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Merge pull request #39 from ruivieira/update-0.4.0
Add Levenshtein example. Bump TrustyAI to 0.4.0
2 parents 396305f + 64927fa commit ab9ed19

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‎README.md‎

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@@ -25,10 +25,12 @@ Several example Jupyter notebooks are available.
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- [Energy load forecasting](examples/EnergyLoadForecasting.ipynb)[^1]
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- [Engine fault detection](examples/EngineFaultDetection.ipynb)[^1]
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### Fairness metrics
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- [Group fairness metrics](examples/GroupFairnessMetrics.ipynb)
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### Metrics
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- Bias/fairness
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- [Group fairness metrics](examples/GroupFairnessMetrics.ipynb)
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- Language
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- [Language performance](examples/LanguageMetrics.ipynb)
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[^1]: These explainers require the extra dependencies, installed with `pip install trustyai[extras]`.

‎examples/Counterfactuals.ipynb‎

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‎examples/Dataset.ipynb‎

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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>24</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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" x1 x2 x3 x4 select\n",
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"4 172.601273 7478.442753 76c9587b-e68f-45e8-a39d-ed9fab2b5609 11 False"
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"0 199.691719 8423.634001 aa433471-f2f4-4210-beab-2a5fde4afc93 13 True\n",
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"1 175.015510 5144.954980 8c8cfe67-7257-4f3d-9506-f4816c4e770e 41 True\n",
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"2 181.826085 8659.891995 7bd8c06d-1064-4f13-b372-c65af8e30149 41 True\n",
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"4 157.718382 5539.941095 6c245358-d261-4e15-8517-c3f09ecdac66 24 True"
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},
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"execution_count": 3,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[Feature{name='x1', type=number, value=199.69171905389746}, Feature{name='x2', type=number, value=8423.634000506001}, Feature{name='x3', type=categorical, value=aa433471-f2f4-4210-beab-2a5fde4afc93}, Feature{name='x4', type=number, value=13}]\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[Output{value=false, type=boolean, score=1.0, name='select'}]\n"
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"[Output{value=true, type=boolean, score=1.0, name='select'}]\n"
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]
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}
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],
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"name": "stdout",
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"text": [
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"[Feature{name='x2', type=number, value=8897.843313935722}, Feature{name='x3', type=categorical, value=769f28d7-df70-4907-8f49-f8a5efeadbb2}, Feature{name='select', type=boolean, value=false}]\n"
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"[Feature{name='x2', type=number, value=8423.634000506001}, Feature{name='x3', type=categorical, value=aa433471-f2f4-4210-beab-2a5fde4afc93}, Feature{name='select', type=boolean, value=true}]\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[Output{value=110.77596090318106, type=number, score=1.0, name='x1'}, Output{value=17.0, type=number, score=1.0, name='x4'}]\n"
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"[Output{value=199.69171905389746, type=number, score=1.0, name='x1'}, Output{value=13.0, type=number, score=1.0, name='x4'}]\n"
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}
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{
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]
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},
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"execution_count": 13,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[Feature{name='input-0', type=number, value=0.49418369920147376}, Feature{name='input-1', type=number, value=0.541389237195627}, Feature{name='input-2', type=number, value=0.79744718322189}, Feature{name='input-3', type=number, value=0.7596647445704917}]\n"
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"[Feature{name='input-0', type=number, value=0.6846801847772033}, Feature{name='input-1', type=number, value=0.6070370508287989}, Feature{name='input-2', type=number, value=0.7619673021775413}, Feature{name='input-3', type=number, value=0.5664971655888972}]\n"
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]
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}
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],
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"name": "stdout",
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"text": [
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"[Output{value=0.6663965038979389, type=number, score=1.0, name='output-0'}]\n"
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"[Output{value=0.7213026353546987, type=number, score=1.0, name='output-0'}]\n"
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}
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"name": "stdout",
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"text": [
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"[Feature{name='input-0', type=number, value=0.6070370508287989}, Feature{name='input-1', type=number, value=0.5664971655888972}, Feature{name='input-2', type=number, value=0.7213026353546987}]\n"
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"text": [
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"[Output{value=0.49418369920147376, type=number, score=1.0, name='output-0'}, Output{value=0.79744718322189, type=number, score=1.0, name='output-1'}]\n"
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"[Output{value=0.6846801847772033, type=number, score=1.0, name='output-0'}, Output{value=0.7619673021775413, type=number, score=1.0, name='output-1'}]\n"
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"source": [
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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"version": "3.11.7"
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"toc": {
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"base_numbering": 1,

‎examples/EnergyLoadForecasting.ipynb‎

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‎examples/EngineFaultDetection.ipynb‎

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‎examples/GroupFairnessMetrics.ipynb‎

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"/Users/rui/.virtualenvs/trustyai-explainability-python-examples/lib/python3.10/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"/home/rui/.virtualenvs/trustyai-explainability-python-examples/lib/python3.11/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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" from pandas import MultiIndex, Int64Index\n",
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"/Users/rui/.virtualenvs/trustyai-explainability-python-examples/lib/python3.10/site-packages/xgboost/data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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"/home/rui/.virtualenvs/trustyai-explainability-python-examples/lib/python3.11/site-packages/xgboost/data.py:208: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
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" from pandas import MultiIndex, Int64Index\n"
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}
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" positive_class=[1])\n",
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"print(score)"
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"id": "ad3d2363",
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"metadata": {},
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"outputs": [],
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"source": []
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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"version": "3.11.7"
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}
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},
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"nbformat": 4,

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