diff --git a/.gitignore b/.gitignore index 4fbc167..92999c7 100644 --- a/.gitignore +++ b/.gitignore @@ -55,7 +55,7 @@ temp/ anas-ntbs/ # WIP scripts -src/microbiome_gpt/run_data_preparation.py +src/gut_former/run_data_preparation.py # Personal notes TODO.md diff --git a/Makefile b/Makefile index 2d95af0..6f4b3db 100644 --- a/Makefile +++ b/Makefile @@ -54,7 +54,7 @@ fix: # ----------------------------------------- kernel: - poetry run python -m ipykernel install --user --name=microbiome-gpt --display-name "Python (microbiome-gpt)" + poetry run python -m ipykernel install --user --name=gut-former --display-name "Python (gut-former)" jupyter: poetry run jupyter notebook diff --git a/notebooks/figure1a_data_overview.ipynb b/notebooks/figure1a_data_overview.ipynb index ca59a1a..02ba06d 100644 --- a/notebooks/figure1a_data_overview.ipynb +++ b/notebooks/figure1a_data_overview.ipynb @@ -15,7 +15,7 @@ }, "cell_type": "code", "source": [ - "from microbiome_gpt.utils.fig_style import apply_style\n", + "from gut_former.utils.fig_style import apply_style\n", "\n", "apply_style(font_family=\"DejaVu Serif\", base_font_size=16)" ], @@ -55,7 +55,7 @@ }, "cell_type": "code", "source": [ - "from microbiome_gpt.utils.project_paths import find_data_path, find_figures_path\n", + "from gut_former.utils.project_paths import find_data_path, find_figures_path\n", "\n", "data_path = find_data_path()\n", "figures_path = find_figures_path()" @@ -618,9 +618,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (microbiome-gpt)", + "display_name": "Python (gut-former)", "language": "python", - "name": "microbiome-gpt" + "name": "gut-former" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/figure1b_model_eval.ipynb b/notebooks/figure1b_model_eval.ipynb index ddbeb1a..c3b82b6 100644 --- a/notebooks/figure1b_model_eval.ipynb +++ b/notebooks/figure1b_model_eval.ipynb @@ -18,7 +18,7 @@ } }, "source": [ - "from microbiome_gpt.utils.fig_style import apply_style\n", + "from gut_former.utils.fig_style import apply_style\n", "\n", "apply_style(font_family=\"DejaVu Serif\", base_font_size=16)" ], @@ -48,7 +48,7 @@ } }, "source": [ - "from microbiome_gpt.utils.project_paths import find_data_path, find_figures_path\n", + "from gut_former.utils.project_paths import find_data_path, find_figures_path\n", "\n", "data_path = find_data_path()\n", "figures_path = find_figures_path()\n", @@ -131,7 +131,7 @@ }, "cell_type": "code", "source": [ - "from microbiome_gpt.utils.analysis import get_pcoa_coords, get_disparity\n", + "from gut_former.utils.analysis import get_pcoa_coords, get_disparity\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", @@ -272,9 +272,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python (microbiome-gpt)", + "display_name": "Python (gut-former)", "language": "python", - "name": "microbiome-gpt" + "name": "gut-former" }, "language_info": { "name": "python", diff --git a/notebooks/figure2_signatures.ipynb b/notebooks/figure2_signatures.ipynb index e7ff0d1..04bca94 100644 --- a/notebooks/figure2_signatures.ipynb +++ b/notebooks/figure2_signatures.ipynb @@ -18,7 +18,7 @@ } }, "source": [ - "from microbiome_gpt.utils.fig_style import apply_style\n", + "from gut_former.utils.fig_style import apply_style\n", "\n", "apply_style(font_family=\"DejaVu Serif\", base_font_size=16)" ], @@ -54,7 +54,7 @@ } }, "source": [ - "from microbiome_gpt.utils.project_paths import find_data_path, find_figures_path\n", + "from gut_former.utils.project_paths import find_data_path, find_figures_path\n", "\n", "data_path = find_data_path()\n", "output_path = find_data_path().parent / \"output\"\n", diff --git a/src/gut_former/run_data_preparation.py b/src/gut_former/run_data_preparation.py index 8851404..6263775 100644 --- a/src/gut_former/run_data_preparation.py +++ b/src/gut_former/run_data_preparation.py @@ -15,7 +15,7 @@ def main(): # Handling Args p = argparse.ArgumentParser() - p.add_argument("--dataset", type=str, default="raw", help="TODO") + p.add_argument("--dataset", type=str, default="raw", help="Dataset name; expects taxonomy_relab_{dataset}.csv, pathways_relab_{dataset}.csv, and metadata_{dataset}.csv in the data directory, with sample ID as the (optionally unnamed) first column") args = p.parse_args() diff --git a/src/gut_former/run_inference.py b/src/gut_former/run_inference.py index 7a9481d..ef35ff8 100644 --- a/src/gut_former/run_inference.py +++ b/src/gut_former/run_inference.py @@ -19,11 +19,11 @@ def main(): # Handling Args p = argparse.ArgumentParser() - p.add_argument("--dataset", type=str, default="sample", help="TODO") - p.add_argument("--embedding_dim", type=int, default=128, help="TODO") - p.add_argument("--latent_dim", type=int, default=64, help="TODO") + p.add_argument("--dataset", type=str, default="sample", help="Dataset name; expects taxonomy_{dataset}.csv and pathways_{dataset}.csv in the data directory, with sample ID as the (optionally unnamed) first column") + p.add_argument("--embedding_dim", type=int, default=128, help="Embedding dimension for the model") + p.add_argument("--latent_dim", type=int, default=64, help="Latent space dimension; controls the size of the learned representation") output_path = find_output_path() - p.add_argument("--checkpoint", type=str, default=f"{output_path}/checkpoint_sample.pt", help="TODO") + p.add_argument("--checkpoint", type=str, default=f"{output_path}/checkpoint_sample.pt", help="Path to model checkpoint file to load for inference") args = p.parse_args() # Loading & Preparing Data diff --git a/src/gut_former/run_training.py b/src/gut_former/run_training.py index 1a0b582..3d4b4b1 100644 --- a/src/gut_former/run_training.py +++ b/src/gut_former/run_training.py @@ -29,13 +29,12 @@ def main(): p = argparse.ArgumentParser() - p.add_argument("--dataset", type=str, default="sample", help="TODO") - p.add_argument("--embedding_dim", type=int, default=128, help="TODO") - p.add_argument("--latent_dim", type=int, default=64, help="TODO") - p.add_argument("--batch_size", type=int, default=16, help="TODO") - p.add_argument("--learning_rate", type=float, default=1.893292917167e-4, help="TODO") - p.add_argument("--epochs", type=int, default=55, help="TODO") - p.add_argument("--verbose", type=bool, default=True, help="TODO") + p.add_argument("--dataset", type=str, default="sample", help="Dataset name; expects taxonomy_{dataset}.csv, pathways_{dataset}.csv, and metadata_{dataset}.csv in the data directory, with sample ID as the (optionally unnamed) first column") + p.add_argument("--embedding_dim", type=int, default=128, help="Embedding dimension for the model") + p.add_argument("--latent_dim", type=int, default=64, help="Latent space dimension; controls the size of the learned representation") + p.add_argument("--batch_size", type=int, default=16, help="Mini-batch size for training") + p.add_argument("--learning_rate", type=float, default=1.893292917167e-4, help="Learning rate for the Adam optimizer") + p.add_argument("--epochs", type=int, default=55, help="Number of training epochs") p.add_argument("--checkpoint", type=str, default=None, help="Path to checkpoint to resume training from") args = p.parse_args()