This closes issue #67 memory error on 32bit Python

Minor refinements due PR feedback
- Removed unnecessary variables and inlined one-time used variable.
- Re-introduced the originally intended solver solver='lbfgs'
- Adhered to PEP8, breaking lines and comments accordingly

mglearn/plot_2d_separator.py

@@ -3,30 +3,30 @@
 from .plot_helpers import cm2, cm3, discrete_scatter
 
 def _call_classifier_chunked(classifier_pred_or_decide, X):
-
-
-    # The chunk_size is used to chunk the large arrays to work with x86 memory
-    # models that are restricted to < 2 GB in memory allocation.
-    # The chunk_size value used here is based on a measurement with the MLPClassifier
-    # using the following parameters:
-    # MLPClassifier(solver='lbfgs', random_state=0, hidden_layer_sizes=[1000,1000,1000])
+    # The chunk_size is used to chunk the large arrays to work with x86
+    # memory models that are restricted to < 2 GB in memory allocation. The
+    # chunk_size value used here is based on a measurement with the
+    # MLPClassifier using the following parameters:
+    # MLPClassifier(solver='lbfgs', random_state=0,
+    #               hidden_layer_sizes=[1000,1000,1000])
     # by reducing the value it is possible to trade in time for memory.
-    # It is possible to chunk the array as the calculations are independent of each other.
-    # Note: an intermittent version made a distinction between 32- and 64 bit architectures
-    # avoiding the chunking. Testing revealed that even on 64 bit architectures the chunking
-    # increases the performance by a factor of 3-5, largely due to the avoidance of memory
+    # It is possible to chunk the array as the calculations are independent of
+    # each other.
+    # Note: an intermittent version made a distinction between
+    # 32- and 64 bit architectures avoiding the chunking. Testing revealed
+    # that even on 64 bit architectures the chunking increases the
+    # performance by a factor of 3-5, largely due to the avoidance of memory
     # swapping.
     chunk_size = 10000
-    X_axis0_size = X.shape[0]
 
     # We use a list to collect all result chunks
     Y_result_chunks = []
 
     # Call the classifier in chunks.
-    y_chunk_pos = 0
-    for x_chunk in np.array_split(X, np.arange(chunk_size,X_axis0_size,chunk_size,dtype=np.int32), axis=0):
+    for x_chunk in np.array_split(X, np.arange(chunk_size, X.shape[0],
+                                               chunk_size, dtype=np.int32),
+                                  axis=0):
         Y_result_chunks.append(classifier_pred_or_decide(x_chunk))
-        y_chunk_pos += x_chunk.shape[0]
 
     return np.concatenate(Y_result_chunks)
 
@@ -111,13 +111,15 @@ def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None, alpha=1,
     X1, X2 = np.meshgrid(xx, yy)
     X_grid = np.c_[X1.ravel(), X2.ravel()]
     if hasattr(classifier, "decision_function"):
-        decision_values = _call_classifier_chunked(classifier.decision_function, X_grid)
+        decision_values = _call_classifier_chunked(classifier.decision_function,
+                                                   X_grid)
         levels = [0] if threshold is None else [threshold]
         fill_levels = [decision_values.min()] + levels + [
             decision_values.max()]
     else:
         # no decision_function
-        decision_values = _call_classifier_chunked(classifier.predict_proba, X_grid)[:, 1]
+        decision_values = _call_classifier_chunked(classifier.predict_proba,
+                                                   X_grid)[:, 1]
         levels = [.5] if threshold is None else [threshold]
         fill_levels = [0] + levels + [1]
     if fill:
@@ -138,7 +140,7 @@ def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None, alpha=1,
     from sklearn.datasets import make_blobs
     from sklearn.linear_model import LogisticRegression
     X, y = make_blobs(centers=2, random_state=42)
-    clf = LogisticRegression().fit(X, y)
+    clf = LogisticRegression(solver='lbfgs').fit(X, y)
     plot_2d_separator(clf, X, fill=True)
     discrete_scatter(X[:, 0], X[:, 1], y)
     plt.show()