Simple linear model

autotune/python/model.py

@@ -0,0 +1,44 @@
+from sklearn import *;
+from sklearn import ensemble;
+import numpy as np
+import scipy as sp
+
+def train_model(X, Y, profiles):
+  #Preprocessing
+  scaler = preprocessing.StandardScaler().fit(X);
+  X = scaler.transform(X);
+  ref = np.argmax(np.bincount(np.argmax(Y, axis=1))) #most common profile
+  
+  print Y
+  print np.bincount(np.argmax(Y, axis=1))
+  #Cross-validation data-sets
+  cut = int(0.5*X.shape[0]+1);
+  XTr = X[0:cut, :];
+  YTr = Y[0:cut, :];
+  XTe = X[cut:,:];
+  YTe = Y[cut:,:];
+
+  #Train the model
+  print("Training the model...");
+  clf = linear_model.LinearRegression().fit(XTr,YTr);
+  
+  #Evaluate the model
+  GFlops = np.empty(XTe.shape[0]);
+  speedups = np.empty(XTe.shape[0]);
+  optspeedups = np.empty(XTe.shape[0]);
+  for i,x in enumerate(XTe):
+    predictions = clf.predict(x);
+    label = np.argmax(predictions);
+    speedups[i] = YTe[i,label]/YTe[i,ref];
+    optspeedups[i] = np.max(YTe[i,:])/YTe[i,ref];
+    GFlops[i] = YTe[i,ref];
+    
+  np.set_printoptions(precision=2);
+  print("-----------------");
+  print("Average testing speedup : %f (Optimal : %f)"%(sp.stats.gmean(speedups), sp.stats.gmean(optspeedups)));
+  print("Average GFLOP/s : %f (Default %f, Optimal %f)"%(np.mean(np.multiply(GFlops,speedups)), np.mean(GFlops), np.mean(np.multiply(GFlops,optspeedups))));
+  print("Minimum speedup is %f wrt %i GFlops"%(np.min(speedups), GFlops[np.argmin(speedups)]));
+  print("Maximum speedup is %f wrt %i GFlops"%(np.max(speedups), GFlops[np.argmax(speedups)]));
+  print("--------");
+  
+  print clf