py3

Author: Lazy Programmer

ab_testing/bayesian_bandit.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.stats import beta
@@ -41,7 +47,7 @@ def experiment():
   bandits = [Bandit(p) for p in BANDIT_PROBABILITIES]
 
   sample_points = [5,10,20,50,100,200,500,1000,1500,1999]
-  for i in xrange(NUM_TRIALS):
+  for i in range(NUM_TRIALS):
 
     # take a sample from each bandit
     bestb = None
@@ -54,7 +60,7 @@ def experiment():
         maxsample = sample
         bestb = b
     if i in sample_points:
-      print "current samples: %s" % allsamples
+      print("current samples: %s" % allsamples)
       plot(bandits, i)
 
     # pull the arm for the bandit with the largest sample

ab_testing/chisquare.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.stats import chi2, chi2_contingency
@@ -47,7 +53,7 @@ def run_experiment(p1, p2, N):
   data = DataGenerator(p1, p2)
   p_values = np.empty(N)
   T = np.zeros((2, 2)).astype(np.float32)
-  for i in xrange(N):
+  for i in range(N):
     c1, c2 = data.next()
     T[0,c1] += 1
     T[1,c2] += 1

ab_testing/ci_comparison.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.stats import beta, norm
@@ -10,7 +16,7 @@
 a, b = 1, 1 # beta priors
 plot_indices = (10, 20, 30, 50, 100, 200, 500)
 data = np.empty(T)
-for i in xrange(T):
+for i in range(T):
   x = 1 if np.random.random() < true_ctr else 0
   data[i] = x
 

ab_testing/convergence.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 from bayesian_bandit import Bandit
@@ -11,7 +17,7 @@ def run_experiment(p1, p2, p3, N):
 
   data = np.empty(N)
 
-  for i in xrange(N):
+  for i in range(N):
     # thompson sampling
     j = np.argmax([b.sample() for b in bandits])
     x = bandits[j].pull()

ab_testing/demo.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy.stats import beta
@@ -16,7 +22,7 @@ def plot(a, b, trial, ctr):
 true_ctr = 0.3
 a, b = 1, 1 # beta parameters
 show = [0, 5, 10, 25, 50, 100, 200, 300, 500, 700, 1000, 1500]
-for t in xrange(1501):
+for t in range(1501):
   coin_toss_result = (np.random.random() < true_ctr)
   if coin_toss_result:
     a += 1

ab_testing/ttest.py

@@ -1,6 +1,12 @@
 # From the course: Bayesin Machine Learning in Python: A/B Testing
 # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
 # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+
 import numpy as np
 from scipy import stats
 
@@ -16,8 +22,8 @@
 t = (a.mean() - b.mean()) / (s * np.sqrt(2.0/N)) # t-statistic
 df = 2*N - 2 # degrees of freedom
 p = 1 - stats.t.cdf(np.abs(t), df=df) # one-sided test p-value
-print "t:\t", t, "p:\t", 2*p # two-sided test p-value
+print("t:\t", t, "p:\t", 2*p) # two-sided test p-value
 
 # built-in t-test:
 t2, p2 = stats.ttest_ind(a, b)
-print "t2:\t", t2, "p2:\t", p2
+print("t2:\t", t2, "p2:\t", p2)