import torch
from xrfm import xRFM
from sklearn.model_selection import train_test_split

# Create synthetic data
def target_function(X):
    return torch.cat([
        (X[:, 0] > 0)[:, None], 
        (X[:, 1] < 0.5)[:, None]
    ], dim=1).float()

# Setup device and model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = xRFM(device=device, tuning_metric='mse')

# Generate data
n_samples = 2000
n_features = 100
X = torch.randn(n_samples, n_features, device=device)
y = target_function(X)
X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.2, random_state=0)

model.fit(X_train, y_train, X_val, y_val)
y_pred_test = model.predict(X_test)

# Custom RFM parameters
rfm_params = {
    'model': {
        'kernel': 'l2',           # Kernel type
        'bandwidth': 5.0,         # Kernel bandwidth
        'exponent': 1.0,          # Kernel exponent
        'diag': False,            # Diagonal Mahalanobis matrix
        'bandwidth_mode': 'constant'
    },
    'fit': {
        'reg': 1e-3,              # Regularization parameter
        'iters': 5,               # Number of iterations
        'M_batch_size': 1000,     # Batch size for AGOP
        'verbose': True,          # Verbose output
        'early_stop_rfm': True    # Early stopping
    }
}

# Initialize model with custom parameters
model = xRFM(
    rfm_params=rfm_params,
    device=device,
    min_subset_size=10000,        # Minimum subset size for splitting
    tuning_metric='accuracy',     # Tuning metric
    split_method='top_vector_agop_on_subset'  # Splitting strategy
)

import numpy as np
import torch
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split

# Assume a pandas DataFrame `df` with:
# - numerical feature columns in `num_cols`
# - categorical feature columns in `cat_cols`
# - target column name in `target_col`

# Split
train_df, test_df = train_test_split(df, test_size=0.2, random_state=0)
train_df, val_df = train_test_split(train_df, test_size=0.2, random_state=0)

# Fit preprocessors on train only
scaler = StandardScaler()
ohe = OneHotEncoder(sparse=False, handle_unknown='ignore')

X_num_train = scaler.fit_transform(train_df[num_cols])
X_num_val = scaler.transform(val_df[num_cols])
X_num_test = scaler.transform(test_df[num_cols])

X_cat_train = ohe.fit_transform(train_df[cat_cols])
X_cat_val = ohe.transform(val_df[cat_cols])
X_cat_test = ohe.transform(test_df[cat_cols])

# Concatenate: numerical block first, then categorical block
X_train = np.hstack([X_num_train, X_cat_train]).astype(np.float32)
X_val = np.hstack([X_num_val, X_cat_val]).astype(np.float32)
X_test = np.hstack([X_num_test, X_cat_test]).astype(np.float32)

y_train = train_df[target_col].to_numpy().astype(np.float32)
y_val = val_df[target_col].to_numpy().astype(np.float32)
y_test = test_df[target_col].to_numpy().astype(np.float32)

# Build categorical_info (indices are relative to the concatenated X)
n_num = X_num_train.shape[1]
categorical_indices = []
categorical_vectors = []
start = n_num
for cats in ohe.categories_:
    cat_len = len(cats)
    idxs = torch.arange(start, start + cat_len, dtype=torch.long)
    categorical_indices.append(idxs)
    categorical_vectors.append(torch.eye(cat_len, dtype=torch.float32))  # identity; do not standardize
    start += cat_len

numerical_indices = torch.arange(0, n_num, dtype=torch.long)

categorical_info = dict(
    numerical_indices=numerical_indices,
    categorical_indices=categorical_indices,
    categorical_vectors=categorical_vectors,
)

# Train xRFM with categorical_info
from xrfm import xRFM
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

rfm_params = {
    'model': {
        'kernel': 'l2',
        'bandwidth': 10.0,
        'exponent': 1.0,
        'diag': False,
        'bandwidth_mode': 'constant',
    },
    'fit': {
        'reg': 1e-3,
        'iters': 3,
        'verbose': False,
        'early_stop_rfm': True,
    }
}

model = xRFM(
    rfm_params=rfm_params,
    device=device,
    tuning_metric='mse',
    categorical_info=categorical_info,
)

model.fit(X_train, y_train, X_val, y_val)
y_pred = model.predict(X_test)