issue (complexity): Consider simplifying the code structure and exploring alternative approaches to the neural network implementation.
While the implementation of a neural network for this curve-fitting problem is thorough, it introduces significant complexity. Consider the following suggestions to improve maintainability and potentially simplify the approach:
Simplify the code structure:
Break down large functions like generate_simulated_data into smaller, focused functions.
Use type hints consistently to improve readability and catch potential errors.
def generate_mode_indices(total_number: int, num_modes: int, max_index: int) -> NDArray[np.int64]:
mode_indices = np.random.randint(0, max_index - 1, [total_number, num_modes])
return np.sort(mode_indices, axis=1)
def generate_gsds(total_number: int, num_modes: int, lower_bound: float, upper_bound: float) -> NDArray[np.float64]:
gsds = np.random.uniform(lower_bound, upper_bound, [total_number, num_modes])
return np.sort(gsds, axis=1)
Consider alternative approaches:
Evaluate if a simpler statistical method or curve-fitting algorithm could achieve similar results. For example, you might explore using scipy's curve_fit function with a custom multi-modal lognormal function.
from scipy.optimize import curve_fit
def bimodal_lognormal(x, *params):
Implement bimodal lognormal function
pass
popt, _ = curve_fit(bimodal_lognormal, x_data, y_data, p0=initial_guess)
If ML is necessary, simplify the model:
Consider using a simpler model architecture or a different ML approach like Random Forests, which might be easier to interpret and maintain.
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
Improve documentation:
Add more context about why this complex ML approach is needed.
Document the expected input and output formats clearly.
def lognormal_2mode_ml_guess(
logspace_x: NDArray[np.float64],
concentration_pdf: NDArray[np.float64]
) -> Tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]]:
"""
Predict lognormal distribution parameters using a pre-trained ML model.
This complex approach is necessary due to [explain reasons here].
Args:
logspace_x: Array of particle sizes in log space.
concentration_pdf: Probability density function of particle concentrations.
Returns:
Tuple containing:
- Predicted mode values
- Predicted geometric standard deviations
- Predicted number of particles
"""
# Implementation...
These changes would make the code more maintainable and easier to understand while preserving the ML-based approach if it's truly necessary.
Gorkowski
commented
1 month ago
issue (complexity): Consider simplifying the code structure and exploring alternative approaches to the neural network implementation.
While the implementation of a neural network for this curve-fitting problem is thorough, it introduces significant complexity. Consider the following suggestions to improve maintainability and potentially simplify the approach:
Simplify the code structure: Break down large functions like generate_simulated_data into smaller, focused functions. Use type hints consistently to improve readability and catch potential errors. def generate_mode_indices(total_number: int, num_modes: int, max_index: int) -> NDArray[np.int64]: mode_indices = np.random.randint(0, max_index - 1, [total_number, num_modes]) return np.sort(mode_indices, axis=1)
def generate_gsds(total_number: int, num_modes: int, lower_bound: float, upper_bound: float) -> NDArray[np.float64]: gsds = np.random.uniform(lower_bound, upper_bound, [total_number, num_modes]) return np.sort(gsds, axis=1) Consider alternative approaches: Evaluate if a simpler statistical method or curve-fitting algorithm could achieve similar results. For example, you might explore using scipy's curve_fit function with a custom multi-modal lognormal function. from scipy.optimize import curve_fit
def bimodal_lognormal(x, *params):
Implement bimodal lognormal function
popt, _ = curve_fit(bimodal_lognormal, x_data, y_data, p0=initial_guess) If ML is necessary, simplify the model: Consider using a simpler model architecture or a different ML approach like Random Forests, which might be easier to interpret and maintain. from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) Improve documentation: Add more context about why this complex ML approach is needed. Document the expected input and output formats clearly. def lognormal_2mode_ml_guess( logspace_x: NDArray[np.float64], concentration_pdf: NDArray[np.float64] ) -> Tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]]: """ Predict lognormal distribution parameters using a pre-trained ML model.
These changes would make the code more maintainable and easier to understand while preserving the ML-based approach if it's truly necessary.