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import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import DBSCAN
import pandas as pd
from typing import List, Dict, Any, Tuple
import streamlit as st
class NearestNeighborGrouping:
def __init__(self):
self.scaler = StandardScaler()
self.feature_weights = {
'depth_mid': 0.05, # Depth position (less important for similarity)
'thickness': 0.05, # Layer thickness (less important)
'soil_type_encoded': 0.35, # Soil type (most important)
'consistency_encoded': 0.30, # Consistency/density (very important)
'strength_value': 0.15, # Strength parameter
'moisture_encoded': 0.05, # Moisture content
'color_encoded': 0.05 # Color
}
def encode_categorical_features(self, layers: List[Dict]) -> pd.DataFrame:
"""Convert categorical features to numerical for clustering"""
# Create DataFrame from layers
df_data = []
for i, layer in enumerate(layers):
layer_data = {
'layer_index': i,
'layer_id': layer.get('layer_id', i+1),
'depth_from': layer.get('depth_from', 0),
'depth_to': layer.get('depth_to', 0),
'depth_mid': (layer.get('depth_from', 0) + layer.get('depth_to', 0)) / 2,
'thickness': layer.get('depth_to', 0) - layer.get('depth_from', 0),
'soil_type': layer.get('soil_type', 'unknown').lower(),
'consistency': layer.get('consistency', 'unknown').lower(),
'strength_value': layer.get('strength_value', 0) or layer.get('calculated_su', 0) or 0,
'moisture': layer.get('moisture', 'unknown').lower(),
'color': layer.get('color', 'unknown').lower(),
'description': layer.get('description', '')
}
df_data.append(layer_data)
df = pd.DataFrame(df_data)
# Encode soil types
soil_type_mapping = {
'clay': 1, 'silt': 2, 'sand': 3, 'gravel': 4, 'rock': 5, 'unknown': 0
}
df['soil_type_encoded'] = df['soil_type'].map(soil_type_mapping).fillna(0)
# Encode consistency/density
consistency_mapping = {
'very soft': 1, 'soft': 2, 'medium': 3, 'stiff': 4, 'very stiff': 5, 'hard': 6,
'very loose': 1, 'loose': 2, 'medium dense': 3, 'dense': 4, 'very dense': 5,
'unknown': 0
}
df['consistency_encoded'] = df['consistency'].map(consistency_mapping).fillna(0)
# Encode moisture
moisture_mapping = {
'dry': 1, 'moist': 2, 'wet': 3, 'saturated': 4, 'unknown': 0
}
df['moisture_encoded'] = df['moisture'].map(moisture_mapping).fillna(0)
# Encode colors (simplified)
color_mapping = {
'brown': 1, 'gray': 2, 'black': 3, 'red': 4, 'yellow': 5, 'white': 6, 'unknown': 0
}
df['color_encoded'] = df['color'].map(color_mapping).fillna(0)
return df
def calculate_layer_similarity(self, df: pd.DataFrame) -> np.ndarray:
"""Calculate similarity matrix between layers using weighted features"""
# Select features for similarity calculation
feature_columns = [
'depth_mid', 'thickness', 'soil_type_encoded',
'consistency_encoded', 'strength_value', 'moisture_encoded', 'color_encoded'
]
# Prepare feature matrix
features = df[feature_columns].copy()
# Handle missing values
features = features.fillna(0)
# Apply feature weights
for col in feature_columns:
if col in self.feature_weights:
features[col] = features[col] * self.feature_weights[col]
# Standardize features
features_scaled = self.scaler.fit_transform(features)
# Calculate similarity matrix (using negative euclidean distance)
from sklearn.metrics.pairwise import euclidean_distances
distance_matrix = euclidean_distances(features_scaled)
similarity_matrix = 1 / (1 + distance_matrix) # Convert distance to similarity
return similarity_matrix, features_scaled
def find_nearest_neighbors(self, df: pd.DataFrame, k: int = 3) -> List[Dict]:
"""Find k nearest neighbors for each soil layer"""
similarity_matrix, features_scaled = self.calculate_layer_similarity(df)
# Use NearestNeighbors to find k nearest neighbors
nn_model = NearestNeighbors(n_neighbors=min(k+1, len(df)), metric='euclidean')
nn_model.fit(features_scaled)
distances, indices = nn_model.kneighbors(features_scaled)
nearest_neighbors = []
for i, (layer_distances, layer_indices) in enumerate(zip(distances, indices)):
neighbors = []
for j, (dist, idx) in enumerate(zip(layer_distances[1:], layer_indices[1:])): # Skip self
neighbor_info = {
'neighbor_index': int(idx),
'neighbor_id': df.iloc[idx]['layer_id'],
'distance': float(dist),
'similarity_score': float(similarity_matrix[i, idx]),
'soil_type': df.iloc[idx]['soil_type'],
'consistency': df.iloc[idx]['consistency'],
'depth_range': f"{df.iloc[idx]['depth_from']:.1f}-{df.iloc[idx]['depth_to']:.1f}m"
}
neighbors.append(neighbor_info)
layer_nn = {
'layer_index': i,
'layer_id': df.iloc[i]['layer_id'],
'soil_type': df.iloc[i]['soil_type'],
'consistency': df.iloc[i]['consistency'],
'depth_range': f"{df.iloc[i]['depth_from']:.1f}-{df.iloc[i]['depth_to']:.1f}m",
'nearest_neighbors': neighbors
}
nearest_neighbors.append(layer_nn)
return nearest_neighbors
def group_similar_layers(self, df: pd.DataFrame, similarity_threshold: float = 0.7) -> List[List[int]]:
"""Group layers using DBSCAN clustering based on similarity"""
similarity_matrix, features_scaled = self.calculate_layer_similarity(df)
# Convert similarity to distance for DBSCAN
distance_matrix = 1 - similarity_matrix
# Use DBSCAN for clustering
eps = 1 - similarity_threshold # Convert similarity threshold to distance
clustering = DBSCAN(eps=eps, min_samples=1, metric='precomputed')
cluster_labels = clustering.fit_predict(distance_matrix)
# Group layers by cluster
clusters = {}
for i, label in enumerate(cluster_labels):
if label not in clusters:
clusters[label] = []
clusters[label].append(i)
# Convert to list of groups, filter out single-layer groups
layer_groups = []
for cluster_id, layer_indices in clusters.items():
if len(layer_indices) > 1: # Only groups with multiple layers
layer_groups.append(layer_indices)
return layer_groups, cluster_labels
def analyze_group_properties(self, df: pd.DataFrame, group_indices: List[int]) -> Dict:
"""Analyze properties of a group of similar layers"""
group_layers = df.iloc[group_indices]
analysis = {
'group_size': len(group_indices),
'depth_range': {
'min': group_layers['depth_from'].min(),
'max': group_layers['depth_to'].max(),
'total_thickness': group_layers['thickness'].sum()
},
'soil_types': group_layers['soil_type'].value_counts().to_dict(),
'consistencies': group_layers['consistency'].value_counts().to_dict(),
'strength_stats': {
'mean': group_layers['strength_value'].mean(),
'min': group_layers['strength_value'].min(),
'max': group_layers['strength_value'].max(),
'std': group_layers['strength_value'].std()
},
'layer_ids': group_layers['layer_id'].tolist(),
'depth_ranges': [f"{row['depth_from']:.1f}-{row['depth_to']:.1f}m"
for _, row in group_layers.iterrows()]
}
return analysis
def suggest_layer_merging(self, layers: List[Dict], similarity_threshold: float = 0.8) -> Dict:
"""Suggest which layers should be merged based on nearest neighbor analysis"""
if len(layers) < 2:
return {"groups": [], "recommendations": []}
# Encode features
df = self.encode_categorical_features(layers)
# Find similar layer groups
layer_groups, cluster_labels = self.group_similar_layers(df, similarity_threshold)
# Analyze each group
group_analyses = []
recommendations = []
for i, group_indices in enumerate(layer_groups):
group_analysis = self.analyze_group_properties(df, group_indices)
group_analysis['group_id'] = i + 1
group_analyses.append(group_analysis)
# Check if layers are adjacent or close
group_df = df.iloc[group_indices].sort_values('depth_from')
is_adjacent = self._check_adjacency(group_df)
if is_adjacent:
dominant_soil_type = max(group_analysis['soil_types'].items(), key=lambda x: x[1])[0]
dominant_consistency = max(group_analysis['consistencies'].items(), key=lambda x: x[1])[0]
recommendation = {
'group_id': i + 1,
'action': 'merge',
'reason': f'Similar {dominant_consistency} {dominant_soil_type} layers in adjacent depths',
'layer_ids': group_analysis['layer_ids'],
'depth_ranges': group_analysis['depth_ranges'],
'merged_properties': {
'soil_type': dominant_soil_type,
'consistency': dominant_consistency,
'depth_from': group_analysis['depth_range']['min'],
'depth_to': group_analysis['depth_range']['max'],
'thickness': group_analysis['depth_range']['total_thickness'],
'avg_strength': group_analysis['strength_stats']['mean']
}
}
recommendations.append(recommendation)
return {
'groups': group_analyses,
'recommendations': recommendations,
'cluster_labels': cluster_labels.tolist()
}
def _check_adjacency(self, group_df: pd.DataFrame, max_gap: float = 0.5) -> bool:
"""Check if layers in group are adjacent or nearly adjacent"""
if len(group_df) <= 1:
return True
# Sort by depth
sorted_df = group_df.sort_values('depth_from')
# Check gaps between consecutive layers
for i in range(len(sorted_df) - 1):
current_end = sorted_df.iloc[i]['depth_to']
next_start = sorted_df.iloc[i + 1]['depth_from']
gap = next_start - current_end
if gap > max_gap:
return False
return True
def get_layer_neighbors_report(self, layers: List[Dict], k: int = 3) -> str:
"""Generate a detailed report of nearest neighbors for each layer"""
if len(layers) < 2:
return "Insufficient layers for neighbor analysis."
df = self.encode_categorical_features(layers)
nearest_neighbors = self.find_nearest_neighbors(df, k)
report_lines = [
"NEAREST NEIGHBOR ANALYSIS REPORT",
"=" * 50,
""
]
for layer_info in nearest_neighbors:
report_lines.append(f"Layer {layer_info['layer_id']}: {layer_info['consistency']} {layer_info['soil_type']} ({layer_info['depth_range']})")
report_lines.append(" Nearest Neighbors:")
for i, neighbor in enumerate(layer_info['nearest_neighbors'][:k], 1):
similarity_pct = neighbor['similarity_score'] * 100
report_lines.append(
f" {i}. Layer {neighbor['neighbor_id']}: {neighbor['consistency']} {neighbor['soil_type']} "
f"({neighbor['depth_range']}) - Similarity: {similarity_pct:.1f}%"
)
report_lines.append("")
return "\n".join(report_lines) |