数据集的生成
以下是上述内容的翻译：
从代码来看，异常数据集（绕路和转换）是由训练数据生成的，而不是测试数据。以下是 process_datasets 方法中的相关部分：
# 将训练数据中的正常轨迹进行转换
train_converted = {}
for idx, traj in enumerate(train_data):
train_converted[idx] = self.convert_trajectory(traj)
# 从训练数据中生成异常点
detour_data = {}
switch_data = {}
num_outliers = int(len(train_data) * 0.1) # 10%的异常点
for i in range(num_outliers):
orig_traj = train_data[i] # 从训练数据中获取轨迹
detour_traj = self.generate_detour(orig_traj)
switch_traj = self.generate_switch(orig_traj)
detour_data[i] = self.convert_trajectory(detour_traj)
switch_data[i] = self.convert_trajectory(switch_traj)
具体来看：
- 异常点的数量是训练数据总量的10%（
num_outliers = int(len(train_data) * 0.1)）。 - 每个异常点是从训练数据中选取的一条轨迹（
orig_traj = train_data[i]）。 - 然后，分别为该训练轨迹生成绕路版本和转换版本。
- 测试数据是独立的，未被用于生成异常点。
如果您希望了解代码为何设计成这样，或者需要进一步解析异常生成过程的其他方面，请告诉我！
以下是上述内容的翻译：
绕路生成（generate_detour 方法）：
def generate_detour(self, traj, detour_factor=2):
if len(traj) < 3:
return traj
start = traj[0] # 保留原始起点
end = traj[-1] # 保留原始终点
num_extra_points = int(len(traj) * detour_factor) # 生成比原路径更多的点
min_val = min(start, end)
max_val = max(start, end)
# 在起点和终点之间生成随机点
detour_points = []
for _ in range(num_extra_points):
new_point = np.random.randint(min_val, max_val + 1)
detour_points.append(new_point)
return [start] + sorted(detour_points) + [end]
绕路生成方法通过以下方式生成一条更长的路径：
- 保留原始的起点和终点；
- 在起点和终点之间添加随机点（默认生成的点数量是原路径点数量的两倍，由
detour_factor控制）； - 将这些点排序，从而形成一条看起来合理的路径。
转换生成（generate_switch 方法）：
def generate_switch(self, traj, switch_ratio=0.3):
if len(traj) < 4:
return traj
# 随机选择一段路径进行修改（默认占原路径的30%）
switch_length = int(len(traj) * switch_ratio)
switch_start = np.random.randint(1, len(traj) - switch_length - 1)
switch_end = switch_start + switch_length
# 通过对原始点添加随机偏移量生成新点
new_segment = []
for _ in range(switch_length):
orig_point = traj[switch_start + len(new_segment)]
offset = np.random.randint(-100, 101) # 随机偏移量在-100到100之间
new_point = max(0, orig_point + offset)
new_segment.append(new_point)
return traj[:switch_start] + new_segment + traj[switch_end:]
转换生成方法通过以下步骤修改路径的一部分：
- 随机选择一段路径（默认长度为原路径的30%）；
- 用随机偏移量替换该路径段中的点；
- 保留路径的起始和结束部分不变。
测试数据处理：
代码并不生成人工测试数据，而是：
-
从CSV文件加载真实的测试数据：
test_data = self.load_porto_data(test_path) -
将测试路径转换为所需格式：
test_converted = {} for idx, traj in enumerate(test_data): test_converted[idx] = self.convert_trajectory(traj) -
将其保存到
test.pickle文件：with open(os.path.join(self.output_dir, 'test.pickle'), 'wb') as f: pickle.dump(test_converted, f)
需要注意的是，人工异常生成（绕路和转换生成）仅应用于训练数据，以创建带标签的异常示例；测试数据保持其原始来源数据的完整性，没有被修改。
如果您需要更详细的解释，可以告诉我！
如何创建更难预测得shujujiI'll help you adjust the config parameters to make the anomaly detection task more challenging for supervised learning. Here are some suggested parameter adjustments with explanations:
- For harder detour anomalies:
config = {
# ... other parameters ...
'detour_factor': 1.2, # Smaller detour factor makes detours more subtle
'detour_multiplier': 3.0, # More detour examples to increase variety
}
Explanation: A smaller detour factor makes the detours less obvious by creating paths that deviate less from the normal route. This makes it harder for models to distinguish between normal and detour routes. Increasing the multiplier gives more diverse examples of subtle detours.
- For harder switch anomalies:
config = {
# ... other parameters ...
'switch_ratio': 0.15, # Smaller segments make switches more subtle
'switch_multiplier': 3.0, # More switch examples for variety
}
Explanation: A smaller switch ratio means shorter route switches, making them harder to detect. Like with detours, more examples help create diverse anomaly patterns.
- For harder training/testing split:
config = {
# ... other parameters ...
'train_ratio': 0.05, # Less training data makes learning harder
'client_sd_pair_ratio': 0.7, # More SD pairs = more route variations
}
Explanation: Reducing the training ratio gives models less data to learn from. Increasing the SD pair ratio introduces more route variations per client, making the pattern recognition harder.
- Complete challenging configuration:
config = {
'random_seed': 42,
'base_dir': "/path/to/your/data",
'city_name': "beijing",
'num_clients': 15, # More clients = more variation
'client_sd_pair_ratio': 0.7, # More route variations
'train_ratio': 0.05, # Less training data
'detour_multiplier': 3.0, # More detour examples
'switch_multiplier': 3.0, # More switch examples
'detour_factor': 1.2, # Subtle detours
'switch_ratio': 0.15, # Subtle switches
}
This configuration makes the task more challenging because:
- Less training data (5%) makes it harder to learn patterns
- More clients and SD pairs increase the variety of normal patterns
- Subtle anomalies (smaller detour_factor and switch_ratio) make them harder to distinguish from normal variations
- More anomaly examples (higher multipliers) create diverse anomaly patterns
- Higher client_sd_pair_ratio means more route variations per client, making it harder to establish "normal" patterns
You could make it even harder by:
- Further reducing
train_ratioto 0.03 - Further reducing
detour_factorto 1.1 - Further reducing
switch_ratioto 0.1 - Increasing
client_sd_pair_ratioto 0.8 - Increasing
num_clientsto 20
However, be cautious not to make the task impossible - there should still be some detectable patterns in the anomalies, just more subtle ones.
switch_data的生成
以下是这段话的翻译：
让我来解释代码如何生成切换轨迹。核心逻辑在 ChengduDataProcessor 类的 generate_switch 方法中。它的工作原理如下：
- 方法接受一个轨迹作为输入，并使用一个可配置的
switch_ratio（默认为 0.3 或 30%）来决定修改轨迹的比例。 - 对于每个切换异常，代码执行以下步骤：
- 选取一条至少包含 4 个点的原始轨迹。
- 根据切换比例计算需要切换的点数（例如，如果轨迹有 10 个点，比例为 0.3，则切换 3 个点）。
- 在轨迹中随机选择一个位于中间的起始点（不在开头或结尾）。
- 用新的点替换这一段轨迹。
详细过程和一个示例：
以下是代码的具体流程：
def generate_switch(self, trajectory: Dict) -> Dict:
# 从配置中获取 switch_ratio（例如，0.3）
switch_ratio = self.config['switch_ratio']
n_geo = trajectory['n_geo']
# 至少需要 4 个点才能进行有效切换
if len(n_geo) < 4:
return trajectory
# 计算需要切换的点数
switch_length = int(len(n_geo) * switch_ratio)
# 随机选择切换的起始位置
# +1 和 -1 确保起点不在开头或结尾
switch_start = random.randint(1, len(n_geo) - switch_length - 1)
switch_end = switch_start + switch_length
# 获取需要替换的原始段
original_segment = n_geo[switch_start:switch_end]
start_point = original_segment[0]
end_point = original_segment[-1]
# 在起点和终点的地理边界框内找到有效的节点
valid_nodes = [list(node) for node in self.all_nodes
if min(start_point[0], end_point[0]) <= node[0] <= max(start_point[0], end_point[0])
and min(start_point[1], end_point[1]) <= node[1] <= max(start_point[1], end_point[1])
and tuple(node) not in (tuple(start_point), tuple(end_point))]
# 如果没有找到有效节点，则返回原始轨迹
if not valid_nodes:
return trajectory
# 采样新点并按坐标排序
new_segment = random.sample(valid_nodes, min(len(valid_nodes), switch_length))
new_segment.sort(key=lambda x: (x[0], x[1]))
# 创建新的轨迹，通过组合以下部分：
# 1. 切换前的点
# 2. 新的切换段
# 3. 切换后的点
new_trajectory = trajectory.copy()
new_trajectory['n_geo'] = n_geo[:switch_start] + new_segment + n_geo[switch_end:]
示例
假设有一条包含 10 个点的轨迹：
- 使用
switch_ratio = 0.3时，将切换 3 个点。 - 可能随机选择从第 4 个点开始。
- 它将用来自
valid_nodes的新点替换第 4、5 和 6 个点。 - 新点是从由切换段的起点和终点定义的地理边界框中选出的。
- 结果保持了相同的起点和终点，但中间路径发生了变化。
这种类型的异常模拟了车辆在旅途中间偏离正常路线的情况，但整体起点和终点保持不变。
switch的生成
Let me explain how this code searches for alternative paths step by step:
- First Level - Different Start Positions:
for _ in range(max_start_positions): # Try 3 different starting positions
switch_start = random.randint(1, len(n_geo) - switch_length - 1)
switch_end = switch_start + switch_length
source = tuple(n_geo[switch_start]) # Starting node
dest = tuple(n_geo[switch_end]) # Ending node
This picks different segments of the trajectory to try replacing.
- Initial Connectivity Check:
potential_middle_nodes = self.detour_generator.next_nodes[source] & self.detour_generator.prev_nodes[dest]
This finds all nodes that are:
- Reachable from the source (in
next_nodes[source]) - Can reach the destination (in
prev_nodes[dest])
Essentially creating a pool of valid middle nodes.
- Second Level - Multiple Attempts at Path Creation:
for _ in range(max_attempts): # Try 5 times for each start position
num_middle_nodes = random.randint(1, 3) # Choose to use 1-3 middle nodes
- Path Building Process:
new_segment = [list(source)] # Start with source node
current_node = source
for i in range(num_middle_nodes):
# Find valid next steps
next_possible = (self.detour_generator.next_nodes[current_node] &
self.detour_generator.prev_nodes[dest])
next_possible.discard(tuple(new_segment[-1])) # Don't go back
if not next_possible: # If no valid next step, path fails
valid_path = False
break
# Choose and add next node
next_node = random.choice(list(next_possible))
new_segment.append(list(next_node))
current_node = next_node
Let me illustrate with an example:
Original path: A -> B -> C -> D -> E
1. First try:
- Randomly selects start=B, end=D
- Needs to find path from B to D
- Looks for nodes that can be reached from B AND can reach D
- Decides to use 2 middle nodes
- Path building:
* Starts at B
* Finds all nodes connected to B that can reach D
* Picks F, path now: B -> F
* Finds all nodes connected to F that can reach D
* Picks G, path now: B -> F -> G -> D
Final result: A -> B -> F -> G -> D -> E
Visual representation of the search process:
F -----> G
↗ ↘
A → B → C → D → E (Original)
↘ ↗
H -----> I
Steps:
1. Start at B, need to reach D
2. Can reach: F, H (potential_middle_nodes)
3. Choose F
4. From F, can reach: G (next_possible)
5. Choose G
6. G can reach D
7. Result: B -> F -> G -> D
The code is essentially doing a constrained random walk:
- It knows which nodes can reach the destination
- At each step, it only considers nodes that:
- Are connected to the current node
- Can eventually reach the destination
- Haven't been used yet
- It builds the path one node at a time, ensuring connectivity
This approach ensures:
- All nodes are actually connected
- The path reaches the destination
- The path doesn't backtrack
- The path is different from the original
update 以后
Ah, I understand now. You want to specifically replace the entire chosen sub-trajectory (B->C->D in the example) with alternative nodes. Let me modify the code to better match your requirements.
def generate_switch(self, trajectory: Dict) -> Dict:
"""Generate route switching outlier by replacing a fixed sub-trajectory with alternative nodes"""
switch_ratio = self.config['switch_ratio']
n_geo = trajectory['n_geo']
if len(n_geo) < 4: # Need minimum length for meaningful switch
return trajectory
# Determine switch segment
switch_length = int(len(n_geo) * switch_ratio)
if switch_length < 3: # Ensure minimum segment length
switch_length = 3
max_attempts = 5 # Maximum attempts for each start position
max_start_positions = 3 # Maximum different start positions to try
# Try different start positions
for _ in range(max_start_positions):
# Select sub-trajectory to replace
switch_start = random.randint(0, len(n_geo) - switch_length - 1)
switch_end = switch_start + switch_length
# Get the nodes right before and after the sub-trajectory
before_node = tuple(n_geo[switch_start])
after_node = tuple(n_geo[switch_end])
# Try multiple times for each sub-trajectory
for _ in range(max_attempts):
# Randomly decide whether to use 2 or 3 nodes for replacement
num_nodes = random.randint(2, 3)
# Try to create a new connected path between before_node and after_node
new_segment = []
current = before_node
valid_path = True
used_nodes = {before_node, after_node} # Keep track of used nodes
# Build path with num_nodes intermediate nodes
for i in range(num_nodes - 1): # -1 because we don't include start/end nodes in count
# Find nodes that can be reached from current and can reach after_node
next_possible = (self.detour_generator.next_nodes[current] &
self.detour_generator.prev_nodes[after_node])
# Remove already used nodes
next_possible = next_possible - used_nodes
if not next_possible:
valid_path = False
break
next_node = random.choice(list(next_possible))
new_segment.append(list(next_node))
used_nodes.add(next_node)
current = next_node
if not valid_path:
continue
# Check if the new path is different from original
original_segment = [tuple(node) for node in n_geo[switch_start+1:switch_end]]
new_segment_tuples = [tuple(node) for node in new_segment]
if new_segment_tuples != original_segment:
# Create new trajectory
new_trajectory = trajectory.copy()
new_trajectory['n_geo'] = (n_geo[:switch_start+1] +
new_segment +
n_geo[switch_end:])
new_trajectory['m_geo'] = new_trajectory['n_geo']
# Update path representation
new_trajectory['npath'] = []
for i in range(len(new_trajectory['n_geo']) - 1):
pair = [
str(list(new_trajectory['n_geo'][i])),
str(list(new_trajectory['n_geo'][i+1]))
]
new_trajectory['npath'].append(pair)
return new_trajectory
return trajectory # Return original if no valid switch found
Let me explain the key changes with an example:
Original path: A -> B -> C -> D -> E -> F
If we select B -> C -> D to replace:
1. before_node = B
2. after_node = E
3. Need to find 2-3 nodes to connect B to E
Possible outcomes:
1. With 2 nodes: A -> B -> X -> Y -> E -> F
2. With 3 nodes: A -> B -> P -> Q -> R -> E -> F
The code ensures:
- The replacement happens between fixed points (B and E in example)
- Uses 2-3 nodes to replace the original sub-trajectory
- All nodes in the new path are connected
- The new path is different from the original
Key differences from the previous version:
- We now explicitly select a sub-trajectory to replace
- We preserve the nodes before and after the sub-trajectory
- We replace the entire segment with 2-3 new nodes
- We ensure connectivity from the node before to the node after
- We check that the new path is different from the original segment
The rest of the code (generate_switch_data and process_client_data) remains the same as they just handle the overall generation process. Would you like me to make any adjustments to better match your requirements?
continue to update
I understand you want to optimize the search space for finding the two intermediate nodes when generating switches. Here's an optimized version that makes the search more efficient:
def find_switch_path(self, start_node: Tuple, end_node: Tuple) -> List:
"""Find two intermediate nodes that connect start_node to end_node with minimum path length"""
# Find nodes directly reachable from start that can eventually reach end
first_layer = self.next_nodes[start_node]
if not first_layer:
return None
# For each node in first layer, check if it can reach end_node in one hop
potential_paths = []
for first_node in first_layer:
second_layer = self.next_nodes[first_node] & self.prev_nodes[end_node]
for second_node in second_layer:
if second_node != start_node and second_node != first_node:
potential_paths.append([list(first_node), list(second_node)])
if not potential_paths:
return None
# Return a random valid path
return random.choice(potential_paths)
def generate_switch(self, trajectory: Dict) -> Dict:
"""Generate route switching outlier by replacing a fixed sub-trajectory with 2 alternative nodes"""
n_geo = trajectory['n_geo']
if len(n_geo) < 5: # Need minimum length to replace 3 nodes with 2-3 nodes
return trajectory
max_start_positions = 3 # Maximum different start positions to try
# Try different start positions
for _ in range(max_start_positions):
# Select 3 consecutive nodes to replace
switch_start = random.randint(1, len(n_geo) - 4) # Ensure we have nodes before and after
switch_end = switch_start + 3 # Always replace 3 nodes
# Get the nodes right before and after the sub-trajectory
before_node = tuple(n_geo[switch_start])
after_node = tuple(n_geo[switch_end])
# Find alternative path with exactly 2 intermediate nodes
new_segment = self.find_switch_path(before_node, after_node)
if new_segment:
# Check if the new path is different from original
original_segment = [tuple(node) for node in n_geo[switch_start+1:switch_end]]
new_segment_tuples = [tuple(node) for node in new_segment]
if new_segment_tuples != original_segment:
# Create new trajectory
new_trajectory = trajectory.copy()
new_trajectory['n_geo'] = (n_geo[:switch_start+1] +
new_segment +
n_geo[switch_end:])
new_trajectory['m_geo'] = new_trajectory['n_geo']
# Update path representation
new_trajectory['npath'] = []
for i in range(len(new_trajectory['n_geo']) - 1):
pair = [
str(list(new_trajectory['n_geo'][i])),
str(list(new_trajectory['n_geo'][i+1]))
]
new_trajectory['npath'].append(pair)
return new_trajectory
return trajectory # Return original if no valid switch found
Key optimizations:
- Created a separate
find_switch_pathmethod that:- First finds all nodes reachable from start node
- Then for each of those nodes, finds which ones can reach the end node in one hop
- Returns a valid path if found
- Eliminated the need for multiple attempts at each position
- Removed the need to track used nodes since we're finding complete paths
- Simplified the path finding to always use exactly 2 intermediate nodes
The search process is now:
For a path A -> B -> C -> D -> E:
1. If replacing B -> C -> D:
- Start at B (before_node)
- Find all nodes X that B can reach directly
- For each X, find all nodes Y that can reach E (after_node) directly
- Result is B -> X -> Y -> E
This is more efficient because:
- We only look at immediate neighbors at each step
- We build complete paths rather than trying node by node
- We check connectivity up front rather than discovering failed paths later
- We eliminate the need for multiple attempts with the same start/end points
Would you like me to make any adjustments to this optimization?
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