Bug In Bofire Silent Failure With Missing Curly Braces In Quadratic Terms

Introduction

In the realm of experimental design and optimization, Bofire stands as a powerful tool. However, like any sophisticated software, it's susceptible to bugs. This article delves into a specific bug encountered in Bofire, focusing on the silent failure that occurs when quadratic terms in model_type strings are not properly delimited by curly braces {}. This issue, if unnoticed, can lead to incorrect experimental designs and suboptimal results. This article aims to provide a comprehensive understanding of the bug, its implications, and potential solutions, ensuring that users can effectively leverage Bofire for their experimental endeavors.

Understanding the Bug: Quadratic Terms and Formulaic

The core of the problem lies in how Formulaic, the underlying formula parsing library used by Bofire, handles quadratic terms. Quadratic terms, such as x^2 or x*x, represent the squared effect of a variable in a model. Formulaic requires these terms to be explicitly enclosed in curly braces {} for correct interpretation. When these braces are omitted, Formulaic silently drops the quadratic terms, leading to a model that doesn't accurately capture the relationships between variables. This silent failure is particularly insidious because it doesn't raise an error or warning, making it difficult to detect.

The Silent Failure

The silent failure occurs because Formulaic doesn't throw an error when it encounters quadratic terms without curly braces. Instead, it simply ignores them. This means that the model generated by Bofire will not include the quadratic effects, leading to an underestimation of the true complexity of the system being modeled. This can have significant consequences for experimental design, as the number of experiments required and the optimal experimental conditions may be miscalculated. The user might unknowingly proceed with an incomplete model, leading to potentially flawed conclusions and missed opportunities for optimization. Therefore, understanding this behavior is crucial for accurate and reliable experimental design using Bofire.

Demonstrating the Behavior

To illustrate this behavior, consider the following examples using the get_formula_from_string function in Bofire:

import os

from bofire.data_models.api import Domain, Inputs, Outputs
from bofire.data_models.features.api import (
    ContinuousInput,
    ContinuousOutput,
)
from bofire.strategies.doe.utils import get_formula_from_string

models = {
    "linear-and-quadratic": "linear-and-quadratic",
    "model_1": "1 + x1 + x2 + x3 + x1 ** 2 + x2 ** 2 + x3 ** 2",
    "model_1a": "1 + x1 + x2 + x3 + {x1 ** 2} + {x2 ** 2} + {x3 ** 2}",
    "model_2": "1 + x1 + x2 + x3 + x1*x1 + x2*x2 + x3*x3",
    "model_2a": "1 + x1 + x2 + x3 + {x1*x1} + {x2*x2} + {x3*x3}",
    "model_3": "1 + x1 + x2 + x3 + x1 * x1 + x2 * x2 + x3 * x3",
    "model_3a": "1 + x1 + x2 + x3 + {x1 * x1} + {x2 * x2} + {x3 * x3}",
}

for name, expr in models.items():
    formula = get_formula_from_string(model_type=expr, inputs=inputs)
    print(
        f"{name}: {expr}.\n\tAs a Formula: {formula}.\n\tExpected experiments: {len(formula) + 3}"
    )

In this code snippet, several model strings are defined, some with quadratic terms enclosed in curly braces and others without. The output clearly shows that only the models with curly braces around the quadratic terms are correctly parsed by Formulaic. The models without braces have their quadratic terms dropped, resulting in a simpler formula and a lower expected number of experiments. This discrepancy highlights the importance of using curly braces to ensure that quadratic terms are properly included in the model.

Impact on get_required_number_of_experiments

This issue directly affects the get_required_number_of_experiments function within Bofire's DoE strategy. This function relies on the parsed formula to determine the number of experiments needed for a comprehensive design. When quadratic terms are dropped, the function underestimates the model complexity, leading to a potentially insufficient number of experiments. This can result in a poorly characterized response surface and inaccurate optimization. The ramifications extend to the overall efficiency and effectiveness of the experimental process, emphasizing the need for vigilance in model specification.

Root Cause Analysis

The root cause of this bug can be traced to the parsing logic within Formulaic and how Bofire interfaces with it. While Formulaic's syntax requires curly braces for quadratic terms, Bofire doesn't currently enforce this requirement or provide explicit guidance to users. This disconnect between the expected syntax and the actual implementation creates an opportunity for errors. Users, unaware of this nuance, may inadvertently write model strings that are not correctly parsed, leading to the silent failure. Therefore, a robust solution requires addressing both the parsing behavior and the user experience aspects of model specification.

Exploring the Code

The issue manifests within the get_formula_from_string function in bofire/strategies/doe/utils.py. This function is responsible for converting the model string into a Formulaic formula. The relevant code snippet shows how the model string is processed and passed to Formulaic:

def get_formula_from_string(
    model_type: str,
    inputs: Inputs,
    input_feature_keys: Optional[List[str]] = None,
) -> Formula:
    ...
    if model_type == "linear-and-quadratic":
        formula = Formula(
            "1 + "
            + " + ".join(input_feature_keys)
            + " + "
            + " + ".join([f"{key}**2" for key in input_feature_keys])
        )
    else:
        formula = Formula(model_type)
    ...

In the case of linear-and-quadratic, the code constructs the formula string with explicit **2 for quadratic terms, but without the necessary curly braces. For other model types, it directly passes the model_type string to Formulaic, which then silently drops any improperly formatted quadratic terms. This highlights the need for a more consistent and robust approach to handling quadratic terms in model specifications.

Delving Deeper into bofire/strategies/doe/utils.py

The rabbit hole goes deeper, specifically within the function responsible for converting model strings into Formulaic formulas. Examining the code, we find that the logic for handling quadratic terms is inconsistent. In certain predefined model types, the code explicitly constructs the formula string using **2 for quadratic terms without the necessary curly braces. For other model types, the raw model string is passed directly to Formulaic, which then silently discards any improperly formatted quadratic terms. This inconsistency is a major contributor to the bug, as it creates ambiguity and increases the likelihood of user error. The inconsistency can be observed around line 62 and 141 of bofire/strategies/doe/utils.py.

Proposed Solutions

To address this bug effectively, a multi-pronged approach is recommended:

1. Regex-based Correction or Erroring: Implement a regular expression (regex) check within get_formula_from_string to identify quadratic terms not enclosed in curly braces. The regex should either automatically add the braces or raise an error, prompting the user to correct the model string. This proactive approach prevents silent failures and ensures that quadratic terms are correctly parsed. The regex should handle variations in spacing and notation, such as x**2, x*x, and x * x. Ideally, it would standardize these notations to a consistent format (e.g., {x**2}).
2. Standardization of Quadratic Term Notation: Enforce a consistent notation for quadratic terms throughout Bofire. The preferred notation should be {x**2}, {x*x}, or {x * x}. Update the linear-and-quadratic model type in get_formula_from_string to use this notation. This consistency reduces ambiguity and simplifies the parsing process. It also makes the code more maintainable and easier to understand.
3. Improved Documentation and Examples: Enhance Bofire's documentation and examples to clearly illustrate the correct syntax for specifying quadratic terms. Provide examples of both valid and invalid model strings, highlighting the importance of curly braces. This empowers users to write correct model specifications from the outset, preventing common errors.
4. User-Friendly Error Messages: If a model string contains improperly formatted quadratic terms, provide a clear and informative error message that guides the user to the correct syntax. The error message should explicitly mention the need for curly braces and provide examples of correct notation. This helps users quickly identify and fix errors, improving the overall user experience.
5. Testing and Validation: Add comprehensive unit tests to specifically test the handling of quadratic terms in get_formula_from_string. These tests should cover various scenarios, including different notations, spacing variations, and edge cases. This ensures that the fix is robust and prevents regressions in future releases.

Implementation Details

Regex Implementation

A possible regex implementation could involve the following steps:

1. Identify Quadratic Terms: Use a regex pattern like r'(\w+)\s*\*\s*\1|(\w+)\s*\*\*\s*2' to identify quadratic terms in the model string. This pattern covers both x*x and x**2 notations, allowing for whitespace variations.
2. Add Curly Braces or Raise Error: If a match is found, either automatically add curly braces around the term or raise an error with a descriptive message. The choice between automatic correction and erroring depends on the desired level of user control and the potential for unintended consequences.

Documentation and Examples

The documentation should be updated to include a dedicated section on model specification, with clear examples of how to include quadratic terms. The examples should cover various scenarios, such as linear, quadratic, and interaction terms. The documentation should also emphasize the importance of using curly braces for quadratic terms and provide a rationale for this requirement.

Conclusion

The silent failure for model_type strings with improperly formatted quadratic terms is a significant bug in Bofire that can lead to incorrect experimental designs. By understanding the root cause and implementing the proposed solutions, this issue can be effectively addressed. The recommendations presented encompass a mix of code-level fixes, documentation enhancements, and user experience improvements. By addressing the issue holistically, we can enhance the robustness and usability of Bofire, ensuring it remains a reliable tool for experimental design and optimization. Addressing this bug will not only improve the accuracy of experimental designs but also enhance the user experience, making Bofire a more robust and user-friendly tool for researchers and practitioners in the field of experimental design.

This comprehensive approach, encompassing code modifications, improved documentation, and user-friendly error messages, will significantly enhance the reliability and usability of Bofire. By proactively addressing this issue, we contribute to a more robust and user-friendly software ecosystem for experimental design and optimization.

BoFire Version and Environment

This bug was identified in BoFire version 0.2.1, running on Python 3.10.12 within a WSL environment (Ubuntu-22.04). This information is crucial for reproducibility and helps users identify if they are affected by the same issue.