More packaging ...

python/autotune/pysrc/model.py

@@ -0,0 +1,83 @@
+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 train_model(X, Y, profiles, metric):
+    print("Building the model...")
+
+    Xmean = mean(X)
+    Xstd = std(X)
+    X = (X - Xmean)/Xstd
+
+    Y = Y[:, :]
+    Ymax = max(Y)
+    Y = Y/Ymax
+
+    ref = argmax(bincount(argmin(Y, axis=1))) #most common profile
+    cut = int(0.800*X.shape[0]+1)
+
+    #Train the model
+    clf = ensemble.RandomForestRegressor(10, max_depth=10).fit(X[:cut,:], Y[:cut,:])
+
+    t = argmin(clf.predict(X[cut:,:]), axis = 1)
+    s = array([y[ref]/y[k] for y,k in zip(Y[cut:,:], t)])
+    tt = argmin(Y[cut:,:], axis = 1)
+    ss = array([y[ref]/y[k] for y,k in zip(Y[cut:,:], tt)])
+    print("Testing speedup : mean = %.3f, median = %.3f, min = %.3f,  max %.3f"%(gmean(s), median(s), min(s), max(s)))
+    print("Optimal speedup : mean = %.3f, median = %.3f, min = %.3f,  max %.3f"%(gmean(ss), median(ss), min(ss), max(ss)))