first design changes, change template

Author: floriankozikowski

.DS_Store

@@ -0,0 +1,6 @@
+__version__ = '0.4dev'
+
+from skglm.estimators import (  # noqa F401
+    Lasso, WeightedLasso, ElasticNet, MCPRegression, MultiTaskLasso, LinearSVC,
+    SparseLogisticRegression, GeneralizedLinearEstimator, CoxEstimator, GroupLasso,
+)

build/lib/skglm/__init__.py

@@ -0,0 +1,14 @@
+from .base import BaseDatafit, BaseMultitaskDatafit
+from .single_task import (Quadratic, QuadraticSVC, Logistic, Huber, Poisson, Gamma,
+                          Cox, WeightedQuadratic, QuadraticHessian,)
+from .multi_task import QuadraticMultiTask
+from .group import QuadraticGroup, LogisticGroup
+
+
+__all__ = [
+    BaseDatafit, BaseMultitaskDatafit,
+    Quadratic, QuadraticSVC, Logistic, Huber, Poisson, Gamma, Cox,
+    QuadraticMultiTask,
+    QuadraticGroup, LogisticGroup, WeightedQuadratic,
+    QuadraticHessian
+]

build/lib/skglm/datafits/__init__.py

@@ -0,0 +1,143 @@
+
+class BaseDatafit:
+    """Base class for datafits."""
+
+    def get_spec(self):
+        """Specify the numba types of the class attributes.
+
+        Returns
+        -------
+        spec: Tuple of (attribute_name, dtype)
+            spec to be passed to Numba jitclass to compile the class.
+        """
+
+    def params_to_dict(self):
+        """Get the parameters to initialize an instance of the class.
+
+        Returns
+        -------
+        dict_of_params : dict
+            The parameters to instantiate an object of the class.
+        """
+
+    def initialize(self, X, y):
+        """Pre-computations before fitting on X and y.
+
+        Parameters
+        ----------
+        X : array, shape (n_samples, n_features)
+            Design matrix.
+
+        y : array, shape (n_samples,)
+            Target vector.
+        """
+
+    def initialize_sparse(self, X_data, X_indptr, X_indices, y):
+        """Pre-computations before fitting on X and y when X is a sparse matrix.
+
+        Parameters
+        ----------
+        X_data : array, shape (n_elements,)
+            `data` attribute of the sparse CSC matrix X.
+
+        X_indptr : array, shape (n_features + 1,)
+            `indptr` attribute of the sparse CSC matrix X.
+
+        X_indices : array, shape (n_elements,)
+            `indices` attribute of the sparse CSC matrix X.
+
+        y : array, shape (n_samples,)
+            Target vector.
+        """
+
+    def value(self, y, w, Xw):
+        """Value of datafit at vector w.
+
+        Parameters
+        ----------
+        y : array_like, shape (n_samples,)
+            Target vector.
+
+        w : array_like, shape (n_features,)
+            Coefficient vector.
+
+        Xw: array_like, shape (n_samples,)
+            Model fit.
+
+        Returns
+        -------
+        value : float
+            The datafit value at vector w.
+        """
+
+
+class BaseMultitaskDatafit:
+    """Base class for multitask datafits."""
+
+    def get_spec(self):
+        """Specify the numba types of the class attributes.
+
+        Returns
+        -------
+        spec: Tuple of (attribute_name, dtype)
+            spec to be passed to Numba jitclass to compile the class.
+        """
+
+    def params_to_dict(self):
+        """Get the parameters to initialize an instance of the class.
+
+        Returns
+        -------
+        dict_of_params : dict
+            The parameters to instantiate an object of the class.
+        """
+
+    def initialize(self, X, Y):
+        """Store useful values before fitting on X and Y.
+
+        Parameters
+        ----------
+        X : array, shape (n_samples, n_features)
+            Design matrix.
+
+        Y : array, shape (n_samples, n_tasks)
+            Multitask target.
+        """
+
+    def initialize_sparse(self, X_data, X_indptr, X_indices, Y):
+        """Store useful values before fitting on X and Y, when X is sparse.
+
+        Parameters
+        ----------
+        X_data : array-like
+            `data` attribute of the sparse CSC matrix X.
+
+        X_indptr : array-like
+            `indptr` attribute of the sparse CSC matrix X.
+
+        X_indices : array-like
+            `indices` attribute of the sparse CSC matrix X.
+
+        Y : array, shape (n_samples, n_tasks)
+            Target matrix.
+        """
+
+    def value(self, Y, W, XW):
+        """Value of datafit at matrix W.
+
+        Parameters
+        ----------
+        Y : array_like, shape (n_samples, n_tasks)
+            Target matrix.
+
+        W : array_like, shape (n_features, n_tasks)
+            Coefficient matrix.
+
+        XW: array_like, shape (n_samples, n_tasks)
+            Model fit.
+
+        Returns
+        -------
+        value : float
+            The datafit value evaluated at matrix W.
+        """

build/lib/skglm/datafits/base.py

@@ -0,0 +1,163 @@
+import numpy as np
+from numpy.linalg import norm
+from numba import int32, float64
+
+from skglm.datafits.base import BaseDatafit
+from skglm.datafits.single_task import Logistic
+from skglm.utils.sparse_ops import spectral_norm, sparse_columns_slice
+
+
+class QuadraticGroup(BaseDatafit):
+    r"""Quadratic datafit used with group penalties.
+
+    The datafit reads:
+
+    .. math:: 1 / (2 xx n_"samples") ||y - Xw||_2 ^ 2
+
+    Attributes
+    ----------
+    grp_indices : array, shape (n_features,)
+        The group indices stacked contiguously
+        ([grp1_indices, grp2_indices, ...]).
+
+    grp_ptr : array, shape (n_groups + 1,)
+        The group pointers such that two consecutive elements delimit
+        the indices of a group in ``grp_indices``.
+    """
+
+    def __init__(self, grp_ptr, grp_indices):
+        self.grp_ptr, self.grp_indices = grp_ptr, grp_indices
+
+    def get_spec(self):
+        spec = (
+            ('grp_ptr', int32[:]),
+            ('grp_indices', int32[:]),
+        )
+        return spec
+
+    def params_to_dict(self):
+        return dict(grp_ptr=self.grp_ptr,
+                    grp_indices=self.grp_indices)
+
+    def get_lipschitz(self, X, y):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        n_groups = len(grp_ptr) - 1
+
+        lipschitz = np.zeros(n_groups)
+        for g in range(n_groups):
+            grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+            X_g = X[:, grp_g_indices]
+            lipschitz[g] = norm(X_g, ord=2) ** 2 / len(y)
+
+        return lipschitz
+
+    def get_lipschitz_sparse(self, X_data, X_indptr, X_indices, y):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        n_groups = len(grp_ptr) - 1
+
+        lipschitz = np.zeros(n_groups, dtype=X_data.dtype)
+        for g in range(n_groups):
+            grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+            X_data_g, X_indptr_g, X_indices_g = sparse_columns_slice(
+                grp_g_indices, X_data, X_indptr, X_indices)
+            lipschitz[g] = spectral_norm(
+                X_data_g, X_indptr_g, X_indices_g, len(y)) ** 2 / len(y)
+
+        return lipschitz
+
+    def value(self, y, w, Xw):
+        return norm(y - Xw) ** 2 / (2 * len(y))
+
+    def gradient_g(self, X, y, w, Xw, g):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+
+        grad_g = np.zeros(len(grp_g_indices))
+        for idx, j in enumerate(grp_g_indices):
+            grad_g[idx] = self.gradient_scalar(X, y, w, Xw, j)
+
+        return grad_g
+
+    def gradient_g_sparse(self, X_data, X_indptr, X_indices, y, w, Xw, g):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+
+        grad_g = np.zeros(len(grp_g_indices))
+        for idx, j in enumerate(grp_g_indices):
+            grad_g[idx] = self.gradient_scalar_sparse(
+                X_data, X_indptr, X_indices, y, w, Xw, j)
+
+        return grad_g
+
+    def gradient_scalar_sparse(self, X_data, X_indptr, X_indices, y, w, Xw, j):
+        grad_j = 0.
+        for i in range(X_indptr[j], X_indptr[j+1]):
+            grad_j += X_data[i] * (Xw[X_indices[i]] - y[X_indices[i]])
+
+        return grad_j / len(y)
+
+    def gradient_scalar(self, X, y, w, Xw, j):
+        return X[:, j] @ (Xw - y) / len(y)
+
+    def intercept_update_step(self, y, Xw):
+        return np.mean(Xw - y)
+
+
+class LogisticGroup(Logistic):
+    r"""Logistic datafit used with group penalties.
+
+    The datafit reads:
+
+    .. math:: 1 / n_"samples" sum_(i=1)^(n_"samples") log(1 + exp(-y_i (Xw)_i))
+
+    Attributes
+    ----------
+    grp_indices : array, shape (n_features,)
+        The group indices stacked contiguously
+        ``[grp1_indices, grp2_indices, ...]``.
+
+    grp_ptr : array, shape (n_groups + 1,)
+        The group pointers such that two consecutive elements delimit
+        the indices of a group in ``grp_indices``.
+
+    lipschitz : array, shape (n_groups,)
+        The lipschitz constants for each group.
+    """
+
+    def __init__(self, grp_ptr, grp_indices):
+        self.grp_ptr, self.grp_indices = grp_ptr, grp_indices
+
+    def get_spec(self):
+        spec = (
+            ('grp_ptr', int32[:]),
+            ('grp_indices', int32[:]),
+            ('lipschitz', float64[:])
+        )
+        return spec
+
+    def params_to_dict(self):
+        return dict(grp_ptr=self.grp_ptr,
+                    grp_indices=self.grp_indices)
+
+    def initialize(self, X, y):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        n_groups = len(grp_ptr) - 1
+
+        lipschitz = np.zeros(n_groups)
+        for g in range(n_groups):
+            grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+            X_g = X[:, grp_g_indices]
+            lipschitz[g] = norm(X_g, ord=2) ** 2 / (4 * len(y))
+
+        self.lipschitz = lipschitz
+
+    def gradient_g(self, X, y, w, Xw, g):
+        grp_ptr, grp_indices = self.grp_ptr, self.grp_indices
+        grp_g_indices = grp_indices[grp_ptr[g]: grp_ptr[g+1]]
+        raw_grad_val = self.raw_grad(y, Xw)
+
+        grad_g = np.zeros(len(grp_g_indices))
+        for idx, j in enumerate(grp_g_indices):
+            grad_g[idx] = X[:, j] @ raw_grad_val
+
+        return grad_g