Merge branch 'master' into dependabot/github_actions/codecov/codecov-action-5.1.2

Author: deepanshs

.github/workflows/continuous-integration-pip.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
 
     steps:
       - name: Checkout
@@ -34,75 +34,12 @@ jobs:
           # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
           flake8 . --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics --exclude="examples/* docs/* build/* utils.py"
 
-  testing_unix:
+  test_os:
     needs: [code_lint]
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: [3.9, "3.10", "3.11"]
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install setuptools
-          pip install -r requirements-dev.txt
-
-      - name: Build and install package from source
-        run: python setup.py develop
-
-      - name: Test with pytest
-        run: pytest --cov=./ --cov-report=xml
-
-      - name: Upload coverage
-        uses: codecov/codecov-action@v5.1.2
-
-  # test-windows:
-  #   needs: [code_lint]
-  #   runs-on: "windows-latest"
-  #   strategy:
-  #     matrix:
-  #       python-version: ["3.10", "3.11"]
-
-  #   steps:
-  #     - name: Checkout
-  #       uses: actions/checkout@v4
-
-  #     - name: Set up Python ${{ matrix.python-version }}
-  #       uses: actions/setup-python@v5
-  #       with:
-  #         python-version: ${{ matrix.python-version }}
-
-  #     - name: Install dependencies
-  #       run: |
-  #         C:\msys64\pacman -S mingw-w64-x86_64-openblas
-  #         python -m pip install --upgrade pip
-  #         pip install setuptools
-  #         pip install -r requirements-dev.txt
-
-  #     - name: Build and install package from source
-  #       run: python setup.py develop
-
-  #     - name: Test with pytest
-  #       run: pytest --cov=./ --cov-report=xml
-
-  #     - name: Upload coverage
-  #       uses: codecov/codecov-action@v5.1.2
-
-  testing_mac_intel:
-    needs: [code_lint]
-    runs-on: "macos-13"
+    runs-on: "ubuntu-latest"
     strategy:
       matrix:
-        python-version: [3.9, "3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - name: Checkout
@@ -115,7 +52,6 @@ jobs:
 
       - name: Install dependencies
         run: |
-          brew install gfortran
           python -m pip install --upgrade pip
           pip install setuptools
           pip install -r requirements-dev.txt

examples/MAF/plot_2d_4_MgO.SiO2.py

@@ -144,7 +144,7 @@ def plot2D(csdm_object, **kwargs):
 # Once the ShieldingPALineshape instance is created, use the
 # :meth:`~mrinversion.kernel.nmr.ShieldingPALineshape.kernel` method of the
 # instance to generate the MAF line-shape kernel.
-K = lineshape.kernel(supersampling=1)
+K = lineshape.kernel(supersampling=1).real
 print(K.shape)
 
 # %%

mrinversion/linear_model/_base_l1l2.py

@@ -523,18 +523,18 @@ def cross_validation_curve(self):
 
 def cv(l1, X, y, cv):
     """Return the cross-validation score as negative of mean square error."""
-    if isinstance(l1, (Lasso, MultiTaskLasso)):
-        fit_params = {"check_input": False}
-    if isinstance(l1, LassoLars):
-        fit_params = None  # {"Xy": np.dot(X.T, y)}
+    # if isinstance(l1, (Lasso, MultiTaskLasso)):
+    #     fit_params = {"check_input": False}
+    # if isinstance(l1, LassoLars):
+    #     fit_params = None  # {"Xy": np.dot(X.T, y)}
 
     cv_score = cross_validate(
         l1,
         X=X,
         y=y,
         scoring="neg_mean_squared_error",  # 'neg_mean_absolute_error",
         cv=cv,
-        fit_params=fit_params,
+        # fit_params=fit_params,
         n_jobs=1,
         verbose=0,
     )

mrinversion/linear_model/fista/__init__.py

@@ -0,0 +1,203 @@
+import numba as nb
+import numpy as np
+from numpy.linalg import norm
+
+
+@nb.njit(fastmath=True)
+def l1_soft_threshold(a: np.ndarray, c: float):
+    """Soft threshold without non-negative constraint."""
+    b = np.abs(a) - c
+    b = np.where(b >= 0, b * np.sign(a), 0.0)
+    return b
+
+
+@nb.njit(fastmath=True)
+def l1_soft_threshold_nonnegative(a: np.ndarray, c: float):
+    """Soft threshold with non-negative constraint."""
+    b = a - c
+    b += np.abs(b)
+    b /= 2.0
+    return b
+
+
+@nb.njit(fastmath=True)
+def fista(
+    matrix: np.ndarray,
+    s: np.ndarray,
+    f_k: np.ndarray,
+    lam: float,
+    max_iter: int,
+    tol: float,
+    l_inv: float,
+    nonnegative: bool = False,
+):
+    matrix = np.asarray(matrix)
+    s = np.asarray(s)
+
+    residue = np.zeros(max_iter)
+    data_consistency = np.zeros(max_iter)
+
+    gradient = matrix.T @ matrix
+    c = matrix.T @ s
+
+    last_data_consistency = 0.0
+    normalization_factor = norm(s) ** 2
+    last_data_consistency = float(normalization_factor)
+
+    last_fk_l1 = 0.0
+    t_kp1 = 1.0
+    y_k = f_k.copy()
+
+    for k in range(max_iter):
+        t_k = t_kp1
+        f_km1 = f_k.copy()
+
+        t_kp1 = (1.0 + np.sqrt(1.0 + 4.0 * t_k**2)) * 0.5
+        constant_factor = (t_k - 1.0) / t_kp1
+
+        temp_c = gradient @ y_k
+        temp_c = l_inv * (c - temp_c)
+        temp_c += y_k
+
+        if nonnegative:
+            f_k[:] = l1_soft_threshold_nonnegative(temp_c, lam * l_inv)
+        else:
+            f_k[:] = l1_soft_threshold(temp_c, lam * l_inv)
+
+        temp = (matrix @ f_k) - s
+        residue_temp = norm(temp) ** 2
+        fk_l1 = np.sum(np.abs(f_k))
+
+        residue[k] = residue_temp
+        data_consistency[k] = residue[k] + lam * fk_l1
+        residue[k] = residue[k] / normalization_factor
+
+        if k >= 5:
+            recent_avg = np.mean(residue[k - 5 : k])
+            if abs(1.0 - (recent_avg / residue[k])) <= tol:
+                break
+
+        # data consistency check
+        if data_consistency[k] > last_data_consistency:
+            f_k[:] = f_km1.copy()
+            fk_l1 = last_fk_l1
+            data_consistency[k] = data_consistency[k - 1]
+            residue[k] = residue[k - 1]
+
+        last_data_consistency = data_consistency[k]
+        last_fk_l1 = fk_l1
+
+        y_k = f_k + constant_factor * (f_k - f_km1)
+
+    zf = matrix @ f_k
+    iter = k
+
+    return zf, f_k, residue[: iter + 1], data_consistency[: iter + 1], iter
+
+
+@nb.njit(fastmath=True)
+def fista_cv_nb(
+    matrix: np.ndarray,
+    s: np.ndarray,
+    matrix_test: np.ndarray,
+    s_test: np.ndarray,
+    max_iter: int,
+    lambda_vals: np.ndarray,
+    nonnegative: bool,
+    l_inv: float,
+    tol: float,
+):
+    n_fold = matrix.shape[-1]
+    n_lambda = len(lambda_vals)
+    n_targets = s.shape[1]
+    n_features = matrix.shape[1]
+    prediction_error = np.zeros((n_lambda, n_fold))
+    iter_arr = np.zeros(n_lambda)
+
+    residue = np.zeros(max_iter)
+    data_consistency = np.zeros(max_iter)
+
+    for fold in range(n_fold):
+        x_train = matrix[..., fold]
+        y_train = s[..., fold]
+        x_test = matrix_test[..., fold]
+        y_test = s_test[..., fold]
+        y_points = y_test.shape[0] * y_test.shape[1]
+
+        gradient = x_train.T @ x_train
+        c = x_train.T @ y_train
+
+        norm_factor = norm(y_train) ** 2
+        f_k = np.zeros((n_features, n_targets))
+        y_k = f_k.copy()
+
+        for j, lam in enumerate(lambda_vals):
+            t_kp1 = 1.0
+            y_k[:] = 0.0
+            f_k[:] = 0.0
+            residue[:] = 0
+            fk_l1 = 0.0
+            last_fk_l1 = 0.0
+            data_consistency[:] = 0
+            last_data_consistency = norm_factor
+
+            for k in range(max_iter):
+                t_k = t_kp1
+                f_km1 = f_k.copy()
+                t_kp1 = (1.0 + np.sqrt(1.0 + 4.0 * t_k**2)) / 2.0
+                constant_factor = (t_k - 1.0) / t_kp1
+
+                grad_yk = gradient @ y_k
+                temp_c = y_k - l_inv * (grad_yk - c)
+
+                if nonnegative:
+                    f_k[:] = l1_soft_threshold_nonnegative(temp_c, l_inv * lam)
+                else:
+                    f_k[:] = l1_soft_threshold(temp_c, l_inv * lam)
+
+                residue[k] = norm(x_train @ f_k - y_train) ** 2
+                fk_l1 = np.sum(np.abs(f_k))
+                data_consistency[k] = residue[k] + lam * fk_l1
+
+                if k >= 5:
+                    recent_avg = np.mean(residue[k - 5 : k])
+                    if abs(1.0 - (recent_avg / residue[k])) <= tol:
+                        break
+
+                # data consistency check
+                if data_consistency[k] > last_data_consistency:
+                    f_k[:] = f_km1.copy()
+                    fk_l1 = last_fk_l1
+                    data_consistency[k] = data_consistency[k - 1]
+                    residue[k] = residue[k - 1]
+
+                last_data_consistency = data_consistency[k]
+                last_fk_l1 = fk_l1
+
+                y_k = f_k + constant_factor * (f_k - f_km1)
+
+            err = np.linalg.norm(x_test @ f_k - y_test) ** 2
+            prediction_error[j, fold] = err / y_points
+            iter_arr[j] = k
+
+    return prediction_error, iter_arr
+
+
+def fista_cv(
+    matrix: np.ndarray,
+    s: np.ndarray,
+    matrix_test: np.ndarray,
+    s_test: np.ndarray,
+    max_iter: int,
+    lambda_vals: np.ndarray,
+    nonnegative: bool,
+    l_inv: float,
+    tol: float,
+):
+    prediction_error, iter_arr = fista_cv_nb(
+        matrix, s, matrix_test, s_test, max_iter, lambda_vals, nonnegative, l_inv, tol
+    )
+
+    cv = prediction_error.mean(axis=1)
+    cvstd = prediction_error.std(axis=1)
+    return cv, cvstd, prediction_error, iter_arr