Updates PythonPackageTestAlgorithm to use QCAlgorithm.Log instead of print

Algorithm.Python/PythonPackageTestAlgorithm.py

@@ -68,52 +68,52 @@ class PythonPackageTestAlgorithm(QCAlgorithm):
         self.AddEquity("SPY", Resolution.Daily)
 
         # numpy test
-        print ("numpy test >>> print numpy.pi: " , numpy.pi)
+        self.Log(f"numpy test >>> print numpy.pi: {numpy.pi}")
 
         # scipy test:
-        print ("scipy test >>> print mean of 1 2 3 4 5:", scipy.mean(numpy.array([1, 2, 3, 4, 5])))
+        self.Log(f"scipy test >>> print mean of 1 2 3 4 5: {scipy.mean(numpy.array([1, 2, 3, 4, 5]))}")
 
         #sklearn test
-        print ("sklearn test >>> default RandomForestClassifier:", RandomForestClassifier())
+        self.Log(f"sklearn test >>> default RandomForestClassifier: {RandomForestClassifier()}")
 
         # cvxopt matrix test
-        print ("cvxopt >>>", cvxopt.matrix([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], (2,3)))
+        self.Log(f"cvxopt >>> {cvxopt.matrix([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], (2,3))}")
 
         # talib test
-        print ("talib test >>>", talib.SMA(numpy.random.random(100)))
+        self.Log(f"talib test >>> {talib.SMA(numpy.random.random(100))}")
 
         # blaze test
-        blaze_test()
+        self.Log(blaze_test())
 
         # cvxpy test
-        cvxpy_test()
+        self.Log(cvxpy_test())
 
         # statsmodels test
-        statsmodels_test()
+        self.Log(statsmodels_test())
 
         # pykalman test
-        pykalman_test()
+        self.Log(pykalman_test())
 
         # copulalib test
-        copulalib_test()
+        self.Log(copulalib_test())
 
         # theano test
-        theano_test()
+        self.Log(theano_test())
 
         # xgboost test
-        xgboost_test()
+        self.Log(xgboost_test())
 
         # arch test
-        arch_test()
+        self.Log(arch_test())
 
         # keras test
-        keras_test()
+        self.Log(keras_test())
 
         # tensorflow test
-        tensorflow_test()
-        
+        self.Log(tensorflow_test())
+
         # deap test
-        deap_test()
+        self.Log(deap_test())
 
     def OnData(self, data): pass
 
@@ -125,7 +125,7 @@ def blaze_test():
          [3, 'Charlie', 300],
          [4, 'Denis',   400],
          [5, 'Edith',  -500]]
-    print ("blaze test >>>", list(blaze.compute(deadbeats, L)))
+    return f"blaze test >>> {list(blaze.compute(deadbeats, L))}"
 
 def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
     i = bisect(breakpoints, score)
@@ -139,12 +139,12 @@ def cvxpy_test():
     Sigma = Sigma.T.dot(Sigma)
 
     w = cvxpy.Variable(n)
-    gamma = cvxpy.Parameter(sign='positive')
+    gamma = cvxpy.Parameter(nonneg=True)
     ret = mu.T*w
     risk = cvxpy.quad_form(w, Sigma)
-    print ("csvpy test >>> ", cvxpy.Problem(cvxpy.Maximize(ret - gamma*risk),
-               [cvxpy.sum_entries(w) == 1,
-                w >= 0]))
+    result = cvxpy.Problem(cvxpy.Maximize(ret - gamma*risk),
+                           [cvxpy.sum(w) == 1, w >= 0])
+    return f"csvpy test >>> {result}" 
 
 def statsmodels_test():
     nsample = 100
@@ -158,31 +158,31 @@ def statsmodels_test():
 
     model = sm.OLS(y, X)
     results = model.fit()
-    print ("statsmodels tests >>>", results.summary())
+    return f"statsmodels tests >>> {results.summary()}"
 
 def pykalman_test():
     kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
     measurements = numpy.asarray([[1,0], [0,0], [0,1]])  # 3 observations
     kf = kf.em(measurements, n_iter=5)
-    print ("pykalman test >>>", kf.filter(measurements))
+    return f"pykalman test >>> {kf.filter(measurements)}"
 
 def copulalib_test():
     x = numpy.random.normal(size=100)
     y = 2.5 * x + numpy.random.normal(size=100)
 
     #Make the instance of Copula class with x, y and clayton family::
-    print ("copulalib test >>>", Copula(x, y, family='clayton'))
+    return f"copulalib test >>> {Copula(x, y, family='clayton')}"
 
 def theano_test():
     a = theano.tensor.vector() # declare variable
     out = a + a ** 10               # build symbolic expression
     f = theano.function([a], out)   # compile function
-    print ("theano test >>>", f([0, 1, 2])) 
+    return f"theano test >>> {f([0, 1, 2])}"
 
 def xgboost_test():
     data = numpy.random.rand(5,10) # 5 entities, each contains 10 features
     label = numpy.random.randint(2, size=5) # binary target
-    print ("xgboost test >>>", xgboost.DMatrix( data, label=label))
+    return f"xgboost test >>> {xgboost.DMatrix( data, label=label)}"
 
 def arch_test():
     r = numpy.array([0.945532630498276,
@@ -203,7 +203,7 @@ def arch_test():
 
     garch11 = arch_model(r, p=1, q=1)
     res = garch11.fit(update_freq=10)
-    print ("arch test >>>", res.summary())
+    return f"arch test >>> {res.summary()}"
 
 def keras_test():
     # Initialize the constructor
@@ -218,14 +218,14 @@ def keras_test():
     # Add an output layer
     model.add(Dense(1, activation='sigmoid'))
 
-    print ("keras test >>>", model)
+    return f"keras test >>> {model}"
 
 def tensorflow_test():
     node1 = tf.constant(3.0, tf.float32)
     node2 = tf.constant(4.0) # also tf.float32 implicitly
     sess = tf.Session()
     node3 = tf.add(node1, node2)
-    print ("tensorflow test >>>", "sess.run(node3): ", sess.run(node3))
+    return f"tensorflow test >>> sess.run(node3): {sess.run(node3)}"
 
 def deap_test():
     # onemax example evolves to print list of ones: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
@@ -255,4 +255,4 @@ def deap_test():
 
     pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=30, 
                                    stats=stats, halloffame=hof, verbose=False) # change to verbose=True to see evolution table
-    print ("deap test >>>", hof[0])
+    return f"deap test >>> {hof[0]}"