ENH: add plot for convergence

Author: jasmainak

doc/contributing.rst

@@ -161,9 +161,11 @@ The following should be installed in order to build the documentation.
 
 *    `sphinx <https://github.com/sphinx-doc/sphinx/>`_
 *    `sphinx-gallery <https://github.com/sphinx-gallery/sphinx-gallery/>`_
+*    `sphinx_bootstrap_theme <https://github.com/ryan-roemer/sphinx-bootstrap-theme>`_
 *    `pillow <https://github.com/python-pillow/Pillow/>`_
 *    `numpydoc <https://github.com/numpy/numpydoc/>`_
 *    `matplotlib <https://github.com/matplotlib/matplotlib/>`_
+*    `spykes <http://kordinglab.com/spykes/>`_
 
 Shortcut:
 

doc/install.rst

@@ -6,25 +6,22 @@ Check dependencies
 ------------------
 We currently support ``Python 3.5+``.
 
-For the package: ``numpy>=1.11``, ``scipy>=0.17``, ``scikit-learn>=0.18``
+For the package: ``numpy>=1.11`` and ``scipy>=0.17``. For the plotting code,
+optionally install ``matplotlib``.
 
-Additionally, for examples: ``pandas>=0.20``
+Additionally, for examples: ``pandas>=0.20`` and ``scikit-learn>=0.18``.
+Our library is ``scikit-learn`` compatible.
 
 Both `Canopy <https://www.enthought.com/products/canopy/>`__
 and `Anaconda <https://www.continuum.io/downloads>`__
 ship with a recent version of all these packages.
 
-Additionally, for development, tests and coverage: ``pytest``, ``pytest-cov``, ``coverage``, ``flake8``
-
-Additionally, for building documentation: ``sphinx``, ``sphinx-gallery``, ``sphinx_bootstrap_theme``, ``pillow``, ``numpydoc``, ``matplotlib``, ``spykes``
-
 In case you have other distributions of Python, you can install
 the dependencies using ``pip``.
 
 .. code-block:: bash
 
    pip install numpy scipy
-   pip install -U scikit-learn
 
 Get pyglmnet
 ------------

doc/whats_new.rst

@@ -11,6 +11,8 @@ Release notes
 Current
 -------
 
+    - Add method :method:`pyglmnet.GLM.plot_convergence` to inspect convergence by `Mainak Jas`_.
+
 Changelog
 ~~~~~~~~~
 
@@ -20,6 +22,7 @@ BUG
     - Graceful handling of small Hessian term in coordinate descent solver that led to exploding update term by `Pavan Ramkumar`_.
     - Ensure full compatibility of `GLM` class with `scikit-learn` by `Titipat Achakulvisut`_.
 
+
 API
 ~~~
 

examples/plot_community_crime.py

@@ -88,3 +88,7 @@
 ax.spines['top'].set_visible(False)
 ax.spines['right'].set_visible(False)
 plt.show()
+
+########################################################
+# We can also check if the algorithm converged properly
+glmcv.best_estimator_.plot_convergence()

pyglmnet/pyglmnet.py

@@ -819,6 +819,7 @@ def fit(self, X, y):
             # init active set
             ActiveSet = np.ones_like(beta)
 
+        self._convergence = list()
         # Iterative updates
         for t in range(0, self.max_iter):
             self.n_iter_ += 1
@@ -855,7 +856,9 @@ def fit(self, X, y):
 
             # Convergence by relative parameter change tolerance
             norm_update = np.linalg.norm(beta - beta_old)
-            if t > 1 and (norm_update / np.linalg.norm(beta)) < tol:
+            norm_update /= np.linalg.norm(beta)
+            self._convergence.append(norm_update)
+            if t > 1 and self._convergence[-1] < tol:
                 msg = ('\tParameter update tolerance. ' +
                        'Converged in {0:d} iterations'.format(t))
                 logger.info(msg)
@@ -880,6 +883,37 @@ def fit(self, X, y):
         self.is_fitted_ = True
         return self
 
+    def plot_convergence(self, ax=None, show=True):
+        """Plots the convergence.
+
+        Parameters
+        ----------
+        ax : matplotlib.pyplot.axes object
+            If not None, plot in this axis.
+        show : bool
+            If True, call plt.show()
+
+        Returns
+        -------
+        fig : matplotlib.Figure
+            The matplotlib figure handle
+        """
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig, ax = plt.subplots(1, 1)
+
+        ax.semilogy(self._convergence)
+        ax.set_xlim((-20, self.max_iter + 20))
+        ax.axhline(self.tol, linestyle='--', color='r', label='tol')
+        ax.set_ylabel(r'$\Vert\beta_{t} - \beta_{t-1}\Vert/\Vert\beta_t\Vert$')
+        ax.legend()
+
+        if show:
+            plt.show()
+
+        return ax.get_figure()
+
     def predict(self, X):
         """Predict targets.
 

tests/test_pyglmnet.py

@@ -1,3 +1,5 @@
+import matplotlib
+
 import subprocess
 import os.path as op
 
@@ -19,6 +21,8 @@
 from pyglmnet import (GLM, GLMCV, _grad_L2loss, _L2loss, simulate_glm,
                       _gradhess_logloss_1d, _loss, datasets, ALLOWED_DISTRS)
 
+matplotlib.use('agg')
+
 
 def test_glm_estimator():
     """Test GLM class using scikit-learn's check_estimator."""
@@ -482,6 +486,7 @@ def test_api_input():
     glm.fit(X, y)
     glm.predict(X)
     glm.score(X, y)
+    glm.plot_convergence()
     glm = GLM(distr='gaussian', solver='test')
 
     with pytest.raises(ValueError, match="solver must be one of"):