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Python/Autotune: Moved devices from config.ini to command line argument

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Philippe Tillet
2014-10-27 03:28:46 -04:00
parent 7780423fa1
commit ba50960b0f
5 changed files with 124 additions and 100 deletions
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44
python/autotune/pysrc/model.py
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@@ -1,7 +1,6 @@
from sklearn import tree
from sklearn import ensemble
from numpy import array, bincount, mean, std, max, argmax, min, argmin, median
import numpy as np
def gmean(a, axis=0, dtype=None):
if not isinstance(a, np.ndarray): # if not an ndarray object attempt to convert it
@@ -16,25 +15,30 @@ def gmean(a, axis=0, dtype=None):
return np.exp(log_a.mean(axis=axis))
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[:,:]
profiles=profiles[:]
Ymax = np.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,:])
cut = int(0.75*X.shape[0])
clf = ensemble.RandomForestRegressor(10, max_depth=4).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)))
print clf.predict([10000])
t = np.argmin(clf.predict(X[cut:,:]), axis = 1)
s = np.array([y[0]/y[k] for y,k in zip(Y[cut:,:], t)])
tt = np.argmin(Y[cut:,:], axis = 1)
ss = np.array([y[0]/y[k] for y,k in zip(Y[cut:,:], 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)))
return clf