# TinyCP
TinyCP is an experimental Python library for conformal predictions, providing tools to generate valid prediction sets with a specified significance level (alpha). This project aims to facilitate the implementation of personal and future projects on the topic.
For more information on a previous project related to Out-of-Bag (OOB) solutions, visit [this link](https://github.com/HeyLucasLeao/cp-study).
## Changes about previous work
- `calibrate`: instead of `Balanced Accuracy Score`, it can be calibrated either `Matthews Correlation Coefficient` or `Bookmaker Informedness Score`, for better reliability.
- `evaluate`: scores `bm` and `mcc` for more reliability.
Currently, TinyCP supports Out-of-Bag (OOB) solutions for `RandomForestClassifier` in binary classification problems, as well as `RandomForestRegressor` and `RandomForestQuantileRegressor` for regression tasks. For additional options and advanced features, you may want to explore [Crepes](https://github.com/henrikbostrom/crepes).
## Installation
Install TinyCP using pip:
```bash
pip install tinycp
```
> **Note:** If you want to enable plotting capabilities, you need to install the extras using Poetry:
```bash
poetry install --E plot
```
## Usage
### Importing Classifiers
Import the conformal classifiers from the `tinycp.classifier` module:
```python
from tinycp.classifier import BinaryClassConditionalConformalClassifier
from tinycp.classifier import BinaryMarginalConformalClassifier
```
### Importing Regressors
Import the conformal regressors from the `tinycp.regressor` module:
```python
from tinycp.regressor import ConformalizedRegressor
from tinycp.regressor import ConformalizedQuantileRegressor
```
### Example
Example usage of `BinaryClassConditionalConformalClassifier`:
```python
from sklearn.ensemble import RandomForestClassifier
from tinycp.classifier import BinaryClassConditionalConformalClassifier
# Create and fit a RandomForestClassifier
learner = RandomForestClassifier(n_estimators=100, oob_score=True)
X_train, y_train = ... # your training data
learner.fit(X_train, y_train)
# Create and fit the conformal classifier
conformal_classifier = BinaryClassConditionalConformalClassifier(learner)
conformal_classifier.fit(y=y_train, oob=True)
# Make predictions
X_test = ... # your test data
predictions = conformal_classifier.predict(X_test)
```
### Evaluating the Classifier
Evaluate the performance of the conformal classifier using the `evaluate` method:
```python
results = conformal_classifier.evaluate(X_test, y_test)
print(results)
```
## Classes
### BinaryMarginalConformalClassifier
`BinaryMarginalConformalClassifier` A marginal coverage conformal classifier methodology utilizing a classifier as the underlying learner. This classifier supports the option to use Out-of-Bag (OOB) samples for calibration.
### BinaryClassConditionalConformalClassifier
`BinaryClassConditionalConformalClassifier` A class conditional conformal classifier methodology utilizing a classifier as the underlying learner. This classifier supports the option to use Out-of-Bag (OOB) samples for calibration.
### ConformalizedRegressor
`ConformalizedRegressor` A conformal regressor methodology utilizing a regressor as the underlying learner. This regressor supports the option to use Out-of-Bag (OOB) samples for calibration, providing valid prediction intervals for regression tasks.
### ConformalizedQuantileRegressor
`ConformalizedQuantileRegressor` A conformal quantile regressor methodology utilizing a quantile regressor as the underlying learner. This regressor supports the option to use Out-of-Bag (OOB) samples for calibration, offering more robust prediction intervals by leveraging quantile estimates.
## License
This project is licensed under the MIT License.
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"description": "# TinyCP\nTinyCP is an experimental Python library for conformal predictions, providing tools to generate valid prediction sets with a specified significance level (alpha). This project aims to facilitate the implementation of personal and future projects on the topic.\n\nFor more information on a previous project related to Out-of-Bag (OOB) solutions, visit [this link](https://github.com/HeyLucasLeao/cp-study).\n\n## Changes about previous work\n- `calibrate`: instead of `Balanced Accuracy Score`, it can be calibrated either `Matthews Correlation Coefficient` or `Bookmaker Informedness Score`, for better reliability.\n- `evaluate`: scores `bm` and `mcc` for more reliability.\n\nCurrently, TinyCP supports Out-of-Bag (OOB) solutions for `RandomForestClassifier` in binary classification problems, as well as `RandomForestRegressor` and `RandomForestQuantileRegressor` for regression tasks. For additional options and advanced features, you may want to explore [Crepes](https://github.com/henrikbostrom/crepes).\n\n## Installation\n\nInstall TinyCP using pip:\n\n```bash\npip install tinycp\n```\n\n> **Note:** If you want to enable plotting capabilities, you need to install the extras using Poetry:\n\n```bash\npoetry install --E plot\n```\n\n## Usage\n\n### Importing Classifiers\n\nImport the conformal classifiers from the `tinycp.classifier` module:\n\n```python\nfrom tinycp.classifier import BinaryClassConditionalConformalClassifier\nfrom tinycp.classifier import BinaryMarginalConformalClassifier\n```\n### Importing Regressors\n\nImport the conformal regressors from the `tinycp.regressor` module:\n\n```python\nfrom tinycp.regressor import ConformalizedRegressor\nfrom tinycp.regressor import ConformalizedQuantileRegressor\n```\n### Example\n\nExample usage of `BinaryClassConditionalConformalClassifier`:\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nfrom tinycp.classifier import BinaryClassConditionalConformalClassifier\n\n# Create and fit a RandomForestClassifier\nlearner = RandomForestClassifier(n_estimators=100, oob_score=True)\nX_train, y_train = ... # your training data\nlearner.fit(X_train, y_train)\n\n# Create and fit the conformal classifier\nconformal_classifier = BinaryClassConditionalConformalClassifier(learner)\nconformal_classifier.fit(y=y_train, oob=True)\n\n# Make predictions\nX_test = ... # your test data\npredictions = conformal_classifier.predict(X_test)\n```\n\n### Evaluating the Classifier\n\nEvaluate the performance of the conformal classifier using the `evaluate` method:\n\n```python\nresults = conformal_classifier.evaluate(X_test, y_test)\nprint(results)\n```\n\n## Classes\n\n### BinaryMarginalConformalClassifier\n\n`BinaryMarginalConformalClassifier` A marginal coverage conformal classifier methodology utilizing a classifier as the underlying learner. This classifier supports the option to use Out-of-Bag (OOB) samples for calibration.\n\n\n### BinaryClassConditionalConformalClassifier\n\n`BinaryClassConditionalConformalClassifier` A class conditional conformal classifier methodology utilizing a classifier as the underlying learner. This classifier supports the option to use Out-of-Bag (OOB) samples for calibration.\n\n### ConformalizedRegressor\n\n`ConformalizedRegressor` A conformal regressor methodology utilizing a regressor as the underlying learner. This regressor supports the option to use Out-of-Bag (OOB) samples for calibration, providing valid prediction intervals for regression tasks.\n\n### ConformalizedQuantileRegressor\n\n`ConformalizedQuantileRegressor` A conformal quantile regressor methodology utilizing a quantile regressor as the underlying learner. This regressor supports the option to use Out-of-Bag (OOB) samples for calibration, offering more robust prediction intervals by leveraging quantile estimates.\n\n## License\n\nThis project is licensed under the MIT License.\n\n",
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