feat: initial release of GIS classification project with strategy-based classifiers selector

main.py

@@ -0,0 +1,116 @@
+"""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="rbf",
+#     C=1.0,
+#     gamma="scale",
+#     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"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()