"""Main script for GIS classification.

Configure parameters below to run classification.
"""

import os
from src import (
    GISClassifier,
    RandomForestStrategy,
    SVMStrategy,
    LogisticRegressionStrategy,
    MLEStrategy,
)


# ==================== PARAMETERS ====================

# Input files
RASTER_PATH = os.path.join("data", "landsat.tif")  # Path to GeoTIFF (Landsat)
VECTOR_PATH = os.path.join("data", "polygons.shx")  # Path to Shapefile

# Column in Shapefile containing class labels
CLASS_COLUMN = "macroclass"

# Output file for classified raster
OUTPUT_PATH = os.path.join("output", "classified.tif")

# Classification strategy parameters
# Change strategy by uncommenting desired option:

# Option 1: Random Forest (recommended for GIS)
STRATEGY = RandomForestStrategy(
    n_estimators=100,
    max_depth=None,
    random_state=42,
)

# # Option 2: Support Vector Machine
# STRATEGY = SVMStrategy(
#     kernel="linear",  # Fast prediction; use 'rbf' for better accuracy but much slower
#     C=1.0,
#     random_state=42,
# )

# Option 3: Logistic Regression
# STRATEGY = LogisticRegressionStrategy(
#     penalty="l2",
#     C=1.0,
#     max_iter=1000,
#     random_state=42,
# )

# Option 4: Maximum Likelihood Estimation (classic for GIS)
# STRATEGY = MLEStrategy(
#     reg_covar=1e-6,
# )

# Training parameters
TEST_SIZE = 0.2  # Fraction for validation
RANDOM_STATE = 42  # Random seed

# ==================== RUN CLASSIFICATION ====================


def main():
    # Check input files exist
    if not os.path.exists(RASTER_PATH):
        print(f"Error: Raster file not found: {RASTER_PATH}")
        return

    if not os.path.exists(VECTOR_PATH):
        print(f"Error: Vector file not found: {VECTOR_PATH}")
        return

    # Create output directory if needed
    os.makedirs(os.path.dirname(OUTPUT_PATH), exist_ok=True)

    # Initialize classifier with strategy
    print(f"Using classification strategy: {STRATEGY.name}")
    print(f"Strategy parameters: {STRATEGY.get_params()}")
    print()

    classifier = GISClassifier(strategy=STRATEGY)

    # Train
    print("Training classifier...")
    metrics = classifier.train(
        raster_path=RASTER_PATH,
        vector_path=VECTOR_PATH,
        class_column=CLASS_COLUMN,
        test_size=TEST_SIZE,
        random_state=RANDOM_STATE,
    )

    print(f"Training samples: {metrics['train_samples']}")
    print(f"Validation samples: {metrics['val_samples']}")
    print(f"Accuracy: {metrics['accuracy']:.2%}")
    print(f"Cohen's Kappa: {metrics['kappa']:.4f}")
    print(f"Classes: {metrics['classes']}")
    print()

    # Predict
    print(f"Classifying raster: {RASTER_PATH}")
    result = classifier.predict(
        raster_path=RASTER_PATH,
        output_path=OUTPUT_PATH,
    )

    print(f"Classification complete!")
    print(f"Output saved to: {OUTPUT_PATH}")
    print(f"Output shape: {result.predicted_array.shape}")
    print(f"Classes in output: {result.classes}")


if __name__ == "__main__":
    main()