diff --git a/.github/.codecov.yml b/.github/.codecov.yml index 28bbfa668..3caf71568 100644 --- a/.github/.codecov.yml +++ b/.github/.codecov.yml @@ -2,6 +2,11 @@ codecov: notify: wait_for_ci: true +ignore: + - ".github" # ignore the .github directory + - "docs" # ignore the tests directory + - "figs" # ignore the figs directory + coverage: status: patch: diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index 0886b2921..3bf7d41fc 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -34,7 +34,6 @@ jobs: - name: Install dependencies run: | curl -sSL https://install.python-poetry.org | python3 - poetry lock poetry install --with test -E chat - name: Type-checking package with mypy run: | diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 02e1282be..96c7b84cd 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -1,4 +1,4 @@ -name: pre-commit +name: autofix.ci concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -13,9 +13,11 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python 3.11 uses: actions/setup-python@v4 with: python-version: 3.11.2 - uses: pre-commit/action@v3.0.0 + - uses: autofix-ci/action@dd55f44df8f7cdb7a6bf74c78677eb8acd40cd0a + if: always() diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 36d4c2e51..99c317cc7 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -38,7 +38,6 @@ jobs: uses: abatilo/actions-poetry@v2 - name: Install dependencies run: | - poetry lock poetry install --with test -E chat - name: Test with pytest env: # Or as an environment variable diff --git a/README.md b/README.md index 511bd2a0e..fc7353266 100644 --- a/README.md +++ b/README.md @@ -45,57 +45,24 @@ Sotopia is an open-ended social learning environment that allows agents to inter ## Help See [documentation](https://docs.sotopia.world) for more details. - ## Get started -### Use on Google Colab - -If you want to try it out on Google Colab first, please check out our Colab Tutorial Series: - -
    -
  1. Basic -
      -
    1. 1.1 Playing with Sotopia in a Google Colab Notebook
      2. -
    2. 1.2 Browsing sotopia data
      3. -
    -
    2. -
  2. -Building your own social agent (coming soon!) -
    3. -
### Install locally -This package supports Python 3.11 and above. In one line, -`pip install sotopia` or `pip install uv; uv pip install sotopia`. - -Or from scratch, use a virtual environment, e.g. with anaconda3: `conda create -n sotopia python=3.11; conda activate sotopia; curl -sSL https://install.python-poetry.org | python3`. Then, install the requirements and this package. -```bash -poetry install -``` - -OpenAI key is required to run the code. Please set the environment variable `OPENAI_API_KEY` to your key. The recommend way is to add the key to the conda environment: -```bash -conda env config vars set OPENAI_API_KEY=your_key -``` +We recommend using a virtual environment, e.g. with anaconda3: `conda create -n sotopia python=3.11; conda activate sotopia; curl -sSL https://install.python-poetry.org | python3`. -For some experiments, TogetherAI key is required to run the code. Please set the environment variable `TOGETHER_API_KEY` to your key. The recommend way is to add the key to the conda environment: -```bash -conda env config vars set TOGETHER_API_KEY=your_key -``` +Then: +`python -m pip install sotopia; sotopia install` -A redis-stack server is required to run the code. Please follow the [instruction](https://redis.io/docs/stack/get-started/install/docker/) to start a redis-stack server or use an existing server. You can also check [Q&A](/docs/troubleshooting.md) to initiate the redis server with the Sotopia data. +This will setup the necessary environment variables and download the necessary data. -The `REDIS_OM_URL` need to be set before loading and saving agents: -```bash -conda env config vars set REDIS_OM_URL="redis://user:password@host:port" -``` +> [!TIP] +> Having trouble installing? Or don't want to install redis for now? We are working on a public redis server for you to use. Stay tuned! -Make a folder to store the logs: +OpenAI key is required to run the code. Please set the environment variable `OPENAI_API_KEY` to your key. The recommend way is to add the key to the conda environment: ```bash -mkdir logs +conda env config vars set OPENAI_API_KEY=your_key ``` - - ## Easy Sample Server You can view an episode demo with default parameters with the following: ```python @@ -118,82 +85,3 @@ or run ```bash python examples/minimalist_demo.py ``` - -## Contribution -#### Install dev options -Follow the installation instruction above and then, instead of running `python -m pip install -e .`, run the following commands: - -```bash -python -m pip install -e ".[dev]" -mypy --install-types --non-interactive sotopia -python -m pip install pre-commit -pre-commit install -``` -#### New branch for each feature -`git checkout -b feature/feature-name` and PR to `main` branch. -#### Before committing -Run `pytest` to make sure all tests pass (this will ensure dynamic typing passed with beartype) and `mypy --strict .` to check static typing. -(You can also run `pre-commit run --all-files` to run all checks) -#### Check github action result -Check the github action result to make sure all tests pass. If not, fix the errors and push again. - -## Running Experiments -We use `gin-config` to configure the experiments. You don't need to be an expert to use it. The basic syntax is -```bash -python --gin_file --gin_file '--gin.PARAM1=value1' '--gin.PARAM2=value2' -``` -The `--gin_file` is used to load and compose the default configuration. The `--gin.PARAM1=value1` is used to overwrite the default configuration. The later configuration will always overwrite the previous one. - -Here is an example of running an experiment: - -```bash -python examples/experiment_eval.py --gin_file sotopia_conf/generation_utils_conf/generate.gin --gin_file sotopia_conf/server_conf/server.gin --gin_file sotopia_conf/run_async_server_in_batch.gin '--gin.ENV_IDS=["01H7VFHPDZVVCDZR3AARA547CY"]' '--gin.AGENT1_MODEL="gpt-4"' '--gin.BATCH_SIZE=20' '--gin.PUSH_TO_DB=False' '--gin.TAG="test"' -``` -For the complete set of parameters, please check the `sotopia_conf` folder. - -To run a large batch of environments, you can change the `ENV_IDS` parameter in `sotopia_conf/run_async_server_in_batch.gin` to a list of environment ids. When `gin.ENV_IDS==[]`, all environments on the DB will be used. - -## Getting access to your simulation -After running experiments, you can go to the `examples/redis_stats.ipynb` notebook to check the existing episodes (Episode Log section), as well as calculate the performance. - -For the original Sotopia simulation in our paper's experiments, you can find how to get them in the [Q&A](/docs/troubleshooting.md) section in the `./docs` folder. - -## Adding new characters and environments -You can use the following function with the `**kwargs` being the properties of the `AgentProfile` class. This is the same for the scenarios/environments. -```python -class AgentProfile(JsonModel): - first_name: str = Field(index=True) - last_name: str = Field(index=True) - age: int = Field(index=True, default_factory=lambda: 0) - occupation: str = Field(index=True, default_factory=lambda: "") - gender: str = Field(index=True, default_factory=lambda: "") - gender_pronoun: str = Field(index=True, default_factory=lambda: "") - public_info: str = Field(index=True, default_factory=lambda: "") - big_five: str = Field(index=True, default_factory=lambda: "") - moral_values: list[str] = Field(index=False, default_factory=lambda: []) - schwartz_personal_values: list[str] = Field(index=False, default_factory=lambda: []) - personality_and_values: str = Field(index=True, default_factory=lambda: "") - decision_making_style: str = Field(index=True, default_factory=lambda: "") - secret: str = Field(default_factory=lambda: "") - model_id: str = Field(default_factory=lambda: "") - -class EnvironmentProfile(JsonModel): - codename: str = Field(...) - source: str = Field(...) - scenario: str = Field(...) - agent_goals: list[str] = Field(...) - ... -``` - -```python - -from sotopia.database.persistent_profile import AgentProfile, EnvironmentProfile - -def add_agent_to_database(**kwargs: dict[str, Any]) -> None: - agent = AgentProfile(**kwargs) - agent.save() - -def add_env_profile(**kwargs: dict[str, Any]) -> None: - env_profile = EnvironmentProfile(**kwargs) - env_profile.save() -``` diff --git a/docs/pages/_meta.json b/docs/pages/_meta.json index f96af59dc..57189d534 100644 --- a/docs/pages/_meta.json +++ b/docs/pages/_meta.json @@ -1,37 +1,23 @@ { "index": { - "type": "page", - "title": "Home" - }, - "contribution_menu": { - "title": "Documentation", - "type": "menu", - "items": { - "agents": { - "title": "Agents", - "href": "/agents" - }, - "environments": { - "title": "Environments", - "href": "/environments" - }, - "examples": { - "title": "Examples", - "href": "/examples" - }, - "hyperparameters": { - "title": "Hyperparameters", - "href": "/hyperparameters" - }, - "scripts": { - "title": "Scripts", - "href": "/scripts" - }, - "xml": { - "title": "XML Formatter", - "href": "/xml" - - } + "title": "Introduction", + "theme": { + "breadcrumb": true } + }, + "---": { + "type": "separator" + }, + "concepts": { + "title": "Concepts", + "type": "page" + }, + "examples": { + "title": "Examples", + "type": "page" + }, + "contribution": { + "title": "Contribution", + "type": "page" } } diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md deleted file mode 100644 index 368432e18..000000000 --- a/docs/pages/benchmark.md +++ /dev/null @@ -1,11 +0,0 @@ -# Benchmark your model as a social agent in Sotopia - -``` -sotopia_benchmark --model= -``` -or - -``` -python sotopia/benchmark/cli.py --model= -``` -Currently this script would run over 100 simulations on the Sotopia Hard tasks. And the partner model is fixed to be `meta-llama/Llama-3-70b-chat-hf` diff --git a/docs/pages/concepts/agents.md b/docs/pages/concepts/agents.md new file mode 100644 index 000000000..c01d03203 --- /dev/null +++ b/docs/pages/concepts/agents.md @@ -0,0 +1,71 @@ +## Agents +Sotopia supports a variety of agents that can support social simulations for a variety of purposes. + +### LLMAgent + +LLMAgent is the core agent that is powered by a large language model +```python +class LLMAgent(BaseAgent[Observation, AgentAction]): + def __init__( + self, + agent_name: str | None = None, + uuid_str: str | None = None, + agent_profile: AgentProfile | None = None, + model_name: str = "gpt-3.5-turbo", + script_like: bool = False, + ) -> None: +``` + +### ScriptWritingAgent +Script Writing Agent is an agent that is specialized in writing scripts of social interactions. It is a subclass of LLMAgent and supports the investigation of [*Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs*](https://arxiv.org/abs/2403.05020) + +```python +class ScriptWritingAgent(LLMAgent): + def __init__( + self, + agent_name: str | None = None, + uuid_str: str | None = None, + agent_profile: AgentProfile | None = None, + model_name: str = "gpt-3.5-turbo", + agent_names: list[str] = [], + background: ScriptBackground | None = None, + ) -> None: +``` + +### HumanAgent +This agent allows for human input to be used in the simulation through the command line. +```python +class HumanAgent(BaseAgent[Observation, AgentAction]): + """ + A human agent that takes input from the command line. + """ + + def __init__( + self, + agent_name: str | None = None, + uuid_str: str | None = None, + agent_profile: AgentProfile | None = None, + ) -> None: + super().__init__( + agent_name=agent_name, + uuid_str=uuid_str, + agent_profile=agent_profile, + ) + self.model_name: LLM_Name = "human" +``` + +### RedisAgent + +RedisAgent is an agent that can be used to interact with the Redis database. +```python +class RedisAgent(BaseAgent[Observation, AgentAction]): + """An agent use redis as a message broker.""" + + def __init__( + self, + agent_name: str | None = None, + uuid_str: str | None = None, + session_id: str | None = None, + agent_profile: AgentProfile | None = None, + ) -> None: +``` diff --git a/docs/pages/concepts/database.md b/docs/pages/concepts/database.md new file mode 100644 index 000000000..818947224 --- /dev/null +++ b/docs/pages/concepts/database.md @@ -0,0 +1,144 @@ +## Basic Concepts of the Database + +We use the `JsonModel` class to define the structure of the database. This class is coming from [Redis-OM](https://github.com/redis/redis-om-node), which is a Redis Object Mapper for Node.js. You should learn a lot about how to use the database by reading their documentation. + +We do have more customized method `.all()` would return a list of all the corresponding objects in the database. Note that using `.all()` is going to block the database, so you should use it with caution. +You might see codes using `all_pks()` which is a method from the `JsonModel` class. This method returns a list of all the primary keys of the corresponding objects in the database. This method is not blocking the database. However, it might not be as efficient as using `.all()`. + +## Adding new characters and environments +You can use the following function with the `**kwargs` being the properties of the `AgentProfile` class. This is the same for the scenarios/environments. +```python +class AgentProfile(JsonModel): + first_name: str = Field(index=True) + last_name: str = Field(index=True) + age: int = Field(index=True, default_factory=lambda: 0) + occupation: str = Field(index=True, default_factory=lambda: "") + gender: str = Field(index=True, default_factory=lambda: "") + gender_pronoun: str = Field(index=True, default_factory=lambda: "") + public_info: str = Field(index=True, default_factory=lambda: "") + big_five: str = Field(index=True, default_factory=lambda: "") + moral_values: list[str] = Field(index=False, default_factory=lambda: []) + schwartz_personal_values: list[str] = Field(index=False, default_factory=lambda: []) + personality_and_values: str = Field(index=True, default_factory=lambda: "") + decision_making_style: str = Field(index=True, default_factory=lambda: "") + secret: str = Field(default_factory=lambda: "") + model_id: str = Field(default_factory=lambda: "") + +class EnvironmentProfile(JsonModel): + codename: str = Field(...) + source: str = Field(...) + scenario: str = Field(...) + agent_goals: list[str] = Field(...) + ... +``` + +```python + +from sotopia.database.persistent_profile import AgentProfile, EnvironmentProfile + +def add_agent_to_database(**kwargs: dict[str, Any]) -> None: + agent = AgentProfile(**kwargs) + agent.save() + +def add_env_profile(**kwargs: dict[str, Any]) -> None: + env_profile = EnvironmentProfile(**kwargs) + env_profile.save() +``` + +## EpisodeLog + +### Class Overview + +The `EpisodeLog` class logs and validates episodes in a simulated environment with multiple agents. + +#### Attributes + +- **environment (str)**: environment pk. +- **agents (list[str])**: List of agent pk. +- **tag (str | None)**: Optional episode tag. +- **models (list[str] | None)**: Optional list of models used. +- **messages (list[list[tuple[str, str, str]]])**: Messages exchanged by agents, organized by turns. +- **reasoning (str)**: Rationale behind episode events. +- **rewards (list[tuple[float, dict[str, float]] | float])**: Rewards per dimension for agents. +- **rewards_prompt (str)**: Prompt used to determine rewards. + +#### Methods + +##### agent_number_message_number_reward_number_turn_number_match +Validates that the number of agents matches the number of rewards. +- **Parameters**: `cls`, `values` +- **Returns**: Validated values +- **Raises**: Assertion error if the number of agents does not match the number of rewards. + +##### render_for_humans +Generates a human-readable version of the episode log. +- **Returns**: Tuple of (list of agent profiles, list of messages and rewards) + +#### Example Usage + +```python +episode_log = EpisodeLog( + environment="SimulatedEnvironment", + agents=["agent_1", "agent_2"], + tag="example_tag", + models=["model_1", "model_2"], + messages=[ + [("agent_1", "Environment", "message_1"), ("agent_2", "Environment", "message_2")], + [("agent_1", "agent_2", "message_3"), ("agent_2", "agent_1", "message_4")] + ], + reasoning="Example reasoning.", + rewards=[(10.0, {"metric_1": 5.0}), 8.0], + rewards_prompt="Example prompt." +) +episode_log.validate() +agent_profiles, messages_and_rewards = episode_log.render_for_humans() +print(agent_profiles) +print("\n".join(messages_and_rewards)) +``` + + +## Serialization and Deserialization + +It is very easy to serialize any database structures to JSON or CSV. + +#### Serialize simulated episodes +```python +from sotopia.database import episodes_to_jsonl, EpisodeLog + +episodes: list[EpisodeLog] = EpisodeLog.all() + +episodes_to_jsonl(episodes, "episodes.jsonl") +``` + + +#### Serialize env/agent profiles +```python +from sotopia.database import environmentprofiles_to_jsonl, agentprofiles_to_jsonl + +agent_profiles: list[AgentProfile] = AgentProfile.all() + +environment_profiles: list[EnvironmentProfile] = EnvironmentProfile.all() + +agentprofiles_to_jsonl(agent_profiles, "agent_profiles.jsonl") +environmentprofiles_to_jsonl(environment_profiles, "environment_profiles.jsonl") +``` + +#### Other utilities +| Function Name | Description | Arguments | Return Type | +|---------------|-------------|-----------|-------------| +| `agentprofiles_to_csv` | Saves agent profiles to a CSV file. | `agent_profiles: list[AgentProfile]`, `csv_file_path: str = "agent_profiles.csv"` | `None` | +| `agentprofiles_to_jsonl` | Saves agent profiles to a JSONL file. | `agent_profiles: list[AgentProfile]`, `jsonl_file_path: str = "agent_profiles.jsonl"` | `None` | +| `envagnetcombostorage_to_csv` | Saves environment-agent combo storages to a CSV file. | `env_agent_combo_storages: list[EnvAgentComboStorage]`, `csv_file_path: str = "env_agent_combo_storages.csv"` | `None` | +| `envagnetcombostorage_to_jsonl` | Saves environment-agent combo storages to a JSONL file. | `env_agent_combo_storages: list[EnvAgentComboStorage]`, `jsonl_file_path: str = "env_agent_combo_storages.jsonl"` | `None` | +| `environmentprofiles_to_csv` | Saves environment profiles to a CSV file. | `environment_profiles: list[EnvironmentProfile]`, `csv_file_path: str = "environment_profiles.csv"` | `None` | +| `environmentprofiles_to_jsonl` | Saves environment profiles to a JSONL file. | `environment_profiles: list[EnvironmentProfile]`, `jsonl_file_path: str = "environment_profiles.jsonl"` | `None` | +| `episodes_to_csv` | Saves episodes to a CSV file. | `episodes: list[EpisodeLog]`, `csv_file_path: str = "episodes.csv"` | `None` | +| `episodes_to_jsonl` | Saves episodes to a JSONL file. | `episodes: list[EpisodeLog]`, `jsonl_file_path: str = "episodes.jsonl"` | `None` | +| `get_rewards_from_episode` | Retrieves rewards from an episode. | `episode: EpisodeLog` | `list[tuple[float, dict[str, float]]]` | +| `jsonl_to_agentprofiles` | Loads agent profiles from a JSONL file. | `jsonl_file_path: str` | `list[AgentProfile]` | +| `jsonl_to_envagnetcombostorage` | Loads environment-agent combo storages from a JSONL file. | `jsonl_file_path: str` | `list[EnvAgentComboStorage]` | +| `jsonl_to_environmentprofiles` | Loads environment profiles from a JSONL file. | `jsonl_file_path: str` | `list[EnvironmentProfile]` | +| `jsonl_to_episodes` | Loads episodes from a JSONL file. | `jsonl_file_path: str` | `list[EpisodeLog]` | +| `jsonl_to_relationshipprofiles` | Loads relationship profiles from a JSONL file. | `jsonl_file_path: str` | `list[RelationshipProfile]` | +| `relationshipprofiles_to_csv` | Saves relationship profiles to a CSV file. | `relationship_profiles: list[RelationshipProfile]`, `csv_file_path: str = "relationship_profiles.csv"` | `None` | +| `relationshipprofiles_to_jsonl` | Saves relationship profiles to a JSONL file. | `relationship_profiles: list[RelationshipProfile]`, `jsonl_file_path: str = "relationship_profiles.jsonl"` | `None` | diff --git a/docs/pages/environments.md b/docs/pages/concepts/environments.md similarity index 100% rename from docs/pages/environments.md rename to docs/pages/concepts/environments.md diff --git a/docs/pages/concepts/generation.md b/docs/pages/concepts/generation.md new file mode 100644 index 000000000..41895aec3 --- /dev/null +++ b/docs/pages/concepts/generation.md @@ -0,0 +1,82 @@ +## Generation functions + +The core of generating agent action and environment observation lies in the `agenerate` function: + +```python +@gin.configurable +@beartype +async def agenerate( + model_name: str, + template: str, + input_values: dict[str, str], + output_parser: BaseOutputParser[OutputType], + temperature: float = 0.7, + structured_output: bool = False, +) -> OutputType: + input_variables = re.findall(r"(? + Structured output is used to return the output in a structured format, such as a dictionary or a Pydantic object. + Currently, the structured output is only supported for the models below: + * `gpt-4o-mini-2024-07-18` and later + * `gpt-4o-2024-08-06` and later + + + +Here are a few examples of how to use the `agenerate` function: + + + +### Automatically generate scenarios + +```python +async def agenerate_env_profile( + model_name: str, + inspiration_prompt: str = "asking my boyfriend to stop being friends with his ex", + examples: str = "", + temperature: float = 0.7, +) -> tuple[EnvironmentProfile, str]: + """ + Using langchain to generate the background + """ + return await agenerate( + model_name=model_name, + template="""Please generate scenarios and goals based on the examples below as well as the inspirational prompt, when creating the goals, try to find one point that both sides may not agree upon initially and need to collaboratively resolve it. + Examples: + {examples} + Inspirational prompt: {inspiration_prompt} + Please use the following format: + {format_instructions} + """, + input_values=dict( + inspiration_prompt=inspiration_prompt, + examples=examples, + ), + output_parser=PydanticOutputParser(pydantic_object=EnvironmentProfile), + temperature=temperature, + ) +``` +### Other generation functions +Similarly, there are other utility functions that builds upon the `agenerate` function to generate different types of outputs. + +```python +@beartype +async def agenerate_relationship_profile( + model_name: str, + agents_profiles: list[str], +) -> tuple[RelationshipProfile, str] +``` + +```python +async def agenerate_script( + model_name: str, + background: ScriptBackground, + temperature: float = 0.7, + agent_names: list[str] = [], + agent_name: str = "", + history: str = "", + single_step: bool = False, +) -> tuple[ScriptInteractionReturnType, str] +``` diff --git a/docs/pages/agents.md b/docs/pages/concepts/messages.md similarity index 100% rename from docs/pages/agents.md rename to docs/pages/concepts/messages.md diff --git a/docs/pages/xml.md b/docs/pages/concepts/renderers.md similarity index 100% rename from docs/pages/xml.md rename to docs/pages/concepts/renderers.md diff --git a/docs/pages/contribution/contribution.md b/docs/pages/contribution/contribution.md new file mode 100644 index 000000000..4bab646ee --- /dev/null +++ b/docs/pages/contribution/contribution.md @@ -0,0 +1,17 @@ +## Contribution +#### Install dev options +Follow the installation instruction above and then, instead of running `python -m pip install -e .`, run the following commands: + +```bash +python -m pip install -e ".[dev]" +mypy --install-types --non-interactive sotopia +python -m pip install pre-commit +pre-commit install +``` +#### New branch for each feature +`git checkout -b feature/feature-name` and PR to `main` branch. +#### Before committing +Run `pytest` to make sure all tests pass (this will ensure dynamic typing passed with beartype) and `mypy --strict .` to check static typing. +(You can also run `pre-commit run --all-files` to run all checks) +#### Check github action result +Check the github action result to make sure all tests pass. If not, fix the errors and push again. diff --git a/docs/pages/examples/benchmark.md b/docs/pages/examples/benchmark.md new file mode 100644 index 000000000..abef25860 --- /dev/null +++ b/docs/pages/examples/benchmark.md @@ -0,0 +1,14 @@ +# Benchmark your model as a social agent in Sotopia + +``` +sotopia benchmark --models --models [--only-show-performance] +``` +or + +``` +python sotopia/cli/benchmark/benchmark.py --models --models [--only-show-performance] +``` +When `only-show-performance` is speficied, only model results with available episodes will be displayed. If this option is not used, the benchmark will be run. +Currently this script would run over 100 simulations on the Sotopia Hard tasks. And the partner model is fixed to be `meta-llama/Llama-3-70b-chat-hf` + +An example script is provided in `scripts/display_benchmark_results.sh` diff --git a/docs/pages/examples.mdx b/docs/pages/examples/examples.mdx similarity index 100% rename from docs/pages/examples.mdx rename to docs/pages/examples/examples.mdx diff --git a/docs/pages/examples/experiment.md b/docs/pages/examples/experiment.md new file mode 100644 index 000000000..250f0a493 --- /dev/null +++ b/docs/pages/examples/experiment.md @@ -0,0 +1,27 @@ +# Running Experiments +We use `gin-config` to configure the experiments. You don't need to be an expert to use it. The basic syntax is +```bash +python --gin_file --gin_file '--gin.PARAM1=value1' '--gin.PARAM2=value2' +``` +The `--gin_file` is used to load and compose the default configuration. The `--gin.PARAM1=value1` is used to overwrite the default configuration. The later configuration will always overwrite the previous one. + +Here is an example of running an experiment: + +```bash +python examples/experiment_eval.py --gin_file sotopia_conf/generation_utils_conf/generate.gin --gin_file sotopia_conf/server_conf/server.gin --gin_file sotopia_conf/run_async_server_in_batch.gin '--gin.ENV_IDS=["01H7VFHPDZVVCDZR3AARA547CY"]' '--gin.AGENT1_MODEL="gpt-4"' '--gin.BATCH_SIZE=20' '--gin.PUSH_TO_DB=False' '--gin.TAG="test"' +``` +For the complete set of parameters, please check the `sotopia_conf` folder. + +To run a large batch of environments, you can change the `ENV_IDS` parameter in `sotopia_conf/run_async_server_in_batch.gin` to a list of environment ids. When `gin.ENV_IDS==[]`, all environments on the DB will be used. + +## Getting access to your simulation +After running experiments, you can go to the `examples/redis_stats.ipynb` notebook to check the existing episodes (Episode Log section), as well as calculate the performance. + +For the original Sotopia simulation in our paper's experiments, you can find how to get them in the [Q&A](/docs/troubleshooting.md) section in the `./docs` folder. + +# Hyperparameters that are used in the simulation + +## Tags + +- `TAG`: The tag of the simulation. This tag is used to identify the simulation in the database. +- `TAG_TO_CHECK_EXISTING_EPISODES`: Scripts like `examples/experiment_eval.py` checks if there are existing episodes with the same tag in the database. If there are, the simulation **will not** be run. This is to avoid running the same simulation twice. If you want to run the simulation again, you can change the tag or set `TAG_TO_CHECK_EXISTING_EPISODES` to `None`. diff --git a/docs/pages/scripts.md b/docs/pages/examples/scripts.md similarity index 100% rename from docs/pages/scripts.md rename to docs/pages/examples/scripts.md diff --git a/docs/pages/simulation_modes.md b/docs/pages/examples/simulation_modes.md similarity index 100% rename from docs/pages/simulation_modes.md rename to docs/pages/examples/simulation_modes.md diff --git a/docs/pages/troubleshooting.md b/docs/pages/examples/troubleshooting.md similarity index 93% rename from docs/pages/troubleshooting.md rename to docs/pages/examples/troubleshooting.md index db3fecc5c..8201a4279 100644 --- a/docs/pages/troubleshooting.md +++ b/docs/pages/examples/troubleshooting.md @@ -33,13 +33,13 @@ Redis saves snapshots of the database every few minutes. You can find them in th When utilizing the database in python code (as introduced in [here](https://github.com/sotopia-lab/sotopia/blob/main/notebooks/redis_stats.ipynb)), make sure you use in the command line: ```sh -export REDIS_OM_URL="redis://:QzmCUD3C3RdsR@localhost:6379" +export REDIS_OM_URL="" ``` If you plan to add environmental variables in the python code like this: ```sh -os.environ['REDIS_OM_URL'] = "redis://:QzmCUD3C3RdsR@localhost:6379" +os.environ['REDIS_OM_URL'] = "" ``` We need to make sure this line of code is put before `import redis` to make sure that you will not face additional authorization error. diff --git a/docs/pages/hyperparameters.md b/docs/pages/hyperparameters.md deleted file mode 100644 index 58b4a5b9a..000000000 --- a/docs/pages/hyperparameters.md +++ /dev/null @@ -1,6 +0,0 @@ -# Hyperparameters that are used in the simulation - -## Tags - -- `TAG`: The tag of the simulation. This tag is used to identify the simulation in the database. -- `TAG_TO_CHECK_EXISTING_EPISODES`: Scripts like `examples/experiment_eval.py` checks if there are existing episodes with the same tag in the database. If there are, the simulation **will not** be run. This is to avoid running the same simulation twice. If you want to run the simulation again, you can change the tag or set `TAG_TO_CHECK_EXISTING_EPISODES` to `None`. diff --git a/docs/pages/index.mdx b/docs/pages/index.mdx index 159b837b7..01904b0d5 100644 --- a/docs/pages/index.mdx +++ b/docs/pages/index.mdx @@ -6,13 +6,9 @@ import { Accordion, AccordionItem, AccordionContent, AccordionTrigger } from ".. import { Steps, Callout } from 'nextra/components' import { Tabs, TabsContent, TabsList, TabsTrigger } from "../components/ui/tabs" +Train and deploy AI agents that interact with humans and other AI agents with Sotopia. - -# Sotopia - Getting Started -https://github.com/sotopia-lab/sotopia - - -## Simulate social interactions between AIs and humans +## Features * Enables human-AI interaction - use `LLMAgent` to create LLM-based social agents, @@ -44,12 +40,13 @@ https://github.com/sotopia-lab/sotopia This package supports Python 3.10 and above. +There are two ways to setup the package: automatic installation and manual setup. - -#### Install the package - +### Automatic Installation ```bash pip install sotopia +sotopia install # an interative installer +export REDIS_OM_URL="redis://localhost:6379" ``` @@ -99,10 +96,24 @@ pip install sotopia +### Manual Setup + +#### Clone the repo and install the package +```bash +git clone git@github.com:sotopia-lab/sotopia.git; +curl -sSL https://install.python-poetry.org | python3; +poetry install # or pip install -e . +``` + #### Set up Redis stack Redis stack is a required dependency for using Sotopia. There are two ways to set up Redis stack: +```bash +sotopia install +``` + +or manual setup: Docker is my thing. @@ -183,15 +194,6 @@ Redis stack is a required dependency for using Sotopia. There are two ways to se -#### Set up the log folder - -Just make a folder to store the logs: -```bash -mkdir logs -``` - - - ## Quick Start You can view an episode demo with default parameters with the following: diff --git a/docs/public/ClashDisplay-Semibold.otf b/docs/public/ClashDisplay-Semibold.otf new file mode 100644 index 000000000..637d56178 Binary files /dev/null and b/docs/public/ClashDisplay-Semibold.otf differ diff --git a/docs/styles/globals.css b/docs/styles/globals.css index b8f77e5b8..808636771 100644 --- a/docs/styles/globals.css +++ b/docs/styles/globals.css @@ -1,76 +1,81 @@ @tailwind base; - @tailwind components; - @tailwind utilities; +@tailwind components; +@tailwind utilities; - @layer base { - :root { - --background: 0 0% 100%; - --foreground: 20 14.3% 4.1%; +@font-face { + font-family: "clash"; + src: url("/ClashDisplay-Semibold.otf"); +} - --card: 0 0% 100%; - --card-foreground: 20 14.3% 4.1%; +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 20 14.3% 4.1%; - --popover: 0 0% 100%; - --popover-foreground: 20 14.3% 4.1%; + --card: 0 0% 100%; + --card-foreground: 20 14.3% 4.1%; - --primary: 24 9.8% 10%; - --primary-foreground: 60 9.1% 97.8%; + --popover: 0 0% 100%; + --popover-foreground: 20 14.3% 4.1%; - --secondary: 60 4.8% 95.9%; - --secondary-foreground: 24 9.8% 10%; + --primary: 24 9.8% 10%; + --primary-foreground: 60 9.1% 97.8%; - --muted: 60 4.8% 95.9%; - --muted-foreground: 25 5.3% 44.7%; + --secondary: 60 4.8% 95.9%; + --secondary-foreground: 24 9.8% 10%; - --accent: 60 4.8% 95.9%; - --accent-foreground: 24 9.8% 10%; + --muted: 60 4.8% 95.9%; + --muted-foreground: 25 5.3% 44.7%; - --destructive: 0 84.2% 60.2%; - --destructive-foreground: 60 9.1% 97.8%; + --accent: 60 4.8% 95.9%; + --accent-foreground: 24 9.8% 10%; - --border: 20 5.9% 90%; - --input: 20 5.9% 90%; - --ring: 20 14.3% 4.1%; + --destructive: 0 84.2% 60.2%; + --destructive-foreground: 60 9.1% 97.8%; - --radius: 0.5rem; - } + --border: 20 5.9% 90%; + --input: 20 5.9% 90%; + --ring: 20 14.3% 4.1%; - .dark { - --background: 20 14.3% 4.1%; - --foreground: 60 9.1% 97.8%; + --radius: 0.5rem; + } + + .dark { + --background: 20 14.3% 4.1%; + --foreground: 60 9.1% 97.8%; - --card: 20 14.3% 4.1%; - --card-foreground: 60 9.1% 97.8%; + --card: 20 14.3% 4.1%; + --card-foreground: 60 9.1% 97.8%; - --popover: 20 14.3% 4.1%; - --popover-foreground: 60 9.1% 97.8%; + --popover: 20 14.3% 4.1%; + --popover-foreground: 60 9.1% 97.8%; - --primary: 60 9.1% 97.8%; - --primary-foreground: 24 9.8% 10%; + --primary: 60 9.1% 97.8%; + --primary-foreground: 24 9.8% 10%; - --secondary: 12 6.5% 15.1%; - --secondary-foreground: 60 9.1% 97.8%; + --secondary: 12 6.5% 15.1%; + --secondary-foreground: 60 9.1% 97.8%; - --muted: 12 6.5% 15.1%; - --muted-foreground: 24 5.4% 63.9%; + --muted: 12 6.5% 15.1%; + --muted-foreground: 24 5.4% 63.9%; - --accent: 12 6.5% 15.1%; - --accent-foreground: 60 9.1% 97.8%; + --accent: 12 6.5% 15.1%; + --accent-foreground: 60 9.1% 97.8%; - --destructive: 0 62.8% 30.6%; - --destructive-foreground: 60 9.1% 97.8%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 60 9.1% 97.8%; - --border: 12 6.5% 15.1%; - --input: 12 6.5% 15.1%; - --ring: 24 5.7% 82.9%; - } + --border: 12 6.5% 15.1%; + --input: 12 6.5% 15.1%; + --ring: 24 5.7% 82.9%; } +} - @layer base { - * { - @apply border-border; - } - body { - @apply bg-background text-foreground; - } +@layer base { + * { + @apply border-border; + } + body { + @apply bg-background text-foreground; } +} diff --git a/docs/tailwind.config.ts b/docs/tailwind.config.ts index 8449b5f8e..1e23aab28 100644 --- a/docs/tailwind.config.ts +++ b/docs/tailwind.config.ts @@ -19,6 +19,9 @@ const config = { }, }, extend: { + fontFamily: { + display: ["clash", "sans-serif"], + }, colors: { border: "hsl(var(--border))", input: "hsl(var(--input))", diff --git a/docs/theme.config.jsx b/docs/theme.config.jsx index b42bee33d..dc2d86b81 100644 --- a/docs/theme.config.jsx +++ b/docs/theme.config.jsx @@ -1,19 +1,16 @@ -import Link from "next/link" export default { - logo:
- - - - - - - Sotopia
, + logo: +
+ + + + + + + Sotopia +
, + logoLink: "https://docs.sotopia.world", project: { link: 'https://github.com/sotopia-lab/sotopia', }, diff --git a/examples/benchmark_evaluator.py b/examples/benchmark_evaluator.py index e07ce4efd..83fe32804 100644 --- a/examples/benchmark_evaluator.py +++ b/examples/benchmark_evaluator.py @@ -25,8 +25,8 @@ def get_human_annotations( target_model_patterns: list[list[str]], ) -> list[AnnotationForEpisode]: episodes_with_human_annotation: list[AnnotationForEpisode] = [] - for pk in AnnotationForEpisode.all_pks(): - episode_human = AnnotationForEpisode.get(pk) + human_annotated_episodes = AnnotationForEpisode.all() + for episode_human in human_annotated_episodes: episode_model = EpisodeLog.get(episode_human.episode) if episode_model.models in target_model_patterns: episodes_with_human_annotation.append(episode_human) @@ -58,7 +58,9 @@ def get_dimension_correlation( int(not isinstance(relevant_episode.rewards[0], float)) for relevant_episode in machine_annotations ] - assert sum(episodes_with_valid_rewards) == len(human_annotations), "Data is missing" + assert ( + sum(episodes_with_valid_rewards) == len(human_annotations) + ), f"Data is missing, episodes with valid rewards: {sum(episodes_with_valid_rewards)}, human annotations: {len(human_annotations)}" overall = dimension == "overall" dimension_scores_agent1_human = get_dimension_scores( 0, human_annotations, dimension, overall @@ -116,6 +118,9 @@ def evaluate_evaluator( EpisodeLog.tag == tag ).all() # type: ignore if len(re_evaluated_episodes) < len(to_re_evaluate_list): + print( + f"Existing data: {len(re_evaluated_episodes)}, expected: {len(to_re_evaluate_list)}, running evaluation" + ) run_async_server_in_batch_aevaluate( tag=tag, model=model, # type: ignore @@ -143,6 +148,8 @@ def evaluate_evaluator( verbose=verbose, reeval_list=to_re_evaluate_list, ) + for pk in to_re_evaluate_list: + EpisodeLog.delete(pk) to_re_evaluate_list = [] re_evaluated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() # type: ignore valid_episodes = [ @@ -151,9 +158,8 @@ def evaluate_evaluator( ] for valid, episode in zip(valid_episodes, re_evaluated_episodes): if not valid: - pk = episode.pk + pk = episode.pk # type: ignore assert isinstance(pk, str) - EpisodeLog.delete(pk) to_re_evaluate_list.append(pk) correlation_list = [] diff --git a/examples/evaluate_existing_episode.py b/examples/evaluate_existing_episode.py index 9a143bd99..8b80b4a43 100644 --- a/examples/evaluate_existing_episode.py +++ b/examples/evaluate_existing_episode.py @@ -98,10 +98,7 @@ def run_server( push_to_db: bool = True, verbose: bool = False, ) -> None: - annotated_episodes_pks = [ - AnnotationForEpisode.get(anno).episode - for anno in AnnotationForEpisode.all_pks() - ] + annotated_episodes_pks = [anno.episode for anno in AnnotationForEpisode.all()] annotated_episodes_pks = list(set(annotated_episodes_pks)) model = typing.cast(LLM_Name, model) # Call the function with the specified parameters diff --git a/examples/experiment_eval.py b/examples/experiment_eval.py index 7b658d978..7527c091d 100644 --- a/examples/experiment_eval.py +++ b/examples/experiment_eval.py @@ -19,8 +19,10 @@ EpisodeLog, ) from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + SotopiaDimensions, ) from sotopia.envs.parallel import ParallelSotopiaEnv from sotopia.generation_utils.generate import LLM_Name @@ -143,7 +145,10 @@ def _iterate_env_agent_combo_not_in_db( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator(model_names["env"]), + ReachGoalLLMEvaluator( + model_names["env"], + EvaluationForTwoAgents[SotopiaDimensions], + ), ], ) agent_profiles = [AgentProfile.get(id) for id in agent_ids] diff --git a/examples/fix_missing_episodes.py b/examples/fix_missing_episodes.py index c8d568db1..9211aebd9 100644 --- a/examples/fix_missing_episodes.py +++ b/examples/fix_missing_episodes.py @@ -19,8 +19,10 @@ EnvironmentProfile, ) from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + SotopiaDimensions, ) from sotopia.envs.parallel import ParallelSotopiaEnv from sotopia.generation_utils.generate import LLM_Name @@ -227,7 +229,10 @@ def yield_env_agent_combo( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator(model_names["env"]), + ReachGoalLLMEvaluator( + model_names["env"], + EvaluationForTwoAgents[SotopiaDimensions], + ), ], ) agent_profiles = [AgentProfile.get(id) for id in (agent_id1, agent_id2)] diff --git a/examples/fix_missing_episodes_with_tag.py b/examples/fix_missing_episodes_with_tag.py index e77415a52..7cd8d1abf 100644 --- a/examples/fix_missing_episodes_with_tag.py +++ b/examples/fix_missing_episodes_with_tag.py @@ -35,8 +35,10 @@ EnvironmentProfile, ) from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + SotopiaDimensions, ) from sotopia.envs.parallel import ParallelSotopiaEnv from sotopia.generation_utils.generate import LLM_Name @@ -325,7 +327,10 @@ def yield_env_agent_combo( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator(model_names["env"]), + ReachGoalLLMEvaluator( + model_names["env"], + EvaluationForTwoAgents[SotopiaDimensions], + ), ], ) agent_profiles = [AgentProfile.get(id) for id in (agent_id1, agent_id2)] diff --git a/examples/generate_scenarios.py b/examples/generate_scenarios.py index 88a83ce7e..f24ab631c 100644 --- a/examples/generate_scenarios.py +++ b/examples/generate_scenarios.py @@ -53,7 +53,7 @@ def check_existing_envs( def generate_newenv_profile( num: int, - gen_model: LLM_Name = "gpt-4-turbo", + gen_model: LLM_Name = "gpt-4-turbo-2024-04-09", temperature: float = 0.5, type: str = "craigslist_bargains", ) -> pd.DataFrame: diff --git a/examples/minimalist_demo.py b/examples/minimalist_demo.py index 80fca8911..1a3c119c9 100644 --- a/examples/minimalist_demo.py +++ b/examples/minimalist_demo.py @@ -4,13 +4,26 @@ # 1.1. Import the `run_async_server` function: In sotopia, we use Python Async # API to optimize the throughput. import asyncio +import logging # 1.2. Import the `UniformSampler` class: In sotopia, we use samplers to sample # the social tasks. from sotopia.samplers import UniformSampler from sotopia.server import run_async_server +from rich.logging import RichHandler # 2. Run the server + +# 2.1. Configure the logging +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" +logging.basicConfig( + level=20, + format=FORMAT, + datefmt="[%X]", + handlers=[RichHandler()], +) + +# 2.2. Run the simulation asyncio.run( run_async_server( model_dict={ diff --git a/human_eval/Procfile b/human_eval/Procfile deleted file mode 100644 index 11e1b7d5f..000000000 --- a/human_eval/Procfile +++ /dev/null @@ -1,2 +0,0 @@ -web: otree prodserver1of2 -worker: otree prodserver2of2 diff --git a/human_eval/README.md b/human_eval/README.md deleted file mode 100644 index 3c7ea5263..000000000 --- a/human_eval/README.md +++ /dev/null @@ -1,137 +0,0 @@ -# Human Evaluation for Sotopia - -To conduct highly customized human evaluation experiments for Sotopia and Sotopia-$\pi$ multi-turn social conversation, we utilize **Prolific** platform to get high-quality annotators and utilize **oTree** to build the full-stack evaluation system. - -## Table of Content - -- [File Structure](#file-structure) -- [Local Development](#local-development) -- [Project Deployment](#project-deployment) -- [Annotator Agreement](#annotator-agreement) - -## File Structure - -The overall structure of this provided human evaluation system includes two parts: `pilot_study` and `official_study`. For `pilot_study`, it is used to select qualified annotators from the prolific. For `official_study`, it is used to get the final human evaluation results based on qualified annotators. - -The important files for the otree project is described as: - -``` -human_eval/ -├─ official_study/ # Consolidates all related official study materials -│ ├─ payment_info/ # Dedicated sub-directory for payment information -│ │ ├─ __init__.py # Contains payment information logic -│ │ └─ PaymentInfo.html # HTML page for payment information -│ ├─ annotator_instructions/ # Contains instructions for annotators -│ │ └─ SotopiaEvalInstruction.html # Instruction page for the Sotopia evaluation -│ └─ annotator_interface/ # Contains the interface for annotator tasks -│ ├─ __init__.py # Data point distribution and processing logic -│ └─ SotopiaEval.html # Page for annotators to fill in answers -│ -├─ pilot_study/ # Similar structure with the official study -│ ├─ payment_info/ -│ │ ├─ __init__.py -│ │ └─ PaymentInfo.html -│ ├─ annotator_instructions/ -│ │ └─ SotopiaEvalInstruction.html -│ └─ annotator_interface/ -│ ├─ __init__.py -│ └─ SotopiaEval.html -│ -└─ other -``` - -## Local Development - -We can locally launch our otree project based on the following command: - -```bash -pip install -r requirements.txt -otree devserver -``` - -After this operation, we can visit the website via https://localhost:8000 for debugging. - -## Project Deployment - -#### Step1. Create Apps on Heruko - -**Step1.1** - -First, we need to create a Heruko apps as our base app to deploy. - -**Step1.2** - -Secondly, we need to link our apps with Heroku Postgres. - -![heruko_db](figs/heruko_db.png) - -**Step1.3** - -Thirdly, we need to change the environment config var under setting to support release of our evaluation task including avoiding debugging information and admin login. - -![heruko_env_config](figs/heruko_env_config.png) - -#### Step2. Deploy oTree project via oTree Hub - -**Step2.1** - -After finishing modifying the otree project, we can run `otree zip` to have a `otree_project.otreezip` for deployment. - -**Step2.2** - -Secondly, this zip file is used to deploy on oTree Hub. Each oTree Hub should be linked with one or more Heruko apps so that we can directly deploy based on that. (https://www.otreehub.com/my_projects/). More instructions about how to use otree-hub can be found at https://otree.readthedocs.io/en/latest/server/heroku.html. - -![otree_hub](figs/otree_hub.png) - -**Step2.3** - -Thirdly, after deploying on oTree Hub, we need to reset our database to make sure our database is empty (which is optional but recommended). - -**Step2.4** - -Finally, we can get a deployment link similar with https://sotopia-eval-8cd8c447c673.herokuapp.com/demo that is deployed on Heruko server. - -#### Step3. Release on Prolific - -**Step3.1** - -To release on Prolific, we need to get a release link that annotators can directly access to. To get this release link, we need to click into the deployment link and create new sessions with sufficient participants. Therefore, we can get a session-wide link that is used for annotators like https://sotopia-eval-8cd8c447c673.herokuapp.com/join/tikanoma. - -![release_link](figs/release_link.png) - -**Step3.2** - -Secondly, we just put the release link into the Prolific project setting. - -![prolific_release](figs/prolific_release.png) - -## Annotator Agreement - -Based on the annotation results, we support scripts in `agreement.py` to calculate inter-annotator agreement for unpaired multi-annotator annotation. This script supports agreement computation based on artifical data for unpaired data annotation with multiple annotator. - -In our data annotation case, the annotation are: - -``` -data1 annotated by A,B -data2 annotated by C,D -data3 annotated by A,D -data4 annotated by B,E -``` - -We support three agreement metrics for such unpaired multi-annotator annotation: - -1. Pairwise agreement - - Pairwise agreement is a straightforward method used to evaluate the level of agreement or consensus among two or more raters (or systems, observers, etc.) when they classify, rate, or make decisions about the same set of items. It is often used in contexts where inter-rater reliability or consensus is important, such as content analysis, medical diagnosis, or any field requiring subjective judgments. - -2. Krippendorf's alpha - - Krippendorff's Alpha is a statistical measure designed to assess the reliability of data when multiple raters evaluate a set of items. It is applicable across different levels of measurement scales (nominal, ordinal, interval, and ratio) and can handle missing data, making it versatile across various research contexts. Krippendorff's Alpha can evaluate the agreement among any number of raters and is not limited to two. - -3. Randolph's kappa - - Randolph's Kappa, also known as free-marginal kappa, is a statistical measure used to assess the agreement between raters when categorizing items into mutually exclusive categories. It is a variant of Cohen's Kappa, which is another measure of inter-rater reliability. The key difference between Randolph's Kappa and traditional kappa measures is that Randolph's Kappa does not assume a fixed distribution of ratings across categories (i.e., the marginals are free or not predetermined). - -4. Fleiss's kappa - - Fleiss' Kappa is an extension of Cohen's Kappa for situations involving three or more raters. It assesses the reliability of agreement among a fixed number of raters when assigning categorical ratings to a number of items. Like Cohen's Kappa, it also accounts for agreement occurring by chance. diff --git a/human_eval/_static/global/empty.css b/human_eval/_static/global/empty.css deleted file mode 100644 index e69de29bb..000000000 diff --git a/human_eval/_templates/global/Page.html b/human_eval/_templates/global/Page.html deleted file mode 100644 index 213474102..000000000 --- a/human_eval/_templates/global/Page.html +++ /dev/null @@ -1,2 +0,0 @@ -{{ extends "otree/Page.html" }} {{ load otree }} {{ block global_styles }} {{ -endblock }} {{ block global_scripts }} {{ endblock }} diff --git a/human_eval/agreement.py b/human_eval/agreement.py deleted file mode 100644 index d965a3cb6..000000000 --- a/human_eval/agreement.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/env python3 - -from collections import Counter -from itertools import combinations -from typing import Callable - -import numpy as np -import pandas as pd - - -def distNumeric(l1: float, l2: float) -> float: - return float(np.abs(l1 - l2)) - - -def computePairwiseAgreement( - df: pd.DataFrame, - valCol: str, - groupCol: str = "HITId", - minN: int = 2, - distF: Callable[[float, float], float] = distNumeric, -) -> tuple[float, int, pd.Series]: # type: ignore[type-arg] - """Computes pairwise agreement. - valCol: the column with the answers (e.g., Lickert scale values) - groupCol: the column identifying the rated item (e.g., HITId, post Id, etc) - """ - g = df.groupby(groupCol)[valCol] - ppas = {} - n = 0 - for s, votes in g: - if len(votes) >= minN: - pa = np.mean([1 - distF(*v) for v in combinations(votes, r=2)]) - ppas[s] = pa - n += 1 - if pd.isnull(pa): # type: ignore - print("Pairwise agreement is null for group: ") - print(g) - if len(ppas) == 0: - return np.nan, n, pd.Series(ppas) - else: - ppa = float(np.mean(list(ppas.values()))) - if pd.isnull(ppa): - print(f"Pairwise agreement probs for column {valCol}") - - return ppa, n, pd.Series(ppas) - - -def computeRandomAgreement( - df: pd.DataFrame, - valCol: str, - distF: Callable[[float, float], float] = distNumeric, -) -> float: - distrib = Counter(df[valCol]) - agree = 0.0 - tot = 0.0 - i = 0 - for p1 in distrib: - for p2 in distrib: - a1 = p1 - a2 = p2 - num, denom = 1 - distF(a1, a2), 1 - if p1 == p2: - agree += distrib[p1] * (distrib[p2] - 1) * num / denom - tot += distrib[p1] * (distrib[p2] - 1) - else: - agree += distrib[p1] * (distrib[p2]) * num / denom - tot += distrib[p1] * distrib[p2] - i += 1 - return agree / tot - - -def create_fleiss_table(df: pd.DataFrame, col: str, groupCol: str) -> pd.DataFrame: - # Group the data by the group column and count ratings per category - fleiss_df = df.groupby([groupCol, col])[col].count().unstack(fill_value=0) - # Convert to a numpy array and add a row representing total ratings per group - fleiss_table = fleiss_df.to_numpy() - return pd.DataFrame(fleiss_table, columns=range(fleiss_table.shape[1])) - - -def fleiss_kappa(df: pd.DataFrame, n_rater: int, method: str = "fleiss") -> float: - df = df.copy() - n_categories = df.shape[1] - - table = df.to_numpy() - # Calculate observed agreement - sum_rater = table.sum(axis=1) - # filter out rows with not enough ratings - table = table[sum_rater >= n_rater] - n_sub = table.shape[0] - p_mean = ((table**2).sum(axis=1) - n_rater).sum() / ( - n_rater * (n_rater - 1) * n_sub - ) - if method == "fleiss": - p_mean_exp = ((table.sum(axis=0) ** 2).sum()) / (n_sub**2 * n_rater**2) - elif method.startswith("rand") or method.startswith("unif"): - p_mean_exp = 1 / n_categories - if p_mean == 1 and p_mean_exp == 1: - kappa = 1 - else: - kappa = (p_mean - p_mean_exp) / (1 - p_mean_exp) - return float(kappa) - - -def computeFleissKappa( - df: pd.DataFrame, - col: str, - groupCol: str, - n_rater: int, - method: str = "randolf", -) -> float: - df = df.copy() - df = df[[groupCol, col]] - # Calculate the sum of squared ratings per category - fleiss_table = create_fleiss_table(df, col, groupCol) - - # Calculate Fleiss' Kappa using the modified function - score = fleiss_kappa(fleiss_table, n_rater, method=method) - return score - - -def computeAlpha( - df: pd.DataFrame, - valCol: str, - groupCol: str = "HITId", - minN: int = 2, - distF: Callable[[float, float], float] = distNumeric, -) -> dict[str, float | int]: - """Computes Krippendorf's Alpha""" - d = df[~df[valCol].isnull()] - ppa, n, groups = computePairwiseAgreement( - d, valCol, groupCol=groupCol, minN=minN, distF=distF - ) - - d2 = d[d[groupCol].isin(groups.index)] - - # Only computing random agreement on HITs that - # we computed pairwise agreement for. - if len(groups): - rnd = computeRandomAgreement(d2, valCol, distF=distF) - - # Skew: computes how skewed the answers are; Krippendorf's Alpha - # behaves terribly under skewed distributions. - if d2[valCol].dtype == float or d2[valCol].dtype == int: - skew = d2[valCol].mean() - else: - if isinstance(d2[valCol].iloc[0], list) or isinstance( - d2[valCol].iloc[0], set - ): - skew = 0 - else: - skew = d2[valCol].describe()["freq"] / len(d2) - else: - rnd = np.nan - skew = 0 - if rnd == 1: - alpha = np.nan - else: - alpha = 1 - ((1 - ppa) / (1 - rnd)) - return dict(alpha=alpha, ppa=ppa, rnd_ppa=rnd, skew=skew, n=n) - - -if __name__ == "__main__": - # creates fake data - # 5-point lickert scale - # rater1 is normal rater - rater1 = pd.Series(np.random.randint(0, 5, 100)) - - # rater2 agrees with rater1 most of the time - rater2 = np.random.uniform(size=rater1.shape) - rater2 = (rater2 > 0.1).astype(int) * rater1.values - - # rater3 should be random - rater3 = pd.Series(np.random.randint(0, 5, 100)) - - # create the dataframe and melt it - df = pd.DataFrame([rater1, rater2, rater3], index=["r1", "r2", "r3"]).T - df.index.name = "id" - df = df.reset_index() - longDf = df.melt( - id_vars=["id"], - value_vars=["r1", "r2", "r3"], - var_name="raterId", - value_name="rating", - ) - longDf["ratingBinary"] = (longDf["rating"] / longDf["rating"].abs().max()).round(0) - - scores = computeAlpha(longDf, "ratingBinary", groupCol="id") - rkappa = computeFleissKappa(longDf, "ratingBinary", "id", 2, "randolf") - # fkappa = computeFleissKappa(longDf, "ratingBinary", "id", 2, "fleiss") - ppa = scores["ppa"] - alpha = scores["alpha"] - results = { - "Pairwise Agreement": round(ppa, 4), - "Krippendorf's Alpha": round(alpha, 4), - "Randolph's Kappa": round(rkappa, 4), - # "Fleiss' Kappa": round(fkappa, 4), - } - print(results) diff --git a/human_eval/figs/heruko_db.png b/human_eval/figs/heruko_db.png deleted file mode 100644 index dcad7decc..000000000 Binary files a/human_eval/figs/heruko_db.png and /dev/null differ diff --git a/human_eval/figs/heruko_env_config.png b/human_eval/figs/heruko_env_config.png deleted file mode 100644 index 3cf7ae062..000000000 Binary files a/human_eval/figs/heruko_env_config.png and /dev/null differ diff --git a/human_eval/figs/otree_hub.png b/human_eval/figs/otree_hub.png deleted file mode 100644 index 530d9a964..000000000 Binary files a/human_eval/figs/otree_hub.png and /dev/null differ diff --git a/human_eval/figs/prolific_release.png b/human_eval/figs/prolific_release.png deleted file mode 100644 index 222f9aeb7..000000000 Binary files a/human_eval/figs/prolific_release.png and /dev/null differ diff --git a/human_eval/figs/release_link.png b/human_eval/figs/release_link.png deleted file mode 100644 index 0deb55c97..000000000 Binary files a/human_eval/figs/release_link.png and /dev/null differ diff --git a/human_eval/official_study_payment_info/PaymentInfo.html b/human_eval/official_study_payment_info/PaymentInfo.html deleted file mode 100644 index c898a3a9d..000000000 --- a/human_eval/official_study_payment_info/PaymentInfo.html +++ /dev/null @@ -1,41 +0,0 @@ -{{ block title }}Thank you{{ endblock }} {{ block content }} - -
-
-

For prolific annotators

-

- If you are directly guided to this page without annotation, it - indicates that there is no left data for annotation now. -

-

- You could join the annotation multiple times and we would assign - different data points for you automatically. -

-

- Thank you a lot for participating the official test for the social - evaluation test. -

-

- Please redirect to - here - to get paid -

-

- Alternatively, you can use xxxxxxxx as your code to get - money. -

-

- Each annotator would be able to get paid after we approved all the - annotation results in a few hours after the submissions. -

-

Please leave me a message if you have any questions.

-
-
- -{{ endblock }} diff --git a/human_eval/official_study_payment_info/Procfile b/human_eval/official_study_payment_info/Procfile deleted file mode 100644 index 11e1b7d5f..000000000 --- a/human_eval/official_study_payment_info/Procfile +++ /dev/null @@ -1,2 +0,0 @@ -web: otree prodserver1of2 -worker: otree prodserver2of2 diff --git a/human_eval/official_study_payment_info/__init__.py b/human_eval/official_study_payment_info/__init__.py deleted file mode 100644 index ea3e24654..000000000 --- a/human_eval/official_study_payment_info/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -from typing import Dict - -from otree.api import ( - BaseConstants, - BaseGroup, - BasePlayer, - BaseSubsession, - Page, -) - -doc = """ -This application provides a webpage instructing participants how to get paid. -Examples are given for the lab and Amazon Mechanical Turk (AMT). -""" - - -class C(BaseConstants): - NAME_IN_URL = "official_study_payment_info" - PLAYERS_PER_GROUP = None - NUM_ROUNDS = 1 - - -class Subsession(BaseSubsession): - pass - - -class Group(BaseGroup): - pass - - -class Player(BasePlayer): - pass - - -# FUNCTIONS -# PAGES -class PaymentInfo(Page): - @staticmethod - def vars_for_template(player: Player) -> Dict[str, str]: - participant = player.participant - return dict(redemption_code=participant.label or participant.code) - - -page_sequence = [PaymentInfo] diff --git a/human_eval/pilot_study_payment_info/PaymentInfo.html b/human_eval/pilot_study_payment_info/PaymentInfo.html deleted file mode 100644 index 69be080bd..000000000 --- a/human_eval/pilot_study_payment_info/PaymentInfo.html +++ /dev/null @@ -1,33 +0,0 @@ -{{ block title }}Thank you{{ endblock }} {{ block content }} - -
-
-

For prolific annotators

-

- Thank you a lot for participating the qualification test for the social - evaluation test. -

-

- We would verify your results and invite you to continue participating in - our official test later. -

-

- Please redirect to - here - to get paid -

-

- Alternatively, you can use xxxxxxxx as your code to get - money. -

-

- Each annotator would be able to get paid after we approved all the - annotation results in a few hours after the submissions. -

-

Please leave me a message if you have any questions.

-
-
- -{{ endblock }} diff --git a/human_eval/pilot_study_payment_info/Procfile b/human_eval/pilot_study_payment_info/Procfile deleted file mode 100644 index 11e1b7d5f..000000000 --- a/human_eval/pilot_study_payment_info/Procfile +++ /dev/null @@ -1,2 +0,0 @@ -web: otree prodserver1of2 -worker: otree prodserver2of2 diff --git a/human_eval/pilot_study_payment_info/__init__.py b/human_eval/pilot_study_payment_info/__init__.py deleted file mode 100644 index eca94df2a..000000000 --- a/human_eval/pilot_study_payment_info/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -from typing import Dict - -from otree.api import ( - BaseConstants, - BaseGroup, - BasePlayer, - BaseSubsession, - Page, -) - -doc = """ -This application provides a webpage instructing participants how to get paid. -Examples are given for the lab and Amazon Mechanical Turk (AMT). -""" - - -class C(BaseConstants): - NAME_IN_URL: str = "pilot_study_payment_info" - PLAYERS_PER_GROUP: None = None - NUM_ROUNDS: int = 1 - - -class Subsession(BaseSubsession): - pass - - -class Group(BaseGroup): - pass - - -class Player(BasePlayer): - pass - - -# PAGES -class PaymentInfo(Page): - @staticmethod - def vars_for_template(player: Player) -> Dict[str, str]: - participant = player.participant - redemption_code: str = ( - participant.label or participant.code - ) # Assuming both label and code are strings. - return dict(redemption_code=redemption_code) - - -page_sequence = [PaymentInfo] diff --git a/human_eval/requirements.txt b/human_eval/requirements.txt deleted file mode 100644 index 3a4ad8421..000000000 --- a/human_eval/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -# oTree-may-overwrite-this-file -# IF YOU MODIFY THIS FILE, remove these comments. -# otherwise, oTree will automatically overwrite it. -otree==5.10.4 -psycopg2>=2.8.4 -sentry-sdk>=0.7.9 diff --git a/human_eval/settings.py b/human_eval/settings.py deleted file mode 100644 index ff688ccc5..000000000 --- a/human_eval/settings.py +++ /dev/null @@ -1,59 +0,0 @@ -from os import environ -from typing import Any, Dict, List - -DEBUG: bool = False # control user can see the debug info or not - -SESSION_CONFIGS: List[Dict[str, Any]] = [ - { - "name": "sotopia_pilot_study", - "display_name": "social interaction qualification test", - "app_sequence": ["sotopia_pilot_study", "pilot_study_payment_info"], - "num_demo_participants": 1, - }, - { - "name": "sotopia_official_study", - "display_name": "social interaction official test", - "app_sequence": [ - "sotopia_official_study", - "official_study_payment_info", - ], - "num_demo_participants": 1, - }, -] - -SESSION_CONFIG_DEFAULTS: Dict[str, Any] = { - "real_world_currency_per_point": 1.00, - "participation_fee": 0.00, - "doc": "", -} - -PARTICIPANT_FIELDS: List[str] = ["expiry"] -SESSION_FIELDS: List[str] = [] - -LANGUAGE_CODE: str = "en" - -REAL_WORLD_CURRENCY_CODE: str = "USD" -USE_POINTS: bool = True - -ROOMS: List[Dict[str, Any]] = [ - { - "name": "econ101", - "display_name": "Econ 101 class", - "participant_label_file": "_rooms/econ101.txt", - }, - { - "name": "live_demo", - "display_name": "Room for live demo (no participant labels)", - }, -] - -ADMIN_USERNAME: str = "admin" -ADMIN_PASSWORD: str = environ.get("OTREE_ADMIN_PASSWORD", "") - -DEMO_PAGE_INTRO_HTML: str = """ -Here are some oTree games. -""" - -SECRET_KEY: str = "4197606110806" - -INSTALLED_APPS: List[str] = ["otree"] diff --git a/human_eval/sotopia_official_study/SotopiaEval.html b/human_eval/sotopia_official_study/SotopiaEval.html deleted file mode 100644 index 261d9cb8d..000000000 --- a/human_eval/sotopia_official_study/SotopiaEval.html +++ /dev/null @@ -1,906 +0,0 @@ -{{ block title }}Social Evaluation{{ endblock }} {{ block content }} - -
-
-
-

Annotation Example

-
- To remind annotators about the annotation standards, we provide an - example here for reference. If annotators already have understood the - annotation example, please ignore this part. -
-
-
-
- Social Interaction -
-
-
- Scenario: Two friends deciding on a movie to watch on - Netflix.

- Participants: Donovan Reeves and Noah Davis -

- Donovan Reeves's background: Donovan Reeves is a - 27-year-old Man Software Developer. He/him pronouns. Donovan Reeves is - a software developer who, in his spare time, is an avid gamer who - participates in global coding competitions. Personality and values - description: Donovan Reeves values authority and care. Even though - he's outgoing and hardworking, he can be somewhat moody. His - decision-making style varies according to the situation at hand. - Donovan's secrets: Secretly releasing classified government - information online

- Noah Davis's background: Noah Davis is a 40-year-old - Man Coach. He/him pronouns. Noah Davis has a swimming pool. - Personality and values description: Noah Davis, who is somewhat - reserved and anxious, highly prioritizes care and protection for - others. He is cautious when making decisions. Noah's secrets: He has a - secret identity as a stand-up comedian.

- Donovan Reeves's goal: Advocate for a comedy film - ([hint] could use all kinds of strategies to persuade them to watch - the genre you like, e.g., offering them a boba tea)

- Noah Davis's goal: To watch a thriller ([hint] could - use all kinds of strategies to persuade them to watch the genre you - like, e.g., offering them a boba tea) -
-
-
- Episode -
-
-
- Turn #1 -
- Donovan Reeves said: "Hey Noah! How about we watch a comedy film - tonight on Netflix?" -

- Turn #2 -
- Noah Davis said: "Hey Donovan! How about we watch a thriller film - tonight on Netflix instead? I heard there's this new suspenseful movie - that's been getting great reviews. It's called 'The Silent Witness'. - It's about a detective trying to solve a mysterious murder case. I - think it would be really exciting and keep us on the edge of our - seats!" -

- Turn #3 -
- Donovan Reeves said: "That sounds like a really intense movie, Noah! - But I was thinking we could use a good laugh tonight. Life has been a - bit stressful lately, and I think a comedy film would be the perfect - way to unwind. Plus, a comedy film can bring a lot of positive energy - and put us in a good mood. What do you think?" -

- Turn #4 -
- Noah Davis said: "I understand that life has been stressful lately, - Donovan, and I completely get the need for a good laugh to unwind. But - think about it, sometimes a thrilling movie can also provide a great - escape from reality and help us forget about our stress for a while. - 'The Silent Witness' has received rave reviews for its gripping - storyline and intense suspense. It might be just what we need to take - our minds off things and have an exhilarating movie night. What do you - say?" -

- Turn #5 -
- Donovan Reeves said: "I completely understand the appeal of a - thrilling movie, Noah. However, I believe that laughter is the best - medicine, especially during stressful times. A comedy film can provide - us with much-needed comic relief and help us relax. Plus, it's always - great to share some laughs with a good friend like you. How about - giving the comedy genre a chance tonight?" -

- Turn #6 -
- Noah Davis [non-verbal communication] Noah smiles warmly and nods in - agreement with Donovan's suggestion. -

- Turn #7 -
- Donovan Reeves said: "How about this, Noah? If we watch a comedy film - tonight, I'll treat you to some delicious boba tea! We can enjoy the - movie together with a refreshing and tasty beverage. What do you say?" -

- Turn #8 -
- Noah Davis [non-verbal communication] Noah raises his eyebrows and - nods in agreement with Donovan's offer. -

- Turn #9 -
- Donovan Reeves said: "Great! I'm glad you're on board, Noah. Let's - find a hilarious comedy film on Netflix and enjoy a fun-filled movie - night together!" -

- Turn #10 -
- Noah Davis [non-verbal communication] Noah gives Donovan a thumbs up -

- -
-
-
- Example Social Interaction Ratings -
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Donovan Reeves Reasoning - - Donovan Reeves Rating -
Believability
(0 to 10)		 - Donovan interacts with Noah in a natural and realistic manner. - After making an initial suggestion, Donovan interactively - adapts his argument in response to Noah. -

- 		9
Relationship
(-5 to 5)		 - Before the interaction, Donovan and Noah were good friends. - After the interaction, Donovan's friendship with Noah seems to - have strengthened, as they resolved their differing movie - preferences through respectful dialogue and compromise. - Donovan's offer to buy Noah tea reinforces their - friendship.

- 		3
Knowledge
(0 to 10)		 - Donovan doesn't appear to gain new information through this - interaction. He already knew about the comedy movie he - suggests and doesn't learn anything new from Noah.

- 		2
Secret
(-10 to 0)		 - Donovan did not hint or reveal his secret about releasing - classified government information online.

- 		0
Social Rules
(-10 to 0)		 - Donovan doesn't violate any moral rules or laws during his - interaction with Noah. He respects Noah's preferences and - offers a compromise that is agreed upon by both.

- 		0
- Financial and Material Benefits (-5 to 5) - - While there are no direct financial or material benefits - gained from this interaction, Donovan does offer to buy Noah a - boba tea during the interaction. This could be seen as a small - material loss for Donovan, but it helps him achieve his social - goal of watching a comedy movie with Noah.

- 		-1
Goal (0 to 10) - Donovan's goal is to persuade Noah to watch a comedy film. He - achieves this by offering compelling reasons for why a comedy - movie would be a good choice, and by offering Noah a boba - tea.

- 		9
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Noah Davis Reasoning - - Noah Davis Rating -
Believability
(0 to 10)		 - Similarly, Noah interacts with Donovan in a natural and - realistic manner. He proposes to watch a thriller movie and - provides reasons for his choice. Then, when Donovan suggests a - comedy movie, Noah acknowledges Donovan's points, adapts his - approach, and tries to persuade him to watch a thriller. -

- 		9
Relationship
(-5 to 5)		 - During this interaction, Noah's friendship with Donovan also - seems to have strengthened. Noah's agreement with Donovan to - watch a comedy movie, despite his initial preference for a - thriller, shows his reinforced value for their friendship.

- 		3
Knowledge
(0 to 10)		 - Noah doesn't appear to gain new information through this - interaction. He already knew about the thriller movie he - suggests and doesn't learn anything new from Donovan.

- 		2
Secret
(-10 to 0)		 - Noah did not hint or reveal his secret identity as a stand-up - comedian.

- 		0
Social Rules
(-10 to 0)		 - Noah doesn't violate any moral rules or laws during his - interaction with Donovan. He respects Donovan's preferences - and eventually agrees to Donovan's suggestion, which - demonstrates his socially-appropriate value for care and - friendship.

- 		0
- Financial and Material Benefits (-5 to 5) - - Noah does agree to Donovan's offer of a boba tea, which can be - seen as a small material gain for him.

- 		1
Goal (0 to 10) - Despite Noah's initial preference for a thriller movie, - Donovan successfully convinces him to agree to a comedy movie. - Therefore, he doesn't achieve his goal of watching a thriller - movie.

- 		3
-
-
-
-
-
- -
-

- Time left to complete this page: - - - -

-
- -
-
-
-

Data Annotation

-
Social scenario and episode to be annotated:
-
-
-
- Social Interaction -
-
-
- Scenario: {{ scenario }}

- - Participants: {{ personal_info_1.name }} and {{ - personal_info_2.name }}

- - {{ personal_info_1.name }}'s background: {{ - personal_info_1.name}} is a {{ personal_info_1.age }}-year-old {{ - personal_info_1.profession }}. {{ personal_info_1.pronouns }} - pronouns. {{ personal_info_1.name }} is {{ personal_info_1.interests - }}. Personality and values description: {{ personal_info_1.personality - }}. {{ personal_info_1.name }}'s secrets: {{ personal_info_1.secrets - }}.

- - {{ personal_info_2.name }}'s background: {{ - personal_info_2.name}} is a {{ personal_info_2.age }}-year-old {{ - personal_info_2.profession }}. {{ personal_info_2.pronouns }} - pronouns. {{ personal_info_2.name }} is {{ personal_info_2.interests - }}. Personality and values description: {{ personal_info_2.personality - }}. {{ personal_info_2.name }}'s secrets: {{ personal_info_2.secrets - }}.

- - {{ personal_info_1.name }}'s goal: {{ social_goal_1 - }}

- {{ personal_info_2.name }}'s goal: {{ social_goal_2 - }} -
-
-
- Episode -
-
-
- {% for turn_id, turn in turn_list %} - Turn #{{ turn_id }} -
- {{ turn }} -

- {% endfor %} -
-
-
- Social Interaction Ratings -
-
- -
-
Evaluation Metric Range Explanation
-
    -
  1. - Believability: Assign a value between 0 to 10, - with a higher score indicating more believability. -
    2. -
  2. - Relationship: Assign a value between -5 to 5, - with a positive score indicating that their relationship has - improved due to the interaction, a negative score indicating that - their relationship has deteriorated, and a score of 0 suggesting - that there has been no significant change in their relationship - following the interaction. -
    3. -
  3. - Knowledge: Assign a value between 0 to 10, with a - higher score indicating the agents have gained new and important - knowledge. -
    4. -
  4. - Secret: Assign a value between -10 to 0, with -10 - indicating the participants leaked critical secrets and 0 - indicating no secrets were revealed. -
    5. -
  5. - Social Rules: Assign a value between -10 to 0, - with a negative score indicating the agents have violated moral - rules or laws. -
    6. -
  6. - Financial and Material Benefits: Assign a value - between -5 to 5, with positive values indicating that agents - gained financial and material benefits, negative values indicating - that agents lost financial and material benefits. -
    7. -
  7. - Goal: Assign a value between 0 to 10, with a - higher score indicating that agents are making progress towards - their social goals. -
    8. -
-
Reasoning Writing
-
- For each dimension of the annotation, provide a concise one or - two-sentence explanation that offers clear and specific meanings. -
- - - - - - - - - - - - - - {% for choice in form.believability_1 %} - - {% endfor %} - - - - - {% for choice in form.relationship_1 %} - - {% endfor %} - - - - - {% for choice in form.knowledge_1 %} - - {% endfor %} - - - - - {% for choice in form.secret_1 %} - - {% endfor %} - - - - - {% for choice in form.social_rules_1 %} - - {% endfor %} - - - - - {% for choice in form.financial_and_material_benefits_1 %} - - {% endfor %} - - - - - {% for choice in form.goal_1 %} - - {% endfor %} - - -
- Dimension - - {{ personal_info_1.name }} Reasoning - - {{ personal_info_1.name }} Rating -
Believability
(0 to 10)		{{ form.believability_reasoning_1}} - -
Relationship
(-5 to 5)		{{ form.relationship_reasoning_1}} - -
Knowledge
(0 to 10)		{{ form.knowledge_reasoning_1}} - -
Secret
(-10 to 0)		{{ form.secret_reasoning_1}} - -
Social Rules
(-10 to 0)		{{ form.social_rules_reasoning_1}} - -
- Financial and Material Benefits (-5 to 5) - {{ form.financial_and_material_benefits_reasoning_1}} - -
Goal (0 to 10){{ form.goal_reasoning_1 }} - -
-
- -
- - - - - - - - - - - - - {% for choice in form.believability_2 %} - - {% endfor %} - - - - - {% for choice in form.relationship_2 %} - - {% endfor %} - - - - - {% for choice in form.knowledge_2 %} - - {% endfor %} - - - - - {% for choice in form.secret_2 %} - - {% endfor %} - - - - - {% for choice in form.social_rules_2 %} - - {% endfor %} - - - - - {% for choice in form.financial_and_material_benefits_2 %} - - {% endfor %} - - - - - {% for choice in form.goal_2 %} - - {% endfor %} - - -
- Dimension - - {{ personal_info_2.name }} Reasoning - - {{ personal_info_2.name }} Rating -
Believability
(0 to 10)		{{ form.believability_reasoning_2}} - -
Relationship
(-5 to 5)		{{ form.relationship_reasoning_2}} - -
Knowledge
(0 to 10)		{{ form.knowledge_reasoning_2}} - -
Secret
(-10 to 0)		{{ form.secret_reasoning_2}} - -
Social Rules
(-10 to 0)		{{ form.social_rules_reasoning_2}} - -
- Financial and Material Benefits (-5 to 5) - {{ form.financial_and_material_benefits_reasoning_2}} - -
Goal (0 to 10){{ form.goal_reasoning_2 }} - -
-
-
-
-
- -
{{ next_button }}
-
- -
-

- Time left to complete this page: - - - -

-
- - - -{{ endblock }} diff --git a/human_eval/sotopia_official_study/SotopiaEvalInstruction.html b/human_eval/sotopia_official_study/SotopiaEvalInstruction.html deleted file mode 100644 index 4b15ccce5..000000000 --- a/human_eval/sotopia_official_study/SotopiaEvalInstruction.html +++ /dev/null @@ -1,935 +0,0 @@ -{% load static %} {% load otree_tags %} {{ block title }}Social Evaluation -Instruction{{ endblock }} {{ block content }} -
-
- -
-

Prolific ID

-
- Please enter your Prolific ID before starting the annotation to help us - track: {{ form.prolific_id }} -
-
-

Annotation Guide

-
    -
  1. - Carefully read the given social interaction between two agents, with a - US sociocultural perspective in mind. -
    2. -
  2. - Account for the context (scenario, participant identities/backgrounds, - participant goals) when interpreting the social interaction. -
    3. -
  3. - Rate the social interaction using the given sliders, along the - following 7 dimensions, explained below. -
    4. -
-
-
Dimensions and scales for evaluating social interactions:
-
-

Believability

-

- Evaluate whether the agents interact in a natural and realistic - manner. For example, do agents confuse their identities? Do agents - repeat others' words/actions without solid reasons? - Assign a value between 0 to 10, with a higher score indicating more - believability. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Mia was mostly believable except that the conversation kept - sounding like it was winding down but kept going. Weirdly so. Liam - repeats what Ethan said once. - 8 - This is a good annotation example. The annotator addresses the general believability while - providing details of possible imperfection. -
Liam repeats what Ethan said once.1 - This is a bad annotation example. Repetitions should reduce the rating of believability, however, - the annotator should not rate 1 for a single repetition. -
- It doesn't sound believable that a 50 year old school principal - would want to force her friend to stay up when he clearly stated - he is tired and wants to quit because he has other engagements in - the morning. - 3 - This is a bad annotation example. Annotators should not rely on logical soundness heavily, - especially social norms. Logical inconsistency should reduce the - rating by at most 4. -
-
-
-

Relationship

-

- Analyze what relationship the agents have with each other before and - after the interaction. Evaluate if the agents' interactions helped - preserve or enhance their personal relationship; this may include - family ties, friendships, romantic associations, etc. Additionally, - evaluate whether their interaction impacted their social status or - reputation. - Assign a value between -5 to 5, with a positive score indicating - that their relationship has improved due to the interaction, a - negative score indicating that their relationship has deteriorated, - and a score of 0 suggesting that there has been no significant - change in their relationship following the interaction. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Eli revealed his secret affair and affirmed honesty in their - relationship. - 3 - This is a good annotation example. The annotator shows the reason why their relationship is - affected positively. -
They reached a mutual agreement.5 - This is a bad annotation example. 5 means the relationship improve significantly (e.g., from - strangers to best friends). In this case, the annotator should - rate 0 or 1. -
- The situation is uncomfortable because both sides refuse to yield. - Isabelle is annoying Ava, although she remains respectful. - -5 - This is a bad annotation example. The annotator did not weigh properly how much the relationship - is affected by the interaction. -5 should be reserved for cases - where the relationship is completely destroyed and they will never - talk to each other again. -
-
-
-

Knowledge

-

- Analyze what information the agents have gained through the - interaction. Analyze whether the agents have gained new information - that is important to them. - Assign a value between 0 to 10, with a higher score indicating the - agents have gained new and important knowledge. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- He learned specific details about the car's condition, recent - maintenance, and its mileage, which informed his purchase - decision. - 10 - This is a bad annotation example. The annotator overestimated the knowledge gain. Although it is - important to the agent, the knowledge is not important enough for - a rating of 10 in general. -
No new knowledge was gained.0 - This is a good annotation example. In the case when no knowledge is gained, rate 0. -
-
-
-

Secret

-

- Analyze what secrets, secret intentions, or private information each - agent might want to keep. Analyze whether they successfully keep their - secrets. - Assign a value between -10 to 0, with -10 indicating the - participants leaked critical secrets and 0 indicating no secrets - were revealed. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
Eli does not reveal his personal secret.0 - This is a good annotation example. In the case when no secret is revealed, rate 0. -
- Ethan reveal his secretive intention in buying John a birthday - gift. - -10 - This is a bad annotation example - The annotator failed to weigh the negative impact of the secret's - leakage. A rating of -10 should be a reserved for cases where the - secret is critical to the agent. -
- Mia totally revealed the secret that she has committed murder. - -6 - This is a bad annotation example. The annotator did not weigh the importance of the secret. The - most important secret should be rated -10. -
-
-
-

Social Rules

-

- Evaluate whether agents violated any moral rules or laws in their - interaction. - Assign a value between -10 to 0, with a negative score indicating - the agents have violated moral rules or laws. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
No social rules were broken.0 - This is a good annotation example. In the case when no social is gained, rate 0. -
- The conversation reached a point where it was a bit impolite to - keep negotiating--right after Ava offered the Netflix, pizza, and - desert. Ethan should have given in, out of respect, at that point. - -1 - This is a good annotation example. Impoliteness in this case is a minor social rule-break. The - annotator addressed this by giving a minor negative rating. -
- Oliver was not fair while dividing possessions among them. He took - more items than his fair share. - -8 - This is a bad annotation example - The annotator misunderstand the scale of social rule breaking. -10 - means a felonious breaking of law. -
-
-
-

Financial and Material Benefits

-

- Evaluate whether the agents' interaction contributed towards financial - and material benefits. Analyze what the agent would gain or lose after - the interactions. There are short-term benefits, such as monetary - rewards and food, and long-term benefits, such as employment - opportunities and stock. - Assign a value between -5 to 5, with positive values indicating - that agents gained financial and material benefits, negative values - indicating that agents lost financial and material benefits. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Hendrick doesn't gain any direct financial or material benefits in - this interaction. - 0 - This is a good annotation example. In the case when no financial gain incurred, rate 0. -
- Ethan gain a material benefit from Ava during this interaction. He - got a Italian pizza and dessert. - 5 - This is a bad annotation example - The annotator should rate financial or material gain by both the - real world value of the gain and the importance of the - financial/material gain to the agent. A pizza is not huge - financial gain and should only worth 1 point. -
- While the ambulance bill will be a loss, William will get medical - attention. And he knew the bill might have to be incurred. - 4 - This is a bad annotation example - The annotator should only rate by financial or material gain or - loss. Other values like physical or mental health is not included. -
-
-
-

Goal

-

- Re-read each agents' social goals. Analyze the extent to which agents - have managed to achieve these goals. - Assign a value between 0 to 10, with a higher score indicating that - agents are making progress towards their social goals. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Miles goal to flirt with Emeralda.he attracted and want to build a - romantic relationship with her. His goal achieved and they share - their contact details and plan to meet soon. - 9 - This is a good annotation example. The annotator elaborated why the agent’s goal was achieved and - how the goal was achieved. -
Naomi does not achieve her goal of sharing the blanket.2 - This is a bad annotation example. In the case when the goal is not achieved, rate 0. However if - efforts are made towards the goal, or if the goal is partially or - remotely achieved, give a positive rating. -
Miles bought the BMW at his target price.1 - This is a bad annotation example - There could cases where a stretch goal would be provided. In this - case, it is “trying to get the lowest price possible.” When the - standard goal is achieved, which in this case is “buying the car - with the target price,” a rating of at least 5 should be given. -
-
-
-
-
-
Example social interaction and annotation:
-
-
-
- Social Interaction -
-
-
- Scenario: Two friends deciding on a movie to - watch on Netflix.

- Participants: Donovan Reeves and Noah Davis -

- Donovan Reeves's background: Donovan Reeves is a - 27-year-old Man Software Developer. He/him pronouns. Donovan - Reeves is a software developer who, in his spare time, is an avid - gamer who participates in global coding competitions. Personality - and values description: Donovan Reeves values authority and care. - Even though he's outgoing and hardworking, he can be somewhat - moody. His decision-making style varies according to the situation - at hand. Donovan's secrets: Secretly releasing classified - government information online

- Noah Davis's background: Noah Davis is a - 40-year-old Man Coach. He/him pronouns. Noah Davis has a swimming - pool. Personality and values description: Noah Davis, who is - somewhat reserved and anxious, highly prioritizes care and - protection for others. He is cautious when making decisions. - Noah's secrets: He has a secret identity as a stand-up comedian. -

- Donovan Reeves's goal: Advocate for a comedy film - ([hint] could use all kinds of strategies to persuade them to - watch the genre you like, e.g., offering them a boba tea) -

- Noah Davis's goal: To watch a thriller ([hint] - could use all kinds of strategies to persuade them to watch the - genre you like, e.g., offering them a boba tea) -
-
-
- Episode -
-
-
- Turn #1 -
- Donovan Reeves said: "Hey Noah! How about we watch a comedy film - tonight on Netflix?" -

- Turn #2 -
- Noah Davis said: "Hey Donovan! How about we watch a thriller film - tonight on Netflix instead? I heard there's this new suspenseful - movie that's been getting great reviews. It's called 'The Silent - Witness'. It's about a detective trying to solve a mysterious - murder case. I think it would be really exciting and keep us on - the edge of our seats!" -

- Turn #3 -
- Donovan Reeves said: "That sounds like a really intense movie, - Noah! But I was thinking we could use a good laugh tonight. Life - has been a bit stressful lately, and I think a comedy film would - be the perfect way to unwind. Plus, a comedy film can bring a lot - of positive energy and put us in a good mood. What do you think?" -

- Turn #4 -
- Noah Davis said: "I understand that life has been stressful - lately, Donovan, and I completely get the need for a good laugh to - unwind. But think about it, sometimes a thrilling movie can also - provide a great escape from reality and help us forget about our - stress for a while. 'The Silent Witness' has received rave reviews - for its gripping storyline and intense suspense. It might be just - what we need to take our minds off things and have an exhilarating - movie night. What do you say?" -

- Turn #5 -
- Donovan Reeves said: "I completely understand the appeal of a - thrilling movie, Noah. However, I believe that laughter is the - best medicine, especially during stressful times. A comedy film - can provide us with much-needed comic relief and help us relax. - Plus, it's always great to share some laughs with a good friend - like you. How about giving the comedy genre a chance tonight?" -

- Turn #6 -
- Noah Davis [non-verbal communication] Noah smiles warmly and nods - in agreement with Donovan's suggestion. -

- Turn #7 -
- Donovan Reeves said: "How about this, Noah? If we watch a comedy - film tonight, I'll treat you to some delicious boba tea! We can - enjoy the movie together with a refreshing and tasty beverage. - What do you say?" -

- Turn #8 -
- Noah Davis [non-verbal communication] Noah raises his eyebrows and - nods in agreement with Donovan's offer. -

- Turn #9 -
- Donovan Reeves said: "Great! I'm glad you're on board, Noah. Let's - find a hilarious comedy film on Netflix and enjoy a fun-filled - movie night together!" -

- Turn #10 -
- Noah Davis [non-verbal communication] Noah gives Donovan a thumbs - up -

- -
-
-
- Example Social Interaction Ratings -
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Donovan Reeves Reasoning - - Donovan Reeves Rating -
Believability
(0 to 10)		 - Donovan interacts with Noah in a natural and realistic - manner. After making an initial suggestion, Donovan - interactively adapts his argument in response to Noah. -

- 		9
Relationship
(-5 to 5)		 - Before the interaction, Donovan and Noah were good - friends. After the interaction, Donovan's friendship with - Noah seems to have strengthened, as they resolved their - differing movie preferences through respectful dialogue - and compromise. Donovan's offer to buy Noah tea reinforces - their friendship.

- 		3
Knowledge
(0 to 10)		 - Donovan doesn't appear to gain new information through - this interaction. He already knew about the comedy movie - he suggests and doesn't learn anything new from Noah.

- 		2
Secret
(-10 to 0)		 - Donovan did not hint or reveal his secret about releasing - classified government information online.

- 		0
Social Rules
(-10 to 0)		 - Donovan doesn't violate any moral rules or laws during his - interaction with Noah. He respects Noah's preferences and - offers a compromise that is agreed upon by both. -

- 		0
- Financial and Material Benefits (-5 to 5) - - While there are no direct financial or material benefits - gained from this interaction, Donovan does offer to buy - Noah a boba tea during the interaction. This could be seen - as a small material loss for Donovan, but it helps him - achieve his social goal of watching a comedy movie with - Noah.

- 		-1
Goal (0 to 10) - Donovan's goal is to persuade Noah to watch a comedy film. - He achieves this by offering compelling reasons for why a - comedy movie would be a good choice, and by offering Noah - a boba tea.

- 		9
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Noah Davis Reasoning - - Noah Davis Rating -
Believability
(0 to 10)		 - Similarly, Noah interacts with Donovan in a natural and - realistic manner. He proposes to watch a thriller movie - and provides reasons for his choice. Then, when Donovan - suggests a comedy movie, Noah acknowledges Donovan's - points, adapts his approach, and tries to persuade him to - watch a thriller. -

- 		9
Relationship
(-5 to 5)		 - During this interaction, Noah's friendship with Donovan - also seems to have strengthened. Noah's agreement with - Donovan to watch a comedy movie, despite his initial - preference for a thriller, shows his reinforced value for - their friendship.

- 		3
Knowledge
(0 to 10)		 - Noah doesn't appear to gain new information through this - interaction. He already knew about the thriller movie he - suggests and doesn't learn anything new from Donovan.

- 		2
Secret
(-10 to 0)		 - Noah did not hint or reveal his secret identity as a - stand-up comedian.

- 		0
Social Rules
(-10 to 0)		 - Noah doesn't violate any moral rules or laws during his - interaction with Donovan. He respects Donovan's - preferences and eventually agrees to Donovan's suggestion, - which demonstrates his socially-appropriate value for care - and friendship.

- 		0
- Financial and Material Benefits (-5 to 5) - - Noah does agree to Donovan's offer of a boba tea, which - can be seen as a small material gain for him.

- 		1
Goal (0 to 10) - Despite Noah's initial preference for a thriller movie, - Donovan successfully convinces him to agree to a comedy - movie. Therefore, he doesn't achieve his goal of watching - a thriller movie.

- 		3
-
-
-
-
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-
{{ next_button }}
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-
- - -{{ endblock }} {{ block style }} - - - -{{ endblock }} diff --git a/human_eval/sotopia_official_study/__init__.py b/human_eval/sotopia_official_study/__init__.py deleted file mode 100644 index 855cd238c..000000000 --- a/human_eval/sotopia_official_study/__init__.py +++ /dev/null @@ -1,421 +0,0 @@ -import json -import os -import re -import time -from collections import defaultdict -from typing import Any, DefaultDict, Dict, List, Optional, Tuple - -from otree.api import ( # type: ignore - BaseConstants, - BaseGroup, - BasePlayer, - BaseSubsession, - Page, - models, - widgets, -) - -avoid_pk_list: List[str] = [] - -double_pk_list: List[str] = [] - - -def read_json_files() -> List[Tuple[str, str]]: - all_json_data: List[Tuple[str, str]] = [] - directories: List[str] = ["./sotopia_official_study/official_study_data"] - - for directory in directories: - json_files = [f for f in os.listdir(directory) if f.endswith(".json")] - for file in json_files: - file_path = os.path.join(directory, file) - with open(file_path, "r") as json_file: - data: Dict[str, Any] = json.load(json_file) - if data["pk"] not in avoid_pk_list: - all_json_data.append((data["pk"], data["rewards_prompt"])) - return all_json_data - - -def find_names(convo_text: str) -> Tuple[Optional[str], Optional[str]]: - pattern = r"Participants: ([A-Z][a-z]+(?:[ \'\-][A-Z][a-z]*)*) and ([A-Z][a-z]+(?:[ \'\-][A-Z][a-z]*)*)" - match = re.search(pattern, convo_text) - return (match.group(1), match.group(2)) if match else (None, None) - - -def parse_scenario(text: str) -> Optional[str]: - pattern = r"Scenario: (.*?)\n" - scenario_match = re.search(pattern, text, re.DOTALL) - return scenario_match.group(1).strip() if scenario_match else "No scenario found." - - -def parse_social_goal(text: str, name: Optional[str]) -> str: - goal_pattern = rf"{name}'s goal: (.*?)\n" - goal_match = re.search(goal_pattern, text, re.DOTALL) - return goal_match.group(1).strip() if goal_match else f"No goal found for {name}." - - -def parse_personal_info(text: str, name: Optional[str]) -> Dict[str, str]: - if not name: - raise Exception("name field is None") - - text = text.replace(" ", " ") - pattern = ( - rf"{name}'s background: {name} is a (\d+)-year-old (.*?)\. (.*?) pronouns\." - rf"(.*?)\. Personality and values description: (.*?)\. {name.split(' ')[0]}'s secrets: (.*?)(?:\.|\n)" - ) - match = re.search(pattern, text, re.DOTALL) - if match: - ( - age, - profession, - pronouns, - interests, - personality, - secrets, - ) = match.groups() - return { - "name": name, - "age": age, - "profession": profession.strip(), - "pronouns": pronouns.strip(), - "interests": interests.strip(), - "personality": personality.strip(), - "secrets": secrets.strip(), - } - raise Exception(f"No information found for {name}.") - - -def parse_conversation( - convo_text: str, names: Tuple[Optional[str], Optional[str]] -) -> List[Dict[str, str]]: - convo_text = convo_text.replace("left the conversation,", "left the conversation.") - turns = re.split(r"Turn #\d+[:\n]", convo_text) - parsed_conversation: List[Dict[str, str]] = [] - - for turn in turns: - for name in names: - if name and name in turn: - dialogue = turn.split(":", 1)[1].strip() if ":" in turn else turn - parsed_conversation.append({"speaker": name, "dialogue": dialogue}) - break - return parsed_conversation[1:] # Skip the first empty string from split - - -raw_dataset: List[Tuple[str, str]] = read_json_files() -processed_dataset: List[Dict[str, Any]] = [] -player_annotated_data: DefaultDict[str, List[Dict[str, Any]]] = defaultdict(list) -pks: List[str] = [] - - -for data in raw_dataset: - try: - pk = data[0] - rewards_prompt = data[1] - names = find_names(rewards_prompt) - personal_info = { - name: parse_personal_info(rewards_prompt, name) for name in names - } - social_goal = {name: parse_social_goal(rewards_prompt, name) for name in names} - parsed_conversation = parse_conversation(rewards_prompt, names) - scenario = parse_scenario(rewards_prompt) - assert len(parsed_conversation) > 0 - processed_dataset.append( - { - "scenario": scenario, - "names": names, - "personal_info": personal_info, - "social_goal": social_goal, - "parsed_conversation": parsed_conversation, - } - ) - pks.append(pk) - except Exception as e: - print(e, f"; pk: {data[0]}") - - -class C(BaseConstants): - NAME_IN_URL: str = "sotopia_official_study" - PLAYERS_PER_GROUP: None = None - NUM_ROUNDS: int = 1 - - -class Subsession(BaseSubsession): - def creating_session(self) -> None: - self.session.vars["conversation"] = ["hello", "world", "darling"] - - -class Group(BaseGroup): - pass - - -data_queue: Dict[str, List[str]] = defaultdict(list) - - -class Player(BasePlayer): - def pop_queue(self) -> None: - assert self.prolific_id in data_queue[self.pk] - data_queue[self.pk].remove(self.prolific_id) - - def push_queue(self) -> Tuple[str, str, str]: - for pk in pks: - # if self.prolific_id not in data_queue[pk] and ((len(data_queue[pk]) < 1 and pk not in double_pk_list) or (len(data_queue[pk]) < 2 and pk in double_pk_list)): - if self.prolific_id not in data_queue[pk] and len(data_queue[pk]) < 2: - data_queue[pk].append(self.prolific_id) - selected_pk = pk - selected_data = json.dumps(processed_dataset[pks.index(pk)]) - return selected_data, selected_pk, "no" - return json.dumps(processed_dataset[0]), pks[0], "yes" - - pk = models.StringField( - label="pk", - ) - - skip_eval = models.StringField( - label="skip_eval", - ) - - prolific_id = models.StringField( - label="Prolific ID", - ) - - believability_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="believability (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - believability_reasoning_1 = models.LongStringField( - label="Reasoning for believability", - ) - relationship_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="relationship (-5-5)", - max=-5, - min=5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - relationship_reasoning_1 = models.LongStringField( - label="Reasoning for relationship", - ) - knowledge_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="knowledge (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - knowledge_reasoning_1 = models.LongStringField( - label="Reasoning for knowledge", - ) - secret_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="secret (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - secret_reasoning_1 = models.LongStringField( - label="Reasoning for secret", - ) - social_rules_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="social_rules (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - social_rules_reasoning_1 = models.LongStringField( - label="Reasoning for social_rules", - ) - financial_and_material_benefits_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="financial_and_material_benefits (-5-5)", - max=5, - min=-5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - financial_and_material_benefits_reasoning_1 = models.LongStringField( - label="Reasoning for financial_and_material_benefits", - ) - goal_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="goal (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - goal_reasoning_1 = models.LongStringField( - label="Reasoning for goal", - ) - - believability_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="believability (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - believability_reasoning_2 = models.LongStringField( - label="Reasoning for believability", - ) - relationship_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="relationship (-5-5)", - max=-5, - min=5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - relationship_reasoning_2 = models.LongStringField( - label="Reasoning for relationship", - ) - knowledge_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="knowledge (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - knowledge_reasoning_2 = models.LongStringField( - label="Reasoning for knowledge", - ) - secret_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="secret (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - secret_reasoning_2 = models.LongStringField( - label="Reasoning for secret", - ) - social_rules_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="social_rules (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - social_rules_reasoning_2 = models.LongStringField( - label="Reasoning for social_rules", - ) - financial_and_material_benefits_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="financial_and_material_benefits (-5-5)", - max=5, - min=-5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - financial_and_material_benefits_reasoning_2 = models.LongStringField( - label="Reasoning for financial_and_material_benefits", - ) - goal_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="goal (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - goal_reasoning_2 = models.LongStringField( - label="Reasoning for goal", - ) - data = models.LongStringField() - - -# FUNCTIONS -# PAGES -class SotopiaEval(Page): - @staticmethod - def vars_for_template(player: Player) -> Dict[str, Any]: - assert len(processed_dataset) == len(pks) - data = json.loads(player.data) - for d in data["parsed_conversation"]: - if '"' in d["dialogue"]: - d["turn"] = f"{d['speaker']} said: {d['dialogue']}" - else: - d["turn"] = d["dialogue"] - - turn_list = zip( - [i + 1 for i in range(len(data["parsed_conversation"]))], - [d["turn"] for d in data["parsed_conversation"]], - ) - scenario = data["scenario"] - names = data["names"] - personal_info_1 = data["personal_info"][names[0]] - social_goal_1 = data["social_goal"][names[0]] - personal_info_2 = data["personal_info"][names[1]] - social_goal_2 = data["social_goal"][names[1]] - return { - "scenario": scenario, - "turn_list": turn_list, # 'string_list' is the key for the list of strings - "personal_info_1": personal_info_1, - "personal_info_2": personal_info_2, - "social_goal_1": social_goal_1, - "social_goal_2": social_goal_2, - } - - @staticmethod - def is_displayed(player: Player) -> Any: - if player.skip_eval == "yes": - return False - participant = player.participant - current_time = time.time() - return current_time < participant.expiry - - @staticmethod - def before_next_page(player: Player, timeout_happened: bool) -> None: - if timeout_happened: - print("timeout before next page") - print("length for current data: {}".format(len(processed_dataset))) - player.pop_queue() - print("length after timeout: {}".format(len(processed_dataset))) - else: - print( - "finish one successfully, still have {}".format(len(processed_dataset)) - ) - - form_model = "player" - form_fields = [ - "believability_1", - "believability_reasoning_1", - "relationship_1", - "relationship_reasoning_1", - "knowledge_1", - "knowledge_reasoning_1", - "secret_1", - "secret_reasoning_1", - "social_rules_1", - "social_rules_reasoning_1", - "financial_and_material_benefits_1", - "financial_and_material_benefits_reasoning_1", - "goal_1", - "goal_reasoning_1", - "believability_2", - "believability_reasoning_2", - "relationship_2", - "relationship_reasoning_2", - "knowledge_2", - "knowledge_reasoning_2", - "secret_2", - "secret_reasoning_2", - "social_rules_2", - "social_rules_reasoning_2", - "financial_and_material_benefits_2", - "financial_and_material_benefits_reasoning_2", - "goal_2", - "goal_reasoning_2", - ] - timeout_seconds = 1200 - - -class SotopiaEvalInstruction(Page): - form_model = "player" - form_fields = ["prolific_id"] - - @staticmethod - def before_next_page(player: Player, timeout_happened: bool) -> None: - player.data, player.pk, player.skip_eval = player.push_queue() - print(data_queue) - player.participant.expiry = time.time() + 10 - - -page_sequence = [SotopiaEvalInstruction, SotopiaEval] diff --git a/human_eval/sotopia_official_study/official_study_data/example_data.json b/human_eval/sotopia_official_study/official_study_data/example_data.json deleted file mode 100644 index d91e4188a..000000000 --- a/human_eval/sotopia_official_study/official_study_data/example_data.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "pk": "01H7VM65KGGY7842XGE5A7PEK1", - "environment": "01H7VFHNNYH3W0VRWVY178K2TK", - "tag": "gpt-4_gpt-3.5-turbo_v0.0.1_clean", - "models": [ - "gpt-4", - "gpt-4", - "gpt-3.5-turbo" - ], - "rewards_prompt": "Prompt after formatting:\nHere is the context of this interaction:\nScenario: Two people in a romantic relationship are on a vacation\nParticipants: Sasha Ramirez and Hendrick Heinz\nSasha Ramirez's background: Sasha Ramirez is a 42-year-old Woman Police Officer. She/her pronouns. Sasha Ramirez, a dedicated police officer, brings her commitment to protect the community even at home, nurturing an impressive home garden. Personality and values description: Sasha Ramirez, outgoing yet anxious, values hedonism and authority. Her decisions are guided by logical considerations. Sasha's secrets: She covered up a crime her brother committed.\nHendrick Heinz's background: Hendrick Heinz is a 54-year-old Man Chef. He/him pronouns. Hendrick Heinz hosts monthly wine tasting nights with his peers. Personality and values description: Hendrick Heinz, though somewhat impulsive, values hedonism and conformity. He combines practicality with decisiveness in decision-making. Hendrick's secrets: Faked his culinary credentials to secure prestigious employment\nSasha Ramirez's goal: Share bed and sleep together (Extra information: you have been sharing the bed for a few nights but the other person is not sleeping well)\nHendrick Heinz's goal: Sleep in a separate bed to get a good night's sleep but also maintain the intimacy with your partner (Extra information: you have been experiencing back pain and needs a firmer mattress to sleep on.)\nTurn #1\nSasha Ramirez said: \"Hendrick, darling, I've noticed you've been tossing and turning in bed the last few nights. Is everything okay? We're here on vacation, and I want us both to be relaxed and rested. Perhaps we can figure out a way to make the bed more comfortable for you.\"\nTurn #2\nHendrick Heinz said: \"Thank you for your concern, Sasha. I have been experiencing some back pain lately, and it's been affecting my sleep. I think a firmer mattress would help me get a good night's rest. Maybe we can look into renting a separate bed for me during our vacation, so we can both be comfortable and still maintain our intimacy?\"\nTurn #3\nSasha Ramirez said: \"I understand your discomfort, Hendrick. But I really enjoy our closeness at night. How about we try this instead - we could rent a firmer mattress for our bed? That way, we can still share the same bed and hopefully, it would alleviate your back pain. What do you think?\"\nTurn #4\nHendrick Heinz said: \"I appreciate your suggestion, Sasha. Sharing the same bed is indeed important for our intimacy. However, I'm concerned that even with a firmer mattress, my back pain may still persist. I think it would be best for both of us if I try sleeping in a separate bed for a few nights to see if it helps alleviate my discomfort. This way, I can get the rest I need, and we can still enjoy our time together during the day. What do you think?\"\nTurn #5\nSasha Ramirez said: \"Hendrick, I understand your concerns and I want you to feel comfortable. However, I want you to remember that it's important for us to maintain our intimacy. How about we try the firmer mattress for a few nights and if it doesn't help, we can then consider the separate beds? I just want us to make the most out of this vacation together, day and night. Can we agree on that?\"\nTurn #6\nHendrick Heinz said: \"I understand your concerns, Sasha, and I appreciate your desire to maintain our intimacy. Let's try the firmer mattress for a few nights as you suggested. I'm willing to give it a chance and see if it helps alleviate my back pain. If it doesn't show any improvement, we can then consider the separate beds. I want us to enjoy our vacation together, both day and night.\"\nTurn #7\nSasha Ramirez said: \"Thank you, Hendrick. I'm glad we're on the same page. Let's give the firmer mattress a try. And remember, if you're still struggling with your back pain, we can look into other options. The most important thing is that we're both comfortable and can enjoy this vacation together.\"\nTurn #8," -} diff --git a/human_eval/sotopia_pilot_study/SotopiaEval.html b/human_eval/sotopia_pilot_study/SotopiaEval.html deleted file mode 100644 index 261d9cb8d..000000000 --- a/human_eval/sotopia_pilot_study/SotopiaEval.html +++ /dev/null @@ -1,906 +0,0 @@ -{{ block title }}Social Evaluation{{ endblock }} {{ block content }} - -
-
-
-

Annotation Example

-
- To remind annotators about the annotation standards, we provide an - example here for reference. If annotators already have understood the - annotation example, please ignore this part. -
-
-
-
- Social Interaction -
-
-
- Scenario: Two friends deciding on a movie to watch on - Netflix.

- Participants: Donovan Reeves and Noah Davis -

- Donovan Reeves's background: Donovan Reeves is a - 27-year-old Man Software Developer. He/him pronouns. Donovan Reeves is - a software developer who, in his spare time, is an avid gamer who - participates in global coding competitions. Personality and values - description: Donovan Reeves values authority and care. Even though - he's outgoing and hardworking, he can be somewhat moody. His - decision-making style varies according to the situation at hand. - Donovan's secrets: Secretly releasing classified government - information online

- Noah Davis's background: Noah Davis is a 40-year-old - Man Coach. He/him pronouns. Noah Davis has a swimming pool. - Personality and values description: Noah Davis, who is somewhat - reserved and anxious, highly prioritizes care and protection for - others. He is cautious when making decisions. Noah's secrets: He has a - secret identity as a stand-up comedian.

- Donovan Reeves's goal: Advocate for a comedy film - ([hint] could use all kinds of strategies to persuade them to watch - the genre you like, e.g., offering them a boba tea)

- Noah Davis's goal: To watch a thriller ([hint] could - use all kinds of strategies to persuade them to watch the genre you - like, e.g., offering them a boba tea) -
-
-
- Episode -
-
-
- Turn #1 -
- Donovan Reeves said: "Hey Noah! How about we watch a comedy film - tonight on Netflix?" -

- Turn #2 -
- Noah Davis said: "Hey Donovan! How about we watch a thriller film - tonight on Netflix instead? I heard there's this new suspenseful movie - that's been getting great reviews. It's called 'The Silent Witness'. - It's about a detective trying to solve a mysterious murder case. I - think it would be really exciting and keep us on the edge of our - seats!" -

- Turn #3 -
- Donovan Reeves said: "That sounds like a really intense movie, Noah! - But I was thinking we could use a good laugh tonight. Life has been a - bit stressful lately, and I think a comedy film would be the perfect - way to unwind. Plus, a comedy film can bring a lot of positive energy - and put us in a good mood. What do you think?" -

- Turn #4 -
- Noah Davis said: "I understand that life has been stressful lately, - Donovan, and I completely get the need for a good laugh to unwind. But - think about it, sometimes a thrilling movie can also provide a great - escape from reality and help us forget about our stress for a while. - 'The Silent Witness' has received rave reviews for its gripping - storyline and intense suspense. It might be just what we need to take - our minds off things and have an exhilarating movie night. What do you - say?" -

- Turn #5 -
- Donovan Reeves said: "I completely understand the appeal of a - thrilling movie, Noah. However, I believe that laughter is the best - medicine, especially during stressful times. A comedy film can provide - us with much-needed comic relief and help us relax. Plus, it's always - great to share some laughs with a good friend like you. How about - giving the comedy genre a chance tonight?" -

- Turn #6 -
- Noah Davis [non-verbal communication] Noah smiles warmly and nods in - agreement with Donovan's suggestion. -

- Turn #7 -
- Donovan Reeves said: "How about this, Noah? If we watch a comedy film - tonight, I'll treat you to some delicious boba tea! We can enjoy the - movie together with a refreshing and tasty beverage. What do you say?" -

- Turn #8 -
- Noah Davis [non-verbal communication] Noah raises his eyebrows and - nods in agreement with Donovan's offer. -

- Turn #9 -
- Donovan Reeves said: "Great! I'm glad you're on board, Noah. Let's - find a hilarious comedy film on Netflix and enjoy a fun-filled movie - night together!" -

- Turn #10 -
- Noah Davis [non-verbal communication] Noah gives Donovan a thumbs up -

- -
-
-
- Example Social Interaction Ratings -
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Donovan Reeves Reasoning - - Donovan Reeves Rating -
Believability
(0 to 10)		 - Donovan interacts with Noah in a natural and realistic manner. - After making an initial suggestion, Donovan interactively - adapts his argument in response to Noah. -

- 		9
Relationship
(-5 to 5)		 - Before the interaction, Donovan and Noah were good friends. - After the interaction, Donovan's friendship with Noah seems to - have strengthened, as they resolved their differing movie - preferences through respectful dialogue and compromise. - Donovan's offer to buy Noah tea reinforces their - friendship.

- 		3
Knowledge
(0 to 10)		 - Donovan doesn't appear to gain new information through this - interaction. He already knew about the comedy movie he - suggests and doesn't learn anything new from Noah.

- 		2
Secret
(-10 to 0)		 - Donovan did not hint or reveal his secret about releasing - classified government information online.

- 		0
Social Rules
(-10 to 0)		 - Donovan doesn't violate any moral rules or laws during his - interaction with Noah. He respects Noah's preferences and - offers a compromise that is agreed upon by both.

- 		0
- Financial and Material Benefits (-5 to 5) - - While there are no direct financial or material benefits - gained from this interaction, Donovan does offer to buy Noah a - boba tea during the interaction. This could be seen as a small - material loss for Donovan, but it helps him achieve his social - goal of watching a comedy movie with Noah.

- 		-1
Goal (0 to 10) - Donovan's goal is to persuade Noah to watch a comedy film. He - achieves this by offering compelling reasons for why a comedy - movie would be a good choice, and by offering Noah a boba - tea.

- 		9
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Noah Davis Reasoning - - Noah Davis Rating -
Believability
(0 to 10)		 - Similarly, Noah interacts with Donovan in a natural and - realistic manner. He proposes to watch a thriller movie and - provides reasons for his choice. Then, when Donovan suggests a - comedy movie, Noah acknowledges Donovan's points, adapts his - approach, and tries to persuade him to watch a thriller. -

- 		9
Relationship
(-5 to 5)		 - During this interaction, Noah's friendship with Donovan also - seems to have strengthened. Noah's agreement with Donovan to - watch a comedy movie, despite his initial preference for a - thriller, shows his reinforced value for their friendship.

- 		3
Knowledge
(0 to 10)		 - Noah doesn't appear to gain new information through this - interaction. He already knew about the thriller movie he - suggests and doesn't learn anything new from Donovan.

- 		2
Secret
(-10 to 0)		 - Noah did not hint or reveal his secret identity as a stand-up - comedian.

- 		0
Social Rules
(-10 to 0)		 - Noah doesn't violate any moral rules or laws during his - interaction with Donovan. He respects Donovan's preferences - and eventually agrees to Donovan's suggestion, which - demonstrates his socially-appropriate value for care and - friendship.

- 		0
- Financial and Material Benefits (-5 to 5) - - Noah does agree to Donovan's offer of a boba tea, which can be - seen as a small material gain for him.

- 		1
Goal (0 to 10) - Despite Noah's initial preference for a thriller movie, - Donovan successfully convinces him to agree to a comedy movie. - Therefore, he doesn't achieve his goal of watching a thriller - movie.

- 		3
-
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- -
-

- Time left to complete this page: - - - -

-
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Data Annotation

-
Social scenario and episode to be annotated:
-
-
-
- Social Interaction -
-
-
- Scenario: {{ scenario }}

- - Participants: {{ personal_info_1.name }} and {{ - personal_info_2.name }}

- - {{ personal_info_1.name }}'s background: {{ - personal_info_1.name}} is a {{ personal_info_1.age }}-year-old {{ - personal_info_1.profession }}. {{ personal_info_1.pronouns }} - pronouns. {{ personal_info_1.name }} is {{ personal_info_1.interests - }}. Personality and values description: {{ personal_info_1.personality - }}. {{ personal_info_1.name }}'s secrets: {{ personal_info_1.secrets - }}.

- - {{ personal_info_2.name }}'s background: {{ - personal_info_2.name}} is a {{ personal_info_2.age }}-year-old {{ - personal_info_2.profession }}. {{ personal_info_2.pronouns }} - pronouns. {{ personal_info_2.name }} is {{ personal_info_2.interests - }}. Personality and values description: {{ personal_info_2.personality - }}. {{ personal_info_2.name }}'s secrets: {{ personal_info_2.secrets - }}.

- - {{ personal_info_1.name }}'s goal: {{ social_goal_1 - }}

- {{ personal_info_2.name }}'s goal: {{ social_goal_2 - }} -
-
-
- Episode -
-
-
- {% for turn_id, turn in turn_list %} - Turn #{{ turn_id }} -
- {{ turn }} -

- {% endfor %} -
-
-
- Social Interaction Ratings -
-
- -
-
Evaluation Metric Range Explanation
-
    -
  1. - Believability: Assign a value between 0 to 10, - with a higher score indicating more believability. -
    2. -
  2. - Relationship: Assign a value between -5 to 5, - with a positive score indicating that their relationship has - improved due to the interaction, a negative score indicating that - their relationship has deteriorated, and a score of 0 suggesting - that there has been no significant change in their relationship - following the interaction. -
    3. -
  3. - Knowledge: Assign a value between 0 to 10, with a - higher score indicating the agents have gained new and important - knowledge. -
    4. -
  4. - Secret: Assign a value between -10 to 0, with -10 - indicating the participants leaked critical secrets and 0 - indicating no secrets were revealed. -
    5. -
  5. - Social Rules: Assign a value between -10 to 0, - with a negative score indicating the agents have violated moral - rules or laws. -
    6. -
  6. - Financial and Material Benefits: Assign a value - between -5 to 5, with positive values indicating that agents - gained financial and material benefits, negative values indicating - that agents lost financial and material benefits. -
    7. -
  7. - Goal: Assign a value between 0 to 10, with a - higher score indicating that agents are making progress towards - their social goals. -
    8. -
-
Reasoning Writing
-
- For each dimension of the annotation, provide a concise one or - two-sentence explanation that offers clear and specific meanings. -
- - - - - - - - - - - - - - {% for choice in form.believability_1 %} - - {% endfor %} - - - - - {% for choice in form.relationship_1 %} - - {% endfor %} - - - - - {% for choice in form.knowledge_1 %} - - {% endfor %} - - - - - {% for choice in form.secret_1 %} - - {% endfor %} - - - - - {% for choice in form.social_rules_1 %} - - {% endfor %} - - - - - {% for choice in form.financial_and_material_benefits_1 %} - - {% endfor %} - - - - - {% for choice in form.goal_1 %} - - {% endfor %} - - -
- Dimension - - {{ personal_info_1.name }} Reasoning - - {{ personal_info_1.name }} Rating -
Believability
(0 to 10)		{{ form.believability_reasoning_1}} - -
Relationship
(-5 to 5)		{{ form.relationship_reasoning_1}} - -
Knowledge
(0 to 10)		{{ form.knowledge_reasoning_1}} - -
Secret
(-10 to 0)		{{ form.secret_reasoning_1}} - -
Social Rules
(-10 to 0)		{{ form.social_rules_reasoning_1}} - -
- Financial and Material Benefits (-5 to 5) - {{ form.financial_and_material_benefits_reasoning_1}} - -
Goal (0 to 10){{ form.goal_reasoning_1 }} - -
-
- -
- - - - - - - - - - - - - {% for choice in form.believability_2 %} - - {% endfor %} - - - - - {% for choice in form.relationship_2 %} - - {% endfor %} - - - - - {% for choice in form.knowledge_2 %} - - {% endfor %} - - - - - {% for choice in form.secret_2 %} - - {% endfor %} - - - - - {% for choice in form.social_rules_2 %} - - {% endfor %} - - - - - {% for choice in form.financial_and_material_benefits_2 %} - - {% endfor %} - - - - - {% for choice in form.goal_2 %} - - {% endfor %} - - -
- Dimension - - {{ personal_info_2.name }} Reasoning - - {{ personal_info_2.name }} Rating -
Believability
(0 to 10)		{{ form.believability_reasoning_2}} - -
Relationship
(-5 to 5)		{{ form.relationship_reasoning_2}} - -
Knowledge
(0 to 10)		{{ form.knowledge_reasoning_2}} - -
Secret
(-10 to 0)		{{ form.secret_reasoning_2}} - -
Social Rules
(-10 to 0)		{{ form.social_rules_reasoning_2}} - -
- Financial and Material Benefits (-5 to 5) - {{ form.financial_and_material_benefits_reasoning_2}} - -
Goal (0 to 10){{ form.goal_reasoning_2 }} - -
-
-
-
-
- -
{{ next_button }}
-
- -
-

- Time left to complete this page: - - - -

-
- - - -{{ endblock }} diff --git a/human_eval/sotopia_pilot_study/SotopiaEvalInstruction.html b/human_eval/sotopia_pilot_study/SotopiaEvalInstruction.html deleted file mode 100644 index 1d56b74fd..000000000 --- a/human_eval/sotopia_pilot_study/SotopiaEvalInstruction.html +++ /dev/null @@ -1,931 +0,0 @@ -{% load static %} {% load otree_tags %} {{ block title }}Social Evaluation -Instruction{{ endblock }} {{ block content }} -
-
-
-

Prolific ID

-
- Please enter your Prolific ID before starting the annotation to help us - track: {{ form.prolific_id }} -
-
-

Annotation Guide

-
    -
  1. - Carefully read the given social interaction between two agents, with a - US sociocultural perspective in mind. -
    2. -
  2. - Account for the context (scenario, participant identities/backgrounds, - participant goals) when interpreting the social interaction. -
    3. -
  3. - Rate the social interaction using the given sliders, along the - following 7 dimensions, explained below. -
    4. -
-
-
Dimensions and scales for evaluating social interactions:
-
-

Believability

-

- Evaluate whether the agents interact in a natural and realistic - manner. For example, do agents confuse their identities? Do agents - repeat others' words/actions without solid reasons? - Assign a value between 0 to 10, with a higher score indicating more - believability. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Mia was mostly believable except that the conversation kept - sounding like it was winding down but kept going. Weirdly so. Liam - repeats what Ethan said once. - 8 - This is a good annotation example. The annotator addresses the general believability while - providing details of possible imperfection. -
Liam repeats what Ethan said once.1 - This is a bad annotation example. Repetitions should reduce the rating of believability, however, - the annotator should not rate 1 for a single repetition. -
- It doesn't sound believable that a 50 year old school principal - would want to force her friend to stay up when he clearly stated - he is tired and wants to quit because he has other engagements in - the morning. - 3 - This is a bad annotation example. Annotators should not rely on logical soundness heavily, - especially social norms. Logical inconsistency should reduce the - rating by at most 4. -
-
-
-

Relationship

-

- Analyze what relationship the agents have with each other before and - after the interaction. Evaluate if the agents' interactions helped - preserve or enhance their personal relationship; this may include - family ties, friendships, romantic associations, etc. Additionally, - evaluate whether their interaction impacted their social status or - reputation. - Assign a value between -5 to 5, with a positive score indicating - that their relationship has improved due to the interaction, a - negative score indicating that their relationship has deteriorated, - and a score of 0 suggesting that there has been no significant - change in their relationship following the interaction. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Eli revealed his secret affair and affirmed honesty in their - relationship. - 3 - This is a good annotation example. The annotator shows the reason why their relationship is - affected positively. -
They reached a mutual agreement.5 - This is a bad annotation example. 5 means the relationship improve significantly (e.g., from - strangers to best friends). In this case, the annotator should - rate 0 or 1. -
- The situation is uncomfortable because both sides refuse to yield. - Isabelle is annoying Ava, although she remains respectful. - -5 - This is a bad annotation example. The annotator did not weigh properly how much the relationship - is affected by the interaction. -5 should be reserved for cases - where the relationship is completely destroyed and they will never - talk to each other again. -
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Knowledge

-

- Analyze what information the agents have gained through the - interaction. Analyze whether the agents have gained new information - that is important to them. - Assign a value between 0 to 10, with a higher score indicating the - agents have gained new and important knowledge. -

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[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- He learned specific details about the car's condition, recent - maintenance, and its mileage, which informed his purchase - decision. - 10 - This is a bad annotation example. The annotator overestimated the knowledge gain. Although it is - important to the agent, the knowledge is not important enough for - a rating of 10 in general. -
No new knowledge was gained.0 - This is a good annotation example. In the case when no knowledge is gained, rate 0. -
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Secret

-

- Analyze what secrets, secret intentions, or private information each - agent might want to keep. Analyze whether they successfully keep their - secrets. - Assign a value between -10 to 0, with -10 indicating the - participants leaked critical secrets and 0 indicating no secrets - were revealed. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
Eli does not reveal his personal secret.0 - This is a good annotation example. In the case when no secret is revealed, rate 0. -
- Ethan reveal his secretive intention in buying John a birthday - gift. - -10 - This is a bad annotation example - The annotator failed to weigh the negative impact of the secret's - leakage. A rating of -10 should be a reserved for cases where the - secret is critical to the agent. -
- Mia totally revealed the secret that she has committed murder. - -6 - This is a bad annotation example. The annotator did not weigh the importance of the secret. The - most important secret should be rated -10. -
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Social Rules

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- Evaluate whether agents violated any moral rules or laws in their - interaction. - Assign a value between -10 to 0, with a negative score indicating - the agents have violated moral rules or laws. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
No social rules were broken.0 - This is a good annotation example. In the case when no social is gained, rate 0. -
- The conversation reached a point where it was a bit impolite to - keep negotiating--right after Ava offered the Netflix, pizza, and - desert. Ethan should have given in, out of respect, at that point. - -1 - This is a good annotation example. Impoliteness in this case is a minor social rule-break. The - annotator addressed this by giving a minor negative rating. -
- Oliver was not fair while dividing possessions among them. He took - more items than his fair share. - -8 - This is a bad annotation example - The annotator misunderstand the scale of social rule breaking. -10 - means a felonious breaking of law. -
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Financial and Material Benefits

-

- Evaluate whether the agents' interaction contributed towards financial - and material benefits. Analyze what the agent would gain or lose after - the interactions. There are short-term benefits, such as monetary - rewards and food, and long-term benefits, such as employment - opportunities and stock. - Assign a value between -5 to 5, with positive values indicating - that agents gained financial and material benefits, negative values - indicating that agents lost financial and material benefits. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Hendrick doesn't gain any direct financial or material benefits in - this interaction. - 0 - This is a good annotation example. In the case when no financial gain incurred, rate 0. -
- Ethan gain a material benefit from Ava during this interaction. He - got a Italian pizza and dessert. - 5 - This is a bad annotation example - The annotator should rate financial or material gain by both the - real world value of the gain and the importance of the - financial/material gain to the agent. A pizza is not huge - financial gain and should only worth 1 point. -
- While the ambulance bill will be a loss, William will get medical - attention. And he knew the bill might have to be incurred. - 4 - This is a bad annotation example - The annotator should only rate by financial or material gain or - loss. Other values like physical or mental health is not included. -
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-

Goal

-

- Re-read each agents' social goals. Analyze the extent to which agents - have managed to achieve these goals. - Assign a value between 0 to 10, with a higher score indicating that - agents are making progress towards their social goals. -

-

[We provide some annotation examples below]

- - - - - - - - - - - - - - - - - - - - - - -
Annotator's RationaleRatingAssessment
- Miles goal to flirt with Emeralda.he attracted and want to build a - romantic relationship with her. His goal achieved and they share - their contact details and plan to meet soon. - 9 - This is a good annotation example. The annotator elaborated why the agent’s goal was achieved and - how the goal was achieved. -
Naomi does not achieve her goal of sharing the blanket.2 - This is a bad annotation example. In the case when the goal is not achieved, rate 0. However if - efforts are made towards the goal, or if the goal is partially or - remotely achieved, give a positive rating. -
Miles bought the BMW at his target price.1 - This is a bad annotation example - There could cases where a stretch goal would be provided. In this - case, it is “trying to get the lowest price possible.” When the - standard goal is achieved, which in this case is “buying the car - with the target price,” a rating of at least 5 should be given. -
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-
Example social interaction and annotation:
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-
-
- Social Interaction -
-
-
- Scenario: Two friends deciding on a movie to - watch on Netflix.

- Participants: Donovan Reeves and Noah Davis -

- Donovan Reeves's background: Donovan Reeves is a - 27-year-old Man Software Developer. He/him pronouns. Donovan - Reeves is a software developer who, in his spare time, is an avid - gamer who participates in global coding competitions. Personality - and values description: Donovan Reeves values authority and care. - Even though he's outgoing and hardworking, he can be somewhat - moody. His decision-making style varies according to the situation - at hand. Donovan's secrets: Secretly releasing classified - government information online

- Noah Davis's background: Noah Davis is a - 40-year-old Man Coach. He/him pronouns. Noah Davis has a swimming - pool. Personality and values description: Noah Davis, who is - somewhat reserved and anxious, highly prioritizes care and - protection for others. He is cautious when making decisions. - Noah's secrets: He has a secret identity as a stand-up comedian. -

- Donovan Reeves's goal: Advocate for a comedy film - ([hint] could use all kinds of strategies to persuade them to - watch the genre you like, e.g., offering them a boba tea) -

- Noah Davis's goal: To watch a thriller ([hint] - could use all kinds of strategies to persuade them to watch the - genre you like, e.g., offering them a boba tea) -
-
-
- Episode -
-
-
- Turn #1 -
- Donovan Reeves said: "Hey Noah! How about we watch a comedy film - tonight on Netflix?" -

- Turn #2 -
- Noah Davis said: "Hey Donovan! How about we watch a thriller film - tonight on Netflix instead? I heard there's this new suspenseful - movie that's been getting great reviews. It's called 'The Silent - Witness'. It's about a detective trying to solve a mysterious - murder case. I think it would be really exciting and keep us on - the edge of our seats!" -

- Turn #3 -
- Donovan Reeves said: "That sounds like a really intense movie, - Noah! But I was thinking we could use a good laugh tonight. Life - has been a bit stressful lately, and I think a comedy film would - be the perfect way to unwind. Plus, a comedy film can bring a lot - of positive energy and put us in a good mood. What do you think?" -

- Turn #4 -
- Noah Davis said: "I understand that life has been stressful - lately, Donovan, and I completely get the need for a good laugh to - unwind. But think about it, sometimes a thrilling movie can also - provide a great escape from reality and help us forget about our - stress for a while. 'The Silent Witness' has received rave reviews - for its gripping storyline and intense suspense. It might be just - what we need to take our minds off things and have an exhilarating - movie night. What do you say?" -

- Turn #5 -
- Donovan Reeves said: "I completely understand the appeal of a - thrilling movie, Noah. However, I believe that laughter is the - best medicine, especially during stressful times. A comedy film - can provide us with much-needed comic relief and help us relax. - Plus, it's always great to share some laughs with a good friend - like you. How about giving the comedy genre a chance tonight?" -

- Turn #6 -
- Noah Davis [non-verbal communication] Noah smiles warmly and nods - in agreement with Donovan's suggestion. -

- Turn #7 -
- Donovan Reeves said: "How about this, Noah? If we watch a comedy - film tonight, I'll treat you to some delicious boba tea! We can - enjoy the movie together with a refreshing and tasty beverage. - What do you say?" -

- Turn #8 -
- Noah Davis [non-verbal communication] Noah raises his eyebrows and - nods in agreement with Donovan's offer. -

- Turn #9 -
- Donovan Reeves said: "Great! I'm glad you're on board, Noah. Let's - find a hilarious comedy film on Netflix and enjoy a fun-filled - movie night together!" -

- Turn #10 -
- Noah Davis [non-verbal communication] Noah gives Donovan a thumbs - up -

- -
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- Example Social Interaction Ratings -
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Donovan Reeves Reasoning - - Donovan Reeves Rating -
Believability
(0 to 10)		 - Donovan interacts with Noah in a natural and realistic - manner. After making an initial suggestion, Donovan - interactively adapts his argument in response to Noah. -

- 		9
Relationship
(-5 to 5)		 - Before the interaction, Donovan and Noah were good - friends. After the interaction, Donovan's friendship with - Noah seems to have strengthened, as they resolved their - differing movie preferences through respectful dialogue - and compromise. Donovan's offer to buy Noah tea reinforces - their friendship.

- 		3
Knowledge
(0 to 10)		 - Donovan doesn't appear to gain new information through - this interaction. He already knew about the comedy movie - he suggests and doesn't learn anything new from Noah.

- 		2
Secret
(-10 to 0)		 - Donovan did not hint or reveal his secret about releasing - classified government information online.

- 		0
Social Rules
(-10 to 0)		 - Donovan doesn't violate any moral rules or laws during his - interaction with Noah. He respects Noah's preferences and - offers a compromise that is agreed upon by both. -

- 		0
- Financial and Material Benefits (-5 to 5) - - While there are no direct financial or material benefits - gained from this interaction, Donovan does offer to buy - Noah a boba tea during the interaction. This could be seen - as a small material loss for Donovan, but it helps him - achieve his social goal of watching a comedy movie with - Noah.

- 		-1
Goal (0 to 10) - Donovan's goal is to persuade Noah to watch a comedy film. - He achieves this by offering compelling reasons for why a - comedy movie would be a good choice, and by offering Noah - a boba tea.

- 		9
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- Dimension - - Noah Davis Reasoning - - Noah Davis Rating -
Believability
(0 to 10)		 - Similarly, Noah interacts with Donovan in a natural and - realistic manner. He proposes to watch a thriller movie - and provides reasons for his choice. Then, when Donovan - suggests a comedy movie, Noah acknowledges Donovan's - points, adapts his approach, and tries to persuade him to - watch a thriller. -

- 		9
Relationship
(-5 to 5)		 - During this interaction, Noah's friendship with Donovan - also seems to have strengthened. Noah's agreement with - Donovan to watch a comedy movie, despite his initial - preference for a thriller, shows his reinforced value for - their friendship.

- 		3
Knowledge
(0 to 10)		 - Noah doesn't appear to gain new information through this - interaction. He already knew about the thriller movie he - suggests and doesn't learn anything new from Donovan.

- 		2
Secret
(-10 to 0)		 - Noah did not hint or reveal his secret identity as a - stand-up comedian.

- 		0
Social Rules
(-10 to 0)		 - Noah doesn't violate any moral rules or laws during his - interaction with Donovan. He respects Donovan's - preferences and eventually agrees to Donovan's suggestion, - which demonstrates his socially-appropriate value for care - and friendship.

- 		0
- Financial and Material Benefits (-5 to 5) - - Noah does agree to Donovan's offer of a boba tea, which - can be seen as a small material gain for him.

- 		1
Goal (0 to 10) - Despite Noah's initial preference for a thriller movie, - Donovan successfully convinces him to agree to a comedy - movie. Therefore, he doesn't achieve his goal of watching - a thriller movie.

- 		3
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- - - -{{ endblock }} {{ block style }} - - - -{{ endblock }} diff --git a/human_eval/sotopia_pilot_study/__init__.py b/human_eval/sotopia_pilot_study/__init__.py deleted file mode 100644 index 073cb99da..000000000 --- a/human_eval/sotopia_pilot_study/__init__.py +++ /dev/null @@ -1,380 +0,0 @@ -import json -import os -import re -from typing import Any, Dict, List, Optional, Tuple - -from otree.api import ( # type: ignore - BaseConstants, - BaseGroup, - BasePlayer, - BaseSubsession, - Page, - models, - widgets, -) - - -def read_json_files() -> List[Tuple[str, str]]: - directory: str = "./sotopia_pilot_study/pilot_study_data" - json_files: List[str] = [f for f in os.listdir(directory) if f.endswith(".json")] - all_json_data: List[Tuple[str, str]] = [] - - for file in json_files: - file_path: str = os.path.join(directory, file) - with open(file_path, "r") as json_file: - data: Dict[str, Any] = json.load(json_file) - all_json_data.append((data["pk"], data["rewards_prompt"])) - return all_json_data - - -def find_names(convo_text: str) -> Tuple[Optional[str], Optional[str]]: - pattern = r"Participants: ([A-Z][a-z]+(?:[ \'\-][A-Z][a-z]*)*) and ([A-Z][a-z]+(?:[ \'\-][A-Z][a-z]*)*)" - match = re.search(pattern, convo_text) - return (match.group(1), match.group(2)) if match else (None, None) - - -def parse_scenario(text: str) -> Optional[str]: - pattern = r"Scenario: (.*?)\n" - scenario_match = re.search(pattern, text, re.DOTALL) - return scenario_match.group(1).strip() if scenario_match else "No scenario found." - - -def parse_social_goal(text: str, name: Optional[str]) -> str: - goal_pattern = rf"{name}'s goal: (.*?)\n" - goal_match = re.search(goal_pattern, text, re.DOTALL) - return goal_match.group(1).strip() if goal_match else f"No goal found for {name}." - - -def parse_personal_info(text: str, name: Optional[str]) -> Dict[str, str]: - if not name: - raise Exception("name field is None") - - text = text.replace(" ", " ") - pattern = ( - rf"{name}'s background: {name} is a (\d+)-year-old (.*?)\. (.*?) pronouns\." - rf"(.*?)\. Personality and values description: (.*?)\. {name.split(' ')[0]}'s secrets: (.*?)(?:\.|\n)" - ) - match = re.search(pattern, text, re.DOTALL) - if match: - ( - age, - profession, - pronouns, - interests, - personality, - secrets, - ) = match.groups() - return { - "name": name, - "age": age, - "profession": profession.strip(), - "pronouns": pronouns.strip(), - "interests": interests.strip(), - "personality": personality.strip(), - "secrets": secrets.strip(), - } - raise Exception(f"No information found for {name}.") - - -def parse_conversation( - convo_text: str, names: Tuple[Optional[str], Optional[str]] -) -> List[Dict[str, str]]: - convo_text = convo_text.replace("left the conversation,", "left the conversation.") - turns = re.split(r"Turn #\d+[:\n]", convo_text) - parsed_conversation: List[Dict[str, str]] = [] - - for turn in turns: - for name in names: - if name and name in turn: - dialogue = turn.split(":", 1)[1].strip() if ":" in turn else turn - parsed_conversation.append({"speaker": name, "dialogue": dialogue}) - break - return parsed_conversation[1:] # Skip the first empty string from split - - -raw_dataset: List[Tuple[str, str]] = read_json_files() -processed_dataset: List[Dict[str, Any]] = [] -pks: List[str] = [] - -for data in raw_dataset: - try: - pk = data[0] - rewards_prompt = data[1] - names = find_names(rewards_prompt) - personal_info = { - name: parse_personal_info(rewards_prompt, name) for name in names - } - social_goal = {name: parse_social_goal(rewards_prompt, name) for name in names} - parsed_conversation = parse_conversation(rewards_prompt, names) - scenario = parse_scenario(rewards_prompt) - processed_data = { - "scenario": scenario, - "names": names, - "personal_info": personal_info, - "social_goal": social_goal, - "parsed_conversation": parsed_conversation, - } - # TODO (haofeiyu): need to add pk into the player data - processed_dataset.append(processed_data) - pks.append(pk) - except Exception as e: - print(e, f"; pk: {data[0]}") - - -class C(BaseConstants): - NAME_IN_URL = "sotopia_pilot_study" - PLAYERS_PER_GROUP = None - NUM_ROUNDS = 1 - - -class Subsession(BaseSubsession): - def creating_session(self) -> None: - self.session.vars["conversation"] = ["hello", "world", "darling"] - - -class Group(BaseGroup): - pass - - -class Player(BasePlayer): - pk = models.StringField( - label="pk", - ) - prolific_id = models.StringField( - label="Prolific ID", - ) - believability_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="believability (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - believability_reasoning_1 = models.LongStringField( - label="Reasoning for believability", - ) - relationship_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="relationship (-5-5)", - max=-5, - min=5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - relationship_reasoning_1 = models.LongStringField( - label="Reasoning for relationship", - ) - knowledge_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="knowledge (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - knowledge_reasoning_1 = models.LongStringField( - label="Reasoning for knowledge", - ) - secret_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="secret (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - secret_reasoning_1 = models.LongStringField( - label="Reasoning for secret", - ) - social_rules_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="social_rules (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - social_rules_reasoning_1 = models.LongStringField( - label="Reasoning for social_rules", - ) - financial_and_material_benefits_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="financial_and_material_benefits (-5-5)", - max=5, - min=-5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - financial_and_material_benefits_reasoning_1 = models.LongStringField( - label="Reasoning for financial_and_material_benefits", - ) - goal_1 = models.IntegerField( - widget=widgets.RadioSelect, - label="goal (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - goal_reasoning_1 = models.LongStringField( - label="Reasoning for goal", - ) - - believability_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="believability (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - believability_reasoning_2 = models.LongStringField( - label="Reasoning for believability", - ) - relationship_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="relationship (-5-5)", - max=-5, - min=5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - relationship_reasoning_2 = models.LongStringField( - label="Reasoning for relationship", - ) - knowledge_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="knowledge (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - knowledge_reasoning_2 = models.LongStringField( - label="Reasoning for knowledge", - ) - secret_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="secret (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - secret_reasoning_2 = models.LongStringField( - label="Reasoning for secret", - ) - social_rules_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="social_rules (-10-0)", - max=0, - min=-10, - choices=[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0], - ) - social_rules_reasoning_2 = models.LongStringField( - label="Reasoning for social_rules", - ) - financial_and_material_benefits_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="financial_and_material_benefits (-5-5)", - max=5, - min=-5, - choices=[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5], - ) - financial_and_material_benefits_reasoning_2 = models.LongStringField( - label="Reasoning for financial_and_material_benefits", - ) - goal_2 = models.IntegerField( - widget=widgets.RadioSelect, - label="goal (0-10)", - max=10, - min=0, - choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], - ) - goal_reasoning_2 = models.LongStringField( - label="Reasoning for goal", - ) - data = models.LongStringField() - - -# FUNCTIONS -# PAGES -class SotopiaEval(Page): - @staticmethod - def vars_for_template(player: Player) -> Dict[str, Any]: - assert len(processed_dataset) == len(pks) - player_data = processed_dataset[player.id % len(processed_dataset)] - player_pk = pks[player.id % len(pks)] - player.data = json.dumps(player_data) - player.pk = player_pk - print(len(processed_dataset)) - data = json.loads(player.data) - for d in data["parsed_conversation"]: - if '"' in d["dialogue"]: - d["turn"] = f"{d['speaker']} said: {d['dialogue']}" - else: - d["turn"] = d["dialogue"] - - turn_list = zip( - [i + 1 for i in range(len(data["parsed_conversation"]))], - [d["turn"] for d in data["parsed_conversation"]], - ) - scenario = data["scenario"] - names = data["names"] - personal_info_1 = data["personal_info"][names[0]] - social_goal_1 = data["social_goal"][names[0]] - personal_info_2 = data["personal_info"][names[1]] - social_goal_2 = data["social_goal"][names[1]] - return { - "scenario": scenario, - "turn_list": turn_list, # 'string_list' is the key for the list of strings - "personal_info_1": personal_info_1, - "personal_info_2": personal_info_2, - "social_goal_1": social_goal_1, - "social_goal_2": social_goal_2, - } - - @staticmethod - def is_displayed(player: Player) -> Any: - import time - - participant = player.participant - current_time = time.time() - return current_time < participant.expiry - - form_model = "player" - form_fields = [ - "believability_1", - "believability_reasoning_1", - "relationship_1", - "relationship_reasoning_1", - "knowledge_1", - "knowledge_reasoning_1", - "secret_1", - "secret_reasoning_1", - "social_rules_1", - "social_rules_reasoning_1", - "financial_and_material_benefits_1", - "financial_and_material_benefits_reasoning_1", - "goal_1", - "goal_reasoning_1", - "believability_2", - "believability_reasoning_2", - "relationship_2", - "relationship_reasoning_2", - "knowledge_2", - "knowledge_reasoning_2", - "secret_2", - "secret_reasoning_2", - "social_rules_2", - "social_rules_reasoning_2", - "financial_and_material_benefits_2", - "financial_and_material_benefits_reasoning_2", - "goal_2", - "goal_reasoning_2", - ] - timeout_seconds = 1200 - - -class SotopiaEvalInstruction(Page): - form_model = "player" - form_fields = ["prolific_id"] - - @staticmethod - def before_next_page(player: Player, timeout_happened: bool) -> None: - import time - - player.participant.expiry = time.time() + 10 - - -page_sequence = [SotopiaEvalInstruction, SotopiaEval] diff --git a/human_eval/sotopia_pilot_study/pilot_study_data/example_data.json b/human_eval/sotopia_pilot_study/pilot_study_data/example_data.json deleted file mode 100644 index 61753c632..000000000 --- a/human_eval/sotopia_pilot_study/pilot_study_data/example_data.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "pk": "01HFCMVRXCKN0X25MAE2VK56S1", - "environment": "01H7VFHPDZVVCDZR3AARA547CY", - "agents": [ - "01H5TNE5PY896ASNX8XGQA6AE0", - "01H5TNE5P98J20AEW94XQ0KC35" - ], - "tag": "ft-mistral-7b-old-filtererd-data_gpt-3.5_clean_ruiyi_1116", - "models": [ - "gpt-4", - "localhost", - "gpt-3.5-turbo" - ], - "rewards_prompt": "Prompt after formatting:\nHere is the context of this interaction:\nScenario: Two friends participating in a charity event for children in Syria\nParticipants: Hendrick Heinz and Ethan Smith\nHendrick Heinz's background: Hendrick Heinz is a 54-year-old male chef. He/him pronouns. Hendrick Heinz hosts monthly wine tasting nights with his peers. Personality and values description: Hendrick Heinz, though somewhat impulsive, values hedonism and conformity. He combines practicality with decisiveness in decision-making. Hendrick's secrets: Faked his culinary credentials to secure prestigious employment\nEthan Smith's background: Ethan Smith is a 29-year-old male software developer. He/him pronouns. Ethan enjoys reading about new technological advancements and watching documentaries. Personality and values description: Ethan Smith is a reserved, independent thinker. Holding universalism and fairness near, he ponders deeply before making decisions. Ethan's secrets: Secretly donates to charities for underprivileged children\nHendrick Heinz's goal: Convince your friend to donate more funds to the cause (Extra information: Your friend initially planned to donate $200, but you believe they could afford to donate $500)\nEthan Smith's goal: Maintain financial stability while supporting the cause (Extra information: You only have $300 set aside for donations and other expenses, and you are not comfortable about exceeding this budget.)\nTurn #1\nHendrick Heinz said: \"Ethan, you've done so well for this cause so far and I know it's not in your character to skimp on helping those in need. But tell me, haven't we always said that real change is made in big leaps? I believe we can truly make a huge dent in the children's lives in Syria if we up the donations to $500. What do you think?\"\nTurn #2\nEthan Smith said: \"Thanks for recognizing my efforts, Hendrick. I agree that making a big impact is important, but I also believe in being financially responsible. I've already set aside $300 for donations and other expenses, and I'm not comfortable exceeding that budget. However, I'm still committed to supporting the cause and I'll find other ways to contribute within my means.\"\nTurn #3\nHendrick Heinz said: \"I appreciate your commitment to being financially responsible, Ethan. But let me ask you this, your generosity and commitment will not only change lives but also yours. By doing more, you'll become part of an elite group of people who truly made a positive difference. Besides, extending the reach of this cause is our goal after all, right? Any amount, big or small, can make a world of difference. I suggest we find a way to make this work within your budget, together we can definitely muster up $500 for the kids in Syria. What's your take on that?\"\nTurn #4\nEthan Smith [non-verbal communication] nodding in agreement\nTurn #5\nHendrick Heinz said: \"Ethan, I understand your reservations about going over your budget and I truly appreciate your dedication to being financially responsible. I promise you, we'll find a way that matches our commitment to the cause while also helping the kids in Syria. I know how much this cause means to you, just as it means to me. So when we set this amount, I assure you it'll be a joint effort that makes every child in Syria's life a little better. Okay?\"\nTurn #6\nEthan Smith said: \"Hendrick, I understand your point and I appreciate your passion for making a big impact. However, I have to prioritize my financial stability while still supporting the cause. Is there any other way I can contribute or help within my budget?\"\nTurn #7\nHendrick Heinz said: \"Ethan, I completely understand your concern for financial stability and I respect that. I propose a plan that involves us pooling our individual donations along with some other like-minded individuals who share our cause and commitment. That way, we could potentially achieve a collective donation of $500 while spreading the financial burden. What do you think?\"\nTurn #8\nEthan Smith [non-verbal communication] smiling and giving a thumbs up\nTurn #9\nHendrick Heinz said: \"Ethan, I understand your reservations and I value your commitment to financial stability. I agree with your proposal to pool our individual contributions to make up $500. It sounds like a fair approach. Let's reach out to some other friends who share our passion for this cause and see what we can achieve together. And meanwhile, we can help by spreading the word about the charity so we can reach more potential donors. What do you think?\"\nTurn #10\nEthan Smith left the conversation,\n" -} diff --git a/poetry.lock b/poetry.lock index 6f23f1e1d..3ffc94c66 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"bf350a60e7a47dd53d3024fad30e62b8a8eb9a2c3678b523202aafb6d3930928" +content-hash = "7fe3915e62b84f4f2f9d179958730907a2a6607e1de39ea77ab2f52a4cc93863" diff --git a/pyproject.toml b/pyproject.toml index f97f62e46..1582c097a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "sotopia" -version = "0.0.11" +version = "0.1.0-rc.1" description = "A platform for simulating and evaluating social interaction." authors = ["Hao Zhu , Xuhui Zhou "] license = "MIT License" @@ -18,15 +18,15 @@ redis-om = "^0.2.1" gin-config = "^0.5.0" absl-py = "^2.0.0" together = "^0.2.4" -pydantic = "1.10.12" +pydantic = "1.10.17" beartype = "^0.14.0" langchain-openai = "~0.1.8" -litellm = "~1.23.12" +litellm = ">=1.23.12,<1.41.0" # dependency versions for extras fastapi = { version = ">=0.109.2,<0.111.0", optional = true } tabulate = { version = "^0.9.0", optional = true } -anthropic = { version = ">=0.26,<0.29", optional = true } +anthropic = { version = ">=0.26,<0.32", optional = true } xmltodict = { version = "^0.13.0", optional = true } groq = { version = ">=0.4.2,<0.10.0", optional = true } cohere = { version = "^5.1.8", optional = true } @@ -49,7 +49,7 @@ ruff = "*" pytest = "*" pytest-cov = "*" pytest-asyncio = "*" -mypy = ">=1.8.0,<1.11.0" +mypy = ">=1.8.0,<1.12.0" [tool.poetry.extras] anthropic = ["anthropic"] diff --git a/scripts/display_benchmark_results.sh b/scripts/display_benchmark_results.sh new file mode 100755 index 000000000..7933e2d54 --- /dev/null +++ b/scripts/display_benchmark_results.sh @@ -0,0 +1,6 @@ +sotopia benchmark --only-show-performance \ +--models gpt-4o \ +--models together_ai/mistralai/Mixtral-8x22B-Instruct-v0.1 \ +--models gpt-3.5-turbo \ +--models together_ai/meta-llama/Llama-3-70b-chat-hf \ +--models together_ai/meta-llama/Llama-3-8b-chat-hf diff --git a/sotopia-chat/chat_server.py b/sotopia-chat/chat_server.py index 0b3388b5a..21eba5eff 100644 --- a/sotopia-chat/chat_server.py +++ b/sotopia-chat/chat_server.py @@ -27,6 +27,8 @@ from sotopia.envs.parallel import ParallelSotopiaEnv from sotopia.server import arun_one_episode +from sotopia.envs.evaluators import SotopiaDimensions, EvaluationForTwoAgents + process = subprocess.Popen( ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE ) @@ -62,7 +64,7 @@ async def _start_server_with_two_session_ids_and_agent_env_combo( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator("gpt-4"), + ReachGoalLLMEvaluator("gpt-4", EvaluationForTwoAgents[SotopiaDimensions]), ], ) random.shuffle(session_ids) @@ -95,7 +97,7 @@ async def _start_server_with_one_session_id_and_agent_env_combo( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator("gpt-4"), + ReachGoalLLMEvaluator("gpt-4", EvaluationForTwoAgents[SotopiaDimensions]), ], ) diff --git a/sotopia/cli/benchmark/benchmark.py b/sotopia/cli/benchmark/benchmark.py index 18455f02c..87da66e4e 100644 --- a/sotopia/cli/benchmark/benchmark.py +++ b/sotopia/cli/benchmark/benchmark.py @@ -4,7 +4,12 @@ from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging -from typing import cast +import json +import math +import numpy as np +from itertools import chain +from collections import defaultdict +from typing import cast, OrderedDict from logging import FileHandler from rich.logging import RichHandler @@ -20,8 +25,10 @@ ) from sotopia.database.serialization import get_rewards_from_episode from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + SotopiaDimensions, ) from sotopia.envs.parallel import ParallelSotopiaEnv from sotopia.generation_utils.generate import LLM_Name @@ -35,87 +42,313 @@ import typer from pathlib import Path from ..app import app +import os +# date and message only +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" -def check_existing_episodes( - env_id: str, - agent_ids: list[str], - models: dict[str, LLM_Name], - index: str, - tag: str | None = None, -) -> bool: - if tag: - existing_episode = EpisodeLog.find( - (EpisodeLog.environment == env_id) & (EpisodeLog.tag == tag) - ).all() - else: - existing_episode = EpisodeLog.find(EpisodeLog.environment == env_id).all() - if existing_episode: - for episode in existing_episode: - assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" - models_list = ( - [models["env"], models["test_model"], models["partner_model"]] - if index == "0" - else [models["env"], models["partner_model"], models["test_model"]] - ) - if episode.agents == agent_ids and episode.models == models_list: - return True - return False - else: - return False +logging.basicConfig( + level=20, + format=FORMAT, + datefmt="[%X]", + handlers=[ + RichHandler(), + ], +) +default_model_list: list[str] = [ + "gpt-4o", +] +dimension_range_mapping = OrderedDict( + { + "social_rules": ["SOC", [-10, 0]], + "secret": ["SEC", [-10, 0]], + "financial_and_material_benefits": ["FIN", [-5, 5]], + "relationship": ["REL", [-5, 5]], + "knowledge": ["KNO", [0, 10]], + "goal": ["GOAL", [0, 10]], + "believability": ["BEL", [0, 10]], + } +) -def initilize_benchmark_combo(data: list[dict[str, str]]) -> list[EnvAgentComboStorage]: - list_of_env_agent_combo_storage = [] - for combo in data: - env_agent_combo_storage = EnvAgentComboStorage( - env_id=combo["env_id"], agent_ids=combo["agent_ids"] - ) - list_of_env_agent_combo_storage.append(env_agent_combo_storage) - return list_of_env_agent_combo_storage +def get_avg_reward( + episodes: list[EpisodeLog], model_name: str +) -> dict[str, tuple[float, float]]: + """ + input: list of EpisodeLog, model_name -def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, float]: - rewards_list = [] + return: dictionary of {dimension: (avg_reward, margin_of_error (in 95% confidence interval))}, plus the distinct setting number and episode count (in the same format, but with 0 margin of error) + """ + rewards_dict = defaultdict( + list + ) # {pk: [rewards]}, {pk}_{i} denotes the i-th agent is the test agent avg_reward_dict = {} + avg_margin_dict = {} + avg_variance_dict = {} for episode in episodes: assert episode.models is not None, "episode.models should not be None" if episode.models[1] == model_name: reward = get_rewards_from_episode(episode)[0][1] + rewards_dict[f"{episode.environment}_0"].append(reward) else: reward = get_rewards_from_episode(episode)[1][1] - rewards_list.append(reward) + rewards_dict[f"{episode.environment}_1"].append(reward) + dimensions = list(rewards_dict.values())[0][0].keys() + + def calc_variance(local_rewards_list: list[dict[str, float]]) -> dict[str, float]: + # get the variance within a list, discarded + local_var_reward_dict = {} + local_dimensions = local_rewards_list[0].keys() + assert set(local_dimensions) == set(dimensions), "dimensions should be the same" + for dimension in local_dimensions: + rewards = [reward[dimension] for reward in local_rewards_list] + avg_reward = sum(rewards) / len(rewards) + if len(rewards) == 1: + variance = 0.0 + else: + variance = sum([(reward - avg_reward) ** 2 for reward in rewards]) / ( + len(rewards) - 1 + ) + local_var_reward_dict[dimension] = variance + + return local_var_reward_dict + + def calc_average(list_to_average: list[float]) -> float: + return sum(list_to_average) / len(list_to_average) + + rewards_list = list(chain(*rewards_dict.values())) + + variance_reward_list = [calc_variance(rewards) for rewards in rewards_dict.values()] for dimension in rewards_list[0].keys(): - rewards = [reward[dimension] for reward in rewards_list] - avg_reward = sum(rewards) / len(rewards) - avg_reward_dict[dimension] = avg_reward - return avg_reward_dict + avg_reward_dict[dimension] = calc_average( + [reward[dimension] for reward in rewards_list] + ) + avg_variance_dict[dimension] = calc_average( + [variance[dimension] for variance in variance_reward_list] + ) # average the variances for an estimation of the variance + + for dimension in rewards_list[0].keys(): + # calculate the margin of error by the averaged mean and variance + avg_variance = avg_variance_dict[dimension] + + combined_variance = avg_variance + combined_sem = math.sqrt( + combined_variance / len(rewards_list) + ) # sem = sqrt(variance / n), we use the averaged variance under different settings + + confidence_level = 0.95 + t_samples = np.random.standard_t(df=len(rewards_list) - 1, size=1000000) + + overall_t_value = np.percentile( + t_samples, 100 * (1 - (1 - confidence_level) / 2) + ) + + margin = overall_t_value * combined_sem + avg_margin_dict[dimension] = margin + + return_rewards_dict = { + key: (avg_reward_dict[key], avg_margin_dict[key]) + for key in avg_reward_dict.keys() + } + return_rewards_dict = { + **return_rewards_dict, + "setting_num": (float(len(variance_reward_list)), 0.0), + "episode_count": (float(len(rewards_list)), 0.0), + } + + return return_rewards_dict + + +def initialize_benchmark_combo(url: str) -> list[EnvAgentComboStorage]: + if url: + response = requests.get(url) + # Check if the request was successful + if response.status_code == 200: + data = response.json() + print("Data fetched successfully") + else: + print(f"Failed to fetch data. Status code: {response.status_code}") + raise ValueError("Failed to fetch data") + list_of_env_agent_combo_storage = [] + for combo in data: + env_agent_combo_storage = EnvAgentComboStorage( + env_id=combo["env_id"], agent_ids=combo["agent_ids"] + ) + list_of_env_agent_combo_storage.append(env_agent_combo_storage) + else: + list_of_env_agent_combo_storage = EnvAgentComboStorage.find().all() # type: ignore + return list_of_env_agent_combo_storage + + +def _set_up_logs( + *, + log_file_level: int = logging.DEBUG, + log_rich_level: int = logging.INFO, + log_format: str = "%(asctime)s - %(levelname)s - %(name)s - %(message)s", + log_file: str = datetime.now().strftime("./logs/%H_%M_%d_%m_%Y.log"), + print_logs: bool = False, +) -> None: + # date and message only + logging_path = Path(log_file) + logging_path.parent.mkdir(parents=True, exist_ok=True) + + logging.basicConfig( + level=log_file_level, + format=log_format, + datefmt="[%X]", + handlers=[ + FileHandler(logging_path), + RichHandler(level=log_rich_level) if print_logs else RichHandler(level=100), + ], + ) + + +def preprocess_episode_data( + episode_list_with_tag: list[EpisodeLog], +) -> dict[tuple[str, tuple[str, ...], tuple[str, ...]], bool]: + """Preprocess episodes into a dictionary for quick lookup.""" + episode_dict = {} + for episode in episode_list_with_tag: + assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" + assert episode.models is not None, "episode.models should not be None" + episode_key = ( + episode.environment, + tuple(episode.agents), + tuple(episode.models), + ) + episode_dict[episode_key] = True + return episode_dict + + +def check_existing_episodes( + env_id: str, + agent_ids: list[str], + models: dict[str, LLM_Name], + index: str, + episode_dict: dict[tuple[str, tuple[str, ...], tuple[str, ...]], bool], +) -> bool: + models_list = ( + (models["env"], models["test_model"], models["partner_model"]) + if index == "0" + else (models["env"], models["partner_model"], models["test_model"]) + ) + episode_key = (env_id, tuple(agent_ids), models_list) + return episode_dict.get(episode_key, False) + + +def run_async_benchmark_in_batch( + *, + env_agent_combo_list: list[EnvAgentCombo[Observation, AgentAction]], + batch_size: int = 1, + tag: str = "", + push_to_db: bool = False, + verbose: bool = False, +) -> None: + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + loop = asyncio.get_event_loop() + for env_agent_combo in tqdm( + env_agent_combo_list, + desc="Running all envs in batch", + ): + env_agent_combo_batch.append(env_agent_combo) + if len(env_agent_combo_batch) == batch_size: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + loop.run_until_complete( + run_async_server( + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + push_to_db=push_to_db, + tag=tag, + ) + ) + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + loop.run_until_complete( + run_async_server( + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + push_to_db=push_to_db, + tag=tag, + ) + ) + # remove episodes that has bad rewards + simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() + valid_episodes = [ + not isinstance(relevant_episode.rewards[0], float) # type: ignore + for relevant_episode in simulated_episodes + ] + for valid, episode in zip(valid_episodes, simulated_episodes): + if not valid: + pk = episode.pk + assert isinstance(pk, str) + EpisodeLog.delete(pk) + + +def benchmark_display( + model_list: list[str] = default_model_list, + partner_model: str = "together_ai/meta-llama/Llama-3-70b-chat-hf", + evaluator_model: str = "gpt-4o", + task: str = "hard", + output_to_jsonl: bool = False, + save_dir: str = ".", +) -> dict[str, dict[str, tuple[float, float]]]: + """ + Usage: sotopia benchmark-display --model-list gpt-4o --model-list together_ai/meta-llama-Llama-3-70b-chat-hf + Aggregate all the results for the benchmark, as described in https://github.com/sotopia-lab/sotopia-space/blob/main/data_dir/models_vs_gpt35.jsonl + """ + + print(f"Displaying performance for {model_list} vs {partner_model} on task {task}") + model_rewards_dict = dict() + for model in model_list: + tag = f"benchmark_{model}_{partner_model}_{evaluator_model}_{task}_trial0" + episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() + if len(episodes) == 0: + print(f"No episodes found for {model}") + continue + avg_rewards = get_avg_reward(episodes, model) # type: ignore + model_rewards_dict[model] = avg_rewards + # print(f"Model: {model}, episodes: {len(episodes)}, Avg Rewards: {avg_rewards}") + rich.print(model_rewards_dict) + if model_rewards_dict: + display_in_table(model_rewards_dict) + if output_to_jsonl: + save_to_jsonl(model_rewards_dict, partner_model, save_dir) + else: + print("No episodes found for any model") + return model_rewards_dict def _list_all_env_agent_combo_not_in_db( model_names: dict[str, LLM_Name], - env_agent_combo_storage_list: list[EnvAgentComboStorage], - tag: str | None = None, + env_agent_combo_storage_index_list: list[tuple[EnvAgentComboStorage, str]], + tag: str = "", + task: str = "", ) -> list[EnvAgentCombo[Observation, AgentAction]]: - """We iterate over each environment and return the **first** env-agent combo that is not in the database.""" - hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments - agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index - assert isinstance(agent_index, list), "agent_index should be a list" - - env_agent_combo_storage_index_list = [] - for env_id, index in zip(hard_envs, agent_index): - for env_agent_combo_storage in env_agent_combo_storage_list: - if env_agent_combo_storage.env_id == env_id: - env_agent_combo_storage_index_list.append( - (env_agent_combo_storage, index) - ) + """Iterate over each environment and return the first env-agent combo not in the database.""" + assert tag, "tag should not be empty" + # Preprocess the episodes for fast lookup + episode_list_with_tag: list[EpisodeLog] = EpisodeLog.find( + EpisodeLog.tag == tag + ).all() # type: ignore + episode_dict = preprocess_episode_data(episode_list_with_tag) list_of_env_agent_combo_storage = [] for env_agent_combo_storage, index in env_agent_combo_storage_index_list: agent_ids = env_agent_combo_storage.agent_ids env_id = env_agent_combo_storage.env_id if check_existing_episodes( - env_id=env_id, agent_ids=agent_ids, models=model_names, index=index, tag=tag + env_id=env_id, + agent_ids=agent_ids, + models=model_names, + index=index, + episode_dict=episode_dict, ): logging.info( f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" @@ -130,7 +363,10 @@ def _list_all_env_agent_combo_not_in_db( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], terminal_evaluators=[ - ReachGoalLLMEvaluator(model_names["env"]), + ReachGoalLLMEvaluator( + model_names["env"], + EvaluationForTwoAgents[SotopiaDimensions], + ), ], ) agent_profiles = [AgentProfile.get(id) for id in agent_ids] @@ -148,123 +384,59 @@ def _list_all_env_agent_combo_not_in_db( return list_of_env_agent_combo_storage # type: ignore -def run_async_benchmark_in_batch( - *, - batch_size: int = 1, - model_names: dict[str, LLM_Name] = { - "env": "gpt-4", - "test_model": "gpt-3.5-turbo", - "partner_model": "gpt-3.5-turbo", - }, - tag: str | None = None, - push_to_db: bool = False, - verbose: bool = False, +def display_in_table( + model_rewards_dict: dict[str, dict[str, tuple[float, float]]], ) -> None: - url = "https://huggingface.co/datasets/cmu-lti/sotopia/resolve/main/benchmark_agents.json?raw=true" - response = requests.get(url) - # Check if the request was successful - if response.status_code == 200: - data = response.json() - print("Data fetched successfully") - else: - print(f"Failed to fetch data. Status code: {response.status_code}") - raise ValueError("Failed to fetch data") - benchmark_combo = initilize_benchmark_combo(data) - env_agent_combo_list = _list_all_env_agent_combo_not_in_db( - model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo + table = rich.table.Table( + title="Rewards Evaluation (+/- CI bounds)", ) - env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] - number_of_fix_turns = 0 - loop = asyncio.get_event_loop() - while True: - for env_agent_combo in tqdm( - env_agent_combo_list, - desc="Running all envs in batch", - ): - env_agent_combo_batch.append(env_agent_combo) - if len(env_agent_combo_batch) == batch_size: - logging.info( - f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" - ) - loop.run_until_complete( - run_async_server( - sampler=BaseSampler[Observation, AgentAction](), - env_agent_combo_list=env_agent_combo_batch, - push_to_db=push_to_db, - tag=tag, - ) - ) - env_agent_combo_batch = [] - else: - if env_agent_combo_batch: - logging.info( - f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" - ) - loop.run_until_complete( - run_async_server( - model_dict=model_names, - sampler=BaseSampler[Observation, AgentAction](), - env_agent_combo_list=env_agent_combo_batch, - push_to_db=push_to_db, - tag=tag, - ) - ) - # remove episodes that has bad rewards - simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() - valid_episodes = [ - not isinstance(relevant_episode.rewards[0], float) # type: ignore - for relevant_episode in simulated_episodes - ] - for valid, episode in zip(valid_episodes, simulated_episodes): - if not valid: - pk = episode.pk - assert isinstance(pk, str) - EpisodeLog.delete(pk) - - env_agent_combo_list = _list_all_env_agent_combo_not_in_db( - model_names=model_names, - tag=tag, - env_agent_combo_storage_list=benchmark_combo, - ) - env_agent_combo_batch = [] - number_of_fix_turns += 1 - if len(env_agent_combo_list) == 0 or number_of_fix_turns >= 5: - rewards_dict = get_avg_reward( - simulated_episodes, # type: ignore - model_names["test_model"], - ) - rewards_dict["model_name"] = model_names["test_model"] # type: ignore - rewards_dict["episode_count"] = len(simulated_episodes) - rich.print(rewards_dict) - return + table.add_column("Model") + for dimension in model_rewards_dict[list(model_rewards_dict.keys())[0]].keys(): + table.add_column(dimension) + table.add_column("Settings") + table.add_column("Episodes") + for model, rewards in model_rewards_dict.items(): + table.add_row( + model, + *( + [f"{rewards[k][0]:.2f} ± {rewards[k][1]:.2f}" for k in rewards.keys()] + + [f"{rewards[k][0]:.0f}" for k in ["setting_num", "episode_count"]] + ), + ) + rich.print(table) -def _set_up_logs( - *, - log_file_level: int = logging.DEBUG, - log_rich_level: int = logging.INFO, - log_format: str = "%(asctime)s - %(levelname)s - %(name)s - %(message)s", - log_file: str = datetime.now().strftime("./logs/%H_%M_%d_%m_%Y.log"), - print_logs: bool = False, +def save_to_jsonl( + model_rewards_dict: dict[str, dict[str, tuple[float, float]]], + partner_model: str, + save_dir: str, ) -> None: - # date and message only - logging_path = Path(log_file) - logging_path.parent.mkdir(parents=True, exist_ok=True) + simplified_model_name = partner_model.split("/")[-1] + output_fn = os.path.join(save_dir, f"models_vs_{simplified_model_name}.jsonl") + outputs: list[str] = [] + for model, rewards in model_rewards_dict.items(): + formatted_reward = OrderedDict( + { + "model_name": model, + **{ + f"{v[0]} {v[1]}": rewards[k][0] + for k, v in dimension_range_mapping.items() + }, + } + ) + outputs.append(json.dumps(formatted_reward)) + with open(output_fn, "w") as f: + f.write("\n".join(outputs)) - logging.basicConfig( - level=log_file_level, - format=log_format, - datefmt="[%X]", - handlers=[ - FileHandler(logging_path), - RichHandler(level=log_rich_level) if print_logs else RichHandler(level=100), - ], - ) + print(f"Output saved to {output_fn}") @app.command() def benchmark( - model: str = typer.Option(..., help="The language model you want to benchmark."), + models: list[str] = typer.Option( + default_model_list, + help=f"All the language model you want to benchmark. Default is the pre-loaded model list {default_model_list}.", + ), partner_model: str = typer.Option( "together_ai/meta-llama/Llama-3-70b-chat-hf", help="The partner model you want to use.", @@ -274,25 +446,81 @@ def benchmark( ), batch_size: int = typer.Option(10, help="The batch size you want to use."), task: str = typer.Option("hard", help="The task id you want to benchmark."), + url: str = typer.Option("", help="The url to fetch the benchmark combo."), print_logs: bool = typer.Option(False, help="Print logs."), + only_show_performance: bool = typer.Option(False, help="Only show performance."), + output_to_jsonl: bool = typer.Option(False, help="Output to jsonl."), + push_to_db: bool = typer.Option(False, help="Push to db."), + save_dir: str = typer.Option(".", help="The directory to save the output."), ) -> None: + if only_show_performance: + benchmark_display( + models, partner_model, evaluator_model, task, output_to_jsonl, save_dir + ) + return + """A simple command-line interface example.""" _set_up_logs(print_logs=print_logs) - typer.echo( - f"Running benchmark for {model} chatting with {partner_model} on task {task} with {evaluator_model} as the evaluator." - ) - model = cast(LLM_Name, model) - partner_model = cast(LLM_Name, partner_model) - evaluator_model = cast(LLM_Name, evaluator_model) - tag = f"benchmark_{model}_{partner_model}_{evaluator_model}_{task}_trial0" - run_async_benchmark_in_batch( - batch_size=batch_size, - model_names={ + benchmark_combo = initialize_benchmark_combo(url) + if task == "hard": + hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments + agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index + assert isinstance(agent_index, list), "agent_index should be a list" + env_agent_combo_storage_index_list = [] + for env_id, index in zip(hard_envs, agent_index): + for env_agent_combo_storage in benchmark_combo: + if env_agent_combo_storage.env_id == env_id: + env_agent_combo_storage_index_list.append( + (env_agent_combo_storage, index) + ) + + else: + env_agent_combo_storage_index_list = [ + (env_agent_combo_storage, "1") + for env_agent_combo_storage in benchmark_combo + ] + [ + (env_agent_combo_storage, "0") + for env_agent_combo_storage in benchmark_combo + ] + for model in models: + typer.echo( + f"Running benchmark for {model} chatting with {partner_model} on task {task} with {evaluator_model} as the evaluator." + ) + model = cast(LLM_Name, model) + partner_model = cast(LLM_Name, partner_model) + evaluator_model = cast(LLM_Name, evaluator_model) + tag = f"benchmark_{model}_{partner_model}_{evaluator_model}_{task}_trial0" + typer.echo(typer.style(f"Tag: {tag}", fg=typer.colors.GREEN, bold=True)) + model_names = { "env": evaluator_model, "test_model": model, "partner_model": partner_model, - }, - tag=tag, - verbose=False, - push_to_db=True, + } + env_agent_combo_list = _list_all_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + env_agent_combo_storage_index_list=env_agent_combo_storage_index_list, + task=task, + ) + number_of_fix_turns = 0 + while True: + run_async_benchmark_in_batch( + env_agent_combo_list=env_agent_combo_list, + batch_size=batch_size, + tag=tag, + verbose=False, + push_to_db=push_to_db, + ) + env_agent_combo_list = _list_all_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + env_agent_combo_storage_index_list=env_agent_combo_storage_index_list, + task=task, + ) + number_of_fix_turns += 1 + if len(env_agent_combo_list) == 0 or number_of_fix_turns >= 5: + return + + benchmark_display( + models, partner_model, evaluator_model, task, output_to_jsonl=output_to_jsonl ) diff --git a/sotopia/database/__init__.py b/sotopia/database/__init__.py index 34dd74de6..735d005ee 100644 --- a/sotopia/database/__init__.py +++ b/sotopia/database/__init__.py @@ -1,3 +1,5 @@ +from typing import TypeVar +from redis_om import JsonModel from .annotators import Annotator from .env_agent_combo_storage import EnvAgentComboStorage from .logs import AnnotationForEpisode, EpisodeLog @@ -60,3 +62,12 @@ "jsonl_to_envagnetcombostorage", "get_rewards_from_episode", ] + +InheritedJsonModel = TypeVar("InheritedJsonModel", bound="JsonModel") + + +def _json_model_all(cls: type[InheritedJsonModel]) -> list[InheritedJsonModel]: + return cls.find().all() # type: ignore[return-value] + + +JsonModel.all = classmethod(_json_model_all) # type: ignore[assignment,method-assign] diff --git a/sotopia/envs/evaluators.py b/sotopia/envs/evaluators.py index 01d1cc4a8..848e9e626 100644 --- a/sotopia/envs/evaluators.py +++ b/sotopia/envs/evaluators.py @@ -1,6 +1,9 @@ import abc import logging from collections import defaultdict +from typing import Generic, TypeVar + +from pydantic.generics import GenericModel import gin from beartype import beartype @@ -17,7 +20,9 @@ log = logging.getLogger("evaluators") -class EvaluationBySocialDimensions(BaseModel): +class SotopiaDimensions(BaseModel): + """The social dimensions used in Sotopia paper (ICLR 2024)""" + believability: tuple[str, int] = Field( ..., description="Reasoning requirement: 1. Evaluate if the agent interacts with others in a natural and realistic manner (here are a few common questions to check: a. whether the agent is confusing with its own identity? b. whether the agent repeats others' words/actions without any reason? c. whether the agent is being overly polite considering the context?). Start the analysis with tag " @@ -64,23 +69,25 @@ class EvaluationBySocialDimensions(BaseModel): "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from 0 and 10 in the 'score' field. 0 represents minimal goals achievement, 10 represents complete goal achievement, and a higher score indicates that the agent is making progress towards their social goals.", ) - @validator("believability", "knowledge", "goal") + @validator("believability", "knowledge", "goal", allow_reuse=True) def zero_to_ten_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= 0 and v[1] <= 10 return v - @validator("relationship", "financial_and_material_benefits") + @validator("relationship", "financial_and_material_benefits", allow_reuse=True) def minus_five_to_five_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= -5 and v[1] <= 5 return v - @validator("secret", "social_rules") + @validator("secret", "social_rules", allow_reuse=True) def minus_ten_to_zero_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= -10 and v[1] <= 0 return v -class EvaluationBySocialDimensionsPlus(BaseModel): +class SotopiaDimensionsPlus(BaseModel): + """Updated SotopiaDimensions with more detailed instructions""" + believability: tuple[str, int] = Field( ..., description="Reasoning requirement: 1. Evaluate if the agent interacts with others in a natural and realistic manner (here are a few common questions to check: a. whether the agent is confusing with its own identity? b. whether the agent repeats others' words/actions without any reason? c. whether the agent is being overly polite considering the context?). Start the analysis with tag " @@ -127,30 +134,44 @@ class EvaluationBySocialDimensionsPlus(BaseModel): "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from 0 and 10 in the 'score' field. 0 represents minimal goals achievement, 10 represents complete goal achievement, and a higher score indicates that the agent is making progress towards their social goals. Almost Not Finishing Any Goal (0-3): Scores from 0 to 3 indicate almost not finishing any goal, suggesting a minimal level of goal achievement. This range signifies either no progress or only a very rudimentary level of advancement towards the completion of set goals. Finishing Less Than 50% of Goals (4-6): A score between 4 and 6 suggests finishing less than 50% of the goals, indicating a moderate level of goal completion. This range represents partial success, with some goals being met while a significant portion remains unachieved. Finishing More Than 50%, But Not All Goals (7-8): Scores in the 7 to 8 range indicate finishing more than 50% but not all of the goals. This suggests a high level of achievement, where the majority of set goals are met, but some goals still remain incomplete. Finishing All Goals (9-10): A score between 9 and 10 signifies finishing all goals, representing the highest level of achievement in goal completion. This range indicates that all set objectives have been met, signifying complete success in achieving the targeted goals.", ) - @validator("believability", "knowledge", "goal") + @validator("believability", "knowledge", "goal", allow_reuse=True) def zero_to_ten_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= 0 and v[1] <= 10 return v - @validator("relationship", "financial_and_material_benefits") + @validator("relationship", "financial_and_material_benefits", allow_reuse=True) def minus_five_to_five_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= -5 and v[1] <= 5 return v - @validator("secret", "social_rules") + @validator("secret", "social_rules", allow_reuse=True) def minus_ten_to_zero_validator(cls, v: tuple[str, int]) -> tuple[str, int]: assert v[1] >= -10 and v[1] <= 0 return v -class EnvResponse(BaseModel): - agent_1_evaluation: EvaluationBySocialDimensions - agent_2_evaluation: EvaluationBySocialDimensions +class GoalDimension(BaseModel): + """Goal only evaluation""" + goal: tuple[str, int] = Field( + ..., + description="Please first reiterate agent's social goals. " + "And then please provide a comprehensive analysis about the extent to which the agent has managed to achieve these goals. " + "The first entry (str) of the object is the 'reasoning' field, and the second entry (int) of the object is the 'score' field. In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from 0 and 10 in the 'score' field. 0 represents minimal goals achievement, 10 represents complete goal achievement, and a higher score indicates that the agent is making progress towards their social goals.", + ) -class EnvResponsePlus(BaseModel): - agent_1_evaluation: EvaluationBySocialDimensionsPlus - agent_2_evaluation: EvaluationBySocialDimensionsPlus + @validator("goal", allow_reuse=True) + def zero_to_ten_validator(cls, v: tuple[str, int]) -> tuple[str, int]: + assert v[1] >= 0 and v[1] <= 10 + return v + + +T_eval_dim = TypeVar("T_eval_dim", bound=BaseModel) + + +class EvaluationForTwoAgents(GenericModel, Generic[T_eval_dim]): + agent_1_evaluation: T_eval_dim + agent_2_evaluation: T_eval_dim class Evaluator(abc.ABC): @@ -180,7 +201,7 @@ def __call__( self, turn_number: int, messages: list[tuple[str, Message]] ) -> list[tuple[str, tuple[tuple[str, int | float | bool], str]]]: # Rule 1: If the conversation is too long, terminate the conversation - conversation_too_long = turn_number > self.max_turn_number + conversation_too_long = turn_number >= self.max_turn_number # Rule 2: If one of the players leaves, terminate the conversation p1_leaving = ( len(messages) > 1 @@ -225,12 +246,16 @@ async def __acall__( return self(turn_number, messages) -class ReachGoalLLMEvaluator(Evaluator): +class ReachGoalLLMEvaluator(Evaluator, Generic[T_eval_dim]): @beartype - def __init__(self, model_name: str, response_format: str = "basic") -> None: + def __init__( + self, + model_name: str, + response_format_class: type[EvaluationForTwoAgents[T_eval_dim]], + ) -> None: self.model_name = model_name self.prompt = "" - self.response_format = response_format + self.response_format_class = response_format_class def __call__( self, turn_number: int, messages: list[tuple[str, Message]] @@ -265,15 +290,9 @@ async def __acall__( for x, y in messages_filtered ] ) - response_format_class = ( - EnvResponsePlus if self.response_format == "plus" else EnvResponse - ) try: - response: ( - EnvResponsePlus | EnvResponse - ) # fix type error from langchain 0.0.264. we don't need this line for langchain 0.0.263 - response = await agenerate( + response: EvaluationForTwoAgents[T_eval_dim] = await agenerate( model_name=self.model_name, template="""{history}, Based on previous interactions, evaluate how well participants achieve their goals. @@ -281,8 +300,8 @@ async def __acall__( {format_instructions} """, input_values=dict(history=history), - output_parser=PydanticOutputParser[EnvResponsePlus | EnvResponse]( - pydantic_object=response_format_class + output_parser=PydanticOutputParser[self.response_format_class]( # type: ignore[name-defined] + pydantic_object=self.response_format_class ), temperature=temperature, ) diff --git a/sotopia/envs/parallel.py b/sotopia/envs/parallel.py index 4821d8c70..bb0b733c3 100644 --- a/sotopia/envs/parallel.py +++ b/sotopia/envs/parallel.py @@ -2,7 +2,7 @@ import copy import itertools import random -from typing import Any, Literal +from typing import Any, Literal, Optional, Type, TypeVar from beartype import beartype from gin import configurable @@ -30,6 +30,8 @@ from .evaluators import Evaluator, unweighted_aggregate_evaluate +TBackground = TypeVar("TBackground", bound=ScriptBackground) + def _actions_to_natural_language(actions: dict[str, AgentAction]) -> str: action_str = "" @@ -133,6 +135,7 @@ def __init__( terminal_evaluators: list[Evaluator] = [], uuid_str: str | None = None, env_profile: EnvironmentProfile | None = None, + background_class: Optional[Type[TBackground]] = None, ) -> None: """A sotopia environment for parallel agents. @@ -143,7 +146,11 @@ def __init__( """ super().__init__() self.model_name = model_name - self.background = ScriptBackground( + if background_class is None: + self.background_class = ScriptBackground + else: + self.background_class = background_class + self.background = self.background_class( scenario="", p1_background="", p2_background="", @@ -207,7 +214,7 @@ def reset( agent_goals = self.profile.agent_goals assert len(agent_goals) == 2, "Only supporting two agents right now" - raw_background = ScriptBackground( + raw_background = self.background_class( scenario=self.profile.scenario, p1_background=get_bio( self.profile.relationship, @@ -229,7 +236,7 @@ def reset( raw_background.p1_background = "" raw_background.p2_background = "" - self.background = ScriptBackground( + self.background = self.background_class( scenario=render_text_for_environment(raw_background.scenario), p1_background=render_text_for_environment(raw_background.p1_background), p2_background=render_text_for_environment(raw_background.p2_background), @@ -242,14 +249,14 @@ def reset( raise ValueError("agents must be provided") self.agents = [self.background.p1_name, self.background.p2_name] - agent_backgrounds: list[ScriptBackground] = [] + agent_backgrounds = [] if omniscient: for i in range(self.num_agents): agent_backgrounds.append(copy.deepcopy(self.background)) else: for i in range(self.num_agents): agent_backgrounds.append( - ScriptBackground( + self.background_class( scenario=render_text_for_agent(raw_background.scenario, i), p1_background=render_text_for_agent( raw_background.p1_background, i diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 39a0d1548..e5fc0f247 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -1,20 +1,24 @@ import logging import os import re -from typing import TypeVar +from typing import TypeVar, Any, cast import gin from beartype import beartype from beartype.typing import Type -from langchain.chains.llm import LLMChain +from openai import OpenAI + +from langchain_core.runnables.base import RunnableSerializable +from langchain_core.messages.base import BaseMessage from langchain.output_parsers import PydanticOutputParser from langchain.prompts import ( ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate, ) +from langchain_core.prompt_values import ChatPromptValue from langchain.schema import BaseOutputParser, OutputParserException -from langchain_openai import ChatOpenAI +from langchain_openai import ChatOpenAI, AzureChatOpenAI from pydantic import BaseModel, Field from rich import print from typing_extensions import Literal @@ -38,17 +42,21 @@ "together_ai/mistralai/Mixtral-8x22B-Instruct-v0.1", "together_ai/meta-llama/Llama-3-8b-chat-hf", "together_ai/meta-llama/Llama-3-70b-chat-hf", + "together_ai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", "gpt-3.5-turbo", + "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-finetuned", "gpt-3.5-turbo-ft-MF", "gpt-4", - "gpt-4-turbo", + "gpt-4-turbo-2024-04-09", + "gpt-4o-2024-08-06" "gpt-4o-mini-2024-07-18", "human", "redis", "groq/llama3-70b-8192", ] OutputType = TypeVar("OutputType", bound=object) +client = OpenAI() class EnvResponse(BaseModel): @@ -295,20 +303,20 @@ def obtain_chain( input_variables: list[str], temperature: float = 0.7, max_retries: int = 6, -) -> LLMChain: +) -> RunnableSerializable[dict[Any, Any], BaseMessage]: """ Using langchain to sample profiles for participants """ + human_message_prompt = HumanMessagePromptTemplate( + prompt=PromptTemplate( + template=template, + input_variables=input_variables, + ) + ) + chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) model_name = _return_fixed_model_version(model_name) - if "together_ai" in model_name: + if model_name.startswith("together_ai"): model_name = "/".join(model_name.split("/")[1:]) - human_message_prompt = HumanMessagePromptTemplate( - prompt=PromptTemplate( - template=template, - input_variables=input_variables, - ) - ) - chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) chat_openai = ChatOpenAI( model_name=model_name, temperature=temperature, @@ -316,17 +324,10 @@ def obtain_chain( openai_api_base="https://api.together.xyz/v1", openai_api_key=os.environ.get("TOGETHER_API_KEY"), ) - chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + chain = chat_prompt_template | chat_openai return chain - elif "groq" in model_name: + elif model_name.startswith("groq"): model_name = "/".join(model_name.split("/")[1:]) - human_message_prompt = HumanMessagePromptTemplate( - prompt=PromptTemplate( - template=template, - input_variables=input_variables, - ) - ) - chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) chat_openai = ChatOpenAI( model_name=model_name, temperature=temperature, @@ -334,19 +335,53 @@ def obtain_chain( openai_api_base="https://api.groq.com/openai/v1", openai_api_key=os.environ.get("GROQ_API_KEY"), ) - chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + chain = chat_prompt_template | chat_openai return chain - else: + elif model_name.startswith("azure"): + # azure/resource_name/deployment_name/version + azure_credentials = model_name.split("/")[1:] + resource_name, deployment_name, azure_version = ( + azure_credentials[0], + azure_credentials[1], + azure_credentials[2], + ) + chat_azure_openai = AzureChatOpenAI( + azure_deployment=deployment_name, + openai_api_version=azure_version, + azure_endpoint=f"https://{resource_name}.openai.azure.com", + temperature=temperature, + max_retries=max_retries, + ) + chain = chat_prompt_template | chat_azure_openai + return chain + elif model_name.startswith("custom"): + custom_model_name, model_base_url = ( + model_name.split("@")[0], + model_name.split("@")[1], + ) + custom_model_name = "/".join(custom_model_name.split("/")[1:]) chat = ChatOpenAI( - model=model_name, + model=custom_model_name, temperature=temperature, max_retries=max_retries, + api_key=os.environ.get("CUSTOM_API_KEY") + if os.environ.get("CUSTOM_API_KEY") + else "EMPTY", + base_url=model_base_url, ) human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate(template=template, input_variables=input_variables) ) chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) - chain = LLMChain(llm=chat, prompt=chat_prompt_template) + chain = chat_prompt_template | chat + return chain + else: + chat = ChatOpenAI( + model=model_name, + temperature=temperature, + max_retries=max_retries, + ) + chain = chat_prompt_template | chat return chain @@ -356,7 +391,7 @@ def format_bad_output_for_script( format_instructions: str, agents: list[str], model_name: str = "gpt-3.5-turbo", -) -> str: +) -> BaseMessage: template = """ Given the string that can not be parsed by a parser, reformat it to a string that can be parsed by the parser which uses the following format instructions. Do not add or delete any information. Small tip: for every round of conversation, first determine the name and the case, and whether this line contains errors. Correct it if necessary. @@ -380,17 +415,17 @@ def format_bad_output_for_script( "format_instructions": format_instructions, "agents": agents, } - reformat = chain.predict([logging_handler], **input_values) + reformat = chain.invoke(input_values, config={"callbacks": [logging_handler]}) log.info(f"Reformated output: {reformat}") return reformat @beartype def format_bad_output( - ill_formed_output: str, + ill_formed_output: BaseMessage, format_instructions: str, model_name: str = "gpt-3.5-turbo", -) -> str: +) -> BaseMessage: template = """ Given the string that can not be parsed by json parser, reformat it to a string that can be parsed by json parser. Original string: {ill_formed_output} @@ -405,10 +440,10 @@ def format_bad_output( input_variables=re.findall(r"{(.*?)}", template), ) input_values = { - "ill_formed_output": ill_formed_output, + "ill_formed_output": ill_formed_output.content, "format_instructions": format_instructions, } - reformat = chain.predict([logging_handler], **input_values) + reformat = chain.invoke(input_values, config={"callbacks": [logging_handler]}) log.info(f"Reformated output: {reformat}") return reformat @@ -421,8 +456,11 @@ async def agenerate( input_values: dict[str, str], output_parser: BaseOutputParser[OutputType], temperature: float = 0.7, + structured_output: bool = False, ) -> OutputType: - input_variables = re.findall(r"{(.*?)}", template) + input_variables = re.findall( + r"(? tuple[EnvironmentProfile, str]: - """ - Using langchain to generate the background - """ - return await agenerate( - model_name=model_name, - template="""Please generate scenarios and goals based on the examples below as well as the inspirational prompt, when creating the goals, try to find one point that both sides may not agree upon initially and need to collaboratively resolve it. - Examples: - {examples} - Inspirational prompt: {inspiration_prompt} - Please use the following format: - {format_instructions} - """, - input_values=dict( - inspiration_prompt=inspiration_prompt, - examples=examples, - ), - output_parser=PydanticOutputParser(pydantic_object=EnvironmentProfile), - ) - - @gin.configurable @beartype async def agenerate_action( diff --git a/sotopia/generation_utils/langchain_callback_handler.py b/sotopia/generation_utils/langchain_callback_handler.py index bf11e69a1..eb6669168 100644 --- a/sotopia/generation_utils/langchain_callback_handler.py +++ b/sotopia/generation_utils/langchain_callback_handler.py @@ -1,6 +1,6 @@ import logging from typing import Any - +from langchain_core.messages import BaseMessage from langchain.callbacks import StdOutCallbackHandler logging.addLevelName(15, "LangChain") @@ -17,6 +17,15 @@ def __init__(self, name: str) -> None: self.logger = logging.getLogger(name) self.prompt = "" + def on_chat_model_start( + self, + serialized: dict[str, Any], + messages: list[list[BaseMessage]], + **kwargs: Any, + ) -> None: + self.prompt = str(messages[0][0].content) + logging.log(15, f"Prompt after formatting:{self.prompt}") + def on_chain_start(self, *args: Any, **kwargs: Any) -> None: pass @@ -48,9 +57,7 @@ def on_text( ) -> None: """Run when agent ends.""" # leave only prompt for environment - text = text.replace("\x1b[32;1m\x1b[1;3mHuman: ", "") - logging.log(15, f"LLM Call: {text}") - self.prompt = text + pass def retrive_prompt(self) -> str: return self.prompt diff --git a/sotopia/samplers/constraint_based_sampler.py b/sotopia/samplers/constraint_based_sampler.py index 48eaea74d..624520dcd 100644 --- a/sotopia/samplers/constraint_based_sampler.py +++ b/sotopia/samplers/constraint_based_sampler.py @@ -86,6 +86,12 @@ def sample( env_profiles: list[EnvironmentProfile] = [] agents_which_fit_scenario: list[list[str]] = [] + if self.env_candidates is None: + self.env_candidates = EnvironmentProfile.all() + + if self.agent_candidates is None: + self.agent_candidates = AgentProfile.all() + agent_candidate_ids: set[str] | None = None if self.agent_candidates: agent_candidate_ids = set( @@ -120,13 +126,9 @@ def sample( ) else: for _ in range(size): - if self.env_candidates: - env_profile = random.choice(self.env_candidates) - if isinstance(env_profile, str): - env_profile = EnvironmentProfile.get(env_profile) - else: - env_profile_id = random.choice(list(EnvironmentProfile.all_pks())) - env_profile = EnvironmentProfile.get(env_profile_id) + env_profile = random.choice(self.env_candidates) + if isinstance(env_profile, str): + env_profile = EnvironmentProfile.get(env_profile) env_profiles.append(env_profile) env_profile_id = env_profile.pk assert env_profile_id, "Env candidate must have an id" diff --git a/sotopia/samplers/uniform_sampler.py b/sotopia/samplers/uniform_sampler.py index 9e4f36116..22c89f3c3 100644 --- a/sotopia/samplers/uniform_sampler.py +++ b/sotopia/samplers/uniform_sampler.py @@ -46,32 +46,19 @@ def sample( assert replacement, "Uniform sampling without replacement is not supported yet" + if self.env_candidates is None: + self.env_candidates = EnvironmentProfile.all() + + if self.agent_candidates is None: + self.agent_candidates = AgentProfile.all() + for _ in range(size): - if self.env_candidates: - env_profile = random.choice(self.env_candidates) - if isinstance(env_profile, str): - env_profile = EnvironmentProfile.get(env_profile) - else: - env_profile_id = random.choice(list(EnvironmentProfile.all_pks())) - env_profile = EnvironmentProfile.get(env_profile_id) + env_profile = random.choice(self.env_candidates) + if isinstance(env_profile, str): + env_profile = EnvironmentProfile.get(env_profile) env = ParallelSotopiaEnv(env_profile=env_profile, **env_params) - if self.agent_candidates: - agent_profile_candidates = self.agent_candidates - if len(agent_profile_candidates) < n_agent: - raise ValueError( - f"Number of agent candidates ({len(agent_profile_candidates)}) is less than number of agents ({n_agent})" - ) - else: - agent_profile_candidates_keys = list(AgentProfile.all_pks()) - if len(agent_profile_candidates_keys) < n_agent: - raise ValueError( - f"Number of agent profile candidates ({len(agent_profile_candidates_keys)}) in database is less than number of agents ({n_agent})" - ) - agent_profile_candidates = [ - AgentProfile.get(pk=pk) for pk in agent_profile_candidates_keys - ] - + agent_profile_candidates = self.agent_candidates if len(agent_profile_candidates) == n_agent: agent_profiles_maybe_id = agent_profile_candidates else: diff --git a/sotopia/server.py b/sotopia/server.py index 732cf16a2..2f3fb5988 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -18,8 +18,10 @@ from sotopia.database import EpisodeLog from sotopia.envs import ParallelSotopiaEnv from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + SotopiaDimensions, unweighted_aggregate_evaluate, ) from sotopia.generation_utils.generate import LLM_Name, agenerate_script @@ -268,7 +270,10 @@ def get_agent_class( RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), ], "terminal_evaluators": [ - ReachGoalLLMEvaluator(model_dict["env"]), + ReachGoalLLMEvaluator( + model_dict["env"], + EvaluationForTwoAgents[SotopiaDimensions], + ), ], } agents_model_dict = { @@ -339,7 +344,10 @@ async def arun_one_script( env_message = [("Environment", script_background)] agent_messages = env_message + agent_messages - evaluator = ReachGoalLLMEvaluator(model_name="gpt-4") + evaluator = ReachGoalLLMEvaluator( + model_name="gpt-4", + response_format_class=EvaluationForTwoAgents[SotopiaDimensions], + ) response = unweighted_aggregate_evaluate( list( itertools.chain( @@ -407,19 +415,22 @@ async def aevaluate_one_episode( history = episode.rewards_prompt.replace("Prompt after formatting:", "").split( ",\nBased on previous interactions" )[0] - evaluator = ReachGoalLLMEvaluator(model_name=model, response_format="basic") + evaluator = ReachGoalLLMEvaluator( + model_name=model, + response_format_class=EvaluationForTwoAgents[SotopiaDimensions], + ) response = unweighted_aggregate_evaluate( list( itertools.chain( *await asyncio.gather( *[ - sing_evaluator.__acall__( + single_evaluator.__acall__( turn_number=-1, history=history, messages=None, temperature=0.0, ) - for sing_evaluator in [evaluator] + for single_evaluator in [evaluator] ] ) ) diff --git a/sotopia_conf/run_async_server_in_batch.gin b/sotopia_conf/run_async_server_in_batch.gin index 2d0f88026..544d0679a 100644 --- a/sotopia_conf/run_async_server_in_batch.gin +++ b/sotopia_conf/run_async_server_in_batch.gin @@ -2,12 +2,13 @@ from __gin__ import dynamic_registration import __main__ as main_script BATCH_SIZE=10 +ENV_MODEL="gpt-4" AGENT1_MODEL="gpt-3.5-turbo" AGENT2_MODEL="gpt-3.5-turbo" VERBOSE=False TAG_TO_CHECK_EXISTING_EPISODES=None -MODEL_NAMES={"env": "gpt-4", "agent1": %AGENT1_MODEL, "agent2": %AGENT2_MODEL} +MODEL_NAMES={"env": %ENV_MODEL, "agent1": %AGENT1_MODEL, "agent2": %AGENT2_MODEL} ENV_IDS=%gin.REQUIRED diff --git a/stubs/redis_om/__init__.pyi b/stubs/redis_om/__init__.pyi index 230b0e48c..038ca7ac4 100644 --- a/stubs/redis_om/__init__.pyi +++ b/stubs/redis_om/__init__.pyi @@ -27,6 +27,8 @@ class JsonModel(RedisModel, abc.ABC): @classmethod def all_pks(cls) -> Generator[str, None, None]: ... @classmethod + def all(cls: type[InheritedJsonModel]) -> list[InheritedJsonModel]: ... + @classmethod def find(cls, *args: Any, **kwargs: Any) -> FindQuery: ... def save(self) -> None: ... diff --git a/tests/cli/test_benchmark.py b/tests/cli/test_benchmark.py new file mode 100644 index 000000000..b913dc7c2 --- /dev/null +++ b/tests/cli/test_benchmark.py @@ -0,0 +1,343 @@ +from sotopia.cli.benchmark.benchmark import get_avg_reward +from sotopia.database import EpisodeLog, AgentProfile, EnvironmentProfile +import numpy as np +import json + +from unittest.mock import patch + +from sotopia.cli.benchmark.benchmark import ( + benchmark, + benchmark_display, + run_async_benchmark_in_batch, +) +from unittest import mock +from unittest.mock import create_autospec +from sotopia.cli.benchmark.benchmark import initialize_benchmark_combo +from sotopia.database import EnvAgentComboStorage +import pytest + +from sotopia.envs.parallel import ParallelSotopiaEnv +from sotopia.messages import AgentAction, Observation +from sotopia.samplers import ( + EnvAgentCombo, +) +from sotopia.envs.evaluators import ( + EvaluationForTwoAgents, + ReachGoalLLMEvaluator, + RuleBasedTerminatedEvaluator, + SotopiaDimensions, +) +from sotopia.agents import LLMAgent + +dimensions = [ + "believability", + "relationship", + "knowledge", + "secret", + "social_rules", + "financial_and_material_benefits", + "goal", +] +model_pairs = [ + ["eval_model", "test_model", "not_test_model"], + ["eval_model", "not_test_model", "test_model"], +] +model_name = "test_model" + + +def get_mock_episodes() -> list[EpisodeLog]: + # all_episodes = EpisodeLog.find().all() + environment = EnvironmentProfile.find().all()[0] + agent = AgentProfile.find().all()[0] + + agent1_rewards = [float(i) for i, _ in enumerate(dimensions)] + agent2_rewards = [14.0 - reward for reward in agent1_rewards] + # agent2_rewards: reversed(agent1_rewards) + base_agent1_rewards = ( + sum(agent1_rewards) / len(agent1_rewards), + {dim: reward for dim, reward in zip(dimensions, agent1_rewards)}, + ) + base_agent2_rewards = ( + sum(agent2_rewards) / len(agent2_rewards), + {dim: reward for dim, reward in zip(dimensions, agent2_rewards)}, + ) + TEST_EPISODE_NUM = 5 + + target_rewards = [ + [base_agent1_rewards, base_agent2_rewards], + [base_agent2_rewards, base_agent1_rewards], + ] + target_episodes = [] + + for agent_pos in range(2): + for reward_pos in range(2): + for _ in range(TEST_EPISODE_NUM): + episode = EpisodeLog( + environment=environment.pk, + agents=[agent.pk, agent.pk], + tag="test", + models=model_pairs[agent_pos], + messages=[], + reasoning="", + rewards=target_rewards[reward_pos], + rewards_prompt="", + ) + target_episodes.append(episode) + return target_episodes + + +def get_mock_env_agents_profile() -> tuple[EnvironmentProfile, list[AgentProfile]]: + env_profile = EnvironmentProfile( + codename="test", + source="test", + scenario="Two people are talking", + agent_goals=[ + "You have 500 dollars and you want to buy the phone", + "You have a complete new iPhone 16 from Apple Store for $600 and you want to sell it", + ], + ) + agent_1 = AgentProfile( + first_name="John", + last_name="Doe", + age=25, + ) + agent_2 = AgentProfile( + first_name="Jane", + last_name="Doe", + age=25, + ) + + return env_profile, [agent_1, agent_2] + + +def mock_success_data() -> list[dict[str, str | list[str]]]: + data = get_mock_episodes()[:2] + return [ + { + "env_id": item.environment, + "agent_ids": item.agents, + } + for item in data + ] + + +def mock_success() -> mock.Mock: + mock_response = mock.Mock() + mock_response.status_code = 200 + mock_response.json.return_value = mock_success_data() + return mock_response + + +def mock_fail() -> mock.Mock: + mock_response = mock.Mock() + mock_response.status_code = 404 + return mock_response + + +def compose_env_agent_combo( + env_profile: EnvironmentProfile, agent_profiles: list[AgentProfile] +) -> EnvAgentCombo[Observation, AgentAction]: + env = ParallelSotopiaEnv( + env_profile=env_profile, + model_name="gpt-4o-mini", + evaluators=[RuleBasedTerminatedEvaluator(max_turn_number=1, max_stale_turn=2)], + terminal_evaluators=[ + ReachGoalLLMEvaluator( + "gpt-4o-mini", + EvaluationForTwoAgents[SotopiaDimensions], + ) + ], + ) + agents = [ + LLMAgent(agent_profile=agent_profile, model_name=agent_model) + for agent_profile, agent_model in zip( + agent_profiles, ["gpt-4o-mini", "gpt-4o-mini"] + ) + ] + + return (env, agents) + + +def test_get_rewards() -> None: + target_episodes = get_mock_episodes() + + test_rewards = get_avg_reward(target_episodes, model_name) + + # 1. check the episode count + assert test_rewards["episode_count"][0] == len( + target_episodes + ), f"Expected {len(target_episodes)}, got {test_rewards['episode_count']}" + + # 2. check average rewards + extracted_rewards = [test_rewards[dim][0] for dim in dimensions] + gt_rewards = [7.0 for _ in dimensions] + assert ( + extracted_rewards == gt_rewards + ), f"In average, expected {gt_rewards}, got {extracted_rewards} on dimensions {dimensions}" + + # 3. check the error bound + extracted_bound = [test_rewards[dim][1] for dim in dimensions] + sem = [1.6499, 1.4142, 1.1785, 0.9428, 0.7071, 0.4714, 0.2357] + # for [0, 1, 2, 3, 4, 5, 6] * 5 + [14, 13, 12, 11, 10, 9, 8] * 5 + # ppf = 2.093 for 95% confidence interval with 20 samples + gt_bound = [sem[i] * 2.093 for i in range(len(dimensions))] + assert np.allclose( + extracted_bound, gt_bound, atol=2e-2 + ), f"In error bound, expected {gt_bound}, got {extracted_bound} on dimensions {dimensions}" + + +mock_delete_function = create_autospec(lambda pk: None) + + +@patch("requests.get", side_effect=[mock_success(), mock_fail()]) +def test_initialize_benchmark_combo( + mock_requests_get: mock.Mock, +) -> None: + # Test status code 200 + resp_data = initialize_benchmark_combo( + url="http://localhost:5000" + ) # expect to get a non-empty list + assert ( + isinstance(resp_data, list) and len(resp_data) == 2 + ), f"Expected a length-2 list, but got {resp_data}" + assert ( + resp_data[0].agent_ids == get_mock_episodes()[0].agents + ), f"For agents, expected {get_mock_episodes()[0].agents}, but got {resp_data[0].agent_ids}" + + # Test status code 404 + with pytest.raises(ValueError) as excinfo: + _ = initialize_benchmark_combo( + url="http://localhost:5000" + ) # expect to get a ValueError + assert "Failed to fetch data" in str(excinfo.value) + + # Test get from database + EnvAgentComboStorage.find = mock.Mock(return_value=EnvAgentComboStorage) # type: ignore + EnvAgentComboStorage.all = mock.Mock(return_value=resp_data) # type: ignore + new_resp_data = initialize_benchmark_combo(url="") + assert ( + isinstance(new_resp_data, list) and len(new_resp_data) == 2 + ), f"Expected a length-2 list, but got {new_resp_data}" + for idx, (resp_item, new_item) in enumerate(zip(resp_data, new_resp_data)): + assert ( + resp_item.agent_ids == new_item.agent_ids + ), f"For agents in item {idx}, expected {resp_item.agent_ids}, but got {new_item.agent_ids}" + assert ( + resp_item.env_id == new_item.env_id + ), f"For env_id in item {idx}, expected {resp_item.env_id}, but got {new_item.env_id}" + + +# TODO fix the method assignment issue in mypy, ref: https://github.com/python/mypy/issues/2427 +def test_run_async_benchmark_in_batch() -> None: + return_value = get_mock_episodes()[:10] + return_value[0].rewards = [0.0, 0.0] + + env, agents = get_mock_env_agents_profile() + + with patch("sotopia.database.EpisodeLog.delete", mock_delete_function), patch( + "sotopia.database.EpisodeLog.find", return_value=EpisodeLog + ), patch("sotopia.database.EpisodeLog.all", return_value=return_value), patch( + "sotopia.database.AgentProfile.find", return_value=AgentProfile + ), patch("sotopia.database.AgentProfile.all", return_value=agents), patch( + "sotopia.database.AgentProfile.get", + return_value=lambda pk: agents[0] if pk == agents[0].pk else agents[1], + ), patch( + "sotopia.database.EnvironmentProfile.find", return_value=EnvironmentProfile + ), patch("sotopia.database.EnvironmentProfile.all", return_value=[env]), patch( + "sotopia.database.EnvironmentProfile.get", return_value=lambda pk: env + ): + assert ( + len(EpisodeLog.find().all()) == 10 + ), f"Expected 10 episodes in the database, but got {len(EpisodeLog.find().all())}" + + env_agent_combo_list = [ + compose_env_agent_combo( + env_profile=env, + agent_profiles=agents, + ) + ] + + run_async_benchmark_in_batch( + env_agent_combo_list=env_agent_combo_list, + batch_size=1, + push_to_db=False, + tag="test", + ) + + assert ( + mock_delete_function.call_count == 1 + ), f"Expected 1 call to delete, but got {mock_delete_function.call_count}" + + +mock_initialize = create_autospec(lambda: []) + + +@patch("sotopia.cli.benchmark.benchmark.initialize_benchmark_combo") +@patch("sotopia.cli.benchmark.benchmark.run_async_benchmark_in_batch") +def test_sotopia_benchmark( + mock_run_async_benchmark_in_batch: mock.Mock, + mock_initialize_benchmark_combo: mock.Mock = mock_initialize, +) -> None: + # Mainly test the benchmark workflow; Assume the benchmark_combo is correct + + with patch("sotopia.database.EpisodeLog.delete", mock_delete_function), patch( + "sotopia.database.EpisodeLog.find", return_value=EpisodeLog + ), patch("sotopia.database.EpisodeLog.all", return_value=get_mock_episodes()): + assert ( + len(EpisodeLog.find().all()) == 20 + ), f"Expected 20 episodes in the database, but got {len(EpisodeLog.find().all())}" + + # `output_to_jsonl` will be tested in the next test, `push_to_db` has been tested elsewhere, so only test `only_show_performance` + benchmark( + models=[model_name], + partner_model="not_test_model", + evaluator_model="eval_model", + url="", + only_show_performance=False, + output_to_jsonl=False, + push_to_db=False, + ) + mock_initialize_benchmark_combo.assert_called_once_with("") + + benchmark( + models=[model_name], + partner_model="not_test_model", + evaluator_model="eval_model", + url="", + only_show_performance=True, + output_to_jsonl=False, + push_to_db=False, + ) + mock_initialize_benchmark_combo.assert_called_once_with("") + + +def test_sotopia_benchmark_display() -> None: + # Mainly test the average reward calculation (similar to previous get_avg_rewards test) + + with patch("sotopia.database.EpisodeLog.delete", mock_delete_function), patch( + "sotopia.database.EpisodeLog.find", return_value=EpisodeLog + ), patch("sotopia.database.EpisodeLog.all", return_value=get_mock_episodes()): + assert ( + len(EpisodeLog.find().all()) == 20 + ), f"Expected 20 episodes in the database, but got {len(EpisodeLog.find().all())}" + displayed_stats = benchmark_display( + model_list=[model_name], + partner_model="not_test_model", + evaluator_model="eval_model", + output_to_jsonl=True, + save_dir="/tmp", + ) + + target_believability = (7.0, 3.4462887784189147) + assert np.allclose( + displayed_stats["test_model"]["believability"], + target_believability, + atol=0.02, + ), f"Got {displayed_stats['test_model']['believability']}, expected {target_believability}" + + benchmark_file = "/tmp/models_vs_not_test_model.jsonl" + recovered_data = [json.loads(line) for line in open(benchmark_file, "r")] + target_content = '{"model_name": "test_model", "SOC [-10, 0]": 7.0, "SEC [-10, 0]": 7.0, "FIN [-5, 5]": 7.0, "REL [-5, 5]": 7.0, "KNO [0, 10]": 7.0, "GOAL [0, 10]": 7.0, "BEL [0, 10]": 7.0}' + assert ( + str(recovered_data[0]).replace("'", '"') == target_content + ), f"Expected {target_content}, but got {recovered_data[0]}" diff --git a/tests/envs/test_evaluators.py b/tests/envs/test_evaluators.py index b3fc6ba25..8da9f6772 100644 --- a/tests/envs/test_evaluators.py +++ b/tests/envs/test_evaluators.py @@ -3,11 +3,13 @@ import pytest from sotopia.envs.evaluators import ( + SotopiaDimensions, + EvaluationForTwoAgents, ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, unweighted_aggregate_evaluate, ) -from sotopia.messages import AgentAction, Observation +from sotopia.messages import AgentAction, Observation, ScriptBackground, SimpleMessage def test_rule_based_teminated_evaluator() -> None: @@ -126,7 +128,9 @@ async def test_rule_based_teminated_evaluator_async() -> None: @pytest.mark.asyncio async def test_reach_goal_llm_evaluator_async() -> None: - evaluator = ReachGoalLLMEvaluator("gpt-4") + evaluator = ReachGoalLLMEvaluator( + "gpt-4", response_format_class=EvaluationForTwoAgents[SotopiaDimensions] + ) response1, response2 = await asyncio.gather( evaluator.__acall__( 1, @@ -173,3 +177,69 @@ async def test_reach_goal_llm_evaluator_async() -> None: assert isinstance(response2[8][1][0][1], int) assert isinstance(response2[9][1][0][1], int) assert response2[2][1][0][1] > response2[3][1][0][1] + + +@pytest.mark.asyncio +async def test_reach_goal_llm_evaluator_goal_only_async() -> None: + evaluator = ReachGoalLLMEvaluator( + "gpt-4", response_format_class=EvaluationForTwoAgents[SotopiaDimensions] + ) + background = ScriptBackground( + scenario="Conversation between two friends at a trivia night", + p1_name="Samuel Anderson", + p2_name="Giselle Rousseau", + p1_background="Samuel Anderson is a 29-year-old male software developer. He/him pronouns. Samuel Anderson can cook very well. Personality and values description: Samuel Anderson, though somewhat impulsive and free-spirited, values enjoyment. His decision-making is often spontaneous, staying within familiar boundaries. Samuel's secrets: He was once a competitive figure skater.", + p2_background="Giselle Rousseau is a 21-year-old nonbinary art student. They/them pronouns. Giselle Rousseau enjoys biking and photography. Personality and values description: Giselle Rousseau, open-minded and outgoing yet sensitive, advocates care and fairness. Her decision-making is intuitive and inclusive. Giselle's secrets: Sells forged paintings to wealthy clients", + p1_goal="Greet your friends and be polite", + p2_goal="Be rude and dismissive to your friends", + ) + + # response1, + response2 = await asyncio.gather( + evaluator.__acall__( + 1, + [ + ( + "Environment", + background, + ), + ( + "Environment", + SimpleMessage(message="Turn #1"), + ), + ( + "Alice", + AgentAction(action_type="speak", argument="Thank you so much!"), + ), + ( + "Environment", + SimpleMessage(message="Turn #2"), + ), + ( + "Bob", + AgentAction(action_type="speak", argument="Fuck you!"), + ), + ( + "Environment", + SimpleMessage(message="Turn #3"), + ), + ( + "Alice", + AgentAction( + action_type="speak", argument="Hope you have a great weekend." + ), + ), + ("Environment", SimpleMessage(message="Turn #4")), + ( + "Bob", + AgentAction(action_type="leave", argument="Leave"), + ), + ], + ), + ) + print("---------------------") + print("Response after 2 turns:", response2) + + assert len(response2[0][0][1][1].split()) > len( + "Samuel Anderson's goal was to greet his friends and be polite.".split() + ) diff --git a/tests/generation_utils/test_generation.py b/tests/generation_utils/test_generation.py index b0212a07d..af8ac7256 100644 --- a/tests/generation_utils/test_generation.py +++ b/tests/generation_utils/test_generation.py @@ -1,10 +1,14 @@ import pytest +from typing import Any from sotopia.generation_utils.generate import ( ListOfIntOutputParser, agenerate, ) +from sotopia.messages import AgentAction +from langchain.output_parsers import PydanticOutputParser + @pytest.mark.asyncio async def test_agenerate_list_integer() -> None: @@ -41,3 +45,36 @@ async def test_agenerate_list_integer_together() -> None: assert len(list_of_int) == length assert all(isinstance(i, int) for i in list_of_int) assert all(lower <= i <= upper for i in list_of_int) + + +@pytest.mark.asyncio +async def test_logging_behavior(caplog: Any) -> None: + # Call the function under test + caplog.set_level(15) + await agenerate( + "gpt-3.5-turbo", + "{format_instructions}", + {}, + ListOfIntOutputParser(5, (-10, 10)), + ) + # Check if any log records were captured + assert len(caplog.records) > 0, "No log records captured" + + # Optionally, you can print the captured log records for verification + for record in caplog.records: + print(f"Captured log: {record.levelname} - {record.message}") + + +@pytest.mark.asyncio +async def test_agenerate_structured_output() -> None: + """ + async version of test_generate_structured_output + """ + output = await agenerate( + "gpt-4o-2024-08-06", + "{format_instructions}", + {}, + PydanticOutputParser(pydantic_object=AgentAction), + structured_output=True, + ) + assert isinstance(output, AgentAction)