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# MLWiz: the Machine Learning Research Wizard
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[](https://mlwiz.readthedocs.io/en/latest/?badge=latest)
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[](https://pepy.tech/project/mlwiz)
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## [Documentation](https://mlwiz.readthedocs.io/en/latest/index.html)
MLWiz is a Python library that fosters machine learning research by reducing the boilerplate code
to run reproducible experiments. It provides automatic management of data splitting, loading and common
experimental settings. It especially handles both model selection and risk assessment procedures, by trying many different
configurations in parallel (CPU or GPU). It is a generalized version of [PyDGN](https://github.com/diningphil/PyDGN)
that can handle different kinds of data and models (vectors, images, time-series, graphs).
## Installation:
Requires at least Python 3.10. Simply run
pip install mlwiz
## Quickstart:
#### Build dataset and data splits
mlwiz-data --config-file examples/DATA_CONFIGS/config_NCI1.yml [--debug]
#### Launch experiments
mlwiz-exp --config-file examples/MODEL_CONFIGS/config_SupToyDGN.yml [--debug]
#### Stop experiments
Use ``CTRL-C``, then type ``ray stop --force`` to stop **all** ray processes you have launched.
### Using the Trained Models
It's very easy to load the model from the experiments (see also the [Tutorial](https://mlwiz.readthedocs.io/en/latest/tutorial.html)):
from mlwiz.evaluation.util import *
config = retrieve_best_configuration('RESULTS/supervised_grid_search_toy_NCI1/MODEL_ASSESSMENT/OUTER_FOLD_1/MODEL_SELECTION/')
splits_filepath = 'examples/DATA_SPLITS/CHEMICAL/NCI1/NCI1_outer10_inner1.splits'
device = 'cpu'
# instantiate dataset
dataset = instantiate_dataset_from_config(config)
# instantiate model
model = instantiate_model_from_config(config, dataset, config_type="supervised_config")
# load model's checkpoint, assuming the best configuration has been loaded
checkpoint_location = 'RESULTS/supervised_grid_search_toy_NCI1/MODEL_ASSESSMENT/OUTER_FOLD_1/final_run1/best_checkpoint.pth'
load_checkpoint(checkpoint_location, model, device=device)
# you can now call the forward method of your model
y, embeddings = model(dataset[0])
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"description": "<p align=\"center\">\n <img src=\"https://github.com/diningphil/MLWiz/blob/main/docs/_static/mlwiz-logo.png\" width=\"300\"/>\n</p>\n\n# MLWiz: the Machine Learning Research Wizard \n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://mlwiz.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/diningphil/mlwiz/actions/workflows/python-publish-package.yml)\n[](https://pepy.tech/project/mlwiz)\n[](https://github.com/psf/black)\n[](https://interrogate.readthedocs.io/en/latest/)\n[]()\n\n## [Documentation](https://mlwiz.readthedocs.io/en/latest/index.html)\n\nMLWiz is a Python library that fosters machine learning research by reducing the boilerplate code \nto run reproducible experiments. It provides automatic management of data splitting, loading and common \nexperimental settings. It especially handles both model selection and risk assessment procedures, by trying many different\nconfigurations in parallel (CPU or GPU). It is a generalized version of [PyDGN](https://github.com/diningphil/PyDGN)\nthat can handle different kinds of data and models (vectors, images, time-series, graphs).\n\n## Installation:\n\nRequires at least Python 3.10. Simply run\n \n pip install mlwiz\n\n## Quickstart:\n\n#### Build dataset and data splits\n\n mlwiz-data --config-file examples/DATA_CONFIGS/config_NCI1.yml [--debug]\n\n#### Launch experiments\n\n mlwiz-exp --config-file examples/MODEL_CONFIGS/config_SupToyDGN.yml [--debug]\n\n\n#### Stop experiments\nUse ``CTRL-C``, then type ``ray stop --force`` to stop **all** ray processes you have launched.\n\n### Using the Trained Models\n\nIt's very easy to load the model from the experiments (see also the [Tutorial](https://mlwiz.readthedocs.io/en/latest/tutorial.html)):\n\n from mlwiz.evaluation.util import *\n\n config = retrieve_best_configuration('RESULTS/supervised_grid_search_toy_NCI1/MODEL_ASSESSMENT/OUTER_FOLD_1/MODEL_SELECTION/')\n splits_filepath = 'examples/DATA_SPLITS/CHEMICAL/NCI1/NCI1_outer10_inner1.splits'\n device = 'cpu'\n\n # instantiate dataset\n dataset = instantiate_dataset_from_config(config)\n\n # instantiate model\n model = instantiate_model_from_config(config, dataset, config_type=\"supervised_config\")\n\n # load model's checkpoint, assuming the best configuration has been loaded\n checkpoint_location = 'RESULTS/supervised_grid_search_toy_NCI1/MODEL_ASSESSMENT/OUTER_FOLD_1/final_run1/best_checkpoint.pth'\n load_checkpoint(checkpoint_location, model, device=device)\n\n # you can now call the forward method of your model\n y, embeddings = model(dataset[0])\n",
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