diff --git a/flaml/autogen/agentchat/responsive_agent.py b/flaml/autogen/agentchat/responsive_agent.py index 0884b88913..bac14bed39 100644 --- a/flaml/autogen/agentchat/responsive_agent.py +++ b/flaml/autogen/agentchat/responsive_agent.py @@ -610,7 +610,7 @@ def generate_code_execution_reply( return False, None if messages is None: messages = self._oai_messages[sender] - last_n_messages = code_execution_config.pop("last_n_messages", 1) + last_n_messages = min(len(messages), code_execution_config.pop("last_n_messages", 1)) for i in range(last_n_messages): message = messages[-(i + 1)] code_blocks = extract_code(message["content"]) diff --git a/notebook/autogen_agentchat_groupchat.ipynb b/notebook/autogen_agentchat_groupchat.ipynb index af4f1e6c7a..b4abdc8b6e 100644 --- a/notebook/autogen_agentchat_groupchat.ipynb +++ b/notebook/autogen_agentchat_groupchat.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -122,26 +122,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ - "llm_config = {\"config_list\": config_list_gpt4}\n", - "human = autogen.UserProxyAgent(\n", - " name=\"Human\",\n", + "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n", + "user_proxy = autogen.UserProxyAgent(\n", + " name=\"User_proxy\",\n", " system_message=\"A human admin.\",\n", " code_execution_config={\"last_n_messages\": 2, \"work_dir\": \"groupchat\"},\n", + " human_input_mode=\"TERMINATE\"\n", ")\n", - "alice = autogen.AssistantAgent(\n", - " name=\"Alice\",\n", + "coder = autogen.AssistantAgent(\n", + " name=\"Coder\",\n", " llm_config=llm_config,\n", ")\n", - "bob = autogen.AssistantAgent(\n", - " name=\"Bob\",\n", - " system_message=\"Scientist. Provide expert knowledge.\",\n", + "pm = autogen.AssistantAgent(\n", + " name=\"Product_manager\",\n", + " system_message=\"Creative in software product ideas.\",\n", " llm_config=llm_config,\n", ")\n", - "groupchat = autogen.GroupChat(agents=[human, alice, bob], messages=[], max_round=12)\n", + "groupchat = autogen.GroupChat(agents=[user_proxy, coder, pm], messages=[], max_round=12)\n", "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)" ] }, @@ -155,133 +156,112 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "Find a latest paper about gpt-4 on arxiv and find its potential application.\n", + "Find a latest paper about gpt-4 on arxiv and find its potential applications in software.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mAlice\u001b[0m (to chat_manager):\n", + "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "To find the latest paper about GPT-4 on arxiv, I will use the arxiv API to query and filter the search results. First, I'll execute the following code to get the latest paper information.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mAlice\u001b[0m (to chat_manager):\n", + "To find the latest paper about GPT-4 on arxiv, I'll provide you with a Python code that fetches the most recent papers from the arxiv API and filters the results to get the most relevant paper related to GPT-4. After fetching the paper, I'll extract the information for potential applications in software. Please execute the following Python code:\n", "\n", "```python\n", "import requests\n", "from bs4 import BeautifulSoup\n", "import re\n", "\n", + "def fetch_arxiv_papers(query):\n", + " base_url = \"http://export.arxiv.org/api/query?\"\n", + " search_query = \"all:\" + query\n", + " response = requests.get(base_url, params={\"search_query\": search_query, \"sortBy\": \"submittedDate\", \"sortOrder\": \"descending\"})\n", + " return BeautifulSoup(response.content, \"xml\")\n", "\n", - "def search_arxiv(query, sort_by=\"submittedDate\", order=\"descending\", max_results=1):\n", - " search_url = \"http://export.arxiv.org/api/query\"\n", - " query_string = f\"search_query=all:{query.replace(' ', '+')}&sortBy={sort_by}&sortOrder={order}&max_results={max_results}\"\n", - " url = f\"{search_url}?{query_string}\"\n", - " response = requests.get(url)\n", - " return response.text\n", - "\n", - "\n", - "def parse_paper_info(xml_response):\n", - " soup = BeautifulSoup(xml_response, \"lxml-xml\")\n", - " entry = soup.find(\"entry\")\n", - " if not entry:\n", - " return {}\n", - "\n", - " paper_info = {\n", - " \"title\": entry.find(\"title\").text.strip(),\n", - " \"authors\": [author.text for author in entry.find_all(\"author\")],\n", - " \"published\": entry.find(\"published\").text,\n", - " \"updated\": entry.find(\"updated\").text,\n", - " \"summary\": entry.find(\"summary\").text.strip(),\n", - " \"url\": entry.find(\"id\").text,\n", - " }\n", - " return paper_info\n", - "\n", + "def find_gpt4_paper():\n", + " papers = fetch_arxiv_papers(\"gpt-4\")\n", + " for entry in papers.find_all(\"entry\"):\n", + " title = entry.title.text.strip()\n", + " summary = entry.summary.text.strip()\n", + " if \"gpt-4\" in title.lower() or \"gpt-4\" in summary.lower():\n", + " return {\"title\": title, \"summary\": summary}\n", "\n", - "query = \"gpt-4\"\n", - "arxiv_response = search_arxiv(query)\n", - "paper_info = parse_paper_info(arxiv_response)\n", - "\n", - "if paper_info:\n", - " print(f\"Title: {paper_info['title']}\")\n", - " print(f\"Authors: {', '.join(paper_info['authors'])}\")\n", - " print(f\"Published: {paper_info['published']}\")\n", - " print(f\"Updated: {paper_info['updated']}\")\n", - " print(f\"Abstract: {paper_info['summary']}\")\n", - " print(f\"URL: {paper_info['url']}\")\n", + "gpt4_paper = find_gpt4_paper()\n", + "if gpt4_paper:\n", + " print(\"Title:\", gpt4_paper[\"title\"])\n", + " print(\"Summary:\", gpt4_paper[\"summary\"])\n", "else:\n", - " print(\"No papers about GPT-4 were found on arxiv.\")\n", + " print(\"No recent GPT-4 papers found.\")\n", "```\n", "\n", + "Once we have the paper details, I'll analyze the summary to identify potential applications in software development.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[31m\n", - ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n", - "\u001b[31m\n", ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", "\u001b[31m\n", ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "exitcode: 0 (execution succeeded)\n", "Code output: \n", - "Title: Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance\n", - "Authors: \n", - "Lefteris Loukas\n", - ", \n", - "Ilias Stogiannidis\n", - ", \n", - "Prodromos Malakasiotis\n", - ", \n", - "Stavros Vassos\n", + "Title: FIMO: A Challenge Formal Dataset for Automated Theorem Proving\n", + "Summary: We present FIMO, an innovative dataset comprising formal mathematical problem\n", + "statements sourced from the International Mathematical Olympiad (IMO)\n", + "Shortlisted Problems. Designed to facilitate advanced automated theorem proving\n", + "at the IMO level, FIMO is currently tailored for the Lean formal language. It\n", + "comprises 149 formal problem statements, accompanied by both informal problem\n", + "descriptions and their corresponding LaTeX-based informal proofs. Through\n", + "initial experiments involving GPT-4, our findings underscore the existing\n", + "limitations in current methodologies, indicating a substantial journey ahead\n", + "before achieving satisfactory IMO-level automated theorem proving outcomes.\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mProduct_manager\u001b[0m (to chat_manager):\n", + "\n", + "Based on the paper titled \"FIMO: A Challenge Formal Dataset for Automated Theorem Proving\" and its summary, the potential applications of GPT-4 in software development can be related to the field of automated theorem proving.\n", "\n", - "Published: 2023-08-28T15:04:16Z\n", - "Updated: 2023-08-28T15:04:16Z\n", - "Abstract: We propose the use of conversational GPT models for easy and quick few-shot\n", - "text classification in the financial domain using the Banking77 dataset. Our\n", - "approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes\n", - "the technical expertise required and eliminates the need for expensive GPU\n", - "computing while yielding quick and accurate results. Additionally, we fine-tune\n", - "other pre-trained, masked language models with SetFit, a recent contrastive\n", - "learning technique, to achieve state-of-the-art results both in full-data and\n", - "few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can\n", - "outperform fine-tuned, non-generative models even with fewer examples. However,\n", - "subscription fees associated with these solutions may be considered costly for\n", - "small organizations. Lastly, we find that generative models perform better on\n", - "the given task when shown representative samples selected by a human expert\n", - "rather than when shown random ones. We conclude that a) our proposed methods\n", - "offer a practical solution for few-shot tasks in datasets with limited label\n", - "availability, and b) our state-of-the-art results can inspire future work in\n", - "the area.\n", - "URL: http://arxiv.org/abs/2308.14634v1\n", + "1. **Automated theorem proving**: GPT-4 can be utilized in the development of automated theorem proving software that attempts to prove complex mathematical problems taken from International Mathematical Olympiad (IMO) or other challenging sources. By fine-tuning GPT-4 with a dataset like FIMO consisting of formal mathematical problems, the model can potentially better understand the problem statements and generate appropriate proofs.\n", "\n", + "2. **Mathematical problem-solving assistants**: Software tools can be developed using GPT-4 to guide users in solving complex mathematical problems. The AI model can be integrated into educational platforms, online math tutoring services, or even standalone tools to help make solving problems easier and faster for students and professionals alike.\n", + "\n", + "3. **Formal language translation**: GPT-4 can potentially be integrated into software for translating between formal languages, assisting in the understanding and comparison of various formal systems. This would be especially useful in research communities employing different formal languages and wanting to share ideas and results.\n", + "\n", + "4. **Mathematical proof checking**: GPT-4 can be employed in proof-checking software to identify and correct inconsistencies. By improving the correctness of proofs, this application would ultimately help users save time and contribute to the overall quality of mathematical research.\n", + "\n", + "Please note that this paper highlights the current limitations of GPT-4 in the context of IMO-level theorem proving. Nevertheless, these potential applications suggest directions for further research and software development as the model and related techniques continue to improve.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mBob\u001b[0m (to chat_manager):\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "The latest paper about GPT-4 on arxiv is titled \"Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance\" by Lefteris Loukas, Ilias Stogiannidis, Prodromos Malakasiotis, and Stavros Vassos. It was published on 2023-08-28.\n", "\n", - "The potential application of GPT-4 in this paper is for few-shot text classification in the financial domain using the Banking77 dataset. The authors propose using conversational GPT models like GPT-3.5 and GPT-4 for in-context learning, which minimizes the technical expertise required and eliminates the need for expensive GPU computing while yielding quick and accurate results.\n", "\n", - "Additionally, the paper explores fine-tuning other pre-trained, masked language models with SetFit, a recent contrastive learning technique, to achieve state-of-the-art results in both full-data and few-shot settings.\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> USING AUTO REPLY...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", "\n", - "The findings of this paper show that querying GPT-3.5 and GPT-4 can outperform fine-tuned, non-generative models even with fewer examples. However, subscription fees associated with these solutions may be considered costly for small organizations. The authors also find that generative models perform better on the given task when shown representative samples selected by a human expert rather than when shown random ones. The proposed methods offer a practical solution for few-shot tasks in datasets with limited label availability, and the state-of-the-art results can inspire future work in the area.\n", "\n", - "You can access the full paper [here](http://arxiv.org/abs/2308.14634v1).\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "\n", + "TERMINATE\n", "\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ - "human.initiate_chat(manager, message=\"Find a latest paper about gpt-4 on arxiv and find its potential application.\")\n", + "user_proxy.initiate_chat(manager, message=\"Find a latest paper about gpt-4 on arxiv and find its potential applications in software.\")\n", "# type exit to terminate the chat" ] } diff --git a/notebook/autogen_agentchat_groupchat_research.ipynb b/notebook/autogen_agentchat_groupchat_research.ipynb index 6f405b6452..973a2863c6 100644 --- a/notebook/autogen_agentchat_groupchat_research.ipynb +++ b/notebook/autogen_agentchat_groupchat_research.ipynb @@ -118,7 +118,7 @@ " \"config_list\": config_list_gpt4,\n", " \"request_timeout\": 120,\n", "}\n", - "human = autogen.UserProxyAgent(\n", + "user_proxy = autogen.UserProxyAgent(\n", " name=\"Admin\",\n", " system_message=\"A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.\",\n", " code_execution_config=False,\n", @@ -155,7 +155,7 @@ " system_message=\"Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.\",\n", " llm_config=gpt4_config,\n", ")\n", - "groupchat = autogen.GroupChat(agents=[human, engineer, scientist, planner, executor, critic], messages=[], max_round=50)\n", + "groupchat = autogen.GroupChat(agents=[user_proxy, engineer, scientist, planner, executor, critic], messages=[], max_round=50)\n", "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_config)" ] }, @@ -473,7 +473,7 @@ } ], "source": [ - "human.initiate_chat(\n", + "user_proxy.initiate_chat(\n", " manager,\n", " message=\"\"\"\n", "find papers on LLM applications from arxiv in the last week, create a markdown table of different domains.\n", @@ -482,6 +482,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -527,11 +528,11 @@ } ], "source": [ - "groupchat_nocritic = autogen.GroupChat(agents=[human, engineer, scientist, planner, executor], messages=[], max_round=50)\n", + "groupchat_nocritic = autogen.GroupChat(agents=[user_proxy, engineer, scientist, planner, executor], messages=[], max_round=50)\n", "for agent in groupchat.agents:\n", " agent.reset()\n", "manager_nocritic = autogen.GroupChatManager(groupchat=groupchat_nocritic, llm_config=gpt4_config)\n", - "human.initiate_chat(\n", + "user_proxy.initiate_chat(\n", " manager_nocritic,\n", " message=\"\"\"\n", "find papers on LLM applications from arxiv in the last week, create a markdown table of different domains.\n", diff --git a/notebook/autogen_agentchat_groupchat_vis.ipynb b/notebook/autogen_agentchat_groupchat_vis.ipynb index 9f6a55b112..0e844cee43 100644 --- a/notebook/autogen_agentchat_groupchat_vis.ipynb +++ b/notebook/autogen_agentchat_groupchat_vis.ipynb @@ -124,9 +124,9 @@ "metadata": {}, "outputs": [], "source": [ - "llm_config = {\"config_list\": config_list_gpt4}\n", - "human_proxy = autogen.UserProxyAgent(\n", - " name=\"Human\",\n", + "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n", + "user_proxy = autogen.UserProxyAgent(\n", + " name=\"User_proxy\",\n", " system_message=\"A human admin.\",\n", " code_execution_config={\"last_n_messages\": 3, \"work_dir\": \"groupchat\"},\n", " human_input_mode=\"NEVER\",\n", @@ -139,19 +139,21 @@ " name=\"Critic\",\n", " system_message=\"\"\"Critic. You are a helpful assistant highly skilled in evaluating the quality of a given visualization code by providing a score from 1 (bad) - 10 (good) while providing clear rationale. YOU MUST CONSIDER VISUALIZATION BEST PRACTICES for each evaluation. Specifically, you can carefully evaluate the code across the following dimensions\n", "- bugs (bugs): are there bugs, logic errors, syntax error or typos? Are there any reasons why the code may fail to compile? How should it be fixed? If ANY bug exists, the bug score MUST be less than 5.\n", - "- Data transformation (transformation): Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped if needed?\n", + "- Data transformation (transformation): Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped if needed? If a date field is used, is the date field first converted to a date object etc?\n", "- Goal compliance (compliance): how well the code meets the specified visualization goals?\n", - "- Visualization type (type): CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? If a different visualization type is more appropriate, the score MUST be less than 5.\n", + "- Visualization type (type): CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? If a different visualization type is more appropriate, the score MUST BE LESS THAN 5.\n", "- Data encoding (encoding): Is the data encoded appropriately for the visualization type?\n", "- aesthetics (aesthetics): Are the aesthetics of the visualization appropriate for the visualization type and the data?\n", "\n", - "You must provide a score for each of the above dimensions.\n", - "Do not suggest code.\n", + "YOU MUST PROVIDE A SCORE for each of the above dimensions.\n", + "{bugs: 0, transformation: 0, compliance: 0, type: 0, encoding: 0, aesthetics: 0}\n", + "Do not suggest code. \n", + "Finally, based on the critique above, suggest a concrete list of actions that the coder should take to improve the code.\n", "\"\"\",\n", " llm_config=llm_config,\n", ")\n", "\n", - "groupchat = autogen.GroupChat(agents=[human_proxy, coder, critic], messages=[], max_round=20)\n", + "groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=20)\n", "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)" ] }, @@ -172,263 +174,495 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "First, I will provide you with a Python script that downloads the dataset and prints its fields. After you execute the script, I will create the code for visualizing the relationship between weight and horsepower and saving the plot to a file.\n", - "\n", - "Please execute the following code block to download the dataset and print the fields:\n", + "First, let's download the data and print its fields:\n", "\n", "```python\n", "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", + "# Download the data\n", "url = \"https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv\"\n", - "dataset = pd.read_csv(url)\n", + "df = pd.read_csv(url)\n", "\n", "# Print the fields in the dataset\n", - "print(\"Fields in the dataset:\")\n", - "print(dataset.columns)\n", + "print(df.columns)\n", + "\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + "\n", + "# Save the plot to a file\n", + "plt.savefig('weight_vs_horsepower.png')\n", + "\n", + "# Show the plot\n", + "plt.show()\n", "```\n", "\n", - "After executing the above code, let me know the output, and I will provide you with the code for visualization.\n", + "This script will download the data from the provided URL, print the fields in the dataset, and create a scatter plot of the relationship between weight and horsepower. It will then save the plot to a file named \"weight_vs_horsepower.png\" and display the plot.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "Based on the provided information, here is an evaluation of the code that downloads the dataset and prints its fields. I will rate each dimension according to visualization best practices.\n", + "I have reviewed your code and here's the evaluation based on the specified dimensions:\n", "\n", - "1. Bugs (bugs): 10/10\n", - "The provided code is free of bugs, logic errors, and syntax errors. It successfully downloads the dataset from the given URL and prints the fields.\n", + "- bugs (bugs): 10/10; There are no bugs, logic errors, syntax errors, or typos I can identify.\n", + "- Data transformation (transformation): 10/10; There is no need for data transformation in this case, as the data can be directly plotted with the desired variables for a scatter plot.\n", + "- Goal compliance (compliance): 10/10; The code properly downloads the dataset, prints the fields, and creates a visualization for the relationship between weight and horsepower. Finally, it saves the plot to a file.\n", + "- Visualization type (type): 10/10; Scatter plot is an appropriate visualization type for depicting the relationship between weight and horsepower, which are both continuous variables.\n", + "- Data encoding (encoding): 10/10; The code properly encodes the variables in the x and y axes using the 'Weight_in_lbs' and 'Horsepower' columns from the dataset.\n", + "- aesthetics (aesthetics): 10/10; The aesthetics of the plot are appropriate. By default, the seaborn scatter plot provides a clean and modern look.\n", "\n", - "2. Data transformation (transformation): N/A\n", - "As the code does not include any data transformation operations, this dimension is not applicable in this evaluation.\n", + "To improve the code:\n", "\n", - "3. Goal compliance (compliance): 7/10\n", - "The code partially meets the specified goal of downloading the dataset and printing the fields. However, it does not visualize the relationship between weight and horsepower, nor does it save the plot to a file.\n", + "1. Notice that there are no identified issues or suggestions for improvement, as the code is concise and properly meets the specified visualization goals.\n", "\n", - "4. Visualization type (type): N/A\n", - "As the provided code does not include any visualization operations, this dimension is not applicable in this evaluation.\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "5. Data encoding (encoding): N/A\n", - "As the provided code does not include any data encoding or visualization operations, this dimension is not applicable in this evaluation.\n", + "exitcode: 1 (execution failed)\n", + "Code output: \n", + "Traceback (most recent call last):\n", + " File \"\", line 14, in \n", + " sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 742, in scatterplot\n", + " p = _ScatterPlotter(data=data, variables=variables, legend=legend)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 538, in __init__\n", + " super().__init__(data=data, variables=variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 640, in __init__\n", + " self.assign_variables(data, variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 701, in assign_variables\n", + " plot_data, variables = self._assign_variables_longform(\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 938, in _assign_variables_longform\n", + " raise ValueError(err)\n", + "ValueError: Could not interpret value `Weight_in_lbs` for parameter `x`\n", "\n", - "6. Aesthetics (aesthetics): N/A\n", - "As the provided code does not include any visualization operations, this dimension is not applicable in this evaluation.\n", "\n", - "Once you execute the provided code and obtain the dataset's fields, you can share the output with me, and I will help you create the visualization code for the relationship between weight and horsepower. Remember that the visualization code is needed to evaluate the remaining dimensions, such as visualization type, data encoding, and aesthetics.\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "\n", + "I apologize for the error. It seems that the column names for weight and horsepower are different in the dataset. Let me print the dataframe to find out the correct column names and update the code accordingly.\n", + "\n", + "```python\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Download the data\n", + "url = \"https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv\"\n", + "df = pd.read_csv(url)\n", + "\n", + "# Print the fields in the dataset\n", + "print(df.columns)\n", + "\n", + "# Print the DataFrame\n", + "print(df)\n", + "\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='weight', y='horsepower')\n", + "\n", + "# Save the plot to a file\n", + "plt.savefig('weight_vs_horsepower.png')\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "```\n", + "\n", + "This updated script should work as expected. Please run it to visualize the relationship between weight and horsepower.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "\n", + "I have reviewed the updated code, and here's the new evaluation based on the specified dimensions:\n", + "\n", + "- bugs (bugs): 10/10; The issue with column names is now resolved, and there are no bugs, logic errors, syntax errors, or typos.\n", + "- Data transformation (transformation): 10/10; As before, there is no need for data transformation in this case.\n", + "- Goal compliance (compliance): 10/10; The updated code properly downloads the dataset, prints the fields and the DataFrame, creates a visualization of the relationship between weight and horsepower, saves the plot to a file, and displays the plot.\n", + "- Visualization type (type): 10/10; As before, a scatter plot remains an appropriate visualization type for depicting the relationship between weight and horsepower.\n", + "- Data encoding (encoding): 10/10; With the corrected column names, the code now properly encodes the variables in the x and y axes using the 'weight' and 'horsepower' columns.\n", + "- aesthetics (aesthetics): 10/10; The aesthetics of the plot are appropriate, as the seaborn scatter plot provides a clean and modern look.\n", + "\n", + "To improve the code:\n", + "\n", + "1. There are no identified issues in the updated code, and it should now work as expected.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[31m\n", ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "exitcode: 0 (execution succeeded)\n", + "exitcode: 1 (execution failed)\n", "Code output: \n", - "Fields in the dataset:\n", - "Index(['Name', 'Type', 'AWD', 'RWD', 'Retail Price', 'Dealer Cost',\n", - " 'Engine Size (l)', 'Cyl', 'Horsepower(HP)', 'City Miles Per Gallon',\n", - " 'Highway Miles Per Gallon', 'Weight', 'Wheel Base', 'Len', 'Width'],\n", - " dtype='object')\n", + "Traceback (most recent call last):\n", + " File \"\", line 17, in \n", + " sns.scatterplot(data=df, x='weight', y='horsepower')\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 742, in scatterplot\n", + " p = _ScatterPlotter(data=data, variables=variables, legend=legend)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 538, in __init__\n", + " super().__init__(data=data, variables=variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 640, in __init__\n", + " self.assign_variables(data, variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 701, in assign_variables\n", + " plot_data, variables = self._assign_variables_longform(\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 938, in _assign_variables_longform\n", + " raise ValueError(err)\n", + "ValueError: Could not interpret value `weight` for parameter `x`\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "Thank you for providing the dataset's fields. Now, I will give you a Python script to visualize the relationship between weight and horsepower and save the plot to a file. Please execute the following code block:\n", + "I apologize for the confusion. It looks like there is still an issue with the column names. Let's print the column names and the first few rows of the dataset to verify the correct names for the weight and horsepower columns.\n", "\n", "```python\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Download the data\n", + "url = \"https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv\"\n", + "df = pd.read_csv(url)\n", "\n", - "# Prepare the data for plotting\n", - "weight = dataset[\"Weight\"]\n", - "horsepower = dataset[\"Horsepower(HP)\"]\n", + "# Print the fields in the dataset and the first few rows\n", + "print(df.columns)\n", + "print(df.head())\n", "\n", - "# Create a scatter plot\n", - "plt.scatter(weight, horsepower)\n", - "plt.xlabel(\"Weight\")\n", - "plt.ylabel(\"Horsepower(HP)\")\n", - "plt.title(\"Relationship between Weight and Horsepower\")\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", "\n", "# Save the plot to a file\n", - "plt.savefig(\"weight_horsepower_plot.png\")\n", + "plt.savefig('weight_vs_horsepower.png')\n", "\n", "# Show the plot\n", "plt.show()\n", - "\n", - "print(\"The plot has been saved to 'weight_horsepower_plot.png'.\")\n", "```\n", "\n", - "This script will create a scatter plot of the relationship between weight and horsepower, save the plot to a file called \"weight_horsepower_plot.png\", and display the plot.\n", + "Run the code above to print the column names and the first few rows to verify the correct column names for weight and horsepower. Then, we'll adjust the column names in the scatter plot as needed.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "exitcode: 1 (execution failed)\n", + "Code output: \n", + "Traceback (most recent call last):\n", + " File \"\", line 15, in \n", + " sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 742, in scatterplot\n", + " p = _ScatterPlotter(data=data, variables=variables, legend=legend)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 538, in __init__\n", + " super().__init__(data=data, variables=variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 640, in __init__\n", + " self.assign_variables(data, variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 701, in assign_variables\n", + " plot_data, variables = self._assign_variables_longform(\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 938, in _assign_variables_longform\n", + " raise ValueError(err)\n", + "ValueError: Could not interpret value `Weight_in_lbs` for parameter `x`\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "exitcode: 1 (execution failed)\n", + "Code output: \n", + "Traceback (most recent call last):\n", + " File \"\", line 15, in \n", + " sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 742, in scatterplot\n", + " p = _ScatterPlotter(data=data, variables=variables, legend=legend)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 538, in __init__\n", + " super().__init__(data=data, variables=variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 640, in __init__\n", + " self.assign_variables(data, variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 701, in assign_variables\n", + " plot_data, variables = self._assign_variables_longform(\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 938, in _assign_variables_longform\n", + " raise ValueError(err)\n", + "ValueError: Could not interpret value `Weight_in_lbs` for parameter `x`\n", + "\n", "\n", - "Based on the provided code that visualizes the relationship between weight and horsepower, here is an evaluation of the code across the six dimensions:\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "1. Bugs (bugs): 9/10\n", - "The code is generally free of bugs, logic errors, and syntax errors. It successfully creates a scatter plot using the weight and horsepower fields from the dataset, saves the plot to a file, and displays the plot. There might be minor improvements, like checking if the dataset is not empty before plotting.\n", + "I apologize for the confusion. Since the previous step did not resolve the issue, let me try a different library for downloading the CSV file and inspect the DataFrame.\n", + "\n", + "```python\n", + "import requests\n", + "import io\n", "\n", - "2. Data transformation (transformation): 8/10\n", - "The code gets the \"Weight\" and \"Horsepower(HP)\" columns from the dataset for plotting, which is a simple and appropriate transformation for a scatter plot. However, it might be beneficial to handle missing data if necessary.\n", + "# Download the data using requests\n", + "response = requests.get(url)\n", + "response.raise_for_status()\n", + "content = response.content.decode('utf-8')\n", + "csv_file = io.StringIO(content)\n", + "df = pd.read_csv(csv_file)\n", "\n", - "3. Goal compliance (compliance): 9/10\n", - "The code achieves the specified visualization goals: it plots the relationship between weight and horsepower, saves the plot to a file, and displays it.\n", + "# Print the fields in the dataset and the first few rows\n", + "print(df.columns)\n", + "print(df.head())\n", "\n", - "4. Visualization type (type): 9/10\n", - "The scatter plot is an appropriate visualization type for showing the relationship between two continuous variables, such as weight and horsepower. It allows for easy identification of trends or patterns between the variables.\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", "\n", - "5. Data encoding (encoding): 9/10\n", - "The data is encoded appropriately for a scatter plot. Each point in the plot represents a unique data point with its x-coordinate as the weight and y-coordinate as the horsepower.\n", + "# Save the plot to a file\n", + "plt.savefig('weight_vs_horsepower.png')\n", "\n", - "6. Aesthetics (aesthetics): 9/10\n", - "The aesthetics of the scatter plot are suitable for displaying the relationship between weight and horsepower. Labels for the x-axis (Weight) and y-axis (Horsepower(HP)) are added to provide clear context for the viewers. A title (\"Relationship between Weight and Horsepower\") is included to describe the plot's intent. The default visuals are clear and easy to interpret, but there might be room for enhancement, such as adding a grid or customizing colors.\n", + "# Show the plot\n", + "plt.show()\n", + "```\n", + "\n", + "This new code should download the CSV file correctly and inspect the DataFrame. Run the code above and then proceed to create the scatter plot.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[31m\n", ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "exitcode: 1 (execution failed)\n", "Code output: \n", "Traceback (most recent call last):\n", - " File \"\", line 4, in \n", - " weight = dataset[\"Weight\"]\n", - "NameError: name 'dataset' is not defined\n", + " File \"\", line 5, in \n", + " response = requests.get(url)\n", + "NameError: name 'url' is not defined\n", "\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "I apologize for the confusion. I assumed that the `dataset` variable would be available from the previous code execution. To resolve this issue, I will provide you with the complete code, which includes downloading the dataset, printing its fields, and creating the scatter plot. Please execute the following code block:\n", + "I apologize for missing the URL variable in the code I provided. Here is the corrected code:\n", "\n", "```python\n", + "import requests\n", + "import io\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", + "# URL to download the data\n", "url = \"https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv\"\n", - "dataset = pd.read_csv(url)\n", "\n", - "# Print the fields in the dataset\n", - "print(\"Fields in the dataset:\")\n", - "print(dataset.columns)\n", + "# Download the data using requests\n", + "response = requests.get(url)\n", + "response.raise_for_status()\n", + "content = response.content.decode('utf-8')\n", + "csv_file = io.StringIO(content)\n", + "df = pd.read_csv(csv_file)\n", "\n", - "# Prepare the data for plotting\n", - "weight = dataset[\"Weight\"]\n", - "horsepower = dataset[\"Horsepower(HP)\"]\n", + "# Print the fields in the dataset and the first few rows\n", + "print(df.columns)\n", + "print(df.head())\n", "\n", - "# Create a scatter plot\n", - "plt.scatter(weight, horsepower)\n", - "plt.xlabel(\"Weight\")\n", - "plt.ylabel(\"Horsepower(HP)\")\n", - "plt.title(\"Relationship between Weight and Horsepower\")\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", "\n", "# Save the plot to a file\n", - "plt.savefig(\"weight_horsepower_plot.png\")\n", + "plt.savefig('weight_vs_horsepower.png')\n", "\n", "# Show the plot\n", "plt.show()\n", - "\n", - "print(\"The plot has been saved to 'weight_horsepower_plot.png'.\")\n", "```\n", "\n", - "This script will download the dataset, print its fields, create a scatter plot of the relationship between weight and horsepower, save the plot to a file called \"weight_horsepower_plot.png\", and display the plot.\n", + "Please run the updated code to download the CSV file, inspect the DataFrame, and create the scatter plot.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "exitcode: 1 (execution failed)\n", + "Code output: \n", + "Traceback (most recent call last):\n", + " File \"\", line 23, in \n", + " sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 742, in scatterplot\n", + " p = _ScatterPlotter(data=data, variables=variables, legend=legend)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/relational.py\", line 538, in __init__\n", + " super().__init__(data=data, variables=variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 640, in __init__\n", + " self.assign_variables(data, variables)\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 701, in assign_variables\n", + " plot_data, variables = self._assign_variables_longform(\n", + " File \"/home/vscode/.local/lib/python3.9/site-packages/seaborn/_oldcore.py\", line 938, in _assign_variables_longform\n", + " raise ValueError(err)\n", + "ValueError: Could not interpret value `Weight_in_lbs` for parameter `x`\n", + "\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "Now that you have the complete code including downloading the dataset, printing its fields, and creating the scatter plot, let's revisit the evaluation of the code across the six dimensions:\n", + "I apologize for the ongoing confusion. After re-evaluating the problem, it has come to my attention that the dataset URL provided points to a newer version of the \"cars\" dataset, causing the column name discrepancies. The appropriate URL to use is `https://raw.githubusercontent.com/vega/vega-datasets/gh-pages/data/cars.json`. \n", "\n", - "1. Bugs (bugs): 10/10\n", - "The complete code is free of bugs, logic errors, and syntax errors. It successfully downloads the dataset, prints its fields, creates a scatter plot using the weight and horsepower fields, saves the plot to a file, and displays the plot.\n", + "Here's the updated code:\n", "\n", - "2. Data transformation (transformation): 8/10\n", - "The code gets the \"Weight\" and \"Horsepower(HP)\" columns from the dataset for plotting, which is a simple and appropriate transformation for a scatter plot. However, it might be beneficial to handle missing data if necessary.\n", + "```python\n", + "import requests\n", + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# URL to download the data\n", + "url = \"https://raw.githubusercontent.com/vega/vega-datasets/gh-pages/data/cars.json\"\n", "\n", - "3. Goal compliance (compliance): 10/10\n", - "The complete code achieves all specified goals: It downloads the dataset, prints its fields, plots the relationship between weight and horsepower, saves the plot to a file, and displays it.\n", + "# Download the data using requests\n", + "response = requests.get(url)\n", + "response.raise_for_status()\n", + "content = response.content.decode('utf-8')\n", + "data = json.loads(content)\n", + "df = pd.DataFrame(data)\n", "\n", - "4. Visualization type (type): 9/10\n", - "The scatter plot is an appropriate visualization type for showing the relationship between two continuous variables, such as weight and horsepower. It allows for easy identification of trends or patterns between the variables.\n", + "# Print the fields in the dataset and the first few rows\n", + "print(df.columns)\n", + "print(df.head())\n", "\n", - "5. Data encoding (encoding): 9/10\n", - "The data is encoded appropriately for a scatter plot. Each point in the plot represents a unique data point with its x-coordinate as the weight and y-coordinate as the horsepower.\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", + "\n", + "# Save the plot to a file\n", + "plt.savefig('weight_vs_horsepower.png')\n", "\n", - "6. Aesthetics (aesthetics): 9/10\n", - "The aesthetics of the scatter plot are suitable for displaying the relationship between weight and horsepower. Labels for the x-axis (Weight) and y-axis (Horsepower(HP)) are added to provide clear context for the viewers. A title (\"Relationship between Weight and Horsepower\") is included to describe the plot's intent. The default visuals are clear and easy to interpret, but there might be room for enhancement, such as adding a grid or customizing colors.\n", + "# Show the plot\n", + "plt.show()\n", + "```\n", + "\n", + "Please run the updated code to download the correct dataset, inspect the DataFrame, and create the scatter plot.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[31m\n", ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "exitcode: 0 (execution succeeded)\n", + "exitcode: 1 (execution failed)\n", "Code output: \n", - "Fields in the dataset:\n", - "Index(['Name', 'Type', 'AWD', 'RWD', 'Retail Price', 'Dealer Cost',\n", - " 'Engine Size (l)', 'Cyl', 'Horsepower(HP)', 'City Miles Per Gallon',\n", - " 'Highway Miles Per Gallon', 'Weight', 'Wheel Base', 'Len', 'Width'],\n", - " dtype='object')\n", - "Figure(640x480)\n", - "The plot has been saved to 'weight_horsepower_plot.png'.\n", + "Traceback (most recent call last):\n", + " File \"\", line 12, in \n", + " response.raise_for_status()\n", + " File \"/usr/local/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n", + " raise HTTPError(http_error_msg, response=self)\n", + "requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://raw.githubusercontent.com/vega/vega-datasets/gh-pages/data/cars.json\n", "\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "Great! The code executed successfully, and the plot has been saved to the file 'weight_horsepower_plot.png'. You now have a visualization that shows the relationship between weight and horsepower based on the dataset provided.\n", + "I apologize for the incorrect URL in the previous code. Please use the URL `https://raw.githubusercontent.com/vega/vega-datasets/main/data/cars.json` to download the correct dataset. Here's the modified code:\n", "\n", - "If you have any more questions or need further assistance, feel free to ask. Otherwise, if everything is done, please type \"TERMINATE\" to end our interaction.\n", + "```python\n", + "import requests\n", + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "# URL to download the data\n", + "url = \"https://raw.githubusercontent.com/vega/vega-datasets/main/data/cars.json\"\n", "\n", - "Your satisfaction is important to me. If you require any further assistance or guidance, please don't hesitate to reach out. If you're finished with the task, you may end our conversation by typing \"TERMINATE\". Have a great day!\n", + "# Download the data using requests\n", + "response = requests.get(url)\n", + "response.raise_for_status()\n", + "content = response.content.decode('utf-8')\n", + "data = json.loads(content)\n", + "df = pd.DataFrame(data)\n", "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "# Print the fields in the dataset and the first few rows\n", + "print(df.columns)\n", + "print(df.head())\n", "\n", + "# Prepare the plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df, x='Weight_in_lbs', y='Horsepower')\n", "\n", + "# Save the plot to a file\n", + "plt.savefig('weight_vs_horsepower.png')\n", "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "# Show the plot\n", + "plt.show()\n", + "```\n", "\n", - "Your satisfaction is important to me. If you require any further assistance or guidance, please don't hesitate to reach out. If you're finished with the task, you may end our conversation by typing \"goodbye\". Have a great day!\n", + "Please run the updated code to download the correct dataset, inspect the DataFrame, and create the scatter plot.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", - "\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", + "exitcode: 0 (execution succeeded)\n", + "Code output: \n", + "Index(['Name', 'Miles_per_Gallon', 'Cylinders', 'Displacement', 'Horsepower',\n", + " 'Weight_in_lbs', 'Acceleration', 'Year', 'Origin'],\n", + " dtype='object')\n", + " Name Miles_per_Gallon ... Year Origin\n", + "0 chevrolet chevelle malibu 18.0 ... 1970-01-01 USA\n", + "1 buick skylark 320 15.0 ... 1970-01-01 USA\n", + "2 plymouth satellite 18.0 ... 1970-01-01 USA\n", + "3 amc rebel sst 16.0 ... 1970-01-01 USA\n", + "4 ford torino 17.0 ... 1970-01-01 USA\n", "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "[5 rows x 9 columns]\n", + "Figure(800x600)\n", "\n", - "I apologize for the confusion in my previous messages. I'm glad I could help with your task. If you have any more questions or need further assistance, feel free to ask. If everything is done, please type \"TERMINATE\" to end our interaction. Have a great day!\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "It seems there was a technical issue with my responses earlier. I'm glad I could be of help to you. If you need any more assistance or have further questions, feel free to ask. If everything is done, please type \"TERMINATE\" to end our interaction. Have a great day!\n", + "I'm glad the updated code worked. Here's the updated evaluation based on the specified dimensions:\n", "\n", - "--------------------------------------------------------------------------------\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "- bugs (bugs): 10/10; The column name issue is now resolved, and there are no bugs, logic errors, syntax errors, or typos.\n", + "- Data transformation (transformation): 10/10; As before, there is no need for data transformation in this case.\n", + "- Goal compliance (compliance): 10/10; The updated code properly downloads the correct dataset, prints the fields and the first few rows, creates a visualization of the relationship between weight and horsepower, saves the plot to a file, and displays the plot.\n", + "- Visualization type (type): 10/10; A scatter plot remains an appropriate visualization type for depicting the relationship between weight and horsepower.\n", + "- Data encoding (encoding): 10/10; With the corrected column names, the code now properly encodes the variables in the x and y axes using the 'Weight_in_lbs' and 'Horsepower' columns.\n", + "- aesthetics (aesthetics): 10/10; The aesthetics of the plot are appropriate, as the seaborn scatter plot provides a clean and modern look.\n", "\n", + "To improve the code:\n", "\n", + "1. No further improvements are needed as the updated code works correctly and meets the specified visualization goals.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "exitcode: 0 (execution succeeded)\n", + "Code output: \n", + "Index(['Name', 'Miles_per_Gallon', 'Cylinders', 'Displacement', 'Horsepower',\n", + " 'Weight_in_lbs', 'Acceleration', 'Year', 'Origin'],\n", + " dtype='object')\n", + " Name Miles_per_Gallon ... Year Origin\n", + "0 chevrolet chevelle malibu 18.0 ... 1970-01-01 USA\n", + "1 buick skylark 320 15.0 ... 1970-01-01 USA\n", + "2 plymouth satellite 18.0 ... 1970-01-01 USA\n", + "3 amc rebel sst 16.0 ... 1970-01-01 USA\n", + "4 ford torino 17.0 ... 1970-01-01 USA\n", + "\n", + "[5 rows x 9 columns]\n", + "Figure(800x600)\n", "\n", - "TERMINATE\n", "\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ - "human_proxy.initiate_chat(manager, message=\"download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.\")\n", + "user_proxy.initiate_chat(manager, message=\"download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.\")\n", "# type exit to terminate the chat" ] }, @@ -447,7 +681,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "" ] @@ -460,7 +694,7 @@ "source": [ "from IPython.display import Image\n", "\n", - "Image(filename='groupchat/weight_horsepower_plot.png')" + "Image(filename='groupchat/weight_vs_horsepower.png')" ] }, { @@ -473,208 +707,224 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "download data from https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv and show me a plot that tells me about the amount of each weather . Save the plot to a file. Print the fields in a dataset before visualizing it. Take the feedback from the critic to improve the code.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "Since I cannot receive feedback from the critic, I will provide you with the code to download the data, print the fields in the dataset, and create the initial plot. If you have any suggestions to improve the plot later, please provide them, and I will make the necessary changes.\n", - "\n", - "First, we will download the data and print the fields in the dataset. Execute the following Python code:\n", + "I will first download the data and print the fields of the dataset. Then, I'll create a plot and save it to a file. Please execute the following Python code to download the data, print the fields, and generate the weather plot.\n", "\n", "```python\n", + "# filename: weather_plot.py\n", + "\n", "import pandas as pd\n", + "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", - "# Download the dataset\n", "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n", - "weather_data = pd.read_csv(url)\n", + "data = pd.read_csv(url)\n", + "print(\"Fields in the dataset:\")\n", + "print(data.columns)\n", "\n", - "# Print the fields in the dataset\n", - "print(weather_data.columns)\n", + "# Count the occurrences of each weather type\n", + "weather_counts = data['weather'].value_counts()\n", "\n", - "# Frequency of each type of weather\n", - "weather_counts = weather_data['weather'].value_counts()\n", + "# Create a bar plot of weather occurrences\n", + "sns.set(style='whitegrid', font_scale=1.2)\n", + "plt.figure(figsize=(10, 6))\n", + "weather_plot = sns.barplot(x=weather_counts.index, y=weather_counts.values)\n", "\n", - "# Create the initial plot\n", - "weather_counts.plot(kind='bar')\n", - "plt.xlabel('Weather Type')\n", - "plt.ylabel('Frequency')\n", - "plt.title('Frequency of Different Types of Weather in Seattle')\n", + "# Add labels and title\n", + "weather_plot.set(xlabel='Weather Types', ylabel='Number of Days', title='Seattle Weather Types Frequency')\n", + "plt.savefig('weather_plot.png')\n", "\n", - "# Save the plot to a file\n", - "plt.savefig(\"seattle_weather_plot.png\")\n", - "plt.close()\n", + "print(\"Plot has been saved to 'weather_plot.png'.\")\n", "```\n", "\n", - "Now, you can see the output for the fields in the dataset, and the saved plot in \"seattle_weather_plot.png\" file. If you want to make any improvements to the plot, please let me know.\n", + "Run this script by executing the command `python weather_plot.py`. This will output the weather dataset's fields and create a plot named 'weather_plot.png' in your current directory.\n", + "\n", + "After running the code, let me know any improvement that the critic suggests in the code.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "Here is an evaluation of your code across different dimensions:\n", + "Based on the provided code, here is my critique:\n", + "\n", + "- bugs: 10/10 - The code is clean, and I didn't find any syntax errors, logic errors, or bugs in it.\n", + "- transformation: 9/10 - The data is being transformed appropriately. The count of each weather type is calculated from the dataset. However, there is a minor issue: The raw data should have been preprocessed to handle possible null or unexpected values.\n", + "- compliance: 10/10 - The code meets the specified visualization goals. It downloads the data, prints the fields, and saves the plot to a file.\n", + "- type: 10/10 - The bar plot is an appropriate visualization type for showing the count of each weather type, and it effectively conveys the required insight.\n", + "- encoding: 10/10 - The encoding is appropriate; the x-axis represents weather types, and the y-axis represents the number of days, as required.\n", + "- aesthetics: 9/10 - The aesthetics used are appropriate, but the plot would benefit from the addition of a color palette that reflects the meaning of the data.\n", + "\n", + "To improve the code, the coder should:\n", + "1. Load and preprocess the raw data to handle possible null or unexpected values.\n", + "2. Apply a meaningful color palette to the bar plot to enhance the visual appearance.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "1. Bugs (bugs): 8\n", - "- The code does not present any major bugs, logic errors, or syntax errors that would prevent it from running successfully. However, there's a small issue with not showing the plot, which can be fixed by adding `plt.show()` after the `plt.savefig(\"seattle_weather_plot.png\")` line.\n", + "Thank you for the feedback. Based on the critique, I will address the suggestions and improve the code. I will preprocess the raw data to handle possible null or unexpected values and use a meaningful color palette for the bar plot.\n", "\n", - "2. Data transformation (transformation): 9\n", - "- The data is transformed appropriately for the visualization type. You have calculated the frequency of each weather type using value_counts() method in the weather_counts variable.\n", + "Please execute the updated Python code below.\n", + "\n", + "```python\n", + "# filename: improved_weather_plot.py\n", "\n", - "3. Goal compliance (compliance): 8\n", - "- The code meets the specified visualization goal of showing the frequency of different types of weather in Seattle, but it is missing minor details like displaying the plot to the user.\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n", + "data = pd.read_csv(url)\n", + "\n", + "# Preprocess raw data: remove null or unexpected values\n", + "data = data.dropna()\n", + "data = data[data['weather'].isin(['sun', 'fog', 'drizzle', 'rain', 'snow'])]\n", + "\n", + "print(\"Fields in the dataset:\")\n", + "print(data.columns)\n", "\n", - "4. Visualization type (type): 8\n", - "- The chosen visualization type, a bar chart, is appropriate and efficient for displaying the frequency of categorical data like different weather types.\n", + "# Count the occurrences of each weather type\n", + "weather_counts = data['weather'].value_counts()\n", "\n", - "5. Data encoding (encoding): 8\n", - "- Data is encoded appropriately for the plot. The weather type is displayed on the x-axis, and the frequency is displayed on the y-axis.\n", + "# Create a bar plot of weather occurrences\n", + "weather_palette = {'sun': 'yellow', 'fog': 'gray', 'drizzle': 'lightblue', 'rain': 'blue', 'snow': 'white'}\n", + "sns.set(style='whitegrid', font_scale=1.2)\n", + "plt.figure(figsize=(10, 6))\n", + "weather_plot = sns.barplot(x=weather_counts.index, y=weather_counts.values, palette=weather_palette)\n", "\n", - "6. Aesthetics (aesthetics): 8\n", - "- The aesthetics of the visualization are appropriate for the plot. The x and y-axis labels and plot title provide sufficient information for the user to understand the plot.\n", + "# Add labels and title\n", + "weather_plot.set(xlabel='Weather Types', ylabel='Number of Days', title='Seattle Weather Types Frequency')\n", + "plt.savefig('improved_weather_plot.png')\n", "\n", - "Based on the evaluation above, here is a list of actions the coder should take to improve the code:\n", + "print(\"Improved plot has been saved to 'improved_weather_plot.png'.\")\n", + "```\n", + "\n", + "Run this script by executing the command `python improved_weather_plot.py`. This will output the dataset's fields, preprocess the data, and create an improved plot named 'improved_weather_plot.png' in your current directory.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "\n", + "Based on the updated code, here is my critique:\n", + "\n", + "- bugs: 10/10 - The code is clean, and I didn't find any syntax errors, logic errors, or bugs in it.\n", + "- transformation: 10/10 - The data is being transformed appropriately. The preprocessing of the raw data now rightly handles null or unexpected values.\n", + "- compliance: 10/10 - The updated code meets the specified visualization goals even better. It downloads the data, prints the fields, preprocesses the data, and saves the improved plot.\n", + "- type: 10/10 - The bar plot remains an appropriate visualization type for showing the count of each weather type, and it effectively conveys the required insight.\n", + "- encoding: 10/10 - The encoding is still appropriate; the x-axis represents weather types, and the y-axis represents the number of days, as required.\n", + "- aesthetics: 10/10 - The aesthetics used are now improved, and the plot has a color palette that reflects the meaning of the data.\n", + "\n", + "The updated code has successfully addressed the previous suggestions, and the resulting plot is more accurate and visually appealing.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[31m\n", + ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "exitcode: 0 (execution succeeded)\n", + "Code output: \n", + "Fields in the dataset:\n", + "Index(['date', 'precipitation', 'temp_max', 'temp_min', 'wind', 'weather'], dtype='object')\n", + "Improved plot has been saved to 'improved_weather_plot.png'.\n", "\n", - "1. Add `plt.show()` after the `plt.savefig(\"seattle_weather_plot.png\")` line to display the plot.\n", - "2. Consider adding gridlines to the plot for better readability.\n", - "3. (Optional) Customize the colors of the bars to better represent the weather types.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCoder\u001b[0m (to chat_manager):\n", "\n", - "Thank you for the valuable feedback. Here is the updated code with the suggested improvements. Execute the following Python code:\n", + "I'm glad that the updated code worked well and the improved plot has been saved to 'improved_weather_plot.png'. If you have any more questions or tasks, feel free to ask. Otherwise, I'll consider this task completed.\n", "\n", - "```python\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", + "TERMINATE\n", "\n", - "# Download the dataset\n", - "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n", - "weather_data = pd.read_csv(url)\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "# Print the fields in the dataset\n", - "print(weather_data.columns)\n", "\n", - "# Frequency of each type of weather\n", - "weather_counts = weather_data['weather'].value_counts()\n", "\n", - "# Create the plot\n", - "weather_counts.plot(kind='bar', color=['blue', 'gray', 'yellow', 'green'])\n", - "plt.xlabel('Weather Type')\n", - "plt.ylabel('Frequency')\n", - "plt.title('Frequency of Different Types of Weather in Seattle')\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "# Customize the plot\n", - "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", "\n", - "# Save the plot to a file\n", - "plt.savefig(\"seattle_weather_plot.png\")\n", "\n", - "# Show the plot\n", - "plt.show()\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "# Close the plot\n", - "plt.close()\n", - "```\n", "\n", - "Now, the plot will be displayed to the user, the gridlines have been added for better readability, and customized bar colors have been added to better represent the weather types. The updated plot will also be saved in the \"seattle_weather_plot.png\" file.\n", "\n", "--------------------------------------------------------------------------------\n", "\u001b[33mCritic\u001b[0m (to chat_manager):\n", "\n", - "Great job! Here's an updated evaluation of your code across the different dimensions:\n", + "It seems like there is no more information to add or any other request. If you have any more questions or tasks in the future, don't hesitate to ask. Have a great day!\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", "\n", - "1. Bugs (bugs): 10\n", - "- The code now includes the missing `plt.show()` command, and there are no major bugs, logic errors, syntax errors, or typos.\n", "\n", - "2. Data transformation (transformation): 9\n", - "- The data is transformed appropriately for the visualization type, and the frequency of each weather type is calculated correctly using the value_counts() method.\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "3. Goal compliance (compliance): 10\n", - "- The updated code meets the specified visualization goal of showing the frequency of different types of weather in Seattle, and it now displays the plot to the user.\n", "\n", - "4. Visualization type (type): 8\n", - "- The chosen visualization type, a bar chart, is appropriate and efficient for displaying the frequency of categorical data like different weather types.\n", "\n", - "5. Data encoding (encoding): 8\n", - "- Data is encoded appropriately for the plot. The weather type is displayed on the x-axis, and the frequency is displayed on the y-axis.\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "6. Aesthetics (aesthetics): 9\n", - "- With the addition of gridlines and customized colors for the bars, the aesthetics of the visualization are improved. The x and y-axis labels and plot title provide sufficient information for the user to understand the plot.\n", "\n", - "You have addressed the previous feedback and made improvements to the code. The updated code now provides a better visualization of the data and achieves its intended goal.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "exitcode: 0 (execution succeeded)\n", - "Code output: \n", - "Index(['date', 'precipitation', 'temp_max', 'temp_min', 'wind', 'weather'], dtype='object')\n", - "Figure(640x480)\n", "\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", - "Great! The code executed successfully, and you should now see the output of the fields in the dataset: ['date', 'precipitation', 'temp_max', 'temp_min', 'wind', 'weather']. The updated plot is also displayed to the user and saved in the \"seattle_weather_plot.png\" file.\n", "\n", - "The improvements you made to the code based on the feedback have resulted in a more effective and visually appealing presentation of the data. Good job!\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCritic\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mHuman\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", "\n", "\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001b[33mCoder\u001b[0m (to chat_manager):\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", "\n", - "TERMINATE\n", "\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ - "human_proxy.reset()\n", + "user_proxy.reset()\n", "coder.reset()\n", - "critic = autogen.AssistantAgent(\n", - " name=\"Critic\",\n", - " system_message=\"\"\"Critic. You are a helpful assistant highly skilled in evaluating the quality of a given visualization code by providing a score from 1 (bad) - 10 (good) while providing clear rationale. YOU MUST CONSIDER VISUALIZATION BEST PRACTICES for each evaluation. Specifically, you can carefully evaluate the code across the following dimensions\n", - "- bugs (bugs): are there bugs, logic errors, syntax error or typos? Are there any reasons why the code may fail to compile? How should it be fixed? If ANY bug exists, the bug score MUST be less than 5.\n", - "- Data transformation (transformation): Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped if needed? If a date field is used, is the date field first converted to a date object etc?\n", - "- Goal compliance (compliance): how well the code meets the specified visualization goals?\n", - "- Visualization type (type): CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? If a different visualization type is more appropriate, the score MUST BE LESS THAN 5.\n", - "- Data encoding (encoding): Is the data encoded appropriately for the visualization type?\n", - "- aesthetics (aesthetics): Are the aesthetics of the visualization appropriate for the visualization type and the data?\n", - "\n", - "YOU MUST PROVIDE A SCORE for each of the above dimensions.\n", - "{bugs: 0, transformation: 0, compliance: 0, type: 0, encoding: 0, aesthetics: 0}\n", - "Do not suggest code. \n", - "Finally, based on the critique above, suggest a concrete list of actions that the coder should take to improve the code.\n", - "\"\"\",\n", - " llm_config=llm_config,\n", - ")\n", - "groupchat = autogen.GroupChat(agents=[human_proxy, coder, critic], messages=[], max_round=20)\n", + "critic.reset()\n", + "groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=20)\n", "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n", - "human_proxy.initiate_chat(manager, message=\"download data from https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv and show me a plot that tells me about the amount of each weather . Save the plot to a file. Print the fields in a dataset before visualizing it. Take the feedback from the critic to improve the code.\")\n" + "user_proxy.initiate_chat(manager, message=\"download data from https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv and show me a plot that tells me about the amount of each weather . Save the plot to a file. Print the fields in a dataset before visualizing it. Take the feedback from the critic to improve the code.\")\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -683,21 +933,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Index(['date', 'precipitation', 'temp_max', 'temp_min', 'wind', 'weather'], dtype='object')\n" + "Fields in the dataset:\n", + "Index(['date', 'precipitation', 'temp_max', 'temp_min', 'wind', 'weather'], dtype='object')\n", + "Plot has been saved to 'weather_plot.png'.\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -706,29 +958,31 @@ ], "source": [ "import pandas as pd\n", + "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", - "# Download the dataset\n", "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n", - "weather_data = pd.read_csv(url)\n", + "data = pd.read_csv(url)\n", + "print(\"Fields in the dataset:\")\n", + "print(data.columns)\n", "\n", - "# Print the fields in the dataset\n", - "print(weather_data.columns)\n", + "# Count the occurrences of each weather type\n", + "weather_counts = data['weather'].value_counts()\n", "\n", - "# Frequency of each type of weather\n", - "weather_counts = weather_data['weather'].value_counts()\n", + "# Create a bar plot of weather occurrences\n", + "sns.set(style='whitegrid', font_scale=1.2)\n", + "plt.figure(figsize=(10, 6))\n", + "weather_plot = sns.barplot(x=weather_counts.index, y=weather_counts.values)\n", "\n", - "# Create the initial plot\n", - "weather_counts.plot(kind='bar')\n", - "plt.xlabel('Weather Type')\n", - "plt.ylabel('Frequency')\n", - "plt.title('Frequency of Different Types of Weather in Seattle')\n", + "# Add labels and title\n", + "weather_plot.set(xlabel='Weather Types', ylabel='Number of Days', title='Seattle Weather Types Frequency')\n", + "plt.savefig('weather_plot.png')\n", "\n", - "plt.show()\n", - "plt.close()" + "print(\"Plot has been saved to 'weather_plot.png'.\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -737,17 +991,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -755,7 +1009,7 @@ "source": [ "from IPython.display import Image\n", "\n", - "Image(filename='groupchat/seattle_weather_plot.png')" + "Image(filename='groupchat/improved_weather_plot.png')" ] } ],