v1.0.0

Author: xRiskLab

.DS_Store

@@ -0,0 +1,9 @@
+MIT License
+
+Copyright (c) [2025] [Denis Burakov]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

LICENSE.md

@@ -0,0 +1,76 @@
+# 💡 Pearsonify
+## Probabilistic Classification with Conformalized Intervals
+
+**Pearsonify** is a lightweight 🐍 Python package for generating **classification intervals** around predicted probabilities in binary classification tasks.
+
+It uses **Pearson residuals** and **principles of conformal prediction** to quantify uncertainty without making strong distributional assumptions.
+
+![Image](ims/Slide_1.jpeg)
+
+### 🚀 Why Pearsonify?
+
+* 📊 **Intuitive Classification Intervals**: Get reliable intervals for binary classification predictions.
+* 🧠 **Statistically Grounded**: Uses Pearson residuals, a well-established metric from classical statistics.
+* ⚡ **Model-Agnostic**: Works with any model that provides probability estimates.
+* 🛠️ **Lightweight**: Minimal dependencies, easy to integrate into existing projects.
+
+### 📦 How to install?
+
+Use `pip` to install the package from GitHub:
+
+```bash
+pip install git+https://github.com/xRiskLab/pearsonify.git
+```
+
+### 💻 How to use?
+
+```python
+import numpy as np
+from pearsonify import Pearsonify
+from sklearn.svm import SVC
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+
+# Generate synthetic classification data
+np.random.seed(42)
+X, y = make_classification(
+    n_samples=1000, n_features=20, n_informative=10, n_classes=2, random_state=42
+)
+
+# Split data into train, calibration, and test sets
+X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)
+X_cal, X_test, y_cal, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)
+
+# Initialize Pearsonify with an SVC model
+clf = SVC(probability=True, random_state=42)
+model = Pearsonify(estimator=clf, alpha=0.05)
+
+# Fit the model on training and calibration sets
+model.fit(X_train, y_train, X_cal, y_cal)
+
+# Generate prediction intervals for test set
+y_test_pred_proba, lower_bounds, upper_bounds = model.predict_intervals(X_test)
+
+# Calculate coverage
+coverage = model.evaluate_coverage(y_test, lower_bounds, upper_bounds)
+print(f"Coverage: {coverage:.2%}")
+
+# Plot the intervals
+model.plot_intervals(y_test_pred_proba, lower_bounds, upper_bounds)
+```
+
+Running `example.py` will generate the following plot:
+
+![Image](ims/Figure_1.png)
+
+This plot shows predicted probabilities with 95% confidence intervals, sorted by prediction score.
+
+### 📖 References
+
+Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. John Wiley & Sons.
+
+Tibshirani, R. (2023). Conformal Prediction. Advanced Topics in Statistical Learning, Spring 2023.
+
+### 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

README.md

@@ -0,0 +1,32 @@
+import numpy as np
+from sklearn.svm import SVC
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from pearsonify import Pearsonify
+
+# Generate synthetic classification data
+np.random.seed(42)
+X, y = make_classification(
+    n_samples=1000, n_features=20, n_informative=10, n_classes=2, random_state=42
+)
+
+# Split data into train, calibration, and test sets
+X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)
+X_cal, X_test, y_cal, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)
+
+# Initialize Pearsonify with an SVC model
+clf = SVC(probability=True, random_state=42)
+model = Pearsonify(estimator=clf, alpha=0.05)
+
+# Fit the model (trains on training set and calculates q_alpha from calibration set)
+model.fit(X_train, y_train, X_cal, y_cal)
+
+# Generate prediction intervals for the test set
+y_test_pred_proba, lower_bounds, upper_bounds = model.predict_intervals(X_test)
+
+# Calculate coverage
+coverage = model.evaluate_coverage(y_test, lower_bounds, upper_bounds)
+print(f"Coverage: {coverage:.2%}")
+
+# Plot the intervals
+model.plot_intervals(y_test_pred_proba, lower_bounds, upper_bounds, y_test)