+
+    def _set_data(
+        self,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        x_test: np.ndarray,
+        y_test: np.ndarray,
+    ) -> None:
+        """
+        Set the training and test data as class attributes.
+
+        Parameters:
+            x_train (np.ndarray): Training data features.
+            y_train (np.ndarray): Training data labels.
+            x_test (np.ndarray): Test data features.
+            y_test (np.ndarray): Test data labels.
+        """
+        self._x_train = np.array(x_train)
+        self._y_train = np.array(y_train)
+        self._x_test = np.array(x_test)
+        self._y_test = np.array(y_test)
+
+    def _setup_mlflow(self) -> None:
+        """Set up MLflow configuration."""
+        self._launch_mlflow_server()
+        mlflow.set_experiment(self._experiment_name)
+        mlflow.set_tracking_uri("http://localhost:5000/")
+        mlflow.enable_system_metrics_logging()
+        mlflow.autolog()
+
+    def _perform_search(self) -> None:
+        """Perform Bayesian optimization for hyperparameters."""
+        if self._x_train is None or self._y_train is None:
+            raise ValueError("Training data has not been set. Call `_set_data` first.")
+
+        # Set up TimeSeriesSplit for cross-validation
+        time_series_split = TimeSeriesSplit(n_splits=self._cv_splits)
+
+        # Initialize and perform BayesSearchCV
+        self._bayes_search = BayesSearchCV(
+            estimator=self._regressor,
+            search_spaces=self._param_space,
+            n_iter=self._n_iter,  # Number of iterations for the search
+            cv=time_series_split,  # Cross-validation scheme
+            scoring="neg_mean_squared_error",  # Metric for scoring
+            n_jobs=-1,  # Use all available CPU cores
+            verbose=1,  # To display progress
+            random_state=RANDOM_SEED,  # Ensures reproducibility
+        )
+
+        self._bayes_search.fit(self._x_train, self._y_train)
+
+    def _evaluate_model(self) -> None:
+        """Evaluate the best model on the test data and log metrics."""
+        if self._x_test is None or self._y_test is None:
+            raise ValueError("Test data has not been set. Call `_set_data` first.")
+        if self._bayes_search is None:
+            raise ValueError(
+                "Bayesian search has not been performed. Call `_perform_search` first."
+            )
+
+        best_regressor = self._bayes_search.best_estimator_
+
+        # Evaluate the best model
+        test_mse = mean_squared_error(
+            self._y_test, best_regressor.predict(self._x_test)
+        )
+        test_rmse = root_mean_squared_error(
+            self._y_test, best_regressor.predict(self._x_test)
+        )
+
+        print(
+            "Best hyperparameters found by Bayesian optimization:",
+            self._bayes_search.best_params_,
+        )
+        print("Test MSE: ", test_mse)
+        print("Test RMSE: ", test_rmse)
+
+    def run(
+        self,
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        x_test: np.ndarray,
+        y_test: np.ndarray,
+    ) -> None:
+        self._set_data(x_train, y_train, x_test, y_test)
+        self._setup_mlflow()
+        self._perform_search()
+        self._evaluate_model()
+
+
+def set_path() -> None:
+    """
+    Set the path to include the parent directory of the current file.
+
+    This is needed to import modules from the parent directory.
+
+    Parameters:
+        None
+
+    Returns:
+        None
+    """
+    currentdir = os.path.dirname(os.path.realpath(__file__))
+    parentdir = os.path.dirname(currentdir)
+    sys.path.insert(0, parentdir)
+
+
+def run_bayesian_optimization(
+    x_train: np.ndarray,
+    y_train: np.ndarray,
+    x_test: np.ndarray,
+    y_test: np.ndarray,
+    experiment_name: str,
+    regressor: BaseEstimator,
+    param_space: Dict[str, Any],
+    n_iter: int,
+) -> None:
+    """
+    Run Bayesian optimization to search for the best hyperparameters for a given regressor.
+
+    Parameters:
+        x_train (np.ndarray): Training data features.
+        y_train (np.ndarray): Training data labels.
+        x_test (np.ndarray): Test data features.
+        y_test (np.ndarray): Test data labels.
+        experiment_name (str): The name of the MLflow experiment.
+        regressor (sklearn model): The regressor to optimize.
+        param_space (dict): The parameter space for Bayesian optimization.
+        n_iter (int): Number of iterations for the search.
+
+    """
+    trainer = RegressorTrainer(
+        experiment_name=experiment_name,
+        regressor=regressor,
+        param_space=param_space,
+        n_iter=n_iter,
+    )
+    trainer.run(x_train, y_train, x_test, y_test)
+
+
+def train_all_models():
+    set_path()
+
+    np.random.seed(RANDOM_SEED)
+
+    x_train, y_train = (
+        pd.read_csv("data/processed/x_train.csv", index_col=0),
+        pd.read_csv("data/processed/y_train.csv", index_col=0),
+    )
+    x_test, y_test = (
+        pd.read_csv("data/processed/x_test.csv", index_col=0),
+        pd.read_csv("data/processed/y_test.csv", index_col=0),
+    )
+
+    with open("configs/hyperparameter_search_spaces.yaml", "r") as stream:
+        param_space_config = yaml.safe_load(stream)
+
+    run_bayesian_optimization(
+        x_train,
+        y_train,
+        x_test,
+        y_test,
+        experiment_name="DecisionTree-BayesianOptimization",
+        regressor=DecisionTreeRegressor(),
+        param_space=param_space_config["decision_tree"],
+        n_iter=1,
+    )
+    run_bayesian_optimization(
+        x_train,
+        y_train,
+        x_test,
+        y_test,
+        experiment_name="XGBoost-BayesianOptimization",
+        regressor=XGBRegressor(),
+        param_space=param_space_config["xgboost"],
+        n_iter=1,
+    )
+    run_bayesian_optimization(
+        x_train,
+        y_train,
+        x_test,
+        y_test,
+        experiment_name="RandomForest-BayesianOptimization",
+        regressor=RandomForestRegressor(),
+        param_space=param_space_config["random_forest"],
+        n_iter=1,
+    )
+
+
+if __name__ == "__main__":
+    train_all_models()

configs/hyperparameter_search_spaces.yaml

@@ -0,0 +1,8 @@
+decision_tree:
+    max_depth: [20, 50]
+
+random_forest:
+    max_depth: [20, 50]
+
+xgboost:
+    max_depth: [20, 50]

data/interim/correlation_matrix.csv

@@ -1,30 +0,0 @@
-,pm25,pm10,o3,no2,so2,tempmax,tempmin,temp,feelslikemax,feelslikemin,feelslike,dew,humidity,precip,precipprob,precipcover,snow,snowdepth,windgust,windspeed,winddir,sealevelpressure,cloudcover,visibility,solarradiation,solarenergy,uvindex,severerisk,moonphase
-pm25,1,0.602,-0.239,0.397,0.044,-0.293,-0.434,-0.38,-0.293,-0.419,-0.374,-0.361,0.177,-0.224,-0.24,-0.241,0.103,0.099,-0.333,-0.243,-0.225,0.274,-0.106,-0.599,-0.223,-0.224,-0.209,0.053,0.07
-pm10,0.602,1,-0.146,0.504,0.085,-0.114,-0.245,-0.191,-0.116,-0.227,-0.184,-0.212,0.018,-0.182,-0.252,-0.229,0.052,0.036,-0.251,-0.199,-0.157,0.246,-0.175,-0.356,-0.097,-0.097,-0.074,0.113,0.02
-o3,-0.239,-0.146,1,-0.505,0.001,0.599,0.412,0.555,0.577,0.42,0.533,0.356,-0.587,-0.048,-0.166,-0.133,-0.051,-0.053,-0.006,-0.018,-0.062,0.036,-0.275,0.418,0.636,0.636,0.608,0.073,0.029
-no2,0.397,0.504,-0.505,1,0.012,-0.384,-0.453,-0.444,-0.377,-0.43,-0.424,-0.379,0.285,-0.113,-0.083,-0.076,0.038,0.039,-0.243,-0.209,-0.038,0.146,-0.088,-0.35,-0.372,-0.372,-0.358,-0.026,0.018
-so2,0.044,0.085,0.001,0.012,1,0.044,0.023,0.037,0.052,0.036,0.046,0.045,-0.003,-0.021,-0.018,-0.022,-0.016,-0.033,-0.019,-0.045,0.082,0.03,-0.008,-0.103,0.038,0.037,0.058,,0.078
-tempmax,-0.293,-0.114,0.599,-0.384,0.044,1,0.828,0.967,0.993,0.861,0.964,0.849,-0.494,-0.063,-0.203,-0.24,-0.165,-0.173,-0.139,-0.172,0,0.045,-0.242,0.353,0.726,0.726,0.711,0.182,0.026
-tempmin,-0.434,-0.245,0.412,-0.453,0.023,0.828,1,0.934,0.829,0.984,0.923,0.935,-0.233,0.117,0.077,-0.002,-0.141,-0.179,0.063,-0.002,0.147,-0.158,0.107,0.284,0.477,0.477,0.459,0.146,0.017
-temp,-0.38,-0.191,0.555,-0.444,0.037,0.967,0.934,1,0.963,0.949,0.993,0.925,-0.412,0.009,-0.09,-0.146,-0.162,-0.186,-0.052,-0.102,0.071,-0.04,-0.097,0.35,0.656,0.657,0.636,0.166,0.021
-feelslikemax,-0.293,-0.116,0.577,-0.377,0.052,0.993,0.829,0.963,1,0.867,0.97,0.857,-0.467,-0.055,-0.197,-0.235,-0.175,-0.19,-0.152,-0.19,0.007,0.049,-0.221,0.337,0.715,0.715,0.703,0.181,0.026
-feelslikemin,-0.419,-0.227,0.42,-0.43,0.036,0.861,0.984,0.949,0.867,1,0.952,0.946,-0.242,0.078,0.026,-0.057,-0.162,-0.192,-0.028,-0.098,0.144,-0.099,0.066,0.281,0.528,0.528,0.512,0.144,0.014
-feelslike,-0.374,-0.184,0.533,-0.424,0.046,0.964,0.923,0.993,0.97,0.952,1,0.927,-0.389,0,-0.104,-0.159,-0.176,-0.198,-0.097,-0.153,0.076,-0.016,-0.096,0.334,0.66,0.66,0.642,0.159,0.019
-dew,-0.361,-0.212,0.356,-0.379,0.045,0.849,0.935,0.925,0.857,0.946,0.927,1,-0.04,0.13,0.072,0.001,-0.15,-0.185,-0.063,-0.128,0.154,-0.12,0.103,0.127,0.43,0.43,0.416,0.156,0.024
-humidity,0.177,0.018,-0.587,0.285,-0.003,-0.494,-0.233,-0.412,-0.467,-0.242,-0.389,-0.04,1,0.285,0.391,0.387,0.076,0.05,-0.065,-0.081,0.155,-0.171,0.472,-0.658,-0.698,-0.698,-0.685,-0.063,0.007
-precip,-0.224,-0.182,-0.048,-0.113,-0.021,-0.063,0.117,0.009,-0.055,0.078,0,0.13,0.285,1,0.48,0.682,0.039,0.001,0.374,0.32,0.12,-0.403,0.278,-0.119,-0.219,-0.219,-0.223,-0.043,-0.021
-precipprob,-0.24,-0.252,-0.166,-0.083,-0.018,-0.203,0.077,-0.09,-0.197,0.026,-0.104,0.072,0.391,0.48,1,0.642,0.069,0.012,0.433,0.37,0.279,-0.476,0.419,-0.113,-0.356,-0.356,-0.355,-0.005,-0.02
-precipcover,-0.241,-0.229,-0.133,-0.076,-0.022,-0.24,-0.002,-0.146,-0.235,-0.057,-0.159,0.001,0.387,0.682,0.642,1,0.101,0.034,0.41,0.335,0.187,-0.466,0.378,-0.175,-0.375,-0.375,-0.387,-0.047,-0.032
-snow,0.103,0.052,-0.051,0.038,-0.016,-0.165,-0.141,-0.162,-0.175,-0.162,-0.176,-0.15,0.076,0.039,0.069,0.101,1,0.346,0.027,0.041,-0.057,-0.061,0.056,-0.096,-0.097,-0.097,-0.102,-0.011,0.034
-snowdepth,0.099,0.036,-0.053,0.039,-0.033,-0.173,-0.179,-0.186,-0.19,-0.192,-0.198,-0.185,0.05,0.001,0.012,0.034,0.346,1,-0.012,0.016,-0.062,-0.007,0.002,-0.042,-0.076,-0.076,-0.075,-0.015,0.016
-windgust,-0.333,-0.251,-0.006,-0.243,-0.019,-0.139,0.063,-0.052,-0.152,-0.028,-0.097,-0.063,-0.065,0.374,0.433,0.41,0.027,-0.012,1,0.907,0.191,-0.418,0.201,0.173,-0.174,-0.174,-0.176,-0.022,-0.021
-windspeed,-0.243,-0.199,-0.018,-0.209,-0.045,-0.172,-0.002,-0.102,-0.19,-0.098,-0.153,-0.128,-0.081,0.32,0.37,0.335,0.041,0.016,0.907,1,0.101,-0.374,0.136,0.148,-0.187,-0.187,-0.184,-0.045,-0.015
-winddir,-0.225,-0.157,-0.062,-0.038,0.082,0,0.147,0.071,0.007,0.144,0.076,0.154,0.155,0.12,0.279,0.187,-0.057,-0.062,0.191,0.101,1,-0.096,0.224,-0.003,-0.029,-0.028,-0.037,0.003,-0.047
-sealevelpressure,0.274,0.246,0.036,0.146,0.03,0.045,-0.158,-0.04,0.049,-0.099,-0.016,-0.12,-0.171,-0.403,-0.476,-0.466,-0.061,-0.007,-0.418,-0.374,-0.096,1,-0.325,-0.045,0.216,0.216,0.213,-0.032,-0.005
-cloudcover,-0.106,-0.175,-0.275,-0.088,-0.008,-0.242,0.107,-0.097,-0.221,0.066,-0.096,0.103,0.472,0.278,0.419,0.378,0.056,0.002,0.201,0.136,0.224,-0.325,1,-0.208,-0.452,-0.452,-0.438,-0.063,-0.026
-visibility,-0.599,-0.356,0.418,-0.35,-0.103,0.353,0.284,0.35,0.337,0.281,0.334,0.127,-0.658,-0.119,-0.113,-0.175,-0.096,-0.042,0.173,0.148,-0.003,-0.045,-0.208,1,0.45,0.45,0.447,0.033,-0.023
-solarradiation,-0.223,-0.097,0.636,-0.372,0.038,0.726,0.477,0.656,0.715,0.528,0.66,0.43,-0.698,-0.219,-0.356,-0.375,-0.097,-0.076,-0.174,-0.187,-0.029,0.216,-0.452,0.45,1,1,0.965,0.118,0.007
-solarenergy,-0.224,-0.097,0.636,-0.372,0.037,0.726,0.477,0.657,0.715,0.528,0.66,0.43,-0.698,-0.219,-0.356,-0.375,-0.097,-0.076,-0.174,-0.187,-0.028,0.216,-0.452,0.45,1,1,0.965,0.119,0.007
-uvindex,-0.209,-0.074,0.608,-0.358,0.058,0.711,0.459,0.636,0.703,0.512,0.642,0.416,-0.685,-0.223,-0.355,-0.387,-0.102,-0.075,-0.176,-0.184,-0.037,0.213,-0.438,0.447,0.965,0.965,1,0.115,0.004
-severerisk,0.053,0.113,0.073,-0.026,,0.182,0.146,0.166,0.181,0.144,0.159,0.156,-0.063,-0.043,-0.005,-0.047,-0.011,-0.015,-0.022,-0.045,0.003,-0.032,-0.063,0.033,0.118,0.119,0.115,1,0.023
-moonphase,0.07,0.02,0.029,0.018,0.078,0.026,0.017,0.021,0.026,0.014,0.019,0.024,0.007,-0.021,-0.02,-0.032,0.034,0.016,-0.021,-0.015,-0.047,-0.005,-0.026,-0.023,0.007,0.007,0.004,0.023,1

{notebooks/mlartifacts/803928810609079892/e684490d78f54fcf8b51f3a2b8018698 → mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e}/artifacts/estimator.html

@@ -409,7 +409,7 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor(max_depth=15)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;DecisionTreeRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html">?<span>Documentation for DecisionTreeRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeRegressor(max_depth=15)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
   </body>
 </html>
     

{notebooks/mlartifacts/475209732522917118/d6de58a8b1b9445a8da3f306598e1754 → mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e}/artifacts/model/MLmodel

@@ -14,12 +14,12 @@ flavors:
     serialization_format: cloudpickle
     sklearn_version: 1.5.2
 mlflow_version: 2.16.2
-model_size_bytes: 31005013
-model_uuid: 14b114586bf9463db4c497b4508d35ab
-run_id: d6de58a8b1b9445a8da3f306598e1754
+model_size_bytes: 34743349
+model_uuid: 2ece0aa5720e4d60a908faa9d0e3d000
+run_id: 5479a322736e4663944d983720a6648e
 signature:
   inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
   outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
     6]}}]'
   params: null
-utc_time_created: '2024-09-29 16:02:11.207015'
+utc_time_created: '2024-09-29 20:35:11.167642'

{notebooks/mlartifacts/475209732522917118/6bfd7856e3624d38aadfd33e3fc79343/artifacts/best_model → mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model}/model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:abe041262c93657c930e8324ddd788dbdba25acb647e245a71ffd18ce004cae8
-size 27144425
+oid sha256:0bf0697af096e259e722ff4149548875d48514deded04ec7fac97f0eca1d5b20
+size 34743349

{notebooks/mlartifacts/803928810609079892/bb1075c63ad14a51ba070d2a3e945f22 → mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210}/artifacts/estimator.html

@@ -409,7 +409,7 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
   /* fitted */
   background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor(max_depth=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;DecisionTreeRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html">?<span>Documentation for DecisionTreeRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeRegressor(max_depth=20)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
   </body>
 </html>
     

{notebooks/mlartifacts/475209732522917118/e8a145a55c094cdc9e55c7b9d5a89bf5 → mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210}/artifacts/model/MLmodel

@@ -14,12 +14,12 @@ flavors:
     serialization_format: cloudpickle
     sklearn_version: 1.5.2
 mlflow_version: 2.16.2
-model_size_bytes: 27144373
-model_uuid: 56947e681896418498166a990e508991
-run_id: e8a145a55c094cdc9e55c7b9d5a89bf5
+model_size_bytes: 34762837
+model_uuid: d6a4b42658cb4824b3125c7ec63d1ed4
+run_id: d256fba7a7fe43c39749afef37154210
 signature:
   inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
   outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
     6]}}]'
   params: null
-utc_time_created: '2024-09-29 14:04:08.170828'
+utc_time_created: '2024-09-29 20:41:11.947759'

{notebooks/mlartifacts/475209732522917118/d6de58a8b1b9445a8da3f306598e1754 → mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210}/artifacts/model/model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a1092fd6d474fafb4da32f866f250d60516be40b9d0ea045b3b46670cf1b6a27
-size 31005013
+oid sha256:72ba8a18da708a814986b0edbe6b5a4a7e931fea667acabeb4040aee0c038e02
+size 34762837

mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/feature_importance_weight.json

@@ -0,0 +1 @@
+{"f0": 30335.0, "f1": 12283.0, "f2": 8903.0, "f3": 6526.0, "f4": 8117.0, "f5": 7541.0, "f6": 6146.0, "f7": 6050.0, "f8": 3123.0, "f9": 3844.0, "f10": 6510.0, "f11": 4622.0, "f12": 3717.0, "f13": 3799.0, "f14": 2906.0, "f15": 3587.0, "f16": 3904.0, "f17": 4225.0, "f18": 3999.0, "f19": 2503.0, "f20": 2764.0, "f21": 4179.0, "f22": 4006.0, "f23": 3549.0, "f24": 3621.0, "f25": 2669.0, "f26": 2954.0, "f27": 3504.0, "f28": 3645.0, "f29": 3696.0, "f30": 2283.0, "f31": 2338.0, "f32": 3725.0}

{notebooks/mlartifacts/588532547813609546/29a7ce3e5aff4004b017460bf6d2274b → mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf}/artifacts/model/MLmodel

@@ -14,12 +14,12 @@ flavors:
     model_format: xgb
     xgb_version: 2.1.1
 mlflow_version: 2.16.2
-model_size_bytes: 11660654
-model_uuid: 3ba468a8c7f44dc18fe6561fc452fc93
-run_id: 29a7ce3e5aff4004b017460bf6d2274b
+model_size_bytes: 12354728
+model_uuid: ae7825e16b794647905010b3fabdf5ee
+run_id: 2ad059c5d4704ed088a288d572818bcf
 signature:
   inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
   outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float32", "shape": [-1,
     6]}}]'
   params: null
-utc_time_created: '2024-09-29 16:10:49.744301'
+utc_time_created: '2024-09-29 20:34:46.696536'

{notebooks/mlartifacts/588532547813609546/4e8ce91d81c549cf80846c249e959c20 → mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf}/artifacts/model/model.xgb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e66c019d2c5ad70910dca6fe39fbed43695be2d1b5871084c3a8aef52725c245
-size 11660654
+oid sha256:c823873e6e051da216bde5618c3db8390d1cdc8e1edd143c59272d69548fc05f
+size 12354728

mlartifacts/674375719018272828/613e592c317949abb0b86f18100a354c/artifacts/feature_importance_weight.json

@@ -0,0 +1 @@
+{"f0": 30335.0, "f1": 12283.0, "f2": 8903.0, "f3": 6526.0, "f4": 8117.0, "f5": 7541.0, "f6": 6146.0, "f7": 6050.0, "f8": 3123.0, "f9": 3844.0, "f10": 6510.0, "f11": 4622.0, "f12": 3717.0, "f13": 3799.0, "f14": 2906.0, "f15": 3587.0, "f16": 3904.0, "f17": 4225.0, "f18": 3999.0, "f19": 2503.0, "f20": 2764.0, "f21": 4179.0, "f22": 4006.0, "f23": 3549.0, "f24": 3621.0, "f25": 2669.0, "f26": 2954.0, "f27": 3504.0, "f28": 3645.0, "f29": 3696.0, "f30": 2283.0, "f31": 2338.0, "f32": 3725.0}

{notebooks/mlartifacts/588532547813609546/4e8ce91d81c549cf80846c249e959c20 → mlartifacts/674375719018272828/613e592c317949abb0b86f18100a354c}/artifacts/model/MLmodel

@@ -14,12 +14,12 @@ flavors:
     model_format: xgb
     xgb_version: 2.1.1
 mlflow_version: 2.16.2
-model_size_bytes: 11660654
-model_uuid: 283a41e11ba04ccab1f3995d5a820c21
-run_id: 4e8ce91d81c549cf80846c249e959c20
+model_size_bytes: 12354728
+model_uuid: fa950ae2169a47128401ab637dab6f5c
+run_id: 613e592c317949abb0b86f18100a354c
 signature:
   inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
   outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float32", "shape": [-1,
     6]}}]'
   params: null
-utc_time_created: '2024-09-29 16:13:38.960625'
+utc_time_created: '2024-09-29 20:40:34.164593'

{notebooks/mlartifacts/588532547813609546/29a7ce3e5aff4004b017460bf6d2274b → mlartifacts/674375719018272828/613e592c317949abb0b86f18100a354c}/artifacts/model/model.xgb

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e66c019d2c5ad70910dca6fe39fbed43695be2d1b5871084c3a8aef52725c245
-size 11660654
+oid sha256:c823873e6e051da216bde5618c3db8390d1cdc8e1edd143c59272d69548fc05f
+size 12354728

mlruns/0/meta.yaml

@@ -0,0 +1,6 @@
+artifact_location: file:///C:/Users/User/ML4I/air-quality-forecast/mlruns/0
+creation_time: 1727640956735
+experiment_id: '0'
+last_update_time: 1727640956735
+lifecycle_stage: active
+name: Default

{notebooks/mlruns/588532547813609546/29a7ce3e5aff4004b017460bf6d2274b → mlruns/149819317988706962/5479a322736e4663944d983720a6648e}/meta.yaml

@@ -1,15 +1,15 @@
-artifact_uri: mlflow-artifacts:/588532547813609546/29a7ce3e5aff4004b017460bf6d2274b/artifacts
-end_time: 1727626258834
+artifact_uri: mlflow-artifacts:/149819317988706962/5479a322736e4663944d983720a6648e/artifacts
+end_time: 1727642116447
 entry_point_name: ''
-experiment_id: '588532547813609546'
+experiment_id: '149819317988706962'
 lifecycle_stage: active
-run_id: 29a7ce3e5aff4004b017460bf6d2274b
-run_name: whimsical-skunk-46
-run_uuid: 29a7ce3e5aff4004b017460bf6d2274b
+run_id: 5479a322736e4663944d983720a6648e
+run_name: spiffy-penguin-165
+run_uuid: 5479a322736e4663944d983720a6648e
 source_name: ''
 source_type: 4
 source_version: ''
-start_time: 1727626186793
+start_time: 1727642102185
 status: 3
 tags: []
 user_id: User

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/cpu_utilization_percentage

@@ -0,0 +1 @@
+1727642112343 47.2 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/disk_available_megabytes

@@ -0,0 +1 @@
+1727642112343 90980.2 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/disk_usage_megabytes

@@ -0,0 +1 @@
+1727642112343 420156.9 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/disk_usage_percentage

@@ -0,0 +1 @@
+1727642112343 82.2 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/gpu_0_memory_usage_megabytes

@@ -0,0 +1 @@
+1727642112343 308.2 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/gpu_0_memory_usage_percentage

@@ -0,0 +1 @@
+1727642112343 14.4 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/gpu_0_utilization_percentage

@@ -0,0 +1 @@
+1727642112343 0.0 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/network_receive_megabytes

@@ -0,0 +1 @@
+1727642112343 0.0 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/network_transmit_megabytes

@@ -0,0 +1 @@
+1727642112343 0.0 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/system_memory_usage_megabytes

@@ -0,0 +1 @@
+1727642112343 11362.1 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/system/system_memory_usage_percentage

@@ -0,0 +1 @@
+1727642112343 66.9 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/training_mean_absolute_error

@@ -0,0 +1 @@
+1727642111000 1.4734425695577313 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/training_mean_squared_error

@@ -0,0 +1 @@
+1727642111000 4.507790763032156 0

mlruns/149819317988706962/5479a322736e4663944d983720a6648e/metrics/training_r2_score

@@ -0,0 +1 @@
+1727642111000 0.9340423340912295 0