From 6c95d8f7651737f6f085f40b716e3f1b8151a140 Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Thu, 25 Jul 2024 17:10:20 +0800 Subject: [PATCH 1/9] Proposal of Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning Signed-off-by: Frank-lilinjie --- ...ud-Edge Collaborative Lifelong Learning.md | 112 ++++++++++++++++++ .../lifelong-learning/images/DAHLIA.png | Bin 0 -> 2469607 bytes .../images/Q2A_task_plan.png | Bin 0 -> 326585 bytes .../images/agent-overview.png | Bin 0 -> 139506 bytes .../images/continual_llm_agent.png | Bin 0 -> 197817 bytes .../images/llm_agent_demo.png | Bin 0 -> 63280 bytes .../images/llm_agent_ianvs.png | Bin 0 -> 159902 bytes 7 files changed, 112 insertions(+) create mode 100644 docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md create mode 100644 docs/proposals/algorithms/lifelong-learning/images/DAHLIA.png create mode 100644 docs/proposals/algorithms/lifelong-learning/images/Q2A_task_plan.png create mode 100644 docs/proposals/algorithms/lifelong-learning/images/agent-overview.png create mode 100644 docs/proposals/algorithms/lifelong-learning/images/continual_llm_agent.png create mode 100644 docs/proposals/algorithms/lifelong-learning/images/llm_agent_demo.png create mode 100644 docs/proposals/algorithms/lifelong-learning/images/llm_agent_ianvs.png diff --git a/docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md b/docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md new file mode 100644 index 00000000..8bb7d931 --- /dev/null +++ b/docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md @@ -0,0 +1,112 @@ +# Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning + +# 1 Motivation + +## 1.1 Background + +### 1.1.1 LLM is Not Equivalent to AGI + +Large Language Models (LLMs) possess excellent reasoning and thinking abilities due to extensive training data and various training methods. However, current LLMs are not truly Artificial General Intelligence (AGI). The following are three obstacles that prevent LLMs from achieving AGI: + +1. **Lack of support for multimodal input**. AGI requires models to solve complex real-world multimodal tasks like humans and possess lifelong learning capabilities. LLMs only support single-modal text input and do not support the integration of multimodal inputs such as video or voice. + +2. **Inability to decompose and plan complex tasks**. In the real world, humans decompose complex problems into multiple sub-tasks and possess task-planning abilities when solving them. LLMs do not have this capability. + +3. **Inferior to expert models in specific domains**. Although LLMs demonstrate powerful zero-shot or few-shot learning capabilities, they are weaker compared to expert models fine-tuned for specific domains. + +This project aims to address the above issues by developing a multi-expert intelligent agent system based on LLM Agents, where the LLM serves as the system's controller (analogous to the human brain). By connecting, managing, and invoking expert models, the system completes complex tasks. Specifically, the LLM's text understanding ability is used to decompose user instructions, distribute different instructions to task-specific expert models, and link the inputs and outputs of various experts to collaboratively complete complex tasks. The system employs the LLM as a controller to receive text and multimodal inputs, understand user requirements through text, and plan sub-tasks. By leveraging the strengths of expert models, the system completes a complex task step by step, addressing the obstacles preventing LLMs from achieving AGI. + +### 1.1.2 Edge Requirements for Personalized LLM Agent + +This project aims to provide personalized LLM Agent services for edge users, facing three challenges: privacy, timeliness, and efficiency. + +1. **Privacy**: Uploading users' personal data to the cloud poses security risks of data breaches. +2. **Timeliness**: Edge computing allows edge users to receive faster feedback. +3. **Efficiency**: Edge computing power is limited, making the deployment of LLM models costly. + +This project aims to achieve a personalized LLM Agent by utilizing a cloud-edge collaborative framework, combining answers from cloud-based large models with those generated based on privacy data at the edge. We plan to develop a personalized LLM Agent based on the KubeEdge-lanvs cloud-edge collaborative platform for lifelong learning (maintaining user-specific data in the knowledge base, with lifelong learning supporting different models on different edges). This system integrates the generalization capabilities of large cloud-based LLMs with personalized user data on edge devices to generate high-quality, personalized responses. + +## 1.2 Goals + +1. Research evaluation benchmarks for LLM and LLM Agents. + +2. Develop a personalized LLM Agent based on lifelong learning using the KubeEdge-lanvs cloud-edge collaborative platform." + +# 2. Proposal + +## 2.1 LLM Agent + +agent + +In an LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: + +- Planning + - Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks. + - Reflection and refinement: The agent can do self-criticism and self-reflection on past actions, learn from mistakes, and refine them for future steps, thereby improving the quality of final results. +- Memory + - Short-term memory: I would consider all the in-context learning as utilizing short-term memory of the model to learn. + - Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval. +- Tool use + - The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources, and more. + +## 2.2 Cloud-Edge Collaborative Lifelong Learning + +LLM agent Continual learning + + + +Large-scale LLM agent models possess powerful generalization capabilities, enabling them to output personalized task plans based on different user instructions. However, edge devices have limited computing resources and cannot support the operation of large-scale LLM agent models. If a small-scale LLM agent can learn from the habits of different users and perform comparably to large-scale LLM agents, this issue can be resolved. Since user data is streamed and large amounts of training data cannot be immediately obtained, continuous learning of the small-scale edge LLM agent is necessary. + +Therefore, this project proposes a **Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning**. The goal is to enable small-scale edge LLM agents to gradually achieve capabilities comparable to large-scale models by leveraging the guidance of large-scale cloud LLM agent models. Specifically, edge users provide a small number of instructions to both the cloud and edge LLM agent models. The output of the cloud LLM agent model serves as pseudo-labels for the edge LLM agent, which is gradually trained through knowledge distillation. This process transforms the edge model into a **Personalized LLM Agent**. + +# 3 Design Detail + +## 3.1 Demo + +LLM agent Demo + + +This project plans to complete a demo for human pose detection through video. The steps are as follows: + +1. Users input commands to query the corresponding video based on time. +2. The video is split into frames for object detection, detecting the position of people. +3. Perform human identity recognition.\3. Perform action pose recognition." + +## 3.2 DataSet + +### 3.2.1 Q2A Task-plan Dataset + +"Q2A (Query to Answer) Task-plan Dataset is a custom dataset created for this project. This dataset includes multiple user instructions and the task planning results generated by the LLM. We can use several data points as prompts, leveraging the few-shot capability of the pretrained LLM to fine-tune it, enabling it to perform task planning and become an LLM Agent." + +Q2A + +### 3.2.2 The DAily Home LIfe Activity Dataset + +DAHLIA dataset is devoted to human activity recognition, which is a major issue for adapting smart-home services such as user assistance. + +DAHLIA + +## 3.3 Ianvs Implement + +llm_agent_ianvs + +This project implements automated task definition and task orchestration through the LLM Agent on Ianvs and evaluates it using the self-built Q2A dataset. Specifically, in the Test Environment Manager, we integrate the Q2A and DAHLIA datasets, as well as three evaluation metrics: task decomposition, tool invocation, and parameter prediction, to establish a benchmark for LLM Agent evaluation. In the Test Case Controller, we perform automated task definition and task orchestration, providing user instructions to the LLM Agent and outputting task plan results. 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.../images/DAHLIA.png | Bin .../images/Q2A_task_plan.png | Bin .../images/agent-overview.png | Bin .../single-task-learning/images/bert_score.png | Bin 0 -> 280783 bytes .../images/continual_llm_agent.png | Bin .../images/llm_agent_demo.png | Bin .../images/llm_agent_ianvs.png | Bin 8 files changed, 15 insertions(+), 2 deletions(-) rename docs/proposals/algorithms/{lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md => single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md} (83%) rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/DAHLIA.png (100%) rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/Q2A_task_plan.png (100%) rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/agent-overview.png (100%) create mode 100644 docs/proposals/algorithms/single-task-learning/images/bert_score.png rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/continual_llm_agent.png (100%) rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/llm_agent_demo.png (100%) rename docs/proposals/algorithms/{lifelong-learning => single-task-learning}/images/llm_agent_ianvs.png (100%) diff --git a/docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md similarity index 83% rename from docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md rename to docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index 8bb7d931..dad37bfb 100644 --- a/docs/proposals/algorithms/lifelong-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -1,4 +1,4 @@ -# Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaborative Lifelong Learning +# Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration # 1 Motivation @@ -49,7 +49,7 @@ In an LLM-powered autonomous agent system, LLM functions as the agent’s brain, - Tool use - The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources, and more. -## 2.2 Cloud-Edge Collaborative Lifelong Learning +## 2.2 Cloud-Edge Collaboration LLM agent Continual learning @@ -92,6 +92,19 @@ DAHLIA dataset is devoted to human activity recognition, which is a major issue This project implements automated task definition and task orchestration through the LLM Agent on Ianvs and evaluates it using the self-built Q2A dataset. Specifically, in the Test Environment Manager, we integrate the Q2A and DAHLIA datasets, as well as three evaluation metrics: task decomposition, tool invocation, and parameter prediction, to establish a benchmark for LLM Agent evaluation. In the Test Case Controller, we perform automated task definition and task orchestration, providing user instructions to the LLM Agent and outputting task plan results. In the Story Manager, we set up the output of test reports. +## 3.4 Metrics +### 3.4.1 Task Decomposition +#### Rouge-N +The ROUGE-N metric is a method used to evaluate the quality of automatic summarization or machine translation, focusing on recall, which is the degree of overlap between the system-generated text and the reference text. The "N" in ROUGE-N represents the length of the longest common subsequence considered. Here is the calculation formula for ROUGE-N: +$$ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)}$$ + +#### Bertscore +[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. + +bert_score + + +$$R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}}$$ # 4 Roadmap diff --git a/docs/proposals/algorithms/lifelong-learning/images/DAHLIA.png b/docs/proposals/algorithms/single-task-learning/images/DAHLIA.png similarity index 100% rename from docs/proposals/algorithms/lifelong-learning/images/DAHLIA.png rename to docs/proposals/algorithms/single-task-learning/images/DAHLIA.png diff --git a/docs/proposals/algorithms/lifelong-learning/images/Q2A_task_plan.png 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100% rename from docs/proposals/algorithms/lifelong-learning/images/llm_agent_ianvs.png rename to docs/proposals/algorithms/single-task-learning/images/llm_agent_ianvs.png From dbd7a2a1643021451d405d654858eaa595ca8a9e Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Wed, 11 Sep 2024 12:29:11 +0800 Subject: [PATCH 3/9] modify proposal of LLM Agent Signed-off-by: Frank-lilinjie --- ...KubeEdge-Ianvs Cloud-Edge Collaboration.md | 18 +++++++----------- .../images/Personal_Instruction.png | Bin 0 -> 230473 bytes 2 files changed, 7 insertions(+), 11 deletions(-) create mode 100644 docs/proposals/algorithms/single-task-learning/images/Personal_Instruction.png diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index dad37bfb..156f64ea 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -63,15 +63,9 @@ Therefore, this project proposes a **Personalized LLM Agent based on KubeEdge-Ia ## 3.1 Demo -LLM agent Demo - - -This project plans to complete a demo for human pose detection through video. The steps are as follows: - -1. Users input commands to query the corresponding video based on time. -2. The video is split into frames for object detection, detecting the position of people. -3. Perform human identity recognition.\3. Perform action pose recognition." +Personal_Instruction +This project presents three scenarios, which are audio task, human posture classification task and face and object recognition task. ## 3.2 DataSet ### 3.2.1 Q2A Task-plan Dataset @@ -96,15 +90,17 @@ This project implements automated task definition and task orchestration through ### 3.4.1 Task Decomposition #### Rouge-N The ROUGE-N metric is a method used to evaluate the quality of automatic summarization or machine translation, focusing on recall, which is the degree of overlap between the system-generated text and the reference text. The "N" in ROUGE-N represents the length of the longest common subsequence considered. Here is the calculation formula for ROUGE-N: -$$ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)}$$ +$$ ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)} $$ #### Bertscore -[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. +[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measures, which can be useful for evaluating different language generation tasks. bert_score -$$R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}}$$ +$$ R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ +$$P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ +$$F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}} $$ # 4 Roadmap diff --git 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KubeEdge-Ianvs Cloud-Edge Collaboration.md | 7 ++++++- .../images/Personal_Instruction.png | Bin 230473 -> 0 bytes .../images/llm_agent_demo.png | Bin 63280 -> 0 bytes .../images/personalized_agent.png | Bin 0 -> 460871 bytes 4 files changed, 6 insertions(+), 1 deletion(-) delete mode 100644 docs/proposals/algorithms/single-task-learning/images/Personal_Instruction.png delete mode 100644 docs/proposals/algorithms/single-task-learning/images/llm_agent_demo.png create mode 100644 docs/proposals/algorithms/single-task-learning/images/personalized_agent.png diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index 156f64ea..ca47d1c1 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -63,9 +63,14 @@ Therefore, this project proposes a **Personalized LLM Agent based on KubeEdge-Ia ## 3.1 Demo -Personal_Instruction +Personal_Instruction This project presents three scenarios, which are audio task, human posture classification task and face and object recognition task. + +What happens if you put a trained Agent model in a mismatched scenario? Here are two examples: +1. Set ***Agent #1*** to ***Scene #3***: Data source does not match; +2. 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was used to divide the scenarios. + +The proposed algorithm has wide applicability and can operate effectively in a variety of different scenarios. It mainly processes the collected car wireless signal data, and uses the TForest algorithm to intelligently divide the scene tasks according to the distribution characteristics of the data. In this way, the algorithm can not only distinguish different data scenarios, but also allocate the most suitable prediction model according to the specific requirements of each scenario. + +In terms of task allocation, the algorithm will identify the key features in each scene and select the optimal model to process according to these features. This processing method ensures the applicability of the model and the accuracy of prediction, especially when predicting the Angle between vehicles, it can predict and analyze the relative Angle direction between vehicles more accurately. + +Other attempts are in the `other_attempts` folder, but due to the conflict between the data characteristics and the task of this project, and the data is not authorized to be disclosed, looking forward to the subsequent update. diff --git a/examples/aoa/single_task_bench/TForest/allocation.out b/examples/aoa/single_task_bench/TForest/allocation.out new file mode 100644 index 00000000..8b0ee7e1 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/allocation.out @@ -0,0 +1,42 @@ +nohup: ignoring input + 0%| | 0/32916 [00:00(2Y$dEdD+^Ut_+x!~zf?6c24`@EBr5hplHc@{ws0*S|u6c7a06G3oB@lU~T zxH|?X;Xht`Q8jx-D?@uHJzE1rTF>6v+{)hEdC z>=yqxfz8U+i2c?q`7^l58SBUDb_hbGhyKG!6;3fh5MhkOqx(uPqLzl7U6pzck5{+p zp9h}5`i{83Q%{~Gkxp)h0J>ge(A0l`Nl1qV0 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a/examples/aoa/single_task_bench/TForest/other_attempts/Create_Dataset.py b/examples/aoa/single_task_bench/TForest/other_attempts/Create_Dataset.py new file mode 100644 index 00000000..7c6fcec3 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/Create_Dataset.py @@ -0,0 +1,132 @@ +from torch.utils.data import Dataset,DataLoader,random_split +import numpy as np +import torch + +from torch import nn +from torch.nn import functional as F + +# path = r'data/processed/1.txt' +# path = r'data/Scene Partition/1_rural/1.txt' +class AOA_dataset(Dataset): + # all_data = np.loadtxt(path, delimiter=',') + def __init__(self,filepath): + all_data = np.loadtxt(path, delimiter=',') + self.x_data = torch.from_numpy(all_data[:, 10:-1]).float().unsqueeze(1) + + self.y_data = torch.from_numpy(all_data[:, 5]).float().unsqueeze(1) + + def __getitem__(self, index): + return self.x_data[index], self.y_data[index] + def __len__(self): + return len(self.x_data) + + + + +class AoACNN(torch.nn.Module): + def __init__(self, flat_num=13, output_dim=1): + super(AoACNN, self).__init__() + self.output_dim = output_dim + self.flat_num = flat_num + self.conv1 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=1, out_channels=8, kernel_size=3, stride=2, padding=1), + # input [batchsize,1,312], output [batchsize,8,156] + torch.nn.BatchNorm1d(8), + torch.nn.ReLU(), + torch.nn.MaxPool1d(kernel_size=3, stride=3) # input [batchsize,8,156], output [batchsize,8,52] + ) + self.conv2 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3, stride=2, padding=1), + # input [batchsize,8,52], output [batchsize,16, 26] + torch.nn.BatchNorm1d(16), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + self.conv3 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1), + # input [batchsize,16,13], output [batchsize,32, 13] + torch.nn.BatchNorm1d(32), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + # self.conv4 = torch.nn.Sequential( + # torch.nn.Conv1d(in_channels=32, out_channels=128, kernel_size=3, stride=1, padding=1), # input [batchsize,16,13], output [batchsize,32, 13] + # torch.nn.BatchNorm1d(128), + # torch.nn.ReLU(), + # ) + if self.output_dim == 1: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + else: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + # self.dp = torch.nn.Dropout(drop_p) + + def forward(self, x): + # x = x.cuda() + # print(x.shape) + x = self.conv1(x) + # print(x.shape) + x = self.conv2(x) + # print(x.shape) + x = self.conv3(x) + # print(x.shape) + # x = self.conv4(x) + + x = self.fc(x.view(x.size(0), -1)) + # print(x.shape) + return x + + +if __name__ == "__main__": + + path = r'data/Scene Partition/1_rural/1.txt' + mydataset = AOA_dataset(path) + + train_loader = DataLoader(dataset=mydataset, batch_size=32, shuffle=True, num_workers=2) + + model = AoACNN() + criterion = torch.nn.MSELoss() # 损失函数 + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) # 优化器优化参 + for epoch in range(100): + for i, data in enumerate(train_loader, 0): + # 1.准备数据 + inputs, labels = data # Tensor type + + # 2.前向传播 + y_pred = model(inputs) + print(labels.size()) + print(y_pred.size()) + loss = criterion(y_pred, labels) + lss = torch.abs(y_pred-labels) + r2 = torch.sum(lss) + print(r2.item()) + print(epoch, i, loss.item()) + + # 3.后向传播 + optimizer.zero_grad() + loss.backward() + + # 4.更新 + optimizer.step() + + with torch.no_grad(): + for i, data in enumerate(train_loader, 0): 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torch.nn import functional as F + +torch.manual_seed(420) +x = torch.rand((500,20),dytpe = torch.float32) +y = torch.randint(low = 0,high = 3,size =(500,),dytpe = torch.float32) diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/data_processing.py b/examples/aoa/single_task_bench/TForest/other_attempts/data_processing.py new file mode 100644 index 00000000..cacab377 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/data_processing.py @@ -0,0 +1,272 @@ +# -*- coding: utf-8 -*- +import os +import numpy as np +from sklearn.model_selection import train_test_split +import pandas as pd +from sklearn.decomposition import PCA +from sklearn import preprocessing +from sklearn.cluster import KMeans +from scipy.spatial.distance import cdist +import matplotlib.pyplot as plt +def read_files(): + """该函数用于读取对应文件夹下各txt文件的名字""" + path = r'data/processed/' + files = os.listdir(path) + + + file_names = [] + for file in files: + if file.split('.')[-1] == 'txt': # 如果不是txt文件就跳过 + file_names.append(file) + return path, file_names +def mixed_file(path,file_list,t_v): + """该函数用于合并刚才读取的各文件 + 输入:文件路径,read_files()返回的文件名 + 输出:一个合并后的文件""" + content = '' + # for file_name in files: + for i in file_list: + with open(path+ str(i)+'_'+t_v+'.txt', 'r', encoding='utf-8') as file: + content = content + file.read() + file.close() + + with open(r'data/general_data/' + 'gen_'+t_v+'.txt', 'a', encoding='utf-8') as file: + file.write(content) + content = '' + file.close() +# 读取文件,随机 9:1 分数据,数据保存到两个文件里 +def tforest_data(ord_num): + path = r'data/processed/' + + data = np.loadtxt(path+str(ord_num)+'.txt', delimiter=',') + features = data[:, 10:-1] + labels = data[:, 5] + train_features, ver_features, train_labels, ver_labels = train_test_split(features, labels,train_size=0.9,shuffle=True,random_state=42) + # train_features和train_labels合并一个test,再保存到train中 + train_data = np.hstack((train_features,train_labels.reshape(-1,1))) + np.savetxt('data/train/'+str(ord_num)+'_train.txt',train_data , delimiter=',', fmt='%.6f') + # ver_features和ver + ver_data = np.hstack((ver_features,ver_labels.reshape(-1,1))) + np.savetxt('data/verification/' + str(ord_num) + '_ver.txt', ver_data, delimiter=',', fmt='%.6f') + + +def data_process(ord_num,path,situ): + data = np.loadtxt(r'data/processed/' + str(ord_num) + '.txt', delimiter=',') + features = data[:, 10:-1] + labels = data[:, 5] + proce_data = np.hstack((features, labels.reshape(-1, 1))) + np.savetxt(path + str(ord_num) + '_'+situ+'.txt', proce_data, delimiter=',', fmt='%.6f') + + + +def mixed_file_2(path,file_list,str_list): + """该函数用于合并刚才读取的各文件 + 输入:文件路径,read_files()返回的文件名 + 输出:一个合并后的文件""" + file_path = r'data/processed_data/' + content = '' + # for file_name in files: + for i in file_list: + with open(file_path+ str(i)+'_'+'.txt', 'r', encoding='utf-8') as file: + content = content + file.read() + file.close() + + with open(path + str_list+'.txt', 'a', encoding='utf-8') as file: + file.write(content) + content = '' + file.close() +def stp_data(): + path = r'data/stp/stp_train.txt' + + data = np.loadtxt(path, delimiter=',') + features = data[:, :-1] + labels = data[:, -1] + train_features, ver_features, train_labels, ver_labels = train_test_split(features, labels,train_size=0.9,shuffle=True,random_state=42) + # train_features和train_labels合并一个test,再保存到train中 + train_data = np.hstack((train_features,train_labels.reshape(-1,1))) + np.savetxt('data/stp/train/stp_train.txt',train_data , delimiter=',', fmt='%.6f') + # ver_features和ver + ver_data = np.hstack((ver_features,ver_labels.reshape(-1,1))) + np.savetxt('data/stp/ver/stp_ver.txt', ver_data, delimiter=',', fmt='%.6f') + + + +def mtp_data(situ): + path = r'data/mtp/'+situ+'_train.txt' + data = np.loadtxt(path, delimiter=',') + features = data[:, :-1] + labels = data[:, -1] + train_features, ver_features, train_labels, ver_labels = train_test_split(features, labels,train_size=0.9,shuffle=True,random_state=42) + # train_features和train_labels合并一个test,再保存到train中 + train_data = np.hstack((train_features,train_labels.reshape(-1,1))) + np.savetxt('data/mtp/train/'+situ+'_train.txt',train_data , delimiter=',', fmt='%.6f') + # ver_features和ver + ver_data = np.hstack((ver_features,ver_labels.reshape(-1,1))) + np.savetxt('data/mtp/ver/'+situ+'_ver.txt', ver_data, delimiter=',', fmt='%.6f') + + +def fcp_data(): + for i in range(15): + filename = r'data/fcp/'+str(i)+'.txt' + data = np.loadtxt(filename,delimiter=',') + features = data[:,:-1] + labels = data[:,-1] + train_features, ver_features, train_labels, ver_labels = train_test_split(features, labels, train_size=0.9, + shuffle=True, random_state=42) + # train_features和train_labels合并一个test,再保存到train中 + train_data = np.hstack((train_features, train_labels.reshape(-1, 1))) + np.savetxt('data/fcp/train/' + str(i)+ '_train.txt', train_data, delimiter=',', fmt='%.6f') + # ver_features和ver + ver_data = np.hstack((ver_features, ver_labels.reshape(-1, 1))) + np.savetxt('data/fcp/ver/' + str(i) + '_ver.txt', ver_data, delimiter=',', fmt='%.6f') + +def test_data(): + # testdata也是处理过的 + path = r'data/test/' + + test_list = ['rural-1.txt', 'rural-2.txt', 'parkinglot-1.txt', 'highway-1.txt', 'surface-1.txt','surface-2.txt'] + for i in test_list: + + data = np.loadtxt(path + i, delimiter=',') + features = data[:, 10:-1] + labels = data[:, 5] + # train_features, ver_features, train_labels, ver_labels = train_test_split(features, labels, train_size=0.9, + # shuffle=True, random_state=42) + # train_features和train_labels合并一个test,再保存到train中 + test_data = np.hstack((features, labels.reshape(-1, 1))) + np.savetxt(path+ 'test_data/'+ i , test_data, delimiter=',', fmt='%.6f') + + + +if __name__ == '__main__': + + # file_list = [1,2,3,4,5,6,7,8,9,10,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,35,36,37,38,39,40,41,42] + # path = r'data/verification/' + # mixed_file(path,file_list,'ver') + # stp_train_list =[1,2,3,4,5,6,7,8,9,10,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,35,36,37,38,39,40,41,42] + # stp_path = r'data/stp/train' + + # stp_test_list=[] + # mtp_train_list = [] + #stp数据,所有数据合并成一个 + #fcp数据,聚类成几个 + #mtp数据,4个 + + # for i in range(1,43): + # data_process(i,r'data/processed_data/','') + # stp + # 1-10 10 + # 13-20 8 + # 22-32 11 + # 35-41 7 + # stp_file_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, + # 13, 14, 15, 16, 17, 18, 19, 20, + # 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, + # 35, 36, 37, 38, 39, 40, 41] + # stp_path = r'data/stp/' + # mixed_file_2(stp_path,stp_file_list,'stp_train') + # stp_data() + + # mtp + + # rural_file_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + # mtp_path =r'data/mtp/' + # mixed_file_2( mtp_path, rural_file_list, 'rural_train') + # + # parkinglot_file_list = [13, 14, 15, 16, 17, 18, 19, 20] + # mtp_path = r'data/mtp/' + # mixed_file_2(mtp_path, parkinglot_file_list, 'parkinglot_train') + # + # surface_file_list = [22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32] + # mtp_path = r'data/mtp/' + # mixed_file_2(mtp_path, surface_file_list, 'surface_train') + # + # highway_file_list = [35, 36, 37, 38, 39, 40, 41] + # mtp_path = r'data/mtp/' + # mixed_file_2(mtp_path,highway_file_list, 'highway_train') + # + # list = ['rural', 'parkinglot','surface','highway'] + # for i in list: + # mtp_data(i) + + test_data() + #fcp + # path = r'data/fcp/' + # data = np.loadtxt(path+'fcp.txt', delimiter=',') + # all_features = data[:, :-1] + # all_labels = data[:, -1] + # + # all_features = pd.DataFrame(all_features) + # # 数据标准化 + # z_scaler = preprocessing.StandardScaler() + # data_fcp1 = z_scaler.fit_transform(all_features) + # # # 要不要转成dataframe另说 + # # data_fcp1 = pd.DataFrame(data_fcp1) + # # 降维 + # pca = PCA(n_components=6) + # data_fcp1 = pca.fit_transform(data_fcp1) + # # 数据归一化 + # minmax_scale = preprocessing.MinMaxScaler().fit(data_fcp1) + # data_fcp2 = minmax_scale.transform(data_fcp1) + # + # k_means = KMeans(init='k-means++', n_init="auto", n_clusters=15, + # random_state=10) # init='k-means++'表示用kmeans++的方法来选择初始质数 n_clusters=8表示数据聚为8类 max_iter=500表示最大的迭代次数是500(缺省300) + # k_means.fit(data_fcp2) + # labels = k_means.labels_ + # y_pred = k_means.fit_predict(data_fcp2) + # print(y_pred) + # + # all_features['labels'] = labels + # all_features['label'] = all_labels + # path = "data/fcp/" + # for i in range(15): + # m = all_features.loc[all_features['labels'] == i] + # filepath = path + str(i) + ".txt" + # print(filepath) + # m = m.drop(['labels'], axis=1) + # + # m.to_csv(path + str(i) + ".txt", index=False, header=False) + # data = np.loadtxt(r'data/fcp/1.txt',delimiter=',') + # # fcp_data = np.loadtxt(r'data/fcp/fcp.txt',delimiter=',') + # fcp_data() + # print(1) + # + + + + + +""" + # 绘制图像判断折点 + K = range(1, 37) + + meandistortions = [] + for k in K: + kmeans = KMeans(n_clusters=k) + kmeans.fit(data_fcp2) + # 计算各个点分别到k个质心的距离,取最小值作为其到所属质心的距离,并计算这些点到各自所属质心距离的平均距离 + meandistortions.append( + sum( + np.min(cdist(data_fcp2, kmeans.cluster_centers_, 'euclidean'), axis=1) + ) / data_fcp2.shape[0] + ) + plt.plot(K, meandistortions, 'bx--') + plt.xlabel('k') + plt.savefig("Gravel figure_pca_2.png") + plt.show() + +""" + + + + + + + + + + + + + +# 读取文件,随机 9:1 分数据,数据保存到两个文件里 \ No newline at end of file diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/main.py b/examples/aoa/single_task_bench/TForest/other_attempts/main.py new file mode 100644 index 00000000..f311afe2 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/main.py @@ -0,0 +1,386 @@ +import logging +import numpy as np +import joblib +import matplotlib.pyplot as plt + +# plt.style.use(['science', 'ieee']) +logging.basicConfig(level=logging.INFO) +from tree import TForest_Tree,AOA_dataset,Tree +import os +import statsmodels.api as sm +import scienceplots +from tqdm import tqdm +from torch.utils.data import DataLoader +# plt.style.use(['science', 'ieee']) + +import torch +from sklearn.ensemble import AdaBoostRegressor, AdaBoostClassifier +device = torch.device('cuda') +def TForest_tree_initial(): + #输入每个trace数据,然后训练一个tree存到列表 + tree_list = [] + # 11,12,21,33,34,42为测试集 + path = r'data/' + # 后面再改成11啊 + for i in range(1,11): + train_dataset = AOA_dataset(path+'train/'+str(i)+'_train.txt') + ver_dataset = AOA_dataset(path+'verification/'+str(i)+'_ver.txt') + tree = TForest_Tree(train_dataset,ver_dataset) + tree.train() + tree_list.append(tree) + for i in range(13,21): + train_dataset = AOA_dataset(path+'train/'+str(i)+'_train.txt') + ver_dataset = AOA_dataset(path+'verification/'+str(i)+'_ver.txt') + tree = TForest_Tree(train_dataset,ver_dataset) + tree.train() + tree_list.append(tree) + for i in range(22,33): + train_dataset = AOA_dataset(path+'train/'+str(i)+'_train.txt') + ver_dataset = AOA_dataset(path+'verification/'+str(i)+'_ver.txt') + tree = TForest_Tree(train_dataset,ver_dataset) + tree.train() + tree_list.append(tree) + for i in range(35,42): + train_dataset = AOA_dataset(path+'train/'+str(i)+'_train.txt') + ver_dataset = AOA_dataset(path+'verification/'+str(i)+'_ver.txt') + tree = TForest_Tree(train_dataset,ver_dataset) + tree.train() + tree_list.append(tree) + + return tree_list + +# def tree_update(tree_list): + +def sim_compare(dataset1,dataset2,sim=30): + + + mean1 = torch.mean(dataset1.x_data, dim=0) + mean2 = torch.mean(dataset2.x_data, dim=0) + dist = torch.linalg.norm(mean1 - mean2).item() + if dist > sim: + return True + else: + return False + +# def general_condition(): +def check_fusion_type(score1, score2, tree1, tree2): + + # score1 = tree1.predict_score(tree_fused.model) + # score2 = tree2.predict_score(tree_fused.model) + + if (score1 > tree1.r2) and (score2 > tree2.r2): + return 0 # entire benefical + elif score1 > tree1.r2: + return 1 # self benefical on tree1 + elif score2 > tree2.r2: + return 2 # self benefical on tree2 + else: + return 3 + +def try_fusion(tree_list): + tree1 = tree_list[0] + best_tree = None + best_index = 1 + best_type = 3 + sim = 40 + set_lambda_G = 3 + for tree_index in range(1, len(tree_list)): + print(tree_index) + tree2 = tree_list[tree_index] + if sim_compare(tree1.train_dataset,tree2.train_dataset,sim): + # 相似度大于sim时返回Ture,即跳过整个不合并 + continue + fused_train_data = torch.utils.data.ConcatDataset([tree1.train_dataset, tree2.train_dataset]) + # fused_train_dataloader = DataLoader(fused_train_data, batch_size=32, shuffle=True) + fused_ver_data = torch.utils.data.ConcatDataset([tree1.ver_dataset, tree2.ver_dataset]) + # fused_ver_dataloader = DataLoader(fused_ver_data, batch_size=32, shuffle=True) + tree_fused = TForest_Tree(fused_train_data,fused_ver_data) + tree_fused.train() + # 这里求得本来是个负值 r2 + lamda_g = tree_fused.lambda_G(tree_fused.model) + if -lamda_g>set_lambda_G: + # 如果误差大于lamda_G则跳过,不合并 + continue + score1 = tree1.predict_score(tree_fused.model) + score2 = tree2.predict_score(tree_fused.model) + fusion_type = check_fusion_type(score1, score2, tree1, tree2) + print(score1, score2, fusion_type) + if best_tree is None: + best_tree = tree_fused + best_type = fusion_type + else: + if fusion_type < best_type: + best_tree = tree_fused + best_index = tree_index + best_type = fusion_type + elif fusion_type == best_type and tree_fused.r2 > best_tree.r2: + best_tree = tree_fused + best_index = tree_index + logging.debug('Best Fusion: {} and {}'.format(str(0), str(best_index))) + + return best_tree, best_type, best_index + +def tree_update(tree_list): + finish_count = 0 + + def take_r2(elem): + if elem.checked is True: + return 1 + else: + return elem.r2 + + while finish_count < 20: + + + + tree_list.sort(key=take_r2) + logging.info("Before Updating!") + # for tree_each in tree_list: + # print(tree_each.r2) + tree_fused, fusion_flag, best_index = try_fusion(tree_list) + if fusion_flag == 0: + del tree_list[0] # delete all two trees + del tree_list[best_index - 1] # the index changed when the first on deleted. + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 1: + del tree_list[0] # delete the 2nd tree + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 2: + del tree_list[best_index] # delete the 1st tree + tree_list.append(tree_fused) + finish_count = 0 + else: + finish_count += 1 + tree_list[0].checked = True + + logging.info("Tree Updating, Fusion Flag: " + str(fusion_flag)) + print(f'finish_count:{finish_count}') + for tree_each in tree_list: + print(tree_each.r2,end=' ') + return tree_list + + +def tforest_main(): + + logging.info("Tree Initializating") + tree_list = TForest_tree_initial() + joblib.dump(tree_list, 'initial_tree.joblib') + tree_list = joblib.load('initial_tree.joblib') + tree_final_list = tree_update(tree_list) + joblib.dump(tree_final_list, 'final_tree.joblib') + + + + + logging.info("STP Tree Initializating") + stp_list = stp_tree() + joblib.dump(stp_list, 'stp_tree.joblib') + + logging.info("MTP Tree Initializating") + mtp_list = mtp_tree() + joblib.dump(mtp_list,'mtp_tree.joblib') + + logging.info('Fcp Tree Initializating') + fcp_list = fcp_tree() + joblib.dump(fcp_list,'fcp_tree.joblib') + # logging.info('Fcp Tree Initializating') + # fcp_list = fcp_tree() + # joblib.dump(fcp_list,'fcp_tree.joblib') + + +def stp_tree(): + path = r'data/stp/' + train_dataset = AOA_dataset(path + 'train/' + 'stp_train.txt') + ver_dataset = AOA_dataset(path + 'ver/'+'stp_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + return tree + + #输入4大场景所有的训练数据,然后训练1个tree + + +def mtp_tree(): + # 输入四大场景的训练数据,然后训练4个tree + mtp_tree_list = [] + path = r'data/mtp/' + list = ['rural', 'parkinglot','surface','highway'] + for i in list: + + train_dataset = AOA_dataset(path + 'train/' + i+'_train.txt') + ver_dataset = AOA_dataset(path + 'ver/' + i+'_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + mtp_tree_list.append(tree) + return mtp_tree_list + + + +def fcp_tree(): + fcp_tree_list = [] + path = r'data/fcp/' + for i in range(15): + train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + ver_dataset = AOA_dataset(path + 'ver/' + str(i) + '_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + fcp_tree_list.append(tree) + return fcp_tree_list + + # 聚完类之后重新训练几个tree + +def task_selector(): + task_final = joblib.load('final_tree.joblib') + features = [] + task_labels = [] + + for index, task_each in enumerate(task_final): + for feature_each in task_each.train_dataset.x_data: + feature = feature_each.numpy().reshape(312,) + features.append(feature) + task_labels.append(index) + #把每个task的模型提取出来,输入数据和task label ,用模型训练 + selector=AdaBoostClassifier(random_state=36) + selector.fit(features, task_labels) + joblib.dump(selector, 'selector.joblib') + +def task_allocated(test_dataset,selector): + for i in test_dataset.x_data: + feature = i.numpy().reshape(312,) + TF_task = selector.predict([feature])[0] + + MTP_task = 1 + FCP_task = 2 + + RTP_task = np.random.randint(0,36) + + return TF_task,MTP_task,FCP_task,RTP_task + + pass + + +def test_main(): + # 载入4大模型和分配器 + tree_final = joblib.load('final_tree.joblib') + fcp_tree = joblib.load('fcp_tree.joblib') + mtp_tree = joblib.load('mtp_tree.joblib') + stp_tree = joblib.load('stp_tree.joblib') + selector = joblib.load('selector.joblib') + + # 载入6个数据集,对应四个场景 + path = r'data/test/test_data/' + test_list = ['rural-1.txt','rural-2.txt','parkinglot-1.txt','highway-1.txt','surface-1.txt','surface-2.txt'] + for index in tqdm(range(len(test_list))): + # 6个测试场景 + test_dataset = AOA_dataset(path + test_list[index]) + # test_loader = DataLoader(dataset=test_dataset, batch_size=32, shuffle=True, num_workers=2) + + + + TF_error = [] + STP_error = [] + MTP_error = [] + FCP_error = [] + RTP_error = [] + # 求四个task编号 TF_task,MTP_task,FCP_task,RTP_task,STP不需要求 + y_labels = test_dataset.y_data.numpy().reshape(1,-1) + for i in test_dataset.x_data: + feature = i.numpy().reshape(312, ) + # TF_task + TF_task = selector.predict([feature])[0] + """ + # MTP_task + Mtp_dist = [] + for mtp_index in mtp_tree: + mtp_feature = mtp_index.train_dataset.x_data.numpy().reshape(-1,312) + # mtp_feature2 = mtp_feature.reshape(12363,312) + #feature与每行求欧氏距离,再求绝对值 + mtp_list = np.linalg.norm(mtp_feature-feature,axis = 1) + mtp_dist = np.mean(mtp_list) + # mtp_dist= np.mean(np.linalg.norm(mtp_feature,axis = 1)) + Mtp_dist.append(mtp_dist) + # 是其中最小值的位置 + MTP_task = Mtp_dist.index(min(Mtp_dist)) + # FCP_task + FCP_dist = [] + for fcp_index in fcp_tree: + fcp_feature = fcp_index.train_dataset.x_data.numpy().reshape(-1,312) + # mtp_feature2 = mtp_feature.reshape(12363,312) + #feature与每行求欧氏距离,再求绝对值 + fcp_list = np.linalg.norm(fcp_feature-feature,axis = 1) + fcp_dist = np.mean(fcp_list) + # mtp_dist= np.mean(np.linalg.norm(mtp_feature,axis = 1)) + FCP_dist.append(fcp_dist) + # 是其中最小值的位置 + FCP_task = FCP_dist.index(min(FCP_dist)) + # RTP_task + RTP_task = np.random.randint(0, 36) + # TF_task,MTP_task,FCP_task,RTP_task + """ + + + # 预测值 + # TForest + inputs = torch.unsqueeze(i, dim=1).to(device) + with torch.no_grad(): + model = tree_final[TF_task].model.to(device) + pre = model(inputs) + pre = pre.item() + TF_error.append(pre) + # TF_error = np.array(TF_error) + """ + # stp预测 + with torch.no_grad(): + model = stp_tree.model.to(device) + pre = model(inputs).item() + STP_error.append(pre) + # mtp预测 + with torch.no_grad(): + model = mtp_tree[MTP_task].model.to(device) + pre = model(inputs).item() + MTP_error.append(pre) + # fcp预测 + with torch.no_grad(): + model = fcp_tree[FCP_task].model.to(device) + pre = model(inputs).item() + FCP_error.append(pre) + # rtp预测 + with torch.no_grad(): + model = tree_final[RTP_task].model.to(device) + pre = model(inputs).item() + RTP_error.append(pre) + + """ + + + TF_error = np.array(TF_error) + # STP_error = np.array(STP_error) + # MTP_error = np.array(MTP_error) + # FCP_error = np.array(FCP_error) + # RTP_error = np.array(RTP_error) + + TF_error = np.array(TF_error)-y_labels + # STP_error = np.array(STP_error)-y_labels + # MTP_error = np.array(MTP_error)-y_labels + # FCP_error = np.array(FCP_error)-y_labels + # RTP_error = np.array(RTP_error)-y_labels + + + + + # pass + # tf预测 + # stp预测 + # mtp预测 + # fcp预测 + + pass + +if __name__ == '__main__': + # tforest_main() + # test里面开始,构建test的数据集 + # task_selector() + test_main() + print(1) + diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/model.pth b/examples/aoa/single_task_bench/TForest/other_attempts/model.pth new file mode 100644 index 0000000000000000000000000000000000000000..2e4ed97289ac7403d6dabe28fe6951098dfe4c3d GIT binary patch literal 650260 zcmb@t30O{D+yCD@&x1xPlr(8X*Iwr?Nud&%5(z0qhAWjZLNY``hC;?rM8?!!=T;gh zN+JmvQZiJ=jHUk-_kF)SzxVgP|KD>wmt)sqpV$7Z@45DO?|rVlR-?TnrNqSK@cf8~kl?8B`STVk_(skQi42+P z9v&Q}AQbEGClojLR}rCu=g*6=v$3^V6cRowEJ`R595r8rwh4-k3XfQ*5EL0QBRV`{ zW>8efyoK{4gJSFzgp%gw{<5MY7mB_)eO8E2>enF(9uf1WM~(fpQ$Z;0FE09ytx(2a 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zya;0aP4z<#i=k=h`OGyNNk8q`&@;uPpKBQx_sc-?K6~}*5#oL1_mL)d)R*G>ZyZ7Q zzdnT8@i|rUYo-7A)|16w-)y5kXxBwC6to{L`Z@47)z`tQ0b}=%x2cA#nRCbZ46203 zfrme&y)1)qcXwZl8C?wP{jz`2U0eX`nmtC@eEbYyw-)(*a>xg9A=3{xo^QPaPY42hhM=W1D_?YAASMLN%fa+%zO`8omXmbERA5zL29z&~k z^i|97r*Qt*6}#5LXD};yee?(47cf6)qj0iGE;xLetf%%O7mU`t3O%I!8frA2WX$%= zg;2eZm!s2O!>yt2cODhy!D?Z$&F8^+@bSW-b^A@ tree1.r2) and (score2 > tree2.r2): + return 0 # entire benefical + elif score1 > tree1.r2: + return 1 # self benefical on tree1 + elif score2 > tree2.r2: + return 2 # self benefical on tree2 + else: + return 3 + + +def try_fusion(tree_list): + tree1 = tree_list[0] + best_tree = None + best_index = 1 + best_type = 3 + for tree_index in range(1, len(tree_list)): + print(f'\n与第{tree_index}个tree合并的情况') + tree2 = tree_list[tree_index] + tree_fused = Tree(np.concatenate((tree1.train_features, tree2.train_features), axis=0), + np.concatenate((tree1.test_features, tree2.test_features), axis=0), + np.concatenate((tree1.train_labels, tree2.train_labels), axis=0), + np.concatenate((tree1.test_labels, tree2.test_labels), axis=0)) + tree_fused.train() + + score1 = tree1.predict_score(tree_fused.model) + score2 = tree2.predict_score(tree_fused.model) + fusion_type = check_fusion_type(score1, score2, tree1, tree2) + print(f'score1:{score1}, score2:{score2},合并类型: {fusion_type}') + if best_tree is None: + print('best_tree is None') + else: + + print(f'tree_fused.r2:{tree_fused.r2},best_tree.r2:{best_tree.r2}') + if best_tree is None: + best_tree = tree_fused + best_type = fusion_type + else: + if fusion_type < best_type: + best_tree = tree_fused + best_index = tree_index + best_type = fusion_type + elif fusion_type == best_type and tree_fused.r2 > best_tree.r2: + best_tree = tree_fused + best_index = tree_index + print(f'best_index为:{best_index}') + logging.debug('Best Fusion: {} and {}'.format(str(fusion_type), str(best_index))) + + return best_tree, best_type, best_index + + +def tree_update(tree_list): + finish_count = 0 + def take_r2(elem): + if elem.checked is True: + return 1 + else: + return elem.r2 + + while finish_count < 20: + + tree_list.sort(key=take_r2) + logging.info("Before Updating!") + print(f'tree的数量为:{len(tree_list)}') + for tree_each in tree_list: + print(tree_each.r2,end = ',') + tree_fused, fusion_flag, best_index = try_fusion(tree_list) + if fusion_flag == 0: + del tree_list[0] # delete all two trees + del tree_list[best_index-1] # the index changed when the first on deleted. + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 1: + del tree_list[0] # delete the 2nd tree + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 2: + del tree_list[best_index] # delete the 1st tree + tree_list.append(tree_fused) + finish_count = 0 + else: + finish_count += 1 + tree_list[0].checked = True + + logging.info("Tree Updating, Fusion Flag: " + str(fusion_flag)) + for tree_each in tree_list: + print(tree_each.r2) + return tree_list + + +def tforest_main(): + # logging.info("Tree Initializating") + # tree_list, single_tree = tree_initial() + # + # joblib.dump(tree_list, 'initial_tree.joblib') + # joblib.dump(single_tree, 'single_tree.joblib') + tree_list = joblib.load('initial_tree.joblib') + logging.info("Tree Updating") + tree_list = tree_update(tree_list) + for tree_each in tree_list: + print(tree_each.r2) + + joblib.dump(tree_list, 'tree_final.joblib') + + +def cdf_draw_each(data, label, color='#8ea3c2', marker='*', linestyle='-', markersize=3): + ecdf = sm.distributions.ECDF(data) + x = np.linspace(min(data), max(data)) + y = ecdf(x) + # plt.step(x, y, label=label, color=color, markersize=markersize, marker=marker, linestyle=linestyle) + plt.step(x, y, label=label) + + +def model_selector_training(all_error_list, test_features): + for index in range(8): + error_list = np.array(all_error_list)[:, index] + selector=AdaBoostRegressor(random_state=42) + selector.fit(test_features, error_list) + joblib.dump(selector, 'selectors/task-'+str(index)+'.m') + + +def task_allocation_comi(test_features, test_labels, single_tree): + tree_final = joblib.load('tree_final.joblib') + + # model_list = [] + # for index, tree_each in enumerate(tree_final): + + # # tree_each.train() + # # model_list.append(tree_each.model) + # print(index, tree_each.r2) + # model_list.append(tree_each.model) + + # # joblib.dump(tree_each.model, 'models/task-'+str(index)+'.m') + + model_list = [] + for tree_index in range(len(tree_final)): + model = joblib.load('models/task-'+str(tree_index)+'.joblib') + model_list.append(model) + + selector_list = [] + for tree_index in range(len(tree_final)): + selector = joblib.load('selectors/task-'+str(tree_index)+'.joblib') + selector_list.append(selector) + + # selector = joblib.load('selector.joblib') + + mse_tomi = [] + mse_bsm = [] + ssm_list = [] + for test_index in tqdm(range(len(test_features))): + features_each = test_features[test_index] + labels_each = test_labels[test_index] + + best_task = 0 + min_error = 100 + + for index, selector in enumerate(selector_list): + predict_error = selector.predict([features_each])[0] + # print(predict_error) + if predict_error < min_error: + min_error = predict_error + best_task = index + # print(min_error) + # print() + + # best_task = selector.predict([features_each])[0] + + # print(best_task) + prediction = model_list[best_task].predict([features_each])[0] + # print(abs(prediction-labels_each)) + + mse_tomi.append(abs(prediction-labels_each) * 180 / 3.14) + + ssm = abs(single_tree.predict([features_each])[0]-labels_each) * 180 / 3.14 + # print(ssm) + ssm_list.append(ssm) + # print() + + error_list = [] + for task_index in range(len(model_list)): + prediction = model_list[task_index].predict([features_each])[0] + error = abs(prediction-labels_each) * 180 / 3.14 + error_list.append(error) + mse_bsm.append(min(error_list)) + + cdf_draw_each(mse_tomi, 'COMI') + cdf_draw_each(ssm_list, 'Single Model') + cdf_draw_each(mse_bsm, 'Best Model Selection') + plt.title('Mean error on Aoa') + plt.legend() + plt.savefig('cdf_comi.png') + + +def task_allocation_tforest(test_features, test_labels, single_tree): + tree_final = joblib.load('tree_final.joblib') + + # model_list = [] + # for index, tree_each in enumerate(tree_final): + + # # tree_each.train() + # # model_list.append(tree_each.model) + # print(index, tree_each.r2) + # model_list.append(tree_each.model) + + # # joblib.dump(tree_each.model, 'models/task-'+str(index)+'.m') + + model_list = [] + for tree_index in range(len(tree_final)): + model = joblib.load('models/task-'+str(tree_index)+'.joblib') + model_list.append(model) + + # selector_list = [] + # for tree_index in range(len(tree_final)): + # selector = joblib.load('selectors/task-'+str(tree_index)+'.joblib') + # selector_list.append(selector) + + selector = joblib.load('selector.joblib') + + mse_tomi = [] + mse_bsm = [] + ssm_list = [] + for test_index in tqdm(range(len(test_features))): + features_each = test_features[test_index] + labels_each = test_labels[test_index] + + # best_task = 0 + # min_error = 100 + + # for index, selector in enumerate(selector_list): + # predict_error = selector.predict([features_each])[0] + # # print(predict_error) + # if predict_error < min_error: + # min_error = predict_error + # best_task = index + # # print(min_error) + # # print() + + best_task = selector.predict([features_each])[0] + + # print(best_task) + prediction = model_list[best_task].predict([features_each])[0] + # print(abs(prediction-labels_each)) + + mse_tomi.append(abs(prediction-labels_each) * 180 / 3.14) + + ssm = abs(single_tree.predict([features_each])[0]-labels_each) * 180 / 3.14 + # print(ssm) + ssm_list.append(ssm) + # print() + + error_list = [] + for task_index in range(len(model_list)): + prediction = model_list[task_index].predict([features_each])[0] + error = abs(prediction-labels_each) * 180 / 3.14 + error_list.append(error) + mse_bsm.append(min(error_list)) + + cdf_draw_each(mse_tomi, 'TForest') + cdf_draw_each(ssm_list, 'Single Model') + cdf_draw_each(mse_bsm, 'Best Model Selection') + plt.title('Mean error on Aoa') + plt.legend() + plt.savefig('cdf_tforest.png') + + +def selector_training(error_list, test_features, index): + error_list = np.array(error_list) + selector=AdaBoostRegressor(random_state=42) + selector.fit(test_features, error_list) + joblib.dump(selector, 'selectors/task-'+str(index)+'.joblib') + + +def gen_error_list(): + single_tree = joblib.load('single_tree.joblib') + train_features = single_tree.train_features + train_labels = single_tree.train_labels + + tree_final = joblib.load('tree_final.joblib') + + model_list = [] + for tree_index in range(len(tree_final)): + model = joblib.load('models/task-'+str(tree_index)+'.joblib') + model_list.append(model) + + for index, model in enumerate(model_list): + error_list = [] + # print(model.estimator_weights_) + for sample_index in tqdm(range(len(train_features))): + train_feature = train_features[sample_index] + train_label = train_labels[sample_index] + prediction = model.predict(train_feature.reshape(1, -1))[0] + # print(prediction, train_label) + error_list.append(abs(prediction - train_label)) + + # print(error_list[0]) + selector_training(error_list, train_features, index) + + +def test_main(): + # load single task + single_tree = joblib.load('single_tree.joblib') + + test_features = single_tree.test_features + test_labels = single_tree.test_labels + + task_allocation_comi(test_features, test_labels, single_tree) + + +def save_model(): + tree_final = joblib.load('tree_final.joblib') + for model_index in range(len(tree_final)): + tree_final[model_index].train() + print(tree_final[model_index].model.estimator_weights_) + joblib.dump(tree_final[model_index].model, 'models/task-'+str(model_index)+'.joblib') + + +def task_selector(): + task_final = joblib.load('tree_final.joblib') + features = [] + task_labels = [] + + for index, task_each in enumerate(task_final): + for feature_each in task_each.train_features: + features.append(feature_each) + task_labels.append(index) + + selector=AdaBoostClassifier(random_state=42) + selector.fit(features, task_labels) + joblib.dump(selector, 'selector.joblib') + + +if __name__ == '__main__': + # tforest_main() + # save_model() + # gen_error_list() + + # task_selector() + test_main() + + # def take_r2(elem): + # if elem.checked is True: + # return 1 + # else: + # return elem.r2 + # # tforest_main() + # final_tree = joblib.load('tree_final.joblib') + # + # init_tree = joblib.load('initial_tree.joblib') + # init_tree.sort(key=take_r2,reverse = True) + # SUM = 0 + # for i in range(len(final_tree)): + # if final_tree[i].r2 == init_tree[i].r2: + # SUM+=1 + # print('final:',final_tree[i].r2) + # print('init:',init_tree[i].r2) + # print('final:',len(final_tree)) + # print('init:',len(init_tree)) + # print('SUM:',SUM) + # # print(len(tree_list)) + # print(1) + diff --git a/examples/aoa/single_task_bench/TForest/tools.py b/examples/aoa/single_task_bench/TForest/tools.py new file mode 100644 index 00000000..2292099d --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/tools.py @@ -0,0 +1,65 @@ +import os +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split + + +def data_io(data_index=1): + path = 'data/processed/' + # names = [str(i) for i in range(64, 86)] #64~86 + # CHANNEL_NUM = 52 + names = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] + + all_data = np.loadtxt(os.path.join(path + str(names[data_index]) + '.txt'), delimiter=',') +# print(os.path.join(path + str(names[data_index]) + '.txt')) +# # all_data = all_data.sample(frac=1.0) +# print(all_data.shape) + # snr = all_data[:, 6:10] + all_features = all_data[:, 10:-1] + + # all_features = np.concatenate((snr, angles), axis = 1) + # pl = PolynomialFeatures() + # all_features = pl.fit_transform(all_features) + all_labels = all_data[:, 5] + + # train_features, test_features, train_labels, test_labels = train_test_split(features, labels, + # random_state=42, test_size=.01, shuffle=False) + + # print(features.shape) + + # regr = AdaBoostRegressor() + # regr.fit(train_features, train_labels) + # print(regr.score(test_features, test_labels)) + + # test_predict = regr.predict(test_features) + # plt.plot(test_predict, label = 'test_predict') + # plt.plot(test_labels, label = 'test_labels') + # plt.legend() + # plt.savefig('test.png') + + return all_features, all_labels + + +def load_test(test_file='seq/seq83_new.txt'): + CHANNEL_NUM = 52 + test_seq = np.array(pd.read_table(test_file, header = None, sep=',')) + snr = test_seq[:, 6:10] + angles = test_seq[:, 10:] + angles = np.concatenate((angles[:, 0:CHANNEL_NUM] - angles[:, CHANNEL_NUM:2*CHANNEL_NUM], + angles[:, 0:CHANNEL_NUM] - angles[:, 2*CHANNEL_NUM:3*CHANNEL_NUM], + angles[:, :CHANNEL_NUM] - angles[:, 3*CHANNEL_NUM:4*CHANNEL_NUM], + angles[:, CHANNEL_NUM:2*CHANNEL_NUM] - angles[:, 2*CHANNEL_NUM:3*CHANNEL_NUM], + angles[:, CHANNEL_NUM:2*CHANNEL_NUM] - angles[:, 3*CHANNEL_NUM:4*CHANNEL_NUM], + angles[:, 2*CHANNEL_NUM:3*CHANNEL_NUM] - angles[:, 3*CHANNEL_NUM:4*CHANNEL_NUM]), + axis = 1) + + all_features = np.concatenate((snr, angles), axis = 1) + # pl = PolynomialFeatures() + # all_features = pl.fit_transform(all_features) + all_labels = test_seq[:, 5] + return all_features, all_labels + + +if __name__ == '__main__': + features, labels = load_test() + print(features.shape) diff --git a/examples/aoa/single_task_bench/TForest/tree.py b/examples/aoa/single_task_bench/TForest/tree.py new file mode 100644 index 00000000..3826f660 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/tree.py @@ -0,0 +1,22 @@ +from sklearn.ensemble import AdaBoostRegressor +from sklearn.metrics import mean_absolute_error + +class Tree: + def __init__(self, train_features, test_features, train_labels, test_labels, model=AdaBoostRegressor(random_state=42)): + self.train_features = train_features + self.test_features = test_features + self.train_labels = train_labels + self.test_labels = test_labels + self.model = model + self.r2 = None + self.checked = False + + def train(self): + self.model.fit(self.train_features, self.train_labels) + self.r2 = -mean_absolute_error(self.model.predict(self.test_features), self.test_labels) + + def predict(self, features): + return self.model.predict(features) + + def predict_score(self, model): + return -mean_absolute_error(model.predict(self.test_features), self.test_labels) From f2355ce7ae08110c1395d171b65cf7cb159905a2 Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Thu, 26 Sep 2024 18:08:27 +0800 Subject: [PATCH 8/9] Delete DAHLIA.png Signed-off-by: Frank-lilinjie --- ...KubeEdge-Ianvs Cloud-Edge Collaboration.md | 6 ------ .../single-task-learning/images/DAHLIA.png | Bin 2469607 -> 0 bytes 2 files changed, 6 deletions(-) delete mode 100644 docs/proposals/algorithms/single-task-learning/images/DAHLIA.png diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index ca47d1c1..e8e347ca 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -79,12 +79,6 @@ What happens if you put a trained Agent model in a mismatched scenario? 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zrGUKj9EKq&CPsnE&{l&if;{EzbMVSP-{~2)SZ*dY?$f_vL3=kw2TyBRi=gum&63Re=z)l31(!!zOkt)s@#z^LWTv zC$*e&w@K!8Q2auY)UmvF9pAduOn-IIitOKZMNQ|pbEPS?3U3A7bn$J@yP?xdIVU`* zx{>*B&C;o+_3!$yS4HWi6PTvC0M7-#i`}MZ2dqBsx(|yw(JkEP*$@4#+tNri(&$r}e)|JzXWe?5SG3u93C6Q2qw`~McP#&~vp YbFwBhA@c;Oaf5iY9_g#sKD3GW52XsYkpKVy From de4b2447dc788e1aebf6aea939dcdd7471de93c6 Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Thu, 26 Sep 2024 18:16:09 +0800 Subject: [PATCH 9/9] modify Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md Signed-off-by: Frank-lilinjie --- ...t based on KubeEdge-Ianvs Cloud-Edge Collaboration.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index 60214a48..e8e347ca 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -69,11 +69,7 @@ This project presents three scenarios, which are audio task, human posture class What happens if you put a trained Agent model in a mismatched scenario? Here are two examples: 1. Set ***Agent #1*** to ***Scene #3***: Data source does not match; -<<<<<<< HEAD 2. Place ***Agent #3*** in ***Scene #2***: A task planning error occurred. -======= -2. Set ***Agent #3*** in ***Scene #2***: A task planning error occurred. ->>>>>>> 1e635499adc6b2b7a5d1e56fd146bb609de668ad ## 3.2 DataSet @@ -83,11 +79,6 @@ What happens if you put a trained Agent model in a mismatched scenario? Here are Q2A -<<<<<<< HEAD -======= - - ->>>>>>> 1e635499adc6b2b7a5d1e56fd146bb609de668ad ## 3.3 Ianvs Implement llm_agent_ianvs

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zeoH=Y{TJZg?ZN%pgXQaWRsIFotUcJFJ$PoBfy=)Do3{r$wgd$&FIkIwjeAZ?5Fd)ChHAARxnAlMdL(H{FpSNuJEwY|Z9&;RN7A3@X4 zTTYJk?!St@H0v?{D0=N)AGeN3Sh>NzqO~xbeCuo8oBw_<`PbiM|N4}kgq}p}=l}b& F{{^6JKD+<` literal 0 HcmV?d00001 diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/models.py b/examples/aoa/single_task_bench/TForest/other_attempts/models.py new file mode 100644 index 00000000..41db1c3d --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/models.py @@ -0,0 +1,135 @@ +import torch +from torch import nn +from torch.nn import functional as F + + +class Autoencoder(nn.Module): + def __init__(self, input_dim=316, hidden_num=4, output_dim=316): + super().__init__() + self.encoder = nn.Sequential( + nn.Linear(input_dim, 256), nn.ReLU(), + nn.Linear(256, 128), nn.ReLU(), + nn.Linear(128, 64), nn.ReLU(), + nn.Linear(64, hidden_num), nn.ReLU() + ) + + self.decoder = nn.Sequential( + nn.Linear(hidden_num, 64), nn.ReLU(), + nn.Linear(64, 128), nn.ReLU(), + nn.Linear(128, 256), nn.ReLU(), + nn.Linear(256, output_dim) + ) + + def forward(self, input_seqs): + encoded = self(input_seqs) + decoded = self.decoder(encoded) + return decoded + + +class AoACNN(torch.nn.Module): + def __init__(self, flat_num=13, output_dim=1): + super(AoACNN, self).__init__() + self.output_dim = output_dim + self.flat_num = flat_num + self.conv1 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=1, out_channels=8, kernel_size=3, stride=2, padding=1), + # input [batchsize,1,312], output [batchsize,8,156] + torch.nn.BatchNorm1d(8), + torch.nn.ReLU(), + torch.nn.MaxPool1d(kernel_size=3, stride=3) # input [batchsize,8,156], output [batchsize,8,52] + ) + self.conv2 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3, stride=2, padding=1), + # input [batchsize,8,52], output [batchsize,16, 26] + torch.nn.BatchNorm1d(16), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + self.conv3 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1), + # input [batchsize,16,13], output [batchsize,32, 13] + torch.nn.BatchNorm1d(32), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + # self.conv4 = torch.nn.Sequential( + # torch.nn.Conv1d(in_channels=32, out_channels=128, kernel_size=3, stride=1, padding=1), # input [batchsize,16,13], output [batchsize,32, 13] + # torch.nn.BatchNorm1d(128), + # torch.nn.ReLU(), + # ) + if self.output_dim == 1: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + else: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + # self.dp = torch.nn.Dropout(drop_p) + + def forward(self, x): + # x = x.cuda() + # print(x.shape) + x = self.conv1(x) + # print(x.shape) + x = self.conv2(x) + # print(x.shape) + x = self.conv3(x) + # print(x.shape) + # x = self.conv4(x) + + x = self.fc(x.view(x.size(0), -1)) + # print(x.shape) + return x + + +class DCNN(nn.Module): + def __init__(self, seq_len, flat_num=120, input=None, output=None): # seq_len=1 输入的segment的长度,input维度是312 + super().__init__() + self.conv1 = nn.Conv2d(1, 50, (5, 1)) + self.bn1 = nn.BatchNorm2d(50) + self.conv2 = nn.Conv2d(50, 40, (5, 1)) + self.bn2 = nn.BatchNorm2d(40) + if seq_len <= 20: + self.conv3 = nn.Conv2d(40, 20, (2, 1)) + else: + self.conv3 = nn.Conv2d(40, 20, (3, 1)) + self.bn3 = nn.BatchNorm2d(20) + self.pool = nn.MaxPool2d((2, 1)) + self.lin1 = nn.Linear(input * flat_num, 400) + self.lin2 = nn.Linear(400, output) + self.lin3 = nn.Linear(21600, 10) + + def forward(self, input_seqs): + h = input_seqs.unsqueeze(1) + h = F.relu(F.tanh(self.conv1(h))) + h = self.pool(h) + h = F.relu(F.tanh(self.conv2(h))) + h = self.pool(h) + h = F.relu(F.tanh(self.conv3(h))) + h = h.view(h.size(0), h.size(1), h.size(2) * h.size(3)) + # print(h.shape) + h = self.lin1(h) + h = F.relu(F.tanh(torch.sum(h, dim=1))) + h = self.normalize(h[:, :, None, None]) + h = self.lin2(h[:, :, 0, 0]) + return h + + def normalize(self, x, k=1, alpha=2e-4, beta=0.75): + # x = x.view(x.size(0), x.size(1) // 5, 5, x.size(2), x.size(3))# + # y = x.clone() + # for s in range(x.size(0)): + # for j in range(x.size(1)): + # for i in range(5): + # norm = alpha * torch.sum(torch.square(y[s, j, i, :, :])) + k + # norm = torch.pow(norm, -beta) + # x[s, j, i, :, :] = y[s, j, i, :, :] * norm + # x = x.view(x.size(0), x.size(1) * 5, x.size(3), x.size(4)) + return x + diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/mycode/data_set.py b/examples/aoa/single_task_bench/TForest/other_attempts/mycode/data_set.py new file mode 100644 index 00000000..18bc78bc --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/mycode/data_set.py @@ -0,0 +1,29 @@ +import torch +import numpy as np +from torch.utils.data import Dataset,DataLoader + +class AOA_dataset(Dataset): + # all_data = np.loadtxt(path, delimiter=',') + #在 init 中载入数据,先读入数据 + def __init__(self,path): + self.path = path + all_data = np.loadtxt(path, delimiter=',') + self.x_data = torch.from_numpy(all_data[:, :-1]).float().unsqueeze(1) + self.y_data = torch.from_numpy(all_data[:, -1]).float().unsqueeze(1) + self.len = len(all_data) + # 在 getitem 中返回相应位置的数据 + def __getitem__(self, index): + return self.x_data[index], self.y_data[index] + #在 len 中返回整个数据的长度 + def __len__(self): + return self.len + + + +if __name__ == '__main__': + path = '../data/train/1_train.txt' + data = AOA_dataset(path) + for i in iter(data): + print(i) + print(len(data)) + 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z1uOz?2EH((_kd_KsVKAaCcltbs80KF5q9k?I(V#imVIDG!Ypyh!Rf!%PP zIM7SX>AG<%X_^8rKs*$5BIw(oD?u}%-dxcBKzCplt2N_F%Of*Ug1yOg z2h7_Q2RLTOWTHr}kQiV7NM_oL!EyJuXQ9)FPdr+;BNJuI4#r;h$wG={+{uK)W`hXKaJFz|1m$3@PU00)z49Zr>XeCj;Yl1{GxWA zl%AGbN3|bUnoj9p`R^3<-t^M(Cp8o|pYfvj;DrTseNPs;Q@WsG4|Sd$doNSk(b0qA zNo{xqz3rUEoW;HE9FFiiZ1LK#+1AU=(|Vi31}A5)E!HQTE`)s zuPW~kANV!+lxtp4{E>M=rHMydtDU(uz>?E@Lp#%KT7=Z=v+c~IvXUBWU$--hGX%9x z8MHGsSX8IiRX)I^EH1XPUTM zALlV{XR-*SB{<`F^%$jfT=UwQa&--UOiXL>=23duxSw`g%#v{auKO}ePUC!2=7H2t z8tu$UPxai`aNg1voA?m^Ie5n8^KZn|s-19nHnW|nsM4AB0q2$PW~lXGa-DuDx)9fk zY^|%;!R-oSY^}Tv+nKe84&}Gtcu~IIG?G7g;X2(r^V^v(tAZMrX|;KT best_tree.r2: +# best_tree = tree_fused +# best_index = tree_index +# logging.debug('Best Fusion: {} and {}'.format(str(0), str(best_index))) +# +# return best_tree, best_type, best_index + + +# def tree_update(tree_list): +# finish_count = 0 +# +# def take_r2(elem): +# if elem.checked is True: +# return 1 +# else: +# return elem.r2 +# +# while finish_count < 20: +# +# tree_list.sort(key=take_r2) +# logging.info("Before Updating!") +# for tree_each in tree_list: +# print(tree_each.r2) +# tree_fused, fusion_flag, best_index = try_fusion(tree_list) +# if fusion_flag == 0: +# del tree_list[0] # delete all two trees +# del tree_list[best_index - 1] # the index changed when the first on deleted. +# tree_list.append(tree_fused) +# finish_count = 0 +# elif fusion_flag == 1: +# del tree_list[0] # delete the 2nd tree +# tree_list.append(tree_fused) +# finish_count = 0 +# elif fusion_flag == 2: +# del tree_list[best_index] # delete the 1st tree +# tree_list.append(tree_fused) +# finish_count = 0 +# else: +# finish_count += 1 +# tree_list[0].checked = True +# +# logging.info("Tree Updating, Fusion Flag: " + str(fusion_flag)) +# for tree_each in tree_list: +# print(tree_each.r2) +# return tree_list + + +def tforest_main(): + logging.info("Tree Initializating") + tree_list = TForest_tree_initial() + joblib.dump(tree_list, 'initial_tree.joblib') + +def sim_compare(dataset1,dataset2,sim=30): + + + mean1 = torch.mean(dataset1.x_data, dim=0) + mean2 = torch.mean(dataset2.x_data, dim=0) + dist = torch.linalg.norm(mean1 - mean2).item() + if dist > sim: + return True + else: + return False + +# def general_condition(): +def check_fusion_type(score1, score2, tree1, tree2): + + # score1 = tree1.predict_score(tree_fused.model) + # score2 = tree2.predict_score(tree_fused.model) + + if (score1 > tree1.r2) and (score2 > tree2.r2): + return 0 # entire benefical + elif score1 > tree1.r2: + return 1 # self benefical on tree1 + elif score2 > tree2.r2: + return 2 # self benefical on tree2 + else: + return 3 + +def try_fusion(tree_list): + tree1 = tree_list[0] + best_tree = None + best_index = 1 + best_type = 3 + sim = 40 + set_lambda_G = 1 + for tree_index in range(1, len(tree_list)): + print(tree_index) + tree2 = tree_list[tree_index] + if sim_compare(tree1.train_dataset,tree2.train_dataset,sim): + # 相似度大于sim时返回Ture,即跳过整个不合并 + continue + fused_train_data = torch.utils.data.ConcatDataset([tree1.train_dataset, tree2.train_dataset]) + # fused_train_dataloader = DataLoader(fused_train_data, batch_size=32, shuffle=True) + fused_ver_data = torch.utils.data.ConcatDataset([tree1.ver_dataset, tree2.ver_dataset]) + # fused_ver_dataloader = DataLoader(fused_ver_data, batch_size=32, shuffle=True) + tree_fused = TForest_Tree(fused_train_data,fused_ver_data) + tree_fused.train() + lamda_g = tree_fused.lambda_G() + # if lambda_G < lamda_g: + # # 如果误差大于lamda_G则跳过,不合并 + # continue + score1 = tree1.predict_score(tree_fused.model) + score2 = tree2.predict_score(tree_fused.model) + fusion_type = check_fusion_type(score1, score2, tree1, tree2) + print(score1, score2, fusion_type) + if best_tree is None: + best_tree = tree_fused + best_type = fusion_type + else: + if fusion_type < best_type: + best_tree = tree_fused + best_index = tree_index + best_type = fusion_type + elif fusion_type == best_type and tree_fused.r2 > best_tree.r2: + best_tree = tree_fused + best_index = tree_index + logging.debug('Best Fusion: {} and {}'.format(str(0), str(best_index))) + + return best_tree, best_type, best_index + +def tree_update(tree_list): + finish_count = 0 + + def take_r2(elem): + if elem.checked is True: + return 1 + else: + return elem.r2 + + while finish_count < 20: + + tree_list.sort(key=take_r2) + logging.info("Before Updating!") + for tree_each in tree_list: + print(tree_each.r2) + tree_fused, fusion_flag, best_index = try_fusion(tree_list) + if fusion_flag == 0: + del tree_list[0] # delete all two trees + del tree_list[best_index - 1] # the index changed when the first on deleted. + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 1: + del tree_list[0] # delete the 2nd tree + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 2: + del tree_list[best_index] # delete the 1st tree + tree_list.append(tree_fused) + finish_count = 0 + else: + finish_count += 1 + tree_list[0].checked = True + + logging.info("Tree Updating, Fusion Flag: " + str(fusion_flag)) + for tree_each in tree_list: + print(tree_each.r2) + return tree_list + +if __name__ == '__main__': + # tforest_main() + tree_list = joblib.load('test_init_tree.joblib') + # print(len(tree_list)) + # tree_list = tree_update(tree_list) + + + tree_final_list = tree_update(tree_list) + + + print(1) + diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/testvar.py b/examples/aoa/single_task_bench/TForest/other_attempts/testvar.py new file mode 100644 index 00000000..a188af98 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/testvar.py @@ -0,0 +1,77 @@ +import logging +import numpy as np +import joblib +import matplotlib.pyplot as plt +from tree import AoACNN +# plt.style.use(['science', 'ieee']) +logging.basicConfig(level=logging.INFO) +from tree import TForest_Tree,AOA_dataset,Tree +import os +import statsmodels.api as sm +import scienceplots +from tqdm import tqdm +from torch.utils.data import DataLoader +# plt.style.use(['science', 'ieee']) + +import torch +from sklearn.ensemble import AdaBoostRegressor, AdaBoostClassifier +device = torch.device('cuda') + + +def test_main(): + # 载入4大模型和分配器 + # tree_final = joblib.load('final_tree.joblib') + # fcp_tree = joblib.load('fcp_tree.joblib') + # mtp_tree = joblib.load('mtp_tree.joblib') + # stp_tree = joblib.load('stp_tree.joblib') + # selector = joblib.load('selector.joblib') + tree_initial = joblib.load('1_tree.joblib') + path = r'data/verification/1_ver.txt' + test_dataset = AOA_dataset(path) + pre_all = [] + + y_labels = test_dataset.y_data.numpy().reshape(1, -1) + for i in test_dataset.x_data: + feature = i.numpy().reshape(312, ) + inputs = torch.unsqueeze(i, dim=1).to(device) + model = AoACNN() + model.load_state_dict(torch.load('model.pth')) + model = model.to(device) + with torch.no_grad(): + + pre = model(inputs) + pre = pre.item() + pre_all.append(pre) + print(pre_all-y_labels) + + + + # with torch.no_grad(): + # test_loader = DataLoader(dataset=test_dataset, batch_size=32, shuffle=True, num_workers=2) + # # y_labels = test_dataset.y_data.numpy().reshape(1, -1) + # # for i in test_dataset.x_data: + # # feature = i.numpy().reshape(312, ) + # # inputs = torch.unsqueeze(i, dim=1).to(device) + # + # # model = tree_initial[0].model.to(device) + # model = AoACNN() + # model.load_state_dict(torch.load('model.pth')) + # model = model.to(device) + # for i, data in enumerate(test_loader, 0): + # inputs, labels = data # Tensor type + # + # inputs = inputs.to(device) + # labels = labels.to(device) + # y_pred = model(inputs) + # pre_loss = (y_pred-labels).cpu().numpy() + # print(pre_loss) + # print(i) + + + +if __name__ == '__main__': + # tforest_main() + # test里面开始,构建test的数据集 + # task_selector() + test_main() + print(1) \ No newline at end of file diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/train_test.py b/examples/aoa/single_task_bench/TForest/other_attempts/train_test.py new file mode 100644 index 00000000..e17a6106 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/train_test.py @@ -0,0 +1,378 @@ +import logging +import numpy as np +import joblib +import matplotlib.pyplot as plt + +# plt.style.use(['science', 'ieee']) +logging.basicConfig(level=logging.INFO) +from tree import TForest_Tree, AOA_dataset, Tree +import os +import statsmodels.api as sm +import scienceplots +from tqdm import tqdm +from torch.utils.data import DataLoader +# plt.style.use(['science', 'ieee']) + +import torch +from sklearn.ensemble import AdaBoostRegressor, AdaBoostClassifier + +device = torch.device('cuda') + + +def TForest_tree_initial(): + # 输入每个trace数据,然后训练一个tree存到列表 + tree_list = [] + # 11,12,21,33,34,42为测试集 + path = r'data/' + # 后面再改成11啊 + for i in range(1, 2): + train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + ver_dataset = AOA_dataset(path + 'verification/' + str(i) + '_ver.txt') + tree = TForest_Tree(train_dataset, ver_dataset) + tree.train() + tree_list.append(tree) + # for i in range(13, 21): + # train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + # ver_dataset = AOA_dataset(path + 'verification/' + str(i) + '_ver.txt') + # tree = TForest_Tree(train_dataset, ver_dataset) + # tree.train() + # tree_list.append(tree) + # for i in range(22, 33): + # train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + # ver_dataset = AOA_dataset(path + 'verification/' + str(i) + '_ver.txt') + # tree = TForest_Tree(train_dataset, ver_dataset) + # tree.train() + # tree_list.append(tree) + # for i in range(35, 42): + # train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + # ver_dataset = AOA_dataset(path + 'verification/' + str(i) + '_ver.txt') + # tree = TForest_Tree(train_dataset, ver_dataset) + # tree.train() + # tree_list.append(tree) + + return tree_list + + +# def tree_update(tree_list): + +def sim_compare(dataset1, dataset2, sim=30): + mean1 = torch.mean(dataset1.x_data, dim=0) + mean2 = torch.mean(dataset2.x_data, dim=0) + dist = torch.linalg.norm(mean1 - mean2).item() + if dist > sim: + return True + else: + return False + + +# def general_condition(): +def check_fusion_type(score1, score2, tree1, tree2): + # score1 = tree1.predict_score(tree_fused.model) + # score2 = tree2.predict_score(tree_fused.model) + + if (score1 > tree1.r2) and (score2 > tree2.r2): + return 0 # entire benefical + elif score1 > tree1.r2: + return 1 # self benefical on tree1 + elif score2 > tree2.r2: + return 2 # self benefical on tree2 + else: + return 3 + + +def try_fusion(tree_list): + tree1 = tree_list[0] + best_tree = None + best_index = 1 + best_type = 3 + sim = 40 + set_lambda_G = 3 + for tree_index in range(1, len(tree_list)): + print(tree_index) + tree2 = tree_list[tree_index] + if sim_compare(tree1.train_dataset, tree2.train_dataset, sim): + # 相似度大于sim时返回Ture,即跳过整个不合并 + continue + fused_train_data = torch.utils.data.ConcatDataset([tree1.train_dataset, tree2.train_dataset]) + # fused_train_dataloader = DataLoader(fused_train_data, batch_size=32, shuffle=True) + fused_ver_data = torch.utils.data.ConcatDataset([tree1.ver_dataset, tree2.ver_dataset]) + # fused_ver_dataloader = DataLoader(fused_ver_data, batch_size=32, shuffle=True) + tree_fused = TForest_Tree(fused_train_data, fused_ver_data) + tree_fused.train() + # 这里求得本来是个负值 r2 + lamda_g = tree_fused.lambda_G(tree_fused.model) + if -lamda_g > set_lambda_G: + # 如果误差大于lamda_G则跳过,不合并 + continue + score1 = tree1.predict_score(tree_fused.model) + score2 = tree2.predict_score(tree_fused.model) + fusion_type = check_fusion_type(score1, score2, tree1, tree2) + print(score1, score2, fusion_type) + if best_tree is None: + best_tree = tree_fused + best_type = fusion_type + else: + if fusion_type < best_type: + best_tree = tree_fused + best_index = tree_index + best_type = fusion_type + elif fusion_type == best_type and tree_fused.r2 > best_tree.r2: + best_tree = tree_fused + best_index = tree_index + logging.debug('Best Fusion: {} and {}'.format(str(0), str(best_index))) + + return best_tree, best_type, best_index + + +def tree_update(tree_list): + finish_count = 0 + + def take_r2(elem): + if elem.checked is True: + return 1 + else: + return elem.r2 + + while finish_count < 20: + + tree_list.sort(key=take_r2) + logging.info("Before Updating!") + # for tree_each in tree_list: + # print(tree_each.r2) + tree_fused, fusion_flag, best_index = try_fusion(tree_list) + if fusion_flag == 0: + del tree_list[0] # delete all two trees + del tree_list[best_index - 1] # the index changed when the first on deleted. + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 1: + del tree_list[0] # delete the 2nd tree + tree_list.append(tree_fused) + finish_count = 0 + elif fusion_flag == 2: + del tree_list[best_index] # delete the 1st tree + tree_list.append(tree_fused) + finish_count = 0 + else: + finish_count += 1 + tree_list[0].checked = True + + logging.info("Tree Updating, Fusion Flag: " + str(fusion_flag)) + print(f'finish_count:{finish_count}') + for tree_each in tree_list: + print(tree_each.r2, end=' ') + return tree_list + + +def tforest_main(): + logging.info("Tree Initializating") + tree_list = TForest_tree_initial() + joblib.dump(tree_list, '1_tree.joblib') + tree_list = joblib.load('1_tree.joblib') + # tree_final_list = tree_update(tree_list) + # joblib.dump(tree_final_list, 'final_tree.joblib') + # + # logging.info("STP Tree Initializating") + # stp_list = stp_tree() + # joblib.dump(stp_list, 'stp_tree.joblib') + # + # logging.info("MTP Tree Initializating") + # mtp_list = mtp_tree() + # joblib.dump(mtp_list, 'mtp_tree.joblib') + # + # logging.info('Fcp Tree Initializating') + # fcp_list = fcp_tree() + # joblib.dump(fcp_list, 'fcp_tree.joblib') + # logging.info('Fcp Tree Initializating') + # fcp_list = fcp_tree() + # joblib.dump(fcp_list,'fcp_tree.joblib') + + +def stp_tree(): + path = r'data/stp/' + train_dataset = AOA_dataset(path + 'train/' + 'stp_train.txt') + ver_dataset = AOA_dataset(path + 'ver/' + 'stp_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + return tree + + # 输入4大场景所有的训练数据,然后训练1个tree + + +def mtp_tree(): + # 输入四大场景的训练数据,然后训练4个tree + mtp_tree_list = [] + path = r'data/mtp/' + list = ['rural', 'parkinglot', 'surface', 'highway'] + for i in list: + train_dataset = AOA_dataset(path + 'train/' + i + '_train.txt') + ver_dataset = AOA_dataset(path + 'ver/' + i + '_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + mtp_tree_list.append(tree) + return mtp_tree_list + + +def fcp_tree(): + fcp_tree_list = [] + path = r'data/fcp/' + for i in range(15): + train_dataset = AOA_dataset(path + 'train/' + str(i) + '_train.txt') + ver_dataset = AOA_dataset(path + 'ver/' + str(i) + '_ver.txt') + tree = Tree(train_dataset, ver_dataset) + tree.train() + fcp_tree_list.append(tree) + return fcp_tree_list + + # 聚完类之后重新训练几个tree + + +def task_selector(): + task_final = joblib.load('final_tree.joblib') + features = [] + task_labels = [] + + for index, task_each in enumerate(task_final): + for feature_each in task_each.train_dataset.x_data: + feature = feature_each.numpy().reshape(312, ) + features.append(feature) + task_labels.append(index) + # 把每个task的模型提取出来,输入数据和task label ,用模型训练 + selector = AdaBoostClassifier(random_state=36) + selector.fit(features, task_labels) + joblib.dump(selector, 'selector.joblib') + + +def task_allocated(test_dataset, selector): + for i in test_dataset.x_data: + feature = i.numpy().reshape(312, ) + TF_task = selector.predict([feature])[0] + + MTP_task = 1 + FCP_task = 2 + + RTP_task = np.random.randint(0, 36) + + return TF_task, MTP_task, FCP_task, RTP_task + + pass + + +def test_main(): + # 载入4大模型和分配器 + tree_final = joblib.load('final_tree.joblib') + fcp_tree = joblib.load('fcp_tree.joblib') + mtp_tree = joblib.load('mtp_tree.joblib') + stp_tree = joblib.load('stp_tree.joblib') + selector = joblib.load('selector.joblib') + + # 载入6个数据集,对应四个场景 + path = r'data/test/test_data/' + test_list = ['rural-1.txt', 'rural-2.txt', 'parkinglot-1.txt', 'highway-1.txt', 'surface-1.txt', 'surface-2.txt'] + for index in tqdm(range(len(test_list))): + # 6个测试场景 + test_dataset = AOA_dataset(path + test_list[index]) + # test_loader = DataLoader(dataset=test_dataset, batch_size=32, shuffle=True, num_workers=2) + + TF_error = [] + STP_error = [] + MTP_error = [] + FCP_error = [] + RTP_error = [] + # 求四个task编号 TF_task,MTP_task,FCP_task,RTP_task,STP不需要求 + y_labels = test_dataset.y_data.numpy().reshape(1, -1) + for i in test_dataset.x_data: + feature = i.numpy().reshape(312, ) + # TF_task + TF_task = selector.predict([feature])[0] + """ + # MTP_task + Mtp_dist = [] + for mtp_index in mtp_tree: + mtp_feature = mtp_index.train_dataset.x_data.numpy().reshape(-1,312) + # mtp_feature2 = mtp_feature.reshape(12363,312) + #feature与每行求欧氏距离,再求绝对值 + mtp_list = np.linalg.norm(mtp_feature-feature,axis = 1) + mtp_dist = np.mean(mtp_list) + # mtp_dist= np.mean(np.linalg.norm(mtp_feature,axis = 1)) + Mtp_dist.append(mtp_dist) + # 是其中最小值的位置 + MTP_task = Mtp_dist.index(min(Mtp_dist)) + # FCP_task + FCP_dist = [] + for fcp_index in fcp_tree: + fcp_feature = fcp_index.train_dataset.x_data.numpy().reshape(-1,312) + # mtp_feature2 = mtp_feature.reshape(12363,312) + #feature与每行求欧氏距离,再求绝对值 + fcp_list = np.linalg.norm(fcp_feature-feature,axis = 1) + fcp_dist = np.mean(fcp_list) + # mtp_dist= np.mean(np.linalg.norm(mtp_feature,axis = 1)) + FCP_dist.append(fcp_dist) + # 是其中最小值的位置 + FCP_task = FCP_dist.index(min(FCP_dist)) + # RTP_task + RTP_task = np.random.randint(0, 36) + # TF_task,MTP_task,FCP_task,RTP_task + """ + + # 预测值 + # TForest + inputs = torch.unsqueeze(i, dim=1).to(device) + with torch.no_grad(): + model = tree_final[TF_task].model.to(device) + pre = model(inputs) + pre = pre.item() + TF_error.append(pre) + # TF_error = np.array(TF_error) + """ + # stp预测 + with torch.no_grad(): + model = stp_tree.model.to(device) + pre = model(inputs).item() + STP_error.append(pre) + # mtp预测 + with torch.no_grad(): + model = mtp_tree[MTP_task].model.to(device) + pre = model(inputs).item() + MTP_error.append(pre) + # fcp预测 + with torch.no_grad(): + model = fcp_tree[FCP_task].model.to(device) + pre = model(inputs).item() + FCP_error.append(pre) + # rtp预测 + with torch.no_grad(): + model = tree_final[RTP_task].model.to(device) + pre = model(inputs).item() + RTP_error.append(pre) + + """ + + TF_error = np.array(TF_error) + # STP_error = np.array(STP_error) + # MTP_error = np.array(MTP_error) + # FCP_error = np.array(FCP_error) + # RTP_error = np.array(RTP_error) + + TF_error = np.array(TF_error) - y_labels + # STP_error = np.array(STP_error)-y_labels + # MTP_error = np.array(MTP_error)-y_labels + # FCP_error = np.array(FCP_error)-y_labels + # RTP_error = np.array(RTP_error)-y_labels + + # pass + # tf预测 + # stp预测 + # mtp预测 + # fcp预测 + + pass + + +if __name__ == '__main__': + tforest_main() + # test里面开始,构建test的数据集 + # task_selector() + # test_main() + print(1) + diff --git a/examples/aoa/single_task_bench/TForest/other_attempts/tree.py b/examples/aoa/single_task_bench/TForest/other_attempts/tree.py new file mode 100644 index 00000000..76d2a569 --- /dev/null +++ b/examples/aoa/single_task_bench/TForest/other_attempts/tree.py @@ -0,0 +1,211 @@ +import numpy as np +import Create_Dataset +from Create_Dataset import AoACNN,AOA_dataset +import time +from sklearn.metrics import mean_absolute_error +from torch.utils.data import Dataset,DataLoader,random_split +import torch +from torch import nn +from torch.nn import functional as F + +device = torch.device('cuda') + + +class AOA_dataset(Dataset): + # all_data = np.loadtxt(path, delimiter=',') + # 在 init 中载入数据,先读入数据 + def __init__(self, path): + self.path = path + all_data = np.loadtxt(path, delimiter=',') + + self.x_data = torch.from_numpy(all_data[:, :-1]).float().unsqueeze(1) + self.y_data = torch.from_numpy(all_data[:, -1]).float().unsqueeze(1) + self.len = len(all_data) + + # 在 getitem 中返回相应位置的数据 + def __getitem__(self, index): + return self.x_data[index], self.y_data[index] + + # 在 len 中返回整个数据的长度 + def __len__(self): + return self.len + +class AoACNN(torch.nn.Module): + def __init__(self, flat_num=13, output_dim=1): + super(AoACNN, self).__init__() + self.output_dim = output_dim + self.flat_num = flat_num + self.conv1 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=1, out_channels=8, kernel_size=3, stride=2, padding=1), + # input [batchsize,1,312], output [batchsize,8,156] + torch.nn.BatchNorm1d(8), + torch.nn.ReLU(), + torch.nn.MaxPool1d(kernel_size=3, stride=3) # input [batchsize,8,156], output [batchsize,8,52] + ) + self.conv2 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3, stride=2, padding=1), + # input [batchsize,8,52], output [batchsize,16, 26] + torch.nn.BatchNorm1d(16), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + self.conv3 = torch.nn.Sequential( + torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1), + # input [batchsize,16,13], output [batchsize,32, 13] + torch.nn.BatchNorm1d(32), + torch.nn.ReLU(), + # torch.nn.MaxPool1d(kernel_size=3, stride=2) # input [batchsize,16,26], output [batchsize,16,13] + ) + # self.conv4 = torch.nn.Sequential( + # torch.nn.Conv1d(in_channels=32, out_channels=128, kernel_size=3, stride=1, padding=1), # input [batchsize,16,13], output [batchsize,32, 13] + # torch.nn.BatchNorm1d(128), + # torch.nn.ReLU(), + # ) + if self.output_dim == 1: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + else: + self.fc = torch.nn.Sequential( + torch.nn.Linear(32 * self.flat_num, 256), torch.nn.ReLU(), + # torch.nn.Dropout(drop_p), + torch.nn.Linear(256, 200), torch.nn.ReLU(), + torch.nn.Linear(200, self.output_dim) + ) + # self.dp = torch.nn.Dropout(drop_p) + + def forward(self, x): + # x = x.cuda() + # print(x.shape) + x = self.conv1(x) + # print(x.shape) + x = self.conv2(x) + # print(x.shape) + x = self.conv3(x) + # print(x.shape) + # x = self.conv4(x) + + x = self.fc(x.view(x.size(0), -1)) + # print(x.shape) + return x + + def train(self,train_dataset,ver_dataset): + # path = r'data/' + start_time = time.time() + + train_loader = DataLoader(dataset=train_dataset, batch_size=32, shuffle=True, num_workers=2) + ver_loader = DataLoader(dataset=ver_dataset, batch_size=32, shuffle=True, num_workers=2) + model = AoACNN() + model = model.to(device) + criterion = torch.nn.MSELoss() # 损失函数 + criterion = criterion.to(device) + optimizer = torch.optim.SGD(model.parameters(), lr=0.001) # 优化器优化参 + print('tree训练开始') + for epoch in range(100): + for i, data in enumerate(train_loader, 0): + + # 1.准备数据 + inputs, labels = data # Tensor type + inputs = inputs.to(device) + labels = labels.to(device) + # 2.前向传播 + y_pred = model(inputs) + # print(labels.size()) + # print(y_pred.size()) + loss = criterion(y_pred, labels) + if epoch%10 == 0: + if i%10 == 0: + print(f'epoch:{epoch},i:{i},loss.item:{loss.item()}') + # 3.后向传播 + optimizer.zero_grad() + loss.backward() + # 4.更新 + optimizer.step() + + with torch.no_grad(): + loss_abs = 0 + for i, data in enumerate(ver_loader, 0): + inputs, labels = data # Tensor type + inputs = inputs.to(device) + labels = labels.to(device) + y_pred = model(inputs) + loss_abs = loss_abs+torch.abs(y_pred-labels).sum().item() + # print(len(ver_dataset)) + r2 = -loss_abs/len(ver_dataset) + end_time = time.time() + print(f'文件对应的tree训练完成,用时{end_time-start_time}秒,r2为{r2}') + torch.save(model.state_dict(),'model.pth') + return r2 + def predict(self,pre_dataset,model): + pre_dataloader = DataLoader(pre_dataset,batch_size=32,shuffle=True) + with torch.no_grad(): + loss_abs = 0 + for i, data in enumerate(pre_dataloader, 0): + inputs, labels = data # Tensor type + inputs = inputs.to(device) + labels = labels.to(device) + model = model.to(device) + y_pred = model(inputs) + loss_abs = loss_abs + torch.abs(y_pred - labels).sum().item() + # print(len(ver_dataset)) + r2 = -loss_abs / len(pre_dataset) + end_time = time.time() + print(f'predict运行完毕,r2为{r2}') + return r2 + + +class Tree(): + def __init__(self,train_dataset,ver_dataset,model = AoACNN()): + self.train_dataset = train_dataset + self.ver_dataset = ver_dataset + # self.ord_num = ord_num + self.model = model + self.r2 = None + self.checked = False + def train(self): + # train_dataset = AOA_dataset(path+'train/'+str(ord_num)+'_train.txt') + # ver_dataset = AOA_dataset(path+'verification/'+str(ord_num)+'_ver.txt') + self.r2 = self.model.train(self.train_dataset,self.ver_dataset) + + def predict_score(self,model): + # tree1 的测试集用合并后的模型 + return self.model.predict(self.ver_dataset,model) + + def lambda_G(self,model): + #general_dataset , 用合并后的模型 + return self.model.predict(general_dataset,model) + + + + + + + +general_dataset = AOA_dataset(r'data/general_data/gen_ver.txt') +class TForest_Tree(): + def __init__(self,train_dataset,ver_dataset,model = AoACNN()): + self.train_dataset = train_dataset + self.ver_dataset = ver_dataset + # self.ord_num = ord_num + self.model = model + self.r2 = None + self.checked = False + def train(self): + # train_dataset = AOA_dataset(path+'train/'+str(ord_num)+'_train.txt') + # ver_dataset = AOA_dataset(path+'verification/'+str(ord_num)+'_ver.txt') + self.r2 = self.model.train(self.train_dataset,self.ver_dataset) + + def predict_score(self,model): + # tree1 的测试集用合并后的模型 + return self.model.predict(self.ver_dataset,model) + + def lambda_G(self,model): + #general_dataset , 用合并后的模型 + return self.model.predict(general_dataset,model) + + + +# if __name__ == "__main__": diff 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KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index ca47d1c1..907b172c 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -69,7 +69,7 @@ This project presents three scenarios, which are audio task, human posture class What happens if you put a trained Agent model in a mismatched scenario? Here are two examples: 1. Set ***Agent #1*** to ***Scene #3***: Data source does not match; -2. Place ***Agent #3*** in ***Scene #2***: A task planning error occurred. +2. Set ***Agent #3*** in ***Scene #2***: A task planning error occurred. ## 3.2 DataSet From 2ce0fa8ef20b60cc230066a3a9e7565ed54e2a91 Mon Sep 17 00:00:00 2001 From: Frank-lilinjie Date: Thu, 26 Sep 2024 17:05:40 +0800 Subject: [PATCH 6/9] Delete Sec 3.2.2 Signed-off-by: Frank-lilinjie --- ... Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index 907b172c..77e41348 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -79,11 +79,7 @@ What happens if you put a trained Agent model in a mismatched scenario? Here are Q2A -### 3.2.2 The DAily Home LIfe Activity Dataset -DAHLIA dataset is devoted to human activity recognition, which is a major issue for adapting smart-home services such as user assistance. - -DAHLIA ## 3.3 Ianvs Implement From aa0580f8d78f2ff2e7c6ed98fc3a9e17ca050ab5 Mon Sep 17 00:00:00 2001 From: QiaoZheyu <15030126286@163.com> Date: Sat, 27 Jul 2024 20:54:18 +0800 Subject: [PATCH 7/9] AOA Code Signed-off-by: QiaoZheyu <15030126286@163.com> Update README.md Update CI badge modify proposal of LLM Agent Signed-off-by: Frank-lilinjie :modify the image of demo Signed-off-by: Frank-lilinjie --- README.md | 2 +- ...KubeEdge-Ianvs Cloud-Edge Collaboration.md | 19 +- .../images/llm_agent_demo.png | Bin 63280 -> 0 bytes .../images/personalized_agent.png | Bin 0 -> 460871 bytes .../aoa/single_task_bench/TForest/README.md | 9 + .../single_task_bench/TForest/allocation.out | 42 + .../aoa/single_task_bench/TForest/cdf.png | Bin 0 -> 16397 bytes .../single_task_bench/TForest/cdf_comi.png 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a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Ianvs -[![CI](https://github.com/kubeedge/ianvs/workflows/CI/badge.svg?branch=main)](https://github.com/sedna/ianvs/actions) +[![CI](https://github.com/kubeedge/ianvs/actions/workflows/main-doc.yaml/badge.svg?branch=main)](https://github.com/kubeedge/ianvs/actions/workflows/main-doc.yaml) [![LICENSE SCAN](https://app.fossa.com/api/projects/custom%2B32178%2Fgithub.com%2Fkubeedge%2Fianvs.svg?type=shield)](https://app.fossa.com/projects/custom%2B32178%2Fgithub.com%2Fkubeedge%2Fianvs?ref=badge_shield) [![LICENSE](https://img.shields.io/github/license/kubeedge-sedna/ianvs.svg)](/LICENSE) diff --git a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md index dad37bfb..ca47d1c1 100644 --- a/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md +++ b/docs/proposals/algorithms/single-task-learning/Personalized LLM Agent based on KubeEdge-Ianvs Cloud-Edge Collaboration.md @@ -63,14 +63,13 @@ Therefore, this project proposes a **Personalized LLM Agent based on KubeEdge-Ia ## 3.1 Demo -LLM agent Demo +Personal_Instruction +This project presents three scenarios, which are audio task, human posture classification task and face and object recognition task. -This project plans to complete a demo for human pose detection through video. The steps are as follows: - -1. Users input commands to query the corresponding video based on time. -2. The video is split into frames for object detection, detecting the position of people. -3. Perform human identity recognition.\3. Perform action pose recognition." +What happens if you put a trained Agent model in a mismatched scenario? Here are two examples: +1. Set ***Agent #1*** to ***Scene #3***: Data source does not match; +2. Place ***Agent #3*** in ***Scene #2***: A task planning error occurred. ## 3.2 DataSet @@ -96,15 +95,17 @@ This project implements automated task definition and task orchestration through ### 3.4.1 Task Decomposition #### Rouge-N The ROUGE-N metric is a method used to evaluate the quality of automatic summarization or machine translation, focusing on recall, which is the degree of overlap between the system-generated text and the reference text. The "N" in ROUGE-N represents the length of the longest common subsequence considered. Here is the calculation formula for ROUGE-N: -$$ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)}$$ +$$ ROUGE\_N=\frac{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count_{match} (gram_n) }{\displaystyle\sum^{}_{S\in\{ReferenceSummaries\}}\sum^{}_{grams_n\in S}Count (gram_n)} $$ #### Bertscore -[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language generation tasks. +[Bert_score](https://github.com/Tiiiger/bert_score?tab=readme-ov-file#readme) leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been shown to correlate with human judgment on sentence-level and system-level evaluation. Moreover, BERTScore computes precision, recall, and F1 measures, which can be useful for evaluating different language generation tasks. bert_score -$$R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}, \quad F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}}$$ +$$ R_{\mathrm{BERT}}=\frac{1}{|x|} \sum_{x_{i} \in x} \max _{\hat{x}_{j} \in \hat{x}} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ +$$P_{\mathrm{BERT}}=\frac{1}{|\hat{x}|} \sum_{\hat{x}_{j} \in \hat{x}} \max _{x_{i} \in x} \mathbf{x}_{i}^{\top} \hat{\mathbf{x}}_{j}$$ +$$F_{\mathrm{BERT}}=2 \frac{P_{\mathrm{BERT}} \cdot R_{\mathrm{BERT}}}{P_{\mathrm{BERT}}+R_{\mathrm{BERT}}} $$ # 4 Roadmap diff --git 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