Replaced cxfreeze with pyinstaller. Works better.

python/autotune/pysrc/model.py

@@ -1,63 +1,20 @@
 from sklearn import tree
 from sklearn import ensemble
-
 from numpy import array, bincount, mean, std, max, argmax, min, argmin, median
-from scipy.stats import gmean
 
 
-# def random_forest(Xtr, Ytr):
-#     clf = ensemble.RandomForestRegressor(10, max_depth=7).fit(Xtr,Ytr)
-#
-#     def predict_tree(tree, x):
-#         tree_ = tree.tree_
-#         children_left = tree_.children_left
-#         children_right = tree_.children_right
-#         threshold = tree_.threshold
-#         feature = tree_.feature
-#         value = tree_.value
-#         idx = 0
-#         while children_left[idx]!=-1:
-#             if x[0, feature[idx]] <= threshold[idx]:
-#                 idx = children_left[idx]
-#             else:
-#                 idx = children_right[idx]
-#         return value[[idx],:,:][:,:,0]
-#
-#     s = 0
-#     for e in clf.estimators_:
-#         tree_ = e.tree_
-#         children_left = tree_.children_left
-#         children_right = tree_.children_right
-#         threshold = tree_.threshold
-#         feature = tree_.feature
-#         value = tree_.value
-#         s = s + value.size + feature.size + threshold.size + children_right.size + children_left.size
-#     print s*4*1e-3
-#     return clf, clf.predict
-#
-# def train_nn(layer_sizes, XTr, YTr, XTe, YTe):
-#     optimizer = HF(open(os.devnull, 'w'), 15)
-#     optimizer.doCGBacktracking = True
-#     net = FeedforwardNeuralNet(layer_sizes, [Act.Tanh() for i in range(len(layer_sizes)-2)], Act.Linear(), 1e-5)
-#
-#     nbatch=10
-#     bsize = XTr.shape[0]/nbatch
-#     data = ((XTr[(i%nbatch)*bsize:(i%nbatch+1)*bsize,:], YTr[(i%nbatch)*bsize:(i%nbatch+1)*bsize,:]) for i in range(nbatch))
-#     data = HFDataSource(data, bsize, gradBatchSize = nbatch*bsize, curvatureBatchSize = bsize, lineSearchBatchSize =nbatch*bsize, gradBatchIsTrainingSet=True)
-#     iters = optimizer.optimize(HFModel(net), data, 300, otherPrecondDampingTerm=net.L2Cost)
-#     bestte = collections.deque([float("inf")]*5, maxlen=5)
-#     for i,w in enumerate(iters):
-#         Diffte = YTe - net.predictions(XTe).as_numpy_array()
-#         Difftr = YTr - net.predictions(XTr).as_numpy_array()
-#         Ete = np.sum(Diffte**2)
-#         Etr = np.sum(Difftr**2)
-#         bestte.append(min(min(bestte),Ete))
-#         if min(bestte)==max(bestte):
-#             print 'Final test error: ', Ete
-#             return net, net.predictions
-#         print 'Iteration %d | Test error = %.2f | Train error = %.2f'%(i, Ete, Etr)
-#     return net, net.predictions
-
+def gmean(a, axis=0, dtype=None):
+    if not isinstance(a, np.ndarray):  # if not an ndarray object attempt to convert it
+        log_a = np.log(np.array(a, dtype=dtype))
+    elif dtype:  # Must change the default dtype allowing array type
+        if isinstance(a,np.ma.MaskedArray):
+            log_a = np.log(np.ma.asarray(a, dtype=dtype))
+        else:
+            log_a = np.log(np.asarray(a, dtype=dtype))
+    else:
+        log_a = np.log(a)
+    return np.exp(log_a.mean(axis=axis))
+    
 def train_model(X, Y, profiles, metric):
     print("Building the model...")