EXA Fixed Convergence Warnings On MLP Training Curves (scikit-learn#14144)

martinoywa · jnothman · commit 23d93f06ebcd · 2019-07-29T10:33:55.000+10:00
* fix convergence warnings

* fix convergence warnings

* PEP8

* PEP8

* Fix Convergence Warning by changing the Optimization Algorithm

* PEP8

* Fixed Future Warnings by explicitly defining n_estimators.

* PEP8

* deleted all

* Fixed Convergence Warnings

* removed changes on unrelated examples

* add comment and with statement

* PEP8

* context manager fix

* fixed indentation

* PEP8

* flake8
diff --git a/examples/neural_networks/plot_mlp_training_curves.py b/examples/neural_networks/plot_mlp_training_curves.py
@@ -14,10 +14,15 @@
 """
 
 print(__doc__)
+
+import warnings
+
 import matplotlib.pyplot as plt
+
 from sklearn.neural_network import MLPClassifier
 from sklearn.preprocessing import MinMaxScaler
 from sklearn import datasets
+from sklearn.exceptions import ConvergenceWarning
 
 # different learning rate schedules and momentum parameters
 params = [{'solver': 'sgd', 'learning_rate': 'constant', 'momentum': 0,
@@ -52,6 +57,7 @@ def plot_on_dataset(X, y, ax, name):
     # for each dataset, plot learning for each learning strategy
     print("\nlearning on dataset %s" % name)
     ax.set_title(name)
+
     X = MinMaxScaler().fit_transform(X)
     mlps = []
     if name == "digits":
@@ -64,12 +70,19 @@ def plot_on_dataset(X, y, ax, name):
         print("training: %s" % label)
         mlp = MLPClassifier(verbose=0, random_state=0,
                             max_iter=max_iter, **param)
-        mlp.fit(X, y)
+
+        # some parameter combinations will not converge as can be seen on the
+        # plots so they are ignored here
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=ConvergenceWarning,
+                                    module="sklearn")
+            mlp.fit(X, y)
+
         mlps.append(mlp)
         print("Training set score: %f" % mlp.score(X, y))
         print("Training set loss: %f" % mlp.loss_)
     for mlp, label, args in zip(mlps, labels, plot_args):
-            ax.plot(mlp.loss_curve_, label=label, **args)
+        ax.plot(mlp.loss_curve_, label=label, **args)
 
 
 fig, axes = plt.subplots(2, 2, figsize=(15, 10))
