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import pandas as pd
from catboost import CatBoostRegressor, Pool
from sklearn.model_selection import KFold
import numpy as np
from sklearn.preprocessing import LabelEncoder
def smape(y_true, y_pred):
denominator = (np.abs(y_true) + np.abs(y_pred)) / 2.0
diff = np.abs(y_true - y_pred) / denominator
diff[denominator == 0] = 0.0
return 100 * np.mean(diff)
# Load data
train = pd.read_csv("./input/train.csv")
test = pd.read_csv("./input/test.csv")
# Convert date to datetime and extract features
for df in [train, test]:
df["date"] = pd.to_datetime(df["date"])
df["year"] = df["date"].dt.year
df["month"] = df["date"].dt.month
df["day"] = df["date"].dt.day
df["day_of_week"] = df["date"].dt.dayofweek # Extract day of the week
# Create interaction terms
df["country_store"] = df["country"] + "_" + df["store"]
df["country_product"] = df["country"] + "_" + df["product"]
df["store_product"] = df["store"] + "_" + df["product"]
# Encode new categorical features
label_encoders = {}
for col in ["country_store", "country_product", "store_product"]:
le = LabelEncoder()
train[col] = le.fit_transform(train[col])
test[col] = le.transform(test[col])
label_encoders[col] = le
# Define categorical features including new interaction terms
cat_features = [
"country",
"store",
"product",
"country_store",
"country_product",
"store_product",
]
# Prepare data for training
X = train.drop(["num_sold", "date", "id"], axis=1)
y = train["num_sold"]
# Cross-validation
kf = KFold(n_splits=10, shuffle=True, random_state=42)
smape_scores = []
for train_index, test_index in kf.split(X):
X_train, X_valid = X.iloc[train_index], X.iloc[test_index]
y_train, y_valid = y.iloc[train_index], y.iloc[test_index]
model = CatBoostRegressor(
iterations=2000, # Increase iterations
learning_rate=0.05, # Decrease learning rate
depth=8, # Increase depth
loss_function="MAE",
cat_features=cat_features,
verbose=200,
)
model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)
preds = model.predict(X_valid)
score = smape(y_valid, preds)
smape_scores.append(score)
print(f"Average SMAPE: {np.mean(smape_scores)}")
# Prepare test data and make predictions
test_data = test.drop(["date", "id"], axis=1)
predictions = model.predict(test_data)
# Save submission
submission = pd.DataFrame({"id": test["id"], "num_sold": predictions})
submission.to_csv("./working/submission.csv", index=False)
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