feat: change to sklearn-compatible interface

pyproject.toml

@@ -4,5 +4,7 @@ requires = [
     "setuptools>=42",
     "wheel",
     "pybind11>=2.6.0",
+    "scipy",
+    "sklearn",
     "numpy"
 ]

sortedl1/__init__.py

@@ -1 +1 @@
-from .slope import slope
+from .estimators import Slope

sortedl1/estimators.py

@@ -0,0 +1,73 @@
+import _sortedl1 as sl1
+import numpy as np
+from sklearn.base import BaseEstimator, RegressorMixin
+from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
+
+
+class Slope(BaseEstimator, RegressorMixin):
+    """
+    Sorted L-One Penalized Estimation
+
+    Parameters
+    ----------
+    lam : array_like
+        The lambda parameter vector for the Sorted L1 Penalty
+    alph : array_like
+        A multiplier for the Sorted L1 Penalty
+
+    Attributes
+    ----------
+    coef_ : ndarray
+        The estimated regression coefficients.
+    """
+
+    def __init__(self, lam=None, alph=1.0):
+        self.lam = lam
+        self.alph = alph
+
+    def fit(self, X, y):
+        """
+        Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : array_like, shape (n_samples, n_features)
+            Training vector, where n_samples is the number of samples and
+            n_features is the number of features.
+        y : array_like, shape (n_samples,)
+            Target vector relative to X.
+
+        Returns
+        -------
+        self : object
+        """
+        X, y = check_X_y(X, y, accept_sparse=True, order="F", y_numeric=True)
+
+        self.X_ = X
+        self.y_ = y
+
+        self.coef_ = sl1.fit_slope(
+            self.X_, self.y_, np.array(self.lam), np.array(self.alph)
+        )
+
+        return self
+
+    def predict(self, X):
+        """
+        Predict using the linear model.
+
+        Parameters
+        ----------
+        X : array_like, shape (n_samples, n_features)
+            Samples.
+
+        Returns
+        -------
+        C : array, shape (n_samples,)
+            Returns predicted values.
+        """
+        check_is_fitted(self)
+
+        X = check_array(X, accept_sparse=True, order="F")
+
+        return np.dot(X, self.coef_)