diff --git a/ch01/gen_webstats.py b/ch01/gen_webstats.py
index fa133d76..61d0b738 100644
--- a/ch01/gen_webstats.py
+++ b/ch01/gen_webstats.py
@@ -17,26 +17,22 @@
 
 sp.random.seed(3)  # to reproduce the data later on
 
-x = sp.arange(1, 31 * 24)
-y = sp.array(200 * (sp.sin(2 * sp.pi * x / (7 * 24))), dtype=int)
+x = sp.arange(1, 31*24)
+y = sp.array(200*(sp.sin(2*sp.pi*x/(7*24))), dtype=int)
 y += gamma.rvs(15, loc=0, scale=100, size=len(x))
-y += 2 * sp.exp(x / 100.0)
-y = sp.ma.array(y, mask=[y < 0])
-print(sum(y), sum(y < 0))
+y += 2 * sp.exp(x/100.0)
+y = sp.ma.array(y, mask=[y<0])
+print(sum(y), sum(y<0))
 
 plt.scatter(x, y)
 plt.title("Web traffic over the last month")
 plt.xlabel("Time")
 plt.ylabel("Hits/hour")
-plt.xticks([w * 7 * 24 for w in [0, 1, 2, 3, 4]], ['week %i' % (w + 1) for w in
-                                                   [0, 1, 2, 3, 4]])
-
+plt.xticks([w*7*24 for w in range(5)], 
+           ['week %i' %(w+1) for w in range(5)])
 plt.autoscale(tight=True)
 plt.grid()
 plt.savefig(os.path.join(CHART_DIR, "1400_01_01.png"))
 
-# sp.savetxt(os.path.join("..", "web_traffic.tsv"),
-# zip(x[~y.mask],y[~y.mask]), delimiter="\t", fmt="%i")
-
-sp.savetxt(os.path.join(
-    DATA_DIR, "web_traffic.tsv"), list(zip(x, y)), delimiter="\t", fmt="%s")
+sp.savetxt(os.path.join(DATA_DIR, "web_traffic.tsv"), 
+           list(zip(x, y)), delimiter="\t", fmt="%s")
diff --git a/ch02/chapter.py b/ch02/chapter.py
index ac887650..c68b45ab 100644
--- a/ch02/chapter.py
+++ b/ch02/chapter.py
@@ -100,7 +100,6 @@ def is_virginica_test(fi, t, reverse, example):
     training[ei] = False
     testing = ~training
     model = fit_model(features[training], is_virginica[training])
-    predict(model, features[testing])
     predictions = predict(model, features[testing])
     correct += np.sum(predictions == is_virginica[testing])
 acc = correct/float(len(features))
diff --git a/ch04/README.rst b/ch04/README.rst
index 7fe0a92f..99a3c186 100644
--- a/ch04/README.rst
+++ b/ch04/README.rst
@@ -4,6 +4,16 @@ Chapter 4
 
 Support code for *Chapter 4: Topic Modeling*
 
+
+AP Data
+-------
+
+To download the AP data, use the ``download_ap.sh`` script inside the ``data``
+directory::
+
+    cd data
+    ./download_ap.sh
+
 Word cloud creation
 -------------------
 
diff --git a/ch04/blei_lda.py b/ch04/blei_lda.py
index bbad9d1f..7f6ac2b3 100644
--- a/ch04/blei_lda.py
+++ b/ch04/blei_lda.py
@@ -36,9 +36,9 @@
 # Iterate over all the topics in the model
 for ti in range(model.num_topics):
     words = model.show_topic(ti, 64)
-    tf = sum(f for f, w in words)
+    tf = sum(f for _, f in words)
     with open('topics.txt', 'w') as output:
-        output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for f, w in words))
+        output.write('\n'.join('{}:{}'.format(w, int(1000. * f / tf)) for w, f in words))
         output.write("\n\n\n")
 
 # We first identify the most discussed topic, i.e., the one with the
diff --git a/ch04/data/download_ap.sh b/ch04/data/download_ap.sh
index 6de8ded8..da27814a 100755
--- a/ch04/data/download_ap.sh
+++ b/ch04/data/download_ap.sh
@@ -1,3 +1,3 @@
 #!/bin/sh
-wget http://www.cs.princeton.edu/~blei/lda-c/ap.tgz
+wget http://www.cs.columbia.edu/~blei/lda-c/ap.tgz
 tar xzf ap.tgz
diff --git a/ch04/wordcloud.py b/ch04/wordcloud.py
index 6c5302ea..accca2d6 100644
--- a/ch04/wordcloud.py
+++ b/ch04/wordcloud.py
@@ -24,8 +24,6 @@ def create_cloud(oname, words,maxsize=120, fontname='Lobster'):
     # gensim returns a weight between 0 and 1 for each word, while pytagcloud
     # expects an integer word count. So, we multiply by a large number and
     # round. For a visualization this is an adequate approximation.
-    # We also need to flip the order as gensim returns (value, word), whilst
-    # pytagcloud expects (word, value):
-    words = [(w,int(v*10000)) for v,w in words]
+    words = [(w,int(v*10000)) for w,v in words]
     tags = make_tags(words, maxsize=maxsize)
     create_tag_image(tags, oname, size=(1800, 1200), fontname=fontname)
diff --git a/ch12/image-classification.py b/ch12/image-classification.py
index 09dbd5b4..6f76d26d 100644
--- a/ch12/image-classification.py
+++ b/ch12/image-classification.py
@@ -39,7 +39,7 @@ def compute_texture(im):
 
 @TaskGenerator
 def chist(fname):
-    from features import color_histogram
+    from features import chist as color_histogram
     im = mh.imread(fname)
     return color_histogram(im)