Covariate Std Err with baselines #245
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- Implemented Task and Agent classes to simulate agent performance on tasks. - Added methods for calculating task success rates and overall success rates. - Included functions for sampling rewards from a Bernoulli distribution based on task success rates. - Created plotting functions for visualizing task difficulty and Gaussian distributions. - Introduced a utility function to augment matrices with averages for analysis.
* add llama4-support * llama4 maverick L1 * llama4 maverick L2 --------- Co-authored-by: agentlabtraces <[email protected]>
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Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Status |
|---|---|---|
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Misspelled Parameter Name ▹ view | |
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Missing Type Hint and Unclear Default ▹ view | |
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Missing Gaussian Parameter Validation ▹ view | |
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Excessive Memory Usage in Cross-validation ▹ view | |
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Missing Promised Return Value ▹ view | |
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Missing Type Hints in Function Signature ▹ view | |
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Complex Formula Without Clarity ▹ view | |
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Incorrect return documentation ▹ view | |
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Duplicate GLM Implementation Logic ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| src/agentlab/agents/generic_agent/init.py | ✅ |
| src/agentlab/analyze/covariate_toy_experiment/mock_data.py | ✅ |
| src/agentlab/llm/llm_configs.py | ✅ |
| src/agentlab/agents/generic_agent/agent_configs.py | ✅ |
| src/agentlab/analyze/covariate_std_err.py | ✅ |
| src/agentlab/analyze/agent_xray.py | ✅ |
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| competence: float, | ||
| benchmark: list[Task], | ||
| type: int = None, | ||
| consistancy: float = 10, |
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Misspelled Parameter Name 
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What is the issue?
The parameter 'consistancy' in Agent.init() is misspelled. It should be 'consistency'.
Why this matters
Misspelled variable names can cause confusion and make code harder to understand and maintain.
Suggested change ∙ Feature Preview
consistency: float = 10,Provide feedback to improve future suggestions
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| def agent_on_benchmark( | ||
| agent: Agent, | ||
| benchmark: list[Task], | ||
| n_samples_per_task=None, |
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Missing Type Hint and Unclear Default
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What is the issue?
The parameter n_samples_per_task lacks a type hint and has None as default without explanation of what that means.
Why this matters
Missing type hints and unexplained None defaults make it unclear what values are acceptable for this parameter.
Suggested change ∙ Feature Preview
def agent_on_benchmark(
agent: Agent,
benchmark: list[Task],
n_samples_per_task: int | None = 1, # Number of samples to generate per taskProvide feedback to improve future suggestions
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| plt.title("Distribution of Task Difficulty") | ||
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| def plot_gaussian(mu, sigma, label=None): |
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Missing Gaussian Parameter Validation 
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What is the issue?
The function doesn't validate that sigma is positive, which is required for a valid Gaussian distribution.
Why this matters
Negative or zero sigma values would result in invalid probability distributions or division by zero errors.
Suggested change ∙ Feature Preview
Add parameter validation:
def plot_gaussian(mu, sigma, label=None):
"""
Plot a Gaussian distribution with mean mu and standard deviation sigma.
"""
if sigma <= 0:
raise ValueError("sigma must be positive")
x = np.linspace(0, 1, 1000)
plt.plot(
x, 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((x - mu) / sigma) ** 2), label=label
)Provide feedback to improve future suggestions
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| def std_err_diff_baselines(rewards, baselines): | ||
| """ | ||
| Find the best baseline and compute the adjusted mean and SE. | ||
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| Parameters: | ||
| - rewards: array-like of shape (n,) | ||
| Observed rewards (may contain NaN). | ||
| - baselines: array-like of shape (n, k) | ||
| k baseline estimates per sample (may contain NaN). | ||
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| Returns: | ||
| - adjusted_reward_mean: float | ||
| Mean of valid rewards. | ||
| - adjusted_se: float∫ | ||
| SE of the adjusted mean (differences) using the selected baseline. | ||
| - selected_baseline: np.ndarray of shape (n,) | ||
| The values of the chosen baseline with NaNs filled. | ||
| """ | ||
| rewards = np.asarray(rewards, dtype=float) | ||
| baselines = _replace_nans_by_average(baselines) | ||
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| if rewards.shape[0] != baselines.shape[0]: | ||
| raise ValueError("rewards and baselines must have the same length.") | ||
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| # Identify valid reward samples | ||
| valid = ~np.isnan(rewards) | ||
| reward_valid = rewards[valid] | ||
| if reward_valid.size == 0: | ||
| return np.nan, np.nan | ||
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| selected_baseline_valid = _select_best_baseline(reward_valid, baselines[valid]) | ||
| diffs = reward_valid - selected_baseline_valid | ||
| adjusted_se = np.std(diffs, ddof=1) / np.sqrt(diffs.size) | ||
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| # Adjusted mean reward is the raw mean of valid rewards | ||
| adjusted_reward_mean = reward_valid.mean() | ||
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| return adjusted_reward_mean, adjusted_se |
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Missing Promised Return Value
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What is the issue?
The function's docstring promises to return a 'selected_baseline' value, but the function only returns two values (adjusted_reward_mean and adjusted_se).
Why this matters
Callers expecting three return values will receive a ValueError or unpacking error when using this function.
Suggested change ∙ Feature Preview
Either update the docstring to reflect the actual return values or modify the function to return the selected baseline as documented:
def std_err_diff_baselines(rewards, baselines):
...
return adjusted_reward_mean, adjusted_se, selected_baseline_validProvide feedback to improve future suggestions
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| def std_err_glm_cv_regularized( | ||
| rewards, baselines, lambda_grid=None, n_splits=5, n_boot=200, random_state=None | ||
| ): |
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Missing Type Hints in Function Signature
Tell me more
What is the issue?
Function uses generic names 'rewards' and 'baselines' without type hints, making it unclear what data types and value ranges are expected.
Why this matters
Without proper type hints, developers need to read the docstring or implementation to understand valid inputs, which slows down code comprehension and can lead to runtime errors.
Suggested change ∙ Feature Preview
def std_err_glm_cv_regularized(
rewards: np.ndarray, # binary values (0 or 1)
baselines: np.ndarray, # shape (n, k) baseline estimates
lambda_grid: Optional[np.ndarray] = None,
n_splits: int = 5,
n_boot: int = 200,
random_state: Optional[int] = None
) -> Tuple[float, float]:Provide feedback to improve future suggestions
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The |
Description by Korbit AI
What change is being made?
Add new functionality for covariate standard error analysis with baselines in
covariate_std_err.py, introduce new agent configurations and LLM settings, update environment variable documentation, adjust supported Python version, and extend the reproducibility journal. Also, include mock data for toy experiments undercovariate_toy_experiment.Why are these changes being made?
These changes enhance the analysis toolkit by providing methods for evaluating covariate effects on model performance. Incorporating new agents and LLM configurations improves adaptability for different LLM scenarios. Updating environment documentation provides clarity for users on configuration settings. Raising the required Python version aligns with newer dependencies, ensuring compatibility.