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Fix errors and manage citation
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JesperDramsch committed Dec 14, 2023
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16 changes: 16 additions & 0 deletions CITATION.cff
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cff-version: 1.2.0
message: 'If you use this ML.recipes, please cite it as below.'
authors:
- family-names: Dramsch
given-names: Jesper Sören
orcid: https://orcid.org/0000-0001-8273-905X
- family-names: Maggio
given-names: Valerio
orcid: https://orcid.org/0000-0003-4824-893X
title: 'ML Recipes – Increase citations, ease review & foster collaboration '
version: PyData-Global-2022
identifiers:
- type: doi
value: ###
date-released: 2022-12-03
url: 'https://github.com/JesperDramsch/ml-for-science-reproducibility-tutorial'
9 changes: 9 additions & 0 deletions book/backmatter/cite.md
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# Cite this Work

This book is licensed under the MIT license, but we appreciate a mention!

You can see the contributions to this book in the [Github contributor graph](https://github.com/JesperDramsch/ml-for-science-reproducibility-tutorial/graphs/contributors) and see contributions to events, workshops and talks in the section event section.

You can cite _ML.recipes_ through the Zenodo project archive: ###

The Github page can create citations for you which look something like this for APA:

> Y
Please visit the Github repo and the Zenodo archive for the most up-to-date reference!
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11 changes: 9 additions & 2 deletions book/notebooks/4-testing.ipynb
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" # Define data you definitely know the answer to\n",
" test_data = pd.DataFrame([[34.6, 21.1, 198.0, \"MALE\"],\n",
" [46.1, 18.2, 178.0, \"FEMALE\"],\n",
" [52.5, 15.6, 221.0, \"MALE\"]], \n",
" [52.5, 15.6, 221.0, \"MALE\"]],\n",
" columns=[\"Culmen Length (mm)\", \"Culmen Depth (mm)\", \"Flipper Length (mm)\", \"Sex\"])\n",
" # Define target to the data\n",
" test_target = ['Adelie Penguin (Pygoscelis adeliae)',\n",
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" -------\n",
" pd.DataFrame\n",
" Normalised dataframe with shortened names\n",
" \n",
"\n",
" Examples\n",
" --------\n",
" >>> shorten_class_name(pd.DataFrame([[1,2,3,\"Adelie Penguin (Pygoscelis adeliae)\"]], columns=[\"1\",\"2\",\"3\",\"Species\"]))\n",
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"X_train.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code is supposed to fail to see what happens if the schema doesn't match!"
]
},
{
"cell_type": "code",
"execution_count": 11,
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2 changes: 1 addition & 1 deletion book/notebooks/5-interpretability.ipynb
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}
],
"source": [
"from sklearn.inspection import PartialDependenceDisplay, partial_dependence, plot_partial_dependence\n",
"from sklearn.inspection import PartialDependenceDisplay, partial_dependence\n",
"\n",
"pd_results = partial_dependence(model, X_train.sample(20), num_features)\n",
"pd_results"
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