[READY] ENH - Add Gram Solver for single task Quadratic datafit

skglm/solvers/gram_cd.py

@@ -0,0 +1,157 @@
+import warnings
+import numpy as np
+from numba import njit
+from scipy.sparse import issparse
+
+from skglm.utils import AndersonAcceleration
+
+
+def gram_cd_solver(X, y, penalty, max_iter=100, w_init=None,
+                   use_acc=True, greedy_cd=True, tol=1e-4, verbose=False):
+    r"""Run coordinate descent while keeping the gradients up-to-date with Gram updates.
+
+    This solver should be used when n_features < n_samples, and computes the
+    (n_features, n_features) Gram matrix which comes with an overhead. It is  only
+    suited to Quadratic datafits.
+
+    It minimizes::
+        1 / (2*n_samples) * norm(y - Xw)**2 + penalty(w)
+
+    which can be rewritten as::
+        w.T @ Q @ w / (2*n_samples) - q.T @ w / n_samples + penalty(w)
+
+    where::
+        Q = X.T @ X (gram matrix), and q = X.T @ y
+
+    Parameters
+    ----------
+    X : array or sparse CSC matrix, shape (n_samples, n_features)
+        Design matrix.
+
+    y : array, shape (n_samples,)
+        Target vector.
+
+    penalty : instance of BasePenalty
+        Penalty object.
+
+    max_iter : int, default 100
+        Maximum number of iterations.
+
+    w_init : array, shape (n_features,), default None
+        Initial value of coefficients.
+        If set to None, a zero vector is used instead.
+
+    use_acc : bool, default True
+        Extrapolate the iterates based on the past 5 iterates if set to True.
+
+    greedy_cd : bool, default True
+        Use a greedy strategy to select features to update in coordinate descent epochs
+        if set to True. A cyclic strategy is used otherwise.
+
+    tol : float, default 1e-4
+        Tolerance for convergence.
+
+    verbose : bool, default False
+        Amount of verbosity. 0/False is silent.
+
+    Returns
+    -------
+    w : array, shape (n_features,)
+        Solution that minimizes the problem defined by datafit and penalty.
+
+    objs_out : array, shape (n_iter,)
+        The objective values at every outer iteration.
+
+    stop_crit : float
+        The value of the stopping criterion when the solver stops.
+    """
+    n_samples, n_features = X.shape
+
+    if issparse(X):
+        scaled_gram = X.T.dot(X)
+        scaled_gram = scaled_gram.toarray() / n_samples
+        scaled_Xty = X.T.dot(y) / n_samples
+    else:
+        scaled_gram = X.T @ X / n_samples
+        scaled_Xty = X.T @ y / n_samples
+    # TODO potential improvement: allow to pass scaled_gram (e.g. for path computation)
+
+    scaled_y_norm2 = np.linalg.norm(y)**2 / (2*n_samples)
+
+    all_features = np.arange(n_features)
+    stop_crit = np.inf  # prevent ref before assign
+    p_objs_out = []
+
+    w = np.zeros(n_features) if w_init is None else w_init
+    grad = - scaled_Xty if w_init is None else scaled_gram @ w_init - scaled_Xty
+    opt = penalty.subdiff_distance(w, grad, all_features)
+
+    if use_acc:
+        if greedy_cd:
+            warnings.warn(
+                "Anderson acceleration does not work with greedy_cd, set use_acc=False",
+                UserWarning)
+        accelerator = AndersonAcceleration(K=5)
+        w_acc = np.zeros(n_features)
+        grad_acc = np.zeros(n_features)
+
+    for t in range(max_iter):
+        # check convergences
+        stop_crit = np.max(opt)
+        if verbose:
+            p_obj = (0.5 * w @ (scaled_gram @ w) - scaled_Xty @ w +
+                     scaled_y_norm2 + penalty.value(w))
+            print(
+                f"Iteration {t+1}: {p_obj:.10f}, "
+                f"stopping crit: {stop_crit:.2e}"
+            )
+
+        if stop_crit <= tol:
+            if verbose:
+                print(f"Stopping criterion max violation: {stop_crit:.2e}")
+            break
+
+        # inplace update of w, grad
+        opt = _gram_cd_epoch(scaled_gram, w, grad, penalty, greedy_cd)
+
+        # perform Anderson extrapolation
+        if use_acc:
+            w_acc, grad_acc, is_extrapolated = accelerator.extrapolate(w, grad)
+
+            if is_extrapolated:
+                # omit constant term for comparison
+                p_obj_acc = (0.5 * w_acc @ (scaled_gram @ w_acc) - scaled_Xty @ w_acc +
+                             penalty.value(w_acc))
+                p_obj = 0.5 * w @ (scaled_gram @ w) - scaled_Xty @ w + penalty.value(w)
+                if p_obj_acc < p_obj:
+                    w[:] = w_acc
+                    grad[:] = grad_acc
+
+        # store p_obj
+        p_obj = (0.5 * w @ (scaled_gram @ w) - scaled_Xty @ w + scaled_y_norm2 +
+                 penalty.value(w))
+        p_objs_out.append(p_obj)
+    return w, np.array(p_objs_out), stop_crit
+
+
+@njit
+def _gram_cd_epoch(scaled_gram, w, grad, penalty, greedy_cd):
+    all_features = np.arange(len(w))
+    for cd_iter in all_features:
+        # select feature j
+        if greedy_cd:
+            opt = penalty.subdiff_distance(w, grad, all_features)
+            j = np.argmax(opt)
+        else:  # cyclic
+            j = cd_iter
+
+        # update w_j
+        old_w_j = w[j]
+        step = 1 / scaled_gram[j, j]  # 1 / lipschitz_j
+        w[j] = penalty.prox_1d(old_w_j - step * grad[j], step, j)
+
+        # gradient update with Gram matrix
+        if w[j] != old_w_j:
+            grad += (w[j] - old_w_j) * scaled_gram[:, j]
+
+    return penalty.subdiff_distance(w, grad, all_features)

skglm/tests/test_gram_solver.py

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+import pytest
+from itertools import product
+
+import numpy as np
+from numpy.linalg import norm
+from sklearn.linear_model import Lasso
+
+from skglm.penalties import L1
+from skglm.solvers.gram_cd import gram_cd_solver
+from skglm.utils import make_correlated_data, compiled_clone
+
+
+@pytest.mark.parametrize("rho, X_density, greedy_cd",
+                         product([1e-1, 1e-3], [1., 0.8], [True, False]))
+def test_vs_lasso_sklearn(rho, X_density, greedy_cd):
+    X, y, _ = make_correlated_data(
+        n_samples=18, n_features=8, random_state=0, X_density=X_density)
+    alpha_max = norm(X.T @ y, ord=np.inf) / len(y)
+    alpha = rho * alpha_max
+
+    sk_lasso = Lasso(alpha, fit_intercept=False, tol=1e-9)
+    sk_lasso.fit(X, y)
+
+    l1_penalty = compiled_clone(L1(alpha))
+    w = gram_cd_solver(X, y, l1_penalty, tol=1e-9, verbose=0,
+                       max_iter=1000, greedy_cd=greedy_cd)[0]
+
+    np.testing.assert_allclose(w, sk_lasso.coef_.flatten(), rtol=1e-7, atol=1e-7)
+
+
+if __name__ == '__main__':
+    pass