[MRG+2] ENH : SAGA support for LogisticRegression (+ L1 support), Ridge

benchmarks/bench_mnist.py

@@ -91,7 +91,10 @@ def load_data(dtype=np.float32, order='F'):
         Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
     'SampledRBF-SVM': make_pipeline(
         RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)),
-    'LinearRegression-SAG': LogisticRegression(solver='sag', tol=1e-1, C=1e4),
+    'LogisticRegression-SAG': LogisticRegression(solver='sag', tol=1e-1,
+                                                 C=1e4),
+    'LogisticRegression-SAGA': LogisticRegression(solver='saga', tol=1e-1,
+                                                  C=1e4),
     'MultilayerPerceptron': MLPClassifier(
         hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
         solver='sgd', learning_rate_init=0.2, momentum=0.9, verbose=1,

benchmarks/bench_saga.py

@@ -0,0 +1,244 @@
+"""Author: Arthur Mensch
+
+Benchmarks of sklearn SAGA vs lightning SAGA vs Liblinear. Shows the gain
+in using multinomial logistic regression in term of learning time.
+"""
+import json
+import time
+from os.path import expanduser
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.datasets import fetch_rcv1, load_iris, load_digits, \
+    fetch_20newsgroups_vectorized
+from sklearn.externals.joblib import delayed, Parallel, Memory
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import log_loss
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelBinarizer, LabelEncoder
+from sklearn.utils.extmath import safe_sparse_dot, softmax
+
+
+def fit_single(solver, X, y, penalty='l2', single_target=True, C=1,
+               max_iter=10, skip_slow=False):
+    if skip_slow and solver == 'lightning' and penalty == 'l1':
+        print('skip_slowping l1 logistic regression with solver lightning.')
+        return
+
+    print('Solving %s logistic regression with penalty %s, solver %s.'
+          % ('binary' if single_target else 'multinomial',
+             penalty, solver))
+
+    if solver == 'lightning':
+        from lightning.classification import SAGAClassifier
+
+    if single_target or solver not in ['sag', 'saga']:
+        multi_class = 'ovr'
+    else:
+        multi_class = 'multinomial'
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42,
+                                                        stratify=y)
+    n_samples = X_train.shape[0]
+    n_classes = np.unique(y_train).shape[0]
+    test_scores = [1]
+    train_scores = [1]
+    accuracies = [1 / n_classes]
+    times = [0]
+
+    if penalty == 'l2':
+        alpha = 1. / (C * n_samples)
+        beta = 0
+        lightning_penalty = None
+    else:
+        alpha = 0.
+        beta = 1. / (C * n_samples)
+        lightning_penalty = 'l1'
+
+    for this_max_iter in range(1, max_iter + 1, 2):
+        print('[%s, %s, %s] Max iter: %s' %
+              ('binary' if single_target else 'multinomial',
+               penalty, solver, this_max_iter))
+        if solver == 'lightning':
+            lr = SAGAClassifier(loss='log', alpha=alpha, beta=beta,
+                                penalty=lightning_penalty,
+                                tol=-1, max_iter=this_max_iter)
+        else:
+            lr = LogisticRegression(solver=solver,
+                                    multi_class=multi_class,
+                                    C=C,
+                                    penalty=penalty,
+                                    fit_intercept=False, tol=1e-24,
+                                    max_iter=this_max_iter,
+                                    random_state=42,
+                                    )
+        t0 = time.clock()
+        lr.fit(X_train, y_train)
+        train_time = time.clock() - t0
+
+        scores = []
+        for (X, y) in [(X_train, y_train), (X_test, y_test)]:
+            try:
+                y_pred = lr.predict_proba(X)
+            except NotImplementedError:
+                # Lightning predict_proba is not implemented for n_classes > 2
+                y_pred = _predict_proba(lr, X)
+            score = log_loss(y, y_pred, normalize=False) / n_samples
+            score += (0.5 * alpha * np.sum(lr.coef_ ** 2) +
+                      beta * np.sum(np.abs(lr.coef_)))
+            scores.append(score)
+        train_score, test_score = tuple(scores)
+
+        y_pred = lr.predict(X_test)
+        accuracy = np.sum(y_pred == y_test) / y_test.shape[0]
+        test_scores.append(test_score)
+        train_scores.append(train_score)
+        accuracies.append(accuracy)
+        times.append(train_time)
+    return lr, times, train_scores, test_scores, accuracies
+
+
+def _predict_proba(lr, X):
+    pred = safe_sparse_dot(X, lr.coef_.T)
+    if hasattr(lr, "intercept_"):
+        pred += lr.intercept_
+    return softmax(pred)
+
+
+def exp(solvers, penalties, single_target, n_samples=30000, max_iter=20,
+        dataset='rcv1', n_jobs=1, skip_slow=False):
+    mem = Memory(cachedir=expanduser('~/cache'), verbose=0)
+
+    if dataset == 'rcv1':
+        rcv1 = fetch_rcv1()
+
+        lbin = LabelBinarizer()
+        lbin.fit(rcv1.target_names)
+
+        X = rcv1.data
+        y = rcv1.target
+        y = lbin.inverse_transform(y)
+        le = LabelEncoder()
+        y = le.fit_transform(y)
+        if single_target:
+            y_n = y.copy()
+            y_n[y > 16] = 1
+            y_n[y <= 16] = 0
+            y = y_n
+
+    elif dataset == 'digits':
+        digits = load_digits()
+        X, y = digits.data, digits.target
+        if single_target:
+            y_n = y.copy()
+            y_n[y < 5] = 1
+            y_n[y >= 5] = 0
+            y = y_n
+    elif dataset == 'iris':
+        iris = load_iris()
+        X, y = iris.data, iris.target
+    elif dataset == '20newspaper':
+        ng = fetch_20newsgroups_vectorized()
+        X = ng.data
+        y = ng.target
+        if single_target:
+            y_n = y.copy()
+            y_n[y > 4] = 1
+            y_n[y <= 16] = 0
+            y = y_n
+
+    X = X[:n_samples]
+    y = y[:n_samples]
+
+    cached_fit = mem.cache(fit_single)
+    out = Parallel(n_jobs=n_jobs, mmap_mode=None)(
+        delayed(cached_fit)(solver, X, y,
+                            penalty=penalty, single_target=single_target,
+                            C=1, max_iter=max_iter, skip_slow=skip_slow)
+        for solver in solvers
+        for penalty in penalties)
+
+    res = []
+    idx = 0
+    for solver in solvers:
+        for penalty in penalties:
+            if not (skip_slow and solver == 'lightning' and penalty == 'l1'):
+                lr, times, train_scores, test_scores, accuracies = out[idx]
+                this_res = dict(solver=solver, penalty=penalty,
+                                single_target=single_target,
+                                times=times, train_scores=train_scores,
+                                test_scores=test_scores,
+                                accuracies=accuracies)
+                res.append(this_res)
+            idx += 1
+
+    with open('bench_saga.json', 'w+') as f:
+        json.dump(res, f)
+
+
+def plot():
+    import pandas as pd
+    with open('bench_saga.json', 'r') as f:
+        f = json.load(f)
+    res = pd.DataFrame(f)
+    res.set_index(['single_target', 'penalty'], inplace=True)
+
+    grouped = res.groupby(level=['single_target', 'penalty'])
+
+    colors = {'saga': 'blue', 'liblinear': 'orange', 'lightning': 'green'}
+
+    for idx, group in grouped:
+        single_target, penalty = idx
+        fig = plt.figure(figsize=(12, 4))
+        ax = fig.add_subplot(131)
+
+        train_scores = group['train_scores'].values
+        ref = np.min(np.concatenate(train_scores)) * 0.999
+
+        for scores, times, solver in zip(group['train_scores'], group['times'],
+                                         group['solver']):
+            scores = scores / ref - 1
+            ax.plot(times, scores, label=solver, color=colors[solver])
+        ax.set_xlabel('Time (s)')
+        ax.set_ylabel('Training objective (relative to min)')
+        ax.set_yscale('log')
+
+        ax = fig.add_subplot(132)
+
+        test_scores = group['test_scores'].values
+        ref = np.min(np.concatenate(test_scores)) * 0.999
+
+        for scores, times, solver in zip(group['test_scores'], group['times'],
+                                         group['solver']):
+            scores = scores / ref - 1
+            ax.plot(times, scores, label=solver, color=colors[solver])
+        ax.set_xlabel('Time (s)')
+        ax.set_ylabel('Test objective (relative to min)')
+        ax.set_yscale('log')
+
+        ax = fig.add_subplot(133)
+
+        for accuracy, times, solver in zip(group['accuracies'], group['times'],
+                                           group['solver']):
+            ax.plot(times, accuracy, label=solver, color=colors[solver])
+        ax.set_xlabel('Time (s)')
+        ax.set_ylabel('Test accuracy')
+        ax.legend()
+        name = 'single_target' if single_target else 'multi_target'
+        name += '_%s' % penalty
+        plt.suptitle(name)
+        name += '.png'
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.9)
+        plt.savefig(name)
+        plt.close(fig)
+
+
+if __name__ == '__main__':
+    solvers = ['saga', 'liblinear', 'lightning']
+    penalties = ['l1', 'l2']
+    single_target = True
+    exp(solvers, penalties, single_target, n_samples=None, n_jobs=1,
+        dataset='20newspaper', max_iter=20)
+    plot()

doc/modules/linear_model.rst

@@ -721,7 +721,7 @@ optimization problem
 .. math:: \underset{w, c}{min\,} \|w\|_1 + C \sum_{i=1}^n \log(\exp(- y_i (X_i^T w + c)) + 1) .
 
 The solvers implemented in the class :class:`LogisticRegression`
-are "liblinear", "newton-cg", "lbfgs" and "sag":
+are "liblinear", "newton-cg", "lbfgs", "sag" and "saga":
 
 The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies
 on the excellent C++ `LIBLINEAR library
@@ -739,25 +739,31 @@ The "lbfgs", "sag" and "newton-cg" solvers only support L2 penalization and
 are found to converge faster for some high dimensional data. Setting
 `multi_class` to "multinomial" with these solvers learns a true multinomial
 logistic regression model [5]_, which means that its probability estimates
-should be better calibrated than the default "one-vs-rest" setting. The
-"lbfgs", "sag" and "newton-cg"" solvers cannot optimize L1-penalized models,
-therefore the "multinomial" setting does not learn sparse models.
+should be better calibrated than the default "one-vs-rest" setting.
 
-The solver "sag" uses a Stochastic Average Gradient descent [6]_. It is faster
+The "sag" solver uses a Stochastic Average Gradient descent [6]_. It is faster
 than other solvers for large datasets, when both the number of samples and the
 number of features are large.
 
+The "saga" solver [7]_ is a variant of "sag" that also supports the
+non-smooth `penalty="l1"` option. This is therefore the solver of choice
+for sparse multinomial logistic regression.
+
 In a nutshell, one may choose the solver with the following rules:
 
-=================================  =============================
+=================================  =====================================
 Case                               Solver
-=================================  =============================
-Small dataset or L1 penalty        "liblinear"
-Multinomial loss or large dataset  "lbfgs", "sag" or "newton-cg"
-Very Large dataset                 "sag"
-=================================  =============================
+=================================  =====================================
+L1 penalty                         "liblinear" or "saga"
+Multinomial loss                   "lbfgs", "sag", "saga" or "newton-cg"
+Very Large dataset (`n_samples`)   "sag" or "saga"
+=================================  =====================================
+
+The "saga" solver is often the best choice. The "liblinear" solver is
+used by default for historical reasons.
 
-For large dataset, you may also consider using :class:`SGDClassifier` with 'log' loss.
+For large dataset, you may also consider using :class:`SGDClassifier`
+with 'log' loss.
 
 .. topic:: Examples:
 
@@ -767,6 +773,10 @@ For large dataset, you may also consider using :class:`SGDClassifier` with 'log'
 
   * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`
 
+  * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py`
+
+  * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py`
+
 .. _liblinear_differences:
 
 .. topic:: Differences from liblinear:
@@ -788,20 +798,23 @@ For large dataset, you may also consider using :class:`SGDClassifier` with 'log'
    thus be used to perform feature selection, as detailed in
    :ref:`l1_feature_selection`.
 
-:class:`LogisticRegressionCV` implements Logistic Regression with builtin
-cross-validation to find out the optimal C parameter. "newton-cg", "sag" and
-"lbfgs" solvers are found to be faster for high-dimensional dense data, due to
-warm-starting. For the multiclass case, if `multi_class` option is set to
-"ovr", an optimal C is obtained for each class and if the `multi_class` option
-is set to "multinomial", an optimal C is obtained by minimizing the cross-
-entropy loss.
+:class:`LogisticRegressionCV` implements Logistic Regression with
+builtin cross-validation to find out the optimal C parameter.
+"newton-cg", "sag", "saga" and "lbfgs" solvers are found to be faster
+for high-dimensional dense data, due to warm-starting. For the
+multiclass case, if `multi_class` option is set to "ovr", an optimal C
+is obtained for each class and if the `multi_class` option is set to
+"multinomial", an optimal C is obtained by minimizing the cross-entropy
+loss.
 
 .. topic:: References:
 
     .. [5] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 4.3.4
 
     .. [6] Mark Schmidt, Nicolas Le Roux, and Francis Bach: `Minimizing Finite Sums with the Stochastic Average Gradient. <https://hal.inria.fr/hal-00860051/document>`_
 
+    .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: `SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. <https://arxiv.org/abs/1407.0202>`_
+
 Stochastic Gradient Descent - SGD
 =================================
 

doc/whats_new.rst

@@ -50,6 +50,13 @@ New features
      particularly useful for targets with an exponential trend.
      :issue:`7655` by :user:`Karan Desai <karandesai-96>`.
 
+   - Added solver ``saga`` that implements the improved version of Stochastic
+     Average Gradient, in :class:`linear_model.LogisticRegression` and
+     :class:`linear_model.Ridge`. It allows the use of L1 penalty with
+     multinomial logistic loss, and behaves marginally better than 'sag'
+     during the first epochs of ridge and logistic regression.
+     By `Arthur Mensch`_.
+
 Enhancements
 ............
 
@@ -5037,3 +5044,4 @@ David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.
 .. _Anish Shah: https://github.com/AnishShah
 
 .. _Neeraj Gangwar: http://neerajgangwar.in
+.. _Arthur Mensch: https://amensch.fr