Debugging Guide

This guide provides solutions to common issues you might encounter when using cz-benchmarks. Problems are organized by the stage at which they typically occur, from loading data to running tasks.

Dataset Loading and Validation Errors

These errors usually happen when calling load_dataset(), dataset.load_data(), or dataset.validate().

πŸ“„ File Not Found or Path Issues

  • Error: FileNotFoundError or ValueError: Dataset path does not exist.

    • Cause: The path specified in your YAML configuration file (e.g., datasets.yaml) is incorrect, or the file is not accessible.

    • Solution:

      1. Verify that the path in your YAML config points to the correct .h5ad file.

      2. Ensure the file exists and that you have the necessary read permissions.

      3. If using a custom config with load_dataset(config_path=...), make sure the path to the YAML file itself is correct.

βš™οΈ Dataset Not Found in Configuration

  • Error: ValueError: Dataset {dataset_name} not found in config.

    • Cause: The dataset name you passed to load_dataset() does not have a corresponding entry in the Hydra configuration.

    • Solution:

      1. Check for typos in the dataset name. You can see available datasets with list_available_datasets().

      2. Ensure your custom YAML config is correctly structured and is being loaded.

πŸ”¬ AnnData Validation Errors

These errors originate from the content of your .h5ad file not meeting the requirements of the Dataset class.

  • Error: ValueError: Dataset does not contain valid gene names...

    • Cause: The SingleCellDataset._validate() method failed because gene names in adata.var_names do not start with the required prefix for the specified organism (e.g., "ENSG" for HUMAN).

    • Solution: Ensure your AnnData object’s gene identifiers are correct. The framework can automatically use the ensembl_id column if it exists in adata.var. Check that adata.var['ensembl_id'] contains the correct, prefixed gene IDs.

  • Error: ValueError: Dataset does not contain '{key}' column in obs.

    • Cause: A required metadata column is missing from adata.obs. This often happens with:

      • SingleCellLabeledDataset: The label_column_key (e.g., "cell_type") is missing.

      • SingleCellPerturbationDataset: The condition_key is missing.

    • Solution: Add the required column with the correct data to your AnnData object’s .obs DataFrame and save the .h5ad file again.

  • Error: ValueError: Unexpected condition label: ``{condition}`` not present in control mapping.

    • Cause: One or more values in the condition column in a SingleCellPerturbationDataset are not present in the control_cells_map.

    • Solution:

      • The values in the condition column in a SingleCellPerturbationDataset must all be present in the control_cells_map. Ensure the values are correct in adata.obs[``{condition_key}``] and in the control_cells_map and re-save the dataset.

Task Execution Errors

These errors occur when calling task.run() and are often related to mismatches between the model output (cell_representation) and the dataset inputs.

↔️ Input Shape and Type Mismatches

  • Error: ValueError: This task requires a list of cell representations or ValueError: This task requires a single cell representation.

    • Cause: You are passing the wrong input structure to a task.

    • Solution:

      • For tasks with requires_multiple_datasets = True (like CrossSpeciesIntegrationTask), cell_representation must be a list of embeddings ([emb1, emb2, ...]).

      • For all other tasks, cell_representation must be a single numpy.ndarray or pd.DataFrame.

  • Error: An error related to mismatched array/DataFrame dimensions during filtering, concatenation, or model fitting (e.g., inside filter_minimum_class or sklearn).

    • Cause: The number of cells (rows) in your cell_representation does not match the number of cells in the loaded dataset’s metadata (e.g., dataset.labels or dataset.adata.obs).

    • Solution: Ensure your model’s output embedding has the same number of observations and is in the same order as the cells in the original dataset file loaded by cz-benchmarks.

🎯 Task-Specific Errors

  • Task: MetadataLabelPredictionTask

  • Issue: Very few classes are used for training, or an error occurs in filter_minimum_class.

    • Cause: Many of your label classes have fewer samples than min_class_size (default is 10).

    • Solution: Check the distribution of your labels. If necessary, either use a dataset with more balanced classes or adjust the min_class_size parameter in the MetadataLabelPredictionTaskInput.

  • Task: CrossSpeciesIntegrationTask

  • Error: AssertionError: At least two organisms are required...

    • Cause: The task is being run on datasets that are all from the same species.

    • Solution: This task is specifically for evaluating cross-species alignment and requires inputs from at least two different organisms.

Environment and Dependency Issues

πŸ“¦ Missing Packages

  • Error: ImportError: No module named '...' (e.g., hnswlib, scanpy).

    • Cause: A required dependency is not installed in your Python environment.

    • Solution: Install the missing package. For all development dependencies, run pip install -e ".[dev]" from the repository root.

🧠 Memory Errors

  • Error: MemoryError or your process is killed by the OS.

    • Cause: Loading or processing data (e.g., a large .h5ad file or a dense embedding matrix) requires more RAM than is available.

    • Solution:

      1. Run your code on a machine with more RAM.

      2. If possible, for initial debugging, create a smaller version of your dataset by subsampling cells.

      3. Check for parts of your code that might be making unnecessary copies of large objects.

General Tips for Debugging

  • Start Small: When debugging, use a small, fast-running dataset (like tsv2_bladder) or a subset of your custom data to quickly iterate.

  • Isolate the Problem: Determine if the issue is in data loading or task execution. First, ensure your dataset loads and validates successfully:

    dataset = load_dataset("my_dataset", config_path="my_config.yaml")
dataset.load_data()
dataset.validate()
print(dataset.adata)

  • Check Shapes and Types: Before running a task, print the shapes and types of your inputs to catch mismatches early.

    print(f"Embedding shape: {my_embedding.shape}")
print(f"Labels length: {len(dataset.labels)}")
print(f"Embedding type: {type(my_embedding)}")

  • Use a Debugger: Use an interactive debugger like pdb or your IDE’s built-in debugger to step through the code, inspect variables, and understand the execution flow.

    import pdb; pdb.set_trace()

  • Dependency Conflicts: Ensure all dependencies are installed in a clean virtual environment. Recreate the environment if needed.

  • hnswlib package installation error: If the hnswlib package fails to install with an error like fatal error: Python.h: No such file or directory, ensure you have installed Python development headers files and static libraries. On Ubuntu, this can be done via sudo apt-get install python3-dev.