modernize

Author: soodoku

allocator/__init__.py

@@ -32,16 +32,14 @@
 For more examples: https://geosensing.github.io/allocator/
 """
 
+import logging
+import sys
 import warnings
 
 warnings.filterwarnings("ignore", message=".*SwigPyPacked.*")
 warnings.filterwarnings("ignore", message=".*SwigPyObject.*")
 warnings.filterwarnings("ignore", message=".*swigvarlink.*")
 
-import logging
-import sys
-
-# Import modern API
 from .api import (
     ClusterResult,
     ComparisonResult,
@@ -60,8 +58,6 @@
     tsp_ortools,
     tsp_osrm,
 )
-
-# Import utilities for advanced users
 from .distances import (
     euclidean_distance_matrix,
     get_distance_matrix,
@@ -71,28 +67,24 @@
     osrm_distance_matrix,
     xy2latlog,
 )
-
-# Import visualization functions
 from .viz.plotting import plot_assignments, plot_clusters, plot_comparison, plot_route
 
-# Version
-__version__ = "1.0.0"
+__version__ = "1.2.0"
 
-# Export public API
 __all__ = [
     # Result types
     "ClusterResult",
     "ComparisonResult",
     "ItineraryResult",
     "RouteResult",
     "SortResult",
-    "assign_to_closest",
     # Main functions
+    "assign_to_closest",
     "cluster",
     "create_itineraries",
     "distance_assignment",
-    "euclidean_distance_matrix",
     # Distance utilities
+    "euclidean_distance_matrix",
     "get_distance_matrix",
     "get_logger",
     "google_distance_matrix",
@@ -101,8 +93,8 @@
     "kmeans",
     "latlon2xy",
     "osrm_distance_matrix",
-    "plot_assignments",
     # Visualization
+    "plot_assignments",
     "plot_clusters",
     "plot_comparison",
     "plot_route",
@@ -125,20 +117,16 @@ def setup_logging(level: int = logging.INFO) -> logging.Logger:
     Args:
         level: Logging level (DEBUG, INFO, WARNING, ERROR)
     """
-    # Create formatter
     formatter = logging.Formatter(
         "%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
     )
 
-    # Get root logger for allocator package
     logger = logging.getLogger("allocator")
     logger.setLevel(level)
 
-    # Remove existing handlers to avoid duplicates
     for handler in logger.handlers[:]:
         logger.removeHandler(handler)
 
-    # Console handler
     console_handler = logging.StreamHandler(sys.stdout)
     console_handler.setLevel(level)
     console_handler.setFormatter(formatter)
@@ -160,5 +148,4 @@ def get_logger(name: str) -> logging.Logger:
     return logging.getLogger(f"allocator.{name}")
 
 
-# Set up default logging
 setup_logging()

allocator/api/cluster.py

@@ -2,8 +2,6 @@
 Modern clustering API for allocator package.
 """
 
-from __future__ import annotations
-
 from pathlib import Path
 
 import numpy as np
@@ -66,15 +64,16 @@ def kmeans(
     Args:
         data: Input data as DataFrame or numpy array
         n_clusters: Number of clusters
-        distance: Distance metric ('euclidean', 'haversine', 'osrm', 'google')
+        distance: Distance metric (stored in metadata only; clustering uses Euclidean)
         max_iter: Maximum iterations
         random_state: Random seed for reproducibility
-        **kwargs: Additional distance-specific arguments
+        **kwargs: Additional arguments (unused, kept for API compatibility)
 
     Returns:
         ClusterResult with clustering information
     """
-    # Ensure we have a DataFrame for output
+    del kwargs
+
     if isinstance(data, np.ndarray):
         df = DataHandler._from_numpy(data)
     elif isinstance(data, list):
@@ -84,36 +83,21 @@ def kmeans(
     else:
         df = data.copy()
 
-    # Run clustering algorithm
     result = _kmeans_cluster(
         df,
         n_clusters=n_clusters,
-        distance_method=distance,
         max_iter=max_iter,
         random_state=random_state,
-        **kwargs,
     )
 
-    # Add cluster assignments to DataFrame
     df_result = df.copy()
     df_result["cluster"] = result["labels"]
 
-    # Calculate inertia (sum of squared distances to centroids)
-    inertia = None
-    if distance == "euclidean":
-        from ..distances import euclidean_distance_matrix
-
-        coords = df[["longitude", "latitude"]].values
-        distances = euclidean_distance_matrix(coords, result["centroids"])
-        inertia = np.sum(
-            [distances[i, result["labels"][i]] ** 2 for i in range(len(result["labels"]))]
-        )
-
     return ClusterResult(
         labels=result["labels"],
         centroids=result["centroids"],
         n_iter=result["iterations"],
-        inertia=inertia,
+        inertia=result["inertia"],
         data=df_result,
         converged=result["converged"],
         metadata={

allocator/api/distance.py

@@ -2,8 +2,6 @@
 Modern distance-based assignment API for allocator package.
 """
 
-from __future__ import annotations
-
 import numpy as np
 import pandas as pd
 

allocator/api/itinerary.py

@@ -2,8 +2,6 @@
 API for itinerary generation.
 """
 
-from __future__ import annotations
-
 from typing import Any
 
 import numpy as np

allocator/api/route.py

@@ -2,8 +2,6 @@
 Modern routing API for allocator package.
 """
 
-from __future__ import annotations
-
 import numpy as np
 import pandas as pd
 

allocator/api/types.py

@@ -2,8 +2,6 @@
 Type definitions and dataclasses for allocator API.
 """
 
-from __future__ import annotations
-
 from dataclasses import dataclass
 from typing import Any
 

allocator/cli/main.py

@@ -2,8 +2,6 @@
 Modern CLI interface for allocator package using Click.
 """
 
-from __future__ import annotations
-
 import logging
 
 import click

allocator/core/__init__.py

@@ -1,14 +1,12 @@
 """Core algorithms for clustering and optimization."""
 
 from .algorithms import (
-    CustomKMeans,
     calculate_cluster_statistics,
     kmeans_cluster,
     sort_by_distance_assignment,
 )
 
 __all__ = [
-    "CustomKMeans",
     "calculate_cluster_statistics",
     "kmeans_cluster",
     "sort_by_distance_assignment",