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adding helper to bootstrap the generation of the meta.yaml file
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,176 @@ | ||
| import pandas as pd | ||
| import yaml | ||
| from typing import Dict, Any | ||
| from litellm import completion | ||
|
|
||
| CONSTANT_PROMPT = """ | ||
| Use the following example as a guide for the structure and content, paying special attention to the advanced template formats: | ||
| ```yaml | ||
| name: bicerano_dataset | ||
| description: |- | ||
| This paper outlines a MD simulation workflow based on GPU MD simulation and the | ||
| refined optimized potentials for liquid simulation (OPLS) OPLS3e force field to | ||
| calculate glass transition temperatures (Tgs) of 315 polymers for which Bicerano | ||
| reported experimental values. | ||
| targets: | ||
| - id: Tg_exp | ||
| description: experimental glass transition temperature | ||
| units: K | ||
| type: float | ||
| names: | ||
| - noun: experimental glass transition temperature | ||
| uris: | ||
| - id: Tg_calc | ||
| description: calculated glass transition T | ||
| units: K | ||
| type: float | ||
| names: | ||
| - noun: computed glass transition temperature | ||
| - id: rho_300K_calc | ||
| description: computed density at 300K | ||
| units: g/cm^3 | ||
| type: float | ||
| names: | ||
| - noun: computed polymer density at 300K | ||
| identifiers: | ||
| - id: PSMILES | ||
| type: PSMILES | ||
| description: PSMILES | ||
| - id: compound_name | ||
| type: Other | ||
| names: | ||
| - noun: compound name | ||
| description: polymer name | ||
| license: CC BY 4.0 | ||
| links: | ||
| - url: https://pubs.acs.org/doi/10.1021/acsapm.0c00524# | ||
| description: corresponding publication | ||
| - url: | ||
| - https://raw.githubusercontent.com/AdrianM0/chemnlp/main/data/tabular/bicerano_dataset/HT_MD_polymer_properties.csv | ||
| description: data source | ||
| num_points: 315 | ||
| bibtex: | ||
| - |- | ||
| @article{afzal2021, | ||
| author = {Afzal, Mohammad Atif Faiz and Browning, Andrea R. and Goldberg, Alexander and Halls, Mathew D. and Gavartin, Jacob L. and Morisato, | ||
| Tsuguo and Hughes, Thomas F. and Giesen, David J. and Goose, Joseph E.}, | ||
| title = {High-Throughput Molecular Dynamics Simulations and Validation of Thermophysical Properties of Polymers for Various Applications}, | ||
| journal = {ACS Applied Polymer Materials}, | ||
| volume = {3}, | ||
| number = {2}, | ||
| pages = {620-630}, | ||
| year = {2021}, | ||
| doi = {10.1021/acsapm.0c00524}} | ||
| templates: | ||
| - The polymer with the {PSMILES__description} of {PSMILES#} has an experimental glass transition temperature of {Tg_exp#} K. | ||
| - The polymer with the {PSMILES__description} of {PSMILES#} has a computed glass transition temperature of {Tg_calc#} K. | ||
| - The polymer with the {PSMILES__description} of {PSMILES#} has a computed density at 300 K of {rho_300K_calc#} g/cc. | ||
| - The polymer with the {compound_name__names__noun} of {compound_name#} has an experimental glass transition temperature of {Tg_exp#} K. | ||
| - The polymer with the {compound_name__names__noun} of {compound_name#} has a computed glass transition temperature of {Tg_calc#} K. | ||
| - The polymer with the {compound_name__names__noun} of {compound_name#} has a computed density at 300 K of {rho_300K_calc#} g/cc. | ||
| - |- | ||
| Question: What is a polymer with a computed glass transition temperature of {Tg_calc#} K and a computed density at 300 K of {rho_300K_calc#} g/cc. | ||
| Answer: A polymer with {PSMILES__description} {PSMILES#} | ||
| # Multiple choice template | ||
| - |- | ||
| Question: Which of the following polymers has an experimental glass transition temperature closest to {Tg_exp#} K? | ||
| Options: {%multiple_choice_enum%4%aA1} | ||
| {compound_name%} | ||
| Answer: {%multiple_choice_result} | ||
| # Benchmarking template | ||
| - The experimental glass transition temperature of the polymer with {PSMILES__description} {PSMILES#} is:<EOI>{Tg_exp#} K | ||
| ``` | ||
| Guidelines for advanced templates: | ||
| 1. Multiple Choice Questions: | ||
| - Use the format {%multiple_choice_enum%N%TYPE} where N is the number of choices and TYPE is the enumeration style (e.g., aA1 for lowercase, uppercase, or numbers). | ||
| - Use {COLUMN_NAME%} to indicate where the choices should be inserted. | ||
| - Use {%multiple_choice_result} for the correct answer. | ||
| 2. Benchmarking Templates: | ||
| - Include the <EOI> tag to separate the question from the answer. | ||
| - These templates are used for evaluating model performance. | ||
| 3. Conditional Statements: | ||
| - Use {COLUMN#not &NULL} for conditional text based on column values. | ||
| 4. Random Choices: | ||
| - Use {#option1|option2|option3!} for random selection of text. | ||
| Generate a similar meta.yaml structure for the given dataset, including appropriate targets, identifiers, and templates based on the column names and example data provided. Include at least one multiple choice template and one benchmarking template.""" | ||
|
|
||
|
|
||
| def generate_meta_yaml( | ||
| df: pd.DataFrame, dataset_name: str, description: str, model: str = "gpt-4o" | ||
| ) -> Dict[str, Any]: | ||
| """ | ||
| Generate a meta.yaml structure using an LLM based on a DataFrame, including advanced sampling methods. | ||
| Args: | ||
| df (pd.DataFrame): The input DataFrame. | ||
| dataset_name (str): The name of the dataset. | ||
| description (str): A brief description of the dataset. | ||
| model (str, optional): The LLM model to use. Defaults to 'gpt-4o'. | ||
| Returns: | ||
| Dict[str, Any]: A dictionary representation of the meta.yaml structure. | ||
| """ | ||
| # Extract column names and example data | ||
| columns = df.columns.tolist() | ||
| example_data = df.iloc[0].to_dict() | ||
|
|
||
| # Prepare the prompt for the LLM | ||
| prompt = ( | ||
| f"""Create a meta.yaml structure for a dataset with the following information: | ||
| Dataset name: {dataset_name} | ||
| Description: {description} | ||
| Number of rows: {len(df)} | ||
| Columns: | ||
| {', '.join(columns)} | ||
| Example data: | ||
| {yaml.dump(example_data, default_flow_style=False)}""" | ||
| + CONSTANT_PROMPT | ||
| ) | ||
|
|
||
| # Call the LLM with the prompt | ||
| llm_response = completion( | ||
| model=model, messages=[{"role": "user", "content": prompt}] | ||
| ) | ||
|
|
||
| # Parse the LLM's response and convert it to a dictionary | ||
| try: | ||
| meta_yaml = yaml.safe_load(llm_response) | ||
| except yaml.YAMLError as e: | ||
| print(f"Error parsing LLM response: {e}") | ||
| meta_yaml = None | ||
|
|
||
| return meta_yaml | ||
|
|
||
|
|
||
| # Example usage | ||
| if __name__ == "__main__": | ||
| # Load your DataFrame | ||
| df = pd.read_csv("your_dataset.csv") | ||
|
|
||
| # Generate meta.yaml | ||
| meta_yaml = generate_meta_yaml( | ||
| df, | ||
| dataset_name="Your Dataset Name", | ||
| description="A brief description of your dataset", | ||
| ) | ||
|
|
||
| # Print or save the generated meta.yaml | ||
| if meta_yaml: | ||
| print(yaml.dump(meta_yaml, default_flow_style=False)) | ||
|
|
||
| # Optionally, save to a file | ||
| with open("meta.yaml", "w") as f: | ||
| yaml.dump(meta_yaml, f, default_flow_style=False) | ||
| else: | ||
| print("Failed to generate meta.yaml") |