import pandas as pd

import numpy as np

from lightgbm import LGBMRegressor

from sklearn.model_selection import train_test_split, RandomizedSearchCV

from sklearn.metrics import mean_squared_log_error

# Load the datasets

train = pd.read_csv("./input/train.csv")

test = pd.read_csv("./input/test.csv")

# Define the target variables explicitly

targets = ["target_carbon_monoxide", "target_benzene", "target_nitrogen_oxides"]

# Feature engineering: Convert date_time to datetime and extract useful features

train["date_time"] = pd.to_datetime(train["date_time"])

test["date_time"] = pd.to_datetime(test["date_time"])

# Extracting datetime features

for df in [train, test]:

df["hour"] = df["date_time"].dt.hour

df["day_of_week"] = df["date_time"].dt.dayofweek

df["day_of_month"] = df["date_time"].dt.day

df["month"] = df["date_time"].dt.month

# Creating interaction terms between sensor readings and weather features

for sensor in ["sensor_1", "sensor_2", "sensor_3", "sensor_4", "sensor_5"]:

for weather in ["deg_C", "relative_humidity", "absolute_humidity"]:

train[f"{sensor}_{weather}_interaction"] = train[sensor] * train[weather]

test[f"{sensor}_{weather}_interaction"] = test[sensor] * test[weather]

# Update features list to include the new interaction terms

features = train.columns.drop(["date_time"] targets).tolist()

# Splitting the data into training and validation sets

X_train, X_val, y_train, y_val = train_test_split(

train[features], train[targets], test_size=0.2, random_state=42

)

# Hyperparameter tuning setup

param_grid = {

}

# Retrain models using only the selected features and hyperparameter tuning

rmsle_scores = []

for target in targets:

model = LGBMRegressor()

random_search = RandomizedSearchCV(

model,

param_grid,

n_iter=10,

scoring="neg_mean_squared_log_error",

cv=3,

random_state=42,

)

random_search.fit(X_train, y_train[target])

best_model = random_search.best_estimator_

predictions = best_model.predict(X_val)

rmsle_score = np.sqrt(mean_squared_log_error(y_val[target], predictions))

rmsle_scores.append(rmsle_score)

# Calculate and print the mean RMSLE score

mean_rmsle = np.mean(rmsle_scores)

print(f"Mean RMSLE after hyperparameter tuning: {mean_rmsle}")

# Prepare submission using hyperparameter tuned models

test_predictions = pd.DataFrame({"date_time": test["date_time"]})

for target in targets:

model = LGBMRegressor()

random_search = RandomizedSearchCV(

model,

param_grid,

n_iter=10,

scoring="neg_mean_squared_log_error",

cv=3,

random_state=42,

)

random_search.fit(train[features], train[target])

best_model = random_search.best_estimator_

test_predictions[target] = best_model.predict(test[features])

# Renaming columns as required for submission

test_predictions.rename(

columns={

"target_carbon_monoxide": "target_carbon_monoxide",

"target_benzene": "target_benzene",

"target_nitrogen_oxides": "target_nitrogen_oxides",

},

inplace=True,

)

# Saving the submission file

test_predictions.to_csv("./working/submission.csv", index=False)