Merge pull request #201 from JesseLivezey/index_mask

Index mask in Logistic regression

LICENSE.txt

@@ -1,4 +1,4 @@
-PyUol Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy).  All rights reserved.
+PyUoI Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy).  All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
 

README.md

@@ -46,7 +46,7 @@ PyUoI requires
 
 * numpy>=1.14
 * h5py>=2.8
-* scikit-learn>=0.20
+* scikit-learn>=0.24
 
 and optionally
 
@@ -89,8 +89,8 @@ Please see our <a href="https://pyuoi.readthedocs.io/en/latest/">ReadTheDocs</a>
 
 # Copyright
 
-PyUol Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy).  All rights reserved.
+PyUoI Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy).  All rights reserved.
 
-If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation & Partnerships Office at  IPO@lbl.gov referring to " PyUol" (LBNL Ref 2019-157)."
+If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Innovation & Partnerships Office at  IPO@lbl.gov referring to " PyUoI" (LBNL Ref 2019-157)."
 
 NOTICE.  This software was developed under funding from the U.S. Department of Energy.  As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, and perform publicly and display publicly.  The U.S. Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.

bin/generate_build.sh

@@ -1,6 +1,6 @@
 eval "$(conda shell.bash hook)"
 mkdir dist
-for py in 3.6 3.7; do
+for py in 3.6 3.7 3.8; do
   git clone https://github.com/BouchardLab/pyuoi.git
   cd pyuoi
   conda create -y -n temp_build_env python=$py

docs/source/installation.rst

@@ -28,7 +28,7 @@ PyUoI requires
 
   * numpy>=1.14
   * h5py>=2.8
-  * scikit-learn>=0.20
+  * scikit-learn>=0.24
 
 and optionally
 

pyuoi/datasets/__init__.py

@@ -126,7 +126,10 @@ def make_classification(n_samples=100, n_features=20, n_informative=2,
     if isinstance(random_state, int):
         rng = np.random.RandomState(random_state)
     else:
-        rng = random_state
+        if random_state is None:
+            rng = np.random
+        else:
+            rng = random_state
     n_not_informative = n_features - n_informative
 
     X = rng.randn(n_samples, n_features)

pyuoi/linear_model/logistic.py

@@ -541,9 +541,10 @@ def _logistic_regression_path(X, y, Cs=48, fit_intercept=True,
     _, n_features = X.shape
 
     classes = np.unique(y)
+    n_classes = len(classes)
 
     if multi_class == 'auto':
-        if len(classes) > 2:
+        if n_classes > 2:
             multi_class = 'multinomial'
         else:
             multi_class = 'ovr'
@@ -616,9 +617,19 @@ def _logistic_regression_path(X, y, Cs=48, fit_intercept=True,
         target = Y_multi
         if penalty == 'l2':
             w0 = w0.ravel()
+            if coef_mask is not None:
+                x0 = np.zeros_like(w0)
 
             def func(x, *args):
-                return _multinomial_loss_grad(x, *args)[0:2]
+                mask = args[3]
+                if mask is not None:
+                    x0[mask] = x
+                    args = args[:3] + (None,) + (args[-1],)
+                    f, df = _multinomial_loss_grad(x0, *args)[0:2]
+                    df = df[mask]
+                else:
+                    f, df = _multinomial_loss_grad(x, *args)[0:2]
+                return f, df
         else:
             w0 = w0.T.ravel().copy()
 
@@ -634,7 +645,18 @@ def func(x, g, *args):
     else:
         target = y_bin
         if penalty == 'l2':
-            func = _logistic_loss_and_grad
+            x0 = np.zeros_like(w0)
+
+            def func(x, *args):
+                mask = args[3]
+                if mask is not None:
+                    x0[mask] = x
+                    args = args[:3] + (None,) + (args[-1],)
+                    f, df = _logistic_loss_and_grad(x0, *args)
+                    df = df[mask]
+                else:
+                    f, df = _logistic_loss_and_grad(x, *args)
+                return f, df
         else:
             def func(x, g, *args):
                 loss, grad = _logistic_loss_and_grad(x, *args)
@@ -644,63 +666,79 @@ def func(x, g, *args):
     coefs = list()
     n_iter = np.zeros(len(Cs), dtype=np.int32)
     for i, C in enumerate(Cs):
-        iprint = [-1, 50, 1, 100, 101][
-            np.searchsorted(np.array([0, 1, 2, 3]), verbose)]
-        if penalty == 'l2':
-            w0, loss, info = optimize.fmin_l_bfgs_b(
-                func, w0, fprime=None,
-                args=(X, target, 1. / C, coef_mask, sample_weight),
-                iprint=iprint, pgtol=tol, maxiter=max_iter)
-        else:
-            zeros_seen = [0]
-
-            def zero_coef(x, *args):
-                if multi_class == 'multinomial':
-                    x = x.reshape(-1, classes.size)[:-1]
-                else:
-                    x = x[:-1]
-                now_zeros = np.array_equiv(x, 0.)
-                if now_zeros:
-                    zeros_seen[0] += 1
+        if coef_mask is None or coef_mask.sum():
+            iprint = [-1, 50, 1, 100, 101][
+                np.searchsorted(np.array([0, 1, 2, 3]), verbose)]
+            if penalty == 'l2':
+                if coef_mask is None:
+                    w0, loss, info = optimize.fmin_l_bfgs_b(
+                        func, w0, fprime=None,
+                        args=(X, target, 1. / C, coef_mask, sample_weight),
+                        iprint=iprint, pgtol=tol, maxiter=max_iter)
                 else:
-                    zeros_seen[0] = 0
-                if zeros_seen[0] > 1:
-                    return -2048
-            try:
-                w0 = fmin_lbfgs(func, w0, orthantwise_c=1. / C,
-                                args=(X, target, 0., coef_mask, sample_weight),
-                                max_iterations=max_iter,
-                                epsilon=tol,
-                                orthantwise_end=coef_size,
-                                progress=zero_coef)
-            except AllZeroLBFGSError:
-                w0 *= 0.
-            info = None
-        if info is not None and info["warnflag"] == 1:
-            warnings.warn("lbfgs failed to converge. Increase the number "
-                          "of iterations.", ConvergenceWarning)
-        # In scipy <= 1.0.0, nit may exceed maxiter.
-        # See https://github.com/scipy/scipy/issues/7854.
-        if info is None:
-            n_iter_i = -1
-        else:
-            n_iter_i = min(info['nit'], max_iter)
+                    if fit_intercept:
+                        if multi_class == 'multinomial':
+                            mask = [coef_mask,
+                                    np.ones(n_classes)[:, np.newaxis]]
+                            mask = np.concatenate(mask, axis=1)
+                        else:
+                            mask = np.concatenate([coef_mask, np.ones(1)])
+                    else:
+                        mask = coef_mask
+                    mask = np.nonzero(mask.ravel())[0]
+                    wp = w0[mask]
+                    wp, loss, info = optimize.fmin_l_bfgs_b(
+                        func, wp, fprime=None,
+                        args=(X, target, 1. / C, mask, sample_weight),
+                        iprint=iprint, pgtol=tol, maxiter=max_iter)
+                    w0 = np.zeros_like(w0)
+                    w0[mask] = wp
+
+            else:
+                zeros_seen = [0]
+
+                def zero_coef(x, *args):
+                    if multi_class == 'multinomial':
+                        x = x.reshape(-1, classes.size)[:-1]
+                    else:
+                        x = x[:-1]
+                    now_zeros = np.array_equiv(x, 0.)
+                    if now_zeros:
+                        zeros_seen[0] += 1
+                    else:
+                        zeros_seen[0] = 0
+                    if zeros_seen[0] > 1:
+                        return -2048
+                try:
+                    args = (X, target, 0., coef_mask, sample_weight)
+                    w0 = fmin_lbfgs(func, w0, orthantwise_c=1. / C,
+                                    args=args,
+                                    max_iterations=max_iter,
+                                    epsilon=tol,
+                                    orthantwise_end=coef_size,
+                                    progress=zero_coef)
+                except AllZeroLBFGSError:
+                    w0 *= 0.
+                info = None
+            if info is not None and info["warnflag"] == 1:
+                warnings.warn("lbfgs failed to converge. Increase the number "
+                              "of iterations.", ConvergenceWarning)
+            # In scipy <= 1.0.0, nit may exceed maxiter.
+            # See https://github.com/scipy/scipy/issues/7854.
+            if info is None:
+                n_iter_i = -1
+            else:
+                n_iter_i = min(info['nit'], max_iter)
+
+            n_iter[i] = n_iter_i
 
         if multi_class == 'multinomial':
             n_classes = max(2, classes.size)
             if penalty == 'l2':
-                multi_w0 = np.reshape(w0, (n_classes, -1))
+                w0 = np.reshape(w0, (n_classes, -1))
             else:
-                multi_w0 = np.reshape(w0, (-1, n_classes)).T
-            if coef_mask is not None:
-                multi_w0[:, :n_features] *= coef_mask
-            coefs.append(multi_w0.copy())
-        else:
-            if coef_mask is not None:
-                w0[:n_features] *= coef_mask
-            coefs.append(w0.copy())
-
-        n_iter[i] = n_iter_i
+                w0 = np.reshape(w0, (-1, n_classes)).T
+        coefs.append(w0.copy())
 
     return np.array(coefs), np.array(Cs), n_iter
 

requirements.txt

@@ -1,3 +1,3 @@
 numpy>=1.14
 h5py>=2.8
-scikit-learn>=0.20
+scikit-learn>=0.24

setup.py

@@ -46,7 +46,7 @@ def finalize_options(self):
     # Versions should comply with PEP440.  For a discussion on single-sourcing
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
-    version='1.0.0',
+    version='1.1.0',
 
     description='The Union of Intersections framework in Python.',
     long_description=long_description,