Updated examples

Author: langmore

README.txt

@@ -1,46 +1,3 @@
-What the l1 addition is
-=======================
-A slight modification that allows l1 regularized LikelihoodModel.
-
-Regularization is handled by a fit_regularized method.
-
-Main Files
-==========
-
-l1_demo/demo.py
-    $ python demo.py --get_l1_slsqp_results logit
-    does a quick demo of the regularization using logistic regression.
-
-l1_demo/sklearn_compare.py
-    $ python sklearn_compare.py
-    Plots a comparison of regularization paths.  Modify the source to use
-    different datasets.
-
-statsmodels/base/l1_cvxopt.py
-    fit_l1_cvxopt_cp()
-        Fit likelihood model using l1 regularization.  Use the CVXOPT package.
-    Lots of small functions supporting fit_l1_cvxopt_cp
-
-statsmodels/base/l1_slsqp.py
-    fit_l1_slsqp()
-        Fit likelihood model using l1 regularization.  Use scipy.optimize
-    Lots of small functions supporting fit_l1_slsqp
-
-statsmodels/base/l1_solvers_common.py
-    Common methods used by l1 solvers
-
-statsmodels/base/model.py
-    Likelihoodmodel.fit()
-        3 lines modified to allow for importing and calling of l1 fitting functions
-
-statsmodels/discrete/discrete_model.py
-    L1MultinomialResults class
-        Child of MultinomialResults
-    MultinomialModel.fit()
-        3 lines re-directing l1 fit results to the L1MultinomialResults class
-
-
-
 What Statsmodels is
 ===================
 What it is

README_l1.txt

@@ -0,0 +1,40 @@
+What the l1 addition is
+=======================
+A slight modification that allows l1 regularized LikelihoodModel.
+
+Regularization is handled by a fit_regularized method.
+
+Main Files
+==========
+
+l1_demo/demo.py
+    $ python demo.py --get_l1_slsqp_results logit
+    does a quick demo of the regularization using logistic regression.
+
+l1_demo/sklearn_compare.py
+    $ python sklearn_compare.py
+    Plots a comparison of regularization paths.  Modify the source to use
+    different datasets.
+
+statsmodels/base/l1_cvxopt.py
+    fit_l1_cvxopt_cp()
+        Fit likelihood model using l1 regularization.  Use the CVXOPT package.
+    Lots of small functions supporting fit_l1_cvxopt_cp
+
+statsmodels/base/l1_slsqp.py
+    fit_l1_slsqp()
+        Fit likelihood model using l1 regularization.  Use scipy.optimize
+    Lots of small functions supporting fit_l1_slsqp
+
+statsmodels/base/l1_solvers_common.py
+    Common methods used by l1 solvers
+
+statsmodels/base/model.py
+    Likelihoodmodel.fit()
+        3 lines modified to allow for importing and calling of l1 fitting functions
+
+statsmodels/discrete/discrete_model.py
+    L1MultinomialResults class
+        Child of MultinomialResults
+    MultinomialModel.fit()
+        3 lines re-directing l1 fit results to the L1MultinomialResults class

l1_demo/short_demo.py

@@ -11,9 +11,6 @@
 
 The l1 Solvers
 --------------
-The solvers are slower than standard Newton, and sometimes have
-    convergence issues Nonetheless, the final solution makes sense and
-    is often better than the ML solution.
 The standard l1 solver is fmin_slsqp and is included with scipy.  It
     sometimes has trouble verifying convergence when the data size is
     large.
@@ -36,14 +33,19 @@
 logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
 ## Standard logistic regression
 logit_res = logit_mod.fit()
+
 ## Regularized regression
+
 # Set the reularization parameter to something reasonable
 alpha = 0.05 * N * np.ones(K)
+
 # Use l1, which solves via a built-in (scipy.optimize) solver
 logit_l1_res = logit_mod.fit_regularized(method='l1', alpha=alpha, acc=1e-6)
+
 # Use l1_cvxopt_cp, which solves with a CVXOPT solver
 logit_l1_cvxopt_res = logit_mod.fit_regularized(
         method='l1_cvxopt_cp', alpha=alpha)
+
 ## Print results
 print "============ Results for Logit ================="
 print "ML results"
@@ -58,32 +60,39 @@
 anes_exog = sm.add_constant(anes_exog, prepend=False)
 mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
 mlogit_res = mlogit_mod.fit()
+
 ## Set the regularization parameter.
 alpha = 10 * np.ones((mlogit_mod.J - 1, mlogit_mod.K))
+
 # Don't regularize the constant
 alpha[-1,:] = 0
 mlogit_l1_res = mlogit_mod.fit_regularized(method='l1', alpha=alpha)
 print mlogit_l1_res.params
+
 #mlogit_l1_res = mlogit_mod.fit_regularized(
 #        method='l1_cvxopt_cp', alpha=alpha, abstol=1e-10, trim_tol=1e-6)
 #print mlogit_l1_res.params
+
 ## Print results
 print "============ Results for MNLogit ================="
 print "ML results"
 print mlogit_res.summary()
 print "l1 results"
 print mlogit_l1_res.summary()
 #
+#
 #### Logit example with many params, sweeping alpha
 spector_data = sm.datasets.spector.load()
 X = spector_data.exog
 Y = spector_data.endog
+
 ## Fit
 N = 50  # number of points to solve at
 K = X.shape[1]
 logit_mod = sm.Logit(Y, X)
 coeff = np.zeros((N, K))  # Holds the coefficients
 alphas = 1 / np.logspace(-0.5, 2, N)
+
 ## Sweep alpha and store the coefficients
 # QC check doesn't always pass with the default options.
 # Use the options QC_verbose=True and disp=True
@@ -94,6 +103,7 @@
         method='l1', alpha=alpha, trim_mode='off', QC_tol=0.1, disp=False,
         QC_verbose=True, acc=1e-15)
     coeff[n,:] = logit_res.params
+
 ## Plot
 plt.figure(1);plt.clf();plt.grid()
 plt.title('Regularization Path');