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copy the content in the README.md +RD-Agent will generate some logs during the R&D process. These logs are very useful for debugging and understanding the R&D process. However, just viewing the terminal log is not intuitive enough. RD-Agent provides a web app to visualize the R&D process. You can easily view the R&D process and understand the R&D process better. A Quick Demo ============ -TODO: -- copy the demo content in the README.md -- Quick start for the demo. +Start Web App +------------- +In `RD-Agent/` folder, run: -TODO: link to more demos +.. code-block:: bash + + streamlit run rdagent/log/ui/app.py --server.port -- --log_dir + +This will start a web app on `http://localhost:`. + +**NOTE**: The log_dir parameter is not required. You can manually enter the log_path in the web app. If you set the log_dir parameter, you can easily select a different log_path in the web app. + +Use Web App +----------- + +1. Open the sidebar. + +2. Select the scenario you want to show. There are some pre-defined scenarios: + - Qlib Model + - Qlib Factor + - Data Mining + - Model from Paper + +3. Click the `Config⚙️` button and input the log path (if you set the log_dir parameter, you can select a log_path in the dropdown list). + +4. Click the buttons below Config⚙️ to show the scenario execution process. Buttons are: + - All Loops: Show complete scenario execution process. + - Next Loop: Show one success **R&D Loop**. + - One Evolving: Show one **evolving** step of **development** part. + - refresh logs: clear shown logs. \ No newline at end of file diff --git a/rdagent/app/model_extraction_and_code/GeneralModel.py b/rdagent/app/model_extraction_and_code/GeneralModel.py index 1b9a831b..b3194649 100644 --- a/rdagent/app/model_extraction_and_code/GeneralModel.py +++ b/rdagent/app/model_extraction_and_code/GeneralModel.py @@ -31,10 +31,10 @@ class GeneralModelScenario(Scenario): @property def rich_style_description(self) -> str: return """ -# [Model Research & Development Co-Pilot] (#_scenario) - -## [Overview](#_summary) - +### [Model Research & Development Co-Pilot](#_scenario) + +#### [Overview](#_summary) + This demo automates the extraction and development of PyTorch models from academic papers. It supports various model types through two main components: Reader and Coder. #### [Workflow Components](#_rdloops) diff --git a/rdagent/log/ui/README.md b/rdagent/log/ui/README.md deleted file mode 100644 index b5b54aa0..00000000 --- a/rdagent/log/ui/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# start web app - -in `RD-Agent/` folder, run `streamlit run rdagent/log/ui/app.py --server.port 14000` - -## custom windows - -in `rdagent.log.ui.web.py` - -StWindow is the base window, can show common log messages like logger in terminal. - -Windows can be nested. - -Simply, just write a window for one log object type, and use `isinstance()` to display messages in different windows. - -### some base windows - -- StWindow -- LLMWindow -- CodeWindow - -### multi tabs window - -More convenient to use nested windows - -- ProgressTabsWindow -- ObjectsTabsWindow - -### main trace window - -- QlibFactorTraceWindow - -## TODOS - -- make it like a living trace -- display real living trace -- Window Styles diff --git a/rdagent/log/ui/app.py b/rdagent/log/ui/app.py index 7f22d8dd..5502b072 100644 --- a/rdagent/log/ui/app.py +++ b/rdagent/log/ui/app.py @@ -1,6 +1,8 @@ -import time +import argparse +import textwrap from collections import defaultdict from datetime import datetime, timezone +from pathlib import Path from typing import Callable, Type import pandas as pd @@ -8,9 +10,11 @@ import plotly.express as px import plotly.graph_objects as go import streamlit as st from plotly.subplots import make_subplots +from st_btn_select import st_btn_select from streamlit import session_state as state from streamlit.delta_generator import DeltaGenerator +from rdagent.app.model_extraction_and_code.GeneralModel import GeneralModelScenario from rdagent.components.coder.factor_coder.CoSTEER.evaluators import ( FactorSingleFeedback, ) @@ -21,20 +25,47 @@ from rdagent.core.proposal import Hypothesis, HypothesisFeedback from rdagent.log.base import Message from rdagent.log.storage import FileStorage from rdagent.log.ui.qlib_report_figure import report_figure -from rdagent.scenarios.qlib.experiment.factor_experiment import QlibFactorExperiment +from rdagent.scenarios.data_mining.experiment.model_experiment import DMModelScenario +from rdagent.scenarios.qlib.experiment.factor_experiment import ( + QlibFactorExperiment, + QlibFactorScenario, +) from rdagent.scenarios.qlib.experiment.model_experiment import ( QlibModelExperiment, QlibModelScenario, ) -st.set_page_config(layout="wide") +st.set_page_config(layout="wide", page_title="RD-Agent", page_icon="🎓", initial_sidebar_state="expanded") -if "log_path" not in state: - state.log_path = "" +# 获取log_path参数 +parser = argparse.ArgumentParser(description="RD-Agent Streamlit App") +parser.add_argument("--log_dir", type=str, help="Path to the log directory") +args = parser.parse_args() +if args.log_dir: + main_log_path = Path(args.log_dir) + if not main_log_path.exists(): + st.error(f"Log dir `{main_log_path}` does not exist!") + st.stop() +else: + main_log_path = None + + +SELECTED_METRICS = [ + "IC", + "1day.excess_return_without_cost.annualized_return", + "1day.excess_return_without_cost.information_ratio", + "1day.excess_return_without_cost.max_drawdown", +] if "log_type" not in state: - state.log_type = "qlib_model" + state.log_type = "Qlib Model" + +if "log_path" not in state: + if main_log_path: + state.log_path = next(main_log_path.iterdir()).relative_to(main_log_path) + else: + state.log_path = "" if "fs" not in state: state.fs = None @@ -54,6 +85,9 @@ if "lround" not in state: if "erounds" not in state: state.erounds = defaultdict(int) # Evolving Rounds in each RD Loop +if "e_decisions" not in state: + state.e_decisions = defaultdict(lambda: defaultdict(tuple)) + # Summary Info if "hypotheses" not in state: # Hypotheses in each RD Loop @@ -65,17 +99,26 @@ if "h_decisions" not in state: if "metric_series" not in state: state.metric_series = [] +# Factor Task Baseline +if "alpha158_metrics" not in state: + state.alpha158_metrics = None + def refresh(): - state.fs = FileStorage(state.log_path).iter_msg() + if main_log_path: + state.fs = FileStorage(main_log_path / state.log_path).iter_msg() + else: + state.fs = FileStorage(state.log_path).iter_msg() state.msgs = defaultdict(lambda: defaultdict(list)) state.lround = 0 state.erounds = defaultdict(int) + state.e_decisions = defaultdict(lambda: defaultdict(tuple)) state.hypotheses = defaultdict(None) state.h_decisions = defaultdict(bool) state.metric_series = [] state.last_msg = None state.current_tags = [] + state.alpha158_metrics = None def should_display(msg: Message): @@ -103,54 +146,341 @@ def get_msgs_until(end_func: Callable[[Message], bool] = lambda _: True): state.current_tags = tags state.last_msg = msg - state.msgs[state.lround][msg.tag].append(msg) # Update Summary Info if "model runner result" in tags or "factor runner result" in tags or "runner result" in tags: + # factor baseline exp metrics + if state.log_type == "Qlib Factor" and state.alpha158_metrics is None: + sms = msg.content.based_experiments[0].result.loc[SELECTED_METRICS] + sms.name = "alpha158" + state.alpha158_metrics = sms + + # common metrics if msg.content.result is None: - state.metric_series.append(pd.Series([None], index=["AUROC"])) + state.metric_series.append(pd.Series([None], index=["AUROC"], name=f"Round {state.lround}")) else: - if msg.content.result.name == "AUROC": + if len(msg.content.result) < 4: ps = msg.content.result ps.index = ["AUROC"] + ps.name = f"Round {state.lround}" state.metric_series.append(ps) else: - state.metric_series.append( - msg.content.result.loc[ - [ - "IC", - "1day.excess_return_without_cost.annualized_return", - "1day.excess_return_without_cost.information_ratio", - "1day.excess_return_without_cost.max_drawdown", - ] - ] - ) + sms = msg.content.result.loc[SELECTED_METRICS] + sms.name = f"Round {state.lround}" + state.metric_series.append(sms) elif "hypothesis generation" in tags: state.hypotheses[state.lround] = msg.content elif "ef" in tags and "feedback" in tags: state.h_decisions[state.lround] = msg.content.decision + elif "d" in tags: + if "evolving code" in tags: + msg.content = [i for i in msg.content if i] + if "evolving feedback" in tags: + msg.content = [i for i in msg.content if i] + if len(msg.content) != len(state.msgs[state.lround]["d.evolving code"][-1].content): + st.toast(":red[**Evolving Feedback Length Error!**]", icon="‼️") + right_num = 0 + for wsf in msg.content: + if wsf.final_decision: + right_num += 1 + wrong_num = len(msg.content) - right_num + state.e_decisions[state.lround][state.erounds[state.lround]] = (right_num, wrong_num) + state.msgs[state.lround][msg.tag].append(msg) # Stop Getting Logs if end_func(msg): break except StopIteration: + st.toast(":red[**No More Logs to Show!**]", icon="🛑") break +def evolving_feedback_window(wsf: FactorSingleFeedback | ModelCoderFeedback): + if isinstance(wsf, FactorSingleFeedback): + ffc, efc, cfc, vfc = st.tabs( + ["**Final Feedback🏁**", "Execution Feedback🖥️", "Code Feedback📄", "Value Feedback🔢"] + ) + with ffc: + st.markdown(wsf.final_feedback) + with efc: + st.code(wsf.execution_feedback, language="log") + with cfc: + st.markdown(wsf.code_feedback) + with vfc: + st.markdown(wsf.factor_value_feedback) + elif isinstance(wsf, ModelCoderFeedback): + ffc, efc, cfc, msfc, vfc = st.tabs( + [ + "**Final Feedback🏁**", + "Execution Feedback🖥️", + "Code Feedback📄", + "Model Shape Feedback📐", + "Value Feedback🔢", + ] + ) + with ffc: + st.markdown(wsf.final_feedback) + with efc: + st.code(wsf.execution_feedback, language="log") + with cfc: + st.markdown(wsf.code_feedback) + with msfc: + st.markdown(wsf.shape_feedback) + with vfc: + st.markdown(wsf.value_feedback) + + +def display_hypotheses(hypotheses: dict[int, Hypothesis], decisions: dict[int, bool], success_only: bool = False): + if success_only: + shd = {k: v.__dict__ for k, v in hypotheses.items() if decisions[k]} + else: + shd = {k: v.__dict__ for k, v in hypotheses.items()} + df = pd.DataFrame(shd).T + if "reason" in df.columns: + df.drop(["reason"], axis=1, inplace=True) + df.columns = df.columns.map(lambda x: x.replace("_", " ").capitalize()) + + def style_rows(row): + if decisions[row.name]: + return ["color: green;"] * len(row) + return [""] * len(row) + + def style_columns(col): + if col.name != "Hypothesis": + return ["font-style: italic;"] * len(col) + return ["font-weight: bold;"] * len(col) + + # st.dataframe(df.style.apply(style_rows, axis=1).apply(style_columns, axis=0)) + st.markdown(df.style.apply(style_rows, axis=1).apply(style_columns, axis=0).to_html(), unsafe_allow_html=True) + + +def metrics_window(df: pd.DataFrame, R: int, C: int, *, height: int = 300, colors: list[str] = None): + fig = make_subplots(rows=R, cols=C, subplot_titles=df.columns) + + def hypothesis_hover_text(h: Hypothesis, d: bool = False): + color = "green" if d else "black" + text = h.hypothesis + lines = textwrap.wrap(text, width=60) + return f"{'
'.join(lines)}" + + hover_texts = [ + hypothesis_hover_text(state.hypotheses[int(i[6:])], state.h_decisions[int(i[6:])]) + for i in df.index + if i != "alpha158" + ] + if state.alpha158_metrics is not None: + hover_texts = ["Baseline: alpha158"] + hover_texts + for ci, col in enumerate(df.columns): + row = ci // C + 1 + col_num = ci % C + 1 + fig.add_trace( + go.Scatter( + x=df.index, + y=df[col], + name=col, + mode="lines+markers", + connectgaps=True, + marker=dict(size=10, color=colors[ci]) if colors else dict(size=10), + hovertext=hover_texts, + hovertemplate="%{hovertext}

%{x} Value: %{y}", + ), + row=row, + col=col_num, + ) + fig.update_layout(showlegend=False, height=height) + + if state.alpha158_metrics is not None: + for i in range(1, R + 1): # 行 + for j in range(1, C + 1): # 列 + fig.update_xaxes( + tickvals=[df.index[0]] + list(df.index[1:]), + ticktext=[f'{df.index[0]}'] + list(df.index[1:]), + row=i, + col=j, + ) + st.plotly_chart(fig) + + +def summary_window(): + if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: + st.header("Summary📊", divider="rainbow", anchor="_summary") + with st.container(): + # TODO: not fixed height + with st.container(): + bc, cc = st.columns([2, 2], vertical_alignment="center") + with bc: + st.subheader("Metrics📈", anchor="_metrics") + with cc: + show_true_only = st.toggle("successful hypotheses", value=False) + + # hypotheses_c, chart_c = st.columns([2, 3]) + chart_c = st.container() + hypotheses_c = st.container() + + with hypotheses_c: + st.subheader("Hypotheses🏅", anchor="_hypotheses") + display_hypotheses(state.hypotheses, state.h_decisions, show_true_only) + + with chart_c: + if state.log_type == "Qlib Factor" and state.alpha158_metrics is not None: + df = pd.DataFrame([state.alpha158_metrics] + state.metric_series) + else: + df = pd.DataFrame(state.metric_series) + if show_true_only and len(state.hypotheses) >= len(state.metric_series): + if state.alpha158_metrics is not None: + selected = ["alpha158"] + [i for i in df.index if state.h_decisions[int(i[6:])]] + else: + selected = [i for i in df.index if state.h_decisions[int(i[6:])]] + df = df.loc[selected] + if df.shape[0] == 1: + st.table(df.iloc[0]) + elif df.shape[0] > 1: + if df.shape[1] == 1: + # suhan's scenario + fig = px.line(df, x=df.index, y=df.columns, markers=True) + fig.update_layout(xaxis_title="Loop Round", yaxis_title=None) + st.plotly_chart(fig) + else: + metrics_window(df, 1, 4, height=300, colors=["red", "blue", "orange", "green"]) + + elif state.log_type == "Model from Paper" and len(state.msgs[state.lround]["d.evolving code"]) > 0: + with st.container(border=True): + st.subheader("Summary📊", divider="rainbow", anchor="_summary") + + # pass + ws: list[FactorFBWorkspace | ModelFBWorkspace] = state.msgs[state.lround]["d.evolving code"][-1].content + # All Tasks + + tab_names = [ + w.target_task.factor_name if isinstance(w.target_task, FactorTask) else w.target_task.name for w in ws + ] + for j in range(len(ws)): + if state.msgs[state.lround]["d.evolving feedback"][-1].content[j].final_decision: + tab_names[j] += "✔️" + else: + tab_names[j] += "❌" + + wtabs = st.tabs(tab_names) + for j, w in enumerate(ws): + with wtabs[j]: + # Evolving Code + for k, v in w.code_dict.items(): + with st.expander(f":green[`{k}`]", expanded=False): + st.code(v, language="python") + + # Evolving Feedback + evolving_feedback_window(state.msgs[state.lround]["d.evolving feedback"][-1].content[j]) + + +def tabs_hint(): + st.markdown( + "

You can navigate through the tabs using ⬅️ ➡️ or by holding Shift and scrolling with the mouse wheel🖱️.

", + unsafe_allow_html=True, + ) + + +# TODO: when tab names are too long, some tabs are not shown +def tasks_window(tasks: list[FactorTask | ModelTask]): + if isinstance(tasks[0], FactorTask): + st.markdown("**Factor Tasks🚩**") + tnames = [f.factor_name for f in tasks] + if sum(len(tn) for tn in tnames) > 100: + tabs_hint() + tabs = st.tabs(tnames) + for i, ft in enumerate(tasks): + with tabs[i]: + # st.markdown(f"**Factor Name**: {ft.factor_name}") + st.markdown(f"**Description**: {ft.factor_description}") + st.latex("Formulation") + st.latex(f"{ft.factor_formulation}") + + mks = "| Variable | Description |\n| --- | --- |\n" + for v, d in ft.variables.items(): + mks += f"| ${v}$ | {d} |\n" + st.markdown(mks) + + elif isinstance(tasks[0], ModelTask): + st.markdown("**Model Tasks🚩**") + tnames = [m.name for m in tasks] + if sum(len(tn) for tn in tnames) > 100: + tabs_hint() + tabs = st.tabs(tnames) + for i, mt in enumerate(tasks): + with tabs[i]: + # st.markdown(f"**Model Name**: {mt.name}") + st.markdown(f"**Model Type**: {mt.model_type}") + st.markdown(f"**Description**: {mt.description}") + st.latex("Formulation") + st.latex(f"{mt.formulation}") + + mks = "| Variable | Description |\n| --- | --- |\n" + for v, d in mt.variables.items(): + mks += f"| ${v}$ | {d} |\n" + st.markdown(mks) + + # Config Sidebar with st.sidebar: - st.text_input("log path", key="log_path", on_change=refresh) - st.selectbox("trace type", ["qlib_model", "qlib_factor", "model_extraction_and_implementation"], key="log_type") + st.markdown( + """ +# RD-Agent🤖 +## [Scenario Description](#_scenario) +## [Summary](#_summary) +- [**Hypotheses**](#_hypotheses) +- [**Metrics**](#_metrics) +## [RD-Loops](#_rdloops) +- [**Research**](#_research) +- [**Development**](#_development) +- [**Feedback**](#_feedback) +""" + ) - st.multiselect("excluded log tags", ["llm_messages"], ["llm_messages"], key="excluded_tags") - st.multiselect("excluded log types", ["str", "dict", "list"], ["str"], key="excluded_types") + st.selectbox( + ":green[**Scenario**]", ["Qlib Model", "Data Mining", "Qlib Factor", "Model from Paper"], key="log_type" + ) - if st.button("refresh"): + with st.popover(":orange[**Config⚙️**]"): + with st.container(border=True): + st.markdown(":blue[**log path**]") + if main_log_path: + if st.toggle("Manual Input"): + st.text_input("log path", key="log_path", on_change=refresh) + else: + folders = [ + folder.relative_to(main_log_path) for folder in main_log_path.iterdir() if folder.is_dir() + ] + st.selectbox(f"Select from `{main_log_path}`", folders, key="log_path", on_change=refresh) + else: + st.text_input("log path", key="log_path", on_change=refresh) + + with st.container(border=True): + st.markdown(":blue[**excluded configs**]") + st.multiselect("excluded log tags", ["llm_messages"], ["llm_messages"], key="excluded_tags") + st.multiselect("excluded log types", ["str", "dict", "list"], ["str"], key="excluded_types") + + if st.button("All Loops"): + if not state.fs: + refresh() + get_msgs_until(lambda m: False) + + if st.button("Next Loop"): + if not state.fs: + refresh() + get_msgs_until(lambda m: "ef.feedback" in m.tag) + + if st.button("One Evolving"): + if not state.fs: + refresh() + get_msgs_until(lambda m: "d.evolving feedback" in m.tag) + + if st.button("refresh logs", help="clear all log messages in cache"): refresh() - debug = st.checkbox("debug", value=False) + debug = st.toggle("debug", value=False) if debug: if st.button("Single Step Run"): + if not state.fs: + refresh() get_msgs_until() @@ -174,148 +504,70 @@ if debug: if isinstance(state.last_msg.content, list): st.write(state.last_msg.content[0]) elif not isinstance(state.last_msg.content, str): - st.write(state.last_msg.content) + st.write(state.last_msg.content.__dict__) # Main Window +header_c1, header_c3 = st.columns([1, 6], vertical_alignment="center") +with st.container(): + with header_c1: + st.image("https://img-prod-cms-rt-microsoft-com.akamaized.net/cms/api/am/imageFileData/RE1Mu3b?ver=5c31") + with header_c3: + st.markdown( + """ +

+ RD-Agent:
LLM-based autonomous evolving agents for industrial data-driven R&D +

+ """, + unsafe_allow_html=True, + ) # Project Info with st.container(): - image_c, toc_c = st.columns([3, 3], vertical_alignment="center") + image_c, scen_c = st.columns([3, 3], vertical_alignment="center") with image_c: - st.image("./docs/_static/scen.jpg") - with toc_c: - st.markdown( - """ -# RD-Agent🤖 -## [Scenario Description](#_scenario) -## [Summary](#_summary) -## [RD-Loops](#_rdloops) -### [Research](#_research) -### [Development](#_development) -### [Feedback](#_feedback) -""" - ) -with st.container(border=True): - st.header("Scenario Description📖", divider=True, anchor="_scenario") - # TODO: other scenarios - if state.log_type == "qlib_model": - st.markdown(QlibModelScenario().rich_style_description) - elif state.log_type == "model_extraction_and_implementation": - st.markdown( - """ -# General Model Scenario - -## Overview - -This demo automates the extraction and iterative development of models from academic papers, ensuring functionality and correctness. - -### Scenario: Auto-Developing Model Code from Academic Papers - -#### Overview - -This scenario automates the development of PyTorch models by reading academic papers or other sources. It supports various data types, including tabular, time-series, and graph data. The primary workflow involves two main components: the Reader and the Coder. - -#### Workflow Components - -1. **Reader** -- Parses and extracts relevant model information from academic papers or sources, including architectures, parameters, and implementation details. -- Uses Large Language Models to convert content into a structured format for the Coder. - -2. **Evolving Coder** -- Translates structured information from the Reader into executable PyTorch code. -- Utilizes an evolving coding mechanism to ensure correct tensor shapes, verified with sample input tensors. -- Iteratively refines the code to align with source material specifications. - -#### Supported Data Types - -- **Tabular Data:** Structured data with rows and columns, such as spreadsheets or databases. -- **Time-Series Data:** Sequential data points indexed in time order, useful for forecasting and temporal pattern recognition. -- **Graph Data:** Data structured as nodes and edges, suitable for network analysis and relational tasks. -""" - ) + st.image("./docs/_static/flow.png") + with scen_c: + st.header("Scenario Description📖", divider="violet", anchor="_scenario") + # TODO: other scenarios + if state.log_type == "Qlib Model": + st.markdown(QlibModelScenario().rich_style_description) + elif state.log_type == "Data Mining": + st.markdown(DMModelScenario().rich_style_description) + elif state.log_type == "Qlib Factor": + st.markdown(QlibFactorScenario().rich_style_description) + elif state.log_type == "Model from Paper": + st.markdown(GeneralModelScenario().rich_style_description) # Summary Window -@st.experimental_fragment() -def summary_window(): - if state.log_type in ["qlib_model", "qlib_factor"]: - with st.container(): - st.header("Summary📊", divider=True, anchor="_summary") - hypotheses_c, chart_c = st.columns([2, 3]) - # TODO: not fixed height - with hypotheses_c.container(height=600): - st.markdown("**Hypotheses🏅**") - h_str = "\n".join( - f"{id}. :green[**{h.hypothesis}**]\n\t>:green-background[*{h.__dict__.get('concise_reason', '')}*]" - if state.h_decisions[id] - else f"{id}. {h.hypothesis}\n\t>*{h.__dict__.get('concise_reason', '')}*" - for id, h in state.hypotheses.items() - ) - st.markdown(h_str) - with chart_c.container(height=600): - mt_c, ms_c = st.columns(2, vertical_alignment="center") - with mt_c: - st.markdown("**Metrics📈**") - with ms_c: - show_true_only = st.checkbox("True Decisions Only", value=False) - - labels = [f"Round {i}" for i in range(1, len(state.metric_series) + 1)] - df = pd.DataFrame(state.metric_series, index=labels) - if show_true_only and len(state.hypotheses) >= len(state.metric_series): - df = df.iloc[[i for i in range(df.shape[0]) if state.h_decisions[i + 1]]] - if df.shape[0] == 1: - st.table(df.iloc[0]) - elif df.shape[0] > 1: - # TODO: figure label - # TODO: separate into different figures - if df.shape[1] == 1: - # suhan's scenario - fig = px.line(df, x=df.index, y=df.columns, markers=True) - fig.update_layout(legend_title_text="Metrics", xaxis_title="Loop Round", yaxis_title=None) - else: - # 2*2 figure - fig = make_subplots(rows=2, cols=2, subplot_titles=df.columns) - for ci, col in enumerate(df.columns): - row = ci // 2 + 1 - col_num = ci % 2 + 1 - fig.add_trace( - go.Scatter(x=df.index, y=df[col], mode="lines+markers", name=col), row=row, col=col_num - ) - fig.update_layout(title_text="Metrics", showlegend=False) - st.plotly_chart(fig) - - summary_window() # R&D Loops Window -st.header("R&D Loops♾️", divider=True, anchor="_rdloops") -button_c1, button_c2, round_s_c = st.columns([2, 3, 18], vertical_alignment="center") -with button_c1: - if st.button("Run One Loop"): - get_msgs_until(lambda m: "ef.feedback" in m.tag) -with button_c2: - if st.button("Run One Evolving Step"): - get_msgs_until(lambda m: "d.evolving feedback" in m.tag) +if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: + st.header("R&D Loops♾️", divider="rainbow", anchor="_rdloops") -if len(state.msgs) > 1: - with round_s_c: - round = st.select_slider("Select RDLoop Round", options=state.msgs.keys(), value=state.lround) +if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: + if len(state.msgs) > 1: + r_options = list(state.msgs.keys()) + if 0 in r_options: + r_options.remove(0) + round = st_btn_select(options=r_options, index=state.lround - 1) + else: + round = 1 else: round = 1 -rf_c, d_c = st.columns([2, 2]) -# Research & Feedback Window -with rf_c: - if state.log_type in ["qlib_model", "qlib_factor"]: - # Research Window - with st.container(border=True): - st.subheader("Research🔍", divider=True, anchor="_research") +def research_window(): + with st.container(border=True): + title = "Research🔍" if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"] else "Research🔍 (reader)" + st.subheader(title, divider="blue", anchor="_research") + if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: # pdf image if pim := state.msgs[round]["r.extract_factors_and_implement.load_pdf_screenshot"]: for i in range(min(2, len(pim))): - st.image(pim[i].content) + st.image(pim[i].content, use_column_width=True) # Hypothesis if hg := state.msgs[round]["r.hypothesis generation"]: @@ -328,37 +580,27 @@ with rf_c: ) if eg := state.msgs[round]["r.experiment generation"]: - if isinstance(eg[0].content[0], FactorTask): - st.markdown("**Factor Tasks**") - fts = eg[0].content - tabs = st.tabs([f.factor_name for f in fts]) - for i, ft in enumerate(fts): - with tabs[i]: - # st.markdown(f"**Factor Name**: {ft.factor_name}") - st.markdown(f"**Description**: {ft.factor_description}") - st.latex(f"Formulation: {ft.factor_formulation}") + tasks_window(eg[0].content) - variables_df = pd.DataFrame(ft.variables, index=["Description"]).T - variables_df.index.name = "Variable" - st.table(variables_df) - elif isinstance(eg[0].content[0], ModelTask): - st.markdown("**Model Tasks**") - mts = eg[0].content - tabs = st.tabs([m.name for m in mts]) - for i, mt in enumerate(mts): - with tabs[i]: - # st.markdown(f"**Model Name**: {mt.name}") - st.markdown(f"**Model Type**: {mt.model_type}") - st.markdown(f"**Description**: {mt.description}") - st.latex(f"Formulation: {mt.formulation}") + elif state.log_type == "Model from Paper": + # pdf image + c1, c2 = st.columns([2, 3]) + with c1: + if pim := state.msgs[round]["r.pdf_image"]: + for i in range(len(pim)): + st.image(pim[i].content, use_column_width=True) - variables_df = pd.DataFrame(mt.variables, index=["Value"]).T - variables_df.index.name = "Variable" - st.table(variables_df) + # loaded model exp + with c2: + if mem := state.msgs[round]["d.load_experiment"]: + me: QlibModelExperiment = mem[0].content + tasks_window(me.sub_tasks) - # Feedback Window + +def feedback_window(): + if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: with st.container(border=True): - st.subheader("Feedback📝", divider=True, anchor="_feedback") + st.subheader("Feedback📝", divider="orange", anchor="_feedback") if fbr := state.msgs[round]["ef.Quantitative Backtesting Chart"]: st.markdown("**Returns📈**") fig = report_figure(fbr[0].content) @@ -375,104 +617,82 @@ with rf_c: - **Reason**: {h.reason}""" ) - elif state.log_type == "model_extraction_and_implementation": - # Research Window - with st.container(border=True): - # pdf image - st.subheader("Research🔍", divider=True, anchor="_research") - if pim := state.msgs[round]["r.pdf_image"]: - for i in range(len(pim)): - st.image(pim[i].content) - # loaded model exp - if mem := state.msgs[round]["d.load_experiment"]: - me: QlibModelExperiment = mem[0].content - mts: list[ModelTask] = me.sub_tasks - tabs = st.tabs([m.name for m in mts]) - for i, mt in enumerate(mts): - with tabs[i]: - # st.markdown(f"**Model Name**: {mt.name}") - st.markdown(f"**Model Type**: {mt.model_type}") - st.markdown(f"**Description**: {mt.description}") - st.latex(f"Formulation: {mt.formulation}") +if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"]: + rf_c, d_c = st.columns([2, 2]) +elif state.log_type == "Model from Paper": + rf_c = st.container() + d_c = st.container() - variables_df = pd.DataFrame(mt.variables, index=["Value"]).T - variables_df.index.name = "Variable" - st.table(variables_df) - # Feedback Window - with st.container(border=True): - st.subheader("Feedback📝", divider=True, anchor="_feedback") - if fbr := state.msgs[round]["d.developed_experiment"]: - st.markdown("**Returns📈**") - result_df = fbr[0].content.result - if result_df: - fig = report_figure(result_df) - st.plotly_chart(fig) - else: - st.markdown("Returns is None") +with rf_c: + research_window() + feedback_window() # Development Window (Evolving) with d_c.container(border=True): - st.subheader("Development🛠️", divider=True, anchor="_development") + title = ( + "Development🛠️" + if state.log_type in ["Qlib Model", "Data Mining", "Qlib Factor"] + else "Development🛠️ (evolving coder)" + ) + st.subheader(title, divider="green", anchor="_development") + + # Evolving Status + if state.erounds[round] > 0: + st.markdown("**☑️ Evolving Status**") + es = state.e_decisions[round] + e_status_mks = "".join(f"| {ei} " for ei in range(1, state.erounds[round] + 1)) + "|\n" + e_status_mks += "|--" * state.erounds[round] + "|\n" + for ei, estatus in es.items(): + if not estatus: + estatus = (0, 0) + e_status_mks += "| " + "✔️
" * estatus[0] + "❌
" * estatus[1] + " " + e_status_mks += "|\n" + st.markdown(e_status_mks, unsafe_allow_html=True) + # Evolving Tabs if state.erounds[round] > 0: - etabs = st.tabs([str(i) for i in range(1, state.erounds[round] + 1)]) + if state.erounds[round] > 1: + st.markdown("**🔄️Evolving Rounds**") + evolving_round = st_btn_select( + options=range(1, state.erounds[round] + 1), index=state.erounds[round] - 1, key="show_eround" + ) + else: + evolving_round = 1 - for i in range(0, state.erounds[round]): - with etabs[i]: - ws: list[FactorFBWorkspace | ModelFBWorkspace] = state.msgs[round]["d.evolving code"][i].content - ws = [w for w in ws if w] - # All Tasks + ws: list[FactorFBWorkspace | ModelFBWorkspace] = state.msgs[round]["d.evolving code"][ + evolving_round - 1 + ].content + # All Tasks - tab_names = [ - w.target_task.factor_name if isinstance(w.target_task, FactorTask) else w.target_task.name for w in ws - ] - wtabs = st.tabs(tab_names) - for j, w in enumerate(ws): - with wtabs[j]: - # Evolving Code - for k, v in w.code_dict.items(): - with st.expander(f":green[`{k}`]", expanded=True): - st.code(v, language="python") + tab_names = [ + w.target_task.factor_name if isinstance(w.target_task, FactorTask) else w.target_task.name for w in ws + ] + if len(state.msgs[round]["d.evolving feedback"]) >= evolving_round: + for j in range(len(ws)): + if state.msgs[round]["d.evolving feedback"][evolving_round - 1].content[j].final_decision: + tab_names[j] += "✔️" + else: + tab_names[j] += "❌" + if sum(len(tn) for tn in tab_names) > 100: + tabs_hint() + wtabs = st.tabs(tab_names) + for j, w in enumerate(ws): + with wtabs[j]: + # Evolving Code + for k, v in w.code_dict.items(): + with st.expander(f":green[`{k}`]", expanded=True): + st.code(v, language="python") - # Evolving Feedback - if len(state.msgs[round]["d.evolving feedback"]) > i: - wsf: list[FactorSingleFeedback | ModelCoderFeedback] = state.msgs[round]["d.evolving feedback"][ - i - ].content[j] - if isinstance(wsf, FactorSingleFeedback): - st.markdown( - f"""#### :blue[Factor Execution Feedback] -{wsf.execution_feedback} -#### :blue[Factor Code Feedback] -{wsf.code_feedback} -#### :blue[Factor Value Feedback] -{wsf.factor_value_feedback} -#### :blue[Factor Final Feedback] -{wsf.final_feedback} -#### :blue[Factor Final Decision] -This implementation is {'SUCCESS' if wsf.final_decision else 'FAIL'}. -""" - ) - elif isinstance(wsf, ModelCoderFeedback): - st.markdown( - f"""#### :blue[Model Execution Feedback] -{wsf.execution_feedback} -#### :blue[Model Shape Feedback] -{wsf.shape_feedback} -#### :blue[Model Value Feedback] -{wsf.value_feedback} -#### :blue[Model Code Feedback] -{wsf.code_feedback} -#### :blue[Model Final Feedback] -{wsf.final_feedback} -#### :blue[Model Final Decision] -This implementation is {'SUCCESS' if wsf.final_decision else 'FAIL'}. -""" - ) + # Evolving Feedback + if len(state.msgs[round]["d.evolving feedback"]) >= evolving_round: + evolving_feedback_window(state.msgs[round]["d.evolving feedback"][evolving_round - 1].content[j]) -# TODO: evolving tabs -> slider -# TODO: multi tasks SUCCESS/FAIL -# TODO: evolving progress bar, diff colors + +with st.container(border=True): + st.subheader("Disclaimer", divider="gray") + st.markdown( + "This content is AI-generated and may not be fully accurate or up-to-date; please verify with a professional for critical matters." + ) diff --git a/requirements.txt b/requirements.txt index b6d8f7e4..4d878f59 100644 --- a/requirements.txt +++ b/requirements.txt @@ -61,4 +61,7 @@ python-dotenv # infrastructure related. docker - +# demo related +streamlit +plotly +st-btn-select