Troubleshooting Guide

# Troubleshooting Guide

This guide provides solutions to common errors and issues you might encounter while using the Ensemble Genetic Algorithm project.

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## Environment and Setup Issues

#### Problem: ModuleNotFoundError: No module named ‘…’

This is the most common issue and usually means the project’s virtual environment is not active or was not set up correctly.

Solutions: 1. Activate the Environment: Make sure you have activated the correct virtual environment before running any scripts.

`bash source ga_env/bin/activate  # If you used setup.sh # OR source .venv/bin/activate   # If you installed manually `

  1. Re-install Dependencies: If the error persists, your installation might be incomplete. Re-install the dependencies. `bash pip install . `

#### Problem: GPU is not being used by PyTorch models.

Solutions: 1. Check Installation: Ensure you installed the GPU-enabled version of PyTorch. The easiest way is to re-run the setup script with the –gpu flag: ./setup.sh –gpu –force. 2. Verify CUDA: From your terminal, run nvidia-smi. This command should list your NVIDIA GPU. If it doesn’t, you may have an issue with your NVIDIA drivers. 3. Check Environment Variables: Make sure the CUDA_VISIBLE_DEVICES environment variable is not set to -1, as this explicitly disables GPU access.

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## Data-Related Errors

#### Problem: The experiment fails immediately with a KeyError or ValueError related to a column name.

Solutions: 1. Check Outcome Variable Name: This is a strict requirement. The column name for your target variable must end with the suffix _outcome_var_1. Please review the Data Preparation Guide. 2. Check for Non-Numeric Data: The framework expects all columns to be numeric. Ensure you have preprocessed your data to remove or encode any string or categorical columns.

#### Problem: A specific base learner fails with ValueError: Input contains NaN.

This happens because some models (like scikit-learn’s LogisticRegression or SVC) cannot natively handle missing values, while others (like XGBoost) can.

Solutions: 1. Perform Imputation: Preprocess your dataset to impute (fill in) missing values before running the experiment. 2. Adjust `percent_missing`: In your config.yml, lower the percent_missing threshold under grid_params (e.g., from 99.8 to 90.0) to be more aggressive about removing columns that have any missing data. 3. Prune `model_list`: In your config.yml, remove the model that is causing the error from the model_list under global_params.

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## Runtime and Performance Issues

#### Problem: The experiment runs out of memory (MemoryError).

Solutions: 1. Reduce Population Size: In your config.yml, use smaller values for pop_params under the ga_params section (e.g., [32]). 2. Reduce Data Size: For testing, either use a smaller input CSV file or set testing: True in your config.yml under global_params. You can also set test_sample_n to a small number (e.g., 1000) to sample your data. 3. Disable Model Caching: In your config.yml, set store_base_learners: [False] under grid_params.

#### Problem: The experiment is running very slowly.

Solutions: 1. Start Small: For initial runs, set n_iter to a low number (e.g., 1-3) and testing: True in your config.yml under global_params. 2. Use Model Caching: For subsequent runs, set use_stored_base_learners: [True] in grid_params to avoid retraining models. See Best Practices. 3. Simplify Weighting: In your config.yml, limit the weighted list under grid_params to [“unweighted”] for fast runs. ‘de’ and ‘ann’ are much slower.

#### Problem: The genetic algorithm’s fitness score is not improving (the convergence plot is flat).

Solutions: 1. Increase Mutation/Crossover: The search might be stuck. In your config.yml, try increasing the mutpb (mutation rate) or cxpb (crossover rate) under grid_params to encourage more exploration. 2. Increase Population Size: In config.yml, use larger values for pop_params under ga_params to introduce more diversity. 3. Check Model Suitability: The base learners in your model_list (in config.yml) may not be a good fit for your data. Try adding or swapping in different types of models.