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Fixed over-head bug in the auto-tuner (not in the benchmarks)

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Philippe Tillet
2014-11-06 16:14:46 -05:00
parent 8dd492de23
commit 6595d20c34
9 changed files with 110 additions and 63 deletions
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40
python/autotune/pysrc/model.py
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@@ -1,5 +1,6 @@
from sklearn import tree
from sklearn import ensemble
from sklearn.grid_search import GridSearchCV
import numpy as np
def gmean(a, axis=0, dtype=None):
@@ -20,7 +21,6 @@ def nrmse(y_ground, y):
return rmsd/(np.max(y_ground) - np.min(y_ground))
def train_model(X, Y, profiles, metric):
#Shuffle
p = np.random.permutation(X.shape[0])
X = X[p,:]
Y = Y[p,:]
@@ -28,18 +28,34 @@ def train_model(X, Y, profiles, metric):
Ymax = np.max(Y)
Y = Y/Ymax
#Train the model
cut = int(0.9*X.shape[0])
nrmses = {}
for depth in range(1,10):
clf = ensemble.RandomForestRegressor(5, max_depth=4).fit(X[:cut,:], Y[:cut,:])
t = np.argmin(clf.predict(X[cut:,:]), axis = 1)
y = np.array([Y[cut+i,t[i]] for i in range(t.size)])
y_ground = np.min(Y[cut:,:], axis=1)
# for i in range(t.size):
# print X[cut+i,:], y[i], y_ground[i]
nrmses[clf] = nrmse(y_ground, y)
print depth, nrmses[clf]
cut = int(0.95*X.shape[0])
XTr, YTr = X[:cut,:], Y[:cut,:]
XCv, YCv = X[cut:,:], Y[cut:,:]
nrmses = {}
for N in range(1,10):
for depth in range(1,5):
clf = ensemble.RandomForestRegressor(N, max_depth=depth).fit(XTr, YTr)
t = np.argmin(clf.predict(XCv), axis = 1)
y = np.array([YCv[i,t[i]] for i in range(t.size)])
nrmses[clf] = nrmse(np.min(YCv[:,:], axis=1), y)
clf = min(nrmses, key=nrmses.get)
t = np.argmin(clf.predict(XCv), axis = 1)
s = np.array([y[0]/y[k] for y,k in zip(YCv, t)])
tt = np.argmin(YCv, axis = 1)
ss = np.array([y[0]/y[k] for y,k in zip(YCv, tt)])
p5 = lambda a: np.percentile(a, 5)
p25 = lambda a: np.percentile(a, 25)
p50 = lambda a: np.percentile(a, 50)
p75 = lambda a: np.percentile(a, 75)
p95 = lambda a: np.percentile(a, 95)
print("Percentile :\t 5 \t 25 \t 50 \t 75 \t 95")
print("Testing speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(s), p25(s), p50(s), p75(s), p95(s)))
print("Optimal speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(ss), p25(ss), p50(ss), p75(ss), p95(ss)))
print clf
return clf