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Merge pull request #140 from DataResponsibly/development
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denysgerasymuk799 authored Sep 14, 2024
2 parents 79d64e3 + 2a9a525 commit 05f8e35
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2 changes: 1 addition & 1 deletion .github/workflows/build-virny.yml
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Expand Up @@ -48,7 +48,7 @@ jobs:
- name: Build Virny
run: |
source ~/.venv/bin/activate
pip install -e ".[dev,docs]"
pip install -e ".[test,docs]"
pip install requests-toolbelt==1.0.0
# We should delete the git project from the build cache to avoid conflicts
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3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -9,6 +9,9 @@ notebooks
.ipynb_checkpoints
docs/examples/test.py
tests/results
.pt_tmp
lightning_logs
saved_models

# Remove big files from GitHub repo
virny/datasets/2018
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -92,6 +92,7 @@ In contrast to existing fairness software libraries and model card generating fr
* Metric static and interactive visualizations
* Data loaders with subsampling for popular fair-ML benchmark datasets
* User-friendly parameters input via config yaml files
* Integration with PyTorch Tabular

Check out [our documentation](https://dataresponsibly.github.io/Virny/) for a comprehensive overview.

Expand All @@ -118,4 +119,4 @@ If Virny has been useful to you, and you would like to cite it in a scientific p

## 📝 License

**Virny** is free and open-source software licensed under the [3-clause BSD license](https://github.com/DataResponsibly/Virny/blob/main/LICENSE).
**Virny** is free and open-source software licensed under the [3-clause BSD license](https://github.com/DataResponsibly/Virny/blob/main/LICENSE).
1 change: 1 addition & 0 deletions docs/examples/.pages
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Expand Up @@ -6,3 +6,4 @@ nav:
- Multiple_Models_Interface_With_Multiple_Test_Sets.md
- Multiple_Models_Interface_With_Inprocessor.md
- Multiple_Models_Interface_With_Postprocessor.md
- Multiple_Models_Interface_With_PyTorch_Tabular.md
2 changes: 1 addition & 1 deletion docs/examples/Multiple_Models_Interface_Use_Case.ipynb
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Expand Up @@ -409,7 +409,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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2 changes: 1 addition & 1 deletion docs/examples/Multiple_Models_Interface_Use_Case.md
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Expand Up @@ -267,7 +267,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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Expand Up @@ -288,7 +288,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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2 changes: 1 addition & 1 deletion docs/examples/Multiple_Models_Interface_With_DB_Writer.md
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Expand Up @@ -183,7 +183,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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Expand Up @@ -318,7 +318,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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Expand Up @@ -189,7 +189,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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Expand Up @@ -291,7 +291,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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Expand Up @@ -176,7 +176,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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Expand Up @@ -266,7 +266,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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Expand Up @@ -175,7 +175,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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Expand Up @@ -334,7 +334,7 @@
"outputs": [],
"source": [
"column_transformer = ColumnTransformer(transformers=[\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),\n",
" ('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),\n",
" ('numerical_features', StandardScaler(), data_loader.numerical_columns),\n",
"])"
],
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Expand Up @@ -208,7 +208,7 @@ data_loader.X_data[data_loader.X_data.columns[:5]].head()

```python
column_transformer = ColumnTransformer(transformers=[
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse=False), data_loader.categorical_columns),
('categorical_features', OneHotEncoder(handle_unknown='ignore', sparse_output=False), data_loader.categorical_columns),
('numerical_features', StandardScaler(), data_loader.numerical_columns),
])
```
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