49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
from sklearn import *;
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from sklearn import ensemble;
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import numpy as np
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import scipy as sp
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from pybrain.datasets import SupervisedDataSet
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from pybrain.tools.shortcuts import buildNetwork
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from pybrain.supervised.trainers import BackpropTrainer
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from pybrain.structure import LinearLayer, TanhLayer, SigmoidLayer, SoftmaxLayer, FeedForwardNetwork, BiasUnit
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from pybrain.tools.neuralnets import NNregression, Trainer
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def train_model(X, Y, profiles, metric):
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#Preprocessing
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Xmean = np.mean(X, axis=0)
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Xstd = np.std(X, axis=0)
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X = (X - Xmean)/Xstd
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Ymax = np.max(Y)
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Y = Y/Ymax
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ref = np.argmax(np.bincount(np.argmax(Y, axis=1))) #most common profile
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#Cross-validation data-sets
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cut = int(0.800*X.shape[0]+1)
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XTr = X[0:cut, :]
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YTr = Y[0:cut, :]
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XTe = X[cut:,:]
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YTe = Y[cut:,:]
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#Train the model
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print("Training the model...")
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clf = ensemble.RandomForestRegressor(40).fit(XTr,YTr)
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#Evaluate the model
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GFlops = np.empty(XTe.shape[0])
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speedups = np.empty(XTe.shape[0])
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optspeedups = np.empty(XTe.shape[0])
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for i,x in enumerate(XTe):
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predictions = clf.predict(x)
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label = np.argmax(predictions)
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speedups[i] = YTe[i,label]/YTe[i,ref]
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optspeedups[i] = np.max(YTe[i,:])/YTe[i,ref]
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GFlops[i] = YTe[i,ref]*Ymax
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np.set_printoptions(precision=2)
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print("-----------------")
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print("Average testing speedup : %f (Optimal : %f)"%(sp.stats.gmean(speedups), sp.stats.gmean(optspeedups)))
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print("Average %s: %f (Default %f, Optimal %f)"%(metric, np.mean(np.multiply(GFlops,speedups)), np.mean(GFlops), np.mean(np.multiply(GFlops,optspeedups))))
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print("Minimum speedup is %f wrt %i %s"%(np.min(speedups), GFlops[np.argmin(speedups)], metric))
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print("Maximum speedup is %f wrt %i %s"%(np.max(speedups), GFlops[np.argmax(speedups)], metric))
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print("--------") |