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feat: New competition - Optiver (#356)
* Adding the competition: Optiver Volatility Prediction * Fixing for CI * Updating a new competition @ Optiver * re-writing the optiver competition * Revise for better commit * Further fixes * Further fixes * Fixes
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...kaggle/experiment/optiver-realized-volatility-prediction_template/fea_share_preprocess.py
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import os | ||
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import numpy as np | ||
import pandas as pd | ||
from sklearn.compose import ColumnTransformer | ||
from sklearn.impute import SimpleImputer | ||
from sklearn.model_selection import train_test_split | ||
from sklearn.pipeline import Pipeline | ||
from sklearn.preprocessing import OrdinalEncoder | ||
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def prepreprocess(): | ||
# Load the training data | ||
train_df = pd.read_csv("/kaggle/input/optiver-realized-volatility-prediction/train.csv") | ||
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# Load book and trade data | ||
book_train = pd.read_parquet("/kaggle/input/optiver-realized-volatility-prediction/book_train.parquet") | ||
trade_train = pd.read_parquet("/kaggle/input/optiver-realized-volatility-prediction/trade_train.parquet") | ||
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# Merge book and trade data with train_df | ||
merged_df = pd.merge(train_df, book_train, on=["stock_id", "time_id"], how="left") | ||
merged_df = pd.merge(merged_df, trade_train, on=["stock_id", "time_id"], how="left") | ||
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# Split the data | ||
X = merged_df.drop(["target"], axis=1) | ||
y = merged_df["target"] | ||
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X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42) | ||
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return X_train, X_valid, y_train, y_valid | ||
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def preprocess_fit(X_train: pd.DataFrame): | ||
numerical_cols = [cname for cname in X_train.columns if X_train[cname].dtype in ["int64", "float64"]] | ||
categorical_cols = [cname for cname in X_train.columns if X_train[cname].dtype == "object"] | ||
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categorical_transformer = Pipeline( | ||
steps=[ | ||
("imputer", SimpleImputer(strategy="most_frequent")), | ||
("ordinal", OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)), | ||
] | ||
) | ||
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numerical_transformer = Pipeline(steps=[("imputer", SimpleImputer(strategy="mean"))]) | ||
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preprocessor = ColumnTransformer( | ||
transformers=[ | ||
("num", numerical_transformer, numerical_cols), | ||
("cat", categorical_transformer, categorical_cols), | ||
] | ||
) | ||
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preprocessor.fit(X_train) | ||
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return preprocessor, numerical_cols, categorical_cols | ||
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def preprocess_transform(X: pd.DataFrame, preprocessor, numerical_cols, categorical_cols): | ||
X_transformed = preprocessor.transform(X) | ||
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# Convert arrays back to DataFrames | ||
X_transformed = pd.DataFrame(X_transformed, columns=numerical_cols + categorical_cols, index=X.index) | ||
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return X_transformed | ||
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def preprocess_script(): | ||
if os.path.exists("/kaggle/input/X_train.pkl"): | ||
X_train = pd.read_pickle("/kaggle/input/X_train.pkl") | ||
X_valid = pd.read_pickle("/kaggle/input/X_valid.pkl") | ||
y_train = pd.read_pickle("/kaggle/input/y_train.pkl") | ||
y_valid = pd.read_pickle("/kaggle/input/y_valid.pkl") | ||
X_test = pd.read_pickle("/kaggle/input/X_test.pkl") | ||
others = pd.read_pickle("/kaggle/input/others.pkl") | ||
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return X_train, X_valid, y_train, y_valid, X_test, *others | ||
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X_train, X_valid, y_train, y_valid = prepreprocess() | ||
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preprocessor, numerical_cols, categorical_cols = preprocess_fit(X_train) | ||
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X_train = preprocess_transform(X_train, preprocessor, numerical_cols, categorical_cols) | ||
X_valid = preprocess_transform(X_valid, preprocessor, numerical_cols, categorical_cols) | ||
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submission_df = pd.read_csv("/kaggle/input/test.csv") | ||
ids = submission_df["id"] | ||
submission_df = submission_df.drop(["id"], axis=1) | ||
X_test = preprocess_transform(submission_df, preprocessor, numerical_cols, categorical_cols) | ||
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return X_train, X_valid, y_train, y_valid, X_test, ids |
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...rios/kaggle/experiment/optiver-realized-volatility-prediction_template/feature/feature.py
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import pandas as pd | ||
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""" | ||
Here is the feature engineering code for each task, with a class that has a fit and transform method. | ||
Remember | ||
""" | ||
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class IdentityFeature: | ||
def fit(self, train_df: pd.DataFrame): | ||
""" | ||
Fit the feature engineering model to the training data. | ||
""" | ||
pass | ||
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def transform(self, X: pd.DataFrame): | ||
""" | ||
Transform the input data. | ||
""" | ||
return X | ||
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feature_engineering_cls = IdentityFeature |
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...le/experiment/optiver-realized-volatility-prediction_template/model/model_randomforest.py
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import numpy as np | ||
import pandas as pd | ||
from sklearn.ensemble import RandomForestRegressor | ||
from sklearn.metrics import mean_squared_error | ||
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def select(X: pd.DataFrame) -> pd.DataFrame: | ||
""" | ||
Select relevant features. To be used in fit & predict function. | ||
""" | ||
# For now, we assume all features are relevant. This can be expanded to feature selection logic. | ||
return X | ||
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def fit(X_train: pd.DataFrame, y_train: pd.Series, X_valid: pd.DataFrame, y_valid: pd.Series): | ||
""" | ||
Define and train the Random Forest model. Merge feature selection into the pipeline. | ||
""" | ||
# Initialize the Random Forest model | ||
model = RandomForestRegressor(n_estimators=100, random_state=32, n_jobs=-1) | ||
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# Select features (if any feature selection is needed) | ||
X_train_selected = select(X_train) | ||
X_valid_selected = select(X_valid) | ||
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# Fit the model | ||
model.fit(X_train_selected, y_train) | ||
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# Validate the model | ||
y_valid_pred = model.predict(X_valid_selected) | ||
mse = mean_squared_error(y_valid, y_valid_pred) | ||
rmse = np.sqrt(mse) | ||
print(f"Validation RMSE: {rmse:.4f}") | ||
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return model | ||
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def predict(model, X): | ||
""" | ||
Keep feature selection's consistency and make predictions. | ||
""" | ||
# Select features (if any feature selection is needed) | ||
X_selected = select(X) | ||
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# Predict using the trained model | ||
y_pred = model.predict(X_selected) | ||
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return y_pred.reshape(-1, 1) |
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.../kaggle/experiment/optiver-realized-volatility-prediction_template/model/model_xgboost.py
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import pandas as pd | ||
import xgboost as xgb | ||
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def select(X: pd.DataFrame) -> pd.DataFrame: | ||
# Ignore feature selection logic | ||
return X | ||
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def fit(X_train: pd.DataFrame, y_train: pd.DataFrame, X_valid: pd.DataFrame, y_valid: pd.DataFrame): | ||
"""Define and train the model. Merge feature_select""" | ||
X_train = select(X_train) | ||
X_valid = select(X_valid) | ||
dtrain = xgb.DMatrix(X_train, label=y_train) | ||
dvalid = xgb.DMatrix(X_valid, label=y_valid) | ||
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# Parameters for regression | ||
params = { | ||
"objective": "reg:squarederror", # Use squared error for regression | ||
"nthread": -1, | ||
} | ||
num_round = 200 | ||
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evallist = [(dtrain, "train"), (dvalid, "eval")] | ||
bst = xgb.train(params, dtrain, num_round, evallist) | ||
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return bst | ||
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def predict(model, X): | ||
""" | ||
Keep feature select's consistency. | ||
""" | ||
X = select(X) | ||
dtest = xgb.DMatrix(X) | ||
y_pred = model.predict(dtest) | ||
return y_pred.reshape(-1, 1) |
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rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/train.py
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import importlib.util | ||
import random | ||
from pathlib import Path | ||
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import numpy as np | ||
import pandas as pd | ||
from fea_share_preprocess import preprocess_script | ||
from sklearn.metrics import mean_squared_error | ||
from sklearn.model_selection import TimeSeriesSplit | ||
from sklearn.preprocessing import LabelEncoder | ||
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# Set random seed for reproducibility | ||
SEED = 42 | ||
random.seed(SEED) | ||
np.random.seed(SEED) | ||
DIRNAME = Path(__file__).absolute().resolve().parent | ||
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def compute_rmse(y_true, y_pred): | ||
"""Compute RMSE for regression.""" | ||
mse = mean_squared_error(y_true, y_pred) | ||
rmse = np.sqrt(mse) | ||
return rmse | ||
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def import_module_from_path(module_name, module_path): | ||
spec = importlib.util.spec_from_file_location(module_name, module_path) | ||
module = importlib.util.module_from_spec(spec) | ||
spec.loader.exec_module(module) | ||
return module | ||
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print("begin preprocess") | ||
# 1) Preprocess the data | ||
X_train, X_valid, y_train, y_valid, X_test, ids = preprocess_script() | ||
print("preprocess done") | ||
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# 2) Auto feature engineering | ||
X_train_l, X_valid_l = [], [] | ||
X_test_l = [] | ||
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for f in DIRNAME.glob("feature/feat*.py"): | ||
cls = import_module_from_path(f.stem, f).feature_engineering_cls() | ||
cls.fit(X_train) | ||
X_train_f = cls.transform(X_train) | ||
X_valid_f = cls.transform(X_valid) | ||
X_test_f = cls.transform(X_test) | ||
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X_train_l.append(X_train_f) | ||
X_valid_l.append(X_valid_f) | ||
X_test_l.append(X_test_f) | ||
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X_train = pd.concat(X_train_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_train_l))]) | ||
X_valid = pd.concat(X_valid_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_valid_l))]) | ||
X_test = pd.concat(X_test_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_test_l))]) | ||
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print(X_train.shape, X_valid.shape, X_test.shape) | ||
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# Handle inf and -inf values | ||
X_train.replace([np.inf, -np.inf], np.nan, inplace=True) | ||
X_valid.replace([np.inf, -np.inf], np.nan, inplace=True) | ||
X_test.replace([np.inf, -np.inf], np.nan, inplace=True) | ||
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from sklearn.impute import SimpleImputer | ||
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imputer = SimpleImputer(strategy="mean") | ||
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X_train = pd.DataFrame(imputer.fit_transform(X_train), columns=X_train.columns) | ||
X_valid = pd.DataFrame(imputer.transform(X_valid), columns=X_valid.columns) | ||
X_test = pd.DataFrame(imputer.transform(X_test), columns=X_test.columns) | ||
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# Remove duplicate columns | ||
X_train = X_train.loc[:, ~X_train.columns.duplicated()] | ||
X_valid = X_valid.loc[:, ~X_valid.columns.duplicated()] | ||
X_test = X_test.loc[:, ~X_test.columns.duplicated()] | ||
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# 3) Train the model | ||
def flatten_columns(df: pd.DataFrame) -> pd.DataFrame: | ||
""" | ||
Flatten the columns of a DataFrame with MultiIndex columns, | ||
for (feature_0, a), (feature_0, b) -> feature_0_a, feature_0_b | ||
""" | ||
if df.columns.nlevels == 1: | ||
return df | ||
df.columns = ["_".join(col).strip() for col in df.columns.values] | ||
return df | ||
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X_train = flatten_columns(X_train) | ||
X_valid = flatten_columns(X_valid) | ||
X_test = flatten_columns(X_test) | ||
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model_l = [] # list[tuple[model, predict_func,]] | ||
for f in DIRNAME.glob("model/model*.py"): | ||
m = import_module_from_path(f.stem, f) | ||
model_l.append((m.fit(X_train, y_train, X_valid, y_valid), m.predict)) | ||
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# 4) Evaluate the model on the validation set | ||
y_valid_pred_l = [] | ||
for model, predict_func in model_l: | ||
y_valid_pred_l.append(predict_func(model, X_valid)) | ||
print(predict_func(model, X_valid).shape) | ||
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# 5) Ensemble | ||
y_valid_pred = np.mean(y_valid_pred_l, axis=0) | ||
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rmse = compute_rmse(y_valid, y_valid_pred) | ||
print("Final RMSE on validation set: ", rmse) | ||
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# 6) Save the validation RMSE | ||
pd.Series(data=[rmse], index=["RMSE"]).to_csv("submission_score.csv") | ||
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# 7) Make predictions on the test set and save them | ||
y_test_pred_l = [] | ||
for m, m_pred in model_l: | ||
y_test_pred_l.append(m_pred(m, X_test)) | ||
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y_test_pred = np.mean(y_test_pred_l, axis=0).ravel() | ||
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# 8) Submit predictions for the test set | ||
submission_result = pd.DataFrame({"id": ids, "price": y_test_pred}) | ||
submission_result.to_csv("submission.csv", index=False) |