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| """Data loading and preprocessing for Kepler KOI exoplanet detection.""" | |
| from pathlib import Path | |
| import numpy as np | |
| import pandas as pd | |
| from imblearn.over_sampling import SMOTE | |
| from sklearn.model_selection import train_test_split | |
| RANDOM_STATE = 42 | |
| # Identifier columns to drop (non-numeric, not useful for classification) | |
| ID_COLUMNS = [ | |
| "rowid", | |
| "kepid", | |
| "kepoi_name", | |
| "kepler_name", | |
| "koi_tce_plnt_num", | |
| "koi_tce_delivname", | |
| "ra", | |
| "dec", | |
| ] | |
| def load_and_preprocess(data_path: Path) -> tuple[pd.DataFrame, pd.Series, pd.DataFrame, pd.Series]: | |
| """ | |
| Load cumulative.csv, filter CONFIRMED/FALSE POSITIVE, preprocess, split. | |
| Returns (X_train, y_train, X_test, y_test). | |
| """ | |
| df = pd.read_csv(data_path) | |
| # Filter target classes | |
| valid = df["koi_disposition"].isin(["CONFIRMED", "FALSE POSITIVE"]) | |
| df = df[valid].copy() | |
| # Encode labels: CONFIRMED=1, FALSE POSITIVE=0 | |
| y = (df["koi_disposition"] == "CONFIRMED").astype(int) | |
| df = df.drop(columns=["koi_disposition"]) | |
| # Drop identifier columns (keep only if present) | |
| to_drop = [c for c in ID_COLUMNS if c in df.columns] | |
| df = df.drop(columns=to_drop, errors="ignore") | |
| # Drop non-numeric columns | |
| numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() | |
| df = df[numeric_cols].copy() | |
| # Handle missing values: median imputation, then 0 for any remaining | |
| df = df.fillna(df.median()) | |
| df = df.fillna(0) | |
| df = df.replace([np.inf, -np.inf], 0) | |
| # Stratified 80/20 split | |
| X_train, X_test, y_train, y_test = train_test_split( | |
| df, y, test_size=0.2, stratify=y, random_state=RANDOM_STATE | |
| ) | |
| # SMOTE on training data only | |
| smote = SMOTE(random_state=RANDOM_STATE, k_neighbors=5) | |
| X_train, y_train = smote.fit_resample(X_train, y_train) | |
| return X_train, y_train, X_test, y_test | |