triton/autotune/python/dataset.py

import os
import sys
import re
import random
import numpy as np
from sklearn.neighbors.kde import KernelDensity;
from pyviennacl.atidlas import FetchingPolicy

def decode(y):
    fetch = [FetchingPolicy.FETCH_FROM_LOCAL, FetchingPolicy.FETCH_FROM_GLOBAL_CONTIGUOUS, FetchingPolicy.FETCH_FROM_GLOBAL_STRIDED]
    y[7] = fetch[y[7]]
    y[8] = fetch[y[8]]
    return y

def generate_dataset(TemplateType, execution_handler):
    I = 0
    step = 64;
    max_size = 4000;

    #Retrieves the existing data
    print "Retrieving data..."
    path = "./data"
    files = os.listdir(path)
    X = np.empty((len(files),3))
    t = np.empty(len(files))
    profiles = []
    nonemptyfiles = []
    for i,fname in enumerate(files):
        if os.path.getsize(os.path.join(path,fname))>0:
            nonemptyfiles.append(fname)
    files = nonemptyfiles

    for i,fname in enumerate(files):
        MNK = re.search(r"([0-9]+)-([0-9]+)-([0-9]+).csv", fname)
        fl = open(os.path.join(path,fname),"rb")
        A = np.loadtxt(fl,delimiter=',')
        x = np.array([MNK.group(1), MNK.group(2), MNK.group(3)]).astype(float)
        y = tuple(A[np.argmin(A[:,0]),1:])
        if y not in profiles:
            profiles.append(y)
        idx = profiles.index(y)
        X[i,:] = x
        t[i] = idx

    #Generates new data
    print "Generating new data..."
    kdes = [KernelDensity(kernel='gaussian', bandwidth=2*step).fit(X[t==i,:]) for i in range(int(max(t))+1)] if files else [];
    X.resize((len(files)+I, 3), refcheck=False);
    t.resize(len(files)+I, refcheck=False);

    max_square = max_size/step
    for i in range(I):
        n_per_label = np.bincount(t[0:i+1].astype(int));
        Xtuples = [tuple(x) for x in X];
        r = random.random();
        while(True):
            if(len(kdes)==0 or r<=1.0/len(kdes)):
                x = np.array([step*random.randint(1,40), step*random.randint(1,40), step*random.randint(1,40)]);
            else:
                probs = (1.0/n_per_label)
                distr = np.random.choice(range(n_per_label.size), p = probs/np.sum(probs))
                x = kdes[distr].sample()[0]
                x = np.maximum(np.ones(x.shape),(x - step/2).astype(int)/step + 1)*step
            if tuple(x) not in Xtuples:
                break;
        x = x.astype(int)
        fname = os.path.join(path, `x[0]` +"-"+ `x[1]` +"-"+ `x[2]` +".csv")
        #Execute auto-tuning procedure
        execution_handler(x, fname)
        #Load csv into matrix
        fl = open(fname,"rb");
        A = np.loadtxt(fl,delimiter=',');
        #Update the kernel density estimators
        y = tuple(A[np.argmin(A[:,0]),1:]);
        if y not in profiles:
            profiles.append(y);
            kdes.append(KernelDensity(kernel='gaussian', bandwidth=2*step));
        idx = profiles.index(y);
        #Update data
        X[len(files)+i,:] = x;
        t[len(files)+i] = idx;
        #Update density estimator p(M,N,K | t=idx)
        kdes[idx].fit(X[t[0:len(files)+i+1]==idx,:]);


    print "Exporting data...";
    #Shuffle the list of file
    files = os.listdir(path)
    X = np.empty((len(files),3))
    Y = np.zeros((len(files), len(profiles)))
    for i,fname in enumerate(files):
        MNK = re.search(r"([0-9]+)-([0-9]+)-([0-9]+).csv", fname)
        X[i,:] = map(float,[MNK.group(k) for k in range(1,4)])
        fl = open(os.path.join(path,fname),"rb");
        A = np.loadtxt(fl,delimiter=',')
        for j,y in enumerate(profiles):
            idx = np.where(np.all(A[:,1:]==y,axis=1))[0]
            T = A[idx[0], 0] if idx.size else execution_handler(map(int,X[i,:]), '', decode(map(int, y)))
            Y[i,j] = 2*1e-9*X[i,0]*X[i,1]*X[i,2]/T

    return X, Y, profiles