python3 compatibility for web service example

Author: lazyprogrammer

supervised_class/app.py

@@ -1,3 +1,8 @@
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
 import pickle
@@ -11,7 +16,7 @@
 if not os.path.exists('mymodel.pkl'):
     exit("Can't run without the model!")
 
-with open('mymodel.pkl') as f:
+with open('mymodel.pkl', 'rb') as f:
     model = pickle.load(f)
 
 class MainHandler(tornado.web.RequestHandler):
@@ -27,9 +32,9 @@ def post(self):
         # body: three=four&one=two
         # arguments: {'three': ['four'], 'one': ['two']}
         params = self.request.arguments
-        x = np.array(map(float, params['input']))
+        x = np.array(list(map(float, params['input'])))
         y = model.predict([x])[0]
-        self.write(json.dumps({'prediction': y}))
+        self.write(json.dumps({'prediction': y.item()}))
         self.finish()
 
 if __name__ == "__main__":

supervised_class/app_caller.py

@@ -1,3 +1,8 @@
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
 import requests
@@ -13,13 +18,16 @@
 while True:
     i = np.random.choice(N)
     r = requests.post("http://localhost:8888/predict", data={'input': X[i]})
+    print("RESPONSE:")
+    print(r.content)
     j = r.json()
-    print j
-    print "target:", Y[i]
+    print(j)
+    print("target:", Y[i])
 
     plt.imshow(X[i].reshape(28, 28), cmap='gray')
+    plt.title("Target: %d, Prediction: %d" % (Y[i], j['prediction']))
     plt.show()
 
-    response = raw_input("Continue? (Y/n)\n")
+    response = input("Continue? (Y/n)\n")
     if response in ('n', 'N'):
         break

supervised_class/app_trainer.py

@@ -1,3 +1,8 @@
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
 import pickle
@@ -7,15 +12,15 @@
 
 if __name__ == '__main__':
     X, Y = get_data()
-    Ntrain = len(Y) / 4
+    Ntrain = len(Y) // 4
     Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain]
 
     model = RandomForestClassifier()
     model.fit(Xtrain, Ytrain)
 
     # just in case you're curious
     Xtest, Ytest = X[Ntrain:], Y[Ntrain:]
-    print "test accuracy:", model.score(Xtest, Ytest)
+    print("test accuracy:", model.score(Xtest, Ytest))
 
     with open('mymodel.pkl', 'wb') as f:
         pickle.dump(model, f)

supervised_class/util.py

@@ -1,10 +1,16 @@
 # https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
 # https://www.udemy.com/data-science-supervised-machine-learning-in-python
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import pandas as pd
 
 def get_data(limit=None):
-    print "Reading in and transforming data..."
+    print("Reading in and transforming data...")
     df = pd.read_csv('../large_files/train.csv')
     data = df.as_matrix()
     np.random.shuffle(data)
@@ -30,14 +36,14 @@ def get_donut():
 
     # distance from origin is radius + random normal
     # angle theta is uniformly distributed between (0, 2pi)
-    R1 = np.random.randn(N/2) + R_inner
-    theta = 2*np.pi*np.random.random(N/2)
+    R1 = np.random.randn(N//2) + R_inner
+    theta = 2*np.pi*np.random.random(N//2)
     X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T
 
-    R2 = np.random.randn(N/2) + R_outer
-    theta = 2*np.pi*np.random.random(N/2)
+    R2 = np.random.randn(N//2) + R_outer
+    theta = 2*np.pi*np.random.random(N//2)
     X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T
 
     X = np.concatenate([ X_inner, X_outer ])
-    Y = np.array([0]*(N/2) + [1]*(N/2))
+    Y = np.array([0]*(N//2) + [1]*(N//2))
     return X, Y